The number of employed persons in The United States decreased to 162912 Thousand in February of 2026 from 163097 Thousand in January of 2026. This dataset provides - United States Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Labor Force Participation Rate in the United States decreased to 62 percent in February from 62.10 percent in January of 2026. This dataset provides the latest reported value for - United States Labor Force Participation Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Employment Rate in the United States decreased to 59.30 percent in February from 59.40 percent in January of 2026. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for All Employees, Manufacturing (MANEMP) from Jan 1939 to Feb 2026 about headline figure, establishment survey, manufacturing, employment, and USA.

The Quarterly Census of Employment and Wages (QCEW) Program is a Federal-State cooperative program between the U.S. Department of Labor’s Bureau of Labor Statistics (BLS) and the California EDD’s Labor Market Information Division (LMID). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by California Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit industry codes from the North American Industry Classification System (NAICS) at the national, state, and county levels. At the national level, the QCEW program publishes employment and wage data for nearly every NAICS industry. At the state and local area level, the QCEW program publishes employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. In accordance with the BLS policy, data provided to the Bureau in confidence are used only for specified statistical purposes and are not published. The BLS withholds publication of Unemployment Insurance law-covered employment and wage data for any industry level when necessary to protect the identity of cooperating employers. Data from the QCEW program serve as an important input to many BLS programs. The Current Employment Statistics and the Occupational Employment Statistics programs use the QCEW data as the benchmark source for employment. The UI administrative records collected under the QCEW program serve as a sampling frame for the BLS establishment surveys. In addition, the data serve as an input to other federal and state programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses the QCEW data as the base for developing the wage and salary component of personal income. The U.S. Department of Labor’s Employment and Training Administration (ETA) and California's EDD use the QCEW data to administer the Unemployment Insurance program. The QCEW data accurately reflect the extent of coverage of California’s UI laws and are used to measure UI revenues; national, state and local area employment; and total and UI taxable wage trends. The U.S. Department of Labor’s Bureau of Labor Statistics publishes new QCEW data in its County Employment and Wages news release on a quarterly basis. The BLS also publishes a subset of its quarterly data through the Create Customized Tables system, and full quarterly industry detail data at all geographic levels. Disclaimer: For information regarding future updates or preliminary/final data releases, please refer to the Bureau of Labor Statistics Release Calendar: https://www.bls.gov/cew/release-calendar.htm

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 Many. 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 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Many, the median income for all workers aged 15 years and older, regardless of work hours, was $30,363 for males and $13,399 for females.

These income figures highlight a substantial gender-based income gap in Many. Women, regardless of work hours, earn 44 cents for each dollar earned by men. This significant gender pay gap, approximately 56%, underscores concerning gender-based income inequality in the town of Many.

- Full-time workers, aged 15 years and older: In Many, among full-time, year-round workers aged 15 years and older, males earned a median income of $46,839, while females earned $26,320, leading to a 44% gender pay gap among full-time workers. This illustrates that women earn 56 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 Many, showcasing a consistent income pattern irrespective of employment status.

https://i.neilsberg.com/ch/many-la-income-by-gender.jpeg" alt="Many, LA gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-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 2022
• 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 Many median household income by gender. You can refer the same here

This dataset focuses on the black-white wage gap in the United States. It provides insights into the disparities in hourly wages between black and white workers, as well as different gender and subgroup breakdowns.

The data is derived from the Economic Policy Institute’s State of Working America Data Library, a reputable source for socio-economic research and analysis.

Potential Use Cases:

1. Take a look at how much black and white workers earn on average to see if there are income differences based on race.
2. See if there are any differences in wages among men and women within the same racial groups.
3. Dig deeper into a regression-based wage gap, which considers a bunch of factors like gender, race, education, age, and where people live. It can give us a better picture of why wages vary.
4. Look at the wage gap over time to see if there have been any changes or trends in how much black and white workers earn in the USA.
5. Use statistics to figure out what factors contribute to the wage gap and if any variables stand out.
6. Make cool visualizations with line charts, bar graphs, or heatmaps to show the wage gap trends and help folks understand the disparities better.
7. Compare different groups of people to see if there are any specific areas where people face multiple challenges and use that to suggest policy changes.
8. Build models that can predict future wage gap scenarios and help us see what might happen if policies are changed.

If you find this dataset valuable, don't forget to hit the upvote button! 😊💝

Checkout my other datasets

USA Wage Comparison for College vs. High School

Productivity and Hourly Compensation

Employment-to-Population Ratio for USA

USA Hispanic-White Wage Gap Dataset

Poverty-Level Wages in the USA Dataset

Photo by Ehimetalor Akhere Unuabona on Unsplash

Part Time Employment in the United States decreased to 28478 Thousand in February from 28727 Thousand in January of 2026. This dataset provides - United States Part Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

This dataset provides valuable insights into the wage gap between Hispanic and White workers in the United States.

The wage gap is expressed as a percentage by which hourly wages of Hispanic workers are less than those of White workers.

It is an essential measure for understanding income disparities and examining trends over time.

Interesting Task Ideas:

1. Analyze the Hispanic-White wage gap trends over the years.
2. Explore the variations in median and average wages between Hispanic and White workers.
3. Investigate the gender-specific wage gap and identify any disparities between Hispanic and White men and women.
4. Conduct regression analysis to analyze the wage gap while controlling for relevant factors such as education, age, and geographic location.
5. Visualize the wage gap data to emphasize the magnitude of disparities and highlight any patterns over time.
6. Discover the potential effects of policy changes or economic transformations on the wage gap.

If you find this dataset insightful, don't forget to upvote it! 😊💝

Checkout my other datasets

Poverty-Level Wages in the USA Dataset

1.4M Amazon Products

Black-White Wage Gap in the USA Dataset

Clash of Clans Clans Dataset 2023 (3.5M Clans)

Productivity and Hourly Compensation

Photo by Clay Banks on Unsplash

In 2023, the broadest measure of labor underutilization, designated U-6 (which includes the unemployed, workers employed part-time for economic reasons, and those marginally attached to the labor force), was 5.3 percent in Virginia, significantly lower than the 6.9-percent rate for the nation, the U.S. Bureau of Labor Statistics reported today. Regional Commissioner Alexandra Hall Bovee noted that the six alternative measures of labor underutilization in Virginia were significantly lower than the national rates. In Virginia and nationally, none of the measures significantly differed from the previous year.The official concept of unemployment, U-3 in the U-1 to U-6 range of measures, includes all jobless persons who are available to take a job and have actively sought work in the past 4 weeks. In 2023, 13 states had rates significantly lower than those of the U.S. for all six measures of labor underutilization, while 4 states and the District of Columbia had rates higher than those of the U.S. for all six measures. The U-4 rate includes discouraged workers; thus, the difference between U-3 and U-4 reflects the degree of would-be job-seeker discouragement. At the national level, the difference between U-3 and U-4 was +0.3 percentage point in 2023. No state had a noteworthy difference between these two measures.

The U-5 rate includes all people who are marginally attached to the labor force, and U-6 adds those who are involuntary part-time workers. Therefore, the larger the difference between U-5 and U-6, the higher the incidence of this form of "underemployment." In 2023, 47 states and the District of Columbia had significant differences between their U-5 and U-6 rates. California had the largest gap (+3.5 percentage points), followed by Oregon (+3.3 points) and Washington (+3.0 points). At the national level, the difference between U-5 and U-6 was +2.4 percentage points.

Relative to 2022, Pennsylvania was the only state to experience significant decreases in all 6 measures of labor underutilization, while Mississippi had decreases in 5 of the 6 measures. For each measure, rates declined over the year for at least three states (U-2 and U-6) and as many as seven states (U-4). Only California experienced over-the-year increases in all 6 measures, while New Jersey had increases in 5 of the 6 measures. Three additional states had increases in one measure (U-1 for Indiana and Texas and U-5 for Idaho). At the national level, rates were unchanged over the year for all six measures.

Overview This dataset contains information about the largest companies in the United States by revenue. It includes key attributes such as company name, industry, annual revenue, profit, number of employees, and the state where the company is headquartered. The dataset provides valuable insights into the financial and operational aspects of these major corporations.

Columns Rank: Ranking of the company based on its annual revenue. Name: Name of the company. Industry: Industry in which the company operates. Revenue: Annual revenue of the company in millions of dollars. Profit: Annual profit of the company in millions of dollars. Employees: Number of employees working for the company. State: State where the company’s headquarters are located. Key Insights Revenue Distribution: Significant variation in revenue among the top companies, with some generating much higher revenues. Profit Margins: Wide variation in profit margins, indicating different levels of profitability across industries. Employee Numbers: Disparity in the number of employees, reflecting differences in business models and operational scales. Geographic Spread: Companies are headquartered in various states, with certain states having a higher concentration of large companies. Potential Uses Industry Analysis: Understand trends and performance in different industries. Economic Research: Analyze the economic impact of these large companies. Business Strategy: Inform business strategies and market analysis. Educational Purposes: Use as a case study for business and economic courses. Future Work In-Depth Industry Analysis: Explore specific industries to identify trends and outliers. Time-Series Analysis: Analyze trends over time if historical data becomes available. Comparative Analysis: Compare with similar datasets from other countries. Advanced Visualization: Create interactive dashboards for better data presentation. This dataset is a valuable resource for anyone interested in the financial and operational characteristics of the largest companies in the United States.

Full Time Employment in the United States decreased to 134341 Thousand in February from 134441 Thousand in January of 2026. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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 Fairfax County. 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 Fairfax County, the median income for all workers aged 15 years and older, regardless of work hours, was $84,377 for males and $54,351 for females.

These income figures highlight a substantial gender-based income gap in Fairfax County. Women, regardless of work hours, earn 64 cents for each dollar earned by men. This significant gender pay gap, approximately 36%, underscores concerning gender-based income inequality in the county of Fairfax County.

- Full-time workers, aged 15 years and older: In Fairfax County, among full-time, year-round workers aged 15 years and older, males earned a median income of $114,731, while females earned $88,584, leading to a 23% gender pay gap among full-time workers. This illustrates that women earn 77 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.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Fairfax County.

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 Fairfax County median household income by race. You can refer the same here

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 Cape May Point. 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 Cape May Point, the median income for all workers aged 15 years and older, regardless of work hours, was $64,688 for males and $54,375 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 16% between the median incomes of males and females in Cape May Point. With women, regardless of work hours, earning 84 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in theborough of Cape May Point.

- Full-time workers, aged 15 years and older: In Cape May Point, among full-time, year-round workers aged 15 years and older, males earned a median income of $62,656, while females earned $250,001

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 3.99 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

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 Cape May Point median household income by race. You can refer the same here

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 Disney. 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 Disney, the median income for all workers aged 15 years and older, regardless of work hours, was $34,688 for males and $21,875 for females.

These income figures highlight a substantial gender-based income gap in Disney. Women, regardless of work hours, earn 63 cents for each dollar earned by men. This significant gender pay gap, approximately 37%, underscores concerning gender-based income inequality in the town of Disney.

- Full-time workers, aged 15 years and older: In Disney, among full-time, year-round workers aged 15 years and older, males earned a median income of $63,750, while females earned $35,000, leading to a 45% gender pay gap among full-time workers. This illustrates that women earn 55 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 Disney, 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 Disney median household income by race. You can refer the same here

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 Lake Bronson. 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 Lake Bronson, the median income for all workers aged 15 years and older, regardless of work hours, was $28,472 for males and $29,931 for females.

Contrary to expectations, women in Lake Bronson, women, regardless of work hours, earn a higher income than men, earning 1.05 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

- Full-time workers, aged 15 years and older: In Lake Bronson, among full-time, year-round workers aged 15 years and older, males earned a median income of $38,750, while females earned $32,813, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Lake Bronson.

Surprisingly, across all roles (including non-full-time employment), women had a higher median income compared to men in Lake Bronson. This might indicate a more favorable income scenario for female workers across different employment patterns within the city of Lake Bronson, especially in non-full-time positions.

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 Lake Bronson median household income by race. You can refer the same here

Unemployment Rate in the United States increased to 4.40 percent in February from 4.30 percent in January of 2026. This dataset provides the latest reported value for - United States Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Non Farm Payrolls in the United States decreased by 92 thousand in February of 2026. This dataset provides the latest reported value for - United States Non Farm Payrolls - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

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 Castleton-On-Hudson. 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 Castleton-On-Hudson, the median income for all workers aged 15 years and older, regardless of work hours, was $37,464 for males and $42,760 for females.

Contrary to expectations, women in Castleton-On-Hudson, women, regardless of work hours, earn a higher income than men, earning 1.14 dollars for every dollar earned by men. This analysis indicates a significant shift in income dynamics favoring females.

- Full-time workers, aged 15 years and older: In Castleton-On-Hudson, among full-time, year-round workers aged 15 years and older, males earned a median income of $76,161, while females earned $64,420, resulting in a 15% gender pay gap among full-time workers. This illustrates that women earn 85 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the village of Castleton-On-Hudson.

Surprisingly, across all roles (including non-full-time employment), women had a higher median income compared to men in Castleton-On-Hudson. This might indicate a more favorable income scenario for female workers across different employment patterns within the village of Castleton-On-Hudson, especially in non-full-time positions.

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 Castleton-On-Hudson median household income by race. You can refer the same here