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 National Compensation Survey (NCS) program produces information on wages by occupation for many metropolitan areas.The Modeled Wage Estimates (MWE) 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. The modeled wage estimates are produced using a statistical procedure that combines survey data collected by the National Compensation Survey (NCS) and the Occupational Employment Statistics (OES) programs. Borrowing from the strengths of the NCS, information on job characteristics and work levels, and from the OES, the occupational and geographic detail, the modeled wage estimates provide more detail on occupational average hourly wages than either program is able to provide separately. Wage rates for different work levels within occupation groups also are published. Data are available for private industry, State and local governments, full-time workers, part-time workers, and other workforce characteristics.

This feature service contains employment and wage data for detailed farming, fishing, and forestry occupations by nation, state, and metropolitan and nonmetropolitan areas. Data from Bureau of Labor Statistics' (BLS) Occupation Employment Statistics (OES) series. Data vintage: May 2018.Boundary files came from U.S. Census Bureau's 2018 Cartographic Boundary Files. Nonmetropolitan areas were constructed based on BLS' May 2018 Area Definitions.A few Frequently Asked Questions from BLS' OES FAQ site:How are "employees" defined by the OES Survey? "Employees" are all part-time and full-time workers who are paid a wage or salary. The survey does not cover the self-employed, owners and partners in unincorporated firms, household workers, or unpaid family workers.Do OES wage estimates include benefits? No. OES wage estimates represent wages and salaries only, and do not include nonproduction bonuses or employer costs of nonwage benefits, such as health insurance or employer contributions to retirement plans. Information on cost of benefits, benefit incidence, and detailed plan provisions is available from the National Compensation Survey program.Why does the sum of the areas within a state not equal the statewide employment? The sum of the areas may differ from statewide employment for several reasons:RoundingThe totals include data items that are not released separately due to confidentiality and quality reasons.Many States include metropolitan areas that cross State lines. These cross-State metropolitan area estimates include data from each State, which should not be included in a total for a single State.A small number of establishments indicate the State in which their employees are located, but do not indicate the specific metropolitan or nonmetropolitan area in which they are located. Data for these establishments are used in the calculation of the statewide estimates, but are not included in the estimates of any individual area.Why don't the major group or "all occupations" employment totals equal the sum of the employment estimates for the detailed occupations? The major group and "all occupations" totals may include detailed occupations for which separate employment estimates could not be published. As a result, employment totals at the major group and "all occupations" levels may be greater than the sum of employment estimates for the detailed occupations. Because the major group employment totals include employment for the detailed occupations in that group, summing across both detailed occupations and major groups will result in double counting of occupational employment.

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

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!

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.

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 51,000 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.

Analysis of ‘Occupational Employment Statistics’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/7ff78641-49d8-4322-9db2-1cfced727ced on 30 September 2021.

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

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 51,000 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.

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

The study uses a dataset containing information from 351,297 records and 154 variables related to socioeconomic, demographic, labor, and well-being aspects in the United States, extracted from a publicly accessible source via IPUMS (https://cps.ipums.org/cps/). Data were collected between 2013 and 2020. The Current Population Survey (CPS), conducted monthly by the U.S. Census Bureau, employs a combination of in-person and telephone interviews to obtain representative data from the population. IPUMS CPS consolidates and facilitates access to data from the CPS since 1962 for research purposes. These data include household characteristics, employment information, income, health, access to social programs, family structure, educational attainment, racial and Hispanic origin, citizenship, housing, and participation in social assistance programs.

This occupational wage dataset is based on Occupational Employment Statistics (OES) survey that captures 52,000 businesses. This particular dataset is on healthcare practitioners, technical occupation and healthcare support occupation. The other data set in this series include healthcare support occupations.

About this Dataset

This dataset combines automation probability data with a breakdown of the number of jobs and salary in each occupation by state within the USA. Automation probability was acquired from the work of Carl Benedikt Freyand Michael A. Osborne; State employment data is from the Bureau of Labor Statistics. Note that for simplicity of analysis, all jobs where data was not available or there were less than 10 employees were marked as zero.

How to Cite this Dataset

If you use this dataset in your research, please credit the authors.

Salary Data

@misc{u.s. bureau of labor statistics, title={Occupational Employment Statistics}, url={https://www.bls.gov/oes/current/oes_nat.htm}, journal={U.S. BUREAU OF LABOR STATISTICS}}

Automation Data

@article{frey_osborne_2017, title={The future of employment: How susceptible are jobs to computerisation?}, volume={114}, DOI={10.1016/j.techfore.2016.08.019}, journal={Technological Forecasting and Social Change}, author={Frey, Carl Benedikt and Osborne, Michael A.}, year={2017}, pages={254–280}}

License

License was not specified at the source.

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Each annual salary listed in this report is 12 times that particular employee's monthly adjusted salary rate as of June 30, 2010 (between July 1, 2009 and June 30, 2010). "Annual Salary" includes most differential payments (such as work-out-of-classification and bilingual differential), but excludes payments for overtime, shift differential, benefits, and vacation payout. The report does not account for unpaid furlough leave that management employees began taking in fiscal year 2009-2010; neither does it reflect step decreases and unpaid furlough leave that some classified employees began taking after June 2009.

This report does not include annual salaries for employees of the Oregon University System, semi-independent agencies, temporary employees, or records protected by court order.

For more State of Oregon Workforce/salary information please visit the Oregon Transparency Website: http://oregon.gov/transparency/state_workforce.page

Explore this visualization to see the latest quarter's data. View metadata for key information about this dataset.This data does not necessarily represent current salaries of employees and is intended for informational purposes only. Formal requests to document salary details or other personnel information should be made through the City’s Human Resources department.This dataset shows the earnings for all City employees, including elected officials and Court staff. Data is from Calendar Year (CY) 2019 Q2 to the most recent quarter of this year. Please note that since employee counts fluctuate throughout the year, the sum of the BASE_SALARY field does not reflect the total budgeted amount. Also, when the BASE_SALARY column is blank, it represents part-time, temporary, or seasonal employees paid by the hour.For questions about this dataset, contact catherine.lamb@phila.gov. For technical assistance, email maps@phila.gov.

Colombia Registered Job Vacancy: Salary Offered: Does Not Specify Salary data was reported at 0.000 Job in Nov 2023. This stayed constant from the previous number of 0.000 Job for Oct 2023. Colombia Registered Job Vacancy: Salary Offered: Does Not Specify Salary data is updated monthly, averaging 0.000 Job from Jan 2015 (Median) to Nov 2023, with 106 observations. The data reached an all-time high of 32,811.000 Job in Sep 2015 and a record low of 0.000 Job in Nov 2023. Colombia Registered Job Vacancy: Salary Offered: Does Not Specify Salary data remains active status in CEIC and is reported by Special Administrative Unit of the Public Employment Service. The data is categorized under Global Database’s Colombia – Table CO.G068: Registered Job Vacancy.

NOTE: This data set was updated on 5/15/15 by request of the data owner. Each annual salary listed in this report is 12 times that particular employee's monthly adjusted salary rate as of June 30, 2011 (between July 1, 2010 and June 30, 2011). "Annual Salary" includes most differential payments (such as work-out-of-classification and bilingual differential), but excludes payments for overtime, shift differential, benefits, and vacation payout. The report does not account for unpaid furlough leave that management employees began taking in fiscal year 2010-2011; neither does it reflect step decreases and unpaid furlough leave that some classified employees began taking after June 2009. This report does not include annual salaries for employees of the Oregon University System, semi-independent agencies, temporary employees, or records protected by court order. For more State of Oregon Workforce/salary information please visit the Oregon Transparency Website: http://www.oregon.gov/transparency/.

This data set represents the aggregate total occupational earnings by state. This includes the mean and median hourly wage, as well as the mean annual salary for each state. http://www.bls.gov/OES/

The data was collected as part of a political science research project on political trust.

The two data files contain the raw data from two online survey experiments which sought to test the effect of pay and expenses information on political trust. The surveys together received 1957 responses.

The R code used to analyse the data files is also made available.

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for does not apply see text in question h.14 in the U.S.

This data does not necessarily represent current salaries of employees and is intended for informational purposes only. Formal requests to document salary details or other personnel information should be made through the City's Human Resources department. Earnings for all City employees, including elected officials and Court staff. Data since 2019 up to the most recent quarter of this year. Please note that since employee counts fluctuate throughout the year, the sum of the BASE_SALARY field does not reflect the total budgeted amount. Also, when the BASE_SALARY column is blank, it represents part-time, temporary, or seasonal employees paid by the hour. Please see metadata for detailed explanations of each field.

The payroll data represents the amount paid to an employee during the reported time period. In addition to regular pay, these amounts may include other pay types such as overtime, longevity, shift differential or terminal pay. This amount does not include any state share costs associated with the payroll i.e. FICA, state share retirement, etc. This amount may vary from an employee’s ‘salary’ due to pay adjustments or pay period timing. The payroll information will be updated monthly after the end of the month. For example, July information will be added in August after the 15th of the month.