US productivity-pay gap signals (decoupling gauge, compensation vs output per hour)
Quarterly signals derived from BLS productivity and costs data (redistributed by FRED, 1947 ->): 30-quarter annualized change volatility, 1-quarter momentum, year-over-year change, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-series volatility rank, the real pay-minus-productivity gap (both rebased to 1947 = 100 — the decoupling gauge) and a 10-year gap z-score (the decoupling-regime gauge). 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 (OPHNFB for nonfarm business sector labor productivity — output per hour, COMPNFB for nonfarm business sector hourly compensation); 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 productivity and costs data). |
| value | float | Index value, quarterly, seasonally adjusted annual rate; see the series notes for index base periods and methodology. |
| volatility_30d | float | |
| momentum_3m | float | |
| yoy_change_pct | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| pay_productivity_gap | float | |
| gap_z_10y | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | volatility_30d | momentum_3m | yoy_change_pct | anomaly_flag | forecast_1m | rank | pay_productivity_gap | gap_z_10y |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1947-01-01 | United States | USA | COMPNFB | Nonfarm Business Sector: Hourly Compensation for All Workers | 3.334 | — | — | — | 0 | — | — | 0 | — |
| 1947-01-01 | United States | USA | OPHNFB | Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers | 22.256 | — | — | — | 0 | — | — | 0 | — |
| 1947-04-01 | United States | USA | COMPNFB | Nonfarm Business Sector: Hourly Compensation for All Workers | 3.41 | — | 2.279544091181762 | — | 0 | — | — | -2.4115166183154457 | — |
| 1947-04-01 | United States | USA | OPHNFB | Nonfarm Business Sector: Labor Productivity (Output per Hour) for All Workers | 22.762 | — | 2.273544212796552 | — | 0 | — | — | -2.4115166183154457 | — |
| 1947-07-01 | United States | USA | COMPNFB | Nonfarm Business Sector: Hourly Compensation for All Workers | 3.511 | — | 2.9618768328445677 | — | 0 | — | — | 2.6142006170637666 | — |
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/productivity_pay_signals/us_productivity_pay_gap_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/productivity_pay_signals/us_productivity_pay_gap_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/productivity_pay_signals/us_productivity_pay_gap_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.