Do AKM effects really matter?

Joint work with Aslan Bakirov and Francesco Del Prato. Draft coming soon.

Abstract. We revisit the wage decomposition literature using machine learning. We show empirically that if both worker- and firm-level observable characteristics are treated non-parametrically via generalized random forests, the share of log-wages variance explained by typical “AKM” fixed effects falls precipitously.