US industry-mix employment intelligence (monthly)
Monthly US industry-mix employment intelligence from the BLS Current Employment Statistics national program (keyless BLS Public Data API v2): seasonally adjusted employment for the 11 CES supersectors with shares of total nonfarm, YoY / MoM / 3-month-annualized growth, additive percentage-point contributions that exactly decompose national job growth by industry, a documented 0-100 industry-momentum score with per-month ranks and tiers, expanding / contracting / rapid-growth / 12-month-record-high / top-driver / biggest-drag flags, and month-level context (national headline, goods-vs-services growth gap, industry breadth, industry spread). Consistent methodology window 2017-01 onward. Caveats: the latest month is preliminary and routinely revised; estimates are benchmark-revised annually. BLS material is public domain (commercial reuse allowed); source: U.S. Bureau of Labor Statistics.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 1 276
- Colonnes
- 32
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 27 sept. 2026
- Thème
- economy
| Colonne | Type | Description |
|---|---|---|
| month | string | Reference month: first day of the month, ISO date. The panel covers the consistent methodology window 2017-01 onward (the 2016 fetch year supplies the 12-month lags). (unit: ISO date) |
| country_code | string | ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3) |
| supersector_code | string | CES 2-digit supersector code (10 = mining & logging, 20 = construction, 30 = manufacturing, 40 = trade / transportation / utilities, 50 = information, 55 = financial activities, 60 = professional & business services, 65 = private education & health services, 70 = leisure & hospitality, 80 = other services, 90 = government). Primary join key with month. |
| supersector_name | string | Official BLS CES supersector name. |
| emp_sa_k | float | Published seasonally adjusted supersector employment, all employees, kept verbatim as published. (unit: thousands of jobs) |
| share_pct | float | Supersector share of total nonfarm employment that month; the 11 shares sum to 100 (identity-gated). (unit: percent) |
| emp_yoy_pct | float | Year-over-year percent change of seasonally adjusted employment. (unit: percent) |
| emp_mom_pct | float | Month-over-month percent change of seasonally adjusted employment. (unit: percent) |
| emp_3m_ann_pct | float | 3-month annualized growth: ((lvl_t / lvl_{t-3}) ** 4 - 1) * 100. (unit: percent) |
| chg_12m_k | float | 12-month level change of seasonally adjusted employment. (unit: thousands of jobs) |
| contribution_pp | float | Additive contribution to national job growth: chg_12m_k / total_{t-12} * 100. The 11 contributions sum to the total YoY percent change (identity-gated within 0.05 pp). (unit: percentage points) |
| industry_momentum_score | float | Documented 0-100 composite: 100 * (0.50 * min-max(winsorized emp_yoy_pct, +/-8) + 0.50 * min-max(winsorized contribution_pp, +/-1.5)), min-maxed within each month across the 11 supersectors. Higher = the industry is driving this month's job growth versus peer industries. (unit: 0-100 score) |
| momentum_rank | integer | Per-month rank of industry_momentum_score across the 11 supersectors (1 = hottest). (unit: rank) |
| momentum_tier | string | Quartile bucket of industry_momentum_score within the month: t1 (>=75, hottest) .. t4 (<25). (unit: tier) |
| expanding_flag | integer | 1 when emp_yoy_pct is positive. (unit: binary) |
| contracting_flag | integer | 1 when emp_yoy_pct is negative (employment shrinking year-over-year). (unit: binary) |
| rapid_growth_flag | integer | 1 when emp_yoy_pct >= 3.0 (a hot industry labor market). (unit: binary) |
| record_high_12m_flag | integer | 1 when employment equals the trailing-12-month maximum (inclusive, minimum 12 observations). (unit: binary) |
| top_driver_flag | integer | 1 for the supersector with the largest contribution_pp that month. (unit: binary) |
| biggest_drag_flag | integer | 1 for the supersector with the smallest contribution_pp that month. (unit: binary) |
| total_emp_sa_k | float | Month-level context: published national total-nonfarm employment, seasonally adjusted (series CES0000000001). (unit: thousands of jobs) |
| total_yoy_pct | float | Month-level context: YoY percent change of national total-nonfarm employment. (unit: percent) |
| goods_yoy_pct | float | Month-level context: YoY percent change of goods-producing employment (mining & logging + construction + manufacturing). (unit: percent) |
| services_yoy_pct | float | Month-level context: YoY percent change of private services employment (the seven private-services supersectors). (unit: percent) |
| goods_minus_services_pp | float | Month-level context: goods_yoy_pct minus services_yoy_pct, in percentage points. Positive = goods-producing industries outrun services. (unit: pp) |
| n_growing | integer | Month-level context: how many of the 11 supersectors grew year-over-year (0-11) — the breadth count. (unit: count) |
| breadth_pct | float | Month-level context: 100 * n_growing / 11 — the industry breadth gauge. (unit: percent) |
| top_driver_name | string | Month-level context: supersector name behind top_driver_flag that month. |
| biggest_drag_name | string | Month-level context: supersector name behind biggest_drag_flag that month. |
| industry_spread_pp | float | Month-level context: max minus min supersector emp_yoy_pct that month — the industry divergence gauge. (unit: pp) |
| bls_series_id | string | BLS series id behind the row (CES<ii>00000001). |
| row_hash | string | Deterministic 16-hex row hash of supersector_code + month (idempotency). |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| month | country_code | supersector_code | supersector_name | emp_sa_k | share_pct | emp_yoy_pct | emp_mom_pct | emp_3m_ann_pct | chg_12m_k | contribution_pp | industry_momentum_score | momentum_rank | momentum_tier | expanding_flag | contracting_flag | rapid_growth_flag | record_high_12m_flag | top_driver_flag | biggest_drag_flag | total_emp_sa_k | total_yoy_pct | goods_yoy_pct | services_yoy_pct | goods_minus_services_pp | n_growing | breadth_pct | top_driver_name | biggest_drag_name | industry_spread_pp | bls_series_id | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-01-01 | USA | 10 | Mining and logging | 647 | 0,444 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES1000000001 | 86383ac712907b9a |
| 2017-01-01 | USA | 20 | Construction | 6 840 | 4,697 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES2000000001 | ef05dbccf57ab628 |
| 2017-01-01 | USA | 30 | Manufacturing | 12 334 | 8,47 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES3000000001 | b0fa131398fd86e0 |
| 2017-01-01 | USA | 40 | Trade, transportation, and utilities | 27 314 | 18,756 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES4000000001 | 6ae9a3851fafbf73 |
| 2017-01-01 | USA | 50 | Information | 2 821 | 1,937 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES5000000001 | 81dc0c973a74c1e5 |
| 2017-01-01 | USA | 55 | Financial activities | 8 397 | 5,766 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES5500000001 | d8b51027d4b2d2ae |
| 2017-01-01 | USA | 60 | Professional and business services | 20 415 | 14,019 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES6000000001 | f5d205d9e5b15eb5 |
| 2017-01-01 | USA | 65 | Private education and health services | 22 941 | 15,753 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES6500000001 | 5e82c8185989dec7 |
| 2017-01-01 | USA | 70 | Leisure and hospitality | 15 874 | 10,9 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES7000000001 | 4218c12170f418bb |
| 2017-01-01 | USA | 80 | Other services | 5 729 | 3,934 | — | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | 145 628 | — | — | — | — | 0 | 0 | — | — | — | CES8000000001 | e79cff4053fa3eee |
- Actuelle
20260927T154520Z-3193daa54503 · sha256 3193daa54503…
1 276 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 "https://datazimuts.com/v1/datasets/bls_industry_mix_intel/us_industry_employment_mix_monthly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/bls_industry_mix_intel/us_industry_employment_mix_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/bls_industry_mix_intel/us_industry_employment_mix_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20260927T154520Z-3193daa54503 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
U.S. Bureau of Labor Statistics. (2026). US industry-mix employment intelligence (monthly) [Data set, snapshot 20260927T154520Z-3193daa54503, sha256 3193daa54503]. Datazimuts. Retrieved 2026-09-27, from https://datazimuts.com/fr/datasets/bls_industry_mix_intel/us_industry_employment_mix_monthly?snapshot=20260927T154520Z-3193daa54503
@misc{dz_bls_industry_mix_intel_us_industry_emplo_3193daa5,
title = {{US industry-mix employment intelligence (monthly)}},
author = {{U.S. Bureau of Labor Statistics}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/bls_industry_mix_intel/us_industry_employment_mix_monthly?snapshot=20260927T154520Z-3193daa54503}},
note = {Snapshot 20260927T154520Z-3193daa54503, sha256 3193daa54503e3562982458494789602be752527a9d7ca5b46aa055dd92d399c; accessed 2026-09-27}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=bls_industry_mix_intel%2Fus_industry_employment_mix_monthly&lang=fr&theme=auto&snapshot=20260927T154520Z-3193daa54503&x=month&y=emp_sa_k&agg=avg" title="US industry-mix employment intelligence (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
Posez une question sur ce jeu de données. Les réponses viennent uniquement de sa fiche, de son profil mesuré et de son historique, et citent les faits utilisés.