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 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)

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.

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.

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

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).

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)

Indonesian JobStreet Salary & Hybrid Recommendation Dataset

Overview

The Indonesian JobStreet Salary & Hybrid Recommendation Dataset is a comprehensive, machine learning–ready dataset containing aggregated salary information from JobStreet Indonesia job postings. It was developed through a data scraping and hybrid recommendation system approach to identify average salaries across various job titles, companies, and regions in Indonesia.

This dataset is ideal for salary prediction, labor market analytics, career recommendation systems, and data-driven HR insights.

Dataset Information

• Total Records: 32,976 job postings
• Time Period: JobStreet Indonesia scraping dataset (2024)
• Geographic Coverage: Indonesia (606 unique locations)
• File Format: CSV
• File Size: ~7.8 MB
• Missing Values: None (100% complete dataset)
• Duplicates: Some overlapping listings between companies and job categories
• Target Variable: Gaji_Rata2 (Average monthly salary in IDR)

Key Features

💼 Job Classification

• Unique Job Titles: 8,686
• Unique Companies: 4,969
• Unique Locations: 606
• Average Salary (Gaji_Rata2): IDR 7.24 million/month
• Salary Range: From entry-level to executive-level roles
• Data Source: JobStreet Indonesia (public postings)

⚙️ Data Preparation

• Job data scraped from JobStreet Indonesia job listings in 1 Month on 2024
• Salaries standardized and averaged across identical job titles
• Aggregation and smoothing performed using hybrid recommender modeling (content-based + collaborative filtering)

Feature Description

Data Quality Assessment

• ✅ Zero missing values — dataset is 100% complete
• ✅ Structured and cleaned schema (uniform columns)
• ⚠️ Some duplicates may occur due to overlapping job postings
• 📊 High diversity across job types and cities
• 🇮🇩 Fully localized — all data from JobStreet Indonesia only

Machine Learning Applications

• Salary Prediction: Predict average salary by job title, company, or region
• Career Recommender Systems: Build hybrid models to suggest similar or higher-paying jobs
• Market Analytics: Analyze salary trends by location or sector
• NLP Job Classification: Cluster similar job roles using semantic text embeddings
• HR Decision Support: Compare salary averages across industries

Citation

Original Source: JobStreet Indonesia (public job listings)

License: CC BY 4.0 (Attribution required)

Version: 1.0 (2024)

Dataset Overview:

This dataset represents a small structured dataset containing information about different job positions, their respective levels, salaries, departments, years of experience, and job locations. Although the dataset has only 20 rows and 6 columns, it provides valuable insights into salary structures across roles and industries.

Details

• Number of Rows: 20
• Number of Columns: 6
• Columns:
  1. Position – The job title (e.g., Business Analyst, Manager, CEO).
  2. Level – A numeric indicator of the position hierarchy (1 = entry-level, 10 = highest).
  3. Salary – The annual salary offered for the position.
  4. Department – The department where the position belongs (Analytics, Consulting, Management, etc.).
  5. YearsExperience – The average number of years of experience required or observed for that role.
  6. Location – The geographical location of the position (e.g., New York, London, Paris).

Key Observations

1. Position Hierarchy

   • The dataset has a Level column ranging from 1 (Business Analyst) up to 10 (CEO).
   • This indicates a clear career progression path.

2. Salary Distribution

   • Minimum salary: 45,000
   • Maximum salary: 1,000,000
   • Average salary: 221,900
   • Salaries increase significantly with higher levels, showing a non-linear relationship (CEO salary jumps much higher than mid-level positions).

3. Experience Levels

   • Minimum years of experience: 1 year
   • Maximum years of experience: 25 years
   • Median experience: 7.5 years
   • Higher positions generally require more years of experience.

4. Departments & Locations

   • Departments include Analytics, Consulting, Management, and Leadership roles.
   • Locations are spread across global cities such as New York, London, Berlin, and Paris, suggesting this dataset is international in scope.

Insights and Potential Uses

• Salary Prediction: The dataset can be used to model how salary changes with position Level or YearsExperience.
• Career Path Analysis: Shows how employees move up in hierarchy and compensation.
• Departmental Comparison: Compare salary trends across different departments.
• Location-based Trends: Explore how job location affects pay.

Limitations

• The dataset is very small (only 20 entries), which limits statistical power and generalizability.
• Some important features (e.g., gender, education, company size) are missing.
• It is more suitable for learning/demo purposes rather than real-world decision making.

Conclusion

Despite being a tiny dataset, position_salaries.csv provides a clean and structured representation of hierarchical job positions, salaries, and experience. It is especially useful for teaching machine learning concepts like regression, salary prediction, and visualization of career progression.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Compensation, benefits, and job analysis specialists occupations: 16 years and over (LEU0257856500A) from 2011 to 2023 about second quartile, occupation, compensation, benefits, jobs, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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).

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.

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).

The graph presents the median monthly salary in the United States from 2000 to 2025. The x-axis represents the years, labeled from '00 to '25*, while the y-axis shows the salary amounts in U.S. dollars per month. Throughout this twenty-five-year period, the median monthly salary consistently increased from $2491.67 in 2000 to $5195.67 in 2025. The data highlights a steady upward trend, with annual salaries rising each year without any declines. Notably, the salary grew by approximately $200 each year from 2000 to 2019, surged to $4265.08 in 2020, and continued to climb each subsequent year, reaching $5023.42 by 2024. This consistent growth reflects economic advancements and potential increases in workforce compensation over the decade. The information is depicted in a line graph format, effectively illustrating the continuous rise in median monthly salaries across the specified years.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Greenland. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Greenland, the median income for all workers aged 15 years and older, regardless of work hours, was $35,264 for males and $32,933 for females.

Based on these incomes, we observe a gender gap percentage of approximately 7%, indicating a significant disparity between the median incomes of males and females in Greenland. Women, regardless of work hours, still earn 93 cents to each dollar earned by men, highlighting an ongoing gender-based wage gap.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.07 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Greenland median household income by race. You can refer the same here

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Job printers occupations: 16 years and over (LEU0254569700A) from 2000 to 2010 about second quartile, occupation, jobs, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Average hourly and weekly wage rate, and median hourly and weekly wage rate by North American Industry Classification System (NAICS), type of work, gender, and age group.

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.

Employee_Data.csv: This dataset contains specific information related to employees' positions, represented by Job Code and Job Title. It includes the average and median salaries for each job role within the organization. Fields include:

Job Code: Numeric code representing the job role Job Title: Description of the job role Employee_Average: Average salary for the job role Employee_Median: Median salary for the job role Market_Data.csv: This dataset focuses on the broader market compensation data for various job roles and families. It provides the minimum, midpoint, and maximum market salaries for the corresponding job codes. Fields include:

Job Code: Numeric code representing the job role, aligning with the Employee_Data.csv file Job Family: Description of the job family or category Market_Minimum: Minimum market salary for the job role Market_Midpoint: Midpoint market salary for the job role Market_Max: Maximum market salary for the job role

Primary Purpose: The integration and analysis of these datasets allow for market compa-ratio analysis. By comparing internal compensation (Employee_Data.csv) with external market benchmarks (Market_Data.csv), organizations can assess the competitiveness of their pay structures. This analysis aids in aligning pay practices with industry standards, ensuring fair compensation, and supporting strategic human resource decisions.

Number of employees and salary per person in the construction industry