AT A GLANCE
Self-reported salary data from 62,000+ STEM employees at major tech companies. Examines how gender, race, and education affect compensation. Includes full documentation package with README, codebook, and data file.
Key Highlights:
- Records: 62,000+ salary entries
- Companies: Apple, Amazon, Microsoft, Google, and others
- Variables: Salary, demographics, education, experience, free-response text
- Package: Complete with README, codebook, and data
PROJECT DESCRIPTION
The dataset investigates how controllable factors (experience, education) and uncontrollable factors (gender, race) influence annual compensation. Multiple variable types support diverse analytical approaches from descriptive statistics to machine learning.
Research Applications:
- Compensation equity audits across gender and race
- Regression modeling of salary determinants
- Company-level benchmarking studies
- Text coding exercises with free-response variables
Subject Terms: STEM salaries, gender pay gap, racial disparities, technology industry, compensation equity, data science, United States
SCOPE & METHODOLOGY
- Geographic Coverage: United States (primarily)
- Smallest Geographic Unit: Individual employee
- Time Period: See data files for specific range
- Universe: STEM employees at U.S. technology companies
- Unit of Observation: Individual employee salary record
- Data Type: Self-reported survey / compensation data
Data Collection: Self-reported by employees at major technology companies including Apple, Amazon, Microsoft, and Google. Over 62,000 records with demographic, educational, and compensation variables.
CITATION
University of Maryland, College of Behavioral and Social Sciences [distributor]. STEM Salaries. BSOS Social Science Data Repository, 2026. https://bsos-data.umd.edu/dataset/stem-salaries
FILES & DOCUMENTATION
Available:
- readme_stem (DOCX)
- codebook_stem (XLSX)
- data_stem (XLSX)
Planned Additions:
- Data Dictionary — Salary components, company identifiers, education categories, demographic fields