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.

In the third quarter of 2024, the top ten percent of earners in the United States held over ** percent of total wealth. This is fairly consistent with the second quarter of 2024. Comparatively, the wealth of the bottom ** percent of earners has been slowly increasing since the start of the *****, though remains low. Wealth distribution in the United States by generation can be found here.

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

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.”

This is the replication package for Astorga, Pablo. 2024. Revealing the diversity and complexity of long-term income inequality in Latin America: 1920-2011. Journal of Economic History, 84(4).This paper analyses and documents new long-term income inequality series for Argentina, Brazil, Chile, Colombia, Mexico and Venezuela based on dynamic social tables with four occupational groups. This enables the calculation of comparable Overall (4 groups) and Labor Ginis (3 groups) with their between- and within-groups components. The main findings are: the absence of a unique inequality pattern over time; country outcomes characterized by trajectory diversity and level divergence during industrialization, and by commonality and convergence post 1980; the occurrence of inequality-levelling episodes with different timing and length; and significant changes in trends, but also evidence indicating persistence.

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 Q1 2025 about net worth, wealth, percentile, Net, and USA.

Income Inequality in Bronx County, NY was 25.92554 Ratio in January of 2023, according to the United States Federal Reserve. Historically, Income Inequality in Bronx County, NY reached a record high of 25.92554 in January of 2023 and a record low of 17.84934 in January of 2010. Trading Economics provides the current actual value, an historical data chart and related indicators for Income Inequality in Bronx County, NY - last updated from the United States Federal Reserve on July of 2025.

In 2023, the Gini index for Black households in the United States stood at 0.5, 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.”

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.

This file includes the knitr files (combined LaTeX + R code) that generate the article and the appendix, including all results and figures. An R script that downloads the Pew Research Center datasets employed is included, along with other data and bibliography dependencies. These reproducibility materials along with their intermediate products and the complete revision history of the article are available at https://github.com/fsolt/meritocracy

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.

Goal 10Reduce inequality within and among countriesTarget 10.1: By 2030, progressively achieve and sustain income growth of the bottom 40 per cent of the population at a rate higher than the national averageIndicator 10.1.1: Growth rates of household expenditure or income per capita among the bottom 40 per cent of the population and the total populationSI_HEI_TOTL: Growth rates of household expenditure or income per capita (%)Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other statusIndicator 10.2.1: Proportion of people living below 50 per cent of median income, by sex, age and persons with disabilitiesSI_POV_50MI: Proportion of people living below 50 percent of median income (%)Target 10.3: Ensure equal opportunity and reduce inequalities of outcome, including by eliminating discriminatory laws, policies and practices and promoting appropriate legislation, policies and action in this regardIndicator 10.3.1: Proportion of population reporting having personally felt discriminated against or harassed in the previous 12 months on the basis of a ground of discrimination prohibited under international human rights lawVC_VOV_GDSD: Proportion of population reporting having felt discriminated against, by grounds of discrimination, sex and disability (%)Target 10.4: Adopt policies, especially fiscal, wage and social protection policies, and progressively achieve greater equalityIndicator 10.4.1: Labour share of GDPSL_EMP_GTOTL: Labour share of GDP (%)Indicator 10.4.2: Redistributive impact of fiscal policySI_DST_FISP: Redistributive impact of fiscal policy, Gini index (%)Target 10.5: Improve the regulation and monitoring of global financial markets and institutions and strengthen the implementation of such regulationsIndicator 10.5.1: Financial Soundness IndicatorsFI_FSI_FSANL: Non-performing loans to total gross loans (%)FI_FSI_FSERA: Return on assets (%)FI_FSI_FSKA: Regulatory capital to assets (%)FI_FSI_FSKNL: Non-performing loans net of provisions to capital (%)FI_FSI_FSKRTC: Regulatory Tier 1 capital to risk-weighted assets (%)FI_FSI_FSLS: Liquid assets to short term liabilities (%)FI_FSI_FSSNO: Net open position in foreign exchange to capital (%)Target 10.6: Ensure enhanced representation and voice for developing countries in decision-making in global international economic and financial institutions in order to deliver more effective, credible, accountable and legitimate institutionsIndicator 10.6.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 10.7: Facilitate orderly, safe, regular and responsible migration and mobility of people, including through the implementation of planned and well-managed migration policiesIndicator 10.7.1: Recruitment cost borne by employee as a proportion of monthly income earned in country of destinationIndicator 10.7.2: Number of countries with migration policies that facilitate orderly, safe, regular and responsible migration and mobility of peopleSG_CPA_MIGRP: Proportion of countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (%)SG_CPA_MIGRS: Countries with migration policies to facilitate orderly, safe, regular and responsible migration and mobility of people, by policy domain (1 = Requires further progress; 2 = Partially meets; 3 = Meets; 4 = Fully meets)Indicator 10.7.3: Number of people who died or disappeared in the process of migration towards an international destinationiSM_DTH_MIGR: Total deaths and disappearances recorded during migration (number)Indicator 10.7.4: Proportion of the population who are refugees, by country of originSM_POP_REFG_OR: Number of refugees per 100,000 population, by country of origin (per 100,000 population)Target 10.a: Implement the principle of special and differential treatment for developing countries, in particular least developed countries, in accordance with World Trade Organization agreementsIndicator 10.a.1: Proportion of tariff lines applied to imports from least developed countries and developing countries with zero-tariffTM_TRF_ZERO: Proportion of tariff lines applied to imports with zero-tariff (%)Target 10.b: Encourage official development assistance and financial flows, including foreign direct investment, to States where the need is greatest, in particular least developed countries, African countries, small island developing States and landlocked developing countries, in accordance with their national plans and programmesIndicator 10.b.1: Total resource flows for development, by recipient and donor countries and type of flow (e.g. official development assistance, foreign direct investment and other flows)DC_TRF_TOTDL: Total assistance for development, by donor countries (millions of current United States dollars)DC_TRF_TOTL: Total assistance for development, by recipient countries (millions of current United States dollars)DC_TRF_TFDV: Total resource flows for development, by recipient and donor countries (millions of current United States dollars)Target 10.c: By 2030, reduce to less than 3 per cent the transaction costs of migrant remittances and eliminate remittance corridors with costs higher than 5 per centIndicator 10.c.1: Remittance costs as a proportion of the amount remittedSI_RMT_COST: Remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_BC: Corridor remittance costs as a proportion of the amount remitted (%)SI_RMT_COST_SC: SmaRT corridor remittance costs as a proportion of the amount remitted (%)

The racial wealth gap in the United States is the object of much misrepresentation and misunderstanding. Our paper is intended to provide a corrective. We will address how the mainstream economic view of the drivers of racial disparities in wealth is a human capital view that promotes anti-Black and personal responsibility narratives while ignoring the significance of the racially uneven transmission of resources across generations. It also fails to acknowledge the cumulative impact of U.S. racial history on present wealth gaps. This is the code and data accompanying the article.

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“Meticulously researched and engagingly written . . . a comprehensive indictment of the court’s rulings in areas ranging from campaign finance and voting rights to poverty law and criminal justice.” —Financial Times

A revelatory examination of the conservative direction of the Supreme Court over the last fifty years.

In Supreme Inequality, bestselling author Adam Cohen surveys the most significant Supreme Court rulings since the Nixon era and exposes how, contrary to what Americans like to believe, the Supreme Court does little to protect the rights of the poor and disadvantaged; in fact, it has not been on their side for fifty years. Cohen proves beyond doubt that the modern Court has been one of the leading forces behind the nation’s soaring level of economic inequality, and that an institution revered as a source of fairness has been systematically making America less fair.

A triumph of American legal, political, and social history, Supreme Inequality holds to account the highest court in the land and shows how much damage it has done to America’s ideals of equality, democracy, and justice for all.

ISBN: 9780593165393 Published: Feb 25, 2020 By: Adam Cohen Read by: Dan Woren

©2020 Adam Cohen (P)2020 Penguin Audio

In 2022, about 40 percent of adults in Mexico held a net worth under 10,000 U.S. dollars. In contrast, merely 393,000 Mexicans (that is, 0.4 percent of the total) had a net worth of over one million U.S. dollars. Mexico is one of the most unequal countries in Latin America regarding wealth distribution, with 78.7 percent of the national wealth held by the richest ten percent of the population.

The minimum salaryThe minimum wage per day guaranteed by law in Mexico was decreed to increase by 22 percent between 2021 and 2022, reaching 172.87 Mexican pesos in 2022. In the Free Zone located near the northern border the minimum daily wage was raised to 260.34 Mexican pesos.This represented the fourth consecutive incrase since 2019, but could prove to be insufficient to maintain the wellbeing of Mexican workers after the soaring inflation rate registered in 2022 and the economic impact of the COVID-19 in Mexican households. The legal minimum salary has a long history in the North American country, it was first implemented with the approval of the Political Constitution of the United Mexican States in 1917. Income inequality in Latin AmericaLatin America, as other developing regions in the world, generally records high rates of inequality, with a Gini coefficient ranging between 38 and 54 among the region’s countries. Moreover, many of the countries with the biggest inequality in income distribution worldwide are found in Latin America. According to the Human Development Report 2019, 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.

Part A. Heinz Müller: The income structure in several German states, 1874-1913. The entire material of this study was developed by the institute for regional politics and transportation science at the University of Freiburg. The first part of the study deals with an analysis of the income structure (=personnel income distribution) in chosen German states for the period from 1873 to 1913. Analysis of income structures can be planned and realized with different aims and with the use of different methods. The following structuring of income recipients is most commonly used: a) By sources of income b) By the income of sociologically important recipient groups c) By income level The structuring by income sources addresses a registration of the functional income distribution. This is instructive but difficult to carry out. For example it is hardly possible to subdivide the income resulting from entrepreneurial activity in the structural components as this term includes employer´s salary, basic pension for entrepreneurs, enterprise interests and entrepreneurial profit. Even theoretically it is difficult to subdivide income resulting from entrepreneurial activity in these components but in the practical implementation this division faces insurmountable difficulties. On the other hand such a structuring is an important perquisite for a proper analysis of income sources especially in the area of agriculture. Another criteria used for classification is the division by sociologically important recipient groups. The aim of such an analysis could be to estimate the share of specific groups of persons in the national income and the changes that are taking place in the process of economic development. Alternatively such an analysis can be based on a division in economic sectors; one could estimate for example the share of agriculture or services in the national income and its changes over time. Also this procedure allows interesting conclusions on social and economic development of the national economy. The third and particularly important criterion consists in the division by income level. This type of investigation serves to generate important findings on the social and economic situation and development of different income groups. Development of wealth within a national economy can be assessed looking at the economic situation of the lower income classes in relation to the higher classes and on how fast one class integrates into another. These three different types of structuring of income recipients can be combined with each other. Doings so one can generate more insights on the development of the industrialization process co pared to using only one classification type (Müller, Heinz/Geisenberger, Siegfried, 1972: Die Einkommensstruktur in verschiedenen deutschen Ländern 1874-1913. Berlin: Duncker & Humblot, S. 13f). The first part of the study exclusively deals with the investigation of the income size structure. These are the summarized results for the investigation on the temporal development of distribution coefficients:

(1) Differentiated according to the different states The share of the very highest incomes in the total income is increasing in all states (Besides Hesse) during the investigation period.

(2) Differentiated according to the surveyed areas According to the distribution coefficient and its development over time one can say that developments differ a lot between rural and industrial areas.

Part B. Siegfried Geisenberger: Important determinants for changes in the income structure. An attempt of an economic interpretation of the development in Prussia 1874-1913. The results from the first part of the investigation gave the impulse for further investigations at the institute of regional politics and transportation science (University of Freiburg). They wanted to investigate the development of the distribution situation for further regions and to control for different income tax laws in several states. The typical differences in the development of income distribution between rural and industrial areas could also be detected for the Prussian governmental districts. The second part of the investigation aims to explain this phenomenon using theoretical economic and statistical instruments.

Register of tables in HISTAT: A. Data on income structure in Prussia and in chosen Prussian governmental districts A.1 Data on income structure in Prussia (1874-1913) A.2 Data on income structure in Prussia, governme...

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.

In the U.S., median household income rose from 51,570 U.S. dollars in 1967 to 80,610 dollars in 2023. In terms of broad ethnic groups, Black Americans have consistently had the lowest median income in the given years, while Asian Americans have the highest; median income in Asian American households has typically been around double that of Black Americans.

This data collection is part of a longitudinal survey designed to provide detailed information on the economic situation of households and persons in the United States. These data examine the distribution of income, wealth, and poverty in American society and gauge the effects of federal and state programs on the well-being of families and individuals.

There are three basic elements contained in the survey. The first is a control card that records basic social and demographic characteristics for each person in a household, as well as changes in such characteristics over the course of the interviewing period. These include age, sex, race, ethnic origin, marital status, household relationship, education, and veteran status. Limited data are provided on housing unit characteristics such as units in structure, tenure, access, and complete kitchen facilities. The second element is the core portion of the questionnaire, with questions repeated at each interview on labor force activity, types and amounts of income, and participation in various cash and noncash benefit programs for each month of the four- month reference period. Data for employed persons include number of hours and weeks worked, earnings, and weeks without a job. Nonworkers are classified as unemployed or not in the labor force. In addition to providing income data associated with labor force activity, the core questions cover nearly 50 other types of income. Core data also include postsecondary school attendance, public or private subsidized rental housing, low-income energy assistance, and school breakfast and lunch participation. The third element consists of topical modules, which are a series of supplemental questions asked during selected household visits. Topical modules include some core data to link individuals to the core files.

1. The Wave 1 Topical Module covers recipiency and employment history.

2. The Wave 2 Topical Module includes work disability, education and training, marital, migration, and fertility histories, and household relationships.

3. The Wave 3 Topical Module covers medical expenses and utilization of health care, work-related expenses and child support, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stocks and mutual fund shares, mortgages, and other assets.

4. The Wave 4 Topical Module covers work schedule, taxes, child care, and annual income and retirement accounts.

5. Data in the Wave 5 Topical Module describe child support agreements, school enrollment and financing, support for non-household members, adult and child disability, and employer-provided health benefits.

6. The Wave 6 Topical Module covers medical expenses and utilization of health care, work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earning accounts, rental properties, stock and mutual fund shares, mortgages, and other financial investments.

7. The Wave 7 Topical Module covers informal caregiving, children's well-being, and annual income and retirement accounts.

8. The Wave 8 Topical Module and Wave 8 Welfare Reform Topical Module cover child support agreements, support for nonhousehold members, adult disability, child disability, adult well-being, and welfare reform.

9. The Wave 9 Topical Module covers medical expenses and utilization of heath care (adults and children), work related expenses, child support paid and child care poverty, assets and liabilities, real estate, shelter costs, dependent care, vehicles, value of business, interest earnings accounts, rental properties, stocks and mutual fund shares mortgages, and other financial investments

This special topic poll, conducted April 30 to May 6, 1996, is part of a continuing series of monthly surveys that solicit public opinion on the presidency and on a range of other political and social issues. This poll sought Americans' views on the most important problems facing the United States, their local communities and their own families. Respondents rated the public schools, crime, and drug problems at the national and local levels, their level of optimism about their own future and that of the country, and the reasons they felt that way. Respondents were asked whether they were better off financially than their parents were at their age, whether they expected their own children to be better off financially than they were, and whether the American Dream was still possible for most people. Respondents then compared their expectations about life to their actual experiences in areas such as job security, financial earnings, employment benefits, job opportunities, health care benefits, retirement savings, and leisure time. A series of questions asked whether the United States was in a long-term economic and moral decline, whether the country's main problems were caused more by a lack of economic opportunity or a lack of morality, and whether the United States was still the best country in the world. Additional topics covered immigration policy and the extent to which respondents trusted the federal, state, and local governments. Demographic variables included respondents' sex, age, race, education level, marital status, household income, political party affiliation, political philosophy, voter registration and participation history, labor union membership, the presence of children in the household, whether these children attended a public school, and the employment status of respondents and their spouses. telephone interviewThe data available for download are not weighted and users will need to weight the data prior to analysis.The data collection was produced by Chilton Research Services of Radnor, PA. Original reports using these data may be found via the ABC News Polling Unit Website.According to the data collection instrument, code 3 in the variable Q909 (Education Level) included respondents who answered that they had attended a technical school.The original data file contained four records per case and was reformatted into a data file with one record per case. To protect respondent confidentiality, respondent names were removed from the data file.The CASEID variable was created for use with online analysis. The data contain a weight variable (WEIGHT) that should be used in analyzing the data. This poll consists of "standard" national representative samples of the adult population with sample balancing of sex, race, age, and education. Households were selected by random-digit dialing. Within households, the respondent selected was the adult living in the household who last had a birthday and who was at home at the time of interview. Persons aged 18 and over living in households with telephones in the contiguous 48 United States. Datasets: DS1: ABC News Listening to America Poll, May 1996