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 wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

The Quarterly Census of Employment and Wages (QCEW) program is a cooperative program involving the Bureau of Labor Statistics (BLS) of the United States Department of Labor and the State Employment Security Agencies (SESAs). The QCEW program produces a comprehensive tabulation of employment and wage information for workers covered by State unemployment insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Publicly available data files include information on the number of establishments, monthly employment, and quarterly wages, by NAICS industry, by county, by ownership sector, for the entire United States. These data are aggregated to annual levels, to higher industry levels (NAICS industry groups, sectors, and supersectors), and to higher geographic levels (national, State, and Metropolitan Statistical Area (MSA)). To download and analyze QCEW data, users can begin on the QCEW Databases page. Downloadable data are available in formats such as text and CSV. Data for the QCEW program that are classified using the North American Industry Classification System (NAICS) are available from 1990 forward, and on a more limited basis from 1975 to 1989. These data provide employment and wage information for arts-related NAICS industries, such as: Arts, entertainment, and recreation (NAICS Code 71) Performing arts and spectator sports Museums, historical sites, zoos, and parks Amusements, gambling, and recreation Professional, scientific, and technical services (NAICS Code 54) Architectural services Graphic design services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, book and music stores Book, periodical, and music stores Art dealers For years 1975-2000, data for the QCEW program provide employment and wage information for arts-related industries are based on the Standard Industrial Classification (SIC) system. These arts-related SIC industries include the following: Book stores (SIC 5942) Commercial photography (SIC Code 7335) Commercial art and graphic design (SIC Code 7336) Museums, Botanical, Zoological Gardens (SIC Code 84) Dance studios, schools, and halls (SIC Code 7911) Theatrical producers and services (SIC Code 7922) Sports clubs, managers, & promoters (SIC Code 7941) Motion Picture Services (SIC Code 78) The QCEW program serves as a near census of monthly employment and quarterly wage information by 6-digit NAICS industry at the national, state, and county levels. At the national level, the QCEW program provides employment and wage data for almost every NAICS industry. At the State and area level, the QCEW program provides employment and wage data down to the 6-digit NAICS industry level, if disclosure restrictions are met. Employment data under the QCEW program represent the number of covered workers who worked during, or received pay for, the pay period including the 12th of the month. Excluded are members of the armed forces, the self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers covered by the railroad unemployment insurance system. Wages represent total compensation paid during the calendar quarter, regardless of when services were performed. Included in wages are pay for vacation and other paid leave, bonuses, stock options, tips, the cash value of meals and lodging, and in some States, contributions to deferred compensation plans (such as 401(k) plans). The QCEW program does provide partial information on agricultural industries and employees in private households. Data from the QCEW program serve as an important source for many BLS programs. The QCEW data are used as the benchmark source for employment by the Current Employment Statistics program and the Occupational Employment Statistics program. The UI administrative records collected under the QCEW program serve as a sampling frame for BLS establishment surveys. In addition, data from the QCEW program serve as a source to other Federal and State programs. The Bureau of Economic Analysis (BEA) of the Department of Commerce uses QCEW data as the base for developing the wage and salary component of personal income. The Employment and Training Administration (ETA) of the Department of Labor and the SESAs use QCEW data to administer the employment security program. The QCEW data accurately reflect the ex

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 41,400 establishments. Each year, forms are mailed to two semiannual panels of approximately 6,900 sampled establishments, one panel in May and the other in November.

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 Q2 2025 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

Graph and download economic data for Employment Cost Index: Wages and Salaries: State and Local Government: All Workers (ECIGVTWAG) from Q1 2001 to Q2 2025 about ECI, state & local, salaries, workers, wages, government, inflation, and USA.

Each year, the City of Boston publishes payroll data for employees. This dataset contains employee names, job details, and earnings information including base salary, overtime, and total compensation for employees of the City.

See the "Payroll Categories" document below for an explanation of what types of earnings are included in each category.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

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 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

Annual salary information including gross pay and overtime pay for all active, permanent employees of Montgomery County, MD paid in calendar year 2023. This dataset is a prime candidate for conducting analyses on salary disparities, the relationship between department/division and salary, and the distribution of salaries across gender and grade levels.

Statistical models can be applied to predict base salaries based on factors such as department, grade, and length of service. Machine learning techniques could also be employed to identify patterns and anomalies in the salary data, such as outliers or instances of significant inequity.

Some analysis to be performed with this dataset can include:

• Gender Pay Gap Analysis: An examination of salary differences between genders within similar roles, grades, and departments to identify any disparities that need to be addressed.
• Departmental Salary Analysis: Analyzing the distribution of salaries across different departments and divisions to understand how compensation varies within the organization.
• Impact of Overtime and Longevity Pay: Evaluating how overtime and longevity pay contribute to the overall compensation of employees and identifying trends or patterns in these payments. ​

Graph and download economic data for Employment Cost Index: Wages and Salaries: Private Industry Workers (ECIWAG) from Q1 2001 to Q2 2025 about cost, ECI, salaries, workers, private industries, wages, private, employment, industry, inflation, indexes, and USA.

The Quarterly Census of Employment and Wages (QCEW) program (also known as ES-202) collects employment and wage data from employers covered by New York State's Unemployment Insurance (UI) Law. This program is a cooperative program with the U.S. Bureau of Labor Statistics. QCEW data encompass approximately 97 percent of New York's nonfarm employment, providing a virtual census of employees and their wages as well as the most complete universe of employment and wage data, by industry, at the State, regional and county levels. "Covered" employment refers broadly to both private-sector employees as well as state, county, and municipal government employees insured under the New York State Unemployment Insurance (UI) Act. Federal employees are insured under separate laws, but are considered covered for the purposes of the program. Employee categories not covered by UI include some agricultural workers, railroad workers, private household workers, student workers, the self-employed, and unpaid family workers. QCEW data are similar to monthly Current Employment Statistics (CES) data in that they reflect jobs by place of work; therefore, if a person holds two jobs, he or she is counted twice. However, since the QCEW program, by definition, only measures employment covered by unemployment insurance laws, its totals will not be the same as CES employment totals due to the employee categories excluded by UI. Industry level data from 1975 to 2000 is reflective of the Standard Industrial Classification (SIC) codes.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

Overview with Chart & Report: Employment Wages q/q show changes in US employee wages (without additional pays) in the given quarter compared to the previous one. The military are excluded from calculations. Wages growth is a

The Quarterly Census of Employment and Wages (QCEW) program serves as a near census of employment and wage information. The program produces a comprehensive tabulation of employment and wage information for workers covered by Connecticut Unemployment Insurance (UI) laws and Federal workers covered by the Unemployment Compensation for Federal Employees (UCFE) program. Data on the number of establishments, employment, and wages are reported by industry for Connecticut and for the counties, towns and Labor Market Areas (LMAs) and Workforce Investment Areas (WIAs).

This table contains 393 series, with data for years 1981 - 2012 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (16 items: Canada and outside Canada;Newfoundland and Labrador;Prince Edward Island ...), Public sector, components (2 items: Employment;Wages and salaries ...), Sector (12 items: Public sector;Federal general government;Provincial and territorial general government;Government ...), Seasonal adjustment (2 items: Unadjusted;Seasonally adjusted ...).

Produced monthly by the Current Employment Statistics (CES) program, this data set provides detailed industry data on employment, hours, and earnings of workers on non-farm payrolls.

These occupational employment and wage estimates are calculated with data collected from employers in all industry sectors in metropolitan and nonmetropolitan areas in Virginia.

Additional information, including the hourly and annual 10th, 25th, 75th, and 90th percentile wages and the employment percent relative standard error, is available

Employment rose in all 12 large counties in Virginia from March 2022 to March 2023. Loudoun County and Chesterfield County had the largest over-the-year increases in employment at 4.3 percent and 3.9 percent. Among the 12 largest counties in Virginia, employment was highest in Fairfax County (624,587) in March 2023. Within Fairfax County’s private industry, professional, scientific, and technical services accounted for the largest employment. Together, the 12 largest Virginia counties accounted for 55.0 percent of total employment within the state. Nationwide, the 360 largest counties made up 73.0 percent of total U.S. employment.