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 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

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

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..

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.

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 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)

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Miscellaneous personal appearance workers occupations: 16 years and over: Men (LEU0254656100A) from 2000 to 2018 about second quartile, miscellaneous, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, personal, median, employment, and USA.

In 2024, people working in IT management in the United States, earned an average annual salary worth around *** thousand U.S. dollars. Software developers and project managers all reported being paid on average over *** 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

Annual estimates of paid hours worked and earnings for UK employees by sex, and full-time and part-time, by two-digit Standard Occupational Classification.

Full-time workers in the finance and insurance sector had the highest average annual salaries in the United Kingdom in 2025, at approximately 58,488 British pounds, with those working in accommodation and food service professions having the lowest average salary, at 28,687 pounds per year.

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

Asian households measured the highest median household income among racial and ethnic groups in the United States. In 2024, Asian household incomes reached a median of 121,700 U.S. dollars. On the other hand, Black households had the lowest median income of 56,020 U.S. dollars. Overall, median household incomes in the United States stood at 83,730 U.S. dollars that year.Asian and Caucasian (white not Hispanic) households had relatively high median incomes, while the median income of Hispanic, African American, American Indian, and Alaskan Native households all came in lower than the national median. A number of related statistics illustrate further the current state of racial inequality in the United States. Unemployment is highest among Black or African American individuals in the U.S. nearing nine percent unemployed, according to the Bureau of Labor Statistics in 2024. Hispanic individuals (of any race) were most likely to go without health insurance as of 2024.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Management, professional, and related occupations: 16 years and over: Men (LEU0254631400Q) from Q1 2000 to Q2 2025 about management, second quartile, occupation, professional, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Salary by Profession and Country Over Time

Salary Differences by Country and Profession

By Kelly Garrett [source]

About this dataset

This dataset contains survey responses from 882 data professionals from 46 countries who took part in the 2021 Global Data Professional Salary Survey. Our goal was to understand how much database administrators, data analysts, data architects, developers and data scientists make across the world in 2017-2021.

The survey covers three years of salary trends, allowing you to compare and contrast movements over time. It also includes an optional postal code field which can be used to identify global regions with specific salary trends. In addition, all questions asked this year were also asked in 2017 and 2018 so that you can easily track changes in compensation over three years.

The spreadsheet contains anonymized responses which are provided as public domain making it available for any purpose without attribution or mention of anyone else. With this dataset at your disposal you'll have access to the detailed salary information needed to make informed decisions about your career development!

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How to use the dataset

• Start by familiarizing yourself with the columns in this dataset. The columns range from age of respondent to country of residence. It also includes salary information for each year (average annual income for 2017, 2018, and 2019). Read through each column header carefully to understand what you're looking at.

• Explore some basic summary statistics about the sample group such as median salary levels by profession or average age by nationality are interesting ways to get acquainted with this data set quickly. Excel's native statistical tools may be used here if you're using an excel file version as your source material; otherwise, you can use any programming language or statistics software that supports importing an exportable CSV (Comma Separated Values) format file or conversion thereof into something manipulable form like a spreadsheet or table structure within your preferred platform..

• You'll then want to identify which factors might be influencing salaries such as experience level, gender and geographical location etc., and attempt some correlation testing between those features against salaries across different job roles or countries over time - where possible without having external datasets available terms of area data points matching up perfectly between thematic dimensions presented within the Respondents' Survey Results tab.. Subsets may also prove relevant when carrying out deeper statistical testing—for example isolating particular participation sets like Ireland alone versus looking at just Europe/Middle East/Africa region altogether..

• Finally look at how these factors have changed over time - it's worth bearing in mind that seasonality might play a role here too depending on where respondents originally reside so it could still be relevant if larger trends towards comparing yearly cohorts differs more widely than expected based purely national economic condition context changes during particular quarters throughout those periods tracked in our findings report � comparison purposes if looking country-by-country instead just individual profiles without taking overall stimulant effects into account e.g higher education qualifications among ~2 yr cohorts vs ~3 yr ones across different populations: Comparing annual amounts doled out employers making ultra-quick transitioning easier tracking changes alone isn't feasible because they're normalized

Research Ideas

• Analyzing regional salary gaps amongst data professionals within the same country, or between countries.
• Evaluating trends in salary rates over time by reviewing changes in year over year responses.
• Generating employer profiles by comparing the salary range of employees at different organizations and industries, as well storing demographic info of individuals who participated in the survey (i.e age range, gender etc)

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: 2019_Data_Professional_Salary_Survey_Responses.csv

File: Data_Professional_Salary_Survey_Responses.csv

Acknowledgements

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

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Business operations specialists, all other occupations: 16 years and over: Women (LEU0257859700A) from 2011 to 2019 about second quartile, operating, occupation, full-time, females, salaries, workers, earnings, 16 years +, wages, business, median, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Miscellaneous agricultural workers occupations: 16 years and over (LEU0254558000A) from 2000 to 2024 about second quartile, miscellaneous, occupation, full-time, agriculture, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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).

In the United States, the median income in 2023 was at 65,540 U.S. dollars for Hispanic households. This is a large increase from 1990 when the median income was 47,600 U.S. dollars for Hispanic households (in 2023 U.S. dollars).

Graph and download economic data for Expenditures: Total Average Annual Expenditures by Occupation: Wage and Salary Earners: Service Workers (CXUTOTALEXPLB1206M) from 1984 to 2023 about occupation, salaries, workers, average, expenditures, wages, services, and USA.