100+ datasets found
  1. w

    World Development Indicators (WDI)

    • data360.worldbank.org
    Updated Apr 18, 2025
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    (2025). World Development Indicators (WDI) [Dataset]. https://data360.worldbank.org/en/dataset/WB_WDI
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    Dataset updated
    Apr 18, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    1960 - 2023
    Description

    The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

    For further details, please refer to https://datatopics.worldbank.org/world-development-indicators/

  2. o

    World Bank List of Indicators for Nigeria - Dataset - openAFRICA

    • open.africa
    Updated Nov 6, 2014
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    (2014). World Bank List of Indicators for Nigeria - Dataset - openAFRICA [Dataset]. https://open.africa/dataset/world-bank-list-of-indicators-for-nigeria
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    Dataset updated
    Nov 6, 2014
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Nigeria
    Description

    List of World Bank accepted inicators for various projects for Nigeria

  3. g

    World Bank - Statistical Performance Indicators (SPI)

    • gimi9.com
    Updated Mar 29, 2021
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    (2021). World Bank - Statistical Performance Indicators (SPI) [Dataset]. https://gimi9.com/dataset/worldbank_wb_spi/
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    Dataset updated
    Mar 29, 2021
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0. For further details, please refer to https://documents.worldbank.org/en/publication/documents-reports/documentdetail/815721616086786412/measuring-the-statistical-performance-of-countries-an-overview-of-updates-to-the-world-bank-statistical-capacity-index

  4. u

    The World Bank, DataBank, Grenada

    • rciims.mona.uwi.edu
    Updated Dec 2, 2020
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    (2020). The World Bank, DataBank, Grenada [Dataset]. https://rciims.mona.uwi.edu/dataset/wb-data-bank-grenada
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    Dataset updated
    Dec 2, 2020
    Area covered
    Grenada
    Description

    Databank (databank.worldbank.org) is an online web resource that provides simple and quick access to collections of time series data. It has advanced functions for selecting and displaying data, performing customized queries, downloading data, and creating charts and maps. Users can create dynamic custom reports based on their selection of countries, indicators and years. They offer a growing range of free, easy-to-access tools, research and knowledge to help people address the world's development challenges. For example, the Open Data website offers free access to comprehensive, downloadable indicators about development in countries around the globe.

  5. N

    Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate

    • ceicdata.com
    Updated May 29, 2020
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    CEICdata.com (2020). Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate [Dataset]. https://www.ceicdata.com/en/nicaragua/poverty/ni-gini-coefficient-gini-index-world-bank-estimate
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    Dataset updated
    May 29, 2020
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 1993 - Dec 1, 2014
    Area covered
    Nicaragua
    Description

    Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 46.200 % in 2014. This records an increase from the previous number of 43.900 % for 2009. Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 50.850 % from Dec 1993 (Median) to 2014, with 6 observations. The data reached an all-time high of 57.400 % in 1993 and a record low of 43.900 % in 2009. Nicaragua NI: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Nicaragua – Table NI.World Bank.WDI: Poverty. 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.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

  6. P

    Selection of World Bank Worldwide Governance Indicators (WB WGI) for Pacific...

    • pacificdata.org
    • pacific-data.sprep.org
    csv
    Updated Nov 7, 2024
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    SPC (2024). Selection of World Bank Worldwide Governance Indicators (WB WGI) for Pacific Island Countries and Territories [Dataset]. https://pacificdata.org/data/dataset/selection-of-world-bank-worldwide-governance-indicators-wb-wgi-for-pacific-island-countri-df-wbwgi
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    csvAvailable download formats
    Dataset updated
    Nov 7, 2024
    Dataset provided by
    SPC
    Time period covered
    Jan 1, 1996 - Dec 31, 2023
    Description

    This selection includes data related to SPC member countries and territories for some of the indicators available in the original database published by the World Bank.

    Find more Pacific data on PDH.stat.

  7. G

    Happiness index in High income countries (World Bank classification) |...

    • theglobaleconomy.com
    csv, excel, xml
    Updated Jan 29, 2021
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    Globalen LLC (2021). Happiness index in High income countries (World Bank classification) | TheGlobalEconomy.com [Dataset]. www.theglobaleconomy.com/rankings/happiness/WB-high/
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    xml, excel, csvAvailable download formats
    Dataset updated
    Jan 29, 2021
    Dataset authored and provided by
    Globalen LLC
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 31, 2013 - Dec 31, 2024
    Area covered
    World
    Description

    The average for 2024 based on 48 countries was 6.62 points. The highest value was in Finland: 7.74 points and the lowest value was in Hong Kong: 5.32 points. The indicator is available from 2013 to 2024. Below is a chart for all countries where data are available.

  8. Switzerland - Economic, Social, Environmental, Health, Education,...

    • data.humdata.org
    • data.amerigeoss.org
    csv
    Updated Jun 27, 2025
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    World Bank Group (2025). Switzerland - Economic, Social, Environmental, Health, Education, Development and Energy [Dataset]. https://data.humdata.org/dataset/df1731fd-979e-421e-97ad-508c9b5e2a73?force_layout=desktop
    Explore at:
    csv(8073204), csv(8508)Available download formats
    Dataset updated
    Jun 27, 2025
    Dataset provided by
    World Bankhttp://worldbank.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Switzerland
    Description
  9. Global Data Regulation Diagnostic Survey Dataset 2021 - Afghanistan, Angola,...

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Oct 26, 2023
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    World Bank (2023). Global Data Regulation Diagnostic Survey Dataset 2021 - Afghanistan, Angola, Argentina...and 77 more [Dataset]. https://microdata.worldbank.org/index.php/catalog/3866
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    Dataset updated
    Oct 26, 2023
    Dataset authored and provided by
    World Bankhttp://worldbank.org/
    Time period covered
    2020
    Area covered
    Afghanistan, Argentina...and 77 more, Angola
    Description

    Abstract

    The Global Data Regulation Diagnostic provides a comprehensive assessment of the quality of the data governance environment. Diagnostic results show that countries have put in greater effort in adopting enabler regulatory practices than in safeguard regulatory practices. However, for public intent data, enablers for private intent data, safeguards for personal and nonpersonal data, cybersecurity and cybercrime, as well as cross-border data flows. Across all these dimensions, no income group demonstrates advanced regulatory frameworks across all dimensions, indicating significant room for the regulatory development of both enablers and safeguards remains at an intermediate stage: 47 percent of enabler good practices and 41 percent of good safeguard practices are adopted across countries. Under the enabler and safeguard pillars, the diagnostic covers dimensions of e-commerce/e-transactions, enablers further improvement on data governance environment.

    The Global Data Regulation Diagnostic is the first comprehensive assessment of laws and regulations on data governance. It covers enabler and safeguard regulatory practices in 80 countries providing indicators to assess and compare their performance. This Global Data Regulation Diagnostic develops objective and standardized indicators to measure the regulatory environment for the data economy across countries. The indicators aim to serve as a diagnostic tool so countries can assess and compare their performance vis-á-vis other countries. Understanding the gap with global regulatory good practices is a necessary first step for governments when identifying and prioritizing reforms.

    Geographic coverage

    80 countries

    Analysis unit

    Country

    Kind of data

    Observation data/ratings [obs]

    Sampling procedure

    The diagnostic is based on a detailed assessment of domestic laws, regulations, and administrative requirements in 80 countries selected to ensure a balanced coverage across income groups, regions, and different levels of digital technology development. Data are further verified through a detailed desk research of legal texts, reflecting the regulatory status of each country as of June 1, 2020.

    Mode of data collection

    Mail Questionnaire [mail]

    Research instrument

    The questionnaire comprises 37 questions designed to determine if a country has adopted good regulatory practice on data governance. The responses are then scored and assigned a normative interpretation. Related questions fall into seven clusters so that when the scores are averaged, each cluster provides an overall sense of how it performs in its corresponding regulatory and legal dimensions. These seven dimensions are: (1) E-commerce/e-transaction; (2) Enablers for public intent data; (3) Enablers for private intent data; (4) Safeguards for personal data; (5) Safeguards for nonpersonal data; (6) Cybersecurity and cybercrime; (7) Cross-border data transfers.

    Response rate

    100%

  10. S

    Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate

    • ceicdata.com
    Updated Aug 15, 2018
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    CEICdata.com (2018). Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate [Dataset]. https://www.ceicdata.com/en/sudan/poverty/sd-gini-coefficient-gini-index-world-bank-estimate
    Explore at:
    Dataset updated
    Aug 15, 2018
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2009
    Area covered
    Sudan
    Description

    Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 35.400 % in 2009. Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 35.400 % from Dec 2009 (Median) to 2009, with 1 observations. Sudan SD: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Sudan – Table SD.World Bank.WDI: Poverty. 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.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

  11. Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate

    • ceicdata.com
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    CEICdata.com, Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate [Dataset]. https://www.ceicdata.com/en/latvia/poverty/lv-gini-coefficient-gini-index-world-bank-estimate
    Explore at:
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2004 - Dec 1, 2015
    Area covered
    Latvia
    Description

    Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 34.200 % in 2015. This records a decrease from the previous number of 35.100 % for 2014. Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 35.500 % from Dec 1993 (Median) to 2015, with 15 observations. The data reached an all-time high of 39.000 % in 2005 and a record low of 27.000 % in 1993. Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Latvia – Table LV.World Bank.WDI: Poverty. 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.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

  12. w

    Education Statistics

    • data360.worldbank.org
    • data.opendata.am
    Updated Apr 18, 2025
    + more versions
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    (2025). Education Statistics [Dataset]. https://data360.worldbank.org/en/dataset/WB_EDSTATS
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    Dataset updated
    Apr 18, 2025
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    1970 - 2023
    Description

    The World Bank EdStats All Indicator Query holds over 4,000 internationally comparable indicators that describe education access, progression, completion, literacy, teachers, population, and expenditures. The indicators cover the education cycle from pre-primary to vocational and tertiary education. The query also holds learning outcome data from international and regional learning assessments (e.g. PISA, TIMSS, PIRLS), equity data from household surveys, and projection/attainment data to 2050. For further information, please visit the EdStats website.

    For further details, please refer to https://datatopics.worldbank.org/education/wRsc/about

  13. Data from: World Tables of Economic and Social Indicators, 1950-1981

    • icpsr.umich.edu
    Updated Mar 30, 2006
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    World Bank. Economic and Social Data Division (2006). World Tables of Economic and Social Indicators, 1950-1981 [Dataset]. http://doi.org/10.3886/ICPSR08197.v1
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    Dataset updated
    Mar 30, 2006
    Dataset provided by
    Inter-university Consortium for Political and Social Researchhttps://www.icpsr.umich.edu/web/pages/
    Authors
    World Bank. Economic and Social Data Division
    License

    https://www.icpsr.umich.edu/web/ICPSR/studies/8197/termshttps://www.icpsr.umich.edu/web/ICPSR/studies/8197/terms

    Time period covered
    1950 - 1981
    Area covered
    Sudan, Netherlands, Seychelles, South Korea, Botswana, Greece, Mauritius, Iran, Panama, Madagascar
    Description

    This dataset contains country level economic and social measures for 183 countries. Part 1, World Tables (1980 File), contains, where available, measures of (1)population, (2)national accounts and price data for 1950, 1955, 1960 through 1977, (3)data on external trade for 1962, 1965, 1970, and 1977, (4)data on balance of payments, debt, central government finance and trade indices for 1970-1977, and (5)social data for 1960, 1970, and (estimated) 1977. More specifically, the groupings include population, GDP by industrial origin and expenditures in constant local prices and current local prices, exchange rates and indices, balance of payments and external debt ($US), central government finance in local currency, social indicators, and external trade. Part 2, World Tables (1982 File), contains data on national accounts, prices, exchange rates and population for 1960-1981. The groupings include GDP by industrial origin as well as expenditure in current local prices and constant local prices, area, population, exchange rates, and indices and savings.

  14. w

    Financial Soundness Indicators: Reporting entities

    • data360.worldbank.org
    • db.nomics.world
    Updated Apr 18, 2025
    + more versions
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    (2025). Financial Soundness Indicators: Reporting entities [Dataset]. https://data360.worldbank.org/en/dataset/IMF_FSIRE
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    Dataset updated
    Apr 18, 2025
    Time period covered
    2000 - 2023
    Description

    The Reporting entities dataset provides information on the structure, size, and coverage of the financial institutions that are used for compiling financial soundness indicators. It provides a better understanding of the structure of the reporting entities in terms of the type of institution, number of entities, size of assets, and type of control.

    Reporting entities are domestically incorporated entities but are divided into two: domestically controlled and foreign controlled. The concepts of residency criterion and control are determined based on FSI Guide methodology which is in line with international best practices such as Systems of National Accounts.

  15. N

    Norway NO: Gini Coefficient (GINI Index): World Bank Estimate

    • ceicdata.com
    Updated Mar 15, 2018
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    CEICdata.com (2018). Norway NO: Gini Coefficient (GINI Index): World Bank Estimate [Dataset]. https://www.ceicdata.com/en/norway/poverty/no-gini-coefficient-gini-index-world-bank-estimate
    Explore at:
    Dataset updated
    Mar 15, 2018
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2004 - Dec 1, 2015
    Area covered
    Norway
    Description

    Norway NO: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 27.500 % in 2015. This records an increase from the previous number of 26.800 % for 2014. Norway NO: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 26.800 % from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 31.600 % in 2004 and a record low of 25.300 % in 2011. Norway NO: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Norway – Table NO.World Bank.WDI: Poverty. 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.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

  16. Barbados - Education

    • data.humdata.org
    csv
    Updated Jun 27, 2025
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    World Bank Group (2025). Barbados - Education [Dataset]. https://data.humdata.org/dataset/world-bank-education-indicators-for-barbados
    Explore at:
    csv(1429481), csv(1433)Available download formats
    Dataset updated
    Jun 27, 2025
    Dataset provided by
    World Bankhttp://worldbank.org/
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Area covered
    Barbados
    Description

    Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

    Education is one of the most powerful instruments for reducing poverty and inequality and lays a foundation for sustained economic growth. The World Bank compiles data on education inputs, participation, efficiency, and outcomes. Data on education are compiled by the United Nations Educational, Scientific, and Cultural Organization (UNESCO) Institute for Statistics from official responses to surveys and from reports provided by education authorities in each country.

  17. w

    World Bank Group Country Survey, 2023 - Ghana

    • microdata.worldbank.org
    • catalog.ihsn.org
    Updated Jan 18, 2024
    + more versions
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    Public Opinion Research Group (2024). World Bank Group Country Survey, 2023 - Ghana [Dataset]. https://microdata.worldbank.org/index.php/catalog/6115
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    Dataset updated
    Jan 18, 2024
    Dataset authored and provided by
    Public Opinion Research Group
    Time period covered
    2023
    Area covered
    Ghana
    Description

    Abstract

    The Country Opinion Survey in Ghana assists the World Bank Group (WBG) in better understanding how stakeholders in Ghana perceive the WBG. It provides the WBG with systematic feedback from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society in Ghana on 1) their views regarding the general environment in Ghana; 2) their overall attitudes toward the WBG in Ghana; 3) overall impressions of the WBG’s effectiveness and results, knowledge work and activities, and communication and information sharing in Ghana; and 4) their perceptions of the WBG’s future role in Ghana.

    Geographic coverage

    -Southern Sector: Volta, Oti, Greater Accra, Eastern, Western North, Western, and Central Regions; -Northern Sector: Northern, Savannah, Upper East, North East, and Upper West Regions; -Middle Sector: Ashanti, Bono East, Ahafo, and Bono Regions;

    Kind of data

    Sample survey data [ssd]

    Sampling procedure

    A total of 1,663 stakeholders in Ghana were invited to provide their opinions on the WBG’s work by participating in a Country Opinion Survey from April 2023 to June 2023. A list of potential participants was compiled by the WBG country team and the fielding agency. Participants were drawn from the Office of the President, Prime Minister, Minister, and Parliament, Government Institutions, Local Governments, Bilateral/ Multilateral Agencies, Private Sector, Civil Society, Academia, and the Media.

    Mode of data collection

    Other [oth]

    Research instrument

    The survey was implemented in English

    Response rate

    The response rate was 45% Comparing responses across Country Surveys reflect changes in attitudes over time, but also changes in respondent samples, changes in methodology, and changes to the survey instrument itself. To reduce the influence of the latter factor, only those questions with similar response scales/options were analyzed. This year’s survey saw an increased outreach to and/or response from Civil Society, but a decrease from Government Principals. These differences in stakeholder composition between the two years should be taken into consideration when interpreting the results of the past year's comparison analyses.

  18. w

    World Bank Group Country Survey 2022 - Ecuador

    • microdata.worldbank.org
    • catalog.ihsn.org
    • +1more
    Updated Feb 14, 2023
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    Public Opinion Research Group (2023). World Bank Group Country Survey 2022 - Ecuador [Dataset]. https://microdata.worldbank.org/index.php/catalog/5710
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    Dataset updated
    Feb 14, 2023
    Dataset authored and provided by
    Public Opinion Research Group
    Time period covered
    2022
    Area covered
    Ecuador
    Description

    Abstract

    The Country Opinion Survey in Ecuador assists the World Bank Group (WBG) in gaining a better understanding of how stakeholders in Ecuador perceive the WBG. It provides the WBG with systematic feedback from national and local governments, multilateral/bilateral agencies, media, academia, the private sector, and civil society in Ecuador on 1) their views regarding the general environment in Ecuador; 2) their overall attitudes toward the WBG in Ecuador; 3) overall impressions of the WBG’s effectiveness and results, knowledge work and activities, and communication and information sharing in Ecuador; and 4) their perceptions of the WBG’s future role in Ecuador.

    Geographic coverage

    • Sierra (Andean highlands)
    • Costa (Coastal lowlands)
    • Amazonía (Amazon)
    • Galápagos Islands

    Sampling procedure

    From June to July 2022, 332 stakeholders of the WBG in Ecuador were invited to provide their opinions on the WBG’s work in the country by participating in a Country Opinion Survey. Participants were drawn from the office of the President, office of a Minister, office of a member of Parliament, employees of ministries/ministerial departments/ implementation agencies; Project Management Units (PMUs) overseeing implementation of WBG projects; consultants/ contractors working on WBG-supported projects/programs; local governments; independent government institutions; the judicial system; state-owned enterprises; bilateral and multilateral agencies; private sector organizations; the financial sector/private banks; private foundations; NGOs and community-based organizations; professional/trade associations; faith-based groups; youth groups; academia/research institutes/think tanks; and the media.

    Mode of data collection

    Internet [int]

    Research instrument

    The survey was implemented in English and Spanish. The English version of the questionnaire is available for download.

    Response rate

    The response rate was 61%

  19. k

    Knowledge Economy Index (World Bank)

    • datasource.kapsarc.org
    csv, excel, json
    Updated Dec 20, 2016
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    (2016). Knowledge Economy Index (World Bank) [Dataset]. https://datasource.kapsarc.org/explore/dataset/knowledge-economy-index-world-bank-2012/
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    excel, json, csvAvailable download formats
    Dataset updated
    Dec 20, 2016
    Description

    The World Bank’s Knowledge Assessment Methodology (KAM: www.worldbank.org/kam) is an online interactive tool that produces the Knowledge Economy Index (KEI)–an aggregate index representing a country’s or region’s overall preparedness to compete in the Knowledge Economy (KE). The KEI is based on a simple average of four subindexes, which represent the four pillars of the knowledge economy:  Economic Incentive and Institutional Regime (EIR)  Innovation and Technological Adoption  Education and Training  Information and Communications Technologies (ICT) Infrastructure The EIR comprises incentives that promote the efficient use of existing and new knowledge and the flourishing of entrepreneurship. An efficient innovation system made up of firms, research centers, universities, think tanks, consultants, and other organizations can tap into the growing stock of global knowledge, adapt it to local needs, and create new technological solutions. An educated and appropriately trained population is capable of creating, sharing, and using knowledge. A modern and accessible ICT infrastructure serves to facilitate the effective communication, dissemination, and processing of information.

  20. S

    Switzerland CH: Gini Coefficient (GINI Index): World Bank Estimate

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). Switzerland CH: Gini Coefficient (GINI Index): World Bank Estimate [Dataset]. https://www.ceicdata.com/en/switzerland/poverty/ch-gini-coefficient-gini-index-world-bank-estimate
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEICdata.com
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2006 - Dec 1, 2014
    Area covered
    Switzerland
    Description

    Switzerland Gini Coefficient (GINI Index): World Bank Estimate data was reported at 32.300 % in 2015. This records a decrease from the previous number of 32.500 % for 2014. Switzerland Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 32.550 % from Dec 2006 (Median) to 2015, with 10 observations. The data reached an all-time high of 34.300 % in 2007 and a record low of 31.600 % in 2012. Switzerland Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Switzerland – Table CH.World Bank.WDI: Poverty. 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.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

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(2025). World Development Indicators (WDI) [Dataset]. https://data360.worldbank.org/en/dataset/WB_WDI

World Development Indicators (WDI)

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Dataset updated
Apr 18, 2025
License

Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically

Time period covered
1960 - 2023
Description

The World Development Indicators (WDI) is the primary World Bank collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates.

For further details, please refer to https://datatopics.worldbank.org/world-development-indicators/

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