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 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

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 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)

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)

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).

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.

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.

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.

Occupations are classified using the three digit National Occupational Classification (NOC) codes. Wages include: average hourly wage rate, average weekly wage rate, median hourly wage rate and median weekly wage rate.

View monthly updates and historical trends for ADP Median Annual Pay: Job Changers. from United States. Source: ADP. Track economic data with YCharts anal…

Context

This dataset was originally created to do Hypothesis testings along with the KaggleSurvey2020 data set. That notebook will be published soon. Please find the scrapper code here in my Github: https://github.com/anoopm031/glassdoor_scraper

Content

This dataset contains the Average salary, payment frequency, currency, Average payment in USD, confidence in the report, etc. published on Glassdoor for nearly 700 countries + job combinations. These countries and jobs are the countries and jobs o of interest in my project. You can download and use the original scrapper with slight changes from my Github for creating your project.

Inspiration

I would like to get some insights into the people who attended the Kaggle survey. Do Kaggle survey insights and Glassdoor published salaries match? If yes, find some generalization. If no, what could be the reason, and understanding which is the more reliable source for salary reference and why? Does Kaggle can be considered as a random sample of the whole people in the industry or is it biased?

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).

Description

Scraper Code - https://github.com/ArshKA/LinkedIn-Job-Scraper

Every day, thousands of companies and individuals turn to LinkedIn in search of talent. This dataset contains a nearly comprehensive record of 124,000+ job postings listed in 2023 and 2024. Each individual posting contains dozens of valuable attributes for both postings and companies, including the title, job description, salary, location, application URL, and work-types (remote, contract, etc), in addition to separate files containing the benefits, skills, and industries associated with each posting. The majority of jobs are also linked to a company, which are all listed in another csv file containing attributes such as the company description, headquarters location, and number of employees, and follower count.

With so many datapoints, the potential for exploration of this dataset is vast and includes exploring the highest compensated titles, companies, and locations; predicting salaries/benefits through NLP; and examining how industries and companies vary through their internship offerings and benefits. Future updates will permit further exploration into time-based trends, including company growth, prevalence of remote jobs, and demand of individual job titles over time.

Thank you to @zoeyyuzou for scraping an additional 100,000 jobs ‎

Files

job_postings.csv

• job_id: The job ID as defined by LinkedIn (https://www.linkedin.com/jobs/view/ job_id )
• company_id: Identifier for the company associated with the job posting (maps to companies.csv)
• title: Job title.
• description: Job description.
• max_salary: Maximum salary
• med_salary: Median salary
• min_salary: Minimum salary
• pay_period: Pay period for salary (Hourly, Monthly, Yearly)
• formatted_work_type: Type of work (Fulltime, Parttime, Contract)
• location: Job location
• applies: Number of applications that have been submitted
• original_listed_time: Original time the job was listed
• remote_allowed: Whether job permits remote work
• views: Number of times the job posting has been viewed
• job_posting_url: URL to the job posting on a platform
• application_url: URL where applications can be submitted
• application_type: Type of application process (offsite, complex/simple onsite)
• expiry: Expiration date or time for the job listing
• closed_time: Time to close job listing
• formatted_experience_level: Job experience level (entry, associate, executive, etc)
• skills_desc: Description detailing required skills for job
• listed_time: Time when the job was listed
• posting_domain: Domain of the website with application
• sponsored: Whether the job listing is sponsored or promoted.
• work_type: Type of work associated with the job
• currency: Currency in which the salary is provided.
• compensation_type: Type of compensation for the job.

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job_details/benefits.csv

• job_id: The job ID
• type: Type of benefit provided (401K, Medical Insurance, etc)
• inferred: Whether the benefit was explicitly tagged or inferred through text by LinkedIn

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company_details/companies.csv

• company_id: The company ID as defined by LinkedIn
• name: Company name
• description: Company description
• company_size: Company grouping based on number of employees (0 Smallest - 7 Largest)
• country: Country of company headquarters.
• state: State of company headquarters.
• city: City of company headquarters.
• zip_code: ZIP code of company's headquarters.
• address: Address of company's headquarters
• url: Link to company's LinkedIn page

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company_details/employee_counts.csv

• company_id: The company ID
• employee_count: Number of employees at company
• follower_count: Number of company followers on LinkedIn
• time_recorded: Unix time of data collection

If you find this dataset helpful, your upvote would convince me I didn't waste my summer break 😁

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.

Average Wage per Job in Maryland and Its Jurisdictions (in Constant 2024 Dollars), 2014–2024, based on data from the Quarterly Census of Employment and Wages (QCEW), which includes all workers covered under the State Unemployment Insurance (UI) program and the Unemployment Compensation for Federal Employees (UCFE). The 2024 annual average wage figures are preliminary. Hand-calculated total may differ from the published total due to data suppression and privacy protection. Source: The U.S. Census Bureau of Labor Statistics, Quarterly Census Employment and Wages (QCEW), 2014-2024, June 2025.

Median earnings of residents - Gross Weekly Pay (£) This measure is the median weekly earnings for full-time employees (working 39.1 hours per week – 2017 average) where half the workers earn above that amount, and half earn below that amount This gives an indication of living standards people are able to enjoy through their disposable income. Changes in the composition of the workforce, demonstrated by changes in median earnings, can show the effect of council’s Economic Strategy. For example, creation of lower paid jobs, or loss of highly-paid jobs, can both act to reduce the median.

Are there well-paying jobs for people without a 4-year college degree? What and where are these jobs? How do employer preferences differ from education requirements for good jobs? This report summarizes research conducted by the Federal Reserve Banks of Philadelphia, Cleveland, and Atlanta on employment opportunities for workers with lower levels of formal education. Opportunity occupations are jobs generally considered accessible to someone without a bachelor’s degree and that pay at least the national annual median wage, adjusted for differences in local consumption prices. Focusing on the 100 largest metropolitan economies in the US, the researchers identify the most prevalent opportunity occupations in these economies; highlight differences across metropolitan areas; and, by using data extracted from online job advertisements, explore how employer preferences for education affect access to decent-paying employment.

Global IT Jobs Analysis

A Detailed Investigation of Salary, Location, and Job Requirements

By [source]

About this dataset

This dataset contains valuable insights into current job opportunities in the information technology (IT) sector all around the world. It offers an overview of available jobs and relevant data such as company, location, salary and links to further information. With this insight, one has the chance to better understand what it takes to land a remote or data-science job in today's global market. The ever increasing demand for IT workforce puts technical skills at a premium, so understanding exactly what employers are searching for can give potential employees an edge in catching the eye of these businesses! Digging through this dataset can provide details on current trends in terms of salary expectations and geographical locations where these roles are most popular. Beyond that, get an idea about which abilities seem most valuable when it comes to remote or data-science positions. Use this arsenal of knowledge to take your career goals into your own hands now!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides an opportunity to explore the remote and data-science job opportunities around the world. Using this dataset, you can analyze trends in job requirements, salary packages offered, location of available jobs and more. With the knowledge gained from this data set, individuals and companies can make more informed decisions about pursuing a certain path in their career or hiring for their business.

The dataset includes columns with important information such as Job title, Company offering the job, Location of the position , Salary offered for that position and a Link to its respective posting. Using these columns you can analyze various factors regarding global IT Jobs availability over different locations in alignment with salary offered for positions and any specific skill sets sought out by companies .

To get executable insights from this data set users should first load it into their respective computing environment (Python or R). After loading it in your environment users should start off by exploring Groupby statements along factors like Companies offering jobs ,Salary offered ,Location etc. followed by descriptive statistics like mean & median of Salary Levels per country/region etc. After getting basic insight about summary statistics for various factors belonging all together within “Job” range user could move forward to look over individual cases (specific skill sets) after which they could filter out & generate valueable insights needed .

With our comprehensive understanding of global supply & demand rates individuals/corporations could always use these datasets to help them keep track on talent acquisition landscape when they hire globally or relocating teams as companies who need such information would greatly benefit from versatile tools like this one that offer valuable actionsable insights on an ongoing basis depending upon dayers choosing!

Research Ideas

• Identifying the most in-demand skills and employment requirements for remote data science and IT jobs, across different countries and regions.
• Developing a prediction model to forecast future salary expectations for data science professionals based on location, company, job type, etc.
• Building an interactive dashboard with visualizations showing differences in job requirements (by level of experience or education), salary comparison across geographies as well as potential career paths one can pursue within the IT or Data Science fields

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Job_listing.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Job | The title of the job listing. (String) | | Company | The name of the company offering the job. (String) | | Location | The geographic location of the job. (String) | | Salary | The salary offere...