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

This is a data record which corresponds to the paper "A consistent dataset for the net income distribution for 184 countries, aggregated to 32 geographical regions and the world from 1958-2015" (Narayan et al. 2023, in prep) https://essd.copernicus.org/preprints/essd-2023-137/

Description/Abstract- Data on the income distribution within and across countries are increasingly becoming important to inform analysis on income inequality and human welfare. While datasets on the income distribution collected from household surveys are available for multiple countries, these datasets often do not represent the same income concept and therefore make comparisons across countries and across datasets difficult. Here, we present a consistent dataset on the income distribution across 184 countries which all represent a single income concept namely net-income. We complement the observed values in this dataset with values of the income distribution imputed from summary measures such as the GINI coefficient to generate a consistent time series across countries from 1958 to 2015. For the imputation, we use a recently developed PCA based approach which shows an excellent fit to the latest data on income distributions. We also present another version of this dataset which is aggregated from the country level to 32 geographical regions and the world as a whole. Our aggregation method takes into account both within country and cross- country income inequality when aggregating to the regional level. This dataset will enable more robust analysis of the income distribution at multiple scales.

Global Income Inequality Dataset (2000–2023)

Overview

This dataset provides a comprehensive look at global income inequality from the year 2000 to 2023. It includes key indicators such as Gini index, average income, income distribution across different population percentiles, and income group classifications for 30 countries worldwide. The dataset offers insights into how income is distributed within nations and highlights disparities across different economic groups.

Data Features

• Country: The name of the country.
• Year: The year of the data point (2000–2023).
• Population: The estimated population for the given year.
• Gini Index: A measure of income inequality, where 0 represents perfect equality and 1 represents maximum inequality.
• Average Income (USD): The average income in USD for the country in the given year.
• Top 10% Income Share (%): The percentage of total income held by the top 10% of the population.
• Bottom 10% Income Share (%): The percentage of total income held by the bottom 10% of the population.
• Income Group: Categorization of the country’s income group (Low Income, Lower Middle Income, Upper Middle Income, High Income).

Potential Uses

• Economic Analysis: Understand global income inequality trends and how they vary by country and region.
• Predictive Modeling: Use the dataset to build machine learning models predicting future income inequality based on historical data.
• Policy Research: Study the impact of income distribution on policy decisions and economic growth in different nations.
• Visualization: Create heatmaps, time series charts, and more to visualize the income inequality across various countries and years.

Source

The data has been generated to simulate realistic income inequality patterns based on publicly available data on global economic trends.

United States US: Income Share Held by Highest 10% data was reported at 30.600 % in 2016. This records an increase from the previous number of 30.100 % for 2013. United States US: Income Share Held by Highest 10% data is updated yearly, averaging 30.100 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 30.600 % in 2016 and a record low of 25.300 % in 1979. United States US: Income Share Held by Highest 10% 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: Poverty. Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.; ; World Bank, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are from the Luxembourg Income Study database. For more information and methodology, please see PovcalNet (http://iresearch.worldbank.org/PovcalNet/index.htm).; ; The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than one thousand six hundred household surveys across 164 countries in six regions and 25 other high income countries (industrialized economies). While income distribution data are published for all countries with data available, poverty data are published for low- and middle-income countries and countries eligible to receive loans from the World Bank (such as Chile) and recently graduated countries (such as Estonia) only. See PovcalNet (http://iresearch.worldbank.org/PovcalNet/WhatIsNew.aspx) for definitions of geographical regions and industrialized countries.

The massive wealth inequality in the world is underpinned by this chart: while *** percent of the world's population had fortunes of more than one million U.S. dollars in 2025, over ** percent of the global population had a total wealth of less than 10,000 U.S. dollars.

Context

Income inequality is a global issue reflecting the uneven distribution of wealth within and between countries. Developed nations exhibit varying income levels due to economic policies and labor dynamics, resulting in Gini coefficients of around 0.3 to 0.4. Conversely, developing nations often experience higher income disparities due to limited access to education, healthcare, and jobs, leading to Gini coefficients exceeding 0.4, exacerbating poverty cycles and social tensions. This inequality hampers economic growth, social cohesion, and upward mobility. Addressing it requires comprehensive policies, including progressive taxation and equitable resource distribution, to promote a more just and inclusive society.

Content

This dataset comprises historical information encompassing various indicators concerning Inequality in Income on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Income from 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Income from 2010 to 2021 - Inequality in Income from year 2010 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

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Abstract

Is global inequality (inequality among world citizens) stable, decreasing or increasing? How high it is? Is it mostly due to inequalities within nations or between nations? Is there a global middle class? See the working papers above: "True world income distribution 1988 and 1993: first calculations based on household surveys alone" no. 2244, and "Decomposing global income distribution: Does the world have a middle class?" no. 2562

Household survey data (1988-2002) used in these papers, and subsequent book "Worlds Apart: Measuring International and Global Inequality", Princeton University Press, 2005. The data are for three benchmark years: 1988, 1993 and 1998

Kind of data

Aggregate data [agg]

Mode of data collection

Other [oth]

Between December 2019 and 2021, the top one percent of earners accumulated 63 percent of all new wealth worldwide. This is more than six times more wealth than accumulated by the bottom 90 percent over the same time period.

Global wealth distribution Newly generated wealth landing in the hands of the few is not a new story and has been the focus of international development policy for many years. Looking at a regional level, Latin America was the region with the starkest distribution of wealth. In this region, 77 percent of the wealth was held by the richest 10 percent in 2021, and only 0.5 percent held by the poorest 50 percent. At an individual level, around 2.82 billion adults worldwide had a net worth of less than 10,000 U.S. dollars in 2021.

Billionaires In 2021, the highest concentration of billionaires could be found in North America. However, China had the largest number of billionaires in its population in 2022, with most living in Beijing. Looking at wealth distribution amongst billionaires themselves, 20 people had fortunes of 50 billion U.S. dollars or more, but the majority of billionaires had a personal fortune between two and five billion U.S. dollars.

In December 2022, Elon Musk slipped from the top spot of richest people on Earth. The number one spot was taken by French magnate, Bernard Arnault of Moët Hennessy Louis Vuitton.

Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of the existing inequality datasets: greater coverage across countries and over time has been available from these sources only at the cost of significantly reduced comparability across observations. The goal of the Standardized World Income Inequality Database (SWIID) is to meet the needs of those engaged in broadly cross-national research by maximizing the comparability of income inequality data while maintaining the widest possible coverage across countries and over time. The SWIID’s income inequality estimates are based on thousands of reported Gini indices from hundreds of published sources, including the OECD Income Distribution Database, the Socio-Economic Database for Latin America and the Caribbean generated by CEDLAS and the World Bank, Eurostat, the World Bank’s PovcalNet, the UN Economic Commission for Latin America and the Caribbean, national statistical offices around the world, and academic studies while minimizing reliance on problematic assumptions by using as much information as possible from proximate years within the same country. The data collected and harmonized by the Luxembourg Income Study is employed as the standard. The SWIID currently incorporates comparable Gini indices of disposable and market income inequality for 199 countries for as many years as possible from 1960 to the present; it also includes information on absolute and relative redistribution.

China % of Household grouped by Annual Income: Urban:RMB70000-75000 data was reported at 4.330 % in 2011. This records an increase from the previous number of 3.880 % for 2010. China % of Household grouped by Annual Income: Urban:RMB70000-75000 data is updated yearly, averaging 2.930 % from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 4.330 % in 2011 and a record low of 1.180 % in 2005. China % of Household grouped by Annual Income: Urban:RMB70000-75000 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Household Income Distribution: Urban.

China % of Household grouped by Annual Income: Urban:>100000 data was reported at 18.080 % in 2011. This records an increase from the previous number of 12.220 % for 2010. China % of Household grouped by Annual Income: Urban:>100000 data is updated yearly, averaging 7.470 % from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 18.080 % in 2011 and a record low of 2.070 % in 2005. China % of Household grouped by Annual Income: Urban:>100000 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Household Income Distribution: Urban.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in International Falls: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 214(7.54%) households where the householder is under 25 years old, 668(23.52%) households with a householder aged between 25 and 44 years, 967(34.05%) households with a householder aged between 45 and 64 years, and 991(34.89%) households where the householder is over 65 years old.
• The age group of under 25 years exhibits the highest median household income, while the largest number of households falls within the 65 years and over bracket. This distribution hints at economic disparities within the city of International Falls, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for International Falls median household income by age. You can refer the same here

The Global Database of Light-based Geospatial Income Inequality (LGII) Measures, Version 1 data set contains Gini-coefficients of inequality for 234 countries and territories from 1992 to 2013. The measurement Unit is the Gini-Coefficient (Range: 0-1), with higher values representing higher inequality. These measures are constructed using worldwide geospatial satellite data on nighttime lights emission as a proxy for economic prosperity, matched with varying sources of data on geo-located population counts. The nighttime lights data were supplied by the National Oceanic and Atmospheric Administration (NOAA), National Centers for Environmental Information (NCEI), Earth Observation Group (EOG), and Operational Linescan System (OLS) instruments. The population data used consisted of CIESIN's Gridded Population of the World (GPW) collection, and the Oak Ridge National Laboratory (ORNL) LandScan (LSC) data set. The nighttime lights and population data were combined to produce an array of geospatially-informed Gini-coefficients, which were then weighted to optimize their correlation with a benchmark - specifically, the Standardized World Income Inequality Database (SWIID), to generate a parsimonious composite inequality metric.

China % of Household grouped by Annual Income: Urban:RMB40000-45000 data was reported at 6.880 % in 2011. This records a decrease from the previous number of 7.850 % for 2010. China % of Household grouped by Annual Income: Urban:RMB40000-45000 data is updated yearly, averaging 7.810 % from Dec 2005 (Median) to 2011, with 7 observations. The data reached an all-time high of 8.160 % in 2009 and a record low of 6.070 % in 2005. China % of Household grouped by Annual Income: Urban:RMB40000-45000 data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Household Survey – Table CN.HD: Household Income Distribution: Urban.

The Tax Foundation’s publication Corporate Tax Rates around the World shows how statutory corporate income tax rates have developed since 1980, with data for over 200 jurisdictions for the year 2023. The dataset we compiled for the years 1980 to 2023 is made available as a resource for research.

Scope

The dataset compiled for this publication includes the 2023 statutory corporate income tax rates of 225 sovereign states and dependent territories around the world. Tax rates were researched only for jurisdictions that are among the around 250 sovereign states and dependent territories that have been assigned a country code by the International Organization for Standardization (ISO). (The jurisdictions Netherland Antilles (which was split into different jurisdictions in 2010) and Kosovo (which has not yet officially been assigned a country code) were added to the dataset.) As a result, zones or territories that are independent taxing jurisdictions but do not have their own country code are generally not included in the dataset.

In addition, the dataset includes historic statutory corporate income tax rates for the time period 1980 to 2022. However, these years cover tax rates of fewer than 225 jurisdictions due to missing data points. Please let Tax Foundation know if you are aware of any sources for historic corporate tax rates that are not mentioned in this report, as we constantly strive to improve our datasets.

To be able to calculate average statutory corporate income tax rates weighted by GDP, the dataset includes GDP data for 181 jurisdictions. When used to calculate average statutory corporate income tax rates, either weighted by GDP or unweighted, only these 181 jurisdictions are included (to ensure the comparability of the unweighted and weighted averages).

Definition of Selected Corporate Income Tax Rate

The dataset captures standard top statutory corporate income tax rates levied on domestic businesses. This means:

The dataset does not reflect special tax regimes, including but not limited to patent boxes, offshore regimes, or special rates for specific industries. A number of countries levy lower rates for businesses below a certain revenue threshold. The dataset does not capture these lower rates. A few countries levy gross revenue taxes on businesses instead of corporate income taxes. Since the tax rates of a corporate income tax and a gross revenue tax are not comparable, these countries are excluded from the dataset. Some countries have a separate tax rate for nonresident companies. This dataset does not consider nonresident tax rates that differ from the general corporate rate.

Explanation of Files

source-data

• country_codes.csv Dataset that includes all 250 sovereign states and dependent territories that have been assigned a country code by the International Organization for Standardization (ISO). Includes official country names in various languages, ISO country codes, continents, and further geographical information.

• data_rates_1980_2022.csv Tax Foundation's dataset of statutory corporate income tax rates for the years 1980 to 2022. This dataset has been built in stages since 2015.

• RealGDPValues.xlsx U.S. Department of Agriculture's dataset of historical and projected real Gross Domestic Product (GDP) and growth rates of GDP for 181 countries and various regions (in billions of 2015 dollars) for the years 1970 to 2032.

intermediate-ouptuts

• gdp_iso.csv GDP data paired with ISO country codes for the years 1980 to 2023.

• rates_final.csv Statutory corporate income tax rates for the years 1980 to 2023. Includes rates of all countries for which data was available in 2023 (data from OECD, KPMG, and researched individually).

• rates_preliminary.csv Statutory corporate income tax rates for the years 1980 to 2023. Includes rates of countries for - which OECD data was available for the year 2023. Does not include countries for which the rate was researched and added individually.

final-data

• final_data_2023.csv Statutory corporate income tax rates and GDP levels of countries paired with ISO country codes, continents, and country groups for the year 2023. Only includes countries for which both the corporate income tax rates and GDP data were available.

• final_data_2023_gdp_incomplete.csv Statutory corporate income tax rates and GDP levels of countries paired with ISO country codes, continents, and country groups for the year 2023. Includes all countries for which we have data for the corporate income tax rate, including countries for which we do not have GDP data.

• final_data_long.csv Statutory corporate income tax rates and GDP levels of all countries paired with ISO country codes, continents, and country groups for the years 1980 to 2023. Includes all countries that have an ISO countr...

Graph and download economic data for Population Growth: All Income Levels for East Asia and Pacific (SPPOPGROWEAS) from 1961 to 2024 about East Asia, Pacific, income, population, and rate.

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.

In 2023, roughly 1.49 billion adults worldwide had a net worth of less than 10,000 U.S. dollars. By comparison, 58 million adults had a net worth of more than one million U.S. dollars in the same year. Wealth distribution The distribution of wealth is an indicator of economic inequality. The United Nations says that wealth includes the sum of natural, human, and physical assets. Wealth is not synonymous with income, however, because having a large income can be depleted if one has significant expenses. In 2023, nearly 1,700 billionaires had a total wealth between one to two billion U.S. dollars. Wealth worldwide China had the highest number of billionaires in 2023, with the United States following behind. That same year, New York had the most billionaires worldwide.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within International Falls. The dataset can be utilized to gain insights into gender-based income distribution within the International Falls population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within International Falls, among individuals aged 15 years and older with income, there were 2,325 men and 2,209 women in the workforce. Among them, 1,037 men were engaged in full-time, year-round employment, while 799 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 5.98% fell within the income range of under $24,999, while 3.13% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: 17.07% of men in full-time roles earned incomes exceeding $100,000, while 7.38% of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for International Falls median household income by race. You can refer the same here

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