Date of Award

2022

Degree Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Graduate Group

Economics

First Advisor

Iourii Manovskii

Abstract

Observed worker and firm characteristics only explain a small wage variation. Beyond characteristics that are directly observed from the data, my thesis develops new empirical methods aimed at identifying unobserved heterogeneity in the labor market.

Chapter 1 proposes an empirical method to measure the effects of coworkers on wages. I take advantage of the recent cutting-edge clustering method that combines machine-learning and economic theory to identify groups of workers with similar latent productivity type. I further apply the cluster-based method to identify the effects of coworkers on wages and evaluate their economic implications in empirical-relevant simulations. The proposed method has proven potential to be applied to the real-world data to improve our ability to understand the role of coworkers in substantive questions where existing methods have limitations.

Included in

Economics Commons

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