OES/OEWS
Occupational Employment and Wage Statistics
- Specific Type
- Core firm survey
- Dataset Type
- Cross-sectional
- Institution
- BLS
- Institution Type
- Federal government
- Level of Focus
- Occupation by area
- Most Granular Level
- 6-digit SOC occupation level by MSA/state
- Perspective
- Firm-side
- Time Coverage
- 1988-present
- Frequency
- Annual (May)
- Sample Size
- ~1.1M establishments collected over 3-year period
- Geographic Detail
- MSA; state; national
- Occupational Classification
- 6-digit SOC 2018
- Industrial Classification
- 4-digit NAICS 2022
- Other Classification
- Metropolitan and nonmetropolitan areas
Key Papers
GPTs are GPTs: An Early Look at the Labor Market Impact Potential of Large Language Models
Eloundou, Manning, Mishkin, Rock (2023)
What Can Machines Learn, and What Does It Mean for Occupations and the Economy?
Brynjolfsson, Mitchell, Rock (2018)
Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses
Felten, Raj, Seamans (2021)
Artificial Intelligence and Jobs: Evidence from Online Vacancies
Acemoglu, Autor, Hazell, Restrepo (2020)
The Future of Employment: How Susceptible are Jobs to Computerisation?
Frey, Osborne (2016)
Expertise
Autor, Thompson (2025)
How Are Patented AI, Software and Robot Technologies Related to Wage Changes in the United States?
Fossen, Samaan, Sorgner (2022)
The AI Productivity Index (APEX)
Vidgen, Thrush, Hale, Madnani, Awal, Majumder, Luger, Baines, Klyman, Saifullah, Kirk (2025)
Anthropic Economic Index Report: Economic Primitives
Appel, Massenkoff, McCrory, McCain, Heller, Neylon, Tamkin (2026)
How Adaptable Are American Workers to AI-Induced Job Displacement?
Manning, Aguirre (2026)
Eloundou et al. (2023); Brynjolfsson et al. (2018); Felten et al. (2021); Acemoglu et al. (2020); Frey & Osborne (2016); Autor & Thompson (2025); Fossen et al. (2022); Vidgen et al. (2025); Appel et al. (2026); Manning & Aguirre (2026); Not found