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).

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).

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.

The Gini index quantifies the degree of inequality in the distribution of income (or sometimes consumption) among individuals or households within an economy. It compares the actual income distribution to a perfectly equal one. This is visualized through the Lorenz curve, which charts the cumulative share of total income against the cumulative share of recipients, starting from the poorest. The Gini index is derived from the area between the Lorenz curve and the line representing absolute equality, measured as a percentage of the total area beneath that line. A Gini index of 0 indicates perfect equality, while 100 signifies complete inequality.

Comoros KM: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 45.300 % in 2014. This records a decrease from the previous number of 55.900 % for 2004. Comoros KM: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 50.600 % from Dec 2004 (Median) to 2014, with 2 observations. The data reached an all-time high of 55.900 % in 2004 and a record low of 45.300 % in 2014. Comoros KM: 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 Comoros – Table KM.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).

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.

Graph and download economic data for GINI Index for the United States (SIPOVGINIUSA) from 1963 to 2023 about gini, indexes, and USA.

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 2023

The datasets are 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)

Gini coefficient:

Please note, two distinct datasets for Gini cofficient are given. One based on SWIID national dataset, and another for WID national dataset. These are separated in file names as follows: _disp_ for SWIID (disposable income); _WID_ for WID.

• Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv)
• Income inequality (Gini coefficient) 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)
• Uncertainty (standard deviation for each year and admin area; uncertainty for slope) for Gini (based on SWIID) at admin 1 level (gpkg)
• Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip)

Gross national income (GNI) pear capita PPP (in 2021 USD):

• 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 GNI per capita (log10) 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 GNI per capita PPP (input_data_GNI.zip)

Files are named as follows
Format: 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 level
Product type:

• _gini_disp_ for gini coefficient based on SWIID national dataset (disposable income)
• _gini_WID_ for gini coefficient based on WID national dataset
• _uncertainty_slope_gini_disp_ for slope uncertainty of SWIID based Gini
• _uncertainty_SD_gini_disp_ for standard deviation of SWIID based Gini
• _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

Version 3 changes (24.07.2025)

• both datasets updated to cover 1990-2023 (previously 1990-2021)
• extrapolation method updated
• Gini data now produced also using WID national data as a base (previously only one based on SWIID was provided)
• uncertainty analysis done for Gini (SWIID)

Belarus BY: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 24.400 % in 2020. This records a decrease from the previous number of 25.300 % for 2019. Belarus BY: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 27.600 % from Dec 1998 (Median) to 2020, with 23 observations. The data reached an all-time high of 32.000 % in 1998 and a record low of 24.400 % in 2020. Belarus BY: 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 Belarus – Table BY.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).

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.

GINI Index Data consists of information based on primary household survey data obtained from government statistical agencies and World Bank country departments. In economics, the GINI index (sometimes expressed as a GINI ratio, GINI coefficient or a normalized 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 measure of inequality.

The USA: Gini income inequality index: The latest value from 2022 is 41.3 index points, an increase from 39.7 index points in 2021. In comparison, the world average is 38.33 index points, based on data from 28 countries. Historically, the average for the USA from 1963 to 2022 is 38.7 index points. The minimum value, 34.7 index points, was reached in 1980 while the maximum of 41.5 index points was recorded in 2014.

This statistic shows the inequality of income distribution in China from 2005 to 2023 based on the Gini Index. In 2023, China reached a score of ************ points. The Gini Index is a statistical measure that is used to represent unequal distributions, e.g. income distribution. It can take any value between 1 and 100 points (or 0 and 1). The closer the value is to 100 the greater is the inequality. 40 or 0.4 is the warning level set by the United Nations. The Gini Index for South Korea had ranged at about **** in 2022. Income distribution in China The Gini coefficient is used to measure the income inequality of a country. The United States, the World Bank, the US Central Intelligence Agency, and the Organization for Economic Co-operation and Development all provide their own measurement of the Gini coefficient, varying in data collection and survey methods. According to the United Nations Development Programme, countries with the largest income inequality based on the Gini index are mainly located in Africa and Latin America, with South Africa displaying the world's highest value in 2022. The world's most equal countries, on the contrary, are situated mostly in Europe. The United States' Gini for household income has increased by around ten percent since 1990, to **** in 2023. Development of inequality in China Growing inequality counts as one of the biggest social, economic, and political challenges to many countries, especially emerging markets. Over the last 20 years, China has become one of the world's largest economies. As parts of the society have become more and more affluent, the country's Gini coefficient has also grown sharply over the last decades. As shown by the graph at hand, China's Gini coefficient ranged at a level higher than the warning line for increasing risk of social unrest over the last decade. However, the situation has slightly improved since 2008, when the Gini coefficient had reached the highest value of recent times.

Guinea GN: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 33.700 % in 2012. This records a decrease from the previous number of 39.400 % for 2007. Guinea GN: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 43.000 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 46.800 % in 1991 and a record low of 33.700 % in 2012. Guinea GN: 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 Guinea – Table GN.World Bank.WDI: Poverty. 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, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. 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.

Of the countries included, South Africa had the highest income inequality, with a Gini coefficient of 0.62. It was also the country with the highest inequality level worldwide. Of the OECD members, Costa Rica had the highest income inequality, whereas Slovakia had the lowest.

Graph and download economic data for GINI Index for Costa Rica (SIPOVGINICRI) from 1981 to 2024 about Costa Rica, gini, and indexes.

Graph and download economic data for GINI Index for Indonesia (SIPOVGINIIDN) from 1984 to 2024 about gini, Indonesia, and indexes.

Between 2010 and 2023, Mexico's data on the degree of inequality in income distribution based on the Gini coefficient decreased compared to the previous period, at 45.4. The Gini coefficient measures the deviation of the distribution of income (or consumption) 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. Poverty still one of the major problems During the last four years, the minimum wage in Mexico has been increasing substantially, going from 141.7 to 248.93 Mexican pesos per day. The main reason for this was to pull people out of poverty. In 2014, the population under the poverty line was over 46 percent, that is almost half of Mexicans living with conditions of vulnerability. Eight years later, the rate was about 36 percent, still a significant number of people living in poverty but a considerable decrease. Gender inequality Mexico does not score particularly well in gender inequality, in fact, it ranks 33rd in the world in the Global Gender Gap Index. Despite some advances, the Aztec country performs poorly in most of the metrics that measure inequality. During late 2022, women recorded a pay disparity of –13.15 percent when compared to them male counterparts. That is to say, that for the same job a woman is paid 87.85 MXP when a man receives 100 MXP.

Historical dataset showing World income inequality - gini coefficient by year from N/A to N/A.

Greece GR: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 36.000 % in 2015. This records an increase from the previous number of 35.800 % for 2014. Greece GR: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 34.600 % from Dec 2003 (Median) to 2015, with 13 observations. The data reached an all-time high of 36.200 % in 2012 and a record low of 32.800 % in 2003. Greece GR: 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 Greece – Table GR.World Bank.WDI: Poverty. 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, Development Research Group. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. 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.