Data Sources & Geography
Where the data comes from, how it's organized geographically, and how confident you can be in it.
Data sources
Almost every source below is public and free to access. One important nuance: the Stanford "Canaries" figures are published aggregates — the underlying payroll microdata is ADP's and is not public. The table is generated automatically from the site's source registry (each entry carries its URL, access method, and license note), so it stays in sync with what's actually powering the dashboard, and it shows which metrics each source feeds, how much of the source the dashboard currently covers, and when it was last retrieved.
We could not load the source registry right now — the /api/methodology endpoint did not respond. Refresh the page to try again.
Occupation employment counts are national BLS OEWS May 2025 figures (data_vintage = 'BLS OEWS May 2025'; suppressed estimates are shown as null, never zero). Where an occupation has no OEWS row, no employment figure is shown — pre-2025 imputed placeholders have been removed.
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:
Source: O*NET industry-occupation matrix (for AI exposure scores) and BLS QCEW (for ownership-class employment counts).
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.
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:
We could not load the geography tiers right now — the /api/methodology endpoint did not respond. The page still works without them.
ZIP codes are postal delivery routes, not statistical areas. We resolve your ZIP into the CBSA, county, and PUMA that contain it. If your ZIP spans multiple counties, we show the larger one and note the split. The statistics are native to those areas, not to your ZIP.
Confidence levels
Every number in the underlying database carries its provenance with it — two fields that say where it came from and what was done to it:
Where the value ultimately comes from: official (a government statistical agency), private (a company's published data), research (a peer-reviewed study), or computed (derived by this site from the sources above). The class is derived from the value's source ID through a committed, reviewable mapping — not hand-tagged by whichever job wrote the row.
What happened to the value after collection: raw (as published), derived, interpolated, imputed, or modeled.
When the value was published. A value re-published on a new day is stored as a new vintage, never an overwrite, so the history is preserved exactly as it was known at the time.
Rows that fail validation are quarantined with a recorded reason — never silently dropped and never re-labeled with a fabricated source.
The API also publishes a simple five-label confidence legend — measured, extracted, estimated, computed, and proxy — rendered below as a compatibility layer for existing readers:
We could not load the confidence legend right now — the /api/methodology endpoint did not respond.
Update cadence
Different data sources update at different frequencies. Here's what to expect:
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.