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 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.

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

Graph and download economic data for Income Gini Ratio for Households by Race of Householder, All Races (GINIALLRH) from 1967 to 2024 about gini, households, income, and USA.

The gini index in Canada was forecast to remain on a similar level in 2029 as compared to 2024 with 0.33 points. According to this forecast, the gini will stay nearly the same over the forecast period. The Gini coefficient here measures the degree of income inequality on a scale from 0 (=total equality of incomes) to one (=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 150 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).Find more key insights for the gini index in countries like United States and Mexico.

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

The gini index in Turkey was forecast to remain on a similar level in 2029 as compared to 2024 with **** points. According to this forecast, the gini will stay nearly the same over the forecast period. 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).Find more key insights for the gini index in countries like Croatia and Romania.

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.

Israel IL: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 41.400 % in 2012. This records a decrease from the previous number of 42.600 % for 2010. Israel IL: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 38.900 % from Dec 1979 (Median) to 2012, with 9 observations. The data reached an all-time high of 42.600 % in 2010 and a record low of 35.500 % in 1992. Israel IL: 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 Israel – Table IL.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.

Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 34.200 % in 2015. This records a decrease from the previous number of 35.100 % for 2014. Latvia LV: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 35.500 % from Dec 1993 (Median) to 2015, with 15 observations. The data reached an all-time high of 39.000 % in 2005 and a record low of 27.000 % in 1993. Latvia LV: 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 Latvia – Table LV.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.

The period from 1980 to 2023 saw an increase in the share of national income in Europe taken by the top 10 percent of earners. This period has generally been categorized by economists as a period of rising income inequality, especially when compared with the postwar period (1945-1970s) in Europe which saw a compression of the income distribution, with the middle classes in particular making large gains. As financial and labor markets were liberalized in the 1980s and as the effects of economic globalization took hold, however, a growing share of income went to the top earners. This European trend mirrors increases in inequality across the globe during this period, with the United States seeing a particularly sharp rise in the share taken by its top one percent. Rising income inequality has been linked to the rise of populism in Europe throughout the 2000s and 2010s, as voters sought to hit back at economic elites.

Chile CL: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 43.000 % in 2022. This records a decrease from the previous number of 47.000 % for 2020. Chile CL: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 49.600 % from Dec 1987 (Median) to 2022, with 16 observations. The data reached an all-time high of 57.200 % in 1990 and a record low of 43.000 % in 2022. Chile CL: 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 Chile – Table CL.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 average for 2021 based on 8 countries was 45.11 index points. The highest value was in Colombia: 55.1 index points and the lowest value was in Peru: 40.1 index points. The indicator is available from 1963 to 2023. Below is a chart for all countries where data are available.

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']

Between 2010 and 2022, Panama's data on the degree of inequality in income distribution based on the Gini coefficient totaled 50.9. This coefficient represents a deterioration compared to last year. Panama was deemed as the third most unequal country in Latin America.

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.

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

The Gini coefficient is defined as the relationship of cumulative shares of the population arranged according to the level of equivalised disposable income, to the cumulative share of the equivalised total disposable income received by them.

Educational resources and lesson plans based on Income Inequality (Gini Coefficients) for Australian regions data collection Lineage: Fleming, David; Measham, Tom (2015): Income Inequality (Gini Coefficients) for Australian regions. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/55093772960E4

Nigeria NG: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 43.000 % in 2009. This records an increase from the previous number of 40.100 % for 2003. Nigeria NG: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 43.000 % from Dec 1985 (Median) to 2009, with 5 observations. The data reached an all-time high of 51.900 % in 1996 and a record low of 38.700 % in 1985. Nigeria NG: 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 Nigeria – Table NG.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.

The rising share of national income taken by the top one percent of earners is a common thread amongst almost all European countries over the past half century. As economic globalization took hold throughout the 1980s and 1990s, European countries experienced de-industrialization due to the emergence of international competitors, mostly in East Asia. At the same time, information technology and finance became much more important for most European economies, while growth in these sectors tends to favor high earners. This rise in inequality is also often also attributed to the ascendence of 'neoliberal' economic and political ideas which prioritized free markets and the privatization of government-owned businesses. Russia: the explosion of inequality after the fall of communismAmong the largest European economies, the Russian Federation stands out as the country which experienced the sharpest increase in inequality, as a small number of 'oligarchs' took control of the major industries after the collapse of the Soviet Union and the end of communist rule in 1991. The top one percent in Russia increased their share of national income five-fold over the 20 years from 1987 to 2007, when inequality in the country reached its peak as the oligarchs took home over a quarter of the country's income. Turkey: falling share of national income taken by top earners****** has bucked the trend of the rising income share for the richest over this period, as its extremely concentrated income distribution has in fact become somewhat more equitable. The highest earners in Turkey saw their share of national income drop from almost ** percent in the early *****, to a low of ** percent in 2007, after which it has stabilized between ** and ** percent. Western Europe: gradually rising share of national income for the richThe five western European democracies, Germany, France, Italy, Spain, and the United Kingdom, have all seen increases in their top earners' shares of national income over this period. The United Kingdom, Italy, and Germany have in particular seen their shares increase sharply, while Spain and France have experienced a more gradual increase.