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.
Quality
Attribution
U.S. Census Bureau Business Formation Statistics via FRED; signals by Frontier Data Hub
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD, weekly ending Saturday). |
| country | string | |
| country_code | string | |
| series_id | string | BUSAPP: 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_label | string | BUSAPP: 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). |
| value | float | Number 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_3m | float | |
| yoy_change | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| high_propensity_share | float | |
| share_momentum | float | |
| share_z_2y | float |
Sample rows
| date | country | country_code | series_id | series_label | value | momentum_3m | yoy_change | volatility_30d | anomaly_flag | forecast_1m | high_propensity_share | share_momentum | share_z_2y |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2006-01-07 | United States | USA | BUSAPP | Business Applications for the United States (weekly, ending Saturday; count) | 39250 | — | — | — | 0 | — | 59.210191082802545 | — | — |
| 2006-01-14 | United States | USA | BUSAPP | Business Applications for the United States (weekly, ending Saturday; count) | 36820 | — | — | — | 0 | — | 64.69310157523086 | — | — |
| 2006-01-21 | United States | USA | BUSAPP | Business Applications for the United States (weekly, ending Saturday; count) | 62920 | — | — | — | 0 | — | 60.91862682771774 | — | — |
| 2006-01-28 | United States | USA | BUSAPP | Business Applications for the United States (weekly, ending Saturday; count) | 51520 | — | — | — | 0 | — | 61.06366459627329 | — | — |
| 2006-02-04 | United States | USA | BUSAPP | Business Applications for the United States (weekly, ending Saturday; count) | 60890 | — | — | — | 0 | — | 59.97700771883725 | — | — |
Download sample data
Download the full sample snapshot for this dataset (sample rows, not the complete dataset).
Use with an LLM
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)API endpoint: https://datazimuts.com/v1/datasets/business_formation_signals/us_business_formation_signals
Tip: fetch /llms.txt for the full machine-readable catalog.