In 2024, the West North Central area was the U.S. region with the highest annual physician compensation. Physicians earned some 404,000 U.S. dollars on average in this area, which includes: North Dakota, South Dakota, Nebraska, Kansas, Minnesota, Iowa, Missouri. Low number of physicians in these states with many rural areas means less competition for doctors, potentially increasing pay.

Located in the north of the country, Lombardy had the highest mean gross salary in 2024, while workers in Basilicata earned the lowest average wages nationwide. The figure for Lombardy amounted to ****** euros, around *** euros more than in Lazio, where the capital Rome is situated, as reported by Job Pricing. Trentino-South Tyrol was the region with the second-highest average gross salary, ****** euros per year. The last positions of the raking were occupied by the southern regions, with an average wage of ****** euros. High wages and large pay gap  According to the same source, employees working in banking and financial services had some of the largest salaries in Italy. However, men earned roughly ** percent more than women (****** euros versus ****** euros). Similarly, the annual gross salary in the insurance industry was ** percent higher in favor of men. Low-wage workers The south of Italy was also the place registering the highest percentage of low paid employees. These are employees with an hourly salary of less than ********** of the median salary over the total number of employees. More specifically, in the south and on the islands, the share of low-wage employees was **** and **** percent, respectively. In the northern regions, the share amounted to only *** percent.

This dataset is a listing of all active City of Chicago employees, complete with full names, departments, positions, employment status (part-time or full-time), frequency of hourly employee –where applicable—and annual salaries or hourly rate. Please note that "active" has a specific meaning for Human Resources purposes and will sometimes exclude employees on certain types of temporary leave. For hourly employees, the City is providing the hourly rate and frequency of hourly employees (40, 35, 20 and 10) to allow dataset users to estimate annual wages for hourly employees. Please note that annual wages will vary by employee, depending on number of hours worked and seasonal status. For information on the positions and related salaries detailed in the annual budgets, see https://www.cityofchicago.org/city/en/depts/obm.html

Data Disclosure Exemptions: Information disclosed in this dataset is subject to FOIA Exemption Act, 5 ILCS 140/7 (Link:https://www.ilga.gov/legislation/ilcs/documents/000501400K7.htm)

A structured overview of the average, net, median, and minimum wage in Germany for 2025. This dataset combines original market research conducted by KUMMUNI GmbH with publicly available data from the German Federal Statistical Office. It includes values with and without bonuses, hourly minimum wage, and take-home pay after tax.

This statistic shows average salaries of IT professionals worldwide in 2017, by region. The average salaries of IT professionals in North America amounted to about **** thousand U.S. dollars in 2017.

Graph and download economic data for Median Family Income in West Census Region (MEFAINUSWEA646N) from 1953 to 2023 about West Census Region, family, median, income, and USA.

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.

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Pay Information for calendar year 2023 for the employees of the State of Missouri by their Agency of employment, Position Title or Employee name.

Public authorities are required by Section 2800 of Public Authorities Law to submit annual reports to the Authorities Budget Office that include salary and compensation data. The dataset consists of salary data by employee reported by State Authorities that covers 8 fiscal years, which includes fiscal years ending in the most recently completed calendar year.

This dataset has been published by the Human Resources Department of the City of Virginia Beach and data.virginiabeach.gov. The mission of data.virginiabeach.gov is to provide timely and accurate City information to increase government transparency and access to useful and well organized data by the general public, non-governmental organizations, and City of Virginia Beach employees.

Distributed by

data.virginiabeach.gov

2405 Courthouse Dr.

Virginia Beach, VA 23456

Entity

Employee Salaries

Point of Contact

Human Resources

Attributes

Column: Department

Description: 3-letter department code

Column: Department Division

Description: This is the City Division that the position is assigned to.

Column: PCN

Description: Tracking number used to reference each unique position within the City.

Column: Position Title

Description: This is the title of the position (per the City’s pay plan).

Column: FLSA Status

Description: Represents the position’s status with regards to the Fair Labor Standards Act (FLSA)

“Exempt” - These positions do not qualify for overtime compensation – Generally, a position is classified as FLSA exempt if all three of the following criteria are met: 1) Paid at least $47,476 per year ($913 per week); 2) Paid on a salary basis - generally, salary basis is defined as having a guaranteed minimum amount of pay for any work week in which the employee performs any work; 3) Perform exempt job duties - Job duties are split between three classifications: executive, professional, and administrative. All three have specific job functions which, if present in the employee’s regular work, would exempt the individual from FLSA. Employees may also be exempt from overtime compensation if they are a “highly compensated employee” as defined by the FLSA or the position meets the criteria for other enumerated exemptions in the FLSA.

“Non-exempt” – These positions are eligible for overtime compensation - positions classified as FLSA non-exempt if they fail to meet any of exempt categories specified in the FLSA.

Column: Initial Hire Date

Description: This is the date that the full-time employee first began employment with the City.

Column: Date in Title

Description: This is the date that the full-time employee first began employment in their current position.

Column: Salary

Description: This is the annual salary of the full-time employee or the hourly rate of the part-time employee.

Frequency of dataset update

Monthly

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.

Average full-time hourly wage paid and payroll employment by type of work, economic region and National Occupational Classification (NOC), 2016 and 2017.

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.

Graph and download economic data for Median Family Income in Northeast Census Region (MEFAINUSNEA646N) from 1953 to 2023 about Northeast Census Region, family, median, income, and USA.

Graph and download economic data for Employment Cost Index: Wages and salaries for Private industry workers in the Midwest Census Region (CIU2020000000230I) from Q1 2001 to Q1 2025 about Midwest Census Region, ECI, salaries, workers, private industries, wages, private, industry, and USA.

The motivation behind analyzing salary data is to gain insights into compensation trends, identify factors that influence pay, and understand disparities across industries, locations, or job roles. For businesses, this analysis is crucial in shaping competitive compensation packages, attracting top talent, and ensuring fair pay practices. Additionally, individuals can benefit from understanding how their salaries compare to industry standards, aiding in negotiation strategies.

Context With increasing attention on pay transparency and equity, salary data has become a critical dataset for human resources departments, economists, and policymakers. Companies and industries alike need to assess compensation against benchmarks, inflation, and the evolving job market. Salary datasets often contain variables such as job titles, experience levels, education, locations, and industries, which are essential in determining pay structures. This analysis allows for a deeper dive into trends like gender pay gaps, regional disparities, and the impact of education or experience on earnings.

For the Kaggle community, salary datasets provide rich opportunities for performing exploratory data analysis, statistical modeling, and predictive analytics. It serves as a hands-on opportunity to practice data wrangling, feature engineering, and model building, especially in the realm of HR analytics.

Description This CSV file contains anonymized company salary data across various industries, roles, and locations. The dataset includes key variables such as:

Job Title: The role of the employee (e.g., Data Analyst, Software Engineer). Years of Experience: Number of years the employee has been in the workforce or industry. Education Level: The highest degree obtained by the employee (e.g., Bachelor's, Master's). Location: City or country where the employee works. Industry: The sector in which the company operates (e.g., Finance, Technology). Annual Salary: The employee’s yearly earnings, including bonuses or incentives. Gender: Gender identification of the employee (if available). Remote Work Percentage: The percentage of work conducted remotely, which may influence salary based on location independence. The dataset is perfect for understanding how salaries vary by job role, region, industry, and experience level. It can also be used to uncover trends such as salary growth over time, the impact of education or certifications on compensation, or potential gender pay gaps. Through data visualization, predictive models, and regression analysis, users can extract meaningful insights that could inform corporate strategy, HR policies, or even career decisions.

Graph and download economic data for Employed: Percent of hourly paid workers: Paid total at or below prevailing federal minimum wage: State wage and salary workers: 16 years and over (LEU0204927500A) from 2000 to 2024 about paid, minimum wage, salaries, workers, hours, 16 years +, federal, wages, percent, employment, and USA.