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

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 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).

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.

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.

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)

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).

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

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).

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)

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.

Average weekly earnings, average hourly wage rate and average usual weekly hours by union status and type of work, last 5 years.

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

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

Jobs

This DataSet about Jobs and the salary

We want to do data analysis and processing and make a salary forecast using sklearn and tensorflow

• About data and columns

age work class education education num marital status occupation relationship race gender capital gain capital loss hours per week native country income bracket

This data is for training purposes in the field of data science and analysis

Turkey Annual Income: Avg: Primary Job (PJ) data was reported at 24,976.000 TRY in 2016. This records an increase from the previous number of 21,514.000 TRY for 2015. Turkey Annual Income: Avg: Primary Job (PJ) data is updated yearly, averaging 14,158.690 TRY from Dec 2006 (Median) to 2016, with 11 observations. The data reached an all-time high of 24,976.000 TRY in 2016 and a record low of 8,754.450 TRY in 2006. Turkey Annual Income: Avg: Primary Job (PJ) data remains active status in CEIC and is reported by Turkish Statistical Institute. The data is categorized under Global Database’s Turkey – Table TR.H032: Average Annual Primary Job Income: by Employment Type.

The age groups available in the dataset are: 15+, 25+, 25-34, 25-54 and 25-64.

Type of work includes full-time and part-time.

The educational levels include: 0-8 yrs., some high school, high school graduate, some post-secondary, post-secondary certificate diploma and university degree.

Wages include average weekly wage rate.

The immigration statuses include: total landed immigrants (very recent immigrants, recent immigrants, established immigrants), non-landed immigrants and born in Canada.

Distribution of employment income of individuals by sex and work activity, Canada, provinces and selected census metropolitan areas, annual.

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.