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.

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).

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]

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 World Inequality Database (WID.world) aims to provide open and convenient access to the most extensive available database on the historical evolution of the world distribution of income and wealth, both within countries and between countries.

HISTORY OF WID.WORLD During the past fifteen years, the renewed interest for the long-run evolution of income and wealth inequality gave rise to a flourishing literature. In particular, a succession of studies has constructed top income share series for a large number of countries (see Thomas Piketty 2001, 2003, T. Piketty and Emmanuel Saez 2003, and the two multi-country volumes on top incomes edited by Anthony B. Atkinson and T. Piketty 2007, 2010; see also A. B. Atkinson et al. 2011 and Facundo Alvaredo et al. 2013 for surveys of this literature). These projects generated a large volume of data, intended as a research resource for further analysis, as well as a source to inform the public debate on income inequality. To a large extent, this literature follows the pioneering work of Simon Kuznets 1953, and A. B. Atkinson and Alan Harrison 1978, and extends it to many more countries and years.

for more https://wid.world/wid-world/

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.

Upvote if its helpful for you Thank You Dive into the intricate relationship between happiness and income inequality with our comprehensive dataset sourced from the World Bank. Uncover key insights into how nations' happiness levels may be influenced by economic disparities. Explore the nuances of global well-being and socioeconomic factors, shedding light on the intricate connections between happiness and income distribution on a worldwide scale. Harness the power of data to gain valuable insights into the factors that contribute to societal contentment and address the complexities of global happiness. Columns in dataset are: Column Names: ['country', 'adjusted_satisfaction', 'avg_satisfaction', 'std_satisfaction', 'avg_income', 'median_income', 'income_inequality', 'region', 'happyScore', 'GDP', 'country.1']

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.

Abstract

This file contains data on Gini coefficients, cumulative quintile shares, explanations regarding the basis on which the Gini coefficient was computed, and the source of the information. There are two data-sets, one containing the "high quality" sample and the other one including all the information (of lower quality) that had been collected.

The database was constructed for the production of the following paper:

Deininger, Klaus and Lyn Squire, "A New Data Set Measuring Income Inequality", The World Bank Economic Review, 10(3): 565-91, 1996.

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups’ income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Geographic coverage

In what follows, we provide brief descriptions of main features for individual countries that are included in the data-base. Without being comprehensive, these notes are intended to indicate some of the considerations underlying our decision to include or exclude certain observations.

Argentina Various permanent household surveys, all covering urban centers only, have been regularly conducted since 1972 and are quoted in a wide variety of sources and years, e.g., for 1980 (World Bank 1992), 1985 (Altimir 1994), and 1989 (World Bank 1992). Estimates for 1963, 1965, 1969/70, 1970/71, 1974, 1975, 1980, and 1981 (Altimir 1987) are based only on Greater Buenos Aires. Estimates for 1961, 1963, 1970 (Jain 1975) and for 1970 (van Ginneken 1984) have only limited geographic coverage and do not satisfy our minimum criteria.

Despite the many urban surveys, there are no income distribution data that are representative of the population as a whole. References to national income distribution for the years 1953, 1959, and 1961(CEPAL 1968 in Altimir 1986 ) are based on extrapolation from national accounts and have therefore not been included. Data for 1953 and 1961 from Weisskoff (1970) , from Lecaillon (1984) , and from Cromwell (1977) are also excluded.

Australia Household surveys, the result of which is reported in the statistical yearbook, have been conducted in 1968/9, 1975/6, 1978/9, 1981, 1985, 1986, 1989, and 1990.

Data for 1962 (Cromwell, 1977) and 1966/67 (Sawyer 1976) were excluded as they covered only tax payers. Jain's data for 1970 was excluded because it covered income recipients only. Data from Podder (1972) for 1967/68, from Jain (1975) for the same year, from UN (1985) for 78/79, from Sunders and Hobbes (1993) for 1986 and for 1989 were excluded given the availability of the primary sources. Data from Bishop (1991) for 1981/82, from Buhman (1988) for 1981/82, from Kakwani (1986) for 1975/76, and from Sunders and Hobbes (1993) for 1986 were utilized to test for the effect of different definitions. The values for 1967 used by Persson and Tabellini and Alesina and Rodrik (based on Paukert and Jain) are close to the ones reported in the Statistical Yearbook for 1969.

Austria: In addition to data referring to the employed population (Guger 1989), national household surveys for 1987 and 1991 are included in the LIS data base. As these data do not include income from self-employment, we do not report them in our high quality data-set.

Bahamas Data for Ginis and shares are available for 1973, 1977, 1979, 1986, 1988, 1989, 1991, 1992, and 1993 in government reports on population censuses and household budget surveys, and for 1973 and 1975 from UN (1981). Estimates for 1970 (Jain 1975), 1973, 1975, 1977, and 1979 (Fields 1989) have been excluded given the availability of primary sources.

Bangladesh Data from household surveys for 1973/74, 1976/77, 1977/78, 1981/82, and 1985/86 are available from the Statistical Yearbook, complemented by household-survey based information from Chen (1995) and the World Development Report. Household surveys with rural coverage for 1959, 1960, 1963/64, 1965, 1966/67 and 1968/69, and with urban coverage for 1963/64, 1965, 1966/67, and 1968/69 are also available from the Statistical yearbook. Data for 1963/64 ,1964 and 1966/67, (Jain 1975) are not included due to limited geographic coverage, We also excluded secondary sources for 1973/74, 1976/77, 1981/82 (Fields 1989), 1977 (UN 1981), 1983 (Milanovic 1994), and 1985/86 due to availability of the primary source.

Barbados National household surveys have been conducted in 1951/52 and 1978/79 (Downs, 1988). Estimates based on personal tax returns, reported consistently for 1951-1981 (Holder and Prescott, 1989), had to be excluded as they exclude the non-wage earning population. Jain's figure (used by Alesina and Rodrik) is based on the same source.

Belgium Household surveys with national coverage are available for 1978/79 (UN 1985), and for 1985, 1988, and 1992 (LIS 1995). Earlier data for 1969, 1973, 1975, 1976 and 1977 (UN 1981) refer to taxable households only and are not included.

Bolivia The only survey with national coverage is the 1990 LSMS (World Development Report). Surveys for 1986 and 1989 cover the main cities only (Psacharopoulos et al. 1992) and are therefore not included. Data for 1968 (Cromwell 1977) do not refer to a clear definition and is therefore excluded.

Botswana The only survey with national coverage was conducted in 1985-1986 (Chen et al 1993); surveys in 74/75 and 85/86 included rural areas only (UN 1981). We excluded Gini estimates for 1971/72 that refer to the economically active population only (Jain 1975), as well as 1974/75 and 1985/86 (Valentine 1993) due to lack of national coverage or consistency in definition.

Brazil Data from 1960, 1970, 1974/75, 1976, 1977, 1978, 1980, 1982, 1983, 1985, 1987 and 1989 are available from the statistical yearbook, in addition to data for 1978 (Fields 1987) and for 1979 (Psacharopoulos et al. 1992). Other sources have been excluded as they were either not of national coverage, based on wage earners only, or because a more consistent source was available.

Bulgaria: Data from household surveys are available for 1963-69 (in two year intervals), for 1970-90 (on an annual basis) from the Statistical yearbook and for 1991 - 93 from household surveys by the World Bank (Milanovic and Ying).

Burkina Faso A priority survey has been undertaken in 1995.

Central African Republic: Except for a household survey conducted in 1992, no information was available.

Cameroon The only data are from a 1983/4 household budget survey (World Bank Poverty Assessment).

Canada Gini- and share data for the 1950-61 (in irregular intervals), 1961-81 (biennially), and 1981-91 (annually) are available from official sources (Statistical Yearbook for years before 1971 and Income Distributions by Size in Canada for years since 1973, various issues). All other references seem to be based on these primary sources.

Chad: An estimate for 1958 is available in the literature, and used by Alesina and Rodrik and Persson and Tabellini but was not included due to lack of primary sources.

Chile The first nation-wide survey that included not only employment income was carried out in 1968 (UN 1981). This is complemented by household survey-based data for 1971 (Fields 1989), 1989, and 1994. Other data that refer either only to part of the population or -as in the case of a long series available from World Bank country operations- are not clearly based on primary sources, are excluded.

China Annual household surveys from 1980 to 1992, conducted separately in rural and urban areas, were consolidated by Ying (1995), based on the statistical yearbook. Data from other secondary sources are excluded due to limited geographic and population coverage and data from Chen et al (1993) for 1985 and 1990 have not been included, to maintain consistency of sources..

Colombia The first household survey with national coverage was conducted in 1970 (DANE 1970). In addition, there are data for 1971, 1972, 1974 CEPAL (1986), and for 1978, 1988/89, and 1991 (World Bank Poverty Assessment 1992 and Chen et al. 1995). Data referring to years before 1970 -including the 1964 estimate used in Persson and Tabellini were excluded, as were estimates for the wage earning population only.

Costa Rica Data on Gini coefficients and quintile shares are available for 1961, 1971 (Cespedes 1973),1977 (OPNPE 1982), 1979 (Fields 1989), 1981 (Chen et al 1993), 1983 (Bourguignon and Morrison 1989), 1986 (Sauma-Fiatt 1990), and 1989 (Chen et al 1993). Gini coefficients for 1971 (Gonzalez-Vega and Cespedes in Rottenberg 1993), 1973 and 1985 (Bourguignon and Morrison 1989) cover urban areas only and were excluded.

Cote d'Ivoire: Data based on national-level household surveys (LSMS) are available for 1985, 1986, 1987, 1988, and 1995. Information for the 1970s (Schneider 1991) is based on national accounting information and therefore excluded

Cuba Official information on income distribution is limited. Data from secondary sources are available for 1953, 1962, 1973, and 1978, relying on personal wage income, i.e. excluding the population that is not economically active (Brundenius 1984).

Czech Republic Household surveys for 1993 and 1994 were obtained from Milanovic and Ying. While it is in principle possible to go back further, splitting national level surveys for the former Czechoslovakia into their independent parts, we decided not to do so as the same argument could be used to

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.

This statistic shows the distribution of income worldwide in 2035 by region. By 2035, roughly *** million people in India are projected to earn between zero and ***** U.S. dollars annually.

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

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

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).

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.

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.

The UNU-WIDER World Income Inequality Database (WIID) collects and stores information on income inequality for developed, developing, and transition countries.