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/

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

Employment Rate in the United States remained unchanged at 59.70 percent in June. This dataset provides - United States Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jun 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

In February 2025, the unemployment rate for those aged 16 and over in the United States came to 4.5 percent. Service occupations had an unemployment rate of 6.3 percent in that month. The underemployment rate of the country can be accessed here and the monthly unemployment rate here. Unemployment by occupation in the U.S. The United States Bureau of Labor Statistics publish data on the unemployment situation within certain occupations in the United States on a monthly basis. According to latest data released from May 2023, transportation and material moving occupations experienced the highest level of unemployment that month, with a rate of around 5.6 percent. Second ranked was farming, fishing, and forestry occupations with a rate of 4.9 percent. Total (not seasonally adjusted) unemployment was reported at 3.6 percent in March 2023. Other data on the U.S. unemployment rate by industry and class of worker shows comparable results. It should be noted that the data were not seasonally adjusted to account for normal seasonal fluctuations in unemployment. The monthly unemployment by occupation data can be compared to the seasonally adjusted monthly unemployment rate. In March 2023, the seasonally adjusted unemployment rate was 3.5 percent, which was an increase from the previous month. The annual unemployment rate in 2022 was 3.6 percent, down from a high of 9.6 in 2010. Unemployment in the United States trended downward after the coronavirus pandemic, and is now experiencing consistently low rates - a sign of economic stability. Individuals who opt to leave the workforce and stop looking for employment are not included among the unemployed. The civilian labor force participation rate in the U.S. rose to 62.2 percent in 2022, down from 67.1 percent in 2000, before the financial crisis.

Unemployment Rate in the United States decreased to 4.10 percent in June from 4.20 percent in May of 2025. 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.

AHE: sa: PW: EH: Child & Youth Services data was reported at 24.850 USD in Mar 2025. This records an increase from the previous number of 24.750 USD for Feb 2025. AHE: sa: PW: EH: Child & Youth Services data is updated monthly, averaging 15.010 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 24.850 USD in Mar 2025 and a record low of 8.520 USD in Jan 1990. AHE: sa: PW: EH: Child & Youth Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

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!

The series comes from the 'Current Employment Statistics (Establishment Survey).' The source code is: CES0500000003

The Average Hourly Earnings of All Private Employees is a measure of the average hourly earnings of all private employees on a “gross” basis, including premium pay for overtime and late-shift work. These differ from wage rates in that average hourly earnings measure the actual return to a worker for a set period of time, rather than the amount contracted for a unit of work, the wage rate. This measure excludes benefits, irregular bonuses, retroactive pay, and payroll taxes paid by the employer.

Average Hourly Earnings are collected in the Current Employment Statistics (CES) program and published by the BLS. It is provided on a monthly basis, so this data is used in part by macroeconomists as an initial economic indicator of current trends. Progressions in earnings specifically help policy makers understand some of the pressures driving inflation.

It is important to note that this series measures the average hourly earnings of the pool of workers in each period. Thus, changes in average hourly earnings can be due to either changes in the set of workers observed in a given period, or due to changes in earnings. For instance, in recessions that lead to the disproportionate increase of unemployment in lower-wage jobs, average hourly earnings can increase due to changes in the pool of workers rather than due to the widespread increase of hourly earnings at the worker-level.

For more information, see: U.S. Bureau of Labor Statistics, CES Overview (https://www.bls.gov/web/empsit/cesprog.htm) U.S. Bureau of Labor Statistics, BLS Handbook of Methods: Chapter 2. Employment, Hours, and Earnings from the Establishment Survey (https://www.bls.gov/opub/hom/pdf/ces-20110307.pdf)

AHE: sa: PW: EH: Vocational Rehabilitation Services data was reported at 20.290 USD in Mar 2025. This records an increase from the previous number of 20.120 USD for Feb 2025. AHE: sa: PW: EH: Vocational Rehabilitation Services data is updated monthly, averaging 8.950 USD from Jan 1972 (Median) to Mar 2025, with 639 observations. The data reached an all-time high of 20.290 USD in Mar 2025 and a record low of 1.480 USD in Jan 1972. AHE: sa: PW: EH: Vocational Rehabilitation Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

AHE: sa: PW: FA: Lessors of Residential Buildings data was reported at 27.080 USD in Mar 2025. This records an increase from the previous number of 26.850 USD for Feb 2025. AHE: sa: PW: FA: Lessors of Residential Buildings data is updated monthly, averaging 14.880 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 27.640 USD in Mar 2024 and a record low of 7.820 USD in Jan 1990. AHE: sa: PW: FA: Lessors of Residential Buildings data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

In October 2024, the total nonfarm payroll employment increased by around 12,000 people in the United States. The data are seasonally adjusted. According to the BLS, the data is derived from the Current Employment Statistics (CES) program which surveys about 140,000 businesses and government agencies each month, representing approximately 440,000 individual worksites, in order to provide detailed industry data on employment.

As of 2022, former President Bill Clinton was the president who created the most jobs in the United States, at **** million jobs created during his eight year term in office. Former President Ronald Reagan created the second most jobs during his term, at **** million.

Graph and download economic data for Employed full time: Median usual weekly real earnings: Wage and salary workers: 16 years and over (LES1252881600Q) from Q1 1979 to Q1 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

AHE: sa: PW: EH: Health Care & Social Assistance data was reported at 32.960 USD in Mar 2025. This records an increase from the previous number of 32.840 USD for Feb 2025. AHE: sa: PW: EH: Health Care & Social Assistance data is updated monthly, averaging 18.430 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 32.960 USD in Mar 2025 and a record low of 9.840 USD in Jan 1990. AHE: sa: PW: EH: Health Care & Social Assistance data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

AHE: sa: PW: EH: Ambulatory Health Care Services data was reported at 35.660 USD in Mar 2025. This records an increase from the previous number of 35.480 USD for Feb 2025. AHE: sa: PW: EH: Ambulatory Health Care Services data is updated monthly, averaging 19.850 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 35.660 USD in Mar 2025 and a record low of 10.100 USD in Jan 1990. AHE: sa: PW: EH: Ambulatory Health Care Services data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G076: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

AHE: sa: PW: EH: Residential Mental Health Facilities data was reported at 22.900 USD in Mar 2025. This records an increase from the previous number of 22.860 USD for Feb 2025. AHE: sa: PW: EH: Residential Mental Health Facilities data is updated monthly, averaging 12.650 USD from Jan 1990 (Median) to Mar 2025, with 423 observations. The data reached an all-time high of 22.900 USD in Mar 2025 and a record low of 7.060 USD in Feb 1990. AHE: sa: PW: EH: Residential Mental Health Facilities data remains active status in CEIC and is reported by U.S. Bureau of Labor Statistics. The data is categorized under Global Database’s United States – Table US.G: Current Employment Statistics: Average Hourly Earnings: Production Workers: Seasonally Adjusted.

Replication files for "Job-to-Job Mobility and Inflation" Authors: Renato Faccini and Leonardo Melosi Review of Economics and Statistics Date: February 2, 2023 -------------------------------------------------------------------------------------------- ORDERS OF TOPICS .Section 1. We explain the code to replicate all the figures in the paper (except Figure 6) .Section 2. We explain how Figure 6 is constructed .Section 3. We explain how the data are constructed SECTION 1 Replication_Main.m is used to reproduce all the figures of the paper except Figure 6. All the primitive variables are defined in the code and all the steps are commented in code to facilitate the replication of our results. Replication_Main.m, should be run in Matlab. The authors tested it on a DELL XPS 15 7590 laptop wih the follwoing characteristics: -------------------------------------------------------------------------------------------- Processor Intel(R) Core(TM) i9-9980HK CPU @ 2.40GHz 2.40 GHz Installed RAM 64.0 GB System type 64-bit operating system, x64-based processor -------------------------------------------------------------------------------------------- It took 2 minutes and 57 seconds for this machine to construct Figures 1, 2, 3, 4a, 4b, 5, 7a, and 7b. The following version of Matlab and Matlab toolboxes has been used for the test: -------------------------------------------------------------------------------------------- MATLAB Version: 9.7.0.1190202 (R2019b) MATLAB License Number: 363305 Operating System: Microsoft Windows 10 Enterprise Version 10.0 (Build 19045) Java Version: Java 1.8.0_202-b08 with Oracle Corporation Java HotSpot(TM) 64-Bit Server VM mixed mode -------------------------------------------------------------------------------------------- MATLAB Version 9.7 (R2019b) Financial Toolbox Version 5.14 (R2019b) Optimization Toolbox Version 8.4 (R2019b) Statistics and Machine Learning Toolbox Version 11.6 (R2019b) Symbolic Math Toolbox Version 8.4 (R2019b) -------------------------------------------------------------------------------------------- The replication code uses auxiliary files and save the pictures in various subfolders: \JL_models: It contains the equations describing the model including the observation equations and routine used to solve the model. To do so, the routine in this folder calls other routines located in some fo the subfolders below. \gensystoama: It contains a set of codes that allow us to solve linear rational expectations models. We use the AMA solver. More information are provided in the file AMASOLVE.m. The codes in this subfolder have been developed by Alejandro Justiniano. \filters: it contains the Kalman filter augmented with a routine to make sure that the zero lower bound constraint for the nominal interest rate is satisfied in every period in our sample. \SteadyStateSolver: It contains a set of routines that are used to solved the steady state of the model numerically. \NLEquations: It contains some of the equations of the model that are log-linearized using the symbolic toolbox of matlab. \NberDates: It contains a set of routines that allows to add shaded area to graphs to denote NBER recessions. \Graphics: It contains useful codes enabling features to construct some of the graphs in the paper. \Data: it contains the data set used in the paper. \Params: It contains a spreadsheet with the values attributes to the model parameters. \VAR_Estimation: It contains the forecasts implied by the Bayesian VAR model of Section 2. The output of Replication_Main.m are the figures of the paper that are stored in the subfolder \Figures SECTION 2 The Excel file "Figure-6.xlsx" is used to create the charts in Figure 6. All three panels of the charts (A, B, and C) plot a measure of unexpected wage inflation against the unemployment rate, then fits separate linear regressions for the periods 1960-1985,1986-2007, and 2008-2009. Unexpected wage inflation is given by the difference between wage growth and a measure of expected wage growth. In all three panels, the unemployment rate used is the civilian unemployment rate (UNRATE), seasonally adjusted, from the BLS. The sheet "Panel A" uses quarterly manufacturing sector average hourly earnings growth data, seasonally adjusted (CES3000000008), from the Bureau of Labor Statistics (BLS) Employment Situation report as the measure of wage inflation. The unexpected wage inflation is given by the difference between earnings growth at time t and the average of earnings growth across the previous four months. Growth rates are annualized quarterly values. The sheet "Panel B" uses quarterly Nonfarm Business Sector Compensation Per Hour, seasonally adjusted (COMPNFB), from the BLS Productivity and Costs report as its measure of wage inflation. As in Panel A, expected wage inflation is given by the... Visit https://dataone.org/datasets/sha256%3A44c88fe82380bfff217866cac93f85483766eb9364f66cfa03f1ebdaa0408335 for complete metadata about this dataset.

Non Farm Payrolls in the United States increased by 147 thousand in June of 2025. 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.

The unemployment rate for people ages 16 to 24 in the United States in 202024 23 was 10 percent. However, this rate was much lower for people aged 45 and over, at 2.9 percent. U.S. unemployment The unemployment rate in the United States varies based on several factors, such as race, gender, and level of education. Black and African-American individuals had the highest unemployment rate in 2021 out of any ethnicity, and people who had less than a high school diploma had the highest unemployment rate by education level. Alaska is consistently the state with the highest unemployment rate, although the El Centro, California metropolitan area was the area with the highest unemployment rate in the country in 2019. Additionally, in August 2022, farming, fishing, and forestry occupations had the highest unemployment rate in the United States Unemployment rate The U.S. Bureau of Labor Statistics is the agency that researches and calculates the unemployment rate in the United States. Unemployment rises during recessions, which causes the cost of social welfare programs to increase. The Bureau of Labor Statistics says unemployed people are those who are jobless, have looked for employment within the last four weeks, and are free to work.