US layoff announcements by state, monthly (Challenger)
Agent-curated state panel from Table 3 (job cuts by region/state) of the Challenger, Gray & Christmas monthly Job Cuts Report PDFs, January–September 2026 (read 2026-10-01): 459 state×month rows (50 states + DC) with announced cuts, USPS state code, Challenger region (east/midwest/west/south), YTD cuts, YoY change, share of the month's and YTD cuts, per-month rank (1 = most cuts), and a 0–100 layoff-shock score (100 = hardest-hit state that month). September: 43,281 cuts nationally (YTD 573,195); Washington led September states (10,680), ahead of California (9,920). An online shop joins on period + state_code to read local demand shocks; the shock score is a ready-made severity feature. Blank month cells in the source tables are 0 announced cuts (region and national totals reconcile). Provenance: agent-curated (intel-1 run 2026-10-01); announced plans, not completed layoffs.
- Rows
- 459
- Columns
- 17
- Source cadence
- Monthly
- Last refreshed
- Oct 1, 2026
- Theme
- economy
| Column | Type | Description |
|---|---|---|
| period | string | ISO calendar month 'YYYY-MM'. Join key for the monthly and industry datasets. |
| country_code | string | ISO alpha-3 country (always USA — national panel). |
| state_name | string | US state name, or 'Dist. of Columbia'. Join key with period (with state_code). |
| state_code | string | USPS two-letter state code. Join key with period. |
| region | string | Challenger's own region taxonomy: east, midwest, west, south. |
| cuts_month | integer | Announced job cuts in the state that month. Blank month cells in the source tables are 0 announced cuts (region and national totals reconcile). (unit: jobs) |
| cuts_ytd | integer | Year-to-date announced cuts in the state (January = cuts_month). (unit: jobs) |
| cuts_yoy_pct | float | January: YoY change in the month's cuts vs Jan-2025. February–September: YoY change in YTD cuts vs YTD-2025. NULL where the report gave no prior-year comparator. (unit: percent) |
| share_of_month_pct | float | State's share of the month's national announced cuts, computed in code (round 1). (unit: percent) |
| share_of_ytd_pct | float | State's share of national YTD announced cuts, computed in code (round 1). (unit: percent) |
| rank_month | integer | State rank by month cuts within the month, 1 = most cuts (ties broken alphabetically, deterministic). |
| layoff_shock_score | float | 0–100 layoff-shock score: 100 * cuts_month / max cuts_month that month (round 1). 100 = the hardest-hit state that month; a ready-made demand-shock severity feature. |
| report_url | string | Canonical Challenger blog URL of the month's report. |
| source_basis | string | challenger_report: read from the month's own report; challenger_report;cross_outlet_verified: key figure(s) confirmed across >=2 independent outlets quoting the Challenger release. |
| source_domain | string | Always challengergray.com. |
| collected_at | string | Date the figures were collected (ISO). |
| row_hash | string | SHA-256 (16 hex chars) of the row payload; changes when any field changes. |
- Current
20261001T141622Z-e2ce9fcba546 · sha256 e2ce9fcba546…
459 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/challenger_layoff_intel/us_layoff_region_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/challenger_layoff_intel/us_layoff_region_monthly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)API endpoint: https://datazimuts.com/v1/datasets/challenger_layoff_intel/us_layoff_region_monthly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20261001T141622Z-e2ce9fcba546 and its content hash, so readers get exactly the data you used.
Challenger monthly layoff announcements (agent-curated). (2026). US layoff announcements by state, monthly (Challenger) [Data set, snapshot 20261001T141622Z-e2ce9fcba546, sha256 e2ce9fcba546]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/en/datasets/challenger_layoff_intel/us_layoff_region_monthly?snapshot=20261001T141622Z-e2ce9fcba546
@misc{dz_challenger_layoff_intel_us_layoff_region_e2ce9fcb,
title = {{US layoff announcements by state, monthly (Challenger)}},
author = {{Challenger monthly layoff announcements (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/en/datasets/challenger_layoff_intel/us_layoff_region_monthly?snapshot=20261001T141622Z-e2ce9fcba546}},
note = {Snapshot 20261001T141622Z-e2ce9fcba546, sha256 e2ce9fcba54677857d0f822093d6ce1c212afc13a44e6c100c7e762162d7013b; accessed 2026-10-02}
}Embed a table or a chart
Paste this into any page. The embed is pinned to the same snapshot, follows the reader's light or dark setting, and always shows the source, license and a link back.
<iframe src="https://datazimuts.com/embed/chart?dataset=challenger_layoff_intel%2Fus_layoff_region_monthly&lang=en&theme=auto&snapshot=20261001T141622Z-e2ce9fcba546&x=period&y=cuts_month&agg=avg" title="US layoff announcements by state, monthly (Challenger)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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