US county business vitality (annual, by NAICS sector)

Name: US county business vitality (annual, by NAICS sector)
Creator: U.S. Census Bureau
License: https://www.census.gov/programs-surveys/cbp.html
Keywords: us, annual, county, employment, census, labor, demand-forecasting, business-vitality

Usage commercial OKÀ jourLicence: CC0-1.0

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Le jeu complet n’est pas encore publié — revenez après la prochaine actualisation.Télécharger l'exemple CSV Télécharger l'exemple JSONLignes d’exemple seulement (jusqu’à 20) — pas le jeu de données complet.

Annual US county business-vitality intelligence from the keyless U.S. Census County Business Patterns bulk files (2021-2023): one row per year per county (~3,190 counties) per NAICS 2-digit sector (20 sectors) plus an all-sectors county row. Each row carries establishments, March-12 paid employment, Q1 and annual payroll (normalized to whole dollars), average pay per employee, the small-establishment (<5 employees) share, and the Census noise-infusion flag. Derived: exact year-over-year changes in establishments/employment/payroll (null when the prior year is absent, never interpolated), 2021->2023 two-year CAGRs, each sector's share of county employment, the county employment concentration HHI (0-10000, lower = more diversified), and a documented 0-100 county vitality score (40% winsorized employment momentum + 30% winsorized establishment momentum + 30% sector-mix diversity, min-max over scored county-years) with per-state ranks and quartile tiers, plus per-(state, sector) employment-growth ranks. Method: parse the 21-row sector panel from each CBP county file, resolve FIPS to canonical county names via the Census gazetteer, null suppressed size-class cells (never zero-fill), keep payroll in dollars. Caveats: cells carry Census noise infusion (flagged); payroll is in dollars converted from the published $1,000s; scores are relative history gauges, not forecasts; reference year lags ~2 years. U.S. federal public domain (commercial reuse allowed with attribution to the U.S. Census Bureau).

Les titres et les descriptions proviennent des sources de données, en anglais.

Source: U.S. Census Bureau
Lignes: 158 681
Colonnes: 32
Cadence de la source: Annuelle
Dernière actualisation: 29 sept. 2026
Thème: economy

Colonne	Type	Description
country_code	string	ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3)
year	integer	CBP reference year: the 12 months ending December of the reference year (establishment count as of March 12). (unit: calendar year)
fips	string	Five-digit county FIPS code (2-digit state + 3-digit county), zero-padded; the county-grain join key.
county_name	string	Canonical county name resolved from the 5-digit FIPS via a union of the 2018/2021/2024 Census county gazetteers (verbatim Census labels; later vintages win on conflicts, so e.g. 2021 Connecticut counties keep their 2021 names).
state_fips	string	Two-digit state FIPS code.
state_abbr	string	USPS two-letter state abbreviation.
state_name	string	Canonical state name.
sector_code	string	NAICS 2022 2-digit sector code, or 'all' for the county all-sectors total row; the sector-grain join key within a county-year.
sector_name	string	Canonical NAICS 2-digit sector name ('All NAICS sectors' on the 'all' row).
sector_level	string	'all_sectors' on the county total row, 'naics_sector' on the 20 sector rows.
establishments	integer	Number of business establishments with paid employees (single physical location), verbatim from CBP. (unit: count)
paid_employees	integer	Paid employment for the pay period including March 12, verbatim from CBP (noise-infused; see noise_flag). (unit: persons)
q1_payroll_usd	integer	First-quarter payroll, converted verbatim from the CBP $1,000s cell to whole dollars. (unit: US dollars)
annual_payroll_usd	integer	Annual payroll, converted verbatim from the CBP $1,000s cell to whole dollars. (unit: US dollars)
avg_annual_pay_per_employee_usd	float	Annual payroll / paid employees (derived; null when employment is zero). County-total cells are sanity-bounded at $1M; sector cells can legitimately be extreme (tiny cells, e.g. 2-employee unclassified industries) and are bounded at $5M. (unit: US dollars per employee)
small_est_share_pct	float	Share of establishments with fewer than 5 employees (the CBP n<5 size class); null where the publisher suppresses the size-class cell (never zero-filled). (unit: percent)
noise_flag	string	CBP employment/payroll noise-infusion flag (G/H/J levels), verbatim from the publisher; the numeric cells are noise-infused at the flagged level.
est_yoy_pct	float	Year-over-year change in establishments vs the exact prior reference year; null for the first year, never interpolated. (unit: percent)
emp_yoy_pct	float	Year-over-year change in paid employment vs the exact prior reference year; null for the first year. (unit: percent)
ap_yoy_pct	float	Year-over-year change in annual payroll vs the exact prior reference year; null for the first year. (unit: percent)
est_cagr_2y_pct	float	Two-year CAGR of establishments, 2021 -> 2023 ((v2023/v2021)^(1/2)-1); carried on the 2023 row only. (unit: percent per year)
emp_cagr_2y_pct	float	Two-year CAGR of paid employment, 2021 -> 2023; carried on the 2023 row only. (unit: percent per year)
ap_cagr_2y_pct	float	Two-year CAGR of annual payroll, 2021 -> 2023; carried on the 2023 row only. (unit: percent per year)
sector_emp_share_pct	float	The sector's share of the county's total paid employment that year (sector rows only; null on the 'all' row). Sums to 100% for counties carrying the full 20-sector panel; fully suppressed sectors are omitted from the CBP file (never zero-filled), so partial-panel counties' published shares sum below 100%. (unit: percent)
county_emp_concentration_hhi	float	Herfindahl-Hirschman index of employment across the county's published sector rows that year, 0-10000; lower = a more diversified business base. A county-grain value, repeated on every row of the county-year for join convenience (for partial-panel counties the HHI covers the published sectors only). (unit: HHI (0-10000))
county_vitality_score	string	Documented 0-100 county business-vitality composite: 100(0.40minmax(winsor(emp_yoy_pct,+-25pp)) + 0.30minmax(winsor(est_yoy_pct,+-25pp)) + 0.30minmax(1 - hhi/10000)), min-max over the snapshot's scored county-years. A county-grain value, repeated on every row of the county-year for join convenience; 2021 rows are null (no prior year to score against). Higher = thriving county business base; a relative gauge, not a forecast.
county_vitality_rank_state	string	Deterministic rank of the county's vitality score within its state that year, 1 = strongest (county-grain, repeated on every row).
county_vitality_tier	string	Vitality-score quartile within the scored population: p1 (top quartile) .. p4 (county-grain, repeated on every row).
sector_emp_yoy_rank_state	string	Within each (state, sector, year), deterministic rank of the sector's employment growth, 1 = fastest (sector rows only).
as_of	string	Snapshot date (UTC) when this panel was built; the CBP reference years themselves are annual. (unit: ISO date)
source_url	string	The exact CBP bulk zip the row's reference year was parsed from (per-year provenance).
row_hash	string	Deterministic 16-hex row identity: sha256('CBP-VIT\|year\|fips\|sector_code')[:16].

10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.

country_code	year	fips	county_name	state_fips	state_abbr	state_name	sector_code	sector_name	sector_level	establishments	paid_employees	q1_payroll_usd	annual_payroll_usd	avg_annual_pay_per_employee_usd	small_est_share_pct	noise_flag	est_yoy_pct	emp_yoy_pct	ap_yoy_pct	est_cagr_2y_pct	emp_cagr_2y_pct	ap_cagr_2y_pct	sector_emp_share_pct	county_emp_concentration_hhi	county_vitality_score	county_vitality_rank_state	county_vitality_tier	sector_emp_yoy_rank_state	as_of	source_url	row_hash
USA	2 023	01001	Autauga County	01	AL	Alabama	11	Agriculture, Forestry, Fishing and Hunting	naics_sector	10	50	667 000	2 945 000	58 900	60	H	0	-3,846	-15,301	0	-19,936	-24,062	0,392	1 313,41	64,01	17	p2	29	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	a01743617d755c15
USA	2 023	01001	Autauga County	01	AL	Alabama	21	Mining, Quarrying, and Oil and Gas Extraction	naics_sector	4	26	385 000	1 108 000	42 615,385	—	J	33,333	-16,129	-37,045	-18,35	-45,333	-53,589	0,204	1 313,41	64,01	17	p2	19	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	e78dc6dff7f3b0b8
USA	2 023	01001	Autauga County	01	AL	Alabama	22	Utilities	naics_sector	5	139	3 791 000	14 898 000	107 179,856	—	G	0	8,594	-13,76	0	0,362	-5,586	1,09	1 313,41	64,01	17	p2	2	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	c00aac81dd736f49
USA	2 023	01001	Autauga County	01	AL	Alabama	23	Construction	naics_sector	100	563	6 656 000	27 960 000	49 662,522	66	G	4,167	-6,323	1,426	5,409	5,378	2,928	4,416	1 313,41	64,01	17	p2	59	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	37d3c74596006bca
USA	2 023	01001	Autauga County	01	AL	Alabama	31	Manufacturing	naics_sector	28	1 484	27 587 000	121 140 000	81 630,728	35,714	G	0	15,847	11,45	10,335	16,897	17,308	11,64	1 313,41	64,01	17	p2	6	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	207023b9c30809b4
USA	2 023	01001	Autauga County	01	AL	Alabama	42	Wholesale Trade	naics_sector	29	329	5 571 000	22 767 000	69 200,608	51,724	J	-3,333	-4,638	-0,341	7,703	17,821	19,153	2,581	1 313,41	64,01	17	p2	47	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	e3fdba5adb538046
USA	2 023	01001	Autauga County	01	AL	Alabama	44	Retail Trade	naics_sector	189	2 911	22 207 000	90 608 000	31 126,074	41,27	G	6,78	3,447	11,174	6,383	6,698	16,234	22,833	1 313,41	64,01	17	p2	27	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	2e2314fa2f0d4dc9
USA	2 023	01001	Autauga County	01	AL	Alabama	48	Transportation and Warehousing	naics_sector	17	179	2 562 000	10 259 000	57 312,849	47,059	H	-15	1,13	13,964	0	31,193	25,294	1,404	1 313,41	64,01	17	p2	39	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	c3799e84a867d711
USA	2 023	01001	Autauga County	01	AL	Alabama	51	Information	naics_sector	15	107	989 000	3 854 000	36 018,692	40	H	0	33,75	-2,455	3,51	27,327	10,262	0,839	1 313,41	64,01	17	p2	8	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	e486478a7c600413
USA	2 023	01001	Autauga County	01	AL	Alabama	52	Finance and Insurance	naics_sector	76	374	6 136 000	25 023 000	66 906,417	65,789	G	-1,299	-8,78	0,854	5,719	0,134	6,087	2,934	1 313,41	64,01	17	p2	56	2026-09-29	https://www2.census.gov/programs-surveys/cbp/datasets/2023/cbp23co.zip	f793cbcb79bae909

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/cbp_county_business_intel/us_county_business_vitality_annual" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/cbp_county_business_intel/us_county_business_vitality_annual").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/cbp_county_business_intel/us_county_business_vitality_annual

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

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APA

U.S. Census Bureau. (2026). US county business vitality (annual, by NAICS sector) [Data set, snapshot 20260929T151700Z-7d0f2d407999, sha256 7d0f2d407999]. Datazimuts. Retrieved 2026-09-30, from https://datazimuts.com/fr/datasets/cbp_county_business_intel/us_county_business_vitality_annual?snapshot=20260929T151700Z-7d0f2d407999

BibTeX

@misc{dz_cbp_county_business_intel_us_county_busi_7d0f2d40,
  title        = {{US county business vitality (annual, by NAICS sector)}},
  author       = {{U.S. Census Bureau}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/cbp_county_business_intel/us_county_business_vitality_annual?snapshot=20260929T151700Z-7d0f2d407999}},
  note         = {Snapshot 20260929T151700Z-7d0f2d407999, sha256 7d0f2d40799989c125ea7747255eff09ce18f988c0fceb3e86327adb5e5bf1db; accessed 2026-09-30}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=cbp_county_business_intel%2Fus_county_business_vitality_annual&lang=fr&theme=auto&snapshot=20260929T151700Z-7d0f2d407999&x=year&y=year&agg=avg" title="US county business vitality (annual, by NAICS sector)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

US county business vitality (annual, by NAICS sector)

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