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.

Source: U.S. Census Bureau
Rows: 1,836
Columns: 31
Source cadence: Monthly
Last refreshed: Sep 28, 2026
Theme: economy

Column	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).

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

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

Sep 28, 2026Current
20260928T212917Z-a07057d5dc2f · sha256 a07057d5dc2f…
1,836 rows · first snapshot

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

API endpoint: https://datazimuts.com/v1/datasets/us_business_formation_intel/us_state_business_formation_monthly

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

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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/en/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/en/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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