APProjects/us-layoffs-by-industry-sector-warn-act

US layoffs by industry sector — 61,235 WARN Act notices, 1988-2026, sector per employer

Rebuilt 2026-09-24. 32,169 of 61,235 dated notices (52.5%; 61,431 on record, 196 lack a usable date) carry a sector; the rest are unclassified and stay in every total. In 2026 so far the largest sector by reported workers is Logistics, transport & warehousing (23,761 workers, 193 notices); in the last 90 days it is Healthcare & medical (8,146 workers).

No state WARN portal publishes an industry field — none of the 48 does, so the flagship archive has no NAICS or sector column to filter on. This dataset assigns one by reading the resolved employer name against an auditable rule table (19 sectors, 2441 patterns, first match wins), and redoes it every morning as new employers file. Every row says which pattern fired, so any assignment can be checked or disputed.

Read this before quoting a number

• Sector comes from the NAME, nothing else. "St. John Hospital" is healthcare; "ICU Medical" (a device maker) is also healthcare, because the table reads medical. A staffing agency filing for a factory closure is staffing. If you need NAICS precision, this is not it.
• unclassified is a real row, not an error (47.5% of notices). Names like "Acme" or a bare surname give no sector, and we do not guess. Sector shares are computed over ALL notices, so shares never sum to 100 among named sectors.
• Manual audit: on 2026-09-12 a human read 60 randomly-drawn classified rows (python3 hf_sectors.py --audit-sample) and found 4 with the wrong sector for the employer named (one random row per employer (seed 41), graded by hand after the rule table was frozen; the 4 misses were a produce grower named 'Church Brothers' (read as a church), a hospital food-service contractor (read as healthcare), a plumbing-fixtures maker (read as construction) and a residential community called 'Village of ...' (read as a municipality)). The rule table changes → the audit is redone and this line updated. Removed-for-precision tokens are listed in hf_sectors.py (GENERIC).
• Coverage is uneven before ~2020. Most state portals begin between 2010 and 2023; a sector's early-year count is a count among the states we hold, not the country. The states_with_notices column on by_year says how many states fed that cell.
• workers_reported sums employees_affected as filed. Portals do not say whether a second notice for the same site is cumulative, so rolling programmes can be overstated.
• Employer names are resolved by alias_merge.py (spellings, not corporate parents).

2026 so far, by sector

sector	notices	workers reported
Unclassified (name gives no sector)	1,419	128,462
Logistics, transport & warehousing	193	23,761
Healthcare & medical	223	19,502
Technology, software & electronics	155	18,626
Food & beverage manufacturing, agriculture	89	15,683
Hotels, restaurants, food service & leisure	153	11,636
Retail	127	10,688
Aerospace, defense & airlines	43	10,282
Finance, insurance & real estate	135	8,164
Education	114	6,589
Manufacturing (other)	95	6,346
Media, publishing & telecom	43	4,149
Automotive & auto parts	18	3,831
Pharma, biotech & life sciences	48	3,825
Energy, utilities & mining	42	3,220
Government & non-profit	23	1,268
Construction & building trades	11	1,121
Staffing & employment services	9	617
Facility, security & janitorial services	12	603
Call centers & outsourcing	6	548

Files

file	rows	one row per
data/notices_by_sector.csv	61,235	notice — id joins to the flagship archive; sector, sector_label, sector_rule
data/sector_by_year.csv	690	year × sector — notices, workers, distinct employers, states, share of that year
data/sector_by_state_year.csv	6,537	state × year × sector

The sectors

• call_center_bpo — Call centers & outsourcing: 256 notices, 46,159 workers on file
• staffing — Staffing & employment services: 201 notices, 35,117 workers on file
• aerospace_defense_airlines — Aerospace, defense & airlines: 1,551 notices, 308,032 workers on file
• automotive — Automotive & auto parts: 1,316 notices, 184,301 workers on file
• pharma_biotech — Pharma, biotech & life sciences: 831 notices, 79,158 workers on file
• healthcare — Healthcare & medical: 3,548 notices, 360,489 workers on file
• hospitality_food_service — Hotels, restaurants, food service & leisure: 6,348 notices, 920,524 workers on file
• retail — Retail: 4,327 notices, 427,787 workers on file
• education — Education: 1,165 notices, 90,670 workers on file
• government_nonprofit — Government & non-profit: 715 notices, 116,411 workers on file
• finance_insurance — Finance, insurance & real estate: 2,101 notices, 180,918 workers on file
• tech_software — Technology, software & electronics: 1,748 notices, 192,838 workers on file
• media_telecom — Media, publishing & telecom: 846 notices, 101,859 workers on file
• logistics_transport — Logistics, transport & warehousing: 1,888 notices, 226,657 workers on file
• energy_utilities_mining — Energy, utilities & mining: 1,281 notices, 138,884 workers on file
• facility_services — Facility, security & janitorial services: 228 notices, 18,359 workers on file
• construction — Construction & building trades: 300 notices, 37,203 workers on file
• food_beverage_manufacturing — Food & beverage manufacturing, agriculture: 1,281 notices, 173,590 workers on file
• manufacturing — Manufacturing (other): 2,238 notices, 258,174 workers on file

Who files in each sector — the three employers with the most workers on file

Counted from data/notices_by_sector.csv: every notice for that employer that carries the sector, workers summed from employees_affected as filed. Names are resolved spellings, not corporate parents, so a subsidiary can appear under its own name.

sector	employer	notices	workers reported
Call centers & outsourcing	Sykes	39	6,834
Call centers & outsourcing	Alorica	25	5,880
Call centers & outsourcing	Concentrix	27	5,845
Staffing & employment services	Tend Exchange Subsidiary LLC and Delaware Tender Staffing	1	16,132
Staffing & employment services	Adecco	17	2,417
Staffing & employment services	Kelly Services	3	1,073
Aerospace, defense & airlines	United Airlines	63	81,360
Aerospace, defense & airlines	Boeing	403	42,906
Aerospace, defense & airlines	Northwest Airlines	17	31,875
Automotive & auto parts	General Motors	41	22,412
Automotive & auto parts	Tesla	57	21,316
Automotive & auto parts	Ford Motor	14	7,872
Pharma, biotech & life sciences	Merck &	28	6,155
Pharma, biotech & life sciences	Novartis Pharmaceuticals	45	4,205
Pharma, biotech & life sciences	Amgen	13	3,370
Healthcare & medical	Adventist Health	148	5,897
Healthcare & medical	Verity Health System of California	19	5,386
Healthcare & medical	Continuum Health Partners/	2	4,916
Hotels, restaurants, food service & leisure	Aramark	280	49,229
Hotels, restaurants, food service & leisure	OS Restaurant	132	38,864
Hotels, restaurants, food service & leisure	Sodexo	265	28,690
Retail	Walmart	142	23,084
Retail	Kmart	243	20,049
Retail	Macy's	171	17,428
Education	Durham School Services	43	5,877
Education	Community Development Institute Head Start	33	2,186
Education	SDH Education West	10	1,838
Government & non-profit	New Jersey Sports & Exposition Authority	4	3,750
Government & non-profit	Chanute Air Force Base	1	3,750
Government & non-profit	YMCA of Greater Seattle	4	3,623
Finance, insurance & real estate	Wells Fargo	171	12,517
Finance, insurance & real estate	Bank of America	71	8,478
Finance, insurance & real estate	Capital One	38	7,209
Technology, software & electronics	Meta Platforms	114	14,006
Technology, software & electronics	Microsoft	61	13,083
Technology, software & electronics	Intel	104	9,025
Media, publishing & telecom	Verizon	63	13,379
Media, publishing & telecom	AT&T	95	8,037
Media, publishing & telecom	Universal City Studios	24	6,111
Logistics, transport & warehousing	Amazon	80	15,030
Logistics, transport & warehousing	First Student	84	9,451
Logistics, transport & warehousing	MV Transportation	63	8,468
Energy, utilities & mining	Haliburton Energy Services-N. Sam Houston	2	4,484
Energy, utilities & mining	Chevron	24	3,485
Energy, utilities & mining	Packers Sanitation Services	31	2,669
Facility, security & janitorial services	Universal Protection Service	14	1,879
Facility, security & janitorial services	FJC Security Services	2	1,376
Facility, security & janitorial services	ABM	22	1,050
Construction & building trades	Great Arrow Builders	1	6,000
Construction & building trades	Builders Square	22	1,644
Construction & building trades	RW Constructors	1	1,606
Food & beverage manufacturing, agriculture	Tyson Foods	35	10,680
Food & beverage manufacturing, agriculture	Nestle	40	5,548
Food & beverage manufacturing, agriculture	Kellogg	32	5,054
Manufacturing (other)	United States Steel	7	7,090
Manufacturing (other)	National Steel	8	5,302
Manufacturing (other)	Century Aluminum	14	4,147

Where the rows come from

The free, CC BY 4.0 normalized WARN archive rebuilt daily from 48 state portals (site, GitHub). Every sector in this file also has its own web page with charts, top employers and states — all of them here (c623). Related cuts of the same archive: the largest layoff events and layoffs per capita by state.

Get told the day an employer on your list files, in any of the 48 states: free 30-day watch (no card) or WARN Watch, $49/year for a list of up to 500 employers.

Automated publisher (APProjects). Not affiliated with any government agency. Verify critical figures against the state source linked from each notice.

A layoff record you can audit, not just download

This dataset is one cut of a single daily rebuild: 61,431 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 2026-09-25; 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:

• 2,482 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 →

Prefer a spreadsheet? One formula puts the last 90 days, the last 12 months or any single state into Google Sheets as a live range that refreshes itself — no signup, no add-on: the formulas, one per state → =IMPORTDATA("https://cdn.jsdelivr.net/gh/APVentureEngine/warn-act-notices@main/data/sheets/us-last-90-days.csv")

Backtesting, or citing a figure you published last month? Today's file has look-ahead and survivorship bias baked in: notices get amended after the fact and some rows are later deleted. The same table as it stood on any past day since 2026-08-30 — one immutable vintage per day, 25 so far, plus a first-appearance index giving the first and last day every notice id was in the file — is the point-in-time snapshot archive. Nobody can backfill it.

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
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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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