Joint work with Yaroslav Korobka. Extended abstract (for conference submissions) available on request.

Abstract. Borrowing tools from the practice of neural networks, I design an empirical framework for the analysis of “hierarchical networks:” socio-economic settings featuring multiple, layered networks, whose nodes are linked across layers. I use this framework to revisit questions involving networks of workers and fi rms.

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.