US state payroll-employment industry-mix intelligence (monthly)
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Monthly US state payroll-employment industry-mix intelligence from the BLS Current Employment Statistics state-and-area program (keyless BLS Public Data API v2): seasonally adjusted employment for the 50 states, the District of Columbia and Puerto Rico across total nonfarm and three bellwether supersectors — professional & business services (B2B demand), education & health services (defensive demand), leisure & hospitality (discretionary/tourism demand) — with within-state employment shares, percentage-point tilts versus the US mix, per-month state tilt ranks, 12-month growth and mix-shift gauges, and hospitality-led / B2B-led mix-shift flags. Consistent methodology window 2017-01 onward. Caveats: the latest month is preliminary and routinely revised; state 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
- 6 032
- Colonnes
- 37
- Cadence de la source
- Mensuelle
- Dernière actualisation
- 1 oct. 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) |
| state_fips | string | Census FIPS state code (e.g. 06 = California, 11 = District of Columbia, 72 = Puerto Rico). |
| geo_code | string | Stable geography code: USPS postal abbreviation, lowercase (ca, tx, ny, ...; dc, pr). Primary join key with month. |
| geo_name | string | BLS catalog State field value (e.g. California, District of Columbia, Puerto Rico). |
| emp_total_k | float | Published seasonally adjusted total-nonfarm employment, all employees, kept verbatim as published. (unit: thousands of jobs) |
| emp_prof_k | float | Published SA professional & business services employment, kept verbatim as published. (unit: thousands of jobs) |
| emp_care_k | float | Published SA education & health services employment, kept verbatim as published. (unit: thousands of jobs) |
| emp_leisure_k | float | Published SA leisure & hospitality employment, kept verbatim as published. (unit: thousands of jobs) |
| share_prof_pct | float | Professional & business services share of the state's total nonfarm employment. (unit: percent) |
| share_care_pct | float | Education & health services share of the state's total nonfarm employment. (unit: percent) |
| share_leisure_pct | float | Leisure & hospitality share of the state's total nonfarm employment. (unit: percent) |
| share_tracked_pct | float | Sum of the three tracked supersector shares. (unit: percent) |
| us_share_prof_pct | float | US professional & business services share of US total nonfarm employment (national benchmark, broadcast on every row). (unit: percent) |
| us_share_care_pct | float | US education & health services share (national benchmark). (unit: percent) |
| us_share_leisure_pct | float | US leisure & hospitality share (national benchmark). (unit: percent) |
| tilt_prof_pp | float | State prof share minus US prof share, in percentage points. Positive = a B2B-heavy labor market. (unit: pp) |
| tilt_care_pp | float | State care share minus US care share, in percentage points. Positive = a defensive, acyclical-heavy labor market. (unit: pp) |
| tilt_leisure_pp | float | State leisure share minus US leisure share, in percentage points. Positive = a tourism/discretionary-heavy labor market. (unit: pp) |
| rank_tilt_prof | integer | Per-month rank of tilt_prof_pp across the 52 geos (1 = highest tilt). (unit: rank) |
| rank_tilt_care | integer | Per-month rank of tilt_care_pp across the 52 geos (1 = highest tilt). (unit: rank) |
| rank_tilt_leisure | integer | Per-month rank of tilt_leisure_pp across the 52 geos (1 = highest tilt). (unit: rank) |
| total_yoy_pct | float | Year-over-year percent change of SA total-nonfarm employment. (unit: percent) |
| prof_yoy_pct | float | Year-over-year percent change of SA professional & business services employment. (unit: percent) |
| care_yoy_pct | float | Year-over-year percent change of SA education & health services employment. (unit: percent) |
| leisure_yoy_pct | float | Year-over-year percent change of SA leisure & hospitality employment. (unit: percent) |
| prof_share_chg_yoy_pp | float | Change in the prof employment share versus 12 months ago, in percentage points (the B2B mix-shift gauge). (unit: pp) |
| care_share_chg_yoy_pp | float | Change in the care employment share versus 12 months ago, in percentage points. (unit: pp) |
| leisure_share_chg_yoy_pp | float | Change in the leisure employment share versus 12 months ago, in percentage points (the discretionary mix-shift gauge). (unit: pp) |
| leisure_expanding_flag | integer | 1 when the leisure employment share rose year-over-year. (unit: binary) |
| prof_expanding_flag | integer | 1 when the prof employment share rose year-over-year. (unit: binary) |
| care_expanding_flag | integer | 1 when the care employment share rose year-over-year. (unit: binary) |
| hospitality_led_flag | integer | 1 when the leisure share rose YoY while total employment was flat or falling — the mix is shifting toward hospitality even as the labor market cools. (unit: binary) |
| b2b_led_flag | integer | 1 when the prof share rose YoY while total employment was flat or falling. (unit: binary) |
| preliminary_flag | integer | 1 for the latest month in the panel (BLS marks it preliminary and routinely revises it). (unit: binary) |
| bls_series_ids | string | Comma-separated BLS series ids behind the row (total, prof, care, leisure statewide series). |
| row_hash | string | Deterministic 16-hex row hash of geo_code + month (idempotency). |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| month | country_code | state_fips | geo_code | geo_name | emp_total_k | emp_prof_k | emp_care_k | emp_leisure_k | share_prof_pct | share_care_pct | share_leisure_pct | share_tracked_pct | us_share_prof_pct | us_share_care_pct | us_share_leisure_pct | tilt_prof_pp | tilt_care_pp | tilt_leisure_pp | rank_tilt_prof | rank_tilt_care | rank_tilt_leisure | total_yoy_pct | prof_yoy_pct | care_yoy_pct | leisure_yoy_pct | prof_share_chg_yoy_pp | care_share_chg_yoy_pp | leisure_share_chg_yoy_pp | leisure_expanding_flag | prof_expanding_flag | care_expanding_flag | hospitality_led_flag | b2b_led_flag | preliminary_flag | bls_series_ids | row_hash |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2017-01-01 | USA | 01 | al | Alabama | 2 009,3 | 235,9 | 239,7 | 200,5 | 11,74 | 11,93 | 9,979 | 33,649 | 14,019 | 15,753 | 10,9 | -2,278 | -3,824 | -0,922 | 34 | 49 | 36 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS01000000000000001,SMS01000006000000001,SMS01000006500000001,SMS01000007000000001 | 67b586506ec5369a |
| 2017-01-01 | USA | 02 | ak | Alaska | 330 | 28,1 | 49,3 | 35,2 | 8,515 | 14,939 | 10,667 | 34,121 | 14,019 | 15,753 | 10,9 | -5,503 | -0,814 | -0,234 | 49 | 31 | 22 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS02000000000000001,SMS02000006000000001,SMS02000006500000001,SMS02000007000000001 | 7199395265cbfae2 |
| 2017-01-01 | USA | 04 | az | Arizona | 2 741,4 | 416,2 | 425,2 | 314,2 | 15,182 | 15,51 | 11,461 | 42,154 | 14,019 | 15,753 | 10,9 | 1,163 | -0,243 | 0,561 | 11 | 22 | 14 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS04000000000000001,SMS04000006000000001,SMS04000006500000001,SMS04000007000000001 | 670ad5452a67cae2 |
| 2017-01-01 | USA | 05 | ar | Arkansas | 1 225,4 | 143,1 | 185,8 | 116,1 | 11,678 | 15,162 | 9,474 | 36,315 | 14,019 | 15,753 | 10,9 | -2,341 | -0,591 | -1,426 | 36 | 28 | 44 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS05000000000000001,SMS05000006000000001,SMS05000006500000001,SMS05000007000000001 | db50408db112722b |
| 2017-01-01 | USA | 06 | ca | California | 16 634,9 | 2 575,6 | 2 605,6 | 1 927,7 | 15,483 | 15,663 | 11,588 | 42,735 | 14,019 | 15,753 | 10,9 | 1,465 | -0,09 | 0,688 | 8 | 21 | 13 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS06000000000000001,SMS06000006000000001,SMS06000006500000001,SMS06000007000000001 | a7ad7405aecc1881 |
| 2017-01-01 | USA | 08 | co | Colorado | 2 626,9 | 408,7 | 330,1 | 329,1 | 15,558 | 12,566 | 12,528 | 40,652 | 14,019 | 15,753 | 10,9 | 1,54 | -3,187 | 1,628 | 7 | 47 | 6 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS08000000000000001,SMS08000006000000001,SMS08000006500000001,SMS08000007000000001 | 0ca73bc3462f3d39 |
| 2017-01-01 | USA | 09 | ct | Connecticut | 1 697,5 | 220 | 342,1 | 156,2 | 12,96 | 20,153 | 9,202 | 42,315 | 14,019 | 15,753 | 10,9 | -1,058 | 4,4 | -1,699 | 28 | 7 | 46 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS09000000000000001,SMS09000006000000001,SMS09000006500000001,SMS09000007000000001 | 138e8fd8913ae910 |
| 2017-01-01 | USA | 10 | de | Delaware | 454,9 | 62,2 | 77 | 50,2 | 13,673 | 16,927 | 11,035 | 41,636 | 14,019 | 15,753 | 10,9 | -0,345 | 1,174 | 0,135 | 16 | 11 | 15 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS10000000000000001,SMS10000006000000001,SMS10000006500000001,SMS10000007000000001 | ef2372a0968309fb |
| 2017-01-01 | USA | 11 | dc | District of Columbia | 781,8 | 166,6 | 127,4 | 75,5 | 21,31 | 16,296 | 9,657 | 47,263 | 14,019 | 15,753 | 10,9 | 7,291 | 0,543 | -1,243 | 1 | 18 | 41 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS11000000000000001,SMS11000006000000001,SMS11000006500000001,SMS11000007000000001 | 342a5ed5e941e333 |
| 2017-01-01 | USA | 12 | fl | Florida | 8 521,9 | 1 315,9 | 1 257,7 | 1 195,4 | 15,441 | 14,758 | 14,027 | 44,227 | 14,019 | 15,753 | 10,9 | 1,423 | -0,995 | 3,127 | 9 | 32 | 3 | — | — | — | — | — | — | — | 0 | 0 | 0 | 0 | 0 | 0 | SMS12000000000000001,SMS12000006000000001,SMS12000006500000001,SMS12000007000000001 | 982c485947ee1715 |
Profilé le 1 oct. 2026 à partir de l’instantané 20261001T185416Z-b28358338c12
Mesuré- Complétude
- 98 %
- Lignes
- 6 032
- Colonnes
- 37
- Colonnes incomplètes
- 7
20172026
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| monthvarchar | 0 % | 90 | — |
|
| country_codevarchar | 0 % | 1 | — |
|
| state_fipsvarchar | 0 % | 54 | — |
|
| geo_codevarchar | 0 % | 44 | — |
|
| geo_namevarchar | 0 % | 59 | — |
|
| emp_total_kdouble | 0 % | 5 797 | 248,9 → 18 177médiane 1 933 | 121 hors du 1er–99e centile |
| emp_prof_kdouble | 0 % | 3 231 | 17,8 → 2 916médiane 225,55 | 120 hors du 1er–99e centile |
| emp_care_kdouble | 0 % | 4 344 | 27,2 → 3 635médiane 283,1 | 121 hors du 1er–99e centile |
| emp_leisure_kdouble | 0 % | 2 958 | 14,5 → 2 061médiane 205,4 | 121 hors du 1er–99e centile |
| share_prof_pctdouble | 0 % | 6 782 | 6,33 → 23,07médiane 13,24 | 122 hors du 1er–99e centile |
| share_care_pctdouble | 0 % | 7 605 | 9,71 → 24,52médiane 15,71 | 122 hors du 1er–99e centile |
| share_leisure_pctdouble | 0 % | 6 731 | 4,42 → 26,51médiane 10,18 | 122 hors du 1er–99e centile |
| share_tracked_pctdouble | 0 % | 6 260 | 26,48 → 50,48médiane 39,69 | 122 hors du 1er–99e centile |
| us_share_prof_pctdouble | 0 % | 138 | 14,01 → 14,86médiane 14,2 | 104 hors du 1er–99e centile |
| us_share_care_pctdouble | 0 % | 115 | 15,75 → 17,59médiane 16,14 | 156 hors du 1er–99e centile |
| us_share_leisure_pctdouble | 0 % | 142 | 6,69 → 11,09médiane 10,65 | 208 hors du 1er–99e centile |
| tilt_prof_ppdouble | 0 % | 5 691 | -7,98 → 8,4médiane -1,13 | 122 hors du 1er–99e centile |
| tilt_care_ppdouble | 0 % | 5 881 | -7,51 → 7,1médiane -0,6367 | 122 hors du 1er–99e centile |
| tilt_leisure_ppdouble | 0 % | 5 495 | -4,21 → 15,6médiane -0,2908 | 122 hors du 1er–99e centile |
| rank_tilt_profbigint | 0 % | 47 | 1 → 52médiane 26,5 | |
| rank_tilt_carebigint | 0 % | 47 | 1 → 52médiane 26,5 | |
| rank_tilt_leisurebigint | 0 % | 47 | 1 → 52médiane 26,5 | |
| total_yoy_pctdouble | 10,3 % | 5 941 | -23,6 → 22,5médiane 1,1 | 110 hors du 1er–99e centile |
| prof_yoy_pctdouble | 10,3 % | 4 383 | -20,58 → 18,27médiane 0,8099 | 110 hors du 1er–99e centile |
| care_yoy_pctdouble | 10,3 % | 5 682 | -18,1 → 15,88médiane 2,12 | 110 hors du 1er–99e centile |
| leisure_yoy_pctdouble | 10,3 % | 5 817 | -61,64 → 109,79médiane 1,64 | 110 hors du 1er–99e centile |
| prof_share_chg_yoy_ppdouble | 10,3 % | 5 606 | -1,29 → 1,85médiane 0,007 | 110 hors du 1er–99e centile |
| care_share_chg_yoy_ppdouble | 10,3 % | 5 834 | -1,62 → 2,5médiane 0,1595 | 110 hors du 1er–99e centile |
| leisure_share_chg_yoy_ppdouble | 10,3 % | 5 357 | -11,45 → 7,43médiane 0,0614 | 110 hors du 1er–99e centile |
| leisure_expanding_flagbigint | 0 % | 2 | 0 → 1médiane 1 | |
| prof_expanding_flagbigint | 0 % | 2 | 0 → 1médiane 0 | |
| care_expanding_flagbigint | 0 % | 2 | 0 → 1médiane 1 | |
| hospitality_led_flagbigint | 0 % | 2 | 0 → 1médiane 0 | |
| b2b_led_flagbigint | 0 % | 2 | 0 → 1médiane 0 | |
| preliminary_flagbigint | 0 % | 2 | 0 → 1médiane 0 | |
| bls_series_idsvarchar | 0 % | 50 | — |
|
| row_hashvarchar | 0 % | 5 089 | — |
|
- Actuelle
20261001T185416Z-b28358338c12 · sha256 b28358338c12…
6 032 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/bls_state_industry_mix_intel/us_state_industry_mix_monthly" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/bls_state_industry_mix_intel/us_state_industry_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_state_industry_mix_intel/us_state_industry_mix_monthly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
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Citer cet instantané
Épinglé à l’instantané 20261001T185416Z-b28358338c12 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
APA
U.S. Bureau of Labor Statistics. (2026). US state payroll-employment industry-mix intelligence (monthly) [Data set, snapshot 20261001T185416Z-b28358338c12, sha256 b28358338c12]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/fr/datasets/bls_state_industry_mix_intel/us_state_industry_mix_monthly?snapshot=20261001T185416Z-b28358338c12
BibTeX
@misc{dz_bls_state_industry_mix_intel_us_state_in_b2835833,
title = {{US state payroll-employment industry-mix intelligence (monthly)}},
author = {{U.S. Bureau of Labor Statistics}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/bls_state_industry_mix_intel/us_state_industry_mix_monthly?snapshot=20261001T185416Z-b28358338c12}},
note = {Snapshot 20261001T185416Z-b28358338c12, sha256 b28358338c12c3b80d969083e5f5eb55b48a24811672ce22ba477c74dd97a874; accessed 2026-10-02}
}Intégrer un tableau ou un graphique
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HTML
<iframe src="https://datazimuts.com/embed/chart?dataset=bls_state_industry_mix_intel%2Fus_state_industry_mix_monthly&lang=fr&theme=auto&snapshot=20261001T185416Z-b28358338c12&x=month&y=emp_total_k&agg=avg" title="US state payroll-employment industry-mix intelligence (monthly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
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