APProjects/us-warn-act-employer-layoff-history

US layoff-notice history by employer — 29,900 employers, rebuilt daily

Last rebuilt: 2026-09-16. One row per employer, not per notice. Every US WARN Act layoff notice we can reach from 48 state labor-department portals, grouped by a canonicalized employer name, so you can ask employer-level questions in one line instead of writing the groupby yourself.

29,900	employers with at least one WARN notice on file
6,410	of them have filed more than once
62%	share of all 61,320 notices that come from those repeat filers
2,756	employers that have filed in more than one state
37	states covered by the widest filer (Aramark)
6,819,644	workers named across every notice that publishes a headcount

The single most useful thing in here: layoffs are concentrated in employers who do it repeatedly. 6,410 employers (21% of the list) account for 37,830 of 61,320 notices (62%). A one-notice employer is the common case; the notices themselves are not.

This file is free forever — CC BY 4.0, no login, no API key, rebuilt daily.

Watching for a particular repeat filer? WARN Watch runs your employer and state list against every daily refresh and gives you a private alert page, an RSS feed and an optional Slack/Discord/Teams webhook — each alert carrying that employer's whole filing history, not just the new row.

Try it free for 30 days — no card (up to 3 employers or one state) · then $49/year, one-off, no auto-renew, 14-day refund

Quickstart

from datasets import load_dataset
ds = load_dataset("APProjects/us-warn-act-employer-layoff-history", split="train")
print(ds[0])

import pandas as pd
url = ("https://huggingface.co/datasets/APProjects/us-warn-act-employer-layoff-history"
       "/resolve/main/data/employer_history.csv")
df = pd.read_csv(url)

df.nlargest(20, "workers_affected")[["company_canonical", "notices", "workers_affected", "n_states"]]
df[df.n_states >= 5].sort_values("notices", ascending=False).head(20)   # serial multi-state filers
df[df.latest_activity >= "2026-01-01"]                                   # who is still filing now

Columns

column	meaning
company_canonical	employer name after canonicalization (Amazon.com Services LLC and Amazon roll up together)
notices	WARN notices on file for this employer
notices_with_headcount	how many of those notices publish an integer headcount
workers_affected	sum of headcounts, over notices_with_headcount rows only
states	pipe-separated USPS codes this employer has filed in
n_states	length of that list
first_activity	earliest dated notice (notice date, or effective date when the state publishes none)
latest_activity	most recent dated notice, same rule
dated_by_effective	how many of this employer's rows were dated by effective date because no notice date was published

The largest employers by workers noticed

employer	notices	workers	states	first	latest
United Airlines	64	81,360	22	1993-10-28	2021-09-01
Aramark	279	49,229	37	2002-04-12	2026-07-17
Boeing	420	43,967	14	1998-11-30	2026-04-13
OS Restaurant	132	38,864	13	2019-04-01	2020-05-15
Northwest Airlines	17	31,875	3	1998-08-11	2005-09-22
Sodexo	265	28,690	37	2009-01-22	2026-08-06
Walt Disney Parks and Resorts U.S	27	24,843	2	2015-01-21	2021-01-20
usi services group	1	23,695	1	2020-03-01	2020-03-01
Walmart	142	23,084	30	2015-04-13	2026-06-23
David's Bridal	82	22,433	23	2020-04-02	2023-08-11
General Motors	42	22,412	13	1989-05-09	2027-01-14
Tesla	57	21,316	7	2017-08-30	2024-05-13

Honest scope — read this before you cite it

• workers_affected undercounts. Only rows that publish an integer headcount are summed; notices_with_headcount tells you how many that was for each employer.
• Dates. MI, PA and SC publish no notice date at all. Those rows are dated by their effective date and counted in dated_by_effective rather than silently guessed.
• Grouping is by name, not by legal entity. Canonicalization merges obvious variants; it cannot merge a subsidiary filing under its own name, and it cannot split two unrelated businesses that share a name. There is no employer identifier in WARN data — no EIN, no DUNS — so any employer-level WARN dataset, including this one, is a name-matching exercise. Treat the rollup as a strong hint, not a legal fact.
• Coverage is 48 states, not 50. Not covered yet: AR, HI, ID, MA, MN, MO, ND, NH, NV, OH, WY. The notice-level mirror carries coverage.json with the authoritative per-state list and scrape stamps.
• Compiled from state workforce-agency portals. Independent project, not affiliated with any government agency; not legal, financial or employment advice.

Where this comes from

Derived on every daily refresh from the notice-level dataset: APProjects/us-warn-act-layoffs-notices-daily (61,320 notices, 48 states, back to 1988). Companion from the same morning's run: the monthly time series (one row per month, 1988 → today, plus a per-state table). All three are rebuilt by the same pipeline on the same morning, so they are never out of step with each other. Full site with per-state and per-employer pages, roll-ups, search and RSS: approjects-warn-act-notices.static.hf.space.

A one-off scrape of WARN data starts rotting the week it is posted — states amend headcounts, re-issue notices and drop rows. Compare the rebuild date at the top of this card with the "last modified" date on any other US WARN dataset before you pick one.

Getting told when an employer on this list files again

Everything above is free, CC BY 4.0, no account. The one paid thing this project sells is the watching: WARN Watch — up to 500 employer terms plus whole states, matched on every daily rebuild for a year, with a private alert page, RSS and an optional Slack / Discord / Teams webhook ($49/year). Every alert carries that employer's whole filing record from this dataset. Try it first for nothing: free 30-day watch, no card, nothing renews.

Corrections: open an issue — they ship the same day.

Cite as: "WARN Feed — US layoff-notice history by employer, rebuilt 2026-09-16, huggingface.co/datasets/APProjects/us-warn-act-employer-layoff-history".

A layoff record you can audit, not just download

This dataset is one cut of a single daily rebuild: 61,330 US WARN Act layoff notices from 48 state agencies, 1988 to today, one schema, no login, no delay, CC BY 4.0. Snapshot as of 2026-09-16; the files above are rebuilt every day, so the live count is the truth.

Several projects publish a current WARN scrape and two of them carry more rows than we do. None of them publish what the records used to say:

• 616 observed changes to already-published notices, logged daily since 2026-08-31. data/revisions.csv records every field that differed between two consecutive daily builds — employee counts, effective dates, notice types, company names — with the old value, the new value and the date we saw it. We publish the observation and not the cause: a change is equally explained by the agency amending the notice or by our own parser improving, and we do not guess which (see data/revisions.README.txt). A scrape that starts tomorrow cannot backfill any of it; it only exists if someone was watching.
• 6,799 notices whose state agency page no longer lists them. Agencies take notices down. We keep them, flagged as archive-only, so a count you ran last year still reconciles.
• Point-in-time employer identity. The ticker crosswalk resolves a filer to the company as it existed at the time of the notice — Kmart, Sears Holdings, Symantec — not to whatever is on today's ticker file.

If you have to defend a number to an editor, a referee or a compliance reviewer, that provenance layer is the part you cannot rebuild yourself. How to cite this dataset →

Look something up right now — free, no signup, nothing to install. Check any employer or state against the last 180 days → It runs in your browser against these same files.

Building something with it? The same files are a free HTTP API — JSON and CSV, no key, no signup, access-control-allow-origin: * so fetch() works from a browser: endpoints, schema and curl examples →

Need one industry only? The same filings, cut by an auditable employer-name rule (each row keeps the rule that fired): tech companies · hospitals & healthcare · retail store closings · restaurants & hotels · factory & plant closings · banks, insurance & finance · warehouses, trucking & logistics · all 20 sectors.

Or have it watch a list for you. Coming back to look is the part a CSV cannot do. WARN Watch — $49 for a year, one payment, nothing auto-renews, 14-day refund, no login: up to 500 employer names plus whole states, matched on every daily refresh, delivered to a private alert page + calendar (.ics) + RSS + an optional Slack / Discord / Teams webhook. Every alert carries that employer's whole filing history from the archive, which a keyword rule on an RSS feed cannot see. There is no built-in email — we do not claim one.

• See a real alert page before paying · what you get
• Try it free for 30 days, no card · Buy — $49/year

Not deciding today? Join the update list → — one email when a new dataset or tier is published; nothing promotional. A state added or a column renamed ships in the daily release instead, no address needed. The list is shared across APProjects datasets, holds an email address only, is run by Gumroad, and any message unsubscribes you. Rather give no address at all? Watch the repo's releases — GitHub notifies you on every daily republish, and a new state or changed field is in those notes the day it lands.

Reaching a human. WARN Feed is published by APProjects, an automated data publisher — that is stated plainly rather than dressed up. Corrections, coverage gaps, schema questions and refund requests all go here and are read: open an issue. Payments are handled by Gumroad as merchant of record, so an invoice can carry your company name.

Source, scrapers and methodology · the 48-state site

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