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.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 8
- Colonnes
- 24
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 30 sept. 2026
- Thème
- economy
| Colonne | 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. |
8 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| 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 |
Profilé le 1 oct. 2026 à partir de l’instantané 20260930T191911Z-cc076307a384
Mesuré- Complétude
- 97,4 %
- Lignes
- 8
- Colonnes
- 24
- Colonnes incomplètes
- 2
20262026
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| periodvarchar | 0 % | 9 | — |
|
| country_codevarchar | 0 % | 1 | — |
|
| total_cutsbigint | 0 % | 7 | 33 429 → 108 435médiane 56 751 | 2 hors du 1er–99e centile |
| cuts_mom_pctdouble | 0 % | 7 | -55,5 → 205médiane 20,9 | 2 hors du 1er–99e centile |
| cuts_yoy_pctdouble | 25 % | 6 | -78 → 118médiane -29,5 | 2 hors du 1er–99e centile |
| ytd_cutsbigint | 0 % | 9 | 108 435 → 529 914médiane 349 252 | 2 hors du 1er–99e centile |
| ytd_yoy_pctdouble | 0 % | 7 | -56 → 118médiane -41 | 2 hors du 1er–99e centile |
| hiring_plansbigint | 0 % | 7 | 5 306 → 32 826médiane 12 540 | 2 hors du 1er–99e centile |
| hiring_mom_pctdouble | 0 % | 9 | -69,4 → 157,4médiane 11,9 | 2 hors du 1er–99e centile |
| hiring_yoy_pctdouble | 37,5 % | 4 | -63 → 725médiane -13 | 2 hors du 1er–99e centile |
| cuts_to_hire_ratiodouble | 0 % | 9 | 1,85 → 20,44médiane 4,24 | 2 hors du 1er–99e centile |
| top_industryvarchar | 0 % | 3 | — |
|
| top_industry_cutsbigint | 0 % | 9 | 9 867 → 38 242médiane 17 112 | 2 hors du 1er–99e centile |
| top_reasonvarchar | 0 % | 4 | — |
|
| top_reason_cutsbigint | 0 % | 9 | 10 736 → 38 579médiane 15 757 | 2 hors du 1er–99e centile |
| layoff_intensity_scoredouble | 0 % | 9 | -1,31 → 1,68médiane -0,375 | 2 hors du 1er–99e centile |
| churn_pressure_bandvarchar | 0 % | 4 | — |
|
| publication_datedate | 0 % | 9 | 5 févr. 2026 → 2 sept. 2026 | — |
| report_urlvarchar | 0 % | 7 | — |
|
| source_basisvarchar | 0 % | 2 | — |
|
| source_domainvarchar | 0 % | 1 | — |
|
| collected_atvarchar | 0 % | 1 | — |
|
| notevarchar | 0 % | 9 | — |
|
| row_hashvarchar | 0 % | 7 | — |
|
- Actuelle
20260930T191911Z-cc076307a384 · sha256 cc076307a384…
8 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (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)Point d’accès API : https://datazimuts.com/v1/datasets/challenger_layoff_intel/us_layoff_announcement_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
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Épinglé à l’instantané 20260930T191911Z-cc076307a384 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
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/fr/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/fr/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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<iframe src="https://datazimuts.com/embed/chart?dataset=challenger_layoff_intel%2Fus_layoff_announcement_monthly&lang=fr&theme=auto&snapshot=20260930T191911Z-cc076307a384&x=period&y=total_cuts&agg=avg" title="US layoff announcements, monthly (Challenger)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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