economy_ai_occupation_exposure_county_year
County-year (2024 baseline). Employment-weighted AI occupational exposure. County occupation employment (BLS QCEW county industry mix expanded by OEWS national NAICS×SOC staffing, shift-share) is weighted against two published occupation-level exposure indices: AIOE (Felten-Raj-Seamans; z-scored, >0 = above-average) and Eloundou et al. "GPTs are GPTs" beta (0-1 share of tasks LLM-exposed). Reports employment-weighted exposure, the share of employment above average, the high- vs low-exposure mean-wage split, and an exposure_coverage flag. The d/r-bar displacement shock sweep is parameterized in the analysis layer, not in this table.
overview
3,170 counties for 2024 (single baseline year). exposure_coverage reports the share of each county's employment carrying an AIOE score; counties with thin scored employment have lower coverage and noisier weighted means.
current vintage — 2024
history — No — single 2024 baseline vintage (the exposure indices are static)
source & licensing
fields
| name | type | definition |
|---|---|---|
| country_id | string | ISO alpha-2 country code (always 'US' for domestic tables). |
| county_idkey | string | 5-character FIPS code identifying the county.Part of primary key. Joins dim.counties on county_id. |
| employment_weighted_aioe | float64 · index (z-scored, mean≈0) | Employment-weighted mean AIOE (Felten-Raj-Seamans) occupational exposure index, z-scored so >0 means above-average AI exposure; weighted only over employment carrying an AIOE score. Higher values indicate the county's job mix is more exposed to AI. |
| employment_weighted_eloundou_beta_dv | float64 · share (0–1) | Employment-weighted mean Eloundou et al. ("GPTs are GPTs") GPT exposure beta using the data+vision (dv) rubric, where the beta is the 0–1 share of an occupation's tasks that are LLM-exposed. Higher values indicate a greater share of county work tasks exposed to LLMs. |
| employment_weighted_eloundou_beta_human | float64 · share (0–1) | Employment-weighted mean Eloundou et al. GPT exposure beta using the human-rater rubric, where the beta is the 0–1 share of an occupation's tasks that are LLM-exposed. Higher values indicate a greater share of county work tasks exposed to LLMs. |
| employment_weighted_lm_aioe | float64 · index (z-scored, mean≈0) | Employment-weighted mean of the language-modeling AIOE variant (LM AIOE), the LLM-specific exposure cut of the AIOE index. Higher values indicate greater exposure of the county's job mix to language-model capabilities specifically. |
| exposure_coverage | float64 · share (0–1) | Share of total county employment carrying an AIOE score, serving as a coverage flag. Lower values mean a larger fraction of employment is unscored and excluded from the weighted means, so the exposure metrics are less representative. |
| mean_wage_high_exposure | float64 · dollars (nominal) | Employment-weighted mean OEWS annual wage of high-exposure (AIOE > 0) county employment. Higher values indicate better-paid work among the more AI-exposed occupations. |
| mean_wage_low_exposure | float64 · dollars (nominal) | Employment-weighted mean OEWS annual wage of low-exposure (AIOE <= 0) county employment. Comparison against mean_wage_high_exposure shows whether AI exposure concentrates in higher- or lower-paid work. |
| share_employment_above_avg_aioe | float64 · share (0–1) | Share of scored county employment in occupations with AIOE > 0 (above-average AI exposure). Higher values indicate more of the county workforce is in above-average-exposure occupations. |
| state_id | string | 2-character FIPS code identifying the state.Joins dim.states on state_id. |
| total_employment | float64 · workers | Total county employment summed across occupations, derived from the QCEW county industry mix expanded by OEWS national NAICS×SOC staffing shares (shift-share estimate). Higher values indicate a larger county workforce. |
| yearkey | int64 | Reference year of the observation.Part of primary key. |
joins
how to use this table
County NAICS employment (QCEW) is expanded into occupation employment via OEWS national NAICS×SOC staffing shares (shift-share), priced at the OEWS SOC mean annual wage. Each occupation's employment is weighted by its exposure score and aggregated to county-year. AIOE is z-scored (mean≈0); the Eloundou beta is a 0-1 task share, with O*NET-SOC scores collapsed to 6-digit SOC by unweighted mean before weighting. Employment-weighted means are computed only over employment carrying a score; exposure_coverage records that scored share.
Treating cross-county exposure differences as precise (the substrate is a national- staffing shift-share estimate, not a county occupation census); single-year change detection (single 2024 vintage); reading AIOE z-scores as probabilities or Eloundou betas as realized displacement. The actual displacement shock (d/r-bar sweep) is not in this table.
QCEW statewide aggregate-area pseudo-counties (000, 996-999) and CT/territory FIPS without a dim.geographies match are filtered upstream in int_county_occupation_employment, so a small amount of employment is dropped before exposure weighting. AIOE (SOC-2010) and Eloundou (O*NET-SOC 2019) keys are matched to OEWS 6-digit SOC; occupations without a score are excluded from the weighted means and lower exposure_coverage.