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

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

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.

In 2024, approximately 10.6 percent of the population was living below the national poverty line in the United States. This reflected a 0.5 percentage point decrease from the previous year. Most recently, poverty levels in the country peaked in 2010 at just over 15 percent. Poverty in the U.S. States The number of people living in poverty in the U.S. as well as poverty rates, vary greatly from state to state. With their large populations, California and Texas led that charts in terms of the size of their impoverished residents. On the other hand, Louisiana had the highest rates of poverty, standing at 20 percent in 2024. The state with the lowest poverty rate was New Hampshire at 5.9 percent. Vulnerable populations The poverty rate in the United States varies widely across different ethnic groups. American Indians and Alaska Natives are the ethnic group with the highest levels of poverty in 2024, with about 19 percent earning an income below the official threshold. In comparison, only about 7.5 percent of the White (non-Hispanic) and Asian populations were living below the poverty line. Children are one of the most poverty endangered population groups in the U.S. between 1990 and 2024. Child poverty peaked in 1993 with 22.7 percent of children living in poverty. Despite fluctuations, in 2024, poverty among minors reached its lowest level in decades, falling to 14.3 percent.

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

Time series data for the statistic Official Moderate Poverty Rate-National and country Luxembourg.

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?

Time series data for the statistic Official Moderate Poverty Rate-National and country Paraguay.

Extreme poverty is defined as living below the International Poverty Line, which is $1.90 per day in 2011 prices and $2.15 per day in 2017 prices.

The International Poverty Line is set by the World Bank to be representative of national definitions of poverty adopted in the world’s poorest countries. In addition to this very low poverty line the World Bank also sets two higher global poverty lines for measuring poverty: one that reflects the definitions of poverty adopted in lower-middle income countries, and one that reflects the definitions adopted in upper-middle income countries. Within the updated methodology, these lines are set at $3.65 and $6.85 in 2017 international-$, replacing the previous $3.20 and $5.50 lines expressed in 2011 international-$.

International dollars (int.-$) are a hypothetical currency that is used for this. It is the result of adjusting both for inflation within countries over time and for differences in the cost of living between countries. The goal of international-$ is to provide a unit whose purchasing power is held fixed over time and across countries, such that one int.-$ can buy the same quantity and quality of goods and services no matter where or when it is spent. The price level in the US is used as the benchmark – or ‘numeraire’ – so that one 2017 int.-$ is defined as the value of goods and services that one US dollar would buy in the US in 2017.

Data Source: From $1.90 to $2.15 a day: the updated International Poverty Line Thumbnail Image: Towfiqu barbhuiya's Unspash

Norway Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 9.400 % in 2019. This records an increase from the previous number of 9.000 % for 2018. Norway Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 7.950 % from Dec 1979 (Median) to 2019, with 22 observations. The data reached an all-time high of 9.700 % in 2016 and a record low of 5.700 % in 2000. Norway 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 Norway – Table NO.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).

Time series data for the statistic Poverty_Headcount_Ratio_at_1.90USD_a_Day and country Estonia. Indicator Definition:Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 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.The statistic "Poverty Headcount Ratio at 1.90USD a Day" stands at 0.3 percent as of 12/31/2023, the lowest value since 12/31/1999. Regarding the One-Year-Change of the series, the current value constitutes a decrease of -0.2 percentage points compared to the value the year prior.The 1 year change in percentage points is -0.2.The 3 year change in percentage points is -0.1.The 5 year change in percentage points is -0.1.The 10 year change in percentage points is -0.8.The Serie's long term average value is 1.12 percent. It's latest available value, on 12/31/2023, is 0.822 percentage points lower, compared to it's long term average value.The Serie's change in percentage points from it's minimum value, on 12/31/1998, to it's latest available value, on 12/31/2023, is +0.3.The Serie's change in percentage points from it's maximum value, on 12/31/2003, to it's latest available value, on 12/31/2023, is -3.30.

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.

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

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.

Time series data for the statistic Poverty_Headcount_Ratio_at_1.90USD_a_Day and country Cyprus. Indicator Definition:Poverty headcount ratio at $1.90 a day is the percentage of the population living on less than $1.90 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.

United States Poverty Headcount Ratio at Societal Poverty Lines: % of Population data was reported at 19.200 % in 2022. This records an increase from the previous number of 16.700 % for 2021. United States Poverty Headcount Ratio at Societal Poverty Lines: % of Population data is updated yearly, averaging 19.200 % from Dec 1963 (Median) to 2022, with 60 observations. The data reached an all-time high of 20.500 % in 1993 and a record low of 16.700 % in 2021. United States 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 United States – Table US.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).

To assist communities in identifying racially/ethnically-concentrated areas of poverty (R/ECAPs), HUD has developed a census tract-based definition of R/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: R/ECAPs must have a non-white population of 50 percent or more. Regarding the poverty threshold, Wilson (1980) defines neighborhoods of extreme poverty as census tracts with 40 percent or more of individuals living at or below the poverty line. Because overall poverty levels are substantially lower in many parts of the country, HUD supplements this with an alternate criterion. Thus, a neighborhood can be a R/ECAP if it has a poverty rate that exceeds 40% or is three or more times the average tract poverty rate for the metropolitan/micropolitan area, whichever threshold is lower. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed R/ECAPs. This translates into the following equation: Where i represents census tracts, () is the metropolitan/micropolitan (CBSA) mean tract poverty rate, is the ith tract poverty rate, () is the non-Hispanic white population in tract i, and Pop is the population in tract i.While this definition of R/ECAP works well for tracts in CBSAs, place outside of these geographies are unlikely to have racial or ethnic concentrations as high as 50 percent. In these areas, the racial/ethnic concentration threshold is set at 20 percent.

Data Source: American Community Survey (ACS), 2009-2013; Decennial Census (2010); Brown Longitudinal Tract Database (LTDB) based on decennial census data, 1990, 2000 & 2010.

Related AFFH-T Local Government, PHA Tables/Maps: Table 4, 7; Maps 1-17. Related AFFH-T State Tables/Maps: Table 4, 7; Maps 1-15, 18.

References:Wilson, William J. (1980). The Declining Significance of Race: Blacks and Changing American Institutions. Chicago: University of Chicago Press.

To learn more about R/ECAPs visit:https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 11/2017

Economic Fitness (EF) is both a measure of a country’s diversification and ability to produce complex goods on a globally competitive basis. Countries with the highest levels of EF have capabilities to produce a diverse portfolio of products, ability to upgrade into ever-increasing complex goods, tend to have more predictable long-term growth, and to attain good competitive position relative to other countries. Countries with low EF levels tend to suffer from poverty, low capabilities, less predictable growth, low value-addition, and trouble upgrading and diversifying faster than other countries. The comparison of the Fitness to the GDP reveals hidden information for the development and the growth of the countries.

This dataset is from The World Bank's DataBank, which houses a variety of databases. It contains 44 series on 184 counties over the past five years. Topics range from income distribution, annualized growth per capita, poverty headcount, and much more. Please note that some countries do not have data for particular series and/or years due to data collection challenges. This data can be applied to the Kiva Crowdfunding Data Science for Good challenge by providing insight into what countries are in greatest need of funding.

More about The World Bank: With 189 member countries, staff from more than 170 countries, and offices in over 130 locations, the World Bank Group is a unique global partnership: five institutions working for sustainable solutions that reduce poverty and build shared prosperity in developing countries.

The image is from unsplash.com.

The OECD Income Distribution database (IDD) has been developed to benchmark and monitor countries' performance in the field of income inequality and poverty. It contains a number of standardised indicators based on the central concept of "equivalised household disposable income", i.e. the total income received by the households less the current taxes and transfers they pay, adjusted for household size with an equivalence scale. While household income is only one of the factors shaping people's economic well-being, it is also the one for which comparable data for all OECD countries are most common. Income distribution has a long-standing tradition among household-level statistics, with regular data collections going back to the 1980s (and sometimes earlier) in many OECD countries.

Achieving comparability in this field is a challenge, as national practices differ widely in terms of concepts, measures, and statistical sources. In order to maximise international comparability as well as inter-temporal consistency of data, the IDD data collection and compilation process is based on a common set of statistical conventions (e.g. on income concepts and components). The information obtained by the OECD through a network of national data providers, via a standardized questionnaire, is based on national sources that are deemed to be most representative for each country.

Small changes in estimates between years should be treated with caution as they may not be statistically significant.

Fore more details, please refer to: https://www.oecd.org/els/soc/IDD-Metadata.pdf and https://www.oecd.org/social/income-distribution-database.htm