The Occupational Employment and Wage Statistics (OES) program conducts a semi-annual survey to produce estimates of employment and wages for specific occupations. The OES program collects data on wage and salary workers in nonfarm establishments in order to produce employment and wage estimates for about 800 occupations. Data from self-employed persons are not collected and are not included in the estimates. The OES program produces these occupational estimates by geographic area and by industry. Estimates based on geographic areas are available at the National, State, Metropolitan, and Nonmetropolitan Area levels. The Bureau of Labor Statistics produces occupational employment and wage estimates for over 450 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and 5-digit North American Industry Classification System (NAICS) industrial groups. More information and details about the data provided can be found at http://www.bls.gov/oes

The Occupational Employment and Wage Statistics (OEWS) Survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). The BLS provides the procedures and technical support, draws the sample, and produces the survey materials, while the SWAs collect the data. SWAs from all fifty states, plus the District of Columbia, Puerto Rico, Guam, and the Virgin Islands participate in the survey. Occupational employment and wage rate estimates at the national level are produced by BLS using data from the fifty states and the District of Columbia. Employers who respond to states' requests to participate in the OEWS survey make these estimates possible.

The OEWS survey collects data from a sample of establishments and calculates employment and wage estimates by occupation, industry, and geographic area. The semiannual survey covers all non-farm industries. Data are collected by the Employment Development Department in cooperation with the Bureau of Labor Statistics, US Department of Labor. The OEWS Program estimates employment and wages for approximately 830 occupations. It also produces employment and wage estimates for statewide, Metropolitan Statistical Areas (MSAs), and Balance of State areas. Estimates are a snapshot in time and should not be used as a time series.

The OEWS estimates are published annually.

SOURCE: https://www.bls.gov/oes/oes_emp.htm

The Occupational Employment and Wage Statistics (OEWS) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OEWS estimates are constructed from a sample of about 44,400 establishments. Each year, forms are mailed to two semiannual panels of approximately 7,400 sampled establishments, one panel in May and the other in November.

The Occupational Employment and Wage Statistics (OEWS) Survey collects data from a sample of establishments and calculates employment and wage estimates by occupation, industry, and geographic area. The semiannual survey covers all non-farm industries. Data are collected by the Employment Development Department in cooperation with the Bureau of Labor Statistics, US Department of Labor. The OEWS Program estimates employment and wages for over 800 occupations from an annual sample of approx. 34,000 California employers. It also produces employment and wage estimates for statewide, Metropolitan Statistical Areas (MSAs), and Balance of State areas. Estimates are a snapshot in time and should not be used as a time series.

Analysis of ‘Occupational Employment and Wage Statistics (OEWS)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ec2114b7-6b39-4b40-bb5d-bdd65a1e2382 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

The Occupational Employment and Wage Statistics (OEWS) Survey collects data from a sample of establishments and calculates employment and wage estimates by occupation, industry, and geographic area. The semiannual survey covers all non-farm industries. Data are collected by the Employment Development Department in cooperation with the Bureau of Labor Statistics, US Department of Labor. The OEWS Program estimates employment and wages for over 800 occupations from an annual sample of approx. 34,000 California employers. It also produces employment and wage estimates for statewide, Metropolitan Statistical Areas (MSAs), and Balance of State areas. Estimates are a snapshot in time and should not be used as a time series.

--- Original source retains full ownership of the source dataset ---

Washington State, metropolitan statistical areas (MSA) and nonmetropolitan areas (NMA), 2019 OES is a program of the U.S. Department of Labor, Bureau of Labor Statistics (BLS). This federal-state cooperative program produces employment and wage estimates for nearly 840 occupations. The occupational employment and wage estimates are based on data collected from the OES survey. The survey includes employment counts, occupations and wages from more than 4,800 Washington state employers. Data from six survey panels are combined to create a sample size of more than 29,300 employers. Blanks in the data columns indicate suppressed data.

This dataset has now been discontinued following a user consultation. However figures for employment by occupation, sourced from our Annual Population Survey are available on our NOMIS website.

Analysis of ‘Occupational Employment and Wage Estimates’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/79274f7e-5582-45bd-a1ef-f86f6f3a1c90 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Washington State, metropolitan statistical areas (MSA) and nonmetropolitan areas (NMA), 2020 OES is a program of the U.S. Department of Labor, Bureau of Labor Statistics (BLS). This federal-state cooperative program produces employment and wage estimates for nearly 867 occupations. The occupational employment and wage estimates are based on data collected from the OES survey. The survey includes employment counts, occupations and wages from more than 4,200 Washington state employers. Data from six survey panels are combined to create a sample size of more than 26,400 employers. Blanks in the data columns indicate suppressed data.

--- Original source retains full ownership of the source dataset ---

The most recent statewide occupational estimates available. Produced in cooperation with the United States Bureau of Labor Statistics (BLS).

VITAL SIGNS INDICATOR Jobs by Wage Level (EQ1)

FULL MEASURE NAME Distribution of jobs by low-, middle-, and high-wage occupations

LAST UPDATED January 2019

DESCRIPTION Jobs by wage level refers to the distribution of jobs by low-, middle- and high-wage occupations. In the San Francisco Bay Area, low-wage occupations have a median hourly wage of less than 80% of the regional median wage; median wages for middle-wage occupations range from 80% to 120% of the regional median wage, and high-wage occupations have a median hourly wage above 120% of the regional median wage.

DATA SOURCE California Employment Development Department OES (2001-2017) http://www.labormarketinfo.edd.ca.gov/data/oes-employment-and-wages.html

American Community Survey (2001-2017) http://api.census.gov

CONTACT INFORMATION vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator) Jobs are determined to be low-, middle-, or high-wage based on the median hourly wage of their occupational classification in the most recent year. Low-wage jobs are those that pay below 80% of the regional median wage. Middle-wage jobs are those that pay between 80% and 120% of the regional median wage. High-wage jobs are those that pay above 120% of the regional median wage. Regional median hourly wages are estimated from the American Community Survey and are published on the Vital Signs Income indicator page. For the national context analysis, occupation wage classifications are unique to each metro area. A low-wage job in New York, for instance, may be a middle-wage job in Miami. For the Bay Area in 2017, the median hourly wage for low-wage occupations was less than $20.86 per hour. For middle-wage jobs, the median ranged from $20.86 to $31.30 per hour; and for high-wage jobs, the median wage was above $31.30 per hour.

Occupational employment and wage information comes from the Occupational Employment Statistics (OES) program. Regional and subregional data is published by the California Employment Development Department. Metro data is published by the Bureau of Labor Statistics. The OES program collects data on wage and salary workers in nonfarm establishments to produce employment and wage estimates for some 800 occupations. Data from non-incorporated self-employed persons are not collected, and are not included in these estimates. Wage estimates represent a three-year rolling average.

Due to changes in reporting during the analysis period, subregion data from the EDD OES have been aggregated to produce geographies that can be compared over time. West Bay is San Mateo, San Francisco, and Marin counties. North Bay is Sonoma, Solano and Napa counties. East Bay is Alameda and Contra Costa counties. South Bay is Santa Clara County from 2001-2004 and Santa Clara and San Benito counties from 2005-2017.

Due to changes in occupation classifications during the analysis period, all occupations have been reassigned to 2010 SOC codes. For pre-2009 reporting years, all employment in occupations that were split into two or more 2010 SOC occupations are assigned to the first 2010 SOC occupation listed in the crosswalk table provided by the Census Bureau. This method assumes these occupations always fall in the same wage category, and sensitivity analysis of this reassignment method shows this is true in most cases.

In order to use OES data for time series analysis, several steps were taken to handle missing wage or employment data. For some occupations, such as airline pilots and flight attendants, no wage information was provided and these were removed from the analysis. Other occupations did not record a median hourly wage (mostly due to irregular work hours) but did record an annual average wage. Nearly all these occupations were in education (i.e. teachers). In this case, a 2080 hour-work year was assumed and [annual average wage/2080] was used as a proxy for median income. Most of these occupations were classified as high-wage, thus dispelling concern of underestimating a median wage for a teaching occupation that requires less than 2080 hours of work a year (equivalent to 12 months fulltime). Finally, the OES has missing employment data for occupations across the time series. To make the employment data comparable between years, gaps in employment data for occupations are ‘filled-in’ using linear interpolation if there are at least two years of employment data found in OES. Occupations with less than two years of employment data were dropped from the analysis. Over 80% of interpolated cells represent missing employment data for just one year in the time series. While this interpolating technique may impact year-over-year comparisons, the long-term trends represented in the analysis generally are accurate.

Self-Employment by Occupation by County reports the total population of employed civilian workers aged 16 years and older by occupation. In the Business Type column, individual Business Types are presented as percentages of the Total employed population for the corresponding occupation in the Occupation column. For example, for 2012-2016, there were approximately 320,110 Service employees in Connecticut, 14.6% of those employees worked in Government. 'Self-Employed, Incorporated' includes workers in their own incorporated businesses, 'Self-Employed, Not Incorporated' includes workers in their own non-incorporated businesses and unpaid family workers, 'Government' includes local, state, and federal government workers, 'Private, Profit' includes employees of for-profit private companies, 'Private, Not-for-profit' includes both wage and salary employees of not-for-profit private companies. These data originate from the American Community Survey (ACS) 5-Year estimates, table S2406.

Long-term Occupational Projections for a 10-year time horizon are provided for the State and its labor market regions to provide individuals and organizations with an occupational outlook to make informed decisions on individual career and organizational program development. Long-term projections are revised annually. Data are not available for geographies below the labor market regions. Detail may not add to summary lines due to suppression of data because of confidentiality and/or quality.

The Occupational Employment Statistics (OES) program produces employment and wage estimates annually for over 800 occupations. These estimates are available for the nation as a whole, for individual States, and for metropolitan and nonmetropolitan areas; national occupational estimates for specific industries are also available.

Graph and download economic data for Employment Level - Construction and Extraction Occupations (LNU02032210) from Jan 1983 to Jun 2025 about extraction, occupation, 16 years +, construction, household survey, employment, and USA.

description: The Occupational Employment Statistics (OES) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OES estimates are constructed from a sample of about 52,000 establishments. Each year, forms are mailed to two semiannual panels of approximately 8,500 sampled establishments, one panel in May and the other in November.; abstract: The Occupational Employment Statistics (OES) survey is a semiannual mail survey of employers that measures occupational employment and occupational wage rates for wage and salary workers in nonfarm establishments, by industry. OES estimates are constructed from a sample of about 52,000 establishments. Each year, forms are mailed to two semiannual panels of approximately 8,500 sampled establishments, one panel in May and the other in November.

Number of employees by National Occupational Classification (NOC), last 5 months. Data are also available for the standard error of the estimate, the standard error of the month-to-month change and the standard error of the year-over-year change.

This dataset contains indicators related to employment projections by detailed occupations, workers education and training characteristics, occupational separations and openings. The occupations denominations are based on the 2016 Standard Occupational Classification.

The Connecticut Occupational Employment and Wage data provides employment and wage data by occupation and is based on the results of the Occupational Employment Statistics (OES) survey. The OES program conducts a bi-annual mail survey designed to produce estimates of employment and wages for over 800 occupations. These estimates are generated at the national, state, and metropolitan area levels. For more information, please visit us at http://www1.ctdol.state.ct.us/lmi/wages/default.asp.

Graph and download economic data for Employment Level - Service Occupations (LNU02032204) from Jan 1983 to Jun 2025 about occupation, 16 years +, household survey, services, employment, and USA.

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.