Average Hourly Earnings vs CPI: Correlation Analysis
Pearson correlation of daily returns for Avg Hourly Earnings (Private) and CPI (All Urban). Rolling windows, yearly breakdown, regression beta, and divergence analysis. Data window spans to (56 aligned observations).
What the Number Means
With a correlation of 0.07, Avg Hourly Earnings (Private) and CPI (All Urban) are essentially uncorrelated at daily frequency. Either the relationship operates at a different time horizon or the shared driver has been dominated by idiosyncratic noise during the observation window.
Recent vs Long-Run Behavior
Recent correlation tracks the long-run relationship closely. No meaningful divergence. The historical pattern between Avg Hourly Earnings (Private) and CPI (All Urban) is intact and should continue to serve as a reasonable baseline for positioning.
Statistical Details (1-Year Window)
| Pearson Correlation (r) | +0.072 |
| R-Squared (r²) | 0.005 |
| Beta (Avg Hourly Earnings (Private) vs CPI (All Urban)) | 0.037 |
| Daily Volatility σ(Avg Hourly Earnings (Private)) | 0.15% |
| Daily Volatility σ(CPI (All Urban)) | 0.28% |
| Observations | 56 |
Correlation measures directional co-movement; R² quantifies the fraction of variance explained by the linear relationship. Beta is the slope coefficient from regressing Avg Hourly Earnings (Private) returns on CPI (All Urban) returns. A beta above 1 means the first asset amplifies moves of the second.
Year-by-Year Correlation
| Year | Correlation | Strength | Observations |
|---|---|---|---|
| 2026 | — | Insufficient data | 3 |
| 2025 | -0.052 | Essentially uncorrelated | 11 |
| 2024 | -0.138 | Essentially uncorrelated | 12 |
| 2023 | -0.197 | Essentially uncorrelated | 12 |
| 2022 | -0.037 | Essentially uncorrelated | 12 |
| 2021 | -0.093 | Essentially uncorrelated | 6 |
Year-by-year correlation reveals how the relationship has held up across different macro regimes. Sharp year-over-year swings in correlation often mark the transition between stress and calm periods.
Methodology
Correlations are computed on daily log-adjacent returns for Avg Hourly Earnings (Private) and CPI (All Urban), aligned on shared trading dates. We use the Pearson product-moment coefficient, which measures the linear relationship between two return series.
Windows are the most recent N observations for 30D, 90D, and 1Y (252 trading days); the 5Y figure uses all aligned data up to 1,260 observations. Beta is the OLS slope from regressing the first series on the second. Data updates daily with a 24-hour revalidation cadence.
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Correlation is not causation and backward-looking statistics can fail when regimes shift. Positions sized on historical correlation assumptions should be stress-tested against scenarios where the relationship breaks. For informational purposes only.