Data Sources & Geography

Data sources

Every source below is public — nothing proprietary, nothing behind a paywall. The table is generated automatically from the site's configuration, so it stays in sync with what's actually powering the dashboard, and it shows which metrics each source feeds.

Where detailed occupation-level data isn't available, employment and wage figures are imputed from the SOC major-group average and flagged in the API's data_vintage field as 'group avg'.

Federal Workforce Breakout

The "Government & Public Administration" row on the For Employers panel is really three different workforce segments folded into one number, and each carries a different AI exposure profile. The breakout below is based on BLS QCEW ownership classes and NAICS industry codes:

• Federal Civilian (NAICS 92, Public Administration): Lower AI exposure (~30%) due to security clearance requirements, procurement buffers, and constituent-facing services. Key occupations: Compliance Officers, Budget Analysts, Program Managers.
• Federal Contractor (NAICS 541, Professional, Scientific, and Technical Services): Higher AI exposure (~50%) due to knowledge-work content. Key occupations: Management Analysts, Software Developers, Market Research Analysts.
• 501(c)(3) Policy & Advocacy (NAICS 813, Religious, Grantmaking, Civic, Professional, and Similar Organizations): Mid-range AI exposure (~35%). Key occupations: Social Science Research Assistants, Political Scientists, Grant Writers.

Source: O*NET industry-occupation matrix (for AI exposure scores) and BLS QCEW (for ownership-class employment counts). The rolled-up Government & Public Administration figure (40%/20%/50%) is the headcount-weighted mean of the three sub-rows; it is not a simple average.

Policy Interventions Evidence Rubric

The evidence tags on the For Policymakers page — Strong, Emerging, Moderate, Mixed — follow the rubric below, and they're reviewed quarterly by the policy team.

• Strong: ≥3 peer-reviewed studies or RCTs in OECD jurisdictions with positive effect sizes (e.g., 5-15% reemployment rate improvement). Reviewed quarterly by the policy team.
• Emerging: 1-2 peer-reviewed studies or pilot programs in early-stage jurisdictions. Promising but not yet replicated.
• Moderate: Several peer-reviewed studies with small-to-moderate effect sizes, or a single large study with methodological caveats.
• Mixed: Multiple peer-reviewed studies with conflicting effect sizes. Outcomes vary by jurisdiction, design, and target population.

Last reviewed: July 1, 2026. Review cadence: quarterly.

Geography & ZIP resolution

Labor-market data is published at different geographic levels, each with its own granularity and freshness. The dashboard organizes it into three tiers:

Handling ZIP codes

ZIP codes are postal delivery routes, not statistical areas — worth stating plainly, since it trips people up. When you enter a ZIP code, the dashboard resolves it into the Census geographies that contain it — the CBSA (metro area), county, and PUMA. The statistics shown are native to those areas, not to your ZIP code itself. If your ZIP spans multiple counties, the larger one is shown, and the split is noted.

Confidence levels

Every observation in the underlying database carries a confidence label:

Update cadence

Different data sources update at different frequencies. Here's what to expect:

• Monthly: BLS employment/unemployment (LAUS, CPS), JOLTS, Challenger layoff data, Stanford Canaries slope, metro stress index (MS1)
• Weekly: Indeed Hiring Lab job postings index, state WARN notices
• Quarterly: NY Fed college labor market report, Yale Budget Lab occupational-mix dissimilarity
• Annual: Census ACS PUMA data (occupation mix, education, income, broadband, industry concentration) — typically released in December for the prior year
• Event-driven: Anthropic Economic Index, NBER recession dating, Sahm Rule triggers

The "as of" date on each card shows when its underlying data was last retrieved. Some indicators may lag by 1–2 months because the source publishes with a delay.