Labor Force Participation Rate in the United States decreased to 62.20 percent in July from 62.30 percent in June of 2025. 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.

The labor force participation rate is the percentage of the population that is either employed or unemployed (that is, either working or actively seeking work). People with jobs are employed. People who are jobless, looking for a job, and available for work are unemployed. The labor force is made up of the employed and the unemployed. People who are neither employed nor unemployed are not in the labor force.

The number of employed persons in The United States decreased to 163106 Thousand in July of 2025 from 163366 Thousand in June of 2025. This dataset provides - United States Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Dataset Overview

This dataset provides a comprehensive overview of the U.S. workforce and their median weekly earnings over time, spanning from 2009 to 2021. The data is broken down by gender and includes both current and constant dollar values, providing insight into the economic trends affecting different segments of the workforce.

Columns Description

• Year: The year of the data point.
• Quarter: The quarter of the year (1-4) when the data was collected.
• Number of workers (in thousands) - Total: The total number of workers in the U.S. workforce, reported in thousands.
• Number of workers (in thousands) - Men: The number of male workers in the U.S. workforce, reported in thousands.
• Number of workers (in thousands) - Women: The number of female workers in the U.S. workforce, reported in thousands.
• Median weekly earnings (in current dollars) - Total: The median weekly earnings of all workers in the U.S. workforce, reported in current dollars.
• Median weekly earnings (in current dollars) - Men: The median weekly earnings of male workers in the U.S. workforce, reported in current dollars.
• Median weekly earnings (in current dollars) - Women: The median weekly earnings of female workers in the U.S. workforce, reported in current dollars.
• Median weekly earnings (in constant dollars) - Total: The median weekly earnings of all workers in the U.S. workforce, adjusted for inflation and reported in constant dollars.
• Median weekly earnings (in constant dollars) - Men: The median weekly earnings of male workers in the U.S. workforce, adjusted for inflation and reported in constant dollars.
• Median weekly earnings (in constant dollars) - Women: The median weekly earnings of female workers in the U.S. workforce, adjusted for inflation and reported in constant dollars.

Dataset Summary

This dataset contains 37 entries, each representing a quarter from Q4 2009 to Q4 2021. It offers a valuable perspective on workforce trends and wage disparities between men and women over time, adjusted for inflation. The data can be used for economic research, gender studies, and trend analysis.

Potential Use Cases

1. Economic Analysis: Study the trends in workforce participation and earnings over time.
2. Gender Wage Gap Research: Analyze the wage disparities between men and women.
3. Inflation Impact Study: Assess the impact of inflation on workers' earnings over time.
4. Quarterly Workforce Insights: Gain insights into how the workforce composition and earnings change quarter by quarter.

Acknowledgements

This dataset is compiled from public sources, aiming to provide a clear picture of the U.S. workforce and wage trends over the years.

This dataset contains unemployment rates for the U.S. (1948 - Present) and California (1976 - Present). The unemployment rate represents the number of unemployed as a percentage of the labor force. Labor force data are restricted to people 16 years of age and older, who currently reside in 1 of the 50 states or the District of Columbia, who do not reside in institutions (e.g., penal and mental facilities, homes for the aged), and who are not on active duty in the Armed Forces. This rate is also defined as the U-3 measure of labor underutilization.

The employment and unemployment indicator shows several data points. The first figure is the number of people in the labor force, which includes the number of people who are either working or looking for work. The second two figures, the number of people who are employed and the number of people who are unemployed, are the two subcategories of the labor force. The unemployment rate is a calculation of the number of people who are in the labor force and unemployed as a percentage of the total number of people in the labor force.

The unemployment rate does not include people who are not employed and not in the labor force. This includes adults who are neither working nor looking for work. For example, full-time students may choose not to seek any employment during their college career, and are thus not considered in the unemployment rate. Stay-at-home parents and other caregivers are also considered outside of the labor force, and therefore outside the scope of the unemployment rate.

The unemployment rate is a key economic indicator, and is illustrative of economic conditions in the county at the individual scale.

There are additional considerations to the unemployment rate. Because it does not count those who are outside the labor force, it can exclude individuals who were looking for a job previously, but have since given up. The impact of this on the overall unemployment rate is difficult to quantify, but it is important to note because it shows that no statistic is perfect.

The unemployment rates for Champaign County, the City of Champaign, and the City of Urbana are extremely similar between 2000 and 2023.

All three areas saw a dramatic increase in the unemployment rate between 2006 and 2009. The unemployment rates for all three areas decreased overall between 2010 and 2019. However, the unemployment rate in all three areas rose sharply in 2020 due to the effects of the COVID-19 pandemic. The unemployment rate in all three areas dropped again in 2021 as pandemic restrictions were removed, and were almost back to 2019 rates in 2022. However, the unemployment rate in all three areas rose slightly from 2022 to 2023.

This data is sourced from the Illinois Department of Employment Security’s Local Area Unemployment Statistics (LAUS), and from the U.S. Bureau of Labor Statistics.

Sources: Illinois Department of Employment Security, Local Area Unemployment Statistics (LAUS); U.S. Bureau of Labor Statistics.

The 2014 American Community Survey Public Use Microdata Sample

Context

The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about our nation and its people. Information from the survey generates data that help determine how more than $400 billion in federal and state funds are distributed each year.

• Frequency: Annual
• Period: 2014

Content

Through the ACS, we know more about jobs and occupations, educational attainment, veterans, whether people own or rent their home, and other topics. Public officials, planners, and entrepreneurs use this information to assess the past and plan the future. When you respond to the ACS, you are doing your part to help your community plan hospitals and schools, support school lunch programs, improve emergency services, build bridges, and inform businesses looking to add jobs and expand to new markets, and more. The data dictionary can be found here.

Inspiration

Kernels created using the 2013 ACS can serve as excellent starting points for working with the 2014 ACS. For example, the following analyses were created using ACS data:

• Work arrival times and earnings in the USA
• Inequality in STEM careers

https://www.kaggle.io/svf/40491/67ea0531d5c3084d7f211732d8150afc/output_files/figure-html/unnamed-chunk-6-1.png" alt="Work arrival times and earnings in the USA">

Acknowledgements

The American Community Survey (ACS) is administered, processed, researched and disseminated by the U.S. Census Bureau within the U.S. Department of Commerce.

The report contains thirteen (13) performance metrics for City's workforce development programs. Each metric can be breakdown by three demographic types (gender, race/ethnicity, and age group) and the program target population (e.g., youth and young adults, NYCHA communities) as well. This report is a key output of an integrated data system that collects, integrates, and generates disaggregated data by Mayor's Office for Economic Opportunity (NYC Opportunity). Currently, the report is generated by the integrated database incorporating data from 18 workforce development programs managed by 5 City agencies. There has been no single "workforce development system" in the City of New York. Instead, many discrete public agencies directly manage or fund local partners to deliver a range of different services, sometimes tailored to specific populations. As a result, program data have historically been fragmented as well, making it challenging to develop insights based on a comprehensive picture. To overcome it, NYC Opportunity collects data from 5 City agencies and builds the integrated database, and it begins to build a complete picture of how participants move through the system onto a career pathway. Each row represents a count of unique individuals for a specific performance metric, program target population, a specific demographic group, and a specific period. For example, if the Metric Value is 2000 with Clients Served (Metric Name), NYCHA Communities (Program Target Population), Asian (Subgroup), and 2019 (Period), you can say that "In 2019, 2,000 Asian individuals participated programs targeting NYCHA communities. Please refer to the Workforce Data Portal for further data guidance (https://workforcedata.nyc.gov/en/data-guidance), and interactive visualizations for this report (https://workforcedata.nyc.gov/en/common-metrics).

This dataset provides values for LABOR FORCE PARTICIPATION RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

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 China. 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 China, the median income for all workers aged 15 years and older, regardless of work hours, was $58,750 for males and $30,313 for females.

These income figures highlight a substantial gender-based income gap in China. Women, regardless of work hours, earn 52 cents for each dollar earned by men. This significant gender pay gap, approximately 48%, underscores concerning gender-based income inequality in the city of China.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.12 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 China median household income by race. You can refer the same here

Full Time Employment in the United States decreased to 134837 Thousand in July from 135277 Thousand in June of 2025. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.