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 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

Are you looking for a new career opportunity? Do you want to explore the job market and see what skills and qualifications are in demand? Or are you curious about the characteristics and performance of different companies?

If you answered yes to any of these questions, then this dataset is for you! This dataset contains two files: one with over 100,000 job postings, and another with information about more than 30,000 companies from different industries and locations. All the info is from US market, and comes from the website Indeed.

Check the extraction and cleaning process on my GitHub repository:

jobs-data-cleaning

The jobs file includes the following fields:

• Job title
• Company name
• Job Profile
• Remote/Hybrid
• Location
• Salary
• Job requirements

The companies file includes the following fields:

• Company name
• Industry
• Location
• Website
• Number of employees
• Revenue

With this dataset, you can:

• Analyze the job market trends and identify the most popular and lucrative jobs, skills, and qualifications.
• Compare and contrast different companies and see how they perform in terms of revenue, profit, growth, and employee satisfaction.
• Create visualizations and dashboards to showcase your findings and insights.
• Build machine learning models to predict salary, job satisfaction, company rating, or any other outcome of interest.

This dataset is a valuable resource for anyone interested in career development, business analysis, data science, or machine learning. Download it now and start exploring!

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.

This dataset provides a comprehensive collection of salary information from various industries and regions across the globe. Sourced from reputable employment websites and surveys, it includes details on job titles, salaries, job sectors, geographic locations, and more. Analyze this data to gain insights into job market trends, compare compensation across different professions, and make informed decisions about your career or hiring strategies. The dataset is cleaned and preprocessed for ease of analysis and is available under an open license for research and data analysis purposes.

Education Level: 0 : High School 1 : Bachelor Degree 2 : Master Degree 3 : Phd

Currency : US Dollar

Senior : It shows that is this employee has a senior position or no.(Binary)

The median income in 2023 was at 56,490 U.S. dollars for Black households. In 1990, the median income among Black households was 38,360 U.S. dollars (In 2023 U.S. dollars).

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

In the United States, the median income in 2023 was at 112,800 U.S. dollars for Asian households. This is a large increase from 2002 when the median income for Asian households was 84,770 U.S. dollars (in 2023 U.S. dollars).

Average Wage per Job in Maryland and Its Jurisdictions (in Current Dollars), 2014–2024, based on data from the Quarterly Census of Employment and Wages (QCEW), which includes all workers covered under the State Unemployment Insurance (UI) program and the Unemployment Compensation for Federal Employees (UCFE). The 2024 annual average wage figures are preliminary. Hand-calculated total may differ from the published total due to data suppression and privacy protection. Source: The U.S. Census Bureau of Labor Statistics, Quarterly Census Employment and Wages (QCEW), 2014-2024, June 2025.

This dataset provides insights into data science job salaries from 2020 to 2025, including information on experience levels, employment types, job titles, and company characteristics. It serves as a valuable resource for understanding salary trends and factors influencing compensation in the data science field.

Features:

Usage:

• This dataset can be utilized for analyzing salary trends and variations in data science jobs over time and across different demographics.
• It can aid in benchmarking salaries, understanding the impact of factors such as experience level and company size on compensation, and informing career decisions in the data science field.

License:

This data set is made available by ai-jobs.net Salaries. Thank you for aggregating this information!

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

This dataset provides comprehensive information on the average income and rent in various states across the United States for the year 2022. It aims to offer insights into state-level economic trends and housing market dynamics.

Column Descriptions:

Region: Name of the state within the United States.

Average_Rent: Description: Average monthly rent for residential properties in each state, reflecting prevailing rental costs.

Average_Income: Average per capita income within each state, representing the average earnings of individuals residing in the state over the year.

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.

This dataset provides comprehensive information on salaries for various job roles across different countries. It includes details such as job titles, categories, local currency salaries, currency exchange rates, and converted salaries in US dollars. The data is sourced from worldsalaries.com and is subject to the website's terms of use and license.

Pointers

• The dataset covers a wide range of countries and job roles, allowing for cross-country and cross-industry comparisons.
• Local currency salaries are provided, along with the corresponding exchange rates and converted salaries in US dollars.
• Job roles are categorized into different groups, making it easier to analyze salaries within specific industries or sectors.
• The dataset can be used for various purposes, such as salary benchmarking, cost of living analysis, and labor market research.

How to Use

The dataset is provided in a tabular format, with each row representing a unique combination of country, job title, category, and salary information. Users can filter, sort, and analyze the data based on their specific requirements. It is recommended to handle the dataset using spreadsheet software or data analysis tools for efficient manipulation and analysis.

Data Source

The dataset is sourced from https://worldsalaries.com, a website dedicated to providing comprehensive salary information from around the world.

License and Terms of Use 📄

The license and terms of use for this dataset are as per the worldsalaries.com website's terms of use. Please respect their terms and adhere to them.

Column Descriptions

1. Country: The name of the country for which the salary information is provided.
2. Job Title: The specific job title or role for which the salary is listed.
3. Category: The broader category or industry to which the job title belongs.
4. Salary (Local Currency): The salary amount in the local currency of the respective country.
5. Currency: The currency code or symbol representing the local currency of the country.
6. Exchange Rate: The exchange rate value used to convert the local currency salary to US dollars.
7. Salary (Current USD): The salary amount converted to US dollars using the provided exchange rate.

Example

• Country: Afghanistan
• Job Title: Account Examiner
• Category: Accounting and Finance
• Salary (Local Currency): 501400 AFN (Afghan Afghani)
• Currency: AFN
• Exchange Rate: 71.31
• Salary (Current USD): 7037.74 (Converted salary in US dollars)

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

Detailed labour market outcomes by educational characteristics, including detailed occupation, hours and weeks worked and employment income.

1) Data Introduction • The Data Science Salaries 2023 Dataset is a global annual salary analysis dataset that summarizes a variety of information in a tabular format, including salary, career, employment type, job, remote work rate, and company location and size for data science jobs as of 2023.

2) Data Utilization (1) Data Science Salaries 2023 Dataset has characteristics that: • Each row contains 11 key characteristics, including year, career level, employment type, job name, annual salary (local currency and USD), employee country of residence, remote work rate, company location, and company size. • Data is organized to reflect different countries, jobs, careers, and work patterns to analyze pay and work environments in data science in three dimensions. (2) Data Science Salaries 2023 Dataset can be used to: • Data Science Salary Analysis and Comparison: Analyzing salary levels and distributions by job, career, country, and company size can be used to understand industry trends and market value. • Establishing Recruitment and Career Strategies: It can be applied to recruitment strategies, career development, global talent attraction, etc. by analyzing the correlation between various working conditions and salaries such as remote work rates, employment types, and company location.

Brazil Average Real Income: All Jobs: Usual Earnings data was reported at 2,295.000 BRL in Apr 2019. This records a decrease from the previous number of 2,304.000 BRL for Mar 2019. Brazil Average Real Income: All Jobs: Usual Earnings data is updated monthly, averaging 2,254.500 BRL from Mar 2012 (Median) to Apr 2019, with 86 observations. The data reached an all-time high of 2,312.000 BRL in Feb 2019 and a record low of 2,159.000 BRL in Mar 2012. Brazil Average Real Income: All Jobs: Usual 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.

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

Occupations are classified using the three digit National Occupational Classification (NOC) codes. Wages include: average hourly wage rate, average weekly wage rate, median hourly wage rate and median weekly wage rate.