US state business conditions, biweekly (Census BTOS)

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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.

Biweekly business-conditions intelligence for the 50 US states, DC, Puerto Rico and the BTOS multi-unit national grouping, from the Census Bureau Business Trends and Outlook Survey (~1.2M employer businesses): the 15 published BTOS diffusion indexes (current performance, revenues, demand, employees, hours, input/output prices, delivery time, plus 6-month outlooks) as idx_* columns, one row per (state, biweekly cycle), cycles 202319 to latest (Sep 2023 -> Sep 2026), each joined to its survey reference-period dates. Disclosure-suppressed cells are explicit nulls (never imputed; cycles 202521-202523 were hit by the Oct-Nov 2025 federal funding lapse). Adds three documented composite scores (current-conditions, outlook, business-health), their cycle-over-cycle momentum, per-cycle health ranks and tiers, trailing-year z-scores and expansion/contraction flags — a (date, state) demand driver for retail forecasting, churn models and territory planning. BTOS is the fastest official read on Main-Street conditions, biweekly where monthly series lag by weeks.

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

Source: U.S. Census Bureau Business Trends and Outlook Survey (BTOS) — state business-conditions intelligence
Lignes: 4 187
Colonnes: 34
Cadence de la source: Hebdomadaire
Dernière actualisation: 1 oct. 2026
Thème: economy

Colonne	Type	Description
state_code	string	BTOS state code: 50 states + DC + PR + XX.
state_name	string	State / grouping name.
geo_type	string	'state' \| 'district' (DC) \| 'territory' (PR) \| 'multiunit_national' (XX: BTOS multi-unit companies national grouping, not a geography).
country_code	string	ISO alpha-3: USA (PRI for Puerto Rico).
cycle	string	BTOS biweekly cycle label YYYYPP (PP = 01..26 biweekly periods per year).
ref_period_start	date	Reference-period start: the survey's prior-two-week reference window.
ref_period_end	date	Reference-period end (join key for demand).
idx_current_performance	float	BTOS diffusion index 'Current performance' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_delivery_time	float	BTOS diffusion index 'Delivery time' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_demand	float	BTOS diffusion index 'Demand' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_employees	float	BTOS diffusion index 'Employees' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_future_delivery_time	float	BTOS diffusion index 'Future delivery time' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_future_demand	float	BTOS diffusion index 'Future demand' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_future_employees	float	BTOS diffusion index 'Future employees' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_future_hours	float	BTOS diffusion index 'Future hours' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_future_input_prices	float	BTOS diffusion index 'Future input prices' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_future_output_prices	float	BTOS diffusion index 'Future output prices' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_future_performance	float	BTOS diffusion index 'Future performance' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_hours	float	BTOS diffusion index 'Hours' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_input_prices	float	BTOS diffusion index 'Input prices' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_output_prices	float	BTOS diffusion index 'Output prices' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
idx_revenues	float	BTOS diffusion index 'Revenues' (0-100; 50 = neutral; 100(1p_increase + 0.5*p_same)). Null = disclosure-suppressed upstream.
current_conditions_score	float	Documented composite: mean(idx_current_performance, idx_revenues, idx_demand, idx_employees, idx_hours). Null unless all five components are non-null.
outlook_score	float	Documented composite: mean of the four future idx_* columns. Null unless all four are non-null.
business_health_score	float	Documented composite: mean(current_conditions_score, outlook_score). Null unless both are non-null.
current_mom_pp	float	Cycle-over-cycle change (points) of current_conditions_score per state; null on the base cycle.
outlook_mom_pp	float	Cycle-over-cycle change (points) of outlook_score per state; null on the base cycle.
health_mom_pp	float	Cycle-over-cycle change (points) of business_health_score per state; null on the base cycle.
health_rank	integer	Per-cycle competition rank of business_health_score across geos (1 = healthiest).
health_tier	integer	Fixed-cut tier on business_health_score: 4 strong (>= 55), 3 firm (>= 52), 2 soft (>= 50), 1 weak (< 50).
health_zscore_1y	float	business_health_score vs trailing-26-cycle mean/std per state (min 8 obs); null during warmup.
expanding_flag	boolean	True when business_health_score >= 52; null when the composite is null.
contracting_flag	boolean	True when business_health_score < 48; null when the composite is null.
row_hash	string	Deterministic 16-hex sha256 over 'BTOS\|<state_code>\|<cycle>'.

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

state_code	state_name	geo_type	country_code	cycle	ref_period_start	ref_period_end	idx_current_performance	idx_delivery_time	idx_demand	idx_employees	idx_future_delivery_time	idx_future_demand	idx_future_employees	idx_future_hours	idx_future_input_prices	idx_future_output_prices	idx_future_performance	idx_hours	idx_input_prices	idx_output_prices	idx_revenues	current_conditions_score	outlook_score	business_health_score	current_mom_pp	outlook_mom_pp	health_mom_pp	health_rank	health_tier	health_zscore_1y	expanding_flag	contracting_flag	row_hash
AK	Alaska	state	USA	202319	2023-08-28	2023-09-10	62	55,2	40,8	39,4	59,3	52,8	47,7	44,4	82,1	66,2	59,1	39,6	74,2	54,1	34,5	43,26	51	47,13	—	—	—	53	1	—	false	true	e4043a94349b9751
AK	Alaska	state	USA	202320	2023-09-11	2023-09-24	55,3	50,7	43	45	53,4	52,6	52,3	42,9	82,1	68	52,2	42,6	71,2	52,1	37,6	44,7	50	47,35	1,44	-1	0,22	53	1	—	false	true	3321eb0c5be21f9c
AK	Alaska	state	USA	202321	2023-09-25	2023-10-08	62,8	53,5	50,1	48	56,1	53,7	53,8	48,4	78,3	66,1	57,5	46,4	65,4	50,9	48,4	51,14	53,35	52,245	6,44	3,35	4,895	6	3	—	true	false	171b7097696bebd6
AK	Alaska	state	USA	202322	2023-10-09	2023-10-22	57,4	54,4	43,3	47,2	57,8	56	53,4	52,3	78,3	68	55,1	45	60,2	54,9	46,1	47,8	54,2	51	-3,34	0,85	-1,245	26	2	—	false	false	30158299a1cd943b
AK	Alaska	state	USA	202323	2023-10-23	2023-11-05	59,4	57,2	41,6	45,9	56,4	56,2	53,7	53	82,3	66,6	53	43,4	69,4	57,2	35	45,06	53,975	49,518	-2,74	-0,225	-1,483	42	1	—	false	false	75c6c05bad48c0ac
AK	Alaska	state	USA	202324	2023-11-06	2023-11-19	58	55	43,2	46,7	55,4	62,5	57,3	59,5	78	68,6	54	42,7	68,8	55,7	40,9	46,3	58,325	52,313	1,24	4,35	2,795	5	3	—	true	false	21aa7f22d8150b06
AK	Alaska	state	USA	202325	2023-11-20	2023-12-03	59,8	61,2	44,9	45,9	51,7	59,1	58,5	56,3	76,4	68,4	59,9	43,5	66,1	54,1	42,3	47,28	58,45	52,865	0,98	0,125	0,553	3	3	—	true	false	d04dc58ccd3d8e9d
AK	Alaska	state	USA	202326	2023-12-04	2023-12-17	56,3	56,3	46,6	48,7	49,5	66	59,7	62,6	80,2	65,6	59,2	47,7	65,1	56	47,8	49,42	61,875	55,647	2,14	3,425	2,782	2	4	1,6	true	false	4a399aa6a6941528
AK	Alaska	state	USA	202401	2023-12-18	2023-12-31	59,2	61,6	45,8	46,1	58,8	62,3	57,5	60,9	69,6	64,7	58,4	38,5	63,8	53	40,2	45,96	59,775	52,867	-3,46	-2,1	-2,78	4	3	0,594	true	false	80344da108d637bf
AK	Alaska	state	USA	202402	2024-01-01	2024-01-14	55,5	53,9	42,2	44,7	42,6	53,8	58,8	57,8	76,1	66	53,7	43,6	71,1	56,9	38,1	44,82	56,025	50,423	-1,14	-3,75	-2,445	26	2	-0,271	false	false	392cbe724b2fd8b9

Profilé le 1 oct. 2026 à partir de l’instantané 20261001T230958Z-b14a7a65f768

Mesuré

Complétude: 96,6 %
Lignes: 4 187
Colonnes: 34
Colonnes incomplètes: 26

Lignes par annéeref_period_start · 2023–2026 · 4 ans

20232026

Colonne	Manquant	Distinctes	Plage	Distribution
state_codevarchar	0 %	58	—	AK79 AL79 AR79
state_namevarchar	0 %	60	—	Alabama79 Alaska79 Arizona79
geo_typevarchar	0 %	3	—	state3 950 district79 multiunit_national79
country_codevarchar	0 %	2	—	USA4 108 PRI79
cyclevarchar	0 %	77	—	20231953 20232053 20232153
ref_period_startdate	0 %	100	28 août 2023 → 24 août 2026	—
ref_period_enddate	0 %	91	10 sept. 2023 → 6 sept. 2026	—
idx_current_performancedouble	3,8 %	243	44 → 69médiane 55,4	82 hors du 1er–99e centile
idx_delivery_timedouble	3,8 %	167	36,7 → 65,7médiane 52,7	81 hors du 1er–99e centile
idx_demanddouble	3,8 %	217	32,5 → 59,4médiane 44,8	81 hors du 1er–99e centile
idx_employeesdouble	3,8 %	146	38 → 58médiane 47,9	77 hors du 1er–99e centile
idx_future_delivery_timedouble	3,8 %	184	39 → 69,8médiane 53	78 hors du 1er–99e centile
idx_future_demanddouble	3,8 %	304	35,8 → 70,2médiane 54	81 hors du 1er–99e centile
idx_future_employeesdouble	3,8 %	217	39,4 → 67,1médiane 52,4	81 hors du 1er–99e centile
idx_future_hoursdouble	3,8 %	250	36,6 → 66,9médiane 51,75	79 hors du 1er–99e centile
idx_future_input_pricesdouble	3,8 %	204	60,5 → 90,7médiane 75,2	82 hors du 1er–99e centile
idx_future_output_pricesdouble	3,8 %	206	50,9 → 77,2médiane 64,5	79 hors du 1er–99e centile
idx_future_performancedouble	3,8 %	204	45,4 → 68,6médiane 55,2	81 hors du 1er–99e centile
idx_hoursdouble	3,8 %	214	32,4 → 57,4médiane 46,3	77 hors du 1er–99e centile
idx_input_pricesdouble	3,8 %	213	52,8 → 87,3médiane 68,1	82 hors du 1er–99e centile
idx_output_pricesdouble	3,8 %	148	43,4 → 68,1médiane 55,2	80 hors du 1er–99e centile
idx_revenuesdouble	3,8 %	246	27,4 → 57,1médiane 42,2	80 hors du 1er–99e centile
current_conditions_scoredouble	3,8 %	1 061	38,3 → 56,46médiane 47,32	82 hors du 1er–99e centile
outlook_scoredouble	3,8 %	1 140	41,8 → 64,93médiane 53,35	80 hors du 1er–99e centile
business_health_scoredouble	3,8 %	3 253	42,5 → 59,53médiane 50,43	82 hors du 1er–99e centile
current_mom_ppdouble	6,3 %	1 502	-11,84 → 9,8médiane 0,02	80 hors du 1er–99e centile
outlook_mom_ppdouble	6,3 %	1 119	-12,13 → 11,25médiane 0	80 hors du 1er–99e centile
health_mom_ppdouble	6,3 %	2 812	-9,51 → 8,5médiane -0,0088	80 hors du 1er–99e centile
health_rankbigint	3,8 %	47	1 → 53médiane 27
health_tierbigint	3,8 %	4	1 → 4médiane 2
health_zscore_1ydouble	12,7 %	3 630	-3,14 → 2,93médiane 0,0165	74 hors du 1er–99e centile
expanding_flagboolean	3,8 %	2	—	false3 267 true761
contracting_flagboolean	3,8 %	2	—	false3 610 true418
row_hashvarchar	0 %	3 955	—	000555cfdfc2082f1 00074f37dc43ef761 00228215af1f13e41

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/btos_state_business_intel/us_state_business_conditions_biweekly" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/btos_state_business_intel/us_state_business_conditions_biweekly").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/btos_state_business_intel/us_state_business_conditions_biweekly

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

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Épinglé à l’instantané 20261001T230958Z-b14a7a65f768 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.

APA

U.S. Census Bureau Business Trends and Outlook Survey (BTOS) — state business-conditions intelligence. (2026). US state business conditions, biweekly (Census BTOS) [Data set, snapshot 20261001T230958Z-b14a7a65f768, sha256 b14a7a65f768]. Datazimuts. Retrieved 2026-10-02, from https://datazimuts.com/fr/datasets/btos_state_business_intel/us_state_business_conditions_biweekly?snapshot=20261001T230958Z-b14a7a65f768

BibTeX

@misc{dz_btos_state_business_intel_us_state_busin_b14a7a65,
  title        = {{US state business conditions, biweekly (Census BTOS)}},
  author       = {{U.S. Census Bureau Business Trends and Outlook Survey (BTOS) — state business-conditions intelligence}},
  year         = {2026},
  publisher    = {Datazimuts},
  howpublished = {\url{https://datazimuts.com/fr/datasets/btos_state_business_intel/us_state_business_conditions_biweekly?snapshot=20261001T230958Z-b14a7a65f768}},
  note         = {Snapshot 20261001T230958Z-b14a7a65f768, sha256 b14a7a65f768e1c48bd471a0da749b809da26a29b0c5cee7872e20bebbe88369; accessed 2026-10-02}
}

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<iframe src="https://datazimuts.com/embed/chart?dataset=btos_state_business_intel%2Fus_state_business_conditions_biweekly&lang=fr&theme=auto&snapshot=20261001T230958Z-b14a7a65f768&x=ref_period_start&y=idx_current_performance&agg=avg" title="US state business conditions, biweekly (Census BTOS)" width="100%" height="380" style="border:0" loading="lazy"></iframe>

US state business conditions, biweekly (Census BTOS)

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