The Occupational Outlook Handbook (OOH) is a nationally recognized source of career information, designed to provide valuable assistance to individuals making decisions about their future work lives. The Handbook is revised every two years. The OOH offers information on the hundreds of occupations that provide the majority of jobs in the United States. Each occupational profile describes the typical duties performed by the occupation, the work environment of that occupation, the typical education and training needed to enter the occupation, the median pay for workers in the occupation, and the job outlook over the coming decade for that occupation. For information on occupations, please visit: https://www.bls.gov/ooh/

The Employment Projections (EP) program develops information about the labor market for the Nation as a whole for 10 years in the future. For more information visit: https://www.bls.gov/emp/

The Employment Cost Index (ECI) measures the change in the hourly labor cost to employers over time. The ECI uses a fixed “basket” of labor to produce a pure cost change, free from the effects of workers moving between occupations and industries and includes both the cost of wages and salaries and the cost of benefits.In the private sector, business owners and human resources professionals can use the ECI to make decisions about pay adjustments to help them stay competitive. In the public sector, the Federal Reserve and others use the ECI to gauge the health of the labor market, adjust contracts, and research the labor market.There are two data schemas:NAICS-SOC basis (2001-Present) - ciSIC-OCS basis (1979-2005) - ec

The Local Area Unemployment Statistics (LAUS) program is a federal-state cooperative effort in which monthly estimates of total employment and unemployment are prepared for over 7,500 areas: Census regions and divisionsStatesMetropolitan Statistical AreasMetropolitan DivisionsMicropolitan Statistical AreasCombined Metropolitan Statistical AreasSmall Labor Market AreasCounties and county equivalentsCities of 25,000 population or moreCities and towns in New England regardless of population These estimates are key indicators of local economic conditions. The Bureau of Labor Statistics (BLS) of the U.S. Department of Labor is responsible for the concepts, definitions, technical procedures, validation, and publication of the estimates that state workforce agencies prepare under agreement with BLS. A wide variety of customers use these estimates: Federal programs use the data for allocations to states and areas, as well as eligibility determinations for assistance.State and local governments use the estimates for planning and budgetary purposes and to determine the need for local employment and training services.Private industry, researchers, the media, and other individuals use the data to assess localized labor market developments and make comparisons across areas. The concepts and definitions underlying LAUS data come from the Current Population Survey (CPS), the household survey that is the source of the national unemployment rate. State monthly model-based estimates are controlled in "real time" to sum to national monthly employment and unemployment estimates from the CPS. These models combine current and historical data from the CPS, the Current Employment Statistics (CES) survey, and state unemployment insurance (UI) systems. Estimates for seven large areas and their respective balances of state also are model-based. Estimates for counties are produced through a building-block approach known as the "Handbook method." This procedure also uses data from several sources, including the CPS, the CES program, state UI systems, and the Census Bureau's American Community Survey (ACS), to create estimates that are adjusted to the statewide measures of employment and unemployment. Estimates for cities are prepared using disaggregation techniques based on inputs from the ACS, annual population estimates, and current UI data.

Graph and download economic data for All Employees, Manufacturing (MANEMP) from Jan 1939 to Sep 2025 about headline figure, establishment survey, manufacturing, employment, and USA.

Graph and download economic data for Employment Level (CE16OV) from Jan 1948 to Sep 2025 about civilian, 16 years +, household survey, employment, and USA.

The Job Openings and Labor Turnover Survey (JOLTS) program provides national estimates of rates and levels for job openings, hires, and total separations. Total separations are further broken out into quits, layoffs and discharges, and other separations. Unadjusted counts and rates of all data elements are published by supersector and select sector based on the North American Industry Classification System (NAICS). The number of unfilled jobs—used to calculate the job openings rate—is an important measure of the unmet demand for labor. With that statistic, it is possible to paint a more complete picture of the U.S. labor market than by looking solely at the unemployment rate, a measure of the excess supply of labor. Information on labor turnover is valuable in the proper analysis and interpretation of labor market developments and as a complement to the unemployment rate. For more information and data visit: https://www.bls.gov/jlt/

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

Graph and download economic data for Employed full time: Wage and salary workers: Market and survey researchers occupations: 16 years and over: Men (LEU0254588300A) from 2000 to 2010 about occupation, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

Employment Rate in the United States increased to 59.70 percent in September from 59.60 percent in August of 2025. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The Business Employment Dynamics (BED) is a set of statistics generated from the Quarterly Census of Employment and Wages (QCEW) program. These quarterly data series consist of gross job gains and gross job losses statistics from 1992 forward. These data help to provide a picture of the dynamic state of the labor market. For more information and data visit: https://www.bls.gov/bed/

📊 Dataset Description:

This dataset is sourced from the U.S. Bureau of Labor Statistics (BLS) Employment Situation – Table A-42. It provides detailed insights into employment and unemployment trends by educational attainment for individuals aged 25 years and older in the United States.

🌐 Source:

~**Publisher:** U.S. Bureau of Labor Statistics (BLS)

~**Table Reference:** CPS Table A-42 – Unemployed persons by duration of unemployment

~**URL:** https://www.bls.gov/web/empsit/cpseea42.htm

~**Update Frequency:** Monthly (as part of the Employment Situation release)

📑 Dataset Overview:

---The dataset includes:

~Year and Month of observation

~Employment and Unemployment counts

~Unemployment rates categorized by educational attainment, such as:

~Less than a high school diploma

~High school graduates, no college

~Some college or associate degree

~Bachelor’s degree and higher

🎯 Potential Uses:

~Analyze how education level impacts unemployment rates.

~Study long-term labor market trends across different demographics.

~Build forecasting models for employment/unemployment rates.

~Perform policy analysis to understand the role of education in job security.

⚠️ Notes:

~All values are based on the Current Population Survey (CPS) conducted by the U.S. Census Bureau for the BLS.

~The dataset may contain seasonally adjusted and non-adjusted values.

~Numbers represent civilian noninstitutional population, 25 years and older.

The Employment Projections (EP) program offers insights into the labor market of the United States, projecting trends for the next decade across approximately 300 detailed industries and 800 occupations. The Bureau of Labor Statistics develops the National Employment Matrix as part of its ongoing Employment Projections program. Occupational classifications of the National Employment Matrix are based on the structure used by the Occupational Employment Statistics (OEWS) program, which is using the 2018 Standard Occupational Classification (SOC) system. Self-employed worker data are sourced from the Current Population Survey (CPS) and are distributed to relevant occupations through a crosswalk mechanism. The industrial structure relies on the 2017 North American Industry Classification System (NAICS), treating self-employment as a separate industry for analytical purposes. Arts-related occupations encompass various sectors, including motion picture and sound recording industries, broadcasting, performing arts, museums, amusement, publishing, education, design, media, and related fields. This comprehensive overview aids in understanding employment dynamics and trends within the arts and cultural sectors.

Between 2022 and 2032, the fastest growing jobs in the United States have been predicted to be ************************ and *******************. The growth rate was predicted by the U.S. Bureau of Labor Statistics as the highest projected percent change of employment. The United States was recently second in the list of leading countries by renewable energy consumption, so the demand for wind turbine technicians is perhaps understandably high.

The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

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.

Context:

The Occupational Employment Statistics (OES) and National Compensation Survey (NCS) programs have produced estimates by borrowing from the strength and breadth of each survey to provide more details on occupational wages than either program provides individually. Modeled wage estimates provide annual estimates of average hourly wages for occupations by selected job characteristics and within geographical location. The job characteristics include bargaining status (union and nonunion), part- and full-time work status, incentive- and time-based pay, and work levels by occupation.

Direct estimates are based on survey responses only from the particular geographic area to which the estimate refers. In contrast, modeled wage estimates use survey responses from larger areas to fill in information for smaller areas where the sample size is not sufficient to produce direct estimates. Modeled wage estimates require the assumption that the patterns to responses in the larger area hold in the smaller area.

The sample size for the NCS is not large enough to produce direct estimates by area, occupation, and job characteristic for all of the areas for which the OES publishes estimates by area and occupation. The NCS sample consists of 6 private industry panels with approximately 3,300 establishments sampled per panel, and 1,600 sampled state and local government units. The OES full six-panel sample consists of nearly 1.2 million establishments.

The sample establishments are classified in industry categories based on the North American Industry Classification System (NAICS). Within an establishment, specific job categories are selected to represent broader occupational definitions. Jobs are classified according to the Standard Occupational Classification (SOC) system.

Content:

Summary: Average hourly wage estimates for civilian workers in occupations by job characteristic and work levels. These data are available at the national, state, metropolitan, and nonmetropolitan area levels.

Frequency of Observations: Data are available on an annual basis, typically in May.

Data Characteristics: All hourly wages are published to the nearest cent.

Acknowledgements:

This dataset was taken directly from the Bureau of Labor Statistics and converted to CSV format.

Inspiration:

This dataset contains the estimated wages of civilian workers in the United States. Wage changes in certain industries may be indicators for growth or decline. Which industries have had the greatest increases in wages? Combine this dataset with the Bureau of Labor Statistics Consumer Price Index dataset and find out what kinds of jobs you would need to afford your snacks and instant coffee!

Context

A striking graph from the Social Security Administration (https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.html) shows that median annual earnings for all men above the age of 20 have decreased since 1988: https://www.ssa.gov/policy/docs/factsheets/at-a-glance/earnings-men-1988-2018.svg" alt="">

I wanted to better understand how educational attainment has played a role in the above trend, and to come up with a model to forecast the future trend for earnings by educational attainment.

As I began looking at the data from the Bureau of Labor Statistics website, there was a striking trend: the median weekly earnings for all groups of people who did not have a bachelors degree or higher had decreased from 1979 levels, in constant 2020 dollars.

Content

I collated data from the US Bureau of Labor Statistics (https://www.bls.gov/webapps/legacy/cpsatab4.htm) and (https://www.bls.gov/cps/cpswktabs.htm) and the US Census Bureau (https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html) to create this dataset.

I have omitted details of gender and race, to solely look at the correlation between educational attainment and median weekly earnings over the years. All of the data is for ages 25 and higher unless otherwise stated in the column header.

An important note is that all the earnings data are in constant base 2020 dollars. This removes the effects of inflation and makes it possible to compare the numbers over the years.

The data starts at the year 1960, but unfortunately only overall labor force data, and population percentages of persons with a high school graduation (HSG) and persons with a Bachelors or Higher Degree are available. Median weekly earnings data categorized by educational attainment is available from 1979 onwards, while labor force data i.e., labor force level, labor force participation rate and the employment level by educational attainment is available only from 1992 onwards.

The only columns that have data from 1960 onwards are: (i) overall labor force level, (ii) civilian non-institutional population level, (iii) overall labor force participation rate, (iv) overall employment level, (v) overall percentage of high school graduates, and (vi) overall percentage of persons with a bachelors degree or higher.

Some of the columns can be calculated from other columns, for instance the civilian non-institutional population level can be calculated from the labor force participation rate.

Acknowledgements

All of this data is from the Bureau of Labor Statistics, and the Census Bureau: https://www.bls.gov/webapps/legacy/cpsatab4.htm , https://www.bls.gov/cps/cpswktabs.htm and https://www.census.gov/data/tables/time-series/demo/income-poverty/historical-income-people.html .

A big thank you to all those who worked so hard to collect and organize this data.

Inspiration

The main question is: what is the best way to generate forecasts for median weekly earnings for each educational attainment level?

The Bureau of Labor Statistics (BLS) calculates employment and wage estimates for every state, Metropolitan Statistical Area and Balance-of-State area in the United States. In order to better meet the needs of local users, the Occupational Employment and Wage Statistics (OEWS) staff in the Texas Labor Market Information Department of the Texas Workforce Commission (LMI) has produced wage estimates for geographic areas not produced by BLS. Workforce Development Areas (WDAs) are not published by BLS and are not, therefore, official BLS data series. Due to confidentiality and quality criteria, LMI cannot produce estimates for every occupation in every geographic area.

The Local Area Unemployment Statistics (LAUS) program facilitates a comprehensive Federal-State collaboration to generate monthly estimates of total employment and unemployment for around 7,300 regions, encompassing counties, cities, and metropolitan statistical areas. These estimates serve as pivotal indicators of local economic health. Orchestrated by the U.S. Department of Labor's Bureau of Labor Statistics (BLS), this dataset offers essential insights into employment dynamics, aiding policymakers, economists, businesses, and researchers in understanding regional labor market trends.

Use and Application: Economists and labor market analysts can use this dataset to analyze local employment and unemployment trends, assess economic disparities among different regions, and identify potential areas for job growth or intervention. Policymakers can make informed decisions about workforce development, job training programs, and economic policies. Businesses can gauge local labor market conditions to inform hiring strategies and expansion plans. Researchers can correlate employment trends with various socio-economic factors, contributing to studies on workforce dynamics. This dataset's nuanced view of employment and unemployment at the local level provides a foundation for targeted economic planning, strategic decision-making, and shaping policies to foster robust and inclusive regional economies.