APProjects/us-layoffs-monthly-time-series-warn-act

US layoffs, month by month — 455 months of WARN notices, 1988-11 → 2026-09, rebuilt daily

Last rebuilt: 2026-09-08. One row per calendar month: how many US WARN Act layoff notices were filed, how many workers they named, and how many states contributed — as a regular series with every month present (zeros included), ready for pandas, a chart or a forecasting model. A second table gives the same series per state.

455	consecutive months, 1988-11 → 2026-09, no gaps
49,049	dated notices in the series (274 undated rows excluded, said so below)
3,926	notices in April 2020, the busiest month on record (33 states reporting)
1.0×	April 2020 alone, measured against the entire last 12 months combined (3,848 notices)
262	notices in August 2026, the last complete month; 26,814 workers named
5,568	state-month rows in the by_state table

Notices per month, notice-date basis, all reporting states, through August 2026 (the partial current month is left off the chart). The April 2020 spike is the pandemic; the flat left half is a handful of states' worth of history, not a quiet economy — see "Honest scope" before you compare decades.

Quickstart

from datasets import load_dataset
national = load_dataset("APProjects/us-layoffs-monthly-time-series-warn-act", split="train")
by_state = load_dataset("APProjects/us-layoffs-monthly-time-series-warn-act", "by_state", split="train")

import pandas as pd
base = "https://huggingface.co/datasets/APProjects/us-layoffs-monthly-time-series-warn-act/resolve/main/data/"
m = pd.read_csv(base + "monthly_series.csv", parse_dates=["month"]).set_index("month")

m.loc["2015":, "notices"].plot()                       # the modern, ~30-40 state era
m.loc["2015":, "notices"].rolling(12).mean()            # smoothed
m["workers_affected"].idxmax()                          # 2020-04
s = pd.read_csv(base + "monthly_by_state.csv")
s[s.state == "CA"].set_index("month")["notices"]        # one state's own series

Columns — monthly_series.csv (default config)

column	meaning
month	calendar month, YYYY-MM
notices	WARN notices dated in this month (notice date; effective date when the state publishes no notice date)
notices_with_headcount	how many of those publish an integer headcount
workers_affected	sum of headcounts over notices_with_headcount rows only
states_reporting	distinct states with at least one notice this month — read this before comparing years
dated_by_effective	rows in this month that were dated by effective date because no notice date exists
notices_effective_month	the same notices keyed by effective date (when the separations actually happen)
workers_effective_month	headcount sum on that effective-date basis

monthly_by_state.csv (by_state config): month, state (USPS code), notices, notices_with_headcount, workers_affected — only (month, state) pairs with activity; zero rows are implied.

Honest scope — read this before you cite it

• This is not a national count before roughly 2015. State portals differ wildly in how far back they publish: 2 states reach back to 1988, 39 have reported in the last twelve months, 45 are covered today. A rise from 1995 to 2025 is mostly coverage, not layoffs. Use states_reporting to restrict to comparable periods, or use the by_state table and pick states with long archives.
• Notice month ≠ layoff month. Employers must file 60 days ahead; the notice-date basis leads the effective-date basis by one to three months. Both are in the file; pick the one your question needs and say which.
• Dates. MI, PA and SC publish no notice date. Their rows are dated by effective date and counted in dated_by_effective (1,025 rows in total). 274 rows carry neither date and are excluded; 22 rows are notice-dated after the rebuild month and 295 have effective dates after it — those are held out of the series rather than plotted as the future.
• workers_affected undercounts. Only rows with an integer headcount are summed; notices_with_headcount says how many that was per month.
• The current month is partial and refills every day until it closes; the last complete month is August 2026. Late-arriving notices are added to their own month retroactively, so history can revise slightly between rebuilds.
• Coverage is 45 states, not 50. Not covered yet: AR, HI, ID, MA, MN, MO, ND, NH, NV, OH, WY. The notice-level mirror carries coverage.json with the authoritative per-state list and scrape stamps.
• 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 the notice-level dataset APProjects/us-warn-act-layoffs-notices-daily (49,345 notices, 45 states, back to 1988). Companion rollups from the same morning's run: by employer. Full site with per-state and per-month pages, search and RSS: approjects-warn-act-notices.static.hf.space.

A one-off scrape of WARN data starts rotting the week it is posted — states amend headcounts, re-issue notices and drop rows. Compare the rebuild date at the top of this card with the "last modified" date on any other US layoffs series before you pick one.

Getting told when the next notice lands

Everything above is free, CC BY 4.0, no account. The one paid thing this project sells is the watching: WARN Watch — up to 25 employer terms plus whole states, matched on every daily rebuild for a year, with a private alert page, RSS and an optional Slack / Discord / Teams webhook ($49/year). Try it first for nothing: free 30-day watch, no card, nothing renews.

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

Cite as: "WARN Feed — US layoffs monthly time series (WARN Act), rebuilt 2026-09-08, huggingface.co/datasets/APProjects/us-layoffs-monthly-time-series-warn-act".