Tracing the transmission dynamics of monetary policy to the labor share through Double/Debiased Machine Learning

This study estimates the dynamic effect of monetary policy on the labor share in the Euro Area. We adapt panel local projections to the Double/Debiased Machine Learning estimator, developed for causal inference in high-dimensional control settings. We find robust evidence that the labor share increases after a monetary policy tightening. The response reflects a broad contractionary transmission mechanism: output, labor productivity, employment, investment, and prices decline, while average real wages adjust less persistently. This pattern is consistent with recent evidence showing that monetary contractions raise the labor share because labor productivity falls more than real wages. Our results indicate that a causal Machine Learning estimator can recover plausible monetary policy innovations and deliver price- and production-puzzle-free responses without relying on high-frequency instruments.