This statistic represents the tax burden of the poorest ** percent in the U.S. in year 2018, by state. The tax rate is the total average state and local taxes as a percentage of income. In 2018, the poorest ** percent in Washington paid almost **** percent of their family income as a tax.

In 2024, just over 45 percent of American households had an annual income that was less than 75,000 U.S. dollars. On the other hand, some 16 percent had an annual income of 200,000 U.S. dollars or more. The median household income in the country reached almost 84,000 U.S. dollars in 2024. Income and wealth in the United States After the economic recession in 2009, income inequality in the U.S. is more prominent across many metropolitan areas. The Northeast region is regarded as one of the wealthiest in the country. Massachusetts, New Hampshire, and Maryland were among the states with the highest median household income in 2024. In terms of income by race and ethnicity, the average income of Asian households was highest, at over 120,000 U.S. dollars, while the median income among Black households was around half of that figure. What is the U.S. poverty threshold? The U.S. Census Bureau annually updates the poverty threshold based on the income of various household types. As of 2023, the threshold for a single-person household was 15,480 U.S. dollars. For a family of four, the poverty line increased to 31,200 U.S. dollars. There were an estimated 38.9 million people living in poverty across the United States in 2024, which reflects a poverty rate of 10.6 percent.

Graph and download economic data for Estimated Percent of People of All Ages in Poverty for United States (PPAAUS00000A156NCEN) from 1989 to 2023 about child, poverty, percent, and USA.

United States US: Account: Income: Poorest 40%: % Aged 15+ data was reported at 87.116 % in 2014. This records an increase from the previous number of 80.995 % for 2011. United States US: Account: Income: Poorest 40%: % Aged 15+ data is updated yearly, averaging 84.056 % from Dec 2011 (Median) to 2014, with 2 observations. The data reached an all-time high of 87.116 % in 2014 and a record low of 80.995 % in 2011. United States US: Account: Income: Poorest 40%: % Aged 15+ 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: Banking Indicators. Denotes the percentage of respondents who report having an account (by themselves or together with someone else). For 2011, this can be an account at a bank or another type of financial institution, and for 2014 this can be a mobile account as well (see year-specific definitions for details) (income, poorest 40%, % age 15+). [ts: data are available for multiple waves].; ; Demirguc-Kunt et al., 2015, Global Financial Inclusion Database, World Bank.; Weighted average;

In May 2025, the average hourly earnings of all employees in the United States was at 11.30 U.S. dollars. The data have been seasonally adjusted. The deflators used for constant-dollar earnings shown here come from the Consumer Price Indexes Programs. The Consumer Price Index for All Urban Employees (CPI-U) is used to deflate the data for all employees. A comparison of the rate of wage growth versus the monthly inflation since 2020 rate can be accessed here. Real wages are wages that have been adjusted for inflation.

United States - Gross National Income for Heavily Indebted Poor Countries was 1096642572912.05000 Current $ in January of 2023, according to the United States Federal Reserve. Historically, United States - Gross National Income for Heavily Indebted Poor Countries reached a record high of 1096642572912.05000 in January of 2023 and a record low of 23527111104.68150 in January of 1967. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Gross National Income for Heavily Indebted Poor Countries - last updated from the United States Federal Reserve on November of 2025.

This survey represents the thoughts of the U.S. population concerning the income gap between the rich and the poor in 2012. In 2012, 65 percent of the respondents thought that the income gap between the rich and the poor in the United States has gotten larger in the past ten years. The number of ultra high net worth individuals in each region worldwide can be accessed here.

In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

While most Americans appear to acknowledge the large gap between the rich and the poor in the U.S., it is not clear if the public is aware of recent changes in income inequality. Even though economic inequality has grown substantially in recent decades, studies have shown that the public's perception of growing income disparities has remained mostly unchanged since the 1980s. This research offers an alternative approach to evaluating how public perceptions of inequality are developed. Centrally, it conceptualizes the public's response to growing economic disparities by applying theories of macro-political behavior and place-based contextual effects to the formation of aggregate perceptions about income inequality. It is argued that most of the public relies on basic information about the economy to form attitudes about inequality and that geographic context---in this case, the American states---plays a role in how views of income disparities are produced. A new measure of state perceptions of growing economic inequality over a 25-year period is used to examine whether the public is responsive to objective changes in economic inequality. Time-series cross-sectional analyses suggest that the public's perceptions of growing inequality are largely influenced by objective state economic indicators and state political ideology. This research has implications for how knowledgeable the public is of disparities between the rich and the poor, whether state context influences attitudes about inequality, and what role the public will have in determining how expanding income differences are addressed through government policy.

In Latin America, the relationship between income and support for redistribution is weak and variable despite the region’s extreme income inequality. This article shows that this condition is rooted in the truncated structure of many Latin American welfare states. Heavy spending on contributory social insurance for formal-sector workers, flat or regressive subsidies, and informal access barriers mean that social spending does far less for the poor in Latin America than in advanced industrial economies. Using public opinion data from across Latin America and original survey data from Colombia, the article demonstrates that income is less predictive of attitudes in the countries and social policy areas where the poor gain less from social expenditures. Social policy exclusion leads the poor to doubt that they will benefit from redistribution, thereby dampening their support for it. This article reverses an assumption in political economy models that welfare exclusion unleashes demands for greater redistribution. Instead, truncation reinforces skepticism that social policy helps the poor. Welfare state reforms to promote social inclusion are essential to strengthen redistributive coalitions.

In 2024, New York was the state with the greatest gap between rich and poor, with a Gini coefficient score of just under 0.52. Although not a state, District of Columbia was among the highest Gini coefficients in the United States that year. On the other hand, Utah had the lowest Gini score among U.S. states. Overall, income inequality has been rising in the country over recent decades.

This administrative dataset provides descriptive information about the families and children served through the federal Child Care and Development Fund (CCDF). CCDF dollars are provided to states, territories, and tribes to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, or depending on their state's policy, to attend training or receive education. The Personal Responsibility and Work Opportunity Act of 1996 requires states and territories to collect information on all family units receiving assistance through the CCDF and to submit monthly case-level data to the Office of Child Care. States are permitted to report case-level data for the entire population, or a sample of the population, under approved sampling guidelines.

The Summary Records file contains monthly state-level summary information including the number of families served. The Family Records file contains family-level data including single parent status of the head of household, monthly co-payment amount, date on which child care assistance began, reasons for care (e.g., employment, training/education, protective services, etc.), income used to determine eligibility, source of income, and the family size on which eligibility is based. The Child Records file contains child-level data including ethnicity, race, gender, and date of birth. The Setting Records file contains information about the type of child care setting, the total amount paid to the provider, and the total number of hours of care received by the child. The Pooling Factor file provides state-level data on the percentage of child care funds that is provided through the CCDF, the federal Head Start region the grantee (state) is in and is monitored by, and the state FIPS code for the grantee.

Relationships between disposable income inequality, income redistribution, labour market polarization and risk reduction and the age-standardized death rate, with country fixed effects and country specific time trends.

Abstract

Poverty reduction via formation of community based organizations is a popular approach in regions of high socio-economic marginalization, especially in South Asia. The shortage of evidence on the impacts of such an approach is an outcome of the complexity of these projects, which almost always have a multi-sectoral design to achieve a comprehensive basket of aims. In the current research, we consider results from a rural livelihoods program in Bihar, one of India’s poorest states. Adopting a model prevalent in several Indian states, the Bihar Rural Livelihoods Project, known locally as JEEViKA, relies on mobilizing women from impoverished, socially marginalized households into Self Help Groups. Simultaneously, activities such as micro-finance and technical assistance for agricultural livelihoods are taken up by the project and routed to the beneficiaries via these institutions; these institutions also serve as a platform for women to come together and discuss a multitude of the socio-economic problems that they face. We use a retrospective survey instrument, coupled with PSM techniques to find that JEEViKA, has engendered some significant results in restructuring the debt portfolio of these households; additionally, JEEViKA has been instrumental in providing women with higher levels of empowerment, as measured by various dimensions.

In the current research, we consider a multi-sectoral approach which closely resembles the APDPIP design. We take a close look at the impacts of a rural poverty reduction program in Bihar, one of India’s poorest states. This program JEEViKA, focusses on building Self Help Groups (SHGs) of marginalized women; these groups are then federated into higher order institutions of such women at the village and local level. Cheap credit for a variety of purposes, technical assistance for various livelihood activities and encouraging awareness about various public services are the key agendas of this program. However, due to the very nature of JEEViKA’s target population, and given Bihar’s vicious income and gender inequality, the potential for impacts on women’s empowerment exists. A retrospective survey instrument, coupled with ‘Propensity Score Matching’ methods are used to estimate the impacts.

The results from the survey point out that JEEViKA has played an instrumental role in restructuring the debt portfolio of beneficiary households; households that have SHG members have a significantly lower high cost debt burden, are able to access smaller loans repeatedly and borrow more often for productive purposes, when compared to households without SHG members. Since JEEViKA works by mobilizing marginalized women into institutional platforms, such women demonstrate higher levels of empowerment, when empowerment is measured by mobility, decision making and collective action. Finally, we see some effects on the asset positions, food security and sanitation preferences of beneficiary households. It is worth pointing out here that the extent and significance of the results on debt portfolio and empowerment are robust to various matching modules and various specifications of the matched sample. The results on the other dimensions are subject to specifications or matching modules.

This brings out to the point about the timeline of these interventions and the materialization of impacts. In the context of such iterative, multi-sectoral poverty reduction approach, a well_x0002_designed research question must be able to identify the goals that a project should have achieved, given the time-line of that evaluation; the extent of such achievements are only a part of the evaluation agenda. The short review provided above provides some clues that a regular evaluation horizon of 2/3 years may be insufficient time to observe higher order effects, especially since actual benefits happen only after poor are mobilized into institutions and institutions are federated into higher-order institutions; indeed, the village-level institution, the Village Organization, which is made of 15 SHGs on an average, becomes functional 8-10 months after JEEViKA enters a village for the first time. The retrospective nature of the survey instrument also rules out any meaningful comparison of consumption or income levels between treatment and control areas.

Analysis unit

Household

Sampling procedure

The survey was administered to 10 randomly selected households from the target hamlets in all 200 project and 200 non-project villages; we can assume that had caste compositions changed significantly since 2001 in either the selected project or non-project villages, this should be reflected in the sample statistics. It is to be noted that the survey team did not have a beneficiary list for the treatment villages; thus the selection of interviewed HHs were truly random, and not a sample of beneficiary HHs only. The details on the questionnaire and selection of villages to survey are discussed at greater lengths in the Section 3 of the survey report - Data & Identification Strategy. The report is available for download under the Downloads section.

Mode of data collection

Face-to-face [f2f]

Research instrument

An identical survey instrument covering several broad areas on socio-economic indicators was administered to each of the 4000 households. The instrument had two broad modules; the general module was administered to a responsible adult (preferably HH head), and the women’s module was administered to an ever married adult woman. The general module collected economic information focused on asset ownership, debt portfolio, land holdings, savings habit and food security condition; social indicators attempting to capture changes in women’s empowerment focused on women’s mobility, decision making and networks were part of the women’s module. The demographic profile of each household was captured by an appropriate household roster and caste-religion profile; in addition, a livelihood roster was also administered. Given the retrospective nature of the study, questions on certain indicators were designed to capture the levels at end 2007, along with the current level. However for other indicators, like debt portfolio, questions for end 2007 levels were not asked since the chances for incorrect responses are considerable.

Most-to-Least Influential County-Level Economic Variables Contributing to County Prevalence of Poor Mental Health Based on Dominance Analysis Ranked by Standardized Dominance Weights, Overall and by Urban/Rural Classification, United States, 2019.

In 2024, the median household income in the United States was 83,730 U.S. dollars. This reflected an increase from the previous year. Household income The median household income depicts the income of households, including the income of the householder and all other individuals aged 15 years or over living in the household. Income includes wages and salaries, unemployment insurance, disability payments, child support payments received, regular rental receipts, as well as any personal business, investment, or other kinds of income received routinely. The median household income in the United States varied from state to state. In 2024, Massachusetts recorded the highest median household income in the country, at 113,900 U.S. dollars. On the other hand, Mississippi, recorded the lowest, at 55,980 U.S. dollars.Household income is also used to determine the poverty rate in the United States. In 2024, 10.6 percent of the U.S. population was living below the national poverty line. This was the lowest level since 2019. Similarly, the child poverty rate, which represents people under the age of 18 living in poverty, reached a three-decade low of 14.3 percent of the children. The state with the widest gap between the rich and the poor was New York, with a Gini coefficient score of 0.52 in 2024. The Gini coefficient is calculated by looking at average income rates. A score of zero would reflect perfect income equality, while a score of one indicates complete inequality.

This data collection focuses on the federal budget deficit and on issues dealing with the rich and the poor in America. Respondents were asked if they approved of the way George Bush, Democrats in Congress, and Republicans in Congress were handling the the federal budget deficit, and who was more to blame for the larger deficit. Additionally, respondents were asked how much money it takes to be rich in the United States, whether they would want to be rich, how likely it was that they would ever be rich or poor, whether the percentage of Americans who are rich was increasing, and whether they respected and admired rich people. Other questions asked respondents if they characterized rich people as more likely to be honest, snobbish, intelligent, and a variety of other traits, whether respondents would be more or less likely to vote for a candidate who was a millionaire/self-made millionaire, and which political party better represented the interests of poor, rich, and middle class people. Background information on respondents includes political alignment, 1988 presidential vote choice, registered voter status, education, age, religion, social class, marital status, number of people in the household, labor union membership, employment status, race, income, sex, and state/region of residence.

COVID-19 vaccination has significantly decreased morbidity, hospitalizations, and death during the pandemic. However, disparities in vaccination uptake threatens to stymie the progress made in safeguarding the health of Americans. Using a nationally representative adult (≥18 years old) sample from the 2021 Medical Expenditure Panel Survey (MEPS), we aimed to explore disparities in COVID-19 vaccine and booster uptake by income levels. To reflect the nature of the survey, a weighted logistic regression analysis was used to explore factors associated with COVID-19 vaccine and booster uptake. A total of 241,645,704 (unweighted n = 21,554) adults were included in the analysis. Average (SD) age of the population was 49 (18) years old, and 51% were female. There were disparities in COVID-19 vaccine and booster uptake by income groups. All other income groups were less likely to receive COVID-19 vaccines and booster shot than those in the high-income group. Those in the poor income group had 55% lower odds of being vaccinated for COVID-19 (aOR = 0.45, p

Beta Coefficients, 95% Confidence Interval, and Statistical Significance for County-Level Economic Variables Using Linear Regression with Prevalence of Poor Mental Health as the Dependent Variable, Overall and by Urban/Rural Classification, United States, 2019. Blue-filled cells indicate a positive association between the variable and the dependent variable; red-filled cells indicate a negative association; greyed out cells indicate the variable was not significant; blank cells indicate a variable that was not included in the model.

（停止更新）美国：金融机构账户：收入：15岁以上所占百分比：最差40%在12-01-2014达87.116%，相较于12-01-2011的80.995%有所增长。（停止更新）美国：金融机构账户：收入：15岁以上所占百分比：最差40%数据按年更新，12-01-2011至12-01-2014期间平均值为84.056%，共2份观测结果。该数据的历史最高值出现于12-01-2014，达87.116%，而历史最低值则出现于12-01-2011，为80.995%。CEIC提供的（停止更新）美国：金融机构账户：收入：15岁以上所占百分比：最差40%数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的美国 – 表 US.世行.WDI：银行业指标。