This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

As of 2023, the median wage for employees in healthcare support occupations was about 36,140 U.S. dollars. The occupational group with the highest annual median wage was management occupations. Mean wages for the same occupational groups can be accessed here.

In 2023, the average wage and salary per full-time equivalent employee in the mining industry in the United States was at 126,707 U.S. dollars. The highest wage and salary per FTE was found in the information industry, at 164,400 U.S. dollars.

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.

In 2024, people working in IT management in the United States, earned an average annual salary worth around 168 thousand U.S. dollars. Software developers and project managers all reported being paid on average over 120 thousand U.S. dollars. Despite nearly all categories saw a year-on-year increase in annual compensation, IT support and help desk technicians saw a decrease compared to the previous year

Comparison of the average salary of State of Oklahoma employees and the market average salary beginning in fiscal year 2005.

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.

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

Wages in the United States increased 4.46 percent in January of 2025 over the same month in the previous year. This dataset provides the latest reported value for - United States Wages and Salaries Growth - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Laborers' Average Hourly Rate of Wages, Weighted for United States (A08139USA052NNBR) from 1860 to 1891 about hours, wages, labor, rate, and USA.

Average Wage Per Job in Maryland and its Jurisdictions in Current Dollars from 2012 to 2022. Source data from U.S. Bureau of Economic Analysis, December 2023.

Gross weekly and hourly earnings by level of occupation, UK, quarterly, not seasonally adjusted. Labour Force Survey. These are official statistics in development.

In 2023, the average wage and salary per full-time equivalent employee in the United States was at 81,359 U.S. dollars. Wage and salary accruals include executives' compensation, bonuses, tips, and payments-in-kind. Wage and salary accruals by industry data may be accessed here.

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.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: 16 years and over (LES1252881500Q) from Q1 1979 to Q4 2024 about second quartile, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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 Q4 2024 about full-time, salaries, workers, earnings, 16 years +, wages, median, real, employment, and USA.

This dataset provides values for WAGES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CEU0500000003) from Mar 2006 to Feb 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

Graph and download economic data for Employed full time: Percent of hourly paid workers: Paid total at or below prevailing federal minimum wage: Wage and salary workers: 16 years and over: Women (LEU0253130200A) from 2000 to 2024 about paid, minimum wage, females, full-time, salaries, workers, hours, 16 years +, federal, wages, percent, employment, and USA.

Economically Active Population Survey: Average wages of the main job by period, type of working day, sector of educational level attained and decile. Annual. National.