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.

In the third quarter of 2024, the top ten percent of earners in the United States held over 67 percent of total wealth. This is fairly consistent with the second quarter of 2024. Comparatively, the wealth of the bottom 50 percent of earners has been slowly increasing since the start of the 2010s, though remains low. Wealth distribution in the United States by generation can be found here.

Graph and download economic data for All Sectors; U.S. Wealth, Level (BOGZ1FL892090005Q) from Q4 1945 to Q4 2024 about wealth, sector, and USA.

In the first quarter of 2024, 51.8 percent of the total wealth in the United States was owned by members of the baby boomer generation. In comparison, millennials own around 9.4 percent of total wealth in the U.S. In terms of population distribution, there is almost an equal share of millennials and baby boomers in the United States.

Graph and download economic data for Households and Nonprofit Organizations; Net Worth as a Percentage of Disposable Personal Income, Level (HNONWPDPI) from Q4 1946 to Q4 2024 about net worth, disposable, nonprofit organizations, personal income, Net, percent, personal, households, income, and USA.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in American Falls. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In American Falls, the median income for all workers aged 15 years and older, regardless of work hours, was $37,500 for males and $22,725 for females.

These income figures highlight a substantial gender-based income gap in American Falls. Women, regardless of work hours, earn 61 cents for each dollar earned by men. This significant gender pay gap, approximately 39%, underscores concerning gender-based income inequality in the city of American Falls.

- Full-time workers, aged 15 years and older: In American Falls, among full-time, year-round workers aged 15 years and older, males earned a median income of $49,073, while females earned $33,510, leading to a 32% gender pay gap among full-time workers. This illustrates that women earn 68 cents for each dollar earned by men in full-time roles. This level of income gap emphasizes the urgency to address and rectify this ongoing disparity, where women, despite working full-time, face a more significant wage discrepancy compared to men in the same employment roles.

Remarkably, across all roles, including non-full-time employment, women displayed a similar gender pay gap percentage. This indicates a consistent gender pay gap scenario across various employment types in American Falls, showcasing a consistent income pattern irrespective of employment status.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for American Falls median household income by race. You can refer the same here

Inequality in family wealth is high, yet we know little about how much and how wealth inequality is maintained across generations. We argue that a long-term perspective reflective of wealth’s cumulative nature is crucial to understand the extent and channels of wealth reproduction across generations. Using data from the Panel Study of Income Dynamics that span nearly half a century, we show that a one decile increase in parental wealth position is associated with an increase of about 4 percentiles in offspring wealth position in adulthood. We show that grandparental wealth is a unique predictor of grandchildren’s wealth, above and beyond the role of parental wealth, suggesting that a focus on only parent-child dyads understates the importance of family wealth lineages. Second, considering five channels of wealth transmission — gifts and bequests, education, marriage, homeownership, and business ownership — we find that most of the advantages arising from family wealth begin much earlier in the life-course than the common focus on bequests implies, even when we consider the wealth of grandparents. We also document the stark disadvantage of African-American households in terms of not only their wealth attainment but also their intergenerational downward wealth mobility compared to whites.

Graph and download economic data for Income Gini Ratio for Households by Race of Householder, All Races (GINIALLRH) from 1967 to 2023 about gini, households, income, and USA.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Mokena: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 36(0.50%) households where the householder is under 25 years old, 1,776(24.57%) households with a householder aged between 25 and 44 years, 3,477(48.11%) households with a householder aged between 45 and 64 years, and 1,938(26.82%) households where the householder is over 65 years old.
• In Mokena, the age group of 45 to 64 years stands out with both the highest median income and the maximum share of households. This alignment suggests a financially stable demographic, indicating an established community with stable careers and higher incomes.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Mokena median household income by age. You can refer the same here

This statistic shows the median household wealth in the United States in 2016, by race. In 2016, the median Black household wealth was 17,600 U.S. dollars.

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.

Graph and download economic data for Share of Net Worth Held by the 99th to 99.9th Wealth Percentiles (WFRBS99T999273) from Q3 1989 to Q4 2024 about shares, net worth, wealth, percentile, Net, and USA.

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.

United States Household Income: $100,000 to $149,999 data was reported at 14.500 % in 2017. This records an increase from the previous number of 14.400 % for 2016. United States Household Income: $100,000 to $149,999 data is updated yearly, averaging 13.100 % from Mar 1967 (Median) to 2017, with 51 observations. The data reached an all-time high of 15.000 % in 1999 and a record low of 6.500 % in 1967. United States Household Income: $100,000 to $149,999 data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.H049: Household Income: by Income Level.

PSID data extract for computing per capita white-to-Black wealth gaps and active saving rates of Black and white Americans during 1984-2019.

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Sedro-Woolley, WA, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 19,557, while the mean income for the highest quintile (20% of households with the highest income) is 176,437. This indicates that the top earners earn 9 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 271,934, which is 154.13% higher compared to the highest quintile, and 1390.47% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2023 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Sedro-Woolley median household income. You can refer the same here

As of April 2025, Elon Musk was estimated as the wealthiest person in the United States with a net worth of around 342 billion dollars. Richest people in the United States - additional information Every year since 1982, the American business magazine Forbes has been compiling lists of the 400 richest people in the United States, known as the “Forbes 400.” In addition to that, since 1987, the publication has also been compiling a ranking of the 500 richest people in the world (excluding royalty and dictators), as well as more specialized tops, such as “World's Most Powerful Women,” “America's Richest Families,” “Most Valuable Brands” or “30 Under 30,” which focuses on young entrepreneurs from various fields which have gained millions in the past year by the use of social media, technical innovations and generally new and fresh approaches to business.

This map shows households within high ($200,000 or more) and low (less than $25,000) annual income ranges. This is shown as a percentage of total households. The data is attached to tract, county, and state centroids and shows:Percent of households making less than $25,000 annuallyPercent of households making $200,000 or more annuallyThe data shown is household income in the past 12 months. These are the American Community Survey (ACS) most current 5-year estimates: Table B19001. The data layer is updated annually, so this map always shows the most current values from the U.S. Census Bureau. To find the layer used in this map and see the full metadata, visit this Living Atlas item.These categories were constructed using an Arcade expression, which groups the lowest census income categories and normalizes them by total households.

We quantitatively identify the factors that drive wealth dynamics in the United States and are consistent with its skewed cross-sectional distribution and with social mobility. We concentrate on three critical factors: (i) skewed earnings, (ii) differential saving rates across wealth levels, and (iii) stochastic idiosyncratic returns to wealth. All of these are fundamental for matching both distribution and mobility. The stochastic process for returns which best fits the cross-sectional distribution of wealth and social mobility in the United States shares several statistical properties with those of the returns to wealth uncovered by Fagereng et al. (2017) from tax records in Norway.

Uruguay and Chile were the Latin American countries with the highest wealth per adult. In 2021, an Uruguayan adult had, on average, 57,726 U.S. dollars worth of financial and non-financial assets, whereas Chileans' mean wealth stood at 54,639 dollars per adult. Argentina was the country with the lowest average wealth in the region, at only 10,446 dollars per adult.