Name: US state business-formation intelligence (monthly)
Creator: U.S. Census Bureau
License: https://www.census.gov/about/policies.html
Keywords: us, monthly, signals, rankings, macroeconomics, entrepreneurship, states, economy

Monthly US state business-formation intelligence from the Census Bureau Business Formation Statistics (keyless monthly time-series CSV): seasonally adjusted business applications, high-propensity / planned-wage / corporation subsets, and projected employer-business formations within 4 and 8 quarters for the 50 states plus the District of Columbia, with pipeline-quality shares, projected formation yields, MoM / YoY percent changes, per-state 24-month z-scores, a documented 0-100 formation-dynamism score with per-month ranks and tiers, surge / slump / 24-month-record-high flags, and month-level national context (US applications, state share of the nation, YoY spread, surging/slumping breadth). Trailing 36 complete months. Caveats: the latest months are routinely revised; formation counts are Census projections, not administrative actuals (actuals lag ~3.5 years); Puerto Rico is excluded (applications only, no formation series). Census Bureau material is public domain (commercial reuse allowed); source: U.S. Census Bureau, Business Formation Statistics.

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

Source: U.S. Census Bureau
Lignes: 1 836
Colonnes: 31
Cadence de la source: Mensuelle
Dernière actualisation: 28 sept. 2026
Thème: economy

Colonne	Type	Description
month	string	Reference month: first day of the month, ISO date. The panel covers the trailing 36 complete months of the Census BFS release (60 months are computed so the YoY and 24-month z-score lags are defined from the window start). (unit: ISO date)
country_code	string	ISO alpha-3 country code (USA for every row). (unit: ISO 3166-1 alpha-3)
state_code	string	Stable geography code: the upstream BFS 2-letter code (CA, TX, NY, ...; DC). Primary join key with month.
state_name	string	USPS state name (California, Texas, ..., District of Columbia).
applications	float	Business Applications (BA_BA): all applications for an Employer Identification Number, seasonally adjusted, kept verbatim as published. (unit: count)
high_propensity	float	High-Propensity Business Applications (BA_HBA): the subset likely to become employer businesses, seasonally adjusted. (unit: count)
planned_wages	float	Business Applications with Planned Wages (BA_WBA): HBA indicating a first wages-paid date, seasonally adjusted. (unit: count)
corporations	float	Business Applications from Corporations (BA_CBA), seasonally adjusted. (unit: count)
proj_formations_4q	float	Projected Business Formations within 4 Quarters (BF_PBF4Q): Census-projected employer businesses originating from the month's applications within four quarters. A projection, not an administrative count. (unit: count)
proj_formations_8q	float	Projected Business Formations within 8 Quarters (BF_PBF8Q). A projection, not an administrative count. (unit: count)
hp_share	float	Pipeline quality: high_propensity / applications. (unit: fraction)
corp_share	float	corporations / applications. (unit: fraction)
planned_wage_share	float	planned_wages / applications. (unit: fraction)
formation_yield_4q	float	Projected conversion: proj_formations_4q / applications. (unit: fraction)
formation_yield_8q	float	Projected conversion: proj_formations_8q / applications. (unit: fraction)
applications_mom_pct	float	Month-over-month percent change of applications. (unit: percent)
applications_yoy_pct	float	Year-over-year percent change of applications. (unit: percent)
z_24m_applications	float	Per-state z-score of applications against its own trailing 24-month mean/std (sample std); 0.0 when the window std is 0; null until 24 observations exist. (unit: z-score)
dynamism_score	float	Documented 0-100 composite: 100 * (0.45 * min-max(winsorized yoy, -30..+30) + 0.30 * min-max(winsorized hp_share, 0..0.35) + 0.25 * min-max(winsorized formation_yield_4q, 0..1)), min-maxed within each month across the 51 geos. Higher = most dynamic formation pipeline versus peers this month. (unit: 0-100 score)
dynamism_rank	integer	Per-month rank of dynamism_score across the 51 geos (1 = most dynamic). (unit: rank)
dynamism_tier	string	Quartile bucket of dynamism_score within the month: t1 (least dynamic) .. t4 (most dynamic). (unit: tier)
yoy_rank	integer	Per-month rank of applications_yoy_pct across the 51 geos (1 = fastest growth). (unit: rank)
surge_flag	integer	1 when applications_yoy_pct >= +10%. (unit: binary)
slump_flag	integer	1 when applications_yoy_pct <= -10%. (unit: binary)
record_high_24m_flag	integer	1 when applications equals its trailing-24-month maximum (minimum 24 observations). (unit: binary)
us_applications	float	Month-level context: the national SA business-application total (Census geo='US'). (unit: count)
state_share_of_us_pct	float	Month-level context: state applications as a percent of the national total. (unit: percent)
national_spread_yoy_pp	float	Month-level context: max minus min applications_yoy_pct across states that month — the national divergence gauge. (unit: pp)
n_surging	integer	Month-level context: how many of the 51 geos surged that month (breadth gauge). (unit: count)
n_slumping	integer	Month-level context: how many of the 51 geos slumped that month (breadth gauge). (unit: count)
row_hash	string	Deterministic 16-hex row hash of state_code + month (idempotency).

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

month	country_code	state_code	state_name	applications	high_propensity	planned_wages	corporations	proj_formations_4q	proj_formations_8q	hp_share	corp_share	planned_wage_share	formation_yield_4q	formation_yield_8q	applications_mom_pct	applications_yoy_pct	z_24m_applications	dynamism_score	dynamism_rank	dynamism_tier	yoy_rank	surge_flag	record_high_24m_flag	us_applications	state_share_of_us_pct	national_spread_yoy_pp	n_surging	row_hash
2023-09-01	USA	AL	Alabama	6 139	1 750	712	252	300	392	0,285	0,041	0,116	0,049	0,064	-0,454	2,625	0,526	32,298	51	t1	43	0	0	470 317	1,305	50,401	26	bfd5e0f1c55291ec
2023-09-01	USA	AK	Alaska	686	224	111	32	58	79	0,327	0,047	0,162	0,085	0,115	-9,737	-9,379	-1,096	40,842	46	t1	51	0	0	470 317	0,146	50,401	26	566c8614de695869
2023-09-01	USA	AZ	Arizona	10 671	3 214	1 113	442	565	797	0,301	0,041	0,104	0,053	0,075	-2,343	10,283	1,317	46,647	40	t1	26	1	0	470 317	2,269	50,401	26	33dba2082def3cae
2023-09-01	USA	AR	Arkansas	3 277	1 073	443	135	189	252	0,327	0,041	0,135	0,058	0,077	-7,56	4,563	0,372	48,62	38	t2	39	0	0	470 317	0,697	50,401	26	5b8176cf7970c3a0
2023-09-01	USA	CA	California	44 250	18 066	6 059	9 511	3 675	4 964	0,408	0,215	0,137	0,083	0,112	-3,792	2,955	0,162	60,759	20	t3	42	0	0	470 317	9,409	50,401	26	9a6533d21a434cd4
2023-09-01	USA	CO	Colorado	11 812	4 078	1 233	1 351	623	853	0,345	0,114	0,104	0,053	0,072	0,957	19,712	1,923	69,164	3	t4	8	1	1	470 317	2,511	50,401	26	03665732de1bccba
2023-09-01	USA	CT	Connecticut	4 054	1 225	396	219	198	282	0,302	0,054	0,098	0,049	0,07	-4,567	4,431	1,054	38,939	48	t1	40	0	0	470 317	0,862	50,401	26	ef603a2cb882f437
2023-09-01	USA	DE	Delaware	4 780	1 610	398	859	174	244	0,337	0,18	0,083	0,036	0,051	-3,862	23,866	1,332	66,549	7	t4	5	1	0	470 317	1,016	50,401	26	3978617402cf0de3
2023-09-01	USA	DC	District of Columbia	1 206	379	153	98	43	64	0,314	0,081	0,127	0,036	0,053	-6,801	2,29	-0,411	35,614	49	t1	44	0	0	470 317	0,256	50,401	26	a4c9af0e9206d24c
2023-09-01	USA	FL	Florida	59 852	24 006	9 043	7 326	2 385	3 517	0,401	0,122	0,151	0,04	0,059	8,353	18,439	2,117	64,956	11	t4	9	1	1	470 317	12,726	50,401	26	da864e574a36f49f

28 sept. 2026Actuelle
20260928T212917Z-a07057d5dc2f · sha256 a07057d5dc2f…
1 836 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/us_business_formation_intel/us_state_business_formation_monthly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/us_business_formation_intel/us_state_business_formation_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/us_business_formation_intel/us_state_business_formation_monthly

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

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APA

U.S. Census Bureau. (2026). US state business-formation intelligence (monthly) [Data set, snapshot 20260928T212917Z-a07057d5dc2f, sha256 a07057d5dc2f]. Datazimuts. Retrieved 2026-09-28, from https://datazimuts.com/fr/datasets/us_business_formation_intel/us_state_business_formation_monthly?snapshot=20260928T212917Z-a07057d5dc2f

BibTeX

@misc{dz_us_business_formation_intel_us_state_bus_a07057d5,
  title        = {{US state business-formation intelligence (monthly)}},
  author       = {{U.S. Census Bureau}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/us_business_formation_intel/us_state_business_formation_monthly?snapshot=20260928T212917Z-a07057d5dc2f}},
  note         = {Snapshot 20260928T212917Z-a07057d5dc2f, sha256 a07057d5dc2f8bf492e1a9c60ad01cc97c06c425cf2d2fabc1e20fa60fb57c57; accessed 2026-09-28}
}

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