Comparing the *** selected regions regarding the gini index , South Africa is leading the ranking (**** points) and is followed by Namibia with **** points. At the other end of the spectrum is Slovakia with **** points, indicating a difference of *** points to South Africa. The Gini coefficient here measures the degree of income inequality on a scale from * (=total equality of incomes) to *** (=total inequality).The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than *** countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

South Africa had the highest inequality in income distribution in 2024, with a Gini score of **. Its South African neighbor, Namibia, followed in second. The Gini coefficient measures the deviation of income (or consumption) distribution among individuals or households within a country from a perfectly equal distribution. A value of 0 represents absolute equality, and a value of 100 represents absolute inequality. All the 20 most unequal countries in the world were either located in Africa or Latin America & The Caribbean.

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.

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.

This table contains data on income inequality. The primary measure is the Gini index – a measure of the extent to which the distribution of income among families/households within a community deviates from a perfectly equal distribution. The index ranges from 0.0, when all families (households) have equal shares of income (implies perfect equality), to 1.0 when one family (household) has all the income and the rest have none (implies perfect inequality). Index data is provided for California and its counties, regions, and large cities/towns. The data is from the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Income is linked to acquiring resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. On average Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. Approximately 119,200 (5%) of the 2.4 million U.S. deaths in 2000 are attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers. More information about the data table and a data dictionary can be found in the About/Attachments section.

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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The Gini index measures economic inequality in a country. Specifically, it is the extent to which the distribution of income (or, in some cases, consumption expenditure) deviates from a perfectly equal distribution among individuals or households within an economy.

Based on the degree of inequality in income distribution measured by the Gini coefficient, Colombia was the most unequal country in Latin America as of 2022. Colombia's Gini coefficient amounted to 54.8. The Dominican Republic recorded the lowest Gini coefficient at 37, even below Uruguay and Chile, which are some of the countries with the highest human development indexes in Latin America. The Gini coefficient explained The Gini coefficient measures the deviation of the distribution of income among individuals or households in a given country from a perfectly equal distribution. A value of 0 represents absolute equality, whereas 100 would be the highest possible degree of inequality. This measurement reflects the degree of wealth inequality at a certain moment in time, though it may fail to capture how average levels of income improve or worsen over time. What affects the Gini coefficient in Latin America? Latin America, as other developing regions in the world, generally records high rates of inequality, with a Gini coefficient ranging between 37 and 55 points according to the latest available data from the reporting period 2010-2023. According to the Human Development Report, wealth redistribution by means of tax transfers improves Latin America's Gini coefficient to a lesser degree than it does in advanced economies. Wider access to education and health services, on the other hand, have been proven to have a greater direct effect in improving Gini coefficient measurements in the region.

This dataset captures key economic indicators for various countries, providing insights into their economic performance and income distribution. The data includes information on GDP per capita, Gini Index (a measure of income inequality), and the total Gross Domestic Product (GDP) for each country. This dataset can be utilized for comparative economic analysis, research on global inequality, and understanding economic trends across different regions.

Columns Description:

Region: The name of the country or region for which the data is recorded.

GDP Per Capita: The average economic output per person, calculated as the Gross Domestic Product (GDP) divided by the population. It is expressed in USD.

Gini Index: A measure of income inequality within a country, where 0 represents perfect equality and 1 indicates maximal inequality.

Gross Domestic Product (GDP): The total monetary value of all goods and services produced within a country's borders in a specific time period, expressed in USD.

This dataset can be used for analyzing global economic disparities, studying the relationship between GDP and income inequality, and conducting country-level comparisons of economic performance. It is valuable for economic research, policy-making, and academic studies focused on development and inequality.

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.

This dataset provides a gridded subnational datasets for Income inequality (Gini coefficient) at admin 1 level Gross national income (GNI) per capita PPP at admin 1 level The datasets are based on reported subnational admin data and spans three decades from 1990 to 2021. The dataset is presented in details in the following publication. Please cite this paper when using data. Chrisendo D, Niva V, Hoffman R, Sayyar SM, Rocha J, Sandström V, Solt F, Kummu M. 2024. Income inequality has increased for over two-thirds of the global population. Preprint. doi: https://doi.org/10.21203/rs.3.rs-5548291/v1 Code is available at following repositories: Gini coefficient data creation: https://github.com/mattikummu/subnatGini GNI per capita data creation: https://github.com/mattikummu/subnatGNI analyses for the article: https://github.com/mattikummu/gini_gni_analyses The following data is given (formats in brackets) Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv) Income inequality (Gini coefficient) at admin 1 level (subnational) (GeoTIFF, gpkg, csv) Gross national income (GNI) per capita PPP at admin 0 level (national) (GeoTIFF, gpkg, csv) Gross national income (GNI) per capita PPP at admin 1 level (subnational) (GeoTIFF, gpkg, csv) Slope for Gini coefficient at admin 1 level (GeoTIFF; slope is given also in gpk and csv files) Slope for GNI per capita at admin 1 level (GeoTIFF; slope is given also in gpk and csv files) Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip) Input data for the script that was used to generate the GNI per capita PPP (input_data_GNI.zip) Files are named as followsFormat: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*. Admin levels: adm0 for admin 0 level, adm1 for admin 1 levelProduct type: _gini_disp_ for gini coefficient based on disposable income _gni_perCapita_ for GNI per capita PPP Metadata Grids Resolution: 5 arc-min (0.083333333 degrees) Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax) Coordinate ref system: EPSG:4326 - WGS 84 Format: Multiband geotiff; one band for each year over 1990-2021 Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita Geospatial polygon (gpkg) files: Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax) Temporal extent: annual over 1990-2021 Coordinate ref system: EPSG:4326 - WGS 84 Format: gkpk Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita

In 2024, New York was the state with the greatest gap between rich and poor, with a Gini coefficient score of just under 0.52. Although not a state, District of Columbia was among the highest Gini coefficients in the United States that year. On the other hand, Utah had the lowest Gini score among U.S. states. Overall, income inequality has been rising in the country over recent decades.

Norway: Gini income inequality index: The latest value from 2019 is 27.7 index points, an increase from 27.6 index points in 2018. In comparison, the world average is 34.98 index points, based on data from 76 countries. Historically, the average for Norway from 1979 to 2019 is 27.04 index points. The minimum value, 24.6 index points, was reached in 1986 while the maximum of 31.6 index points was recorded in 2004.

Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 52.000 % in 2022. This records a decrease from the previous number of 52.900 % for 2021. Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 56.400 % from Dec 1981 (Median) to 2022, with 38 observations. The data reached an all-time high of 63.300 % in 1989 and a record low of 48.900 % in 2020. Brazil BR: Gini Coefficient (GINI Index): World Bank Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Brazil – Table BR.World Bank.WDI: Social: Poverty and Inequality. Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.;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).

Brazil: Gini income inequality index: The latest value from 2022 is 52 index points, a decline from 52.9 index points in 2021. In comparison, the world average is 38.33 index points, based on data from 28 countries. Historically, the average for Brazil from 1981 to 2022 is 56.28 index points. The minimum value, 48.9 index points, was reached in 2020 while the maximum of 63.2 index points was recorded in 1989.

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]

Content

This analysis focuses on income inequailty as measured by the Gini Index* and its association with economic metrics such as GDP per capita, investments as a % of GDP, and tax revenue as a % of GDP. One polical metric, EIU democracy index, is also included.

The data is for years 2006 - 2016

This investigation can be considered a starting point for complex questions such as:

1. Is a higher tax revenue as a % of GDP associated with less income inequality?
2. Is a higher EIU democracy index associated with less income inequality?
3. Is higher GDP per capita associated with less income inequality?
4. Is higher investments as a % of GDP associated with less income inequality?

This analysis uses the gapminder dataset from the Gapminder Foundation. The Gapminder Foundation is a non-profit venture registered in Stockholm, Sweden, that promotes sustainable global development and achievement of the United Nations Millennium Development Goals by increased use and understanding of statistics and other information about social, economic and environmental development at local, national and global levels.

*The Gini Index is a measure of statistical dispersion intended to represent the income or wealth distribution of a nation's residents, and is the most commonly used measurement of inequality. It was developed by the Italian statistician and sociologist Corrado Gini and published in his 1912 paper Variability and Mutability.

The dataset contains data from the following GapMinder datasets:

EIU Democracy Index:

"This democracy index is using the data from the Economist Inteligence Unit to express the quality of democracies as a number between 0 and 100. It's based on 60 different aspects of societies that are relevant to democracy universal suffrage for all adults, voter participation, perception of human rights protection and freedom to form organizations and parties. The democracy index is calculated from the 60 indicators, divided into five ""sub indexes"", which are:

1. Electoral pluralism index;
2. Government index;
3. Political participation indexm;
4. Political culture index;
5. Civil liberty index.

The sub-indexes are based on the sum of scores on roughly 12 indicators per sub-index, converted into a score between 0 and 100. (The Economist publishes the index with a scale from 0 to 10, but Gapminder has converted it to 0 to 100 to make it easier to communicate as a percentage.)" https://docs.google.com/spreadsheets/d/1d0noZrwAWxNBTDSfDgG06_aLGWUz4R6fgDhRaUZbDzE/edit#gid=935776888

Income: GDP per capita, constant PPP dollars

GDP per capita measures the value of everything produced in a country during a year, divided by the number of people. The unit is in international dollars, fixed 2011 prices. The data is adjusted for inflation and differences in the cost of living between countries, so-called PPP dollars. The end of the time series, between 1990 and 2016, uses the latest GDP per capita data from the World Bank, from their World Development Indicators. To go back in time before the World Bank series starts in 1990, we have used several sources, such as Angus Maddison. https://www.gapminder.org/data/documentation/gd001/

Investments (% of GDP)

Capital formation is a term used to describe the net capital accumulation during an accounting period for a particular country. The term refers to additions of capital goods, such as equipment, tools, transportation assets, and electricity. Countries need capital goods to replace the older ones that are used to produce goods and services. If a country cannot replace capital goods as they reach the end of their useful lives, production declines. Generally, the higher the capital formation of an economy, the faster an economy can grow its aggregate income.

Tax revenue (% of GDP)

refers to compulsory transfers to the central governement for public purposes. Does not include social security. https://data.worldbank.org/indicator/GC.TAX.TOTL.GD.ZS

Context

Gapminder is an independent Swedish foundation with no political, religious or economic affiliations. Gapminder is a fact tank, not a think tank. Gapminder fights devastating misconceptions about global development. Gapminder produces free teaching resources making the world understandable based on reliable statistics. Gapminder promotes a fact-based worldview everyone can understand. Gapminder collaborates with universities, UN, public agencies and non-governmental organizations. All Gapminder activities are governed by the board. We do not award grants. Gapminder Foundation is registered at Stockholm County Administration Board. Our constitution can be found here.

Acknowledgements

Thanks to gapminder.org for organizing the above datasets.

Inspiration

Below are some research questions associated with the data and some ...

The World Income Inequality Database (WIID) contains information on income inequality in various countries, and is maintained by the United Nations University-World Institute for Development Economics Research (UNU-WIDER). The database was originally compiled during 1997-99 for the research project Rising Income Inequality and Poverty Reduction, directed by Giovanni Andrea Corina. A revised and updated version of the database was published in June 2005 as part of the project Global Trends in Inequality and Poverty, directed by Tony Shorrocks and Guang Hua Wan. The database was revised in 2007 and a new version was launched in May 2008.

The database contains data on inequality in the distribution of income in various countries. The central variable in the dataset is the Gini index, a measure of income distribution in a society. In addition, the dataset contains information on income shares by quintile or decile. The database contains data for 159 countries, including some historical entities. The temporal coverage varies substantially across countries. For some countries there is only one data entry; in other cases there are over 100 data points. The earliest entry is from 1867 (United Kingdom), the latest from 2003. The majority of the data (65%) cover the years from 1980 onwards. The 2008 update (version WIID2c) includes some major updates and quality improvements, in fact leading to a reduced number of variables in the new version. The new version has 334 new observations and several revisions/ corrections made in 2007 and 2008.

Ireland: Gini income inequality index: The latest value from 2021 is 30.1 index points, an increase from 29.2 index points in 2020. In comparison, the world average is 35.28 index points, based on data from 71 countries. Historically, the average for Ireland from 1987 to 2021 is 32.77 index points. The minimum value, 29.2 index points, was reached in 2020 while the maximum of 37 index points was recorded in 1995.

The Gini index is a measure of income inequality that is widely used to assess the distribution of income within a country. The index ranges from 0 to 1, with 0 representing perfect equality and 1 representing complete inequality, where all income is concentrated in one individual or group. Income inequality is a key issue in economic development and has important implications for social and political stability.

The Gini index per country dataset provides a comprehensive overview of the income inequality of each country. The dataset includes information on the Gini index for each country, covering all countries in the world. It is compiled from various sources, including national statistical agencies, international organizations such as the United Nations Development Programme (UNDP), and other relevant data sources.

The Gini index per country dataset can be used by researchers, policymakers, and the general public to gain insight into the degree of income inequality within different countries and regions, and to compare the relative levels of inequality across the world. It can also be used to monitor changes in income distribution over time and to evaluate the effectiveness of policies and strategies aimed at reducing income inequality.

Overall, the Gini index per country dataset is an important resource for understanding the distribution of income within the world and for developing policies and strategies that promote more equitable and sustainable economic development.