Essays on student debt, unemployment, and labor market outcomes

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2022-05-06

Authors

Yan, Jin (Ph. D. in economics)

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This dissertation focuses on studying the effect of certain life events including accumulating student debt and incidences of unemployment on workers' labor market outcomes. The chapters use economic models to guide empirical analysis and find that both student debt and unemployment experience can negatively impact workers' future earnings. The models in Chapter 2 and 3 both feature imperfect information about workers' underlying ability and learning through noisy productivity signals over time. Both chapters suggest that information frictions in the labor market can be costly. Chapter 2 develops and estimates a model of career choices and experimentation featuring imperfect information, risk aversion, and incomplete markets. The model emphasizes the importance of career experimentation in driving earnings growth and shows that information frictions cause risk-averse graduates with student debt to choose a safe career path with a flatter experience-earnings profile which is consistent with the empirical results. Chapter 3 finds that the longer a worker is unemployed, the less he earns initially coming out of an unemployment spell, but the wage penalty of unemployment duration decreases over time. The findings are consistent with the prediction of the employer learning with statistical discrimination model developed in Altonji and Pierret (2001) which suggests that information frictions can motivate profit-maximizing employers to use unemployment duration as a negative signal to predict workers' productivity at the time of hire, put extra negative weight on unemployment duration when making initial wage offers, and alleviate it only after learning about workers’ underlying ability. Chapter 4 presents a flow-based methodology for real-time unemployment rate projections to characterize the evolution of the unemployment rate during the recession triggered by the COVID-19 pandemic. In particular, the chapter analyzes fluctuations in the unemployment rate through the lens of labor market flows and this approach performed considerably better at the onset of the COVID-19 recession in spring 2020 in predicting the peak unemployment rate as well as its rapid decline in the following months. The predictive power of the methodology comes from its combined use of real-time data with the flow approach.

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