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

Abstract

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).

Documentation

*The passage below comes from the US Census website:*

GINI INDEX OF INCOME INEQUALITYSurvey/Program: American Community SurveyUniverse: HouseholdsYear: 2018Estimates: 1-YearTable ID: B19083

Source: U.S. Census Bureau, 2018 American Community Survey 1-Year Estimates 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. 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: 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.

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.

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).

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

This table contains data on income inequality. The primary measure is the Gini index – a measure of the extent to which the distribution of income among families/households within a community deviates from a perfectly equal distribution. 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. More information about the data table and a data dictionary can be found in the About/Attachments section.

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.

Estimated Gini index by US census tract in 2018.

The table Gini by Census Tract is part of the dataset US Gini Coefficient , available at https://redivis.com/datasets/fme1-3tf0n6q1d. It contains 74016 rows across 4 variables.

Estimated Gini index by US county in 2018.

The table Gini by County is part of the dataset US Gini Coefficient , available at https://redivis.com/datasets/fme1-3tf0n6q1d. It contains 839 rows across 4 variables.

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.

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.

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.

These test data files were used to debug the code used in the following study: "Is the Gini Coefficient Enough? A Microeconomic Data Decomposition Study."

List of test data: 1. it14ih.dta - household-level dataset for Italy. 2. it14ip.dta - person-level dataset for Italy. 3. mx16ih.dta - household-level dataset for Mexico. 4. mx16ip.dta - person-level dataset for Mexico. 5. us18ih.dta - household-level dataset for the USA. 6. us18ip.dta - person-level dataset for the USA.

All files can be used for testing/debugging of the following scripts: lis_theil.R, lis_scv.R, lis_theil_functions.R, lis_scv_functions.R.

These datasets were donloaded from the following website. https://www.lisdatacenter.org/resources/self-teaching/.

Gini Coefficient data was reported at 0.413 NA in 2022. This records an increase from the previous number of 0.397 NA for 2021. Gini Coefficient data is updated yearly, averaging 0.390 NA from Dec 1963 (Median) to 2022, with 60 observations. The data reached an all-time high of 0.415 NA in 2019 and a record low of 0.347 NA in 1980. Gini Coefficient data remains active status in CEIC and is reported by Our World in Data. The data is categorized under Global Database’s United States – Table US.OWID.ESG: Social: Gini Coefficient: Annual.

Key Table Information.Table Title.Gini Index of Income Inequality.Table ID.ACSDT1Y2024.B19083.Survey/Program.American Community Survey.Year.2024.Dataset.ACS 1-Year Estimates Detailed Tables.Source.U.S. Census Bureau, 2024 American Community Survey, 1-Year Estimates.Dataset Universe.The dataset universe of the American Community Survey (ACS) is the U.S. resident population and housing. For more information about ACS residence rules, see the ACS Design and Methodology Report. Note that each table describes the specific universe of interest for that set of estimates..Methodology.Unit(s) of Observation.American Community Survey (ACS) data are collected from individuals living in housing units and group quarters, and about housing units whether occupied or vacant. For more information about ACS sampling and data collection, see the ACS Design and Methodology Report..Geography Coverage.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year.Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Sampling.The ACS consists of two separate samples: housing unit addresses and group quarters facilities. Independent housing unit address samples are selected for each county or county-equivalent in the U.S. and Puerto Rico, with sampling rates depending on a measure of size for the area. For more information on sampling in the ACS, see the Accuracy of the Data document..Confidentiality.The Census Bureau has modified or suppressed some estimates in ACS data products to protect respondents' confidentiality. Title 13 United States Code, Section 9, prohibits the Census Bureau from publishing results in which an individual's data can be identified. For more information on confidentiality protection in the ACS, see the Accuracy of the Data document..Technical Documentation/Methodology.Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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.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.Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..Weights.ACS estimates are obtained from a raking ratio estimation procedure that results in the assignment of two sets of weights: a weight to each sample person record and a weight to each sample housing unit record. Estimates of person characteristics are based on the person weight. Estimates of family, household, and housing unit characteristics are based on the housing unit weight. For any given geographic area, a characteristic total is estimated by summing the weights assigned to the persons, households, families or housing units possessing the characteristic in the geographic area. For more information on weighting and estimation in the ACS, see the Accuracy of the Data document.Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates ...

Data on redistributive spending in the 50 American states from 1974-2012. Also includes two Gini coefficient measures, economic measures, and demographic measures.

This paper calculates the distribution of an adjusted measure of income that deducts damages due to exposure to air pollution from reported market income in the United States from 2011 to 2014. The Gini coefficient for this measure of adjusted income is 0.682 in 2011, as compared to 0.482 for market income. By 2014, we estimate that the Gini for adjusted income fell to 0.646, while the market income Gini did not appreciably change. The inclusion of air pollution damage acts like a regressive tax: with air pollution, the bottom 20% of households lose roughly 10% of the share of income, while the top 20% of households gain 10%. We find that, unlike the case for market income, New England is not the most unequal division with respect to adjusted income. Further, the difference between adjusted income for white and Hispanics is smaller than expected. However, the gap in augmented income between whites and African-Americans is widening.

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

CityPropStats provides aggregated property statistics for 795 cities and towns (i.e., Metropolitan and Micropolitan statistical areas) in the conterminous United States. These statistics include sum, mean, median, Gini index and entropy of residential floor space, cadastral parcel size, floor-area ratio, and property value, approximately for the reference year 2020, aggregated by building construction year in decadal steps (cumulative and incremental) from 1910 to 2020.Cumulative statistics: CBSA_Property_Statistics_1910-2020_cumulative.csvDecadal time slices statistics: CBSA_Property_Statistics_1910-2020_decadal_slices.csvData source: Zillow Transaction and Assessment Dataset (ZTRAX), provided to University of Colorado Boulder via a data share agreement (2016-2023).CityPropStats is a supplementary dataset to:Ortman, Scott G., Amy Bogaard, Jessica Munson, Dan Lawrence, Adam S. Green, Gary M. Feinman, Shadreck Chirikure, Johannes H. Uhl, and Stefan Leyk. "Changes in agglomeration and productivity are poor predictors of inequality across the archaeological record." Proceedings of the National Academy of Sciences 122, no. 16 (2025): e2400693122. https://doi.org/10.1073/pnas.2400693122Column description:cbsa_idCBSA GEOIDcbsa_nameFull namecbsa_typeCBSA type (metro vs micropolitan statistical area)year_fromEarliest year for selection interval of properties based on their construction yearyear_toLatest year for selection interval of properties based on their construction yearcbsa_popCBSA population or population change (US Census)tot_res_propsTotal residential propertiestot_res_area_sqkmTotal indoor area of residential properties in sqkmavg_res_area_sqmAverage indoor area of residential properties in sqmmedian_res_area_sqmMedian indoor area of residential properties in sqmq25_res_area_sqm25th percentile of indoor area of residential properties in sqmq75_res_area_sqm75th percentile of indoor area of residential properties in sqmgini_res_areaGini index of residential property indoor areatot_prop_value_usdTotal residential property value in USDmedian_prop_value_usdMedian residential property value in USDq25_prop_value_usd25th percentile of residential property values in USDq75_prop_value_usd75th percentile of residential property values in USDgini_prop_valueGini index of residential property valuestot_lot_area_sqkmTotal lot (cadastral parcel) area in sqkmavg_lot_area_sqmMean lot area in sqmmedian_lot_area_sqmMedian lot area in sqmq25_lot_area_sqm25th percentile of lot area in sqmq75_lot_area_sqm75th percentile of lot area in sqmgini_lot_areaGini index of lot areaavg_farMean floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesmedian_farMedian floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesq25_far25th percentile of floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesq75_far75th percentile of floor-area-ratio (FAR), with FAR being the ratio of building indoor area and lot area, based on residential propertiesentropy_res_areaShannon entropy of the indoor area of residential properties, based on propertiesentropy_prop_valueShannon entropy of the property value of residential properties, based on propertiesentropy_lot_areaShannon entropy of the lot size of residential properties, based on propertiesarea_completenessRatio of properties with a valid indoor area attribute [0,1]value_completenessRatio of properties with a valid property value attribute [0,1]lotsize_completenessRatio of properties with a valid indoor area, property value, and lot size attribute [0,1]area_value_completenessRatio of properties with a valid lot size attribute [0,1]area_value_lotsize_completenessRatio of properties with both a valid indoor area and property value attribute [0,1]