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 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

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

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).

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.

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).

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

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.

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).

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.

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?

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.

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.

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).

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).

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

Context

The World Bank is an international financial institution that provides loans to countries of the world for capital projects. The World Bank's stated goal is the reduction of poverty. Source: https://en.wikipedia.org/wiki/World_Bank

Content

This dataset combines key education statistics from a variety of sources to provide a look at global literacy, spending, and access.

For more information, see the World Bank website.

Fork this kernel to get started with this dataset.

Acknowledgements

https://bigquery.cloud.google.com/dataset/bigquery-public-data:world_bank_health_population

http://data.worldbank.org/data-catalog/ed-stats

https://cloud.google.com/bigquery/public-data/world-bank-education

Citation: The World Bank: Education Statistics

Dataset Source: World Bank. This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - http://www.data.gov/privacy-policy#data_policy - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

Banner Photo by @till_indeman from Unplash.

Inspiration

Of total government spending, what percentage is spent on education?

This Dataset comes from the R Package wbstats. The World Bank[https://www.worldbank.org/] is a tremendous source of global socio-economic data; spanning several decades and dozens of topics, it has the potential to shed light on numerous global issues. To help provide access to this rich source of information, The World Bank themselves, provide a well structured RESTful API. While this API is very useful for integration into web services and other high-level applications, it becomes quickly overwhelming for researchers who have neither the time nor the expertise to develop software to interface with the API. This leaves the researcher to rely on manual bulk downloads of spreadsheets of the data they are interested in. This too is can quickly become overwhelming, as the work is manual, time consuming, and not easily reproducible. The goal of the wbstats R-package is to provide a bridge between these alternatives and allow researchers to focus on their research questions and not the question of accessing the data. The wbstats R-package allows researchers to quickly search and download the data of their particular interest in a programmatic and reproducible fashion; this facilitates a seamless integration into their workflow and allows analysis to be quickly rerun on different areas of interest and with realtime access to the latest available data.

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. Copied from https://databank.worldbank.org/source/world-development-indicators.

Highlighted features of the wbstats R-package: * Uses version 2 of the World Bank API that provides access to more indicators and metadata than the previous API version * Access to all annual, quarterly, and monthly data available in the API * Support for searching and downloading data in multiple languages * Returns data in either wide (default) or long format * Support for Most Recent Value queries * Support for grep style searching for data descriptions and names * Ability to download data not only by country, but by aggregates as well, such as High Income or South Asia

More information can be found at https://www.rdocumentation.org/packages/wbstats/versions/1.0.4

Note for Version 1. Version 1 published January 2023. Its primary focus is on the featured indicator of climate change. Other versions planned will cover other featured indicators such as economy, education, energy, environment, debt, gender, health, infrastructure, poverty, science and technology.

The new scorecard tracks progress toward the World Bank Group's vision to create a world free of poverty on a livable planet. The Scorecard includes three types of indicators: - Vision indicators - reflect the new vision for the WBG, showing the WBG’s ambition and providing high-level measures to gauge the direction and pace of progress in tackling global challenges. Vision indicators contain aggregated and disaggregated development context data for all countries in the world, where data is available. The Scorecard reports the latest available global updates for each of these indicators. - Client context indicators - reflect the circumstances in client countries, including multidimensional aspects of poverty, and are aligned with the Sustainable Development Goals (SDGs). They serve to frame the challenges clients face, and the context in which the WBG operates. Client Context indicators contain aggregated and disaggregated development context data for World Bank client countries, based on country eligibility for financing and where data is available. The Scorecard also reports the latest available update for each of these indicators. - WBG Results indicators monitor WBG progress on some of the most critical global challenges. Results data include: - Active Portfolio Results: Contain achieved and expected results of WBG operations based on its active portfolio as of end of June 2024. Includes aggregated and disaggregated data. - Results achieved since July 1st, 2023: Contain cumulative results achieved between July 1st, 2023 - June 30, 2024 from active and closed projects. Results achieved before July 1st, 2023 are excluded from this calculation. Includes aggregated data for World Bank, IBRD and IDA only. IFC and MIGA do not currently report this data. - Operations Details: Operation-level detail is provided for World Bank projects. However, in alignment with IFC and MIGA Access to Information Policies, project-level data is available in an aggregated format on the WBG Scorecard, provided the minimum threshold to secure individual clients' data is satisfied.

This collection includes only a subset of indicators from the source dataset.