economy_industry_group_employment_county_year
9 HFAW industry groups + 3 AI exposure aggregate shares, county-year (E4). Private sector only (own_code='5'). Tier A with suppression caveat. See README for group definitions and national validation ranges.
tier —admin_record
overview
overview
3,207 counties. Private sector only (own_code='5'). 9 HFAW industry groups + 3 AI exposure aggregates.
current vintage — 2024
history — Recomputable from QCEW back to 2001 (NAICS-based)
provenance
source & licensing
authority
U.S. Bureau of Labor Statistics
dataset
Quarterly Census of Employment and Wages (QCEW)
methodology
license
—
citation
U.S. Bureau of Labor Statistics. QCEW - derived 9-group industry aggregation, 2024.
schema
fields
| name | type | definition |
|---|---|---|
| construction_employment | int64 · workers | Number of employed workers in the construction industry. Higher values reflect greater construction activity or a larger building-trades workforce. |
| construction_employment_share | float64 · share (0–1) | Construction workers as a share of total classified employment. Higher values reflect a greater concentration of building-trades employment in the county. |
| country_id | string | ISO alpha-2 country code (always 'US' for domestic tables). |
| county_idkey | string | 5-character FIPS code identifying the county.Joins dim.counties on county_id. |
| education_private_employment | int64 · workers | Number of employed workers in private educational services (excludes public school and government education workers). Higher values indicate greater private-sector education employment. |
| education_private_employment_share | float64 · share (0–1) | Private educational services workers as a share of total classified employment. Higher values indicate a larger private education sector relative to the overall local workforce. |
| finance_employment | int64 · workers | Number of employed workers in finance, insurance, and real estate industries. Higher values indicate greater concentration of financial-sector employment. |
| finance_employment_share | float64 · share (0–1) | Finance, insurance, and real estate workers as a share of total classified employment. Higher values indicate greater financial-sector concentration in the local economy. |
| healthcare_employment | int64 · workers | Number of employed workers in healthcare and social assistance industries. Higher values indicate a larger healthcare workforce in the county. |
| healthcare_employment_share | float64 · share (0–1) | Healthcare and social assistance workers as a share of total classified employment. Higher values reflect greater dependence on the healthcare sector as an employer. |
| high_or_higher_ai_exposure_share | float64 · share (0–1) | Share of county employment in occupations rated as having high or very high AI automation exposure, based on occupation-level AI exposure scores mapped to local employment. Higher values indicate a workforce more vulnerable to AI-driven labor displacement. |
| knowledge_economy_employment | int64 · workers | Number of employed workers in knowledge-economy industries (e.g., professional services, information technology, research). Higher values indicate a larger knowledge-sector workforce. |
| knowledge_economy_employment_share | float64 · share (0–1) | Knowledge-economy workers as a share of total classified employment (knowledge_economy_employment / total_classified_employment). Higher values indicate a county economy more concentrated in knowledge industries. |
| logistics_retail_employment | int64 · workers | Number of employed workers in logistics, transportation, warehousing, and retail trade industries. Higher values indicate greater employment in goods movement and consumer sales. |
| logistics_retail_employment_share | float64 · share (0–1) | Logistics, transportation, warehousing, and retail workers as a share of total classified employment. Higher values indicate a local economy more oriented toward goods distribution and retail trade. |
| manufacturing_employment | int64 · workers | Number of employed workers in manufacturing industries. Higher values indicate a larger industrial production workforce. |
| manufacturing_employment_share | float64 · share (0–1) | Manufacturing workers as a share of total classified employment. Higher values indicate a more industrially oriented local economy. |
| medium_or_higher_ai_exposure_share | float64 · share (0–1) | Share of county employment in occupations rated as having medium, high, or very high AI automation exposure. Broader than high_or_higher_ai_exposure_share; higher values indicate a larger portion of the workforce with meaningful AI displacement risk. |
| personal_local_services_employment | int64 · workers | Number of employed workers in personal and locally oriented service industries (e.g., food service, accommodation, personal care). Higher values reflect a larger consumer-services workforce. |
| personal_local_services_employment_share | float64 · share (0–1) | Personal and local services workers as a share of total classified employment. Higher values reflect a local economy more dependent on consumer-facing service industries. |
| resource_economy_employment | int64 · workers | Number of employed workers in resource-extraction industries such as agriculture, forestry, fishing, hunting, and mining. Higher values indicate greater reliance on natural-resource industries. |
| resource_economy_employment_share | float64 · share (0–1) | Resource-extraction workers as a share of total classified employment. Higher values indicate greater economic dependence on agriculture, mining, or related industries. |
| state_id | string | 2-character FIPS code identifying the state.Joins dim.states on state_id. |
| total_classified_employment | int64 · workers | Sum of workers across all classified industry groups in this table. Used as the denominator for employment share calculations; may differ from total county employment if some workers are unclassified. |
| yearkey | int64 | Reference year of the observation. |
relationships
joins
primary key
county_id, year
common joins
dim.geographies on county_id
economy_industry_employment_county_year_naics on (county_id, year)
economy_industrial_concentration_county_year on (county_id, year)
usage
how to use this table
method
Aggregates QCEW NAICS-2 employment into 9 HFAW groups: manufacturing, knowledge_economy, finance, healthcare, education_private, resource_economy, construction, logistics_retail, personal_local_services. Three AI-exposure aggregates from Felten-Raj-Seamans scores. own_code='5' filters to private sector.
do not use for
Government employment shares (excluded - see government_employment_share in employment_levels); cross-year comparison with NAICS revision year (small reclassifications); national-level AI exposure benchmarking (AI exposure scores are county-relative aggregates).
known issues
Sector shares sum to ~1.20 (not 1.00) due to government double-counting noted in scorecard_v4 issues. Investigate before using as denominator.
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last updated · May 5, 2026