In 2023, according to the Gini coefficient, household income distribution in the United States was 0.47. This figure was at 0.43 in 1990, which indicates an increase in income inequality in the U.S. over the past 30 years. What is the Gini coefficient? The Gini coefficient, or Gini index, is a statistical measure of economic inequality and wealth distribution among a population. A value of zero represents perfect economic equality, and a value of one represents perfect economic inequality. The Gini coefficient helps to visualize income inequality in a more digestible way. For example, according to the Gini coefficient, the District of Columbia and the state of New York have the greatest amount of income inequality in the U.S. with a score of 0.51, and Utah has the greatest income equality with a score of 0.43. The Gini coefficient around the world The Gini coefficient is also an effective measure to help picture income inequality around the world. For example, in 2018 income inequality was highest in South Africa, while income inequality was lowest in Slovenia.

France - Inequality of income distribution was 4.66 in December of 2024, according to the EUROSTAT. Trading Economics provides the current actual value, an historical data chart and related indicators for France - Inequality of income distribution - last updated from the EUROSTAT on July of 2025. Historically, France - Inequality of income distribution reached a record high of 4.66 in December of 2024 and a record low of 4.23 in December of 2018.

Over ** percent of all income earned in Angola between 2018 and 2019 were concentrated among the richest ** percent of the population (fifth quintile). By contrast, the lowest ** percent of the population (first quintile) obtained *** percent of all earnings. According to the source, this means that the average income of an Angolan in the richest quintile was more than ** times that of an individual in the poorest quintile. Overall, the Gini Coefficient in Angola was measured at *** in the same period.

Cross-national research on the causes and consequences of income inequality has been hindered by the limitations of existing inequality datasets: greater coverage across countries and over time is 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 overcome these limitations. A custom missing-data algorithm was used to standardize the United Nations University's World Income Inequality Database and data from other sources; data collected by the Luxembourg Income Study served as the standard. The SWIID provides comparable Gini indices of gross and net income inequality for 192 countries for as many years as possible from 1960 to the present along with estimates of uncertainty in these statistics. By maximizing comparability for the largest possible sample of countries and years, the SWIID is better suited to broadly cross-national research on income inequality than previously available sources: it offers coverage double that of the next largest income inequality dataset, and its record of comparability is three to eight times better than those of alternate datasets. In any papers or publications that use the SWIID, authors are asked to cite the article of record for the data set and give the version number as follows: Solt, Frederick. 2016. "The Standardized World Income Inequality Database." Social Science Quarterly 97(5):1267-1281. SWIID Version 7.1, August 2018.

Graph and download economic data for Income Gini Ratio for Households by Race of Householder, All Races (GINIALLRH) from 1967 to 2023 about gini, households, income, and USA.

Australia: Gini income inequality index: The latest value from 2018 is 34.3 index points, an increase from 33.7 index points in 2016. In comparison, the world average is 35.68 index points, based on data from 91 countries. Historically, the average for Australia from 1981 to 2018 is 33.52 index points. The minimum value, 31.3 index points, was reached in 1981 while the maximum of 35.4 index points was recorded in 2008.

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.

Income Inequality in Monona County, IA was 10.11095 Ratio in January of 2023, according to the United States Federal Reserve. Historically, Income Inequality in Monona County, IA reached a record high of 13.15655 in January of 2018 and a record low of 9.81162 in January of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for Income Inequality in Monona County, IA - last updated from the United States Federal Reserve on August of 2025.

Income Inequality in Searcy County, AR was 11.72314 Ratio in January of 2023, according to the United States Federal Reserve. Historically, Income Inequality in Searcy County, AR reached a record high of 14.88334 in January of 2018 and a record low of 10.91290 in January of 2012. Trading Economics provides the current actual value, an historical data chart and related indicators for Income Inequality in Searcy County, AR - last updated from the United States Federal Reserve on August of 2025.

75% of households from the Bangladeshi ethnic group were in the 2 lowest income quintiles (after housing costs were deducted) between April 2021 and March 2024.

This dataset presents information about total income distribution. The data covers the financial year of 2017-2018, and is based on Statistical Area Level 3 (SA3) according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). Total Income is the sum of all reported income derived from Employee income, Own unincorporated business, Superannuation, Investments and Other income. Total income does not include the non-lodger population. Government pensions, benefits or allowances are excluded from the Australian Bureau of Statistics (ABS) income data and do not appear in Other income or Total income. Pension recipients can fall below the income threshold that necessitates them lodging a tax return, or they may only receive tax free pensions or allowances. Hence they will be missing from the personal income tax data set. Recent estimates from the ABS Survey of Income and Housing (which records Government pensions and allowances) suggest that this component can account for between 9% to 11% of Total income. All monetary values are presented as gross pre-tax dollars, as far as possible. This means they reflect income before deductions and loses, and before any taxation or levies (e.g. the Medicare levy or the temporary budget repair levy) are applied. The amounts shown are nominal, they have not been adjusted for inflation. The income presented in this release has been categorised into income types, these categories have been devised by the ABS to closely align to ABS definitions of income. The statistics in this release are compiled from the Linked Employer Employee Dataset (LEED), a cross-sectional database based on administrative data from the Australian taxation system. The LEED includes more than 120 million tax records over seven consecutive years between 2011-12 and 2017-18. Please note: All personal income tax statistics included in LEED were provided in de-identified form with no home address or date of birth. Addresses were coded to the ASGS and date of birth was converted to an age at 30 June of the reference year prior to data provision.

In a developing and highly unequal region like Latin America, it is crucial to understand the determinants that affect people's support for redistribution of resources from the state. A series of theories focused on self-interest have continuously established a negative link between people's income and their support for the reduction of inequalities through redistribution. Despite this, the evidence is scarce and sometimes contradictory while its study in Latin America is almost non-existent. Using data from the LAPOP Survey between 2008 and 2018, a longitudinal dimension is considered for the first time in the measurement of Latin American redistributive preferences, using hybrid multilevel regression models. In contrast to the evidence from studies conducted in other regions, the results reveal that in Latin America it is not possible to detect a clear association between income and redistributive preferences at specific times, but it is possible when changes occur in countries' levels of inequality and economic development. Likewise, other elements that consistently affect preferences are evident, such as educational level, political ideology, and confidence in the political system. In light of this evidence, comparisons are made with previous research findings in industrialized countries, challenging rationalist theories of justice and solidarity.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the .Technical Documentation.. section......Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the .Methodology.. section..Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see .ACS Technical Documentation..). The effect of nonsampling error is not represented in these tables..While the 2018 American Community Survey (ACS) data generally reflect the July 2015 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas, in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineations due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:..An "**" entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An "-" entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution, or the margin of error associated with a median was larger than the median itself..An "-" following a median estimate means the median falls in the lowest interval of an open-ended distribution..An "+" following a median estimate means the median falls in the upper interval of an open-ended distribution..An "***" entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An "*****" entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An "N" entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An "(X)" means that the estimate is not applicable or not available....

Sommet, N., Morselli, D., & Spini, D. (2018). Income inequality affects the psychological health only of the people facing scarcity. Accepted for publication in Psychological Science. https://doi.org/ 10.1177/0956797618798620

Income Inequality in Nance County, NE was 11.22460 Ratio in January of 2023, according to the United States Federal Reserve. Historically, Income Inequality in Nance County, NE reached a record high of 14.56510 in January of 2018 and a record low of 9.49836 in January of 2012. Trading Economics provides the current actual value, an historical data chart and related indicators for Income Inequality in Nance County, NE - last updated from the United States Federal Reserve on August of 2025.

Human capital is a nation’s primary source of inner strength to achieve sustainable economic growth and development. Meanwhile, income inequality is a critical issue preventing sustainable economic growth and social transformation, especially in developing countries. This paper investigates the effect of human capital on income inequality in both the short and long term using the mean group, pooled mean group, and threshold regressions for the ASEAN-7 (including Indonesia, Laos, Malaysia, the Philippines, Singapore, Thailand, and Vietnam) from 1992 to 2018. The paper develops a theoretical linkage between human capital and income inequality by combining the learning theory and the Kuznets hypothesis. This linkage is then tested using data from the ASEAN countries. Findings from the paper indicate that human capital reduces income inequality in the short run in the ASEAN countries. However, the effect is reverted in the long run, suggesting that human capital may increase the income gap in these countries. Particularly, the inverted U-shaped relationship between human capital and income inequality is established for the ASEAN countries whose GDP per capita is lower than USD 8.2 thousand per year. In contrast, the U-shaped relationship is found for the countries with income per capital of more than USD 8.2 thousand. All these findings suggest that social policies targeting reducing income inequality should be prioritized and stay at the centre of any economic policies to achieve sustainable economic growth and development in the ASEAN countries.

What is the relationship between income inequality and redistributive policies? This question carries with it important implications for both scholars of comparative politics and for core political dynamics in contemporary world politics. We contend that the current literature fails to provide satisfactory answers. It generally does not acknowledge heterogeneity in the relationship between inequality and redistributive policies across space and time, nor does it use cross-nationally comparable data on government redistribution and income. In this note, we compare the relationship between inequality and redistribution over time, as well as among clusters of developed and less developed countries. We use a number of statistical models to address the complexity of the relationship. We find a positive, short-term association between inequality and redistribution, controlling for endogeneity between redistribution and market income inequality. We also find that, over the long term, inequality increases redistribution in developed democracies, but appears to decrease it in a number of developing nations.

This publication contains results on the income inequality of individuals in the Netherlands. Various measures of income inequality are given. The standardized disposable household income allocated to the person is taken as the starting point. Persons are further distinguished according to characteristics such as gender, age, ethnic group and region. Data available from 2000 up to and including 2014 Status of the figures The figures for the year 2000 are provisional. The figures for the years 2001-2014 are final. Changes March 15, 2018: None, this table has been discontinued. When will new numbers come out? Not applicable anymore.

This table provides data on inequality indicators such as the Gini index and the P80/P20 income distribution for 2018. The information is disaggregated territorially at the level of sections of the Canary Islands with reference date 1/1/2019.

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.