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 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.

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

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.

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 Statistics (OES) program conducts a semiannual survey designed 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 for the nation as a whole, by state, by metropolitan or nonmetropolitan area, and by industry or ownership. The Bureau of Labor Statistics produces occupational employment and wage estimates for approximately 415 industry classifications at the national level. The industry classifications correspond to the sector, 3-, 4-, and selected 5- and 6-digit North American Industry Classification System (NAICS) industrial groups. The OES program surveys approximately 200,000 establishments per panel (every six months), taking three years to fully collect the sample of 1.2 million establishments. To reduce respondent burden, the collection is on a three-year survey cycle that ensures that establishments are surveyed at most once every three years. The estimates for occupations in nonfarm establishments are based on OES data collected for the reference months of May and November. The OES survey is a federal-state cooperative program between the Bureau of Labor Statistics (BLS) and State Workforce Agencies (SWAs). 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 OES survey make these estimates possible.

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.

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.

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.

Washington State, metropolitan statistical areas (MSA) and nonmetropolitan areas (NMA), 2020

OEWS 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 OEWS 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.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

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 ---

This dataset is from the Iowa Wage survey which is based on the Occupation Employment Statistics (OES) program from the Bureau of Labor Statistics (BLS). This data is updated to reflect more current statistics using cost of living indicators.

Annual occupational employment and annual wage data for Multiple Occupations in Colorado in 2012.

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 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.

This Indicator is measured by the percent of employed individuals who are not employed in industries with a mean annual wage of at least $80,000. In 2016, these industries included management occupations; legal occupations; healthcare practitioners and technical occupations; computer and mathematical occupations; architecture and engineering occupations; life, physical, and social science occupations; and business and financial operations occupations. (Source: Occupational Employment Statistics, CA Employment Development Department https://data.edd.ca.gov/Wages/Occupational-Employment- Statistics-OES-/pwxn-y2g5)

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The dataset contains the name, gender, county or city of residence (when possible), official title, total salary received during each fiscal year, base salary for the employee, and traveling and subsistence expense reimbursed to state personnel beginning with Fiscal Year 2007. A status of "TERMINATED" in the column providing the base salary does not indicate that the employee was fired, only that the person no longer works in that position.

This table contains data on the living wage and the percent of families with incomes below the living wage for California, its counties, regions and cities/towns. Living wage is the wage needed to cover basic family expenses (basic needs budget) plus all relevant taxes; it does not include publicly provided income or housing assistance. The percent of families below the living wage was calculated using data from the Living Wage Calculator and the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. The living wage is the wage or annual income that covers the cost of the bare necessities of life for a worker and his/her family. These necessities include housing, transportation, food, childcare, health care, and payment of taxes. Low income populations and non-white race/ethnic have disproportionately lower wages, poorer housing, and higher levels of food insecurity. More information about the data table and a data dictionary can be found in the About/Attachments section.

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.

The following are a selection of annual outcomes of services provided by the Pennsylvania's Department of Labor & Industry's Office of Vocational Rehabilitation. Outcomes include applicants and case outcomes including employment and wages.

Key Footnotes: 1) Employed in Competitive Labor Market means employment at or above the minimum wage in settings where most employees do not have disabilities. 2) Estimated Taxes Paid are based on a standard deduction for the year, annual tax brackets and rates established by the IRS, and flat-rate FICA, state, and local taxes. 3) Estimated Total Government Savings are estimated federal, state, and local taxes paid plus annualized public support dollars at closure. 4) Average per Person Cost for a Competitive Employment Placement is the average individual "life of case" cost for all persons having a competitive employment outcome regardless of total number of years receiving services. 5) Average per Person Cost of Services is the average individual "life of case" cost for all persons having an employment outcome regardless of total number of years receiving services. 6) Source: U.S. Department of Labor, Bureau of Labor Statistics, May 2016 State Occupational Employment and Wage Estimates, Pennsylvania, https://www.bls.gov/oes/current/oes_pa.htm#00-0000.