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 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.

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

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

🧠 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

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.

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

Graph and download economic data for Personal Taxes: Federal Income Taxes by Occupation: Wage and Salary Earners: Construction Workers and Mechanics (CXUFEDTAXESLB1207M) from 1984 to 2023 about mechanics, occupation, salaries, workers, tax, construction, federal, wages, personal, income, and USA.

Graph and download economic data for Employed full time: Wage and salary workers: Helpers, construction trades occupations: 16 years and over: Women (LEU0254721000A) from 2000 to 2024 about occupation, females, full-time, salaries, workers, trade, 16 years +, construction, wages, employment, and USA.

This statistic shows the median earnings of mid-career college graduates aged 35 to 45 who worked full-time in the United States between 2016 and 2017, by attained major. Between 2016 and 2017, mid-career graduates with a computer science major had a median income of ****** U.S. dollars in the United States.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Service occupations: 16 years and over (LEU0254543400Q) from Q1 2000 to Q1 2025 about second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, services, median, employment, 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.

Unlock valuable salary insights with our comprehensive Salary Dataset, designed for businesses, recruiters, and job seekers to analyze compensation trends, workforce planning, and market competitiveness.

Dataset Features

Job Listings & Salaries: Access structured salary data from top job platforms, including job titles, company names, locations, salary ranges, and compensation types. Employer & Industry Insights: Extract company-specific salary trends, industry benchmarks, and hiring patterns. Geographic Pay Disparities: Compare salaries across different regions, cities, and countries to identify location-based compensation trends. Job Market Trends: Monitor salary fluctuations, demand for specific roles, and hiring trends over time.

Customizable Subsets for Specific Needs Our Salary Dataset is fully customizable, allowing you to filter data based on job titles, industries, locations, experience levels, and salary ranges. Whether you need broad market insights or focused data for recruitment strategy, we tailor the dataset to your needs.

Popular Use Cases

Workforce Planning & Talent Acquisition: Optimize hiring strategies by analyzing salary benchmarks and compensation trends. Market Research & Competitive Intelligence: Compare salaries across industries and competitors to stay ahead in talent acquisition. Career Decision-Making: Help job seekers evaluate salary expectations and identify high-paying opportunities. AI & Predictive Analytics: Use structured salary data to train AI models for job market forecasting and compensation analysis. Geographic Expansion & Business Strategy: Assess salary variations across regions to plan business expansions and remote workforce strategies.

Whether you're optimizing recruitment, analyzing salary trends, or making data-driven career decisions, our Salary Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

In Mexico as of the third quarter of 2023, the sectors of occupation measured by the average monthly salary had the extractive industry as the clear leader, in terms of highest average salary, with 10,612 Mexican pesos, followed by the governmental, education and health areas.

In 2024, graduates of Princeton University and Harvard University had an average early career salary of 95,600 U.S. dollars, which was the highest early career salary of any Ivy League university. In comparison, graduates of Brown University had an average early career salary of 88,000 U.S. dollars.

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.

Dataset

This dataset was created by Arushi

Contents

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.

Introduction

Job Satisfaction Statistics: Companies use the term "job contentment" or "employee satisfaction " to measure how happy or unhappy workers are with their jobs. Companies that want to get the best results can use job satisfaction data because it is closely connected to things like employee performance, retention, and overall happiness at work.

Employers who want to attract and keep the best employees need to understand how important job satisfaction is. In this article, we will look at the key Job Satisfaction Statistics.

The income information is collected as part of the Household Labour Force Survey each year during the June quarter. This dataset in particular refers to the hourly and weekly income of individuals who are employed and are receiving income from wages and salaries.