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

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.

Chiapas, the state with the highest share of population living in poverty, had the highest wealth inequality in the country based on the Gini coefficient as well. This index measures the deviation of the income distribution situation in a given country from a perfectly equal distribution. A value of 0 represents an ideal situation of equality, whereas 1 would be the highest possible degree of inequality. As of 2022, Mexico City, the country's capital, had a Gini coefficient of 0.46, second highest recorded figure.

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.

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

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

Wealth inequality has been sharply rising in the United States and across many other high-income countries. Due to a lack of data, we know little about how this trend has unfolded across locations within countries. Investigating this subnational geography of wealth is crucial, as from one generation to the next, wealth powerfully shapes opportunity and disadvantage across individuals and communities. Using machine-learning-based imputation to link newly assembled national historical surveys conducted by the U.S. Federal Reserve to population survey microdata, the data presented in this paper addresses this gap. The Geographic Wealth Inequality Database ("GEOWEALTH-US") provides the first estimates of the level and distribution of wealth at various geographical scales within the United States from 1960 to 2020. The GEOWEALTH-US database enables new lines investigation into the contribution of inter-regional wealth patterns to major societal challenges including wealth concentration, spatial income inequality, equality of opportunity, housing unaffordability, and political polarization.

In the first quarter of 2024, almost two-thirds percent of the total wealth in the United States was owned by the top 10 percent of earners. In comparison, the lowest 50 percent of earners only owned 2.5 percent of the total wealth. Income inequality in the U.S. Despite the idea that the United States is a country where hard work and pulling yourself up by your bootstraps will inevitably lead to success, this is often not the case. In 2023, 7.4 percent of U.S. households had an annual income under 15,000 U.S. dollars. With such a small percentage of people in the United States owning such a vast majority of the country’s wealth, the gap between the rich and poor in America remains stark. The top one percent The United States follows closely behind China as the country with the most billionaires in the world. Elon Musk alone held around 219 billion U.S. dollars in 2022. Over the past 50 years, the CEO-to-worker compensation ratio has exploded, causing the gap between rich and poor to grow, with some economists theorizing that this gap is the largest it has been since right before the Great Depression.

This is the replication code package for "Income Inequality in the United States: Using Tax Data to Measure Long-Term Trends," accepted in 2023 by the Journal of Political Economy.

Income Inequality Rates by State, Year and Statistic

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Graph and download economic data for Income Inequality in New York County, NY (2020RATIO036061) from 2010 to 2023 about New York County, NY; inequality; New York; NY; income; and USA.

Recent studies of representation at the national and state levels have provided evidence that elected officials’ votes, political parties’ platforms, and enacted policy choices are more responsive to the preferences of the affluent, while those with average incomes and the poor have little or no impact in the political process. Yet, this research on the dominance of the affluent has overlooked key partisan differences in the electorate. In this era of hyper-partisanship, we argue that representation occurs through the party system, and we test whether taking this reality into account changes the story of policy dominance by the rich. We combine data on public preferences and state party positions to test for income bias in parties’ representation of their own co-partisans. The results show an interesting pattern in which under-representation of the poor is driven by Democratic parties pushing the more liberal social policy stances of rich Democrats and Republican parties reflecting the particularly conservative economic policy preferences of Rich Republicans. Thus, we have ample evidence that the wealthy, more often than not, do call the shots, but that the degree to which this disproportionate party responsiveness produces less representative policies depends on the party in power and the policy dimension being considered. We conclude by linking this pattern of influence and “coincidental representation” to familiar changes which define the transformation of the New Deal party system.[insert article abstract]

In recent decades, there has been an institutional shift in the literature on authoritarian regimes, with scholars investigating the role of political institutions, such as elections and political parties, in shaping regime stability and economic performance. However, scant attention has been devoted to the effect of political institutions on policy outcomes, and more specifically, on income inequality. This paper adds to this debate and sheds light on the role of formal and informal institutions, on the one hand, and state capacity, on the other, in influencing levels of income inequality in autocracies. We argue that, while the presence of elections and multiparty competition creates more favourable conditions for the adoption of redistributive policies, state capacity increases the likelihood of successfully implemented policy decisions aimed at reducing the level of inequality. Our empirical analysis rests on a time-series cross-sectional dataset, which includes around 100 countries from 1972 to 2014. The findings indicate that both political institutions and a higher level of state capacity lead to lower levels of income inequality in authoritarian contexts.

This file contains data needed to replicate all time series analyses from my book The Politics of Income Inequality in the United States.

Traditional agrarian elites have often been portrayed as obstacles to the expansion of the state. Because landed actors are particularly exposed to taxation, inequality is expected to exacerbate their resistance to the development of fiscal capacity. This article argues that when propertied actors are politically dominant and obtain benefits from public spending that are proportional to their capital endowments, wealth inequality is associated with greater elite support for capacity investments. Using early 20th-century Brazilian data, I show that where landed elites faced fewer political threats, higher levels of landholding concentration were associated with increased fiscal and administrative capacity. Tests of mechanisms corroborate the idea that this relationship results from elite demands for specific types of public spending. These findings contribute to the broader literature on state-building by providing new insights into the interaction between economic interests and political dominance in shaping subnational variation in the reach of the state.

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 Inequality in Sacramento County, CA was 14.19395 Ratio in January of 2023, according to the United States Federal Reserve. Historically, Income Inequality in Sacramento County, CA reached a record high of 15.11999 in January of 2016 and a record low of 12.04339 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for Income Inequality in Sacramento County, CA - last updated from the United States Federal Reserve on June of 2025.

Income Inequality in Jefferson Parish, LA was 17.14348 Ratio in January of 2023, according to the United States Federal Reserve. Historically, Income Inequality in Jefferson Parish, LA reached a record high of 17.14348 in January of 2023 and a record low of 13.72062 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for Income Inequality in Jefferson Parish, LA - last updated from the United States Federal Reserve on June of 2025.

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