Research · WARN Atlas
The WARN Atlas
12,175 Worker Adjustment and Retraining Notification filings decoded, geocoded, and cross-joined with 9,691 companies' AI-adoption scores — 1,033,499 workers affected across 43 US states since 2009.
24-month trend
Monthly count of layoff + closure + mass-layoff notices since January 2023. Figures reflect filing activity — actual layoff execution may lag by 60 days per WARN Act requirements.
Where the cuts are concentrated
WARN filings by 2-digit NAICS sector (only sectors with matched filings shown). Joined via company-name normalization against our 9,691-company AI-adoption index.
Top sector: Finance & Insurance — 108 notices affecting 10,895 workers across 15 companies. Average AI-adoption score in this sector: 69.01/100.
Does AI adoption predict layoffs?
The right question, asked at the right level of aggregation: for each NAICS-2 industry, what is the industry's mean AI adoption score vs the share of its total US employment impacted by WARN filings? This normalizes for industry size — small sectors with concentrated layoffs and large sectors with absolute-but-proportionally-small layoffs both read on the same scale.
Pearson correlation (industry mean AI score × % employment impacted): Employment-weighted r = -0.034 · unweighted r = 0.331 across 12 NAICS-2 industries. Dot radius scales with industry employment (Health Care is the largest at ~23M workers; Mining is the smallest shown at ~630K).
Why this is the right aggregation. The prior company-level scatter mostly measured company size (bigger companies file larger WARN notices) rather than any underlying AI-adoption effect. Normalizing layoff volume by BLS Current Employment Statistics (CES, May 2025) total employment per sector produces an apples-to-apples comparison: small sectors with concentrated layoffs score higher here than large sectors with absolute-but-proportionally-small layoffs, which is exactly what the underlying question — "is AI adoption a sector-level driver of workforce displacement?" — needs.
Supporting view: company-level scatter (direct-matched + imputed)
Below is the company-level view that preceded the industry rollup. Each dot is one company; x-axis is its (direct or sector-imputed) AI adoption score, y-axis is total workers affected by its WARN filings. The weak correlation here is partly an artifact of firm size — bigger companies file bigger WARN notices regardless of AI adoption — which is why the industry-level plot above is the published headline chart.
Pearson correlation (Workers affected × AI adoption score): Direct-matched companies (n=47): r = -0.106 · Sector-imputed population (n=474): r = 0.061 · Combined (n=521): r = -0.096. 27 outliers with >2,351 affected workers excluded from the view.
Imputation methodology. The 186 companies that appear in both our AI-opportunity index and WARN filings are plotted with their directly-observed AI adoption score. For the remaining filings (whose employers don't appear in our SMB-focused index), we impute an AI adoption score using a public-research-grounded heuristic: 0.70 × sector_mean + 0.15 × state_mean + 0.15 × (sector_mean + size_modifier), where sector/state means come from our 9,691 companies and size_modifier scales log-linearly from −8 (50 workers) to +8 (5,000 workers). The 70/15/15 weighting reflects evidence from the US Census Bureau's Annual Business Survey 2024 and Stanford AI Index 2024 that sector dominates size and geography as an AI-adoption predictor.
Geography — per-capita burden
Top 15 states ranked by workers affected per million civilian labor force (2024 BLS baseline). Ranking corrects for absolute population size — small states with concentrated displacement rise to the top.
| Rank | State | Notices | Workers affected | Per million workforce |
|---|---|---|---|---|
| 1 | ID | 42 | 26,244 | 27055.7 |
| 2 | DC | 55 | 6,857 | 16326.2 |
| 3 | CA | 5,245 | 301,756 | 15602.7 |
| 4 | CT | 90 | 27,334 | 14386.3 |
| 5 | NV | 125 | 19,223 | 12482.5 |
| 6 | WV | 29 | 9,327 | 11806.3 |
| 7 | NE | 42 | 10,965 | 10543.3 |
| 8 | NJ | 340 | 45,315 | 9682.7 |
| 9 | IA | 299 | 16,258 | 9677.4 |
| 10 | RI | 35 | 5,226 | 9332.1 |
| 11 | OR | 118 | 18,443 | 8658.7 |
| 12 | IL | 475 | 55,478 | 8628.0 |
| 13 | MS | 69 | 10,263 | 8412.3 |
| 14 | MD | 346 | 25,524 | 7951.4 |
| 15 | WI | 337 | 24,561 | 7922.9 |
Occupation forecast
Translating NAICS-level filings into SOC-level occupation displacement requires the BLS Occupational Employment and Wage Statistics (OEWS) crosswalk. Our ingest infrastructure is ready (see BL-164); once loaded, 50 occupations with 12-month projected displacement will appear here, each linked to its AI replaceability analysis.
Methodology
Source. Each filing originates from the public WARN roster published by its state Department of Labor. We ingest 61 state-level rosters across 44 US jurisdictions, normalize the 140+ event-type variants into five canonical buckets, geocode to state, and store the canonical record in content.warn_notices. The cleaned, self-hosted CSVs are available at /api/warn/export.
Coverage. 44 of 51 US jurisdictions have release-ready sources. AR, HI, KS, LA, NH, WY, and territories are either statutorily private (AR) or have no published public list. All rates should be interpreted as undercounts in non-release-ready states.
NAICS assignment. The source data contains no industry classification, so NAICS is derived by fuzzy-matching company_name_normalized against our 9,691-company index (which has 100% NAICS coverage). Direct match rate is 0.4%; unmatched filings receive a sector-imputed AI adoption score per the scatter methodology above and are retained in all rollups.
Event classification. 140+ raw event-type strings are bucketized into five classes: layoff, closure, mass_layoff, amendment, other. Only the first three are counted in trend/sector rollups.
Rate model. The per-company layoff probability combines a sector- empirical base rate (12-month filings per matched company in each NAICS-2) with an AI-adoption coefficient fit on the matched subset via Poisson GLM (log link, IRLS solver, 6/6 unit tests). The current matched subset (n≈47 companies) is too small for strong causal claims; the coefficient is published as directional evidence only.
Legal + ethical note. Company-specific risk bands are published only for firms we already list in our public AI-opportunity index. Bands are framed as statistical associations with historical filing behavior, not predictions of future actions. Corrections and takedowns welcome at our contact page.
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