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William Jungerman

© 2024 William Jungerman. Powered by Jekyll.
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About me

Hi! I’m an Assistant Professor of Economics at the University of North Carolina, Chapel Hill. I received my PhD from the University of Minnesota in 2024.

My CV is available here.

Research

Working papers

Learning on the Job

(with Jacob Adenbaum and Fil Babalievsky)

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Abstract (click to expand) 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 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. Finally, we use the model to calculate dynamic markdowns that price in the benefits of learning on the job and find that markdowns are less than 5% on average for workers with more than 1 year of experience at their current firm.


Deep Reinforcement Learning for Economics

(with Jacob Adenbaum and Fil Babalievsky)

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Abstract (click to expand) This paper provides a self-contained guide to deep reinforcement learning methods for economists. These tools allow agents to find the policy function that maximizes their expected discounted stream of rewards under very few assumptions about the structure of the model, and are potentially applicable to a wide class of economic models. We begin by translating the language of reinforcement learning to the language of economics. Next, we introduce neural networks, a class of function approximators that have proven useful for reinforcement learning. We use a standard Bewley (1977)-style consumption-savings problem as our test case since we can easily check for correctness. We conclude by offering a practical guide with implementation details and a discussion of what kinds of problems are more amenable to reinforcement learning than conventional techniques.


Dynamic Monopsony and Human Capital

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Abstract (click to expand) A number of influential papers study monopsony power in static models. Among the papers that model dynamics with a finite number of firms, none model the process of human capital accumulation by workers. In this paper, I show that this has important implications for the measurement and welfare consequences of monopsony power. How large are properly measured markdowns? And what are the welfare gains of implementing competitive allocations once we have accounted for human capital accumulation? To answer these questions, I introduce a novel model of dynamic monopsony in which a large non-atomistic firm competes with a finite number of homogeneous firms for workers who learn on-the-job. The markdown has an additional dynamic term reflecting expected future changes in worker human capital. I estimate the model using rich matched employee-employer administrative data from France and find that the welfare gains from forcing firms to offer workers their marginal product are large. Moreover, the welfare losses are underestimated by 81% when ignoring human capital accumulation.


Average Match Quality over the Business Cycle

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Abstract (click to expand) This paper examines how the business cycle impacts the average quality of an employer-employee match. I study a model of the labor market with on-the-job search, aggregate uncertainty, and heterogeneous match qualities. I test two theories: the cleansing effect, whereby the low quality matches are destroyed during recessions, and the sullying effect, whereby firms post fewer vacancies during recessions and workers have fewer opportunities to move up the job ladder. I find that the sullying effect dominates and that average match quality is procyclical due to increased hiring out of unemployment during recessions. I extend the model to allow for an exogenous minimum wage and show that neglecting to account for the cyclicality of match qualities can lead to miscalculating the effects of the policy.


Work in progress

Production Function Estimation with Missing Data
(with Kyle Herkenhoff)

Monopsony in the Antebellum South
(with Kyle Herkenhoff and James A. Schmitz, Jr.)

Publications

Brexit, the City of London, and the prospects for portfolio investment
(with Barry Eichengreen and Mingyang (Chris) Liu)
Empirica, February 2020
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Abstract (click to expand) This paper examines the international financial consequences of Brexit. It first provides a survey of the still limited literature on EU membership and international capital flows. It then provides new estimates of the impact of Brexit on cross-border investment utilizing data from the IMF’s Consolidated Portfolio Investment Survey. It lastly provides a comparative analysis of these same issues using data on cross- border capital flows from the BIS. The conclusion is that the impact on cross-border capital flows to and from the UK is likely to be substantial.


Teaching

University of North Carolina, Chapel Hill

University of Minnesota, Twin Cities

Contact

E-mail
william dot jungerman at gmail dot com
wjunger at unc dot edu

Mailing Address
University of North Carolina
Department of Economics
305B Gardner Hall
Chapel Hill, NC 27599