演講者簡介 : Professor William Jungerman received his Ph.D. in Economics from the University of Minnesota in 2024. He is currently an Assistant Professor at the University of North Carolina at Chapel Hill. His research interests are Macroeconomics and Labor Economics.
演講摘要 : What are the sources of worker learning within the firm? How much of a worker's human capital growth comes from firm specific factors, such as the learning environment, as opposed to their own ability, and the composition of their coworkers? In this paper, we introduce a novel labor search model with multi-worker firms, learning from coworkers, and heterogeneity in learning-by-doing rates that can vary at the worker and firm level. Despite its complexity, we show that it is possible to solve such a model by leveraging recent advances from the machine learning literature. We use French administrative data to discipline the parameters of the model, specifically by targeting how wage growth varies across workers, firms, and the distribution of coworker wages. With the calibrated model in hand we perform a series of structural and statistical decompositions to test how much of the variance of human capital growth is driven by learning from coworkers and by heterogeneous learning-by-doing, and find that learning from coworkers is the dominant source of learning in the economy. Switching off learning from coworkers lowers human capital and wages by more than 25%, and differences in the composition of coworkers accounts for more than 50% of the variance of human capital growth rates.
