New York was the state with the greatest gap between rich and poor, with a Gini coefficient score of 0.52 in 2023. Although not a state, District of Columbia was among the highest Gini coefficients in the United States that year.

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

This dataset contains tables that match an estimated Gini coefficient to a specific geographic region (either census tract, county, or state) from 2010 to 2018. The 1-year estimates are produced by the American Community Survey (ACS).

United States US: Gini Coefficient (GINI Index): World Bank Estimate data was reported at 41.500 % in 2016. This records an increase from the previous number of 41.000 % for 2013. United States US: Gini Coefficient (GINI Index): World Bank Estimate data is updated yearly, averaging 40.400 % from Dec 1979 (Median) to 2016, with 11 observations. The data reached an all-time high of 41.500 % in 2016 and a record low of 34.600 % in 1979. United States US: 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 United States – Table US.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.

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.

Estimated Gini index by state from 2010 to 2018.

The table Gini by State is part of the dataset US Gini Coefficient , available at https://redivis.com/datasets/fme1-3tf0n6q1d. It contains 468 rows across 4 variables.

In 2023, the Gini index for Black households in the United States stood at ***, which was higher than the national index that year. The Census Bureau defines the Gini index as “a statistical measure of income inequality ranging from zero to one. A measure of one indicates perfect inequality, i.e., one household having all the income and the rest having none. A measure of zero indicates perfect equality, i.e., all households having an equal share of income.”

In 2023, the Gini index for households of Asian origin in the United States stood at ****. The Census Bureau defines the Gini index as “a statistical measure of income inequality ranging from zero to ***. A measure of *** indicates perfect inequality, i.e., *** household having all the income and rest having none. A measure of zero indicates perfect equality, i.e., all households having an equal share of income.”

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

The income distribution Gini-coefficient of individual earnings for full-time, year-round workers was 0.42 in 2023, indicating no change from the year prior. A Gini coefficient of zero expresses perfect equality, where all would have the same income, and a Gini coefficient of one expresses maximal inequality among values. In 2021, the Gini coefficient for individual income was 0.43.

In 2023, the Gini Index for all U.S. households was ****. The U.S. Census Bureau defines the Gini index as “a statistical measure of income inequality ranging from zero to one. A measure of one indicates perfect inequality, i.e., one household having all the income and the rest having none. A measure of zero indicates perfect equality, i.e., all households having an equal share of income.”

United States - Income Gini for Households by Race of Householder, White Alone Not Hispanic was 0.47300 Ratio in January of 2023, according to the United States Federal Reserve. Historically, United States - Income Gini for Households by Race of Householder, White Alone Not Hispanic reached a record high of 0.48400 in January of 2021 and a record low of 0.45200 in January of 2002. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Income Gini for Households by Race of Householder, White Alone Not Hispanic - last updated from the United States Federal Reserve on September of 2025.

Graph and download economic data for Income Gini Ratio of Families by Race of Householder, Black Alone or in Combination (GINIBAOICF) from 2002 to 2024 about African-American, gini, households, income, and USA.

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, 2014-2018 American Community Survey 5-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 2014-2018 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions 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 definitions 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....

United States - Income Gini for Households by Race of Householder, Hispanic Origin (Any Race) was 0.47600 Ratio in January of 2024, according to the United States Federal Reserve. Historically, United States - Income Gini for Households by Race of Householder, Hispanic Origin (Any Race) reached a record high of 0.48100 in January of 2013 and a record low of 0.36700 in January of 1973. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Income Gini for Households by Race of Householder, Hispanic Origin (Any Race) - last updated from the United States Federal Reserve on October of 2025.

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.

Income InequalityThe level of income inequality among households in a county can be measured using the Gini index. A Gini index varies between zero and one. A value of one indicates perfect inequality, where only one household in the county has any income. A value of zero indicates perfect equality, where all households in the county have equal income.The United States, as a country, has a Gini Index of 0.47 for this time period. For comparision in this map, the purple counties have greater income inequality, while orange counties have less inequality of incomes. For reference, Brazil has an index of 0.58 (relatively high inequality) and Denmark has an index of 0.24 (relatively low inequality).The 5-year Gini index for the U.S. was 0.4695 in 2007-2011 and 0.467 in 2006-2010. Appalachian Regional Commission, September 2013Data source: U.S. Census Bureau, 5-Year American Community Survey, 2006-2010 & 2007-2011

Household Gini Ratio: Total data was reported at 0.482 USD in 2017. This records an increase from the previous number of 0.481 USD for 2016. Household Gini Ratio: Total data is updated yearly, averaging 0.433 USD from Dec 1967 (Median) to 2017, with 51 observations. The data reached an all-time high of 0.482 USD in 2017 and a record low of 0.386 USD in 1968. Household Gini Ratio: Total data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s United States – Table US.G085: Gini Ratio: Households. The Gini Ratio (or index of income concentration) is a statistical measure of income equality ranging from 0 to 1. A measure of 1 indicates perfect inequality; one person has all the income and the rest have none. A measure of 0 indicates perfect equality; all people have equal shares of income.

The index ranges from 0.0, when all families (households) have equal shares of income (implies perfect equality), to 1.0 when one family (household) has all the income and the rest have none (implies perfect inequality). Index data is provided for California and its counties, regions, and large cities/towns. The data is from the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Income is linked to acquiring resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. On average Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. Approximately 119,200 (5%) of the 2.4 million U.S. deaths in 2000 are attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers.Dataset taken from https://data.chhs.ca.gov/dataset/income-inequalityData Dictionary: COLUMN NAMEDEFINITIONFORMATCODINGind_idIndicator IDPlain Text770ind_definitionDefinition of indicator in plain languagePlain TextFree textreportyearYear(s) that the indicator was reportedPlain Text2005-2007, 2008-2010, 2006-2010. 2005-2007, 2008-2010, and 2006-2010 data is from the American Community Survey (ACS), U.S. Census Bureau. The ACS is a continuous survey. ACS estimates are period estimates that describe the average characteristics of the population in a period of data collection. The multiyear estimates are averages of the characteristics over several years. For example, the 2005-2007 ACS 3-year estimates are averages over the period from January 1, 2005 to December 31, 2007. Multiyear estimates cannot be used to say what was going on in any particular year in the period, only what the average value is over the full time period (Source: http://www.census.gov/acs/www/about_the_survey/american_community_survey/).race_eth_codenumeric code for a race/ethnicity groupPlain Text9=Totalrace_eth_nameName of race/ethnic groupPlain Text9=TotalgeotypeType of geographic unitPlain TextPL=Place (includes cities, towns, and census designated places -CDP-. It does not include unincorporated communities); CO=County; RE=region; CA=StategeotypevalueValue of geographic unitPlain Text9-digit Census tract code; 5-digit FIPS place code; 5-digit FIPS county code; 2-digit region ID; 2-digit FIPS state codegeonameName of geographic unitPlain Textplace name, county name, region name, or state namecounty_nameName of county that geotype is inPlain TextNot available for geotypes RE and CAcounty_fipsFIPS code of county that geotype is inPlain Text2-digit census state code (06) plus 3-digit census county coderegion_nameMetopolitan Planning Organization (MPO)-based region name: see MPO_County List TabPlain TextMetropolitan Planning Organizations (MPO) regions as reported in the 2010 California Regional Progress Report (http://www.dot.ca.gov/hq/tpp/offices/orip/Collaborative%20Planning/Files/CARegionalProgress_2-1-2011.pdf).region_codeMetopolitan Planning Organization (MPO)-based region code: see MPO_CountyList tabPlain Text01=Bay Area; 08=Sacramento Area; 09=San Diego; 14=Southern CaliforniaNumber_HouseholdsNumber of households in a jurisdictionNumericGini_indexCumulative percentage of household income relative to the cumulative percentage of the number of households expressed on a 0 to 1 scale called the Gini Index. The index ranges from 0.0, when all families (households) have equal shares of income, to 1.0, when one family (household) has all the income and the rest none (https://www.census.gov/prod/2000pubs/p60-204.pdf).NumericLL_95CILower limit of 95% confidence intervalNumericLower limit of 95% confidence interval. The 95% confidence limits depict the range within which the percentage would probably occur in 95 of 100 sets of data (if data similar to the present set were independently acquired on 100 separate occasions). In five of those 100 data sets, the percentage would fall outside the limits.UL_95CIUpper limit of 95% confidence intervalNumericUpper limit of 95% confidence interval. The 95% confidence limits depict the range within which the percentage would probably occur in 95 of 100 sets of data (if data similar to the present set were independently acquired on 100 separate occasions). In five of those 100 data sets, the percentage would fall outside the limits.seStandard error of percent NumericThe standard error (SE) of the estimate of the mean is a measure of the precision of the sample mean. The standard error falls as the sample size increases. (Reference: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1255808/)rseRelative standard error (se/percent * 100) expressed as a percentNumericThe relative standard error (RSE) provides the rational basis for determining which rates may be considered “unreliable.” Conforming to National Center for Health Statistics (NCHS) standards, rates that are calculated from fewer than 20 data elements, the equivalent of an RSE of 23 percent or more, are considered unreliable. From: http://www.cdph.ca.gov/programs/ohir/Documents/OHIRProfiles2014.pdfCA_decileDecilesNumeric"CA_decile" groups places or census tracts into 10 groups (or deciles) according to the distribution of values of the index (Gini_index). The first decile (1) corresponds to the highest Gini indices; the tenth decile (10) corresponds to the lowest Gini indices. Equal values or 'ties' are assigned the mean decile rank. For example, in a database of 100 records where 70 records equal 0, 0 values span from the 1st to 7th deciles (70% of all data records). As a result, all 0 values will be assigned to the 4th decile: the mean between the 1st and 7th deciles. The deciles are only calculated for places and/or census tracts.CA_RRIndex ratio to state indexNumericRatio of local index to state index. This indicates how many times the local index is higher or lower than the state index (Reference: http://health.mo.gov/training/epi/RateRatio-b.html). Values higher than 1 indicate local index is higher than state index.Median_HH_incomeMedian household income data is provided for users to stratify the Gini index by income deciles for places and countiesNumericMedian_HH_decileMedian household income data is provided for users to stratify the Gini index by income deciles for places and countiesNumericversionDate/time stamp of version of dataDate/Timemm/DD/CCYY hh:mm:ss

About 50.4 percent of the household income of private households in the U.S. were earned by the highest quintile in 2023, which are the upper 20 percent of the workers. In contrast to that, in the same year, only 3.5 percent of the household income was earned by the lowest quintile. This relation between the quintiles is indicative of the level of income inequality in the United States. Income inequalityIncome inequality is a big topic for public discussion in the United States. About 65 percent of U.S. Americans think that the gap between the rich and the poor has gotten larger in the past ten years. This impression is backed up by U.S. census data showing that the Gini-coefficient for income distribution in the United States has been increasing constantly over the past decades for individuals and households. The Gini coefficient for individual earnings of full-time, year round workers has increased between 1990 and 2020 from 0.36 to 0.42, for example. This indicates an increase in concentration of income. In general, the Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality and a score of one indicates a society where one person would have all the money and all other people have nothing. Income distribution is also affected by region. The state of New York had the widest gap between rich and poor people in the United States, with a Gini coefficient of 0.51, as of 2019. In global comparison, South Africa led the ranking of the 20 countries with the biggest inequality in income distribution in 2018. South Africa had a score of 63 points, based on the Gini coefficient. On the other hand, the Gini coefficient stood at 16.6 in Azerbaijan, indicating that income is widely spread among the population and not concentrated on a few rich individuals or families. Slovenia led the ranking of the 20 countries with the greatest income distribution equality in 2018.