100+ datasets found
  1. Poverty rates in OECD countries 2022

    • statista.com
    • ai-chatbox.pro
    Updated May 30, 2025
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    Statista (2025). Poverty rates in OECD countries 2022 [Dataset]. https://www.statista.com/statistics/233910/poverty-rates-in-oecd-countries/
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    Dataset updated
    May 30, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

    The significance of the OECD

    The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

    Poverty in the United States

    In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

  2. Countries with the highest poverty rate worldwide 2022

    • statista.com
    • ai-chatbox.pro
    Updated Jun 23, 2025
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    Statista (2025). Countries with the highest poverty rate worldwide 2022 [Dataset]. https://www.statista.com/statistics/1341014/countries-highest-poverty-rate-world/
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    Dataset updated
    Jun 23, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    World
    Description

    All the 20 countries with the highest poverty rates in the world are located in Africa. The Democratic Republic of Congo and Mozambique were the two countries with the highest share of people living on less than 2.15 U.S. dollars a day when adjusting for 2017 Purchasing Power Parities (PPP), both at over ** percent.

  3. China Poverty Headcount Ratio at Societal Poverty Lines: % of Population

    • ceicdata.com
    Updated Feb 1, 2023
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    CEICdata.com (2023). China Poverty Headcount Ratio at Societal Poverty Lines: % of Population [Dataset]. https://www.ceicdata.com/en/china/social-poverty-and-inequality
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    Dataset updated
    Feb 1, 2023
    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, 2010 - Dec 1, 2021
    Area covered
    China
    Description

    Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 19.000 % in 2021. This records a decrease from the previous number of 20.900 % for 2020. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 31.700 % from Dec 1990 (Median) to 2021, with 19 observations. The data reached an all-time high of 72.000 % in 1990 and a record low of 19.000 % in 2021. Poverty Headcount Ratio at Societal Poverty Lines: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s China – Table CN.World Bank.WDI: Social: Poverty and Inequality. 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 2017 U.S. dollars and defined as max($2.15, $1.15 + 0.5*Median). This means that when the national median is sufficiently low, the Societal Poverty line is equivalent to the extreme poverty line, $2.15. For countries with a sufficiently high national median, the Societal Poverty Line grows as countries’ median income grows.;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.;;The World Bank’s 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).

  4. M

    World Poverty Rate

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). World Poverty Rate [Dataset]. https://www.macrotrends.net/global-metrics/countries/wld/world/poverty-rate
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    csvAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Area covered
    world, World
    Description
    World poverty rate for 2023 was 47.00%, a 1% decline from 2022.
    <ul style='margin-top:20px;'>
    
    <li>World poverty rate for 2022 was <strong>48.00%</strong>, a <strong>0.6% decline</strong> from 2021.</li>
    <li>World poverty rate for 2021 was <strong>48.60%</strong>, a <strong>1.8% decline</strong> from 2020.</li>
    <li>World poverty rate for 2020 was <strong>50.40%</strong>, a <strong>4.1% increase</strong> from 2019.</li>
    </ul>Poverty headcount ratio at $5.50 a day is the percentage of the population living on less than $5.50 a day at 2011 international 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.
    
  5. d

    Global Subnational Inequality

    • search.dataone.org
    • dataverse.harvard.edu
    Updated Nov 8, 2023
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    Hai-Anh H. Dang; Minh Cong Nguyen; Trong-Anh Trinh (2023). Global Subnational Inequality [Dataset]. http://doi.org/10.7910/DVN/IOGOYE
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    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Hai-Anh H. Dang; Minh Cong Nguyen; Trong-Anh Trinh
    Description

    The database (version August 2022) is built upon the released Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021). In this database, we assemble a new panel dataset that provides different measures of inequality. This database is generated using household income and consumption surveys from the World Bank’s Global Monitoring Database (GMD), which underlie country official poverty statistics, and offers the most detailed subnational poverty data on a global scale to date. The Global Subnational Atlas of Poverty (GSAP) is produced by the World Bank’s Poverty and Equity Global Practice, coordinated by the Data for Goals (D4G) team, and supported by the six regional statistics teams in the Poverty and Equity Global Practice, and Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) at the World Bank. The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in Poverty and Inequality Platform (PIP), World Bank’s Multidimensional Poverty Measures (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports. Reference: World Bank. (2021). World Bank estimates based on data from the Global Subnational Atlas of Poverty, Global Monitoring Database. World Bank: Washington. https://datacatalog.worldbank.org/search/dataset/0042041

  6. w

    Learning Poverty Global Database

    • data360.worldbank.org
    Updated Apr 18, 2025
    + more versions
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    (2025). Learning Poverty Global Database [Dataset]. https://data360.worldbank.org/en/dataset/WB_LPGD
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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
    2001 - 2023
    Description

    Will all children be able to read by 2030? 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—generally 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. 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. To understand the global picture better, we have worked with the UNESCO Institute for Statistics (UIS) to assemble a new dataset with the most comprehensive measures of this foundational skill yet developed, by linking together data from credible cross-national and national assessments of reading. This dataset covers 115 countries, accounting for 81% of children worldwide and 79% of children in low- and middle-income countries. The new data allow us to estimate the reading proficiency of late-primary-age children, and we also provide what are among the first estimates (and the most comprehensive, for low- and middle-income countries) of the historical rate of progress in improving reading proficiency globally (for the 2000-17 period). The results show that 53% of all children in low- and middle-income countries cannot read age-appropriate material by age 10, and that at current rates of improvement, this “learning poverty” rate will have fallen only to 43% by 2030. Indeed, we find that the goal of all children reading by 2030 will be attainable only with historically unprecedented progress. The high rate of “learning poverty” and slow progress in low- and middle-income countries is an early warning that all the ambitious SDG targets in education (and likely of social progress) are at risk. Based on this evidence, we suggest a new medium-term target to guide the World Bank’s work in low- and middle- income countries: cut learning poverty by at least half by 2030. This target, together with improved measurement of learning, can be as an evidence-based tool to accelerate progress to get all children reading by age 10.

    For further details, please refer to https://thedocs.worldbank.org/en/doc/e52f55322528903b27f1b7e61238e416-0200022022/original/Learning-poverty-report-2022-06-21-final-V7-0-conferenceEdition.pdf

  7. Poverty and Inequality - African Countries - Dataset - ADH Data Portal

    • ckan.africadatahub.org
    Updated Oct 24, 2022
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    africadatahub.org (2022). Poverty and Inequality - African Countries - Dataset - ADH Data Portal [Dataset]. https://ckan.africadatahub.org/dataset/poverty-and-inequality
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    Dataset updated
    Oct 24, 2022
    Dataset provided by
    Africa Data Hub
    License

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

    Area covered
    Africa
    Description

    World Bank has a Poverty and Inequality Platform where country data can be downloaded for Poverty, Inequality and Multi-dimensional Poverty. The link https://pip.worldbank.org/country-profiles will take you to the Country Poverty Profile and from this page you can select any country and choose between one of three the Poverty Lines: $1.9, $3.2 or $5.5 (at 2011 international prices) and that Poverty Profile will be called up. Then you can select the Poverty, Inequality and Multi-dimensional Poverty data that you want to download. The Reporting Years are: 2000, 2008 and 2018.

  8. Extreme poverty as share of global population in Africa 2025, by country

    • statista.com
    Updated Feb 3, 2025
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    Statista (2025). Extreme poverty as share of global population in Africa 2025, by country [Dataset]. https://www.statista.com/statistics/1228553/extreme-poverty-as-share-of-global-population-in-africa-by-country/
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    Dataset updated
    Feb 3, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2025
    Area covered
    Africa
    Description

    In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

  9. Japan JP: Income Share Held by Third 20%

    • ceicdata.com
    Updated May 15, 2018
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    CEICdata.com (2018). Japan JP: Income Share Held by Third 20% [Dataset]. https://www.ceicdata.com/en/japan/poverty
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    Dataset updated
    May 15, 2018
    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, 2008
    Area covered
    Japan
    Description

    JP: Income Share Held by Third 20% data was reported at 17.300 % in 2008. JP: Income Share Held by Third 20% data is updated yearly, averaging 17.300 % from Dec 2008 (Median) to 2008, with 1 observations. JP: Income Share Held by Third 20% data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank.WDI: Poverty. 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.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. 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.

  10. J

    Jordan JO: Poverty Gap at National Poverty Lines: %

    • ceicdata.com
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    CEICdata.com, Jordan JO: Poverty Gap at National Poverty Lines: % [Dataset]. https://www.ceicdata.com/en/jordan/poverty/jo-poverty-gap-at-national-poverty-lines-
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    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, 2008 - Dec 1, 2010
    Area covered
    Jordan
    Description

    Jordan JO: Poverty Gap at National Poverty Lines: % data was reported at 3.600 % in 2010. This records an increase from the previous number of 2.600 % for 2008. Jordan JO: Poverty Gap at National Poverty Lines: % data is updated yearly, averaging 3.100 % from Dec 2008 (Median) to 2010, with 2 observations. The data reached an all-time high of 3.600 % in 2010 and a record low of 2.600 % in 2008. Jordan JO: Poverty Gap at National Poverty Lines: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank: Poverty. Poverty gap at national poverty lines is the mean shortfall from the poverty lines (counting the nonpoor as having zero shortfall) as a percentage of the poverty lines. This measure reflects the depth of poverty as well as its incidence.; ; World Bank, Global Poverty Working Group. Data are compiled from official government sources or are computed by World Bank staff using national (i.e. country–specific) poverty lines.; ; This 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.

  11. Share of population living in extreme poverty, by country, varying years...

    • data.amerigeoss.org
    • data.apps.fao.org
    png, wms, zip
    Updated Mar 14, 2023
    + more versions
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    Food and Agriculture Organization (2023). Share of population living in extreme poverty, by country, varying years (FGGD) [Dataset]. https://data.amerigeoss.org/dataset/847f3f50-8519-11db-b9b2-000d939bc5d8
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    zip, wms, pngAvailable download formats
    Dataset updated
    Mar 14, 2023
    Dataset provided by
    Food and Agriculture Organizationhttp://fao.org/
    License

    Attribution-NonCommercial-ShareAlike 3.0 (CC BY-NC-SA 3.0)https://creativecommons.org/licenses/by-nc-sa/3.0/
    License information was derived automatically

    Description

    The FGGD extreme poverty map is a global vector datalayer at scale 1:5 000 000. The map depicts the differences among countries with respect to the national population estimated to be living in extreme poverty as of the latest year for which data was available in 2005. Data have been compiled by FAO from data reported in World Bank, WDI Online, as of April 2005.

    Data publication: 2007-06-25

    Supplemental Information:

    This dataset is contained in Module 3 "Socio-economics and nutrition indicators" of Food Insecurity, Poverty and Environment Global GIS Database (FGGD) (FAO, 2007).

    Contact points:

    Metadata Contact: FAO-Data

    Resource Contact: Mirella Salvatore

    Resource constraints:

    copyright

    Online resources:

    Share of population living in extreme poverty, by country, varying years

  12. M

    Brazil Poverty Rate

    • macrotrends.net
    csv
    Updated May 31, 2025
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    MACROTRENDS (2025). Brazil Poverty Rate [Dataset]. https://www.macrotrends.net/global-metrics/countries/bra/brazil/poverty-rate
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    csvAvailable download formats
    Dataset updated
    May 31, 2025
    Dataset authored and provided by
    MACROTRENDS
    License

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

    Time period covered
    Jan 1, 1981 - Dec 31, 2023
    Area covered
    Brazil
    Description

    Historical chart and dataset showing Brazil poverty rate by year from 1981 to 2023.

  13. F

    Estimated Percent of People of All Ages in Poverty for United States

    • fred.stlouisfed.org
    json
    Updated Dec 20, 2024
    + more versions
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    (2024). Estimated Percent of People of All Ages in Poverty for United States [Dataset]. https://fred.stlouisfed.org/series/PPAAUS00000A156NCEN
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    jsonAvailable download formats
    Dataset updated
    Dec 20, 2024
    License

    https://fred.stlouisfed.org/legal/#copyright-public-domainhttps://fred.stlouisfed.org/legal/#copyright-public-domain

    Area covered
    United States
    Description

    Graph and download economic data for Estimated Percent of People of All Ages in Poverty for United States (PPAAUS00000A156NCEN) from 1989 to 2023 about percent, child, poverty, and USA.

  14. H

    Data from: A Multifaceted Program Causes Lasting Progress for the Very Poor:...

    • dataverse.harvard.edu
    Updated Nov 13, 2019
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    Abhijit Banerjee; Esther Duflo; Nathanael Goldberg; Dean Karlan; Robert Osei; William Parienté; Jeremy Shapiro; Bram Thuysbaert; Christopher Udry (2019). A Multifaceted Program Causes Lasting Progress for the Very Poor: Evidence From Six Countries [Dataset]. http://doi.org/10.7910/DVN/NHIXNT
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 13, 2019
    Dataset provided by
    Harvard Dataverse
    Authors
    Abhijit Banerjee; Esther Duflo; Nathanael Goldberg; Dean Karlan; Robert Osei; William Parienté; Jeremy Shapiro; Bram Thuysbaert; Christopher Udry
    License

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

    Area covered
    Pakistan, Sindh, Peru, Canas and Acomayo, Kolkata, Murshidabad district, India, Lempira, Honduras, Ethiopia, Tigray, Kilte Awlaelo district, Northern and Upper Eastern regions, Ghana
    Dataset funded by
    International Initiative for Impact Evaluation (3ie)
    Ford Foundation
    USAID
    Description

    We present results from six randomized control trials of an integrated approach to improve livelihoods among the very poor. The approach combines the transfer of a productive asset with consumption support, training, and coaching plus savings encouragement and health education and/or services. Results from the implementation of the same basic program, adapted to a wide variety of geographic and institutional contexts and with multiple implementing partners, show statistically significant cost-effective impacts on consumption (fueled mostly by increases in self-employment income) and psychosocial status of the targeted households. The impact on the poor households lasted at least a year after all implementation ended. It is possible to make sustainable improvements in the economic status of the poor with a relatively short-term intervention.

  15. d

    Global Subnational Atlas of Poverty

    • dataone.org
    Updated Nov 8, 2023
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    Hai-Anh H. Dang; Minh Cong Nguyen; Trong-Anh Trinh (2023). Global Subnational Atlas of Poverty [Dataset]. http://doi.org/10.7910/DVN/MLHFAF
    Explore at:
    Dataset updated
    Nov 8, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Hai-Anh H. Dang; Minh Cong Nguyen; Trong-Anh Trinh
    Description

    The database (version August 2022) is built upon the released Global Subnational Atlas of Poverty (GSAP) (World Bank, 2021). In this database, we assemble a new panel dataset that provides (headcount) poverty rates using the daily poverty lines of US $1.90, $3.20, and $5.50 (based on the revised 2011 Purchasing Power Parity (PPP) dollars). This database is generated using household income and consumption surveys from the World Bank’s Global Monitoring Database (GMD), which underlie country official poverty statistics, and offers the most detailed subnational poverty data on a global scale to date. The Global Subnational Atlas of Poverty (GSAP) is produced by the World Bank’s Poverty and Equity Global Practice, coordinated by the Data for Goals (D4G) team, and supported by the six regional statistics teams in the Poverty and Equity Global Practice, and Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) at the World Bank. The Global Monitoring Database (GMD) is the World Bank’s repository of multitopic income and expenditure household surveys used to monitor global poverty and shared prosperity. The household survey data are typically collected by national statistical offices in each country, and then compiled, processed, and harmonized. The process is coordinated by the Data for Goals (D4G) team and supported by the six regional statistics teams in the Poverty and Equity Global Practice. Global Poverty & Inequality Data Team (GPID) in Development Economics Data Group (DECDG) also contributed historical data from before 1990, and recent survey data from Luxemburg Income Studies (LIS). Selected variables have been harmonized to the extent possible such that levels and trends in poverty and other key sociodemographic attributes can be reasonably compared across and within countries over time. The GMD’s harmonized microdata are currently used in Poverty and Inequality Platform (PIP), World Bank’s Multidimensional Poverty Measures (WB MPM), the Global Database of Shared Prosperity (GDSP), and Poverty and Shared Prosperity Reports. Reference: World Bank. (2021). World Bank estimates based on data from the Global Subnational Atlas of Poverty, Global Monitoring Database. World Bank: Washington. https://datacatalog.worldbank.org/search/dataset/0042041

  16. Poverty gap in OECD countries based on disposable income 2022, by country

    • statista.com
    Updated Feb 9, 2024
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    Statista (2024). Poverty gap in OECD countries based on disposable income 2022, by country [Dataset]. https://www.statista.com/statistics/1461833/poverty-gap-oecd-countries/
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    Dataset updated
    Feb 9, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    OECD, Worldwide
    Description

    Romania was the OECD country with the highest poverty gap in 2022. The poverty gap in Costa Rica was around 47 percentage points. Chile followed behind, whereas the Slovak Republic had the lowest gap at 23 percentage points.

  17. G

    Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total...

    • ceicdata.com
    Updated Feb 15, 2025
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    CEICdata.com (2025). Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population [Dataset]. https://www.ceicdata.com/en/ghana/social-poverty-and-inequality/multidimensional-poverty-headcount-ratio-world-bank--of-total-population
    Explore at:
    Dataset updated
    Feb 15, 2025
    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, 2012 - Dec 1, 2016
    Area covered
    Ghana
    Description

    Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 32.800 % in 2016. This records a decrease from the previous number of 33.200 % for 2012. Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 33.000 % from Dec 2012 (Median) to 2016, with 2 observations. The data reached an all-time high of 33.200 % in 2012 and a record low of 32.800 % in 2016. Ghana Multidimensional Poverty Headcount Ratio: World Bank: % of total population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Ghana – Table GH.World Bank.WDI: Social: Poverty and Inequality. 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 – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.;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.;;The World Bank’s 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).

  18. a

    Data from: Goal 1: End poverty in all its forms everywhere

    • senegal2-sdg.hub.arcgis.com
    • ethiopia-1-sdg.hub.arcgis.com
    • +8more
    Updated Jul 1, 2022
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    arobby1971 (2022). Goal 1: End poverty in all its forms everywhere [Dataset]. https://senegal2-sdg.hub.arcgis.com/items/509af3652f8b4ffb86cdb145e42ed4c6
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    Dataset updated
    Jul 1, 2022
    Dataset authored and provided by
    arobby1971
    Description

    Goal 1End poverty in all its forms everywhereTarget 1.1: By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a dayIndicator 1.1.1: Proportion of the population living below the international poverty line by sex, age, employment status and geographic location (urban/rural)SI_POV_DAY1: Proportion of population below international poverty line (%)SI_POV_EMP1: Employed population below international poverty line, by sex and age (%)Target 1.2: By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsIndicator 1.2.1: Proportion of population living below the national poverty line, by sex and ageSI_POV_NAHC: Proportion of population living below the national poverty line (%)Indicator 1.2.2: Proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitionsSD_MDP_MUHC: Proportion of population living in multidimensional poverty (%)SD_MDP_ANDI: Average proportion of deprivations for people multidimensionally poor (%)SD_MDP_MUHHC: Proportion of households living in multidimensional poverty (%)SD_MDP_CSMP: Proportion of children living in child-specific multidimensional poverty (%)Target 1.3: Implement nationally appropriate social protection systems and measures for all, including floors, and by 2030 achieve substantial coverage of the poor and the vulnerableIndicator 1.3.1: Proportion of population covered by social protection floors/systems, by sex, distinguishing children, unemployed persons, older persons, persons with disabilities, pregnant women, newborns, work-injury victims and the poor and the vulnerableSI_COV_MATNL: [ILO] Proportion of mothers with newborns receiving maternity cash benefit (%)SI_COV_POOR: [ILO] Proportion of poor population receiving social assistance cash benefit, by sex (%)SI_COV_SOCAST: [World Bank] Proportion of population covered by social assistance programs (%)SI_COV_SOCINS: [World Bank] Proportion of population covered by social insurance programs (%)SI_COV_CHLD: [ILO] Proportion of children/households receiving child/family cash benefit, by sex (%)SI_COV_UEMP: [ILO] Proportion of unemployed persons receiving unemployment cash benefit, by sex (%)SI_COV_VULN: [ILO] Proportion of vulnerable population receiving social assistance cash benefit, by sex (%)SI_COV_WKINJRY: [ILO] Proportion of employed population covered in the event of work injury, by sex (%)SI_COV_BENFTS: [ILO] Proportion of population covered by at least one social protection benefit, by sex (%)SI_COV_DISAB: [ILO] Proportion of population with severe disabilities receiving disability cash benefit, by sex (%)SI_COV_LMKT: [World Bank] Proportion of population covered by labour market programs (%)SI_COV_PENSN: [ILO] Proportion of population above statutory pensionable age receiving a pension, by sex (%)Target 1.4: By 2030, ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services, ownership and control over land and other forms of property, inheritance, natural resources, appropriate new technology and financial services, including microfinanceIndicator 1.4.1: Proportion of population living in households with access to basic servicesSP_ACS_BSRVH2O: Proportion of population using basic drinking water services, by location (%)SP_ACS_BSRVSAN: Proportion of population using basic sanitation services, by location (%)Indicator 1.4.2: Proportion of total adult population with secure tenure rights to land, (a) with legally recognized documentation, and (b) who perceive their rights to land as secure, by sex and type of tenureSP_LGL_LNDDOC: Proportion of people with legally recognized documentation of their rights to land out of total adult population, by sex (%)SP_LGL_LNDSEC: Proportion of people who perceive their rights to land as secure out of total adult population, by sex (%)SP_LGL_LNDSTR: Proportion of people with secure tenure rights to land out of total adult population, by sex (%)Target 1.5: By 2030, build the resilience of the poor and those in vulnerable situations and reduce their exposure and vulnerability to climate-related extreme events and other economic, social and environmental shocks and disastersIndicator 1.5.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 1.5.2: Direct economic loss attributed to disasters in relation to global gross domestic product (GDP)VC_DSR_GDPLS: Direct economic loss attributed to disasters (current United States dollars)VC_DSR_LSGP: Direct economic loss attributed to disasters relative to GDP (%)VC_DSR_AGLH: Direct agriculture loss attributed to disasters (current United States dollars)VC_DSR_HOLH: Direct economic loss in the housing sector attributed to disasters (current United States dollars)VC_DSR_CILN: Direct economic loss resulting from damaged or destroyed critical infrastructure attributed to disasters (current United States dollars)VC_DSR_CHLN: Direct economic loss to cultural heritage damaged or destroyed attributed to disasters (millions of current United States dollars)VC_DSR_DDPA: Direct economic loss to other damaged or destroyed productive assets attributed to disasters (current United States dollars)Indicator 1.5.3: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 1.5.4: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 1.a: Ensure significant mobilization of resources from a variety of sources, including through enhanced development cooperation, in order to provide adequate and predictable means for developing countries, in particular least developed countries, to implement programmes and policies to end poverty in all its dimensionsIndicator 1.a.1: Total official development assistance grants from all donors that focus on poverty reduction as a share of the recipient country’s gross national incomeDC_ODA_POVLG: Official development assistance grants for poverty reduction, by recipient countries (percentage of GNI)DC_ODA_POVDLG: Official development assistance grants for poverty reduction, by donor countries (percentage of GNI)DC_ODA_POVG: Official development assistance grants for poverty reduction (percentage of GNI)Indicator 1.a.2: Proportion of total government spending on essential services (education, health and social protection)SD_XPD_ESED: Proportion of total government spending on essential services, education (%)Target 1.b: Create sound policy frameworks at the national, regional and international levels, based on pro-poor and gender-sensitive development strategies, to support accelerated investment in poverty eradication actionsIndicator 1.b.1: Pro-poor public social spending

  19. Data Poverty Index - claculation results

    • figshare.com
    pdf
    Updated Jan 20, 2016
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    Mathias Leidig; Richard Teeuw (2016). Data Poverty Index - claculation results [Dataset]. http://doi.org/10.6084/m9.figshare.1531940.v5
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    pdfAvailable download formats
    Dataset updated
    Jan 20, 2016
    Dataset provided by
    Figsharehttp://figshare.com/
    Authors
    Mathias Leidig; Richard Teeuw
    License

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

    Description

    A table to download the data will be made available on: http://research.mlabs.org.uk/blog/data-poverty/ .

  20. World Development Indicators Data Package

    • johnsnowlabs.com
    csv
    Updated Jan 20, 2021
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    John Snow Labs (2021). World Development Indicators Data Package [Dataset]. https://www.johnsnowlabs.com/marketplace/world-development-indicators-data-package/
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    csvAvailable download formats
    Dataset updated
    Jan 20, 2021
    Dataset authored and provided by
    John Snow Labs
    Description

    This data package contains data on World Development Indicators on Population and Economy, Poverty and Shared Prosperity, People, Environment, Economy, States and Markets and Global links.

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Statista (2025). Poverty rates in OECD countries 2022 [Dataset]. https://www.statista.com/statistics/233910/poverty-rates-in-oecd-countries/
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Poverty rates in OECD countries 2022

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14 scholarly articles cite this dataset (View in Google Scholar)
Dataset updated
May 30, 2025
Dataset authored and provided by
Statistahttp://statista.com/
Area covered
United States
Description

Out of all OECD countries, Cost Rica had the highest poverty rate as of 2022, at over 20 percent. The country with the second highest poverty rate was the United States, with 18 percent. On the other end of the scale, Czechia had the lowest poverty rate at 6.4 percent, followed by Denmark.

The significance of the OECD

The OECD, or the Organisation for Economic Co-operation and Development, was founded in 1948 and is made up of 38 member countries. It seeks to improve the economic and social well-being of countries and their populations. The OECD looks at issues that impact people’s everyday lives and proposes policies that can help to improve the quality of life.

Poverty in the United States

In 2022, there were nearly 38 million people living below the poverty line in the U.S.. About one fourth of the Native American population lived in poverty in 2022, the most out of any ethnicity. In addition, the rate was higher among young women than young men. It is clear that poverty in the United States is a complex, multi-faceted issue that affects millions of people and is even more complex to solve.

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