ESSAYS IN MACROECONOMICS AND LABOR ECONOMICS

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Degree type
Doctor of Philosophy (PhD)
Graduate group
Economics
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Economics
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Copyright date
01/01/2024
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Lam, Justin Franco
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Abstract

This dissertation revolves around two themes: the macroeconomic consequences of heterogeneity in labor markets, and the degree of income risk sharing in an economy. Chapter one examines labor market heterogeneity across geographic locations. I document that within location job-to-job switch wage growth is on average 40% greater in high unemployment area than low unemployment area in the U.S. This difference contrasts with the similar within location job-to-job switch rate across high and low unemployment area. Along the job ladder, the more job-to-job switches a worker has made post-unemployment, the less wage growth they yield in their job switch. Interpreting these facts jointly using a job ladder model, I highlight that a larger job-to-job wage growth signifies greater mismatch in the initial job. In the model, locations only exogenously differ in job destruction probability and firms in each location make job type creation choice. Places with large job destruction probability discourage firms to invest and bad jobs are created. The high job destruction rate frequently forces workers to restart their search from unemployment, while bad job creation increases the chance of workers obtaining low match quality. Therefore, workers in high unemployment area are lower down in the job ladder, more mismatched and have larger wage growth potential when they make a job-to-job switch. The calibrated model offers a novel channel to speak to the empirical finding that productivity in high unemployment area dropped more than low unemployment area during the 2007-09 recession. Even with the same decrease in matching efficiency across regions, productivity in high unemployment area declines more, since it loses job-to-job switches that have larger productivity gains. Chapter two, coauthored with Xincheng Qiu, studies heterogeneity of workers' job search behavior along the life cycle. We document that the share of jobless spells that end with recalls, i.e., returning to the previous job, as opposed to finding a new job, is strongly increasing over the life cycle. Throughout the life cycle, wage changes associated with recalls are concentrated at zero, different from the wage change behavior of new-job findings or job-to-job transitions. We find that the introduction of recall options into a job-ladder search model provides a novel mechanism that reproduces a declining job finding rate over the life cycle. The model quantitatively accounts for empirical patterns of life-cycle labor market dynamics. Deterioration of aggregate matching efficiency in bad times hurts labor market entrants more than experienced workers, because job-finding prospects of entrants rely more on the matching function of the labor market, whereas experienced workers rely less and get more recalls. Chapter three, coauthored with Agustin Diaz, Sean Mccrary and Kharis Sokolov, investigates measurements of the degree of risk sharing in an economy. We explore the properties of the consumption pass-through estimator proposed by Heathcote et al. (HSV) using simulated data from a standard life cycle incomplete-markets model with labor supply decision. We show that, without savings, the HSV wage-to-consumption pass-through estimator accurately captures consumption insurance via labor supply adjustment. On the other hand, when there is savings, HSV has an upward bias and that bias gets more severe when savings accumulate or when the persistence of the wage process deviates from the unit root assumption that underlies the method. Compared to the estimate of Blundell et al. (BPP), HSV performs better when there is little savings in the model, while BPP performs better when there is ample savings. The findings suggest that combining the two methods, such as using HSV for agents close to the borrowing constraint while applying BPP to agents away from the borrowing constraint, may yield more comprehensive consumption insurance estimates.

Advisor
Rios Rull, Jose-Victor
Date of degree
2024
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