APProjects/us-warn-act-layoffs-notices-daily

US WARN Act Layoff Notices — normalized, 48 states, rebuilt every day

Last rebuilt: 2026-09-25. An automated pipeline re-scrapes 48 state labor-department portals every day, re-normalizes, re-deduplicates and re-uploads this file. Compare that date with the "last modified" date on any other US WARN dataset on the Hub before you choose one — WARN data is a moving target and a one-shot upload starts rotting the week it is posted (states amend headcounts, re-issue notices, and silently drop rows).

Every US layoff notice filed under the WARN Act that we can reach, scraped daily from 48 state portals, normalized to one schema, deduplicated, with canonicalized employer names. 61,431 notices from 48 states in this free tier (notices dated 1988-01-01 onward, published with no delay). States in this CSV: AK, AL, AZ, CA, CO, CT, DC, DE, FL, GA, HI, IA, ID, IL, IN, KS, KY, LA, MA, MD, ME, MI, MN, MO, MS, MT, NC, ND, NE, NJ, NM, NV, NY, OH, OK, OR, PA, RI, SC, SD, TN, TX, UT, VA, VT, WA, WI, WV. Not covered: AR, NH, WY — those states publish no public WARN list (surveyed 2026-09-13; the per-state survey with archive depth is its own dataset: which-us-states-publish-warn-act-layoff-notices-coverage), so 48 is every US jurisdiction that publishes one. coverage.json (included) lists what the pipeline currently scrapes, and flags the 6,799 notices that the issuing agencies' live pages no longer list (recovered from archived copies of retired agency pages; published on their own as us-historical-layoffs-archive-warn-act-notices-removed-from-state-websites).

The data above is free forever — CC BY 4.0, no login, no API key, no delay, rebuilt daily.

If you only care about particular employers or states, you do not have to re-download the whole file and diff it yourself. WARN Watch runs your list against every daily refresh and gives you a private alert page, a calendar (.ics), an RSS feed and an optional Slack/Discord/Teams webhook — no login.

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 · see a real alert page first

Quickstart

from datasets import load_dataset
ds = load_dataset("APProjects/us-warn-act-layoffs-notices-daily", split="train")
print(ds[0])

import pandas as pd
df = pd.read_csv("https://huggingface.co/datasets/APProjects/"
                 "us-warn-act-layoffs-notices-daily/resolve/main/data/warn_notices.csv")
# layoffs by month
df.groupby(df.notice_date.str[:7]).employees_affected.sum().tail(12)

How much notice did workers actually get?

The WARN Act is a law about a number of days, and no state publishes that number. Both dates are in this dataset, so we compute it on every refresh:

15,389 of 31,632 notices (49%) that publish both a notice date and a later layoff date gave workers fewer than 60 days — 1,471,272 workers in total. The median notice period is exactly 60 days: employers file at the line, and half land under it. 2,020 notices were filed the same day the layoff took effect.

This is arithmetic on public records, not a finding that anyone broke the law: WARN excuses short notice for a faltering company, for business circumstances that were not reasonably foreseeable, and for natural disasters, and several states run their own notice laws. We also exclude rather than reinterpret the 12,212 records whose layoff date precedes their own notice date, and the 12,076 with no layoff date at all.

• Free CSV, 31,632 rows with a computed notice_days column: notice_period.csv
• Charts, per-state ranking and the full method: notice-period.html
• notice_days is computed for all 61,431 rows back to 1988, free

Rebuilt every day

26 consecutive days of data commits to the public repo, 2026-08-30 through 2026-09-24, none missed. Layoff trackers are usually one person's side project and stop when that person gets busy; this one is a pipeline, and the commit history is the proof. Counted from git log -- data on every build.

This year against last, same months

2,367 WARN notices Jan-Jul 2026 vs 2,428 Jan-Jul 2025 (-2.5%); 211,464 workers this year vs 223,433. Counted by notice_date from data/warn_notices.csv on every build. The two most recent months are left out because state portals post notices dated in the prior month for weeks afterwards; every month since 1988 is in the monthly time-series companion dataset.

How far back does this go?

This file is the whole archive: 61,431 notices back to 1988, with no paywalled years. The free page US layoff notices by year, 1988–2026 charts every one of those years — notices, workers affected, and how many states were filing (the coverage caveat, stated rather than hidden). The starkest thing it shows: 2020 alone holds 16,351 notices covering 2,053,979 workers, more workers than 2021–2025 combined (1,437,811) — and that year, like every other, is inside this file.

For scale on what a paid comparable costs: WARNTracker's public pricing page (checked 2026-09-05) lists $2,000 one-time for its historical CSV and $250/month for a weekly email of new notices, covering 46 states and D.C. This file is free and rebuilt daily across 48 states, and the whole 1988-present archive is in it at no charge. What we sell is the daily watch on YOUR employers: WARN Watch, $49/year.

If this saved you a scrape

Two free things, five seconds, no account changes hands:

• Like this dataset (the heart, top right) — likes are how the next person searching the Hub for "WARN" or "layoffs" finds a file that was rebuilt today instead of one uploaded once in 2023.
• Watch the source repo's releases — a notification whenever the snapshot is republished (not every day; the data files refresh daily), with the row counts and the per-state freshness table in the notes.

Something wrong in a row, or a state you need that is not here? Open an issue — the last reader's fix shipped the same day.

What a fork of the scraper cannot get

Collection uses the Apache-2.0 biglocalnews/warn-scraper, and you should fork it — it is good software. A fork returns whatever each state portal serves today. Five states have taken their history down:

State	On the state portal today	In this dataset	Recovered from deleted pages	Period recovered
Michigan	116	2,202	2,086	2000–2025
Pennsylvania	311	1,896	1,585	2011–2022
New York	5,018	6,515	1,497	2001–2014
Connecticut	26	870	844	2010–2025
North Carolina	299	1,086	787	2014–2021

6,799 notices in this file exist only because they were rebuilt one Internet Archive capture at a time from pages the agencies removed (Michigan's retired milmi.org listings, Pennsylvania L&I's month-by-month pages, Connecticut's year index, New York's retired labor.ny.gov/app/warn application, North Carolina's archived yearly PDFs). Those five portals list 5,770 notices between them right now. Nothing here is estimated or interpolated; fields a source never printed are left empty. The archive-only rows are also published on their own: us-historical-layoffs-archive-warn-act-notices-removed-from-state-websites.

Is there a state feed you could subscribe to instead?

We checked the official source behind all 48 states on 2026-09-18: 0 publish a feed you can subscribe to, 3 post a spreadsheet, and the other 45 publish a web page, a pile of PDFs, a POST-only search form — or no WARN page at all. Two that advertise a feed (Georgia, Idaho) serve the agency's WordPress comments feed with no notices in it. That is why this dataset is rebuilt daily and why each state gets a normalized RSS feed here: the per-state census with every portal linked (re-run weekly; evidence in data/source_formats.json in the GitHub repo).

What a single state's feed cannot tell you

Layoffs cross state lines; WARN portals do not. Because this dataset normalizes 48 portals into one schema and canonicalizes employer names across them, you can ask a question no single state's feed can answer:

2,759 employers filed WARN notices in two or more states since 1988 — 605 of them in five or more, 2,917,877 workers in total, spanning 48 states (build of 2026-09-25).

Employer	States	Which	Workers
Aramark	37	AL, AZ, CA, CO, CT, DC, DE, FL, IA, ID, IL, IN, KS, KY, MA, MD, MI, MN, MO, MS, NC, NJ, NV, NY, OH, OK, OR, PA, RI, SC, SD, TN, TX, UT, VA, WA, WI	49,229
Sodexo	37	AL, AZ, CA, CO, CT, DC, DE, FL, HI, ID, IL, IN, KS, KY, LA, MA, MD, MI, MN, MO, NC, NE, NJ, NV, NY, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, WI, WV	28,690
Walmart	30	AK, AL, AZ, CA, CO, CT, DC, FL, GA, IL, IN, KS, MA, MD, MI, NC, NJ, NM, NY, OH, OK, OR, PA, SC, TN, TX, UT, VA, WA, WI	23,084
Conduent	30	AZ, CA, CO, CT, DE, FL, GA, ID, IN, KY, LA, MD, MI, MO, NC, NE, NM, NV, NY, OH, OK, OR, PA, RI, SC, SD, TN, VA, WA, WI	3,605
ABM	27	AL, AZ, CA, CO, DC, FL, GA, IL, IN, MA, MD, MN, MO, NC, NJ, NV, NY, OH, OK, PA, SC, TN, TX, UT, VA, WA, WI	12,448

Full ranked list, filterable, free and rebuilt daily: companies laying off in multiple states.

Don't want to re-download this every day? (free, no card)

If you only care about a few employers, you do not need the whole CSV. Check them right now, in your browser → — paste any list, see every match from the last 180 days; no account, no email, nothing sent anywhere (?q=Boeing|Kaiser or ?s=CA pre-fills it).

To be told when one of them files next, name up to three employers (or one whole state) and every daily rebuild is matched against them for you — matches land on your own alert page with a calendar (.ics) and an RSS feed, and optionally a Slack/Discord/Teams webhook.

Start a free 30-day watch → — $0, no card, nothing renews. Matching runs on the employer name as filed, on our canonical name and on the trade name, so general motors also catches General Motors, LLC -- Lansing Region.

Want the one column no state publishes? The companion dataset how many days of notice each filing actually gave computes effective_date - notice_date per notice — half of all measurable US WARN filings since 1988 gave workers under 60 days, and the median sits exactly on 60. Same daily rebuild, same ids, so the two files join on id.

Which employers are cutting in more than one state? No single state portal can show that; the companion multi-state employer dataset merges the free window across every state, one row per employer (states, notices, workers as filed) plus every underlying notice keyed by the same id.

Need the aggregate rather than individual notices? BLS killed its Mass Layoff Statistics program on 2013-09-30 and nothing replaced it. The companion US Mass Layoff & Plant Closing Statistics rebuilds a monthly national and per-state series from this same file — events and workers affected, WARN's own 50-worker threshold, back to 1988, refreshed on the same daily run.

Want to know whose job ends NEXT rather than what was announced? The companion upcoming layoffs dataset is only the notices whose separation date falls in the next 90 days: 458 notices across 26 states, of which 266 take effect within 30 days (21,525 workers named). Those people are still employed as of this rebuild. A copy of that file is wrong within a day, which is the point of it.

What did this filing say before it said this? No state portal keeps a history - the page simply changes. The companion observed change log is a diff of this dataset against itself, every day since 2026-08-30: 2,482 observed changes over 25 daily builds, touching 2,209 distinct notices, joinable on id. It labels only what is mechanically decidable, because a difference between two builds can come from the agency amending the notice or from our own parser improving, and a row that stops appearing is not proof the employer withdrew anything. Nobody can reconstruct what those portals said last Tuesday, including us - the file only grows forward.

If you need the table as it stood on a past date rather than the diff - to backtest without look-ahead bias, or to cite the exact file a published figure came from - the point-in-time snapshot archive carries one immutable vintage per day since 2026-08-30 (25 so far, one added every day), each with a published sha256 so a reader can verify they loaded the same bytes you did. Do not try to rebuild a vintage from the change log - the days where our coverage window moved are suppressed there on purpose, and those are exactly the days you would get wrong.

Only want this year? The companion US layoffs 2026 dataset is the 2,798 notices filed in 2026 (254,250 workers named, 45 states), with by-state, by-month and top-employer rollups already computed. Same rebuild, same ids.

Only want the tech layoffs? The companion [US tech company layoffs dataset] (https://huggingface.co/datasets/APProjects/us-tech-company-layoffs-warn-act-notices-daily) is the 1,748 filings from software, internet, semiconductor and electronics employers (192,838 workers, 369 companies, 1989-2026), with the per-company history already rolled up. Unlike the trackers built from news stories, every row there is a filing with a state, a site and an effective date. No state publishes an industry field, so sector is assigned from the employer NAME and the file is a floor, not a census: 47.5% of all notices carry a name that reveals no sector. Same daily rebuild, same ids.

Want it one row per COMPANY instead of one row per notice? The companion employer layoff-history dataset rolls all 61,431 notices up into 29,971 canonicalized employers with their states, first and latest filing and total workers noticed — and shows what the notice-level file hides: the 6,428 employers that have filed more than once account for 62% of every notice on record. Same daily rebuild, same canonical names.

Want it as a TIME SERIES? The companion monthly layoffs time-series dataset is one row per month, 1988-11 to 2026-09 (455 months, zeros included), notice-date and effective-date bases side by side, with a states_reporting column so nobody mistakes growing coverage for growing layoffs, plus a per-state table. Busiest month on record: April 2020, 4,843 notices.

Prefer clicking to coding? The live explorer Space searches every employer in this file, lists the newest notices, and shows per-state freshness — it reads the same files, so it is never staler than the dataset.

This is a live mirror of github.com/APVentureEngine/warn-act-notices — stats site, per-state CSVs, weekly/monthly pages, RSS, and API docs live there.

Schema

Why this repo exists in one paragraph: of the 48 state sources in this refresh, 47 arrive in 37 different column layouts using 193 distinct column names, and the employer's name alone is published under 8 different headings — Doing Business As Name (IL) · Name of Company (MT) · Organization Name (DC). Every one is mapped to the 11 fields below on every refresh, dates parsed to ISO and employers resolved to a canonical name, so one company can be followed across state lines. Counted from the raw files on each build, never hand-written.

column	meaning
id	stable row id (source-issued key where the state provides one)
state	two-letter state code
company	employer name as filed
company_canonical	canonicalized employer name (dedup/rollup-friendly)
company_dba	"doing business as" alias, when filed
location	site location as filed
employees_affected	headcount on the notice (blank when the state omits it)
notice_date	date the notice was filed
effective_date	planned layoff/closure date
notice_type	layoff / closure / amendment etc., as filed
first_seen	date our pipeline first observed this row

Freshness & coverage

Regenerated from state sources every day; broken scrapers are repaired within a cycle, and per-state coverage is tracked in coverage.json (included here). A one-off copy of this dataset rots in weeks — several states silently amend, re-issue, or remove rows; the pipeline reconciles that daily with stable-id retention logic.

Hugging Face lists several other US WARN datasets. Before choosing one, compare their last-modified dates: the site's data-sources comparison reads them from the Hugging Face API on every rebuild and prints them next to this dataset's rebuild date, so the freshness claim above is checkable rather than asserted.

Browse by state

Every state below has a page with its notices, a monthly trend, RSS and JSON, rebuilt from this dataset each day. Latest notice is the most recent notice date that state has published — not the date we checked — so a stale column is the STATE being quiet, not this pipeline being broken.

State	Notices	Latest notice
California WARN notices · own dataset	16,640	2026-09-18
New York WARN notices · own dataset	6,515	2026-08-25
Illinois WARN notices · own dataset	4,847	2026-09-16
Florida WARN notices · own dataset	3,116	2026-09-16
Texas WARN notices · own dataset	2,391	2026-09-15
New Jersey WARN notices · own dataset	2,330	2026-09-01
Michigan WARN notices · own dataset	2,202	2026-09-25
Pennsylvania WARN notices · own dataset	1,896	2026-09-21
Washington WARN notices · own dataset	1,519	2026-09-22
Oregon WARN notices · own dataset	1,371	2026-09-23
Maryland WARN notices · own dataset	1,277	2026-09-14
Indiana WARN notices · own dataset	1,182	2026-09-02
Virginia WARN notices · own dataset	1,126	2026-09-16
North Carolina WARN notices · own dataset	1,086	2026-09-16
Ohio WARN notices · own dataset	1,072	2026-09-21
Alabama WARN notices · own dataset	1,062	2026-09-21
Tennessee WARN notices · own dataset	1,061	2026-09-02
Connecticut WARN notices · own dataset	870	2026-09-09
Nebraska WARN notices · own dataset	845	2026-08-26
Colorado WARN notices · own dataset	841	2026-09-10
Kentucky WARN notices · own dataset	807	2026-09-10
Kansas WARN notices · own dataset	791	2026-05-01
Arizona WARN notices · own dataset	639	2026-09-15
Wisconsin WARN notices · own dataset	617	2026-08-24
Nevada WARN notices · own dataset	613	2026-06-29
South Carolina WARN notices · own dataset	604	2026-08-28
Hawaii WARN notices · own dataset	460	2026-08-20
Minnesota WARN notices · own dataset	457	2026-06-15
Iowa WARN notices · own dataset	419	2026-09-22
Missouri WARN notices · own dataset	375	2026-09-16
Massachusetts WARN notices · own dataset	294	2026-08-31
Georgia WARN notices · own dataset	284	2026-09-14
Utah WARN notices · own dataset	282	2026-08-13
Oklahoma WARN notices · own dataset	220	2026-08-17
Idaho WARN notices · own dataset	200	2026-09-17
District of Columbia WARN notices · own dataset	143	2026-07-27
Mississippi WARN notices · own dataset	136	2026-05-11
Rhode Island WARN notices · own dataset	126	2026-06-29
New Mexico WARN notices · own dataset	116	2026-06-29
Delaware WARN notices · own dataset	100	2026-08-10
Maine WARN notices	86	2026-09-09
South Dakota WARN notices	80	2026-08-10
Vermont WARN notices	70	2026-06-17
Alaska WARN notices	66	2026-07-06
West Virginia WARN notices	58	2026-08-19
North Dakota WARN notices	54	2026-01-15
Montana WARN notices	46	2026-07-21
Louisiana WARN notices	39	2026-09-15

The whole archive is in this file

Since 2026-09-07 there is no paywalled history: all 61,431 notices back to 1988 are in data/warn_notices.csv here and in the GitHub repo, CC BY 4.0.

Paid: have your list watched for you

• WARN Watch — up to 500 employer terms plus whole states, matched on every daily refresh for a year; private alert page + calendar (.ics) + RSS + optional Slack/Discord/Teams webhook, no login. Every alert carries that employer's whole filing record ("Boeing — 403rd WARN notice from this employer since 1998 · 13 states · 42,893 workers to date · last filed 2025-04-04") — history a keyword rule on an RSS feed cannot see. Buy — $49/year · see a real alert page · what it looks like · try it free for 30 days, no card

License & citation

Free tier: CC BY 4.0. Cite as "WARN Feed, github.com/APVentureEngine/warn-act-notices" (see CITATION.cff in the GitHub repo). Underlying scrapers include Apache-2.0 code from biglocalnews/warn-scraper (attributed in-repo).

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
• 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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