STRATAHELM · COMMUNITY AI EXPOSURE & RESILIENCE INDEX

York County, South Carolina

CAERI NationalSouth Carolina › York County · FIPS 45091 · caeri:45091
61.2
CAERI score (national basis)
75th–90th
National percentile band
#454
Ranked of 3142 U.S. counties
#12
Ranked in South Carolina (of 46)
High
Confidence

Two comparisons, both shown. The national basis ranks York 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 South Carolina'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
98
P2 · Economic concentration
58
P3 · Adaptive capacity (inverted)
81
P4 · Regional buffer (inverted)
48
P5 · Fiscal sensitivity
56
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 York County

York County scores 61.2 on the national CAERI basis — 454th of 3,142 scored U.S. counties, placing it in the 75th–90th band nationally by percentile, with a high-confidence rating. Within South Carolina it ranks 12th of 46 counties (75th 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 98th national percentile. The largest concentrations of locally estimated employment in high-exposure occupations are customer service representatives, software developers, office clerks, general. The share of exposed-industry jobs held by workers under 25 is 7% — near the state median of 7%; 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 25% (well below the state median) — a higher share historically means slower workforce adjustment when industries restructure. Fiscal sensitivity ranks at the 56th percentile among South Carolina's counties. Census of Governments data shows state aid at 9% of this county's general revenue (above the state median), and wage- and consumption-sensitive streams (sales, income, and similar own-source taxes, where levied) at 16% 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 214 high-exposure occupations (top quartile of ensemble exposure) with estimated local employment, York County has an estimated 38,804 jobs — 34.7% of county employment — carrying an estimated $3B annual wage bill in high-exposure work. The five largest:

OccupationEst. local employment*Exposure (0–1)Median wage (area)
Customer Service Representatives2,6570.42$44,480
Software Developers1,7700.65$132,100
Office Clerks, General1,7480.44$41,000
Accountants and Auditors1,3430.48$85,450
Management Analysts1,2970.46$104,760
*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

York CountySouth Carolina median
Share of exposed-industry jobs held by workers under 25 (entry rung) 6.7%6.9%
Share of exposed-industry jobs held by workers 55+ (adjustment friction) 24.9%30.6%

🔒 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:45091 — this URL is permanent; if the address scheme ever changes, the old address will redirect. StrataHelm. (2026). York County, South Carolina — Community AI Exposure & Resilience Index (CAERI v0.3.1) [Data set]. Retrieved August 4, 2026, from https://stratahelm.com/counties/south-carolina/york/ “York County, South Carolina — CAERI.” StrataHelm, 2026, stratahelm.com/counties/south-carolina/york/. Accessed 4 August 2026. StrataHelm. “York County, South Carolina — Community AI Exposure and Resilience Index (v0.3.1).” 2026. https://stratahelm.com/counties/south-carolina/york/
Embed this county's score card — attribution to StrataHelm CAERI and the link back are part of the embedded page and cannot be stripped:<iframe src="https://stratahelm.com/counties/south-carolina/york/embed/" width="420" height="275" loading="lazy" title="CAERI — York County, SC"></iframe>

Machine-readable: this county's JSON · national dataset download and data dictionary on the methodology page.

Nearby and comparable counties

Beaufort Co., SC (in state)Edgefield Co., SC (in state)Dorchester Co., SC (in state)Meagher Co., MT (similar score)Warren Co., VA (similar score)Bee Co., TX (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_sc_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_sc.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_sc_county_revenue_mix: 2022 Census of Governments, Survey of Government Finances; lodes_sc_county_flows: LEHD LODES8 OD 2023, JT00/S000; sources: sc_main, sc_aux, ga_aux, nc_aux; qwi_sc_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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