US layoff announcements, monthly (Challenger)
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Agent-curated labor-churn intelligence from the Challenger, Gray & Christmas monthly Job Cuts Reports, January–August 2026 (read 2026-09-30): 8 rows with announced cuts (108,435 in Jan, cooling to 33,429 in Jul, 52,881 in Aug), MoM/YoY momentum, YTD cuts (529,914 through Aug, −41% YoY), announced hiring plans, cuts-to-hire ratio (20.4 in Jan down to 1.8 in Mar), top industry and top cited reason per month (AI led March–July, peaking at a record 38,579 in May), a z-scored layoff-intensity score, and a churn-pressure band (critical/elevated/guarded/balanced). A shop joins on period to read consumer-spending capacity; subscription businesses read the ratio and band as a churn signal. MoM values computed in code from reported totals and checked against stated headlines; May hiring plans (19,536) and June YTD cuts (443,604) derived from reported YTDs. Provenance: agent-curated (intel-1 run 2026-09-30); announced plans, not completed layoffs; national panel, no geo breakdown.
- Rows
- 8
- Columns
- 24
- Source cadence
- Monthly
- Last refreshed
- Sep 30, 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). |
| total_cuts | integer | Announced job cuts in the month (headcount). (unit: jobs) |
| cuts_mom_pct | float | Month-over-month change in announced cuts, computed in code from reported totals (seeded with Dec 2025: 35,553) and checked within 1.5pp of the stated headline. (unit: percent) |
| cuts_yoy_pct | float | Year-over-year change in announced cuts, as stated in the report. NULL where the report gave no comparator. (unit: percent) |
| ytd_cuts | integer | Year-to-date announced cuts. June (443,604) derived as May YTD + June total. (unit: jobs) |
| ytd_yoy_pct | float | YTD year-over-year change; Feb (−29.3) derived from reported Jan+Feb totals of both years. (unit: percent) |
| hiring_plans | integer | Announced hiring plans in the month. May (19,536) derived as May hiring YTD − April hiring YTD. (unit: jobs) |
| hiring_mom_pct | float | Month-over-month change in hiring plans, computed in code (seeded with Dec 2025: 10,431) and checked within 1.5pp of the stated headline. (unit: percent) |
| hiring_yoy_pct | float | Year-over-year change in hiring plans, as stated. NULL where the report gave no comparator. (unit: percent) |
| cuts_to_hire_ratio | float | total_cuts / hiring_plans (round 2). >1 means more announced cuts than hires — the churn signal. |
| top_industry | string | Industry slug with the most announced cuts that month. |
| top_industry_cuts | integer | Announced cuts in the top industry. (unit: jobs) |
| top_reason | string | Most-cited reason for cuts that month (contract_loss, closings, restructuring, artificial_intelligence). |
| top_reason_cuts | integer | Announced cuts attributed to the top reason. (unit: jobs) |
| layoff_intensity_score | float | Z-score of total_cuts against the Jan–Aug 2026 sample (population std, round 2). Positive = above-average layoff month. |
| churn_pressure_band | string | critical (ratio ≥ 8), elevated (≥ 4), guarded (≥ 2), balanced (< 2). Derived from cuts_to_hire_ratio. |
| publication_date | date | Challenger report publication date (first Thursday of the following month). |
| 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). |
| note | string | Month context: records set, drivers, and notable company announcements from the report. |
| row_hash | string | SHA-256 (16 hex chars) of the row payload; changes when any field changes. |
First 8 sample rows — a preview, not the complete dataset.
| period | country_code | total_cuts | cuts_mom_pct | cuts_yoy_pct | ytd_cuts | ytd_yoy_pct | hiring_plans | hiring_mom_pct | hiring_yoy_pct | cuts_to_hire_ratio | top_industry | top_industry_cuts | top_reason | top_reason_cuts | layoff_intensity_score | churn_pressure_band | publication_date | report_url | source_basis | source_domain | collected_at | note | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-01 | USA | 108,435 | 205 | 118 | 108,435 | 118 | 5,306 | -49.1 | -13 | 20.44 | transportation | 31,243 | contract_loss | 30,784 | 1.68 | critical | 2026-02-05 | https://www.challengergray.com/blog/challenger-report-january-job-cuts-surge-lowest-january-hiring-on-record/ | challenger_report | challengergray.com | 2026-09-30 | Highest January total since 2009 (241,749); highest monthly since Oct 2025 (153,074). Transportation led on the UPS-Amazon split (30,000 of 31,243); Technology 22,291 on Amazon's 16,000 restructuring; Health Care 17,107 (most since Apr 2020). Hiring plans 5,306 — lowest January on record (since 2009). | 8963528d88ea6b5f |
| 2026-02 | USA | 48,307 | -55.5 | -72 | 156,742 | -29.3 | 12,755 | 140.4 | -63 | 3.79 | technology | 11,039 | closings | 10,736 | -0.71 | guarded | 2026-03-05 | https://www.challengergray.com/blog/challenger-report-february-cuts-plunge-hiring-falls-56-percent/ | challenger_report | challengergray.com | 2026-09-30 | Lowest January–February total since 2022. Education 5,417 on school budget season (+96% YoY YTD); Industrial Manufacturing YTD +143%. AI cited for 4,680 cuts (~10% of the month). | d0fa5eced304eb0a |
| 2026-03 | USA | 60,620 | 25.5 | -78 | 217,362 | -56 | 32,826 | 157.4 | 149 | 1.85 | technology | 18,720 | artificial_intelligence | 15,341 | -0.22 | balanced | 2026-04-02 | https://www.challengergray.com/blog/challenger-report-march-cuts-rise-25-from-february-ai-leads-reasons/ | challenger_report | challengergray.com | 2026-09-30 | Q1 total 217,362 — lowest Q1 since 2022. AI led reasons for the first month (15,341, 25% of cuts). Transportation YTD 32,241 (+703%, record Q1); Health Care YTD 23,520 (record Q1). Hiring rebounded on seasonal summer jobs (~21% of March plans). | c7a4b1ce3c96b4b5 |
| 2026-04 | USA | 83,387 | 37.6 | -21 | 300,749 | -50 | 10,049 | -69.4 | -38 | 8.3 | technology | 33,361 | artificial_intelligence | 21,490 | 0.68 | critical | 2026-05-07 | https://www.challengergray.com/blog/challenger-report-april-job-cuts-rise-38-from-march-ytd-cuts-down-50/ | challenger_report | challengergray.com | 2026-09-30 | Third-highest April since 2009. AI led for the second straight month (21,490, 26% of cuts). Government 9,149 — highest since Mar 2025's 216,915. Pharma YTD +500%, Chemical YTD +167%. | bea35034c461cd22 |
| 2026-05 | USA | 97,006 | 16.3 | 3 | 397,755 | -43 | 19,536 | 94.4 | — | 4.97 | technology | 38,242 | artificial_intelligence | 38,579 | 1.23 | elevated | 2026-06-04 | https://www.challengergray.com/blog/challenger-report-may-job-cuts-rise-16-from-april-highest-may-total-since-2020/ | challenger_report;cross_outlet_verified | challengergray.com | 2026-09-30 | Highest May since 2020; third straight monthly rise. AI hit a record monthly 38,579 (40% of cuts; YTD 87,714 vs 54,836 in all of 2025). Transportation 6,909; FinTech 5,731; Government 4,499. | eb1024efa9ec70a6 |
| 2026-06 | USA | 45,849 | -52.7 | — | 443,604 | -40 | 10,933 | -44 | — | 4.19 | technology | 15,503 | artificial_intelligence | 14,029 | -0.81 | elevated | 2026-07-01 | https://www.challengergray.com/blog/challenger-report-june-layoffs-cool-to-45849-down-53-from-may-ai-leads-reasons-for-fourth-consecutive-month/ | challenger_report;cross_outlet_verified | challengergray.com | 2026-09-30 | Summer cooling (−53% MoM), lowest monthly since Dec 2025. AI led reasons for the fourth straight month (14,029). Cuts remained concentrated in technology (YTD 139,156, +83% YoY). | 937e851d12c13526 |
| 2026-07 | USA | 33,429 | -27.1 | — | 477,033 | -41 | 16,095 | 47.2 | — | 2.08 | technology | 9,867 | artificial_intelligence | 10,970 | -1.31 | guarded | 2026-08-06 | https://www.challengergray.com/blog/challenger-report-layoffs-fall-hiring-picks-up-ai-leads-for-fifth-straight-month/ | challenger_report | challengergray.com | 2026-09-30 | Lowest monthly total since Jul 2024 (25,885). AI led for the fifth straight month (10,970, 33% of cuts). Hiring 16,095 — highest July since 2022. Technology is 31% of all 2026 cuts. | ef9f62bc76266cea |
| 2026-08 | USA | 52,881 | 58.2 | -38 | 529,914 | -41 | 12,325 | -23.4 | 725 | 4.29 | consumer_products | 10,057 | restructuring | 16,173 | -0.53 | elevated | 2026-09-02 | https://www.challengergray.com/blog/challenger-report-august-job-cuts-up-58-consumer-products-food-lead/ | challenger_report | challengergray.com | 2026-09-30 | Lowest August since 2022. AI's five-month run as top reason ended (3,462); Restructuring led (16,173, 31%). Consumer Products 10,057 and Food 7,982 (Tyson ~⅓) led industries. Hiring 12,325 — highest August since 2022. | 91f6df5951f296aa |
Profiled Oct 1, 2026 from snapshot 20260930T191911Z-cc076307a384
Measured- Completeness
- 97.4%
- Rows
- 8
- Columns
- 24
- Columns with gaps
- 2
20262026
| Column | Missing | Distinct | Range | Distribution |
|---|---|---|---|---|
| periodvarchar | 0% | 9 | — |
|
| country_codevarchar | 0% | 1 | — |
|
| total_cutsbigint | 0% | 7 | 33,429 → 108,435median 56,751 | 2 outside 1st–99th percentile |
| cuts_mom_pctdouble | 0% | 7 | -55.5 → 205median 20.9 | 2 outside 1st–99th percentile |
| cuts_yoy_pctdouble | 25% | 6 | -78 → 118median -29.5 | 2 outside 1st–99th percentile |
| ytd_cutsbigint | 0% | 9 | 108,435 → 529,914median 349,252 | 2 outside 1st–99th percentile |
| ytd_yoy_pctdouble | 0% | 7 | -56 → 118median -41 | 2 outside 1st–99th percentile |
| hiring_plansbigint | 0% | 7 | 5,306 → 32,826median 12,540 | 2 outside 1st–99th percentile |
| hiring_mom_pctdouble | 0% | 9 | -69.4 → 157.4median 11.9 | 2 outside 1st–99th percentile |
| hiring_yoy_pctdouble | 37.5% | 4 | -63 → 725median -13 | 2 outside 1st–99th percentile |
| cuts_to_hire_ratiodouble | 0% | 9 | 1.85 → 20.44median 4.24 | 2 outside 1st–99th percentile |
| top_industryvarchar | 0% | 3 | — |
|
| top_industry_cutsbigint | 0% | 9 | 9,867 → 38,242median 17,112 | 2 outside 1st–99th percentile |
| top_reasonvarchar | 0% | 4 | — |
|
| top_reason_cutsbigint | 0% | 9 | 10,736 → 38,579median 15,757 | 2 outside 1st–99th percentile |
| layoff_intensity_scoredouble | 0% | 9 | -1.31 → 1.68median -0.375 | 2 outside 1st–99th percentile |
| churn_pressure_bandvarchar | 0% | 4 | — |
|
| publication_datedate | 0% | 9 | Feb 5, 2026 → Sep 2, 2026 | — |
| report_urlvarchar | 0% | 7 | — |
|
| source_basisvarchar | 0% | 2 | — |
|
| source_domainvarchar | 0% | 1 | — |
|
| collected_atvarchar | 0% | 1 | — |
|
| notevarchar | 0% | 9 | — |
|
| row_hashvarchar | 0% | 7 | — |
|
- Current
20260930T191911Z-cc076307a384 · sha256 cc076307a384…
8 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (JSON-LD + Croissant).
cURL
curl "https://datazimuts.com/v1/datasets/challenger_layoff_intel/us_layoff_announcement_monthly" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/challenger_layoff_intel/us_layoff_announcement_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_announcement_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 20260930T191911Z-cc076307a384 and its content hash, so readers get exactly the data you used.
APA
Challenger monthly layoff announcements (agent-curated). (2026). US layoff announcements, monthly (Challenger) [Data set, snapshot 20260930T191911Z-cc076307a384, sha256 cc076307a384]. Datazimuts. Retrieved 2026-10-01, from https://datazimuts.com/en/datasets/challenger_layoff_intel/us_layoff_announcement_monthly?snapshot=20260930T191911Z-cc076307a384
BibTeX
@misc{dz_challenger_layoff_intel_us_layoff_announ_cc076307,
title = {{US layoff announcements, 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_announcement_monthly?snapshot=20260930T191911Z-cc076307a384}},
note = {Snapshot 20260930T191911Z-cc076307a384, sha256 cc076307a3848f6a5aadd1f1b82d512a778774e259ba47b195de7a52d452cd85; accessed 2026-10-01}
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