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.
Quality
Attribution
Federal Reserve Bank of St. Louis (FRED; underlying data: U.S. Bureau of Labor Statistics; derived signals by Frontier Data Hub)
Schema
| Column | 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 |
Sample rows
| 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 | — | — | — | — |
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/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)API endpoint: https://datazimuts.com/v1/datasets/wagecost_signals/us_wage_cost_pressure_signals
Tip: fetch /llms.txt for the full machine-readable catalog.