US wage & productivity signals (real earnings momentum, productivity-pay gap, anomalies)
Monthly/quarterly signals derived from FRED's US earnings and productivity data: 30-period annualized change volatility, ~3-month momentum, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-date cross-series volatility rank, the real-wage year-over-year gauge (worker purchasing power) and the productivity-minus-pay gap (the decoupling gauge). Covers nominal average hourly earnings, a connector-derived real-earnings series (CPI-deflated) and nonfarm business labor productivity. All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: U.S. Bureau of Labor Statistics.
Qualité
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Labor Statistics; derived signals by Frontier Data Hub)
Schéma
| Colonne | Type | Description |
|---|---|---|
| date | string | Observation date (FRED API field date; YYYY-MM-DD). |
| country | string | |
| country_code | string | |
| series_id | string | FRED series ID (AHETPI, OPHNFB) or REAL_EARNINGS for the connector-derived real average hourly earnings series; FRED IDs resolve to the series page at https://fred.stlouisfed.org/series/<id>. |
| series_label | string | Official FRED series title as published for the series (U.S. Bureau of Labor Statistics data). |
| value | float | Observation value as published by FRED for this series: average hourly earnings in dollars per hour (AHETPI); nonfarm business labor productivity index (OPHNFB); see the series notes for methodology and revisions. |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| real_wage_yoy | float | |
| prod_wage_gap | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank | real_wage_yoy | prod_wage_gap |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1947-01-01 | United States | USA | OPHNFB | Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers | 22.256 | — | — | 0 | — | — | — | — |
| 1947-04-01 | United States | USA | OPHNFB | Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers | 22.762 | — | 0.5060000000000002 | 0 | — | — | — | — |
| 1947-07-01 | United States | USA | OPHNFB | Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers | 22.065 | — | -0.6969999999999992 | 0 | — | — | — | — |
| 1947-10-01 | United States | USA | OPHNFB | Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers | 22.993 | — | 0.9279999999999973 | 0 | — | — | — | — |
| 1948-01-01 | United States | USA | OPHNFB | Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers | 23.097 | — | 0.10400000000000276 | 0 | 23.167083333333334 | — | — | — |
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/wage_signals/us_wage_productivity_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/wage_signals/us_wage_productivity_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/wage_signals/us_wage_productivity_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.