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.

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 graph displays the twenty largest occupation groups in the United States as of May 2023, ranked by annual mean wage. The annual mean wage among the 7.7 million retail sales workers in the U.S. stood at 34,520 U.S. dollars in 2023.

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

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jun 2025 about earnings, average, establishment survey, hours, wages, private, employment, and USA.

In October 2024, the average hourly earnings for all employees on private nonfarm payrolls in the United States stood at 35.46 U.S. dollars. The data have been seasonally adjusted. Employed persons are employees on nonfarm payrolls and consist of: persons who did any work for pay or profit during the survey reference week; persons who did at least 15 hours of unpaid work in a family-operated enterprise; and persons who were temporarily absent from their regular jobs because of illness, vacation, bad weather, industrial dispute, or various personal reasons.

Average full-time hourly wage paid and payroll employment by type of work, North American Industry Classification System (NAICS) and National Occupational Classification (NOC), 2016 and 2017.

Analysis of ‘Average Salary by Job Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c279deaa-d913-48fe-8693-5899e9291025 on 27 January 2022.

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

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

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

Graph and download economic data for 3-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Movement: Job Stayer (FRBATLWGT3MMAUMHWGJMJST) from Mar 1997 to Jun 2025 about growth, moving average, jobs, 3-month, average, wages, median, and USA.

Brazil Average Real Income: All Jobs: Actual Earnings data was reported at 2,304.000 BRL in Mar 2019. This records a decrease from the previous number of 2,531.000 BRL for Feb 2019. Brazil Average Real Income: All Jobs: Actual Earnings data is updated monthly, averaging 2,269.000 BRL from Feb 2012 (Median) to Mar 2019, with 86 observations. The data reached an all-time high of 2,611.000 BRL in Jan 2019 and a record low of 2,147.000 BRL in Apr 2012. Brazil Average Real Income: All Jobs: Actual Earnings data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Labour Market – Table BR.GBA001: Continuous National Household Sample Survey: Monthly.

A structured overview of the average, net, median, and minimum wage in Germany for 2025. This dataset combines original market research conducted by KUMMUNI GmbH with publicly available data from the German Federal Statistical Office. It includes values with and without bonuses, hourly minimum wage, and take-home pay after tax.

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

Job Postings in Europe

Exploring Salaries, Job Types and Locations

By [source]

About this dataset

The eMedCareers Europe dataset contains more than 30,000 job postings from the top companies across Europe. This comprehensive data set includes detailed information about each job posting such as the associated job category, company name, location, job title and description, as well as types of jobs and salary offered. With this data set, researchers can gain insights into the current trends in salaries and job types in various parts of Europe. Moreover, it provides unique insights into which companies are actively hiring for specific positions and wage levels to assist businesses in forming competitive salaries structures. With this dataset at your fingertips you can start uncovering intriguing patterns in European employment and pay scales - providing deep understandings of the current hiring climate across multiple countries within the region

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How to use the dataset

This dataset contains information on job postings from top companies in Europe. It can be used to analyze job types, salaries and locations of advertised positions. The data fields include: - Category: This field specifies the type or category of the position being advertised, such as Engineering, Marketing or Accounting. - Company Name: This field identifies the company advertising the position. - Job Description: This field provides a short description about what will be expected from an individual in this role. - Job Title: This field displays the title of the role that is being offered. - Job Type: This field specifies full-time, part-time contract work etc which would either be available for direct hire or freelance gigs. - Location: This field denotes where these positions are located in Europe and who could apply for it based on their location/residence. - Salary Offered :This filed provides gross annual salary or pay range that is being offered by employer to employee who takes up this job title and other compensation benefits as part per contract terms and conditions set while signing up for specific roles in company/organization

Using this dataset you can easily analyze all these different aspects related to job openings in Europe available at eMedCareers portal like salary statistics for different industries/categories, job types – full-time vs freelance/contract; location wise jobs availability etc making more informed decision when looking out into market looking out new career opportunities with prospective employers based upon your skillset

Research Ideas

• Analyzing the correlation between salary offered and job type (full-time, part-time, contract) to identify salary trends across different job types in Europe.
• Using the job category and location data to create a geographical analysis of demand for certain roles and skillsets in Europe.
• Tracking changes in the average salaries over time by visualizing posting date vs salary_offered data points

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: emed_careers_eu.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | category | The job category of the posting. (String) | | company_name | The name of the company posting the job. (String) | | job_description | A description of the job. (String) | | job_title | The title of the job. (String) | | job_type | The type of job (full-time, part-time, etc.). (String) | | location | The location of the job. (String) | | post_date | The date the job was posted. (Date) | | salary_offered | The salary offered for the job. (Integer) |

Acknowledgements

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

Explore the progression of average salaries for graduates in Law See Section K, Job 1, Item 9 from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of Law See Section K, Job 1, Item 9 relative to other fields. This data is essential for students assessing the return on investment of their education in Law See Section K, Job 1, Item 9, providing a clear picture of financial prospects post-graduation.

Explore the dataset on average salaries in the private sector by main profession, nationality, and gender in Saudi Arabia. Gain insights into industrial and chemical processes, food industries, total labor force, and more.

Industrial and chemical processes and food industries, Non-Saudis, Total labour force, Agricultural and animal husbandry Poultry and fishing, Services jobs, Auxiliary basic engineering jobs, Scientific, technical and human technicians, Clerical jobs, Saudis, Male, Administrative and business directors, Other, Sales jobs, Scientific, technical and human specialists, Female, Profession, Gender , Saudi, Non Saudi, SAMA Annual

Saudi Arabia Follow data.kapsarc.org for timely data to advance energy economics research..

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

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 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.

Explore the progression of average salaries for graduates in See Section K Job 7 from 2020 to 2023 through this detailed chart. It compares these figures against the national average for all graduates, offering a comprehensive look at the earning potential of See Section K Job 7 relative to other fields. This data is essential for students assessing the return on investment of their education in See Section K Job 7, providing a clear picture of financial prospects post-graduation.