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 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 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.

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

This table comprises yearly figures on the main aspects of employment, wages and working hours in the Netherlands. The information in this table is classified according to Standard Industrial Classification of all Economic Activities (SIC 2008) and can be broken down into: - employee characteristics (age and sex) - job characteristics (type of employment contract and working hours) - company characteristics (size of the firm and collective wage agreements)

Data available from: 2009.

Status of the figures: Figures for the years 2009 to 2023 are final. Figures for 2024 are provisional.

Changes as of 28 April 2025: Provisional figures for 2024 are added. The figures for 2024 are excluding the selections: ‘Type of employment contract: full-time’, ‘Type of employment contract: part-time’ for the branche ‘P Education’

When will new figures be published? The final figures for 2024 will be published in October 2025.

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Switcher (FRBATLWGT12MMUMHWGJSW) from Dec 1997 to Jun 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

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, many of the highest paying jobs were those in the medical field. The mean annual pay for psychiatrists was at 256,930 U.S. dollars in the United States. The highest paying occupation was pediatric surgeon, with a mean annual wage of 449,320 U.S. dollars.

Graph and download economic data for 12-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Stayer (FRBATLWGT12MMUMHWGJST) from Dec 1997 to May 2025 about growth, moving average, 1-year, jobs, average, wages, median, and USA.

Number of UK jobs paid below minimum wage by sex, age, occupation and industry, and region, annual estimates, 1998 to 2023. Annual Survey of Hours and Earnings.

Dataset Card for Data Science Job Salaries

  Dataset Summary





  Content

Column Description

work_year The year the salary was paid.

experience_level The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.

🧠 About the Data

This dataset explores how remote work opportunities intersect with salaries, experience, and employment types across industries. It contains clean, structured records of 500 hypothetical employees in remote or hybrid job roles, suitable for salary modeling, HR analytics, or industry-based salary insights.

📌 Column Descriptions

📈 Potential Use Cases

• Predictive modeling for salary based on role, experience, and location
• Salary benchmarking per industry or employment type
• Visualizing remote vs onsite salary disparities
• Market research for HR and hiring trends
• Exploratory analysis on global employment models

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.

The age groups available in the dataset are: 15+, 25+, 25-34, 25-54 and 25-64.

Type of work includes full-time and part-time.

The educational levels include: 0-8 yrs., some high school, high school graduate, some post-secondary, post-secondary certificate diploma and university degree.

Wages include average weekly wage rate.

The immigration statuses include: total landed immigrants (very recent immigrants, recent immigrants, established immigrants), non-landed immigrants and born in Canada.

The Women’s Regional Index for the theme Work and wages is based on the indicators of employment intensity among foreign-born persons aged 20-64, share (%), employed persons aged 20-64 working in occupations that are well in line with their education, share (%), long-term unemployment 25-64 years, share (%) of the population, Acquisition population aged 20-64, share (%), population 16-84 who are worried about losing their job, percentage (%). The KPIs are normalised so that all regions’ values are placed on a scale from 0 to 100 where 0 is the worst and 100 is best (for some indicators, inverted scale is used). In the next step, the normalised indicator values are weighed together into indices at aspect level (currently, the theme is based on indicators in four aspects). This is done with averages, all indicators weighed together with the same weight in each aspect. The values are also at this level in the range 0 to 100. Then the index at aspect level is weighed together to the thematic level according to the same principle and these values also fall between 0 and 100. The weighting is equal for all aspects of the theme.

This table comprises yearly figures on the main aspects of employment, wages and working hours in the Netherlands. The information in this table is classified according to Standard Industrial Classification of all Economic Activities (SIC 2008) and can be broken down into: - employee characteristics (age and sex) - job characteristics (type of employment contract and working hours) - company characteristics (size of the firm and collective wage agreements) Data available from: 2009. Status of the figures: Figures for the years 2009 to 2023 are final. Figures for 2024 are provisional. Changes as of 28 April 2025: Provisional figures for 2024 are added. The figures for 2024 are excluding the selections: ‘Type of employment contract: full-time’, ‘Type of employment contract: part-time’ for the branche ‘P Education’ When will new figures be published? The final figures for 2024 will be published in October 2025.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Job printers occupations: 16 years and over: Men (LEU0254676500A) from 2000 to 2010 about second quartile, occupation, jobs, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Job printers occupations: 16 years and over: Men (LEU0254623100A) from 2000 to 2010 about occupation, jobs, full-time, males, salaries, workers, 16 years +, wages, employment, and USA.

Number of job vacancies and average offered hourly wage by one-digit National Occupational Classification (NOC) code, last 5 quarters.

Graph and download economic data for Employed full time: Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over (LEU0257856200A) from 2011 to 2024 about occupation, jobs, full-time, compensation, benefits, salaries, workers, 16 years +, wages, employment, and USA.