STRATAHELM · COMMUNITY AI EXPOSURE & RESILIENCE INDEX

Monongalia County, West Virginia

CAERI NationalWest Virginia › Monongalia County · FIPS 54061 · caeri:54061
70.0
CAERI score (national basis)
Top 10%
National percentile band
#70
Ranked of 3142 U.S. counties
#2
Ranked in West Virginia (of 55)
Medium
Confidence

Two comparisons, both shown. The national basis ranks Monongalia County against every scored U.S. county — its percentile band above is a relative national standing, not a condition. The in-state basis ranks it against West Virginia's other counties (rank and percentile only — bands aren't meaningful within a single state). A county can stand out in its own state while sitting mid-pack nationally. Neither is a forecast.

Pillar breakdown — national percentile ranks

P1 · Direct occupational exposure
99
P2 · Economic concentration
94
P3 · Adaptive capacity (inverted)
82
P4 · Regional buffer (inverted)
33
P5 · Fiscal sensitivity
52
Zone shading marks percentile quartiles; darker and warmer means more concerning. Capacity and buffer bars (inverted before aggregation) read mirrored — for those, a higher percentile is the safer pale end.

What this means for Monongalia County

Monongalia County scores 70.0 on the national CAERI basis — 70th of 3,142 scored U.S. counties, placing it in the Top 10% band nationally by percentile, with a medium-confidence rating. Within West Virginia it ranks 2nd of 55 counties (97th in-state percentile). The score summarizes how concentrated local employment is in AI-exposed occupations against the economy's measured capacity to adapt; these are relative-standing bands, not a projection of local job change.

Direct occupational exposure stands at the 99th national percentile. The largest concentrations of locally estimated employment in high-exposure occupations are health specialties teachers, postsecondary, business operations specialists, all other, accountants and auditors. The share of exposed-industry jobs held by workers under 25 is 11% — well above the state median of 8%; research on AI-era payrolls finds early-career roles in exposed work are where hiring patterns shift first, so this share indicates how soon exposure could be felt, not how large it is.

The share of exposed-industry jobs held by workers 55 and over is 22% (well below the state median) — a higher share historically means slower workforce adjustment when industries restructure. Fiscal sensitivity ranks at the 58th percentile among West Virginia's counties. Census of Governments data shows state aid at 7% of this county's general revenue (well above the state median), and wage- and consumption-sensitive streams (sales, income, and similar own-source taxes, where levied) at 12% of own-source revenue. Property taxes, which respond to economic change more slowly, are counted separately and are not part of that sensitive share. How this state's aid formulas and tax structure carry economic change into local budgets is covered in the state-specific fiscal analysis available to subscribers. All county occupation figures on this page are model-based ESTIMATES with the confidence rating shown above.

Largest locally-estimated employment in high-exposure occupations

Across all 194 high-exposure occupations (top quartile of ensemble exposure) with estimated local employment, Monongalia County has an estimated 25,724 jobs — 41.8% of county employment — carrying an estimated $2B annual wage bill in high-exposure work. The five largest:

OccupationEst. local employment*Exposure (0–1)Median wage (area)
Health Specialties Teachers, Postsecondary2,3180.52$106,940
Business Operations Specialists, All Other1,4860.47$59,950
Accountants and Auditors8920.48$72,970
Data Scientists6920.57$75,230
Software Developers6690.65$94,630
*County-level occupation figures are model-based ESTIMATES, not surveyed counts (see methodology §5 summary); every county carries the confidence rating shown above. Wage = area median.

Age structure of exposed-industry employment

Monongalia CountyWest Virginia median
Share of exposed-industry jobs held by workers under 25 (entry rung) 10.6%8.0%
Share of exposed-industry jobs held by workers 55+ (adjustment friction) 21.9%28.3%

🔒 City-level detail for its cities and towns

The county number above averages over every community in it. City- and place-level exposure profiles, employer-mix detail, trend monitoring, and peer benchmarking are part of the CAERI subscription for local governments and regional organizations.

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Cite this page

Canonical identifier: caeri:54061 — this URL is permanent; if the address scheme ever changes, the old address will redirect. StrataHelm. (2026). Monongalia County, West Virginia — Community AI Exposure & Resilience Index (CAERI v0.3.1) [Data set]. Retrieved August 4, 2026, from https://stratahelm.com/counties/west-virginia/monongalia/ “Monongalia County, West Virginia — CAERI.” StrataHelm, 2026, stratahelm.com/counties/west-virginia/monongalia/. Accessed 4 August 2026. StrataHelm. “Monongalia County, West Virginia — Community AI Exposure and Resilience Index (v0.3.1).” 2026. https://stratahelm.com/counties/west-virginia/monongalia/
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Machine-readable: this county's JSON · national dataset download and data dictionary on the methodology page.

Nearby and comparable counties

Boone Co., WV (in state)Pleasants Co., WV (in state)Kanawha Co., WV (in state)Manassas Park Co., VA (similar score)Bethel Co., AK (similar score)Santa Fe Co., NM (similar score)

Links point to the economically nearest counties (in-state, and similar national score). County-level geographic adjacency is not part of the public reference data, so proximity here is by rank, not by shared border.

Sources & provenance

LayerSources (vintage)
Employment & occupation structurecbp_wv_county_naics4: Census CBP 2023 API (NAICS2017 classification; PAYANN in $1,000s; noise infusion G/H/J bands, D = withheld -&gt; NaN); county_soc_estimates.meta_wv.json; exposure_scores: 2026-07-04; matrix_staffing_patterns: 2024-34 National Employment Matrix (base year 2024)
AI-exposure research baseAnthropic Economic Index; Felten, Raj & Seamans (AIOE); Eloundou et al. — combined as a weighted ensemble with cross-source disagreement feeding the confidence rating
Capacity, buffer & fiscalgovfin_wv_county_revenue_mix: 2022 Census of Governments, Survey of Government Finances; lodes_wv_county_flows: LEHD LODES8 OD 2023, JT00/S000; sources: wv_main, wv_aux, ky_aux, md_aux, oh_aux, pa_aux, va_aux; qwi_wv_county_naics3_age: Census QWI (qwi/sa), quarters [&#x27;2024-Q4&#x27;, &#x27;2025-Q1&#x27;, &#x27;2025-Q2&#x27;, &#x27;2025-Q3&#x27;], sex=0, ownercode=A05 (private), 96 NAICS-3 + &#x27;00&#x27; all-industry
MethodologyCAERI v0.3.1 · public methodology · scores generated 2026-07-12 · page generated 2026-08-04
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