HomeCountiesArticlesMethodologyContactAmendment v0.3.1 (2026-08-03) — display bands. Tier labels are relative-standing
percentile bands on the national basis only, not condition assessments; the
in-state basis is reported as rank and percentile with no band. No composite
score, percentile, or rank changed — this is a presentation change (see §1 and
the public changelog).
CAERI is a 0–100 composite score, computed per U.S. county (and rollable to municipalities, workforce development areas, and states), answering:
"How exposed is this community's economy to AI-driven labor disruption, and how well-positioned is it to adapt?"
It is an exposure and resilience index, not a displacement forecast. We explicitly do NOT claim to predict how many jobs will be lost by a given date. We claim, defensibly:
1. Which communities have employment concentrated in occupations that published, peer-reviewed research identifies as highly exposed to current AI capabilities.
2. Which communities have economic structures (concentration, low diversification, weak adaptive infrastructure) that historically amplify labor shocks.
3. How these two interact, relative to all other communities in the state and nation.
Scores are presented as percentile ranks with relative-standing bands and confidence indicators — never as false-precision point predictions. On the national basis the bands are Top 10% / 75th–90th / 50th–75th / 25th–50th / Bottom 25% — cuts on a county's national percentile, i.e. a statement of relative standing among all U.S. counties, not a condition assessment and not a forecast. The in-state basis is reported as rank and percentile with no band, because percentile bands are not meaningful within a single state (some states have only a handful of counties).
| Pillar | Weight* | Direction | Question Answered |
|---|---|---|---|
| P1. Direct Occupational Exposure | 35% | Higher = riskier | What share of local jobs and wages sit in AI-exposed occupations? |
| P2. Economic Concentration | 20% | Higher = riskier | Is the economy diversified enough to absorb a sector shock? |
| P3. Adaptive Capacity | 25% | Higher = safer (inverted) | Can the workforce retrain and the economy regenerate? |
| P4. Regional Buffer | 10% | Higher = safer (inverted) | Can residents reach alternative labor markets? |
| P5. Fiscal Sensitivity | 10% | Higher = riskier | How exposed is the local government's own revenue base? |
*Baseline weights; see Section 6 for weighting rationale and sensitivity analysis requirements. P5 is also sold standalone as the premium Fiscal Resilience module with a deeper model.
| Source | Program | Geography | Cadence | Access |
|---|---|---|---|---|
| BLS | OEWS (Occupational Employment & Wage Statistics) | MSA / nonmetro area | Annual (May release) | Flat files + API |
| Census | County Business Patterns (CBP) | County × NAICS | Annual | API |
| BLS | National Employment Matrix (industry→occupation staffing patterns) | National | Biennial | Flat files |
| Census | ACS 5-year (tables S2401 occupation, S2403 industry, B24010) | County | Annual | API |
| BLS | QCEW (Quarterly Census of Employment & Wages) | County × NAICS | Quarterly | API |
| Census | LEHD/LODES (Origin-Destination commuting flows) | Block → aggregated | Annual | Flat files |
| Source | What It Provides | Notes |
|---|---|---|
| Anthropic Economic Index | Observed AI usage by occupational task; automation vs. augmentation share | Only measure based on revealed usage, not speculation. Update as new releases publish. |
| Felten, Raj & Seamans (AIOE) | AI Occupational Exposure scores, all SOC codes | Peer-reviewed, widely cited |
| Eloundou et al. (task-level LLM exposure) | Share of tasks exposed per occupation | Task-granular |
| O*NET | Task statements, work activities per SOC | Crosswalk backbone |
We use an ensemble: each occupation's exposure score is a weighted blend of the above, with disagreement across sources feeding the confidence indicator (Section 6.4). We separately track automation share (task substitution) vs. augmentation share (task assistance) — a county heavy in augmentation-dominant occupations scores materially lower risk than one heavy in automation-dominant occupations at the same raw exposure.
| Indicator | Source | Geography |
|---|---|---|
| Industry concentration (HHI) | CBP / QCEW | County |
| Large-employer dependence | QCEW size classes; WARN notices (state portals) | County |
| Educational attainment | ACS B15003 | County |
| Broadband subscription | ACS S2801 / FCC BDC | County |
| Higher-ed & training institutions | IPEDS | County (geocoded) |
| Labor force participation, unemployment | BLS LAUS | County |
| Business formation rate | Census BFS | County/state |
| Age structure | ACS S0101 | County |
| Commuting-shed job diversity | LODES OD flows | County |
| Employment by age band × industry | Census QWI (LEHD) | County × NAICS |
| Local gov revenue mix (property/sales/income/state aid) | Census of Governments; Annual Survey of State & Local Gov Finances | Municipality/county |
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[Federal APIs/files] → Ingest → Clean/Conform → County Occupation
Estimation → Exposure Scoring → Pillar Indicators → Normalization →
Weighted Aggregation → Tiering + Confidence → Scorecards/Dashboard
```
Refresh cadence: quarterly (QCEW/LAUS), annual (OEWS, CBP, ACS), event-driven (new exposure research, WARN notices).
County-level occupation figures are model-based ESTIMATES assembled from the federal sources above and validated against area-level observations; every county's scorecard carries a Confidence rating reflecting source agreement, disclosure imputation, and county size. Full estimation and scoring detail is maintained in StrataHelm's internal methodology (v0.3.1) and summarized in the forthcoming whitepaper. The index is comparative and not a forecast.