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

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.

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.

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.

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.

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

This statistic shows the share of automation probability of median jobs in selected European countries as of 2018, according to calculations by the OECD. In Slovakia, the median job has a ** percent chance of being automated, making the country the most vulnerable to automation in Europe. By comparison, the median worker in England had a ** probability of being automated.

Employees in Greece spend an average of 13 years with their employers as of 2023, the longest average job tenure among European countries. Among the provided countries, Denmark had the shortest average job tenure, at 7.5 years. In most European countries, men spend more time with a single employer on average than women. Notable exceptions to this trend come from a number of post-communist countries in central and eastern Europe - Romania, Bulgaria, Lithuania, Latvia, and Estonia-.

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

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.

In 2024, over 25,000 job losses were accounted for in the U.S. media industry, down by 30 percent compared to a year prior. The media industry in the United States has been deeply impacted by the pandemic and the following inflation, as well as the introduction of Generative AI technologies, resulting in an increasing number of layoffs. While about 10,000 job cuts in media were reported in 2019, that value had tripled the following year.

Number of job vacancies and payroll employees, job vacancy rate, and average offered hourly wage by economic region, last 5 quarters.

📂 Dataset Title:

AI Impact on Job Market: Increasing vs Decreasing Jobs (2024–2030)

📝 Dataset Description:

This dataset explores how Artificial Intelligence (AI) is transforming the global job market. With a focus on identifying which jobs are increasing or decreasing due to AI adoption, this dataset provides insights into job trends, automation risks, education requirements, gender diversity, and other workforce-related factors across industries and countries.

The dataset contains 30,000 rows and 13 valuable columns, generated to reflect realistic labor market patterns based on ongoing research and public data insights. It can be used for data analysis, predictive modeling, AI policy planning, job recommendation systems, and economic forecasting.

📊 Columns Description:

Column Name Description

Job Title Name of the job/role (e.g., Data Analyst, Cashier, etc.) Industry Industry sector in which the job is categorized (e.g., IT, Healthcare, Manufacturing) Job Status Indicates whether the job is Increasing or Decreasing due to AI adoption AI Impact Level Estimated level of AI impact on the job: Low, Moderate, or High Median Salary (USD) Median annual salary for the job in USD Required Education Typical minimum education level required for the job Experience Required (Years) Average number of years of experience required Job Openings (2024) Number of current job openings in 2024 Projected Openings (2030) Projected job openings by the year 2030 Remote Work Ratio (%) Estimated percentage of jobs that can be done remotely Automation Risk (%) Probability of the job being automated or replaced by AI Location Country where the job data is based (e.g., USA, India, UK, etc.) Gender Diversity (%) Approximate percentage representation of non-male genders in the job

🔍 Potential Use Cases:

Predict which jobs are most at risk due to automation.

Compare AI impact across industries and countries.

Build dashboards on workforce diversity and trends.

Forecast job market shifts by 2030.

Train ML models to predict job growth or decline.

📚 Source:

This is a synthetic dataset generated using realistic modeling, public job data patterns (U.S. BLS, OECD, McKinsey, WEF reports), and AI simulation to reflect plausible scenarios from 2024 to 2030. Ideal for educational, research, and AI project purposes.

📌 License: MIT

United States Median Wage Growth: 3-Mo Mov Avg: Job Switcher data was reported at 4.300 % in Apr 2025. This records an increase from the previous number of 4.200 % for Mar 2025. United States Median Wage Growth: 3-Mo Mov Avg: Job Switcher data is updated monthly, averaging 4.200 % from Mar 1997 (Median) to Apr 2025, with 338 observations. The data reached an all-time high of 8.500 % in Jul 2022 and a record low of 1.200 % in Jan 2010. United States Median Wage Growth: 3-Mo Mov Avg: Job Switcher data remains active status in CEIC and is reported by Federal Reserve Bank of Atlanta. The data is categorized under Global Database’s United States – Table US.G113: Atlanta Fed Wage Growth Tracker: 3-Month Moving Average.

Graph and download economic data for 3-Month Moving Average of Unweighted Median Hourly Wage Growth: Job Movement: Job Switcher (FRBATLWGT3MMAUMHWGJMJSW) from Mar 1997 to Jun 2025 about growth, moving average, jobs, 3-month, average, wages, median, and USA.

Number of job vacancies and payroll employees, job vacancy rate, and average offered hourly wage by two-digit North American Industry Classification System (NAICS) code, last 5 quarters.

Australia Unemployment: Duration of Job Search: Median data was reported at 10.000 Week in Mar 2025. This stayed constant from the previous number of 10.000 Week for Feb 2025. Australia Unemployment: Duration of Job Search: Median data is updated monthly, averaging 14.000 Week from Jan 1991 (Median) to Mar 2025, with 411 observations. The data reached an all-time high of 30.000 Week in Nov 1993 and a record low of 6.000 Week in Jan 2009. Australia Unemployment: Duration of Job Search: Median data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.G039: Unemployment: by Duration of Job Search and Sex.

Number of job vacancies, proportion of job vacancies and average offered hourly wage by selected characteristics (type of work, minimum level of education sought, minimum experience level sought, duration of job vacancy, type of position, and recruitment strategies) and National Occupational Classification (NOC), last 5 quarters.

Average Wage Per Job in Maryland and its Jurisdictions (Constant 2017 Dollars) from 2012 to 2022. Data source from U.S. Bureau of Economic Analysis (Table CA30), November 2023.