Date of Award
Doctor of Philosophy (PhD)
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.
Xin, Jianhong, "Essays On Machine Learning And Labor Economics" (2022). Publicly Accessible Penn Dissertations. 5028.