TL;DR

Public WARN Act notices — the layoff filings companies must submit to state labor departments — are a goldmine for "talent intelligence": knowing which companies are cutting, how deep, and where. In 2026, with 185,000+ tech layoffs (and roughly 56% of them citing AI as a factor), tracking this signal has real value for recruiters, analysts, and job-seekers alike.

You can build a WARN tracker for free with ScrapeMaster: open a state's public WARN listing page, let AI auto-detect the table (company, location, employees affected, effective date), paginate through all entries, and export to CSV to build your own cross-state tracker. Everything is public data you can already see, and it all stays local in your browser.

  • Public pages only. WARN listings are government-published — this is not LinkedIn scraping.
  • Free, local, no-code. ScrapeMaster is on the Chrome Web Store.
  • Mind the personal data. WARN pages are mostly company-level, but where any personal data appears, data-protection law still applies.

If you want an honest, zero-cost way to turn scattered state WARN pages into one clean tracker, here's how.

What WARN notices are, and why they're useful

The Worker Adjustment and Retraining Notification (WARN) Act requires many US employers to give advance notice of mass layoffs and plant closings. Those notices get filed with state labor/workforce agencies, and most states publish them on public web pages — usually as a table: employer name, location, number of employees affected, notice date, and effective date.

That's structured, public, official data about corporate contraction — before it fully shows up in the news. For different readers it means different things:

  • Recruiters and talent teams — a WARN filing means a pool of skilled people about to hit the market in a specific place. It's an early, lawful signal for sourcing.
  • Analysts and researchers — cross-state WARN data is a leading indicator of industry health, regional trends, and (in 2026) the AI-driven restructuring wave.
  • Job-seekers — knowing which employers are cutting vs. hiring helps you target energy where roles are actually opening, and steer clear of teams mid-contraction.

The 2026 backdrop makes this especially live. With 185,000+ tech layoffs and a majority citing AI, "who's cutting and who's hiring" is one of the year's most-asked questions. WARN data answers part of it, straight from the source.

The talent-intelligence workflow with a free scraper

The problem isn't that WARN data is hidden — it's that it's scattered across ~50 state sites, each with its own page and format, and often paginated. Copy-pasting by hand is miserable. A scraper turns each state page into clean rows in minutes.

Step 1 — Find the public WARN listing page

Each state's labor or workforce development department publishes its WARN notices. Open the state's public WARN page in your browser — the one showing the table of filings. These are public government pages; no login, no paywall.

Step 2 — Auto-detect the table

Open ScrapeMaster in the Chrome side panel and let it auto-detect. In a couple of seconds it recognizes the repeating rows and names the columns — typically company / employer, location or city, number of employees affected, notice date, effective date. No CSS selectors, no code. If a column comes through with an awkward name, rename it.

Because ScrapeMaster runs inside your browser, it reads the page exactly as rendered — handy for the many state portals that load their WARN tables via JavaScript.

We also cover the general table-extraction pattern in scrape tables from websites if you want the fundamentals.

Step 3 — Paginate through every filing

State WARN pages are often long and paginated. ScrapeMaster handles next-page, load-more, numbered pages, and infinite scroll, so it can walk the entire list and collect every filing into one dataset — not just the first screen. Set a reasonable extraction delay to be a courteous guest on a public-sector site.

Step 4 — (Optional) follow detail for filing specifics

Some states link each filing to a detail page (a PDF or a fuller record with union status, reason codes, or contact info). If the extra fields matter, turn on follow detail: ScrapeMaster opens each filing's link in a background tab and merges those fields into the row. Keep in mind this only reaches what's publicly rendered.

Step 5 — Export and normalize

Export to CSV or XLSX, or copy-to-clipboard into Google Sheets. Do this per state, then stack them into one master sheet with a state column so you can slice nationally. Add a date_pulled column — WARN pages update, and dating your pulls lets you track new filings over time.

Step 6 — Build the tracker

Now the analysis is just spreadsheet work:

  • Rank by employees affected to see the biggest cuts.
  • Group by state or metro to spot regional talent pools.
  • Track new filings week over week by comparing dated pulls.
  • Pair with hiring signals. Extract open roles from public job boards and company careers pages (same auto-detect flow) to build a "cutting vs. hiring" board. Our guide on saving job listings as PDFs during layoffs pairs well here for archiving specific roles.

Where this sits legally and ethically

This workflow is deliberately on the defensible side of the line — but "defensible" isn't "anything goes."

  • Public government data. WARN listings are published by state agencies for public awareness. Extracting the visible table is about as clean as web data collection gets. This is emphatically not LinkedIn scraping — no login, no anti-bot gauntlet, no private profiles. (If you are eyeing LinkedIn, read is scraping LinkedIn legal in 2026 first — different, riskier territory.)
  • ScrapeMaster only sees what you see. It doesn't bypass anything. On a public WARN page there's nothing to bypass, which is the point.
  • Personal data still triggers duties. WARN filings are mostly company-level (employer, headcount, dates). But some records include named contacts or, occasionally, more granular info. Wherever personal data appears, data-protection rules (and basic decency) apply — don't build people-level dossiers out of layoff filings. Our social media scraping rules piece covers the personal-data principles.
  • Respect the site. Public-sector pages are a shared resource. Pace your extraction, use delays, and don't hammer them.

None of this is legal advice. But of all the scraping scenarios we write about, WARN-notice extraction is one of the cleaner ones — public, official, mostly non-personal data you're already allowed to read.

Honest limits

  • It's a snapshot, not a live feed. You pull when you pull. To track new filings you re-run and compare dated pulls — there's no push alert.
  • Formats vary by state. Fifty agencies means fifty layouts. Auto-detect handles most tables well, but some states publish PDFs or oddly structured pages that need a bit more manual cleanup.
  • Coverage is partial by law. WARN only covers certain employer sizes and layoff thresholds, and enforcement/filing quality varies. It's a strong signal, not a complete census of every job cut.
  • No bypass, no magic. If a state buries records behind something you can't view, ScrapeMaster can't view it either.

Frequently asked questions

WARN notices are public data that state agencies publish specifically for public awareness, and ScrapeMaster only extracts what's already visible to you without bypassing anything — which makes this one of the cleaner data-collection scenarios. That said, wherever personal data appears in a filing, data-protection duties apply, and you should respect each site's terms. This isn't legal advice; consult counsel for your specifics.

Is this the same as scraping LinkedIn?

No, and the difference is important. WARN pages are public government listings — no login, no anti-bot systems, no private profiles. LinkedIn is a private platform with aggressive anti-bot defenses and its own legal history. WARN-notice extraction is far lower-risk. If you're considering LinkedIn, read our dedicated legality guide first.

How do I track WARN filings across many states?

Extract each state's public WARN page with ScrapeMaster, add a state column, and stack the exports into one master sheet in Google Sheets or Excel. Add a date_pulled column so you can compare pulls over time and spot new filings. The scraper handles pagination so you capture every entry, not just the first page.

Can I get alerts when a new WARN notice is filed?

Not from ScrapeMaster directly — it produces manual snapshots, not push alerts. To approximate alerting, re-run your extraction on a schedule (say weekly) and diff the new pull against the last one in your spreadsheet. New rows are new filings. It's a habit, not a live feed.

Does ScrapeMaster work on state sites that load tables with JavaScript?

Yes. It runs inside your browser via the Chrome side panel, so it sees fully rendered pages — including tables injected by JavaScript — exactly as you do. Many state WARN portals render their tables client-side, and ScrapeMaster reads them fine.

What columns can I expect to extract?

Typically employer/company name, location or city, number of employees affected, notice date, and effective date — though it varies by state. Auto-detect names the columns it finds; you rename or remove as needed. Some states link to detail records you can pull with follow-detail if the extra fields matter.

Where does the extracted data go?

Locally, in your browser's IndexedDB, until you export it — nothing is uploaded. The only network call is auto-detect, which sends the page's HTML structure (not its content) to suggest columns. Your WARN dataset stays on your machine.

Bottom line

WARN notices are one of the best public signals of where talent is about to move — and in a year of 185,000+ tech layoffs, that signal is gold for recruiters, analysts, and job-seekers. ScrapeMaster turns those scattered, paginated state pages into a clean cross-state tracker for free, entirely from public data you can already see, stored locally. No LinkedIn scraping, no bypassing, no subscription — just honest extraction of official public records, with the caveat that it's manual snapshots and you should respect personal-data limits.

Install ScrapeMaster free from the Chrome Web Store and build your layoff-intelligence tracker this week.