APProjects/upcoming-layoffs-job-cuts-plant-closings-us-warn-act

Upcoming US layoffs: 419 WARN notices take effect in the next 90 days - 40,668 workers, rebuilt daily

Rebuilt 2026-09-14. Window: 2026-09-14 to 2026-12-13.

Every other US layoffs dataset - including our own notice-level one - tells you what was announced. This one tells you whose job ends next. One row per WARN Act notice whose separation date falls inside the next 90 days, sorted by how soon. The people in these rows are, as of the rebuild date above, still employed.

48	notices take effect within 7 days - 4,555 workers
234	within 30 days - 21,862 workers
393	within 60 days - 37,183 workers
419	within 90 days - 40,668 workers, across 27 states
1,341	largest single upcoming cut: International Motors, LLC (OH), effective 2026-10-02

Workers with a separation date in each two-week block ahead. Counts are FLOORS - see Honest scope.

The point of this file

A snapshot of it is wrong within a day: rows leave the window as their dates pass, new notices arrive every morning, and states amend headcounts after filing. It is regenerated from the source portals on every daily refresh. If you are reading a mirror of this that has not moved in a week, it is telling you about layoffs that already happened.

Quickstart

from datasets import load_dataset
upcoming = load_dataset("APProjects/upcoming-layoffs-job-cuts-plant-closings-us-warn-act", split="train")
by_state = load_dataset("APProjects/upcoming-layoffs-job-cuts-plant-closings-us-warn-act", "by_state", split="train")

import pandas as pd
base = "https://huggingface.co/datasets/APProjects/upcoming-layoffs-job-cuts-plant-closings-us-warn-act/resolve/main/data/"
up = pd.read_csv(base + "upcoming_separations.csv", parse_dates=["effective_date", "notice_date"])

up[up.days_until <= 14]                                  # separating in the next fortnight
up.groupby("state").employees_affected.sum().nlargest(10)
up[up.company.str.contains("Bank", case=False, na=False)]

Columns

column	meaning
id	stable notice id, joins to the notice-level dataset
state	two-letter filing state
company	employer exactly as the state agency published it
location	city / county as filed
employees_affected	workers named on the notice; blank where the agency published none
notice_date	date the employer filed
effective_date	date the separations take effect - the reason this file exists
days_until	effective_date minus the rebuild date, in days
notice_type	layoff / closure / permanent / temporary, as filed

upcoming_by_state.csv is the same window rolled up: state, notices, workers.

Honest scope - read this before quoting a number

• Totals count only workers an agency actually named. Today all 419 notices in the window carry a headcount, but blanks are common in this data and are counted as zero, never guessed - so treat every worker total as a floor.
• 12,544 of the 61,316 notices in the archive have no effective date at all and therefore cannot appear here, however recent they are. That is a gap in what states publish, not a filter we chose.
• 1 notice inside the window was excluded as rescinded, cancelled or amended away.
• Coverage is 48 state agencies, not 50, and a state that files late is invisible until it files. Absence from this file is not evidence that a company is not cutting.
• Dates are as published by the agency. Employers routinely revise them; some separations in here will slip or be called off.
• 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 APProjects/us-warn-act-layoffs-notices-daily - 61,316 notices from 48 state agencies, back to 1988. Companion series: monthly national and per-state totals. Human-readable version of this exact window, with the full table and per-state pages: approjects-warn-act-notices.static.hf.space/upcoming-layoffs.html.

Getting told when the next one lands

Everything above is free, CC BY 4.0, no account, no API key. The one paid thing this project sells is the watching: WARN Watch runs your employer and state list against every daily rebuild for a year and delivers matches to a private alert page, an RSS feed and an optional Slack / Discord / Teams webhook - $49/year, one payment, no auto-renew, 14-day refund. Try it first for nothing: free 30-day watch, no card, up to 3 employers or one whole state.

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

Cite as: "WARN Feed - Upcoming US layoffs (WARN Act notices taking effect within 90 days), rebuilt 2026-09-14, huggingface.co/datasets/APProjects/upcoming-layoffs-job-cuts-plant-closings-us-warn-act".

A layoff record you can audit, not just download

This dataset is one cut of a single daily rebuild: 61,316 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 2026-09-14; 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:

• 593 field-level changes, logged every day since 2026-08-31. data/revisions.csv records each time a state agency edited a notice after publishing it — employee counts, effective dates, notice types, company names — with the old value, the new value and the date we saw the change. A scrape that starts tomorrow cannot backfill this; 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 →

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 → — used only for a state added, a column change or a tier change; nothing promotional. 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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