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.

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.

This dataset is about books. It has 2 rows and is filtered where the book is The politics of inequality : a political history of the idea of economic inequality in America. It features 7 columns including author, publication date, language, and book publisher.

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.

Graph and download economic data for Income Inequality in Nantucket County, MA (2020RATIO025019) from 2010 to 2023 about Nantucket County, MA; inequality; MA; income; and USA.

Racial disparities arise across many vital areas of American life, including employment, health, and interpersonal treatment. For example, 1 in 3 Black children live in poverty (vs. 1 in 9 White children) and on average, Black Americans live 4 fewer years than White Americans. Which disparity is more likely to spark reduction efforts? We find that highlighting disparities in health-related (vs. economic) outcomes spurs greater social media engagement and support for disparity-mitigating policy. Further, reading about racial health disparities elicits greater support for action (e.g., protesting) than economic or belonging-based disparities. This occurs, in part, because people view health disparities as violating morally-sacred values which enhances perceived injustice. This work elucidates which manifestations of racial inequality are most likely to prompt Americans to action., The data from Studies 1a, 1b, 3, 4a, and 4b were collected via online platfroms (i.e., Mturk.com, Prolific Academic, and NORC’s AmeriSpeak Panel). All analyses were run in R with the R code provided (title: Health_Disparities_Syntax.R)., , # Highlighting Health Consequences of Racial Disparities Sparks Support for Action

There are a total of 5 datasets available (Studies 1a, 1b, 3, 4a, 4b) each collected by the researchers from online survey platforms. All data files are .sav files. We recommed using SPSS or RStudio to work with the data. We provide our code using RStudio and a codebook with the name of all variables in each dataset.

Description of the data and file structure

Study 1a and Study 1b utilized a within-subjects experimental design (S1a: N=191; S1b, preregistered: N=337, 50% White participants, 50% Black participants) where samples of U.S. citizens recruited from MTurk.com and Prolific Academic read nine examples of racial disparities, three each from the domains of health, economics, and belonging. After each example, participants reported whether the disparity was unjust and fair (reverse-coded; 2-items averaged to create a perceived injustice scale). Participants also indicated their agreement (1=s...

Graph and download economic data for Income Inequality in St. Louis city, MO (2020RATIO029510) from 2010 to 2023 about St. Louis City, MO; St. Louis; inequality; MO; income; and USA.

Graph and download economic data for Income Inequality in Washington County, OR (2020RATIO041067) from 2010 to 2023 about Washington County, OR; Portland; inequality; OR; income; and USA.

United States US: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 15.500 % in 2021. This records a decrease from the previous number of 17.000 % for 2020. United States US: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 17.700 % from Dec 1963 (Median) to 2021, with 59 observations. The data reached an all-time high of 19.000 % in 1993 and a record low of 15.500 % in 2021. United States US: Proportion of People Living Below 50 Percent Of Median Income: % 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: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;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).

Recent research has explored the distributive consequences of major historical epidemics, and the current crisis triggered by Covid-19 prompts us to look at the past for insights about how pandemics can affect inequalities in income, wealth, and health. The fourteenth-century Black Death, which is usually believed to have led to a significant reduction in economic inequality, has attracted the greatest attention. However, the picture becomes much more complex if other epidemics are considered. This article covers the worst epidemics of preindustrial times, from Justinian’s Plague of 540-41 to the last great European plagues of the seventeenth century, as well as the cholera waves of the nineteenth. It shows how the distributive outcomes of lethal epidemics do not only depend upon mortality rates, but are mediated by a range of factors, chief among them the institutional framework in place at the onset of each crisis. It then explores how past epidemics affected poverty, arguing that highly lethal epidemics could reduce its prevalence through two deeply different mechanisms: redistribution towards the poor, or extermination of the poor. It concludes by recalling the historical connection between the progressive weakening and spacing in time of lethal epidemics and improvements in life expectancy, and by discussing how epidemics affected inequality in health and living standards.

Replication data for article published in Political Research Quarterly.

Existing research analyzes the effects of cross national and temporal variation in income inequality on public opinion; however, research has failed to explore the impact of variation in inequality across citizens' local residential context. This article analyzes the impact of local inequality on citizens' belief in a core facet of the American ethos--meritocracy. We advance conditional effects hypotheses which collectively argue that the effect of residing in a high inequality context will be moderated by individual income. Utilizing national survey data, we demonstrate that residing in more unequal counties heightens rejection of meritocracy among low income residents and bolsters adherence among high income residents. In relatively equal counties, we find no significant differences between high and low income citizens. We conclude by discussing the implications of class-based polarization found in response to local inequality with respect to current debates over the consequences of income inequality for American democracy.

During a June 2020 survey, registered voters among three different political affiliations were asked how important they thought it was for Congress to pass a bill to reduce economic inequality. Among the survey participants who identified as Democratic, 49 percent responded that such a bill should be a top priority for Congress.

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 Share of Net Worth Held by the Top 1% (99th to 100th Wealth Percentiles) (WFRBST01134) from Q3 1989 to Q4 2024 about net worth, wealth, percentile, Net, and USA.

Economic inequality has been increasing in the U.S. over the past several decades. The contribution of purely economic factors, such as wage divergence within a generation over time, versus demographic and societal contributors, such as selective immigration and changes in the earnings potential of new generations entering the labor market, is not well understood. The distinction between different mechanisms driving inequality may be especially relevant for racial and ethnic groups that experience high rates of immigration and demographic change. Using confidential-use, individual-level Internal Revenue Service and U.S. Census data, we follow the earnings of Hispanics and Asians between the ages of 18--45 with panel data that spans the years 2005--2014. We examine the impact that labor market entrants and new immigrant arrivals within each group have on group earnings inequality. We show that labor market entrants and immigrants increase inequality for both groups.

Graph and download economic data for Income Inequality in Santa Cruz County, CA (2020RATIO006087) from 2010 to 2023 about Santa Cruz County, CA; Santa Cruz; inequality; CA; income; and USA.

Based on the degree of inequality in income distribution measured by the Gini coefficient, Colombia was the most unequal country in Latin America as of 2022. Colombia's Gini coefficient amounted to 54.8. The Dominican Republic recorded the lowest Gini coefficient at 37, even below Uruguay and Chile, which are some of the countries with the highest human development indexes in Latin America. The Gini coefficient explained The Gini coefficient measures the deviation of the distribution of income 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. This measurement reflects the degree of wealth inequality at a certain moment in time, though it may fail to capture how average levels of income improve or worsen over time. What affects the Gini coefficient in Latin America? Latin America, as other developing regions in the world, generally records high rates of inequality, with a Gini coefficient ranging between 37 and 55 points according to the latest available data from the reporting period 2010-2023. According to the Human Development Report, wealth redistribution by means of tax transfers improves Latin America's Gini coefficient to a lesser degree than it does in advanced economies. Wider access to education and health services, on the other hand, have been proven to have a greater direct effect in improving Gini coefficient measurements in the region.

During a June 2020 survey, registered voters were asked about how important they thought it was for Congress to pass a bill to reduce economic inequality. The survey results showed that only 10 percent of respondents believed that Congress should not pass such a bill. Adversely, 34 percent of survey participants thought it should be a top priority.

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.