ACS
American Community Survey
- Specific Type
- Core labor force survey
- Dataset Type
- Pooled cross-sectional
- Institution
- Census Bureau
- Institution Type
- Federal government
- Level of Focus
- Individual/Household
- Most Granular Level
- Block group and tract level
- Perspective
- Worker-side
- Time Coverage
- 2000-present
- Frequency
- Annual
- Sample Size
- ~3.5M addresses/year
- Geographic Detail
- County; tract; PUMA
- Occupational Classification
- 4-digit census code 2018
- Industrial Classification
- 4-digit NAICS 2022
- Other Classification
- County-level (FIPS); tract-level; PUMA
Key Papers
Occupational, Industry, and Geographic Exposure to Artificial Intelligence: A Novel Dataset and Its Potential Uses
Felten, Raj, Seamans (2021)
Skill Requirements across Firms and Labor Markets
Deming, Kahn (2017)
The Growth of Low-Skill Service Jobs and the Polarization of the US Labor Market
Autor, Dorn (2009)
Earnings Dynamics, Changing Job Skills, and STEM Careers
Deming, Noray (2020)
Tasks At Work: Comparative Advantage, Technology and Labor Demand
Acemoglu, Kong, Restrepo (2024)
Expertise
Autor, Thompson (2025)
Who Is Using AI to Code? Global Diffusion and Impact of Generative AI
Daniotti, Wachs, Feng, Neffke (2026)
Which Economic Tasks Are Performed with AI? Evidence from Millions of Claude Conversations
Handa, Tamkin, McCain, Huang (2025)
Technology and Labor Displacement: Evidence from Linking Patents with Worker-Level Data
Kogan, Papanikolaou, Schmidt, Seegmiller (2023)
Labour Market Exposure to AI: Cross-Country Differences and Distributional Implications
Pizzinelli (2023)
How Adaptable Are American Workers to AI-Induced Job Displacement?
Manning, Aguirre (2026)
Felten et al. (2021); Deming & Kahn (2017); Autor & Dorn (2009); Deming & Noray (2020); Acemoglu et al. (2024); Autor & Thompson (2025); Daniotti et al. (2026); Handa et al. (2025); Kogan et al. (2023); Pizzinelli (2023); Manning & Aguirre (2026); Not found