Labor Force Participation Rate in the United States increased to 62.30 percent in August from 62.20 percent in July 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 increased to 163394 Thousand in August of 2025 from 163106 Thousand in July of 2025. This dataset provides - United States Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset uses seasonally adjusted data from the US Bureau of Labor Statistics to present information on Maryland's labor force participation rate, employment rate, and unemployment rate.

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.

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.

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 powerful dataset represents a meticulously curated snapshot of the United States job market throughout 2021, sourced directly from CareerBuilder, a venerable employment website founded in 1995 with a formidable global footprint spanning the US, Canada, Europe, and Asia. It offers an unparalleled opportunity for in-depth research and strategic analysis.

Dataset Specifications:

• Source: CareerBuilder.com (US Listings)
• Crawled by: Crawl Feeds in-house team
• Volume: Over 422,000 unique job records
• Timeliness: Last crawled in May 2021, providing a critical historical benchmark for post-pandemic labor market recovery and shifts.
• Format: Compressed ZIP archive containing structured JSON files, designed for seamless integration into databases, analytical platforms, and machine learning pipelines.
• Accessibility: Published and available immediately for acquisition.

Richness of Detail (22 Comprehensive Fields):

The true analytical power of this dataset stems from its 22 granular data points per job listing, offering a multi-faceted view of each employment opportunity:

1. Core Job & Role Information:

   • id: A unique, immutable identifier for each job posting.
   • title: The specific job role (e.g., "Software Engineer," "Marketing Manager").
   • description: A condensed summary of the role, responsibilities, and key requirements.
   • raw_description: The complete, unformatted HTML/text content of the original job posting – invaluable for advanced Natural Language Processing (NLP) and deeper textual analysis.
   • posted_at: The precise date and time the job was published, enabling trend analysis over daily or weekly periods.
   • employment_type: Clarifies the nature of the role (e.g., "Full-time," "Part-time," "Contract," "Temporary").
   • url: The direct link back to the original job posting on CareerBuilder, allowing for contextual validation or deeper exploration.

2. Compensation & Professional Experience:

   • salary: Numeric ranges or discrete values indicating the compensation offered, crucial for salary benchmarking and compensation strategy.
   • experience: Specifies the level of professional experience required (e.g., "Entry-level," "Mid-senior level," "Executive").

3. Organizational & Sector Context:

   • company: The name of the employer, essential for company-specific analysis, competitive intelligence, and brand reputation studies.
   • domain: Categorizes the job within broader industry sectors or functional areas, facilitating industry-specific talent analysis.

4. Skills & Educational Requirements:

   • skills: A rich collection of keywords, phrases, or structured tags representing the specific technical, soft, or industry-specific skills sought by employers. Ideal for identifying skill gaps and emerging skill demands.
   • education: Outlines the minimum or preferred educational qualifications (e.g., "Bachelor's Degree," "Master's Degree," "High School Diploma").

5. Precise Geographic & Location Data:

   • country: Specifies the country (United States for this dataset).
   • region: The state or province where the job is located.
   • locality: The city or town of the job.
   • address: The specific street address of the workplace (if provided), enabling highly localized analysis.
   • location: A more generalized location string often provided by the job board.
   • postalcode: The exact postal code, allowing for granular geographic clustering and demographic overlay.
   • latitude & longitude: Geospatial coordinates for precise mapping, heatmaps, and proximity analysis.

6. Crawling Metadata:

   • crawled_at: The exact timestamp when each individual record was acquired, vital for understanding data freshness and chronological analysis of changes.

Expanded Use Cases & Analytical Applications:

This comprehensive dataset empowers a wide array of research and commercial applications:

• Deep Labor Market Trend Analysis:

  • Identify the most in-demand job titles, skills, and educational backgrounds across different US regions and industries in 2021.
  • Analyze month-over-month or quarter-over-quarter hiring trends to understand recovery patterns or shifts in specific sectors post-pandemic.
  • Spot emerging job roles or skill combinations that gained prominence during the dataset's period.
  • Assess the volume of remote vs. in-person job postings and their distribution.

• Strategic Talent Acquisition & HR Analytics:

  • Benchmark job requirements, salary ranges, and desired experience levels against market averages for specific roles.
  • Optimize job descriptions by identifying common keywords and phrases used by top employers for similar positions.
  • Understand the competitive landscape for talent in specific geographic areas or specialized skill sets.
  • Develop data-driven recruitment strategies by identifying where and how competitors are hiring.

• Compensation & Benefits Research:

  • Conduct detailed salary analysis broken down by job title, industry, location (state, city, even postal code), experience level, and required skills.
  • Identify potential salary premiums or discrepancies for niche skills or hard-to-fill roles.
  • Support robust compensation planning and negotiation strategies.

• Educational & Workforce Development Planning:

  • Universities and vocational schools can align curriculum with real-world employer demand by analyzing required skills and education fields.
  • Government agencies can identify areas for workforce retraining or development programs based on skill gaps revealed in job postings.
  • Career counselors can advise job seekers on in-demand skills and promising career paths.

• Economic Research & Forecasting:

  • Economists can use the volume and nature of job postings as a leading indicator for economic activity and regional growth.
  • Analyze the impact of economic policies or global events on specific industries' hiring patterns.
  • Study labor mobility and migration patterns based on job locations.

• Competitive Intelligence for Businesses:

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This dataset contains information about the Workforce 1 service, a service offered by the Department of Small Business Services (SBS) that connects New Yorkers to job opportunities. Each row in the dataset represents the number of public housing residents on a Borough-level who receive or utilize this service. For datasets related to other services provided to NYCHA residents, view the data collection “Services available to NYCHA Residents - Local Law 163”.

Full Time Employment in the United States decreased to 134480 Thousand in August from 134837 Thousand in July of 2025. This dataset provides - United States Full Time Employment- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Productivity in the United States increased to 116.14 points in the second quarter of 2025 from 115.21 points in the first quarter of 2025. This dataset provides - United States Productivity - 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 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