US state payroll-employment industry-mix intelligence (monthly)

Name: US state payroll-employment industry-mix intelligence (monthly)
Creator: U.S. Bureau of Labor Statistics
License: https://www.bls.gov/opub/copyright-information.htm
Keywords: us, employment, labor, industry, monthly, signals, rankings, macroeconomics

Commercial use OKFresh

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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.

Source: U.S. Bureau of Labor Statistics
Rows: 6,032
Columns: 37
Source cadence: Monthly
Last refreshed: Oct 1, 2026
Theme: economy

Column	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).

First 10 sample rows — a preview, not the complete dataset.

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	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	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	—	—	—	—	—	—	—	SMS12000000000000001,SMS12000006000000001,SMS12000006500000001,SMS12000007000000001	982c485947ee1715

Profiled Oct 1, 2026 from snapshot 20261001T185416Z-b28358338c12

Measured

Completeness: 98%
Rows: 6,032
Columns: 37
Columns with gaps: 7

Rows per yearmonth · 2017–2026 · 10 years

20172026

Column	Missing	Distinct	Range	Distribution
monthvarchar	0%	90	—	2017-01-0152 2017-02-0152 2017-03-0152
country_codevarchar	0%	1	—	USA6,032
state_fipsvarchar	0%	54	—	01116 02116 04116
geo_codevarchar	0%	44	—	ak116 al116 ar116
geo_namevarchar	0%	59	—	Alabama116 Alaska116 Arizona116
emp_total_kdouble	0%	5,797	248.9 → 18,177median 1,933	121 outside 1st–99th percentile
emp_prof_kdouble	0%	3,231	17.8 → 2,916median 225.55	120 outside 1st–99th percentile
emp_care_kdouble	0%	4,344	27.2 → 3,635median 283.1	121 outside 1st–99th percentile
emp_leisure_kdouble	0%	2,958	14.5 → 2,061median 205.4	121 outside 1st–99th percentile
share_prof_pctdouble	0%	6,782	6.33 → 23.07median 13.24	122 outside 1st–99th percentile
share_care_pctdouble	0%	7,605	9.71 → 24.52median 15.71	122 outside 1st–99th percentile
share_leisure_pctdouble	0%	6,731	4.42 → 26.51median 10.18	122 outside 1st–99th percentile
share_tracked_pctdouble	0%	6,260	26.48 → 50.48median 39.69	122 outside 1st–99th percentile
us_share_prof_pctdouble	0%	138	14.01 → 14.86median 14.2	104 outside 1st–99th percentile
us_share_care_pctdouble	0%	115	15.75 → 17.59median 16.14	156 outside 1st–99th percentile
us_share_leisure_pctdouble	0%	142	6.69 → 11.09median 10.65	208 outside 1st–99th percentile
tilt_prof_ppdouble	0%	5,691	-7.98 → 8.4median -1.13	122 outside 1st–99th percentile
tilt_care_ppdouble	0%	5,881	-7.51 → 7.1median -0.6367	122 outside 1st–99th percentile
tilt_leisure_ppdouble	0%	5,495	-4.21 → 15.6median -0.2908	122 outside 1st–99th percentile
rank_tilt_profbigint	0%	47	1 → 52median 26.5
rank_tilt_carebigint	0%	47	1 → 52median 26.5
rank_tilt_leisurebigint	0%	47	1 → 52median 26.5
total_yoy_pctdouble	10.3%	5,941	-23.6 → 22.5median 1.1	110 outside 1st–99th percentile
prof_yoy_pctdouble	10.3%	4,383	-20.58 → 18.27median 0.8099	110 outside 1st–99th percentile
care_yoy_pctdouble	10.3%	5,682	-18.1 → 15.88median 2.12	110 outside 1st–99th percentile
leisure_yoy_pctdouble	10.3%	5,817	-61.64 → 109.79median 1.64	110 outside 1st–99th percentile
prof_share_chg_yoy_ppdouble	10.3%	5,606	-1.29 → 1.85median 0.007	110 outside 1st–99th percentile
care_share_chg_yoy_ppdouble	10.3%	5,834	-1.62 → 2.5median 0.1595	110 outside 1st–99th percentile
leisure_share_chg_yoy_ppdouble	10.3%	5,357	-11.45 → 7.43median 0.0614	110 outside 1st–99th percentile
leisure_expanding_flagbigint	0%	2	0 → 1median 1
prof_expanding_flagbigint	0%	2	0 → 1median 0
care_expanding_flagbigint	0%	2	0 → 1median 1
hospitality_led_flagbigint	0%	2	0 → 1median 0
b2b_led_flagbigint	0%	2	0 → 1median 0
preliminary_flagbigint	0%	2	0 → 1median 0
bls_series_idsvarchar	0%	50	—	SMS01000000000000001,SMS01000006000000001,SMS01000006500000001,SMS01000007000000001116 SMS02000000000000001,SMS02000006000000001,SMS02000006500000001,SMS02000007000000001116 SMS04000000000000001,SMS04000006000000001,SMS04000006500000001,SMS04000007000000001116
row_hashvarchar	0%	5,089	—	00085f5106eb7c351 002cadbc1d01ec691 004ca574c6ccbc971

Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)

API endpoint: https://datazimuts.com/v1/datasets/bls_state_industry_mix_intel/us_state_industry_mix_monthly

Tip: fetch /llms.txt for the full machine-readable catalog.

Cite this snapshot

Pinned to snapshot 20261001T185416Z-b28358338c12 and its content hash, so readers get exactly the data you used.

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/en/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/en/datasets/bls_state_industry_mix_intel/us_state_industry_mix_monthly?snapshot=20261001T185416Z-b28358338c12}},
  note         = {Snapshot 20261001T185416Z-b28358338c12, sha256 b28358338c12c3b80d969083e5f5eb55b48a24811672ce22ba477c74dd97a874; accessed 2026-10-02}
}

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US state payroll-employment industry-mix intelligence (monthly)

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