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

US biotech and pharma layoffs — the actual WARN Act filings, rebuilt every day

Last rebuilt: 2026-09-24. 831 layoff and closure notices filed by pharmaceutical and biopharma companies, biotech and therapeutics startups, genomics and biologics firms, vaccine and diagnostics makers, contract manufacturers and clinical-research organisations, and reference labs with US state labor departments — 79,158 workers, 270 employers, 36 states, 1992–2026. 190 of the notices (22.9%) were recorded by the state as a closure rather than a layoff. Free, CC BY 4.0, no login, no delay.

Biotech job cuts are tracked company by company from press releases, which arrive late, cover only funded names and omit the site. The WARN filings behind them — which facility, which town, how many workers, notice date, effective date, closure or layoff — sit on 48 separate state portals and are never assembled as one life-sciences series. This file is that series, with the closure/layoff split the states themselves recorded. An automated pipeline re-scrapes 48 state portals every day and rebuilds this file from the legal filings employers must make before a mass layoff: employer, state, site, workers affected, notice date, effective date, and the notice type in the state's own words.

Read this before you quote a number

This is a FLOOR, not a census. No US state WARN portal publishes an industry field — not one. Sector here is assigned from the employer name by an auditable keyword rule table, and 47.5% of all 61,235 notices in the archive carry a name that reveals no sector at all (Rugged Liner, KPR US, Point Designs). A biotech and pharma employer trading under an opaque name is therefore missing from this file. Every row keeps sector_rule — the exact pattern that fired — so any inclusion can be checked or disputed row by row, and the full unfiltered archive is one click away: all 61,235 notices.

WARN also only covers layoffs above a size threshold (broadly 50+ at a site, lower in some states), so smaller biotech and pharma layoffs never generate a filing at all.

What counts as biotech or pharma here: the employer name reads as pharmaceutical, pharma, biopharma, biotech, bioscience, biologics, therapeutics, genomics, biomedical, life sciences, vaccine, diagnostics, clinical research or clinical trials, or is one of ~25 named companies (Pfizer, Merck, Amgen, Genentech, Gilead, Biogen, Regeneron, Moderna, AbbVie, Bristol-Myers, Novartis, Sanofi, AstraZeneca, GlaxoSmithKline, Eli Lilly, Takeda, Catalent, Illumina, Thermo Fisher, Charles River, LabCorp, Quest Diagnostics). A reference lab or clinical-research organisation lands here rather than in healthcare, because the pharma rule is evaluated before the healthcare rule — so compare this file against the hospital and healthcare cut before quoting either as a total. A biotech trading under a founder's surname reveals no sector and is not here; it is in the unclassified remainder of the sector dataset. Every row keeps the sector_rule that fired.

"Closure" is the state's word, not ours. notice_type is copied verbatim from the filing; the closure count above is every row whose type contains clos (Closure, Closure Permanent, Plant Closing…). 244 rows have no type because that state's portal does not publish one.

Files

import pandas as pd
df = pd.read_csv("https://huggingface.co/datasets/APProjects/us-biotech-pharma-layoffs-warn-act-notices-daily/resolve/main/data/pharma_biotech_layoff_notices.csv")
df.groupby(df.notice_date.str[:4]).employees_affected.sum().tail(10)

Biotech and pharma layoffs by year

The employers with the most biotech and pharma layoff workers on file

States with the most biotech and pharma layoff workers on file

Most recent biotech and pharma filings in this build

Names are resolved for spelling variants, not corporate parents: a subsidiary filing under its own name stays under its own name.

Where this comes from

• —Source: official state WARN listings, scraped daily and normalized to one schema. Full method, per-state coverage and freshness: https://approjects-warn-act-notices.static.hf.space.
• —This same cut as a web page — charts by year and state, the employers with the most notices, and what the boundary of this sector is: https://approjects-warn-act-notices.static.hf.space/industry/pharma.html (c623).
• —Public commit log of every daily rebuild: https://github.com/APVentureEngine/warn-act-notices.
• —Whole archive, all sectors: https://huggingface.co/datasets/APProjects/us-warn-act-layoffs-notices-daily.
• —All 20 sectors, not just this one: layoffs by industry sector.
• —Verify anything that matters at the state source before you publish it.

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If this saved you a scrape

• —Like this dataset (the heart, top right) — likes are how the next person searching for biotech and pharma layoffs finds a file that was rebuilt today instead of one uploaded once years ago.
• —[Watch the source repo's releases](https://github.com/APVentureEngine/warn-act-notices/subscription) — one notification whenever the snapshot is republished (not every day; the data files refresh daily).
• —Corrections and questions: open an issue.

Published by APProjects, an automated publisher of US public records. Not affiliated with any state agency.

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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 2026-09-24; 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 <!-- warn-feed:offer:end -->

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