In 2025, 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.

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 Rich Square. 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 Rich Square, the median income for all workers aged 15 years and older, regardless of work hours, was $24,265 for males and $17,431 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 28% between the median incomes of males and females in Rich Square. With women, regardless of work hours, earning 72 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thetown of Rich Square.

- Full-time workers, aged 15 years and older: In Rich Square, among full-time, year-round workers aged 15 years and older, males earned a median income of $45,893, while females earned $36,089, leading to a 21% gender pay gap among full-time workers. This illustrates that women earn 79 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same 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 Rich Square, 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/.

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This table contains statistics regarding income and capital of self-employed persons in the Netherlands. A distinction is made between, on the one hand, persons for whom self-employment provides for the main source of income, and on the other hand all persons with income from self-employed work. The figures in this table are broken down by type of self-employed person, sector, gender, age, migration background, position in the household, and by income and wealth decile groups.

All statistics in this table are at the individual level, this includes capital; (corporate) assets are summed per household and then assigned to all household members, thus serving as a measure of personal prosperity. The sample date for both population and capital is the first of January of the reporting year. For the older years 2007 up to and including 2010, capital is sampled on the first of January of the year following the reporting year.

The General Business Register (ABR) is used to determine the sector (SBI) of self-employed persons. The ABR has been subject to various trend breaks in the period 2007-2011. This leads to a sharp decrease in the number of self-employed persons in the financial services (sector K) in 2010. Therefore caution is advised when consulting sector trends or comparing numbers across sectors.

Data available from: 2007.

Status of the figures: The figures for 2006 to 2022 are final. The figures for 2023 are preliminary.

Changes as of November 1 2024: Figures for 2022 have been finalized. Figures for 2023 have been added.

Changes as of March 2022: Figures on the wealth of the self-employed in 2010 were incorrect, and have been removed. For this year the wealth of 2011 applies, as 2011 marks a shift in sample date from December 31 to January 1. Missing wealth figures for 2013 have been supplemented.

Changes as of July 2021: Revised data for 2006 to 2019 have been added. Due to the availability of new sources and improvements in the methodology, wealth figures have changed. Additionally everyone with personnel is now classified as self-employed with employee (formerly this distinction was based solely on the enterprise constituting the main source of income).

When will new figures be published? New figures for 2024 will be published in December 2025.

Between December 2019 and 2021, the top one percent of earners accumulated 63 percent of all new wealth worldwide. This is more than six times more wealth than accumulated by the bottom 90 percent over the same time period.

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In December 2022, Elon Musk slipped from the top spot of richest people on Earth. The number one spot was taken by French magnate, Bernard Arnault of Moët Hennessy Louis Vuitton.

Delve into the world of salaries with this comprehensive dataset designed for insightful analysis. With a diverse range of entries spanning various industries, job roles, and demographics, this dataset offers a unique opportunity to uncover trends and factors influencing compensation levels. Explore variables such as education level, years of experience, job title, industry sector, and geographic location to gain valuable insights into salary distributions and disparities. Whether you're a data scientist, HR professional, or curious individual, this dataset provides a wealth of information for understanding salary dynamics and making data-driven decisions.

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Rich Square. The dataset can be utilized to gain insights into gender-based income distribution within the Rich Square population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Rich Square, among individuals aged 15 years and older with income, there were 254 men and 420 women in the workforce. Among them, 73 men were engaged in full-time, year-round employment, while 161 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 19.18% fell within the income range of under $24,999, while 25.47% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: none of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

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:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 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 $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

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.

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/.

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Context

Income inequality is a global issue reflecting the uneven distribution of wealth within and between countries. Developed nations exhibit varying income levels due to economic policies and labor dynamics, resulting in Gini coefficients of around 0.3 to 0.4. Conversely, developing nations often experience higher income disparities due to limited access to education, healthcare, and jobs, leading to Gini coefficients exceeding 0.4, exacerbating poverty cycles and social tensions. This inequality hampers economic growth, social cohesion, and upward mobility. Addressing it requires comprehensive policies, including progressive taxation and equitable resource distribution, to promote a more just and inclusive society.

Content

This dataset comprises historical information encompassing various indicators concerning Inequality in Income on a global scale. The dataset prominently features: ISO3, Country, Continent, Hemisphere, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Income from 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Continent - Name of the Continent
• Hemisphere - Name of the Hemisphere
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Income from 2010 to 2021 - Inequality in Income from year 2010 to 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by Image by pch.vector on Freepik

Thumbnail by: Image by Salary icons created by Freepik - Flaticon

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.

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