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US business-formation signals (entrepreneurship pipeline gauges)

Weekly US business-formation signals from Census Bureau Business Formation Statistics (FRED BUSAPPWNSAUS + HBUSAPPWNSAUS, 2006 ->): total and high-propensity business applications with 13-week momentum, year-on-year change, 30-week change volatility, 3-sigma anomaly flags, naive-drift forecasts, and the high-propensity share of applications (the quality mix of the startup pipeline). The entry-margin lens on the US business cycle — applications lead formations, and high-propensity applications lead employer births. Companion to us-labor-market-signals (established firms) and us-state-coincident-activity-signals (output). All rows normalized to country_code USA. Raw series: U.S. Census Bureau Business Formation Statistics via FRED.

Source: US Business-Formation Signals (derived)2,154 lignesMis à jour: 22/09/2026
business-formationentrepreneurshipstartupeinbusiness-applicationsbusiness-cyclecensusmomentumanomaly-detectionforecastingsignalsfred

Qualité

99.5

Attribution

U.S. Census Bureau Business Formation Statistics via FRED; signals by Frontier Data Hub

Schéma

ColonneTypeDescription
datestringObservation date (FRED API field date; YYYY-MM-DD, weekly ending Saturday).
countrystring
country_codestring
series_idstringBUSAPP: Business Applications for the United States (FRED BUSAPPWNSAUS); HBUSAPP: High-Propensity Business Applications (FRED HBUSAPPWNSAUS). Both from the U.S. Census Bureau Business Formation Statistics.
series_labelstringBUSAPP: all applications for an Employer Identification Number (EIN), the broadest entrepreneurship gauge. HBUSAPP: the subset with characteristics (corporation, hiring intent, planned wages, business name) that make them likely to become employer businesses (Census BFS definitions).
valuefloatNumber of business applications in the week (count). BUSAPP includes all EIN applications except tax liens, estates, trusts, certain financial filings, and applications with no state-county geocodes; see Census BFS 'About the Data'.
momentum_3mfloat
yoy_changefloat
volatility_30dfloat
anomaly_flaginteger
forecast_1mfloat
high_propensity_sharefloat
share_momentumfloat
share_z_2yfloat

Exemple de lignes

datecountrycountry_codeseries_idseries_labelvaluemomentum_3myoy_changevolatility_30danomaly_flagforecast_1mhigh_propensity_shareshare_momentumshare_z_2y
2006-01-07United StatesUSABUSAPPBusiness Applications for the United States (weekly, ending Saturday; count)39250059.210191082802545
2006-01-14United StatesUSABUSAPPBusiness Applications for the United States (weekly, ending Saturday; count)36820064.69310157523086
2006-01-21United StatesUSABUSAPPBusiness Applications for the United States (weekly, ending Saturday; count)62920060.91862682771774
2006-01-28United StatesUSABUSAPPBusiness Applications for the United States (weekly, ending Saturday; count)51520061.06366459627329
2006-02-04United StatesUSABUSAPPBusiness Applications for the United States (weekly, ending Saturday; count)60890059.97700771883725

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Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

Utiliser avec un LLM

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/business_formation_signals/us_business_formation_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/business_formation_signals/us_business_formation_signals").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/business_formation_signals/us_business_formation_signals

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

US business-formation signals (entrepreneurship pipeline gauges)