The Poverty and Inequality Platform (PIP), developed by the World Bank, provides global, regional, and country-level estimates of poverty, inequality, and shared prosperity for 170 economies. PIP is the primary source for the World Bank's poverty and inequality estimates, and it informs many Sustainable Development Goal (SDG) indicators on poverty and inequality. The data, governed by the Global Poverty Working Group (GPWG), are expressed in 2017 Purchasing Power Parity (PPP) prices, with global poverty lines set at $2.15, $3.65, and $6.85 per day.

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

The Multidimensional Poverty Measure (MPM) seeks to understand poverty beyond just a monetary dimension by including access to education and basic infrastructure along with the monetary headcount ratio at the $1.90 poverty line. The World Bank’s measure takes inspiration and guidance from other prominent multidimensional measures, particularly the Multidimensional Poverty Index (MPI) developed by UNDP and Oxford University but differs from them in one important aspect: it includes Monetary poverty (measured as having a daily consumption less than $1.90 in 2011 PPP) as one of the dimensions. While monetary poverty is strongly correlated with deprivations in other domains, this correlation is far from perfect. The Poverty and Shared Prosperity 2020 (World Bank, 2020) report shows that over a third of those experiencing multidimensional poverty are not captured by the monetary headcount ratio, in line with the findings of the previous edition of the report (World Bank, 2018). A country’s MPM is at least as high as or higher than the monetary poverty, reflecting the additional role of nonmonetary dimensions in increasing multidimensional poverty and their importance to general well-being.

Latest poverty and inequality indicators compiled from officially recognized international sources. Poverty indicators include the poverty headcount ratio, poverty gap, and number of poor at both international and national poverty lines. Inequality indicators include the Gini index and income or consumption distributions. The database includes national, regional and global estimates. This database is maintained by the Global Poverty Working Group (GPWG), a team of poverty measurement experts from the Poverty Global Practice, the Development Research Group, and the Development Data Group.

The database is part of the Poverty and Inequality Platform, https://pip.worldbank.org/home

United States Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 1.500 % in 2022. This records an increase from the previous number of 0.600 % for 2021. United States Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 1.300 % from Dec 2010 (Median) to 2022, with 13 observations. The data reached an all-time high of 1.500 % in 2022 and a record low of 0.500 % in 2020. United States 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 United States – Table US.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).

As per World Bank's thresholds, in 2022, over 23.9 percent of India's population was living on less than 3 U.S. dollars per day. When the 4.20 U.S. dollars per day threshold is considered, the share increased to over 5.3 percent. The poverty line of 4.20 per day is set by the World Bank to be representative of the definitions of poverty adopted in lower-middle-income countries.

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

Context

Poverty data from the World bank Data includes country and subnational level.

Content

Poverty data available at the administrative unit level 1, based on national poverty line(s). Administrative unit level 1 is the highest subnational unit level, e.g. state or province level.

Annual Coverage: 1999 - 2013 Cite:

Acknowledgements

Data from the world bank. Some descriptions from data.world. This dataset is subject to these license terms, including attribution requirements and linking the license terms to: http://web.worldbank.org/WBSITE/EXTERNAL/0,,contentMDK:22547097~pagePK:50016803~piPK:50016805~theSitePK:13,00.html

Source: http://data.worldbank.org/data-catalog/sub-national-poverty-data

Inspiration

• linkage to kiva dataset
• Differences from Oxford subnational deprivation index (requires matching the geographic regions).
• Connect with other world bank time-series and try to see if any predictors for change in poverty levels over time can be found (despite the short time-scale).
• What causes worsening (rather than a generic improvement/poverty reduction) in some cases/countries/ regions? Especially when the overall country may be doing better, but some regions get worse/poorer?

Russia Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 5.000 % in 2020. This records an increase from the previous number of 4.600 % for 2019. Russia Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 4.050 % from Dec 2010 (Median) to 2020, with 4 observations. The data reached an all-time high of 5.000 % in 2020 and a record low of 0.300 % in 2010. Russia 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 Russian Federation – Table RU.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).

This data compiles projected poverty rates for 2020 and 2021 from seven data vintages released between April 2020 to October 2022. The underlying poverty data is used in this blog.

The following variables are reported: region--regional classifications used in Poverty and Inequality Platform; nowcasts--the date of data release; FGT0_lic--Poverty headcount rate using the poverty lines for low-income countries; FGT0_lmic--Poverty headcount rate using the poverty lines for lower-middle-income countries; FGT0_umic--Poverty headcount rate using the poverty lines for upper-middle-income countries; FGT0_lic_change--change in the poverty rate from the previous year using the LIC poverty line; FGT0_lmic_change--change in the poverty rate from the previous year using the LMIC poverty line; FGT0_umic_change--change in the poverty rate from the previous year using the UMIC poverty line. Poverty rates are calculated using the 2011 PPPs for all data vintages besides the October 2022 vintage when 2017 PPPs were used. The poverty lines for LICs are either $1.90 a day in 2011 PPPs or $2.15 a day in 2017 PPPs. The poverty lines for LMICs are either $3.20 a day in 2011 PPPs or $3.65 a day in 2017 PPPs. The poverty lines for LICs are either $5.50 a day in 2011 PPPs or $6.85 a day in 2017 PPPs.

This dataset contains data from the World Development Indicators on Poverty and Shared Prosperity presenting indicators that measure progress toward the World Bank Group’s twin goals of ending extreme poverty by 2030 and promoting shared prosperity in every country in a sustainable manner.

Context

This dataset was uploaded to support the Data Science For Good Kiva crowdfunding challenge. In particular, in uploading this dataset, I intend to assist with mapping subnational locations in the Kiva dataset to more accurate geocodes.

Content

This dataset contains poverty data at the administrative unit level 1, based on national poverty line(s). Administrative unit level 1 refers to the highest subnational unit level (examples include ‘state’, ‘governorate’, ‘province’). This dataset also provides data and methodology for distinguishing between poverty rates in urban and rural regions.

This dataset includes one main .csv file: Subnational-PovertyData.csv, which includes a set of poverty indicators at the national and subnational level between the years 1996-2013. Many countries are missing data for multiple years, and no country has data for the years 1997-1999.

It also includes three metadata .csv files: 1. Subnational-PovertyCountry.csv, which describes the country codes and subregions. 2.Subnational-PovertySeries.csv, which describes the three series indicators for national, urban, and rural poverty headcount ratios. This metadata file also including limitations, statistical methodologies, and development relevance for these metrics. 3. Subnational-Povertyfootnote.csv, which describes the years and sources for all of the country-series combinations.

Acknowledgements

This dataset is provided openly by the World Bank. Individual sources for the different data series are available in Subnational-Povertyfootnote.csv.

This dataset is classified as Public under the Access to Information Classification Policy. Users inside and outside the World Bank can access this dataset. It is licensed under CC-BY 4.0.

Metadata

Type: Time Series Topics: Economic Growth Poverty Economy Coverage: IBRD Languages Supported: English Number of Economies: 60 Geographical Coverage: World Access Options: Download, Query Tool Temporal Coverage: 1996 - 2013 Last Updated: April 27, 2015

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.

Norway Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 1.800 % in 2019. This records a decrease from the previous number of 2.600 % for 2018. Norway Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 1.550 % from Dec 2008 (Median) to 2019, with 12 observations. The data reached an all-time high of 2.600 % in 2018 and a record low of 0.800 % in 2011. Norway 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 Norway – Table NO.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).

Jamaica Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 0.700 % in 2021. This records an increase from the previous number of 0.300 % for 2018. Jamaica Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 0.500 % from Dec 2018 (Median) to 2021, with 2 observations. The data reached an all-time high of 0.700 % in 2021 and a record low of 0.300 % in 2018. Jamaica 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 Jamaica – Table JM.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).

Definition: The indicator “proportion of the population below the international poverty line” is defined as the percentage of the population living on less than $2.15 a day at 2017 international prices. Concepts: In assessing poverty in a given country, and how best to reduce poverty, one naturally focuses on a poverty line that is considered appropriate for that country. But how do we talk meaningfully about Last updated: 2023-03-31 “global poverty?” Poverty lines across countries vary in terms of their purchasing power, and they have a strong economic gradient, such that richer countries tend to adopt higher standards of living in defining poverty. But to consistently measure global absolute poverty in terms of consumption we need to treat two people with the same purchasing power over commodities the same way—both are either poor or not poor—even if they live in different 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 parity (PPP) exchange rates, was chosen for World Development Report 1990 because it was typical of the poverty lines in lowincome 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 the World Bank adopted $2.15 as the international poverty line using the 2017 PPP. Prior to that, the 2015 update set the international poverty line at $1.90 using the 2011 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries and over time. Unit of measure: Percent (%). The unit of measure is the proportion of people.Validation: The raw data are obtained by poverty economists through their contacts in the NSOs, and checked for quality before being submitted for further analysis. The raw data can be unit-record survey data, or grouped data, depending on the agreements with the country governments. In most cases, the welfare aggregate, the essential element for poverty estimation, is generated by the country governments. Sometimes, the World Bank constructs the welfare aggregate or adjusts the aggregate provided by the countryData availability: Data are available in 160+ economies, (measured in terms of number of economies that have at least 1 data point). References: For more information and methodology, please see : https://worldbank.github.io/PIP-Methodology/. Also, consult: https://openknowledge.worldbank.org/handle/10986/37061

Overview

This dataset provides a comprehensive collection of time series data sourced from the World Bank Open Data Platform, covering a wide range of global indicators from 1960 to the most recently published year. It includes economic, social, environmental, and demographic metrics, making it an ideal resource for researchers, data scientists, and policymakers interested in global development trends, economic forecasting, or socio-economic analysis.

A tutorial on how to combined the dataset topics together into one large dataset can be found here

Why this Dataset?

My motivation for this project was to curate a high-quality collection of datasets for World Bank indicators organized by topics and structured in time-series, making them more accessible for data science projects. Since the World Bank’s Kaggle datasets have not been updated since 2019 https://www.kaggle.com/organizations/theworldbank, I saw an opportunity to provide more current data for the data analysis community.

Dataset Collection Contents

This collection brings together more than 800 World Bank indicators organized into 18 topic‑specific CSV files. Each file is structured as a country‑year panel: every row represents a unique combination of year (1960‑present) and ISO‑3 country code, while the columns hold the topic’s indicators.

The collection includes datasets with a variety of indicators, such as: - Economic Metrics: GDP growth (%), GDP per capita, consumer price inflation, merchandise trade, gross capital formation, and more.
- Social Metrics: School enrollment (primary, secondary, tertiary), infant mortality rate, maternal mortality rate, poverty headcount, and more.
- Environmental Metrics: Forest area, renewable energy consumption, food production indices, and more.
- Demographic Metrics: Urban population, life expectancy, net migration, and more.

Usage

This dataset is ideal for a variety of applications, including: - Economic forecasting and trend analysis (e.g., GDP growth, inflation).
- Socio-economic studies (e.g., education, health, poverty).
- Environmental impact analysis (e.g., renewable energy adoption).
- Demographic research (e.g., population trends, migration).

Topic datasets can be merged with each other using year and country code. This tutorial with notebook code can help you get started quickly.

Collection Methodology

The data is collected via a custom software application that discovers and groups high-quality indicators with rules-based logic & artificial intelligence, generates metadata, and performs ETL for the data from the World Bank API. The result is a clean, up‑to‑date collection of World Bank indicators in time-series format that is ready for analysis—no manual downloads or data wrangling required.

Modifications

The original World Bank data has been aggregated and transformed for ease of use. Missing values have been preserved as provided by the World Bank, and no significant transformations have been applied beyond formatting and aggregation into a single file.

Source & Attribution

The World Bank: World Development Indicators

This dataset is publicly available and sourced from the World Bank Open Data Platform and is made available under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. When using this data, please attribute the World Bank as follows: "Data sourced from the World Bank, licensed under CC BY 4.0." For more details on the World Bank’s terms of use, visit: https://www.worldbank.org/en/about/legal/terms-of-use-for-datasets.

License

This dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Feel free to use this data in Kaggle notebooks, academic research, or policy analysis. If you create a derived dataset or analysis, I encourage you to share it with the Kaggle community.

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.

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

Bangladesh Multidimensional Poverty Headcount Ratio: World Bank: % of total population data was reported at 6.600 % in 2022. This records a decrease from the previous number of 20.500 % for 2016. Bangladesh Multidimensional Poverty Headcount Ratio: World Bank: % of total population data is updated yearly, averaging 20.500 % from Dec 2010 (Median) to 2022, with 3 observations. The data reached an all-time high of 31.300 % in 2010 and a record low of 6.600 % in 2022. Bangladesh 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 Bangladesh – Table BD.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).