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.
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). |
| 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 |
Sample rows
| 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 | — |
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/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)API endpoint: https://datazimuts.com/v1/datasets/productivity_pay_signals/us_productivity_pay_gap_signals
Tip: fetch /llms.txt for the full machine-readable catalog.