US state wage-growth signals (regional pay pressure)
US state-level average hourly earnings signals (BLS via FRED, 50 states + DC, monthly 2007 ->): 12-month wage growth, 3-month momentum, 30-period change volatility, 3-sigma shock flags, drift forecasts, 5-year pay-pressure z-scores, the state-vs-US growth gap and cross-state growth ranks. The regional pay-pressure lens: where wages run hot or cold across the country. BLS data via FRED (free, keyless-by-reuse of the existing FRED key).
Qualité
Attribution
U.S. Bureau of Labor Statistics via FRED; derived signals by Frontier Data Hub
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Reference month (FRED observation date; monthly, seasonally adjusted). |
| country | string | United States (all series are US state-level). |
| country_code | string | ISO 3166-1 alpha-3 code: USA. |
| series_id | string | FRED series ID: SMU<state-FIPS>000000500000003 (average hourly earnings of all employees, total private, by state). |
| series_label | string | Official FRED series title as published in the series metadata (includes the state name). |
| value | float | Average hourly earnings of all employees on private nonfarm payrolls, in dollars, seasonally adjusted; U.S. Bureau of Labor Statistics, Current Employment Statistics program, via FRED. |
| yoy_change_pct | float | |
| momentum_3m | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| wage_z_5y | float | |
| rank | integer | |
| us_gap_pp | float | |
| strong_growth_flag | integer | |
| weak_growth_flag | integer |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | yoy_change_pct | momentum_3m | volatility_30d | anomaly_flag | forecast_1m | wage_z_5y | rank | us_gap_pp | strong_growth_flag | weak_growth_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2007-01-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.24 | — | — | — | 0 | — | — | — | — | 0 | 0 |
| 2007-02-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.29 | — | — | — | 0 | — | — | — | — | 0 | 0 |
| 2007-03-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.4 | — | — | — | 0 | — | — | — | — | 0 | 0 |
| 2007-04-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.53 | — | 1.5072765072765115 | — | 0 | — | — | — | — | 0 | 0 |
| 2007-05-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.42 | — | 0.6739243131156147 | — | 0 | — | — | — | — | 0 | 0 |
Télécharger un échantillon
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/state_wage_signals/us_state_wage_growth_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/state_wage_signals/us_state_wage_growth_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/state_wage_signals/us_state_wage_growth_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.