US wage-cost pressure signals (ECI, real pay, unit labor costs)
Quarterly US labor-cost signals derived from FRED: the Employment Cost Index (total compensation and wages & salaries — the Fed's preferred wage gauge) and nonfarm unit labor costs, with quarter-on-quarter annualized changes, year-on-year percent changes, 30-quarter annualized change volatility, 1-quarter momentum, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-series volatility rank, CPI-deflated real ECI growth and the ECI-minus-unit-labor-cost spread (the wage-push vs productivity 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 survey: 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, first day of the reference quarter). |
| country | string | |
| country_code | string | |
| series_id | string | FRED series ID, e.g. ECIALLCIV, ECIWAG, ULCNFB; resolves to the series page at https://fred.stlouisfed.org/series/<id>. |
| series_label | string | Official FRED series title as published for the series (Employment Cost Index and unit labor costs, U.S. Bureau of Labor Statistics). |
| value | float | Observation value as published by FRED for this series: index points (ECIALLCIV and ECIWAG, December 2005 = 100; ULCNFB, 2017 = 100); see the series notes for methodology and revisions. |
| qoq_ann_change | float | |
| yoy_change_pct | float | |
| volatility_30d | float | |
| momentum_3m | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| rank | integer | |
| real_eci_yoy | float | |
| eci_ulc_spread | float |
Exemple de lignes
| date | country | country_code | series_id | series_label | value | qoq_ann_change | yoy_change_pct | volatility_30d | momentum_3m | anomaly_flag | forecast_1m | rank | real_eci_yoy | eci_ulc_spread |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1947-01-01 | United States | USA | ULCNFB | Nonfarm Business Sector: Unit Labor Costs for All Workers | 14.981 | — | — | — | — | 0 | — | — | — | — |
| 1947-04-01 | United States | USA | ULCNFB | Nonfarm Business Sector: Unit Labor Costs for All Workers | 14.98 | -0.02669781396774651 | — | — | -0.0009999999999994458 | 0 | — | — | — | — |
| 1947-07-01 | United States | USA | ULCNFB | Nonfarm Business Sector: Unit Labor Costs for All Workers | 15.911 | 27.274866303572452 | — | — | 0.9309999999999992 | 0 | — | — | — | — |
| 1947-10-01 | United States | USA | ULCNFB | Nonfarm Business Sector: Unit Labor Costs for All Workers | 15.632 | -6.831676110932506 | — | — | -0.2789999999999999 | 0 | — | — | — | — |
| 1948-01-01 | United States | USA | ULCNFB | Nonfarm Business Sector: Unit Labor Costs for All Workers | 15.929 | 7.8191400324967075 | 6.328015486282634 | — | 0.2970000000000006 | 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/wagecost_signals/us_wage_cost_pressure_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/wagecost_signals/us_wage_cost_pressure_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/wagecost_signals/us_wage_cost_pressure_signals
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.