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Canaries Dashboard

Gains and lossesworking paper and dashboardactive
Canaries Dashboard: a working paper and monthly-updated dashboard, one of the Lab's AI Economic Indicators. Operator: Stanford Digital Economy Lab with ADP Research (paper by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen). Its unit: monthly employment in a five-year balanced sample of firms using ADP payroll services, by occupation (over 730), age group, gender and AI-exposure group. Whose labor: the employment of workers in firms using ADP payroll services, matched to an occupation code, grouped by age and by their occupation's AI exposure.
Visit Canaries Dashboard ↗The /non rowThe DSL audit
In collaboration with ADP Research, we summarize live labor market trends for a sample workers in firms using ADP payroll services.

In its own words: https://digitaleconomy.stanford.edu/project/canaries-dashboard/, read 2026-10-29

Who supplies, who directs, who gains

Supplies the laborworkers in firms using ADP payroll services (the payroll records)
Directs itnot recorded; the paper locates the change at the hiring margin ('reduced hiring of young workers') without recording who decides
Receives the gains, bears the costscosts as recorded: employment of young workers (22-25) in AI-exposed occupations stands 19% below where it would have been; adjustment runs through employment and base compensation shows no comparable adjustment; who receives gains is not recorded

Each as the resource itself records it. Where it records nothing, the census says so.

What it records

IntersectionsDistribution of gains and losses: wages, employment, workload, credit, ownership, and who captures productivity gains
Kind of evidenceobserved employment outcome
Unit of recordmonthly employment in a five-year balanced sample of firms using ADP payroll services, by occupation (over 730), age group, gender and AI-exposure group
Whose laborthe employment of workers in firms using ADP payroll services, matched to an occupation code, grouped by age and by their occupation's AI exposure
Methodhigh-frequency administrative payroll data from ADP, a balanced five-year firm sample rolled forward monthly, matched to AI-exposure measures (Eloundou et al. 2024 and the Anthropic Economic Index) through the BLS 2018 SOC crosswalk; descriptive employment indices and event-study regressions op
Coveragegeography: United States (firms using ADP payroll services); period: employment indexed from November 2022 (ChatGPT's release); payroll data through June 2026 in the August 2026 paper; the dashboard updated monthly, last on 21 October 2026
What it findsno evidence of widespread, economy-wide job displacement; employment of young workers (22-25) in AI-exposed occupations stands 19% below where it would be had it kept pace with less-exposed peers; experienced workers show no comparable gap; earlier versions of the paper headlined regression estimates adjusting for firm-level shocks: a 13% relative decline on July 2025 data and 16% on September 2025 data; the August 2026 version emphasizes the descriptive divergence, 15% at the July 2025 data vintage and 19% as of June 2026; the divergence has widened steadily since August 2025 and operates primarily through reduced hiring; declines concentrate where AI usage primarily substitutes for human tasks; where it complements workers, employment is flat or rising; adjustment is occurring through employment; base compensation is not the margin; early-career women in the most-exposed quintile: employment contracting 4.5% a year since November 2022, against 2.5% for men; the gender gap is driven primarily by occupational composition
Its stated limitsearly, descriptive indicators and not causal estimates; the sample represents neither the entire U.S. labor market nor the universe of ADP firms; the dashboard's balanced sample shuts down firm entry and exit (the dashboard); the paper states its patterns are also robust to allowing for firm entry and exit, though the ADP clientele may not fully reflect new business formation (the paper's PDF); patterns attenuate when controlling for education, show some trends predating generative AI, and are more pronounced in the ADP sample than in national survey benchmarks; the dashboard measures correlation

op the resource's own statement · 3p a third-party account · obs our observation · rd our reading. Hover a tag for its quote; each opens below with its source.

What this evidence can settle

Can answer

  • observed employment change by age, occupation, gender and AI exposure in a large payroll sample
  • whether the change runs through hiring or separations, and through employment or base compensation
  • how employment trends differ by an occupation's automation versus augmentation ratio of AI usage

Cannot answer

  • whether AI exposure causes the employment change
  • employment in firms outside the ADP sample
  • the firm entry and exit margin on the dashboard, whose balanced sample shuts it down (the paper reports its patterns robust to allowing for entry and exit, with the clientele caveat)
  • who directs the hiring decisions, or who receives the gains
  • what workers do with AI at work

In the worked questions

Where it meets Labor Vector

rd none stated. The archive's reading: the dashboard's automation-versus-augmentation grouping, borrowed from the Anthropic Economic Index, is where a usage measure of who directs the task meets an observed employment outcome; the resource itself reads it as correlation.

Entity

Canonical nameCanaries Dashboard
Also known asAI Economic Indicators: Canaries Dashboard, Canaries in the Coal Mine? Six Facts about the Recent Employment Effects of Artificial Intelligence, Canaries in the Coal Mine
Addresshttps://digitaleconomy.stanford.edu/project/indicators/canaries-dashboard/
OperatorStanford Digital Economy Lab with ADP Research (paper by Erik Brynjolfsson, Bharat Chandar and Ruyu Chen) op
FoundedAugust 2025 (the paper's first version) op
Kindworking paper and monthly-updated dashboard, one of the Lab's AI Economic Indicators
Lifecycleactive — the dashboard updated monthly, last updated 21 October 2026; the paper first documented the divergence in August 2025, updated February 2026, revised 12 August 2026; an expanded and reweighted sample is announced op
Count25,000 firms employing 4.6mm workers matched to an occupation code (November 2022), over 730 occupations; the paper: millions of U.S. workers (as of the dashboard launch (sample of the 5 years ending April 2026)) op
Data accessthe dashboard's charts, each with a 'Data' link; the paper states that derived, occupation-level results are published, updated monthly and downloadable through the dashboard, that the payroll records are proprietary to ADP, and that code is available from the authors on request; the paper as PDF; earlier versions of the paper on request by email; the dashboard's canonical address is /project/indicators/canaries-dashboard/ (the census's address /project/canaries-dashboard/ redirects there, and the paper gives it), while its own citation gives /project/indicators/canaries/ op
Licencenot stated in the saved text
Versions and datespaper revised: 2026-08-12; february update: 2026-02-09; gender results: 2026-07-22; dashboard updated: 2026-10-21; read: 2026-10-29
Observation2026-10-29: read

Relations

Uses

Captures: the composition layer's account

The /non row

Evidenceevery field's quote, its source and the hash of the saved copy33
Negative tagswhat it is not4
  • NOT a causal estimate: early, descriptive indicators by its own account
  • NOT nationally representative: a balanced sample of firms using ADP payroll services
  • NOT a finding of economy-wide displacement: it finds none
  • NOT a usage measure: its exposure and automation groupings come from Eloundou et al. (2024) and the Anthropic Economic Index
Sourcesevery source read, with its date and hash3
Not yet checkedwhat remains to read4
  • the paper's online appendix (cited in the PDF and unsaved)
  • the dashboard's chart data and its annualized results (JavaScript charts; 'Data' links not opened)
  • the February 2026 update and the August 2026 notes from the authors
  • the license of the data and the dashboard

Cite this record

Lee Sharks, ed. "Canaries Dashboard." Labor Vector meta-aggregator, laborvector.org, compiled 2026-10-29. https://www.laborvector.org/aggregators/stanford-canaries

Record https://www.laborvector.org/aggregators/stanford-canaries · entity https://www.laborvector.org/aggregators/stanford-canaries#entity · the census as data

@misc{lv-stanfordcanaries2026,
  editor = {Sharks, Lee},
  title = {Canaries Dashboard},
  howpublished = {Labor Vector meta-aggregator, laborvector.org},
  year = {2026},
  note = {Compiled 2026-10-29; every field sourced and dated},
  url = {https://www.laborvector.org/aggregators/stanford-canaries}
}
Editor: Lee Sharks (ORCID 0009-0000-1599-0703). This page is the Labor Vector meta-aggregator's record of the resource above: its reading and fields are ours, and each field carries its source, its basis and the hash of the copy read. The resource's own terms and findings are kept as its own. Compiled 2026-10-29. CC BY 4.0.