Training Contracts, Worker Overconfidence, and the Provision of Firm-Sponsored General Training∗

Training by firms is a central means by which workers accumulate human capital, yet firms may be reluctant to train if workers can quit and use their gained skills elsewhere. “Training contracts” that impose a penalty for premature quitting can help alleviate this inefficiency. This paper studies training contracts in the U.S. trucking industry where they are widely used, focusing on data from one leading firm. Exploiting two plausibly exogenous contract changes that introduced penalties for quitting, I confirm that training contracts significantly reduce quitting. To analyze the optimal design of training contracts and their welfare consequences, I develop and estimate a structural learning model with heterogeneous beliefs that accounts for many key features of the data. The estimation combines weekly productivity data with weekly subjective productivity forecasts for each worker and reveals a pattern of persistent overconfidence whereby many workers believe they will achieve higher productivity than they actually attain. If workers are overconfident about their productivity at the firm relative to their outside option, they will be less likely to quit and more likely to sign training contracts. Counterfactual analysis shows that workers’ estimated overconfidence increases firm profits by over $7,000 per truck, but reduces worker welfare by 1.5%. Banning training contracts decreases profits by $4,600 per truck and decreases retention by 25%, but increases worker welfare by 4%. Despite the positive effect of training contracts on profits, training may not be profitable unless some workers are overconfident. ∗I am indebted to David Card, Stefano DellaVigna, Steve Tadelis, and especially John Morgan for their advice and encouragement. I am extremely grateful to Stephen Burks for sharing his data with me and for his continuous support and advice throughout this project. I also thank Firm A and Firm B for sharing data with me, as well as the numerous trucking industry managers and drivers who shared their insights with me. Thanks also to Andrew Agopsowicz, Constanca Esteves-Sorenson, Ben Handel, Ben Hermalin, Ken Judd, Pat Kline, Botond Koszegi, Mauricio Larrain, Edward Lazear, Jonathan Leonard, Rosario Macera, Ulrike Malmendier, Michael Mattock, Don Moore, Enrico Moretti, Denis Nekipelov, Alex Poirer, Matthew Rabin, Jesse Rothstein, Juan Carlos Suarez Serrato, Dan Silverman, Lowell Taylor, Felix Vardy, and various seminar participants for helpful comments. I thank Mark Gergen, Anthony Kraus, and Gregory Lemmer for guidance in understanding relevant legal issues. Christina Chew, Sandrena Frischer, Will Kuffel, Amol Lingnurkar, and Irina Titova provided outstanding research assistance. Financial support from the National Science Foundation IGERT Fellowship and the Kauffman Foundation is gratefully acknowledged. †Email: [email protected]. The latest version is available at http://econgrads.berkeley.edu/hoffman/.