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

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 paper measures aggregate changes in job characteristics in the U.S. from 2005 to 2015, and decomposes those changes into components representing shifts within occupations and changes in occupational employment shares. Per our title, within-occupation changes dominate, raising doubts about the ability of projections based on expected changes in the occupational composition of employment to capture the likely future of work. Indeed, our data show only weak relationships between automatability, repetitiveness, and other job attributes and changes in occupational employment. The results suggest that analysts give greater attention to within-occupation impacts of technology in assessing the future of work.

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.

Occupations are classified using the three digit National Occupational Classification (NOC) codes. Wages include: average hourly wage rate, average weekly wage rate, median hourly wage rate and median weekly wage rate.

The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

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.

Number of employees by province, economic region and National Occupational Classification (NOC), last 5 years.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by National Occupational Classification (NOC), type of work, gender, and age group.

VITAL SIGNS INDICATOR
Jobs by Industry (EC1)

FULL MEASURE NAME
Employment by place of work by industry sector

LAST UPDATED
December 2022

DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.

DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.

Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.

The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.

Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.

QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.

For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:

Farm:
(aggregation level: 74, NAICS code: 11) - Contra Costa: 2008-2010 - Marin: 1990-2006, 2008-2010, 2014-2020 - Napa: 1990-2004, 2013-2021 - San Francisco: 2019-2020 - San Mateo: 2013

Information:
(aggregation level: 73, NAICS code: 51) - Solano: 2001

Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53) - Solano: 2001

Unclassified:
(aggregation level: 73, NAICS code: 99) - All nine Bay Area counties: 1990-2000 - Marin, Napa, San Mateo, and Solano: 2020 - Napa: 2019 - Solano: 2001

Data on employment income statistics, by occupation unit group (5-digit code) from the National Occupational Classification (NOC) 2021, visible minority, highest level of education, work activity during the reference year, age and gender for the population aged 15 years and over who reported weeks worked and employment income in 2020 in private households in Canada, provinces and territories.

Annual estimates of paid hours worked, weekly, hourly and annual earnings for UK employees by sex, and full-time and part-time, by region and four-digit Standard Occupational Classification.

This collection provides average scores on selected variables from the Dictionary of Occupational Titles for 1980 Census occupational categories. The dataset was prepared using a sample of individuals from the 1970 Census whose occupations had been coded with both 1970 and 1980 Census detailed occupational codes. This file, known as the Treiman file, was merged with selected variables from the FOURTH EDITION DICTIONARY OF OCCUPATIONAL TITLES, using 1970 occupational categories as the merge variable. A score on each Dictionary of Occupational Titles (DOT) variable was then computed for each 1980 occupational category. Included among the DOT variables were such characteristics as (1) level of complexity at which the worker functions in terms of data, people, and things, (2) training required to do the job, (3) verbal, numerical, spatial, and other aptitudes needed to perform the job, (4) percent of workers in the job with a preference for a specific type of job activity such as abstract and creative activity or activity requiring business contact with people, (5) temperamental attributes such as adaptability to performing under stress and adaptability to performing repetitive work, (6) physical demands of the job, and (7) environmental conditions under which the work is performed. Measures of mean hourly and annual earnings for both men and women in each occupation and number of men and women in each occupation also are included.

The 1982 crosswalk consists of all (and only those) Occupational Employment Statistics (OES) survey occupations used during the most recent surveys of all industries covered by the OES survey program. Those OES occupations not surveyed during the referenced cycle are not included on the crosswalk. However, all nine digit fourth edition Dictionary of Occupational Titles (DOT) codes are contained on the tape and have been assigned to either specific OES occupations, OES residual categories, or to an unassigned OES code because the DOT occupation is industry-specific and the industry is not within the scope of the OES survey.

Based on the results of occupational psychology tests, career preference test S-type, entrepreneurship aptitude test, and career value test provided by the Korea Employment Information Service, it provides classification code values and mapping results for occupational groups that are suitable for you. You can look up the results of individual psychological tests using the psychological test API. It provides occupational psychological test occupation codes as Korean Employment Occupational Classification code results. It receives authentication key, return type, and psychological test code (adult occupational psychology test, career value test) as parameters and provides results such as Korean Employment Occupational Classification code list. * Note: In case of items entered as strings, be careful as search results may not be displayed properly if spaces or typos are included. Most parameters can be entered optionally, and searches are possible by entering only the necessary conditions.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties..Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section.Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Source: U.S. Census Bureau, 2015-2019 American Community Survey 5-Year Estimates.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation). The effect of nonsampling error is not represented in these tables..The Class of Worker status "unpaid family workers" may have earnings. Earnings reflect any earnings from all jobs held during the 12 months prior to the ACS interview. The Class of Worker status reflects the job or business held the week prior to the ACS interview, or the last job held by the respondent..Industry titles and their 4-digit codes are based on the North American Industry Classification System (NAICS). The Census industry codes for 2018 and later years are based on the 2017 revision of the NAICS. To allow for the creation of multiyear tables, industry data in the multiyear files (prior to data year 2018) were recoded to the 2017 Census industry codes. We recommend using caution when comparing data coded using 2017 Census industry codes with data coded using Census industry codes prior to data year 2018. For more information on the Census industry code changes, please visit our website at https://www.census.gov/topics/employment/industry-occupation/guidance/code-lists.html..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Beginning in data year 2019, respondents to the Weeks Worked question provided an integer value for the number of weeks worked. For data years 2008 through 2018, respondents selected a category corresponding to the number of weeks worked..Occupation titles and their 4-digit codes are based on the Standard Occupational Classification (SOC). The Census occupation codes for 2018 and later years are based on the 2018 revision of the SOC. To allow for the creation of the multiyear tables, occupation data in the multiyear files (prior to data year 2018) were recoded to the 2018 Census occupation codes. We recommend using caution when comparing data coded using 2018 Census occupation codes with data coded using Census occupation codes prior to data year 2018. For more information on the Census occupation code changes, please visit our website at https://www.census.gov/topics/employment /industry-occupation/guidance/code-lists.html..In 2019, methodological changes were made to the class of worker question. These changes involved modifications to the question wording, the category wording, and the visual format of the categories on the questionnaire. The format for the class of worker categories are now listed under the headings "Private Sector Employee," "Government Employee," and "Self-Employed or Other." Additionally, the category of Active Duty was added as one of the response categories under the "Government Employee" section for the mail questionnaire. For more detailed information about the 2019 changes, see the 2016 American Community Survey Content Test Report for Class of Worker located at http://www.census.gov/library/working-papers/2017/acs/2017_Martinez_01.html..The 2015-2019 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the ...

Data on employment income statistics by occupation minor group (4-digit code) from the National Occupational Classification (NOC) 2021, Indigenous identity, highest level of education, work activity during the reference year, age and gender, for the population aged 15 years and over who reported weeks worked and employment income in 2020, in private households in Canada, provinces and territories and census metropolitan areas with parts.

The age groups available in the data set are: 15+, 15-64, 15-19, 20-24, 25-44, 45-54, 55-64 and 65+. Occupations include major occupational groups including one and two-digit National Occupational Classification codes.

Number of persons in the labour force (employment and unemployment), unemployment rate and employment rate, by National Occupational Classification (NOC) and gender.

Dataset includes occupational licenses by state. Data is provided by the state. All licenses are coded to an O*NET-SOC code and by state. CareerOneStop.org web service available upon request.