US state wage-growth signals (regional pay pressure)
US state-level average hourly earnings signals (BLS via FRED, 50 states + DC, monthly 2007 ->): 12-month wage growth, 3-month momentum, 30-period change volatility, 3-sigma shock flags, drift forecasts, 5-year pay-pressure z-scores, the state-vs-US growth gap and cross-state growth ranks. The regional pay-pressure lens: where wages run hot or cold across the country. BLS data via FRED (free, keyless-by-reuse of the existing FRED key).
Quality
Attribution
U.S. Bureau of Labor Statistics via FRED; derived signals by Frontier Data Hub
Schema
| Column | Type | Description |
|---|---|---|
| date | string | Reference month (FRED observation date; monthly, seasonally adjusted). |
| country | string | United States (all series are US state-level). |
| country_code | string | ISO 3166-1 alpha-3 code: USA. |
| series_id | string | FRED series ID: SMU<state-FIPS>000000500000003 (average hourly earnings of all employees, total private, by state). |
| series_label | string | Official FRED series title as published in the series metadata (includes the state name). |
| value | float | Average hourly earnings of all employees on private nonfarm payrolls, in dollars, seasonally adjusted; U.S. Bureau of Labor Statistics, Current Employment Statistics program, via FRED. |
| yoy_change_pct | float | |
| momentum_3m | float | |
| volatility_30d | float | |
| anomaly_flag | integer | |
| forecast_1m | float | |
| wage_z_5y | float | |
| rank | integer | |
| us_gap_pp | float | |
| strong_growth_flag | integer | |
| weak_growth_flag | integer |
Sample rows
| date | country | country_code | series_id | series_label | value | yoy_change_pct | momentum_3m | volatility_30d | anomaly_flag | forecast_1m | wage_z_5y | rank | us_gap_pp | strong_growth_flag | weak_growth_flag |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2007-01-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.24 | — | — | — | 0 | — | — | — | — | 0 | 0 |
| 2007-02-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.29 | — | — | — | 0 | — | — | — | — | 0 | 0 |
| 2007-03-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.4 | — | — | — | 0 | — | — | — | — | 0 | 0 |
| 2007-04-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.53 | — | 1.5072765072765115 | — | 0 | — | — | — | — | 0 | 0 |
| 2007-05-01 | United States | USA | SMU01000000500000003 | Average Hourly Earnings of All Employees: Total Private in Alabama | 19.42 | — | 0.6739243131156147 | — | 0 | — | — | — | — | 0 | 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/state_wage_signals/us_state_wage_growth_signals" | jq '{title, rows, columns_count, license}'Python
import requests
ds = requests.get("https://datazimuts.com/v1/datasets/state_wage_signals/us_state_wage_growth_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/state_wage_signals/us_state_wage_growth_signals
Tip: fetch /llms.txt for the full machine-readable catalog.