Worker Overconfidence: Field Evidence and Implications for Employee Turnover and Returns from Training

Mitchell Hoffman and Stephen Burks

No 10794, IZA Discussion Papers from Institute of Labor Economics (IZA)

Abstract: Combining weekly productivity data with weekly productivity beliefs for a large sample of truckers over two years, we show that workers tend to systematically and persistently over-predict their productivity. If workers are overconfident about their own productivity at the current firm relative to their outside option, they should be less likely to quit. Empirically, all else equal, having higher productivity beliefs is associated with an employee being less likely to quit. To study the implications of overconfidence for worker welfare and firm profits, we estimate a structural learning model with biased beliefs that ac-counts for many key features of the data. While worker overconfidence moderately decreases worker welfare, it also substantially increases firm profits. This may be critical for firms (such as the main one we study) that make large initial investments in worker training.

Keywords: biased learning; turnover; truckload; firm-sponsored training; overconfidence; truck driver (search for similar items in EconPapers)
JEL-codes: D03 J24 J41 M53 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2017-05
New Economics Papers: this item is included in nep-bec, nep-hrm and nep-lma
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Published - revised version published in: Quantitative Economics, 2020, 11(1), 315-348

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Working Paper: Worker Overconfidence: Field Evidence and Implications for Employee Turnover and Returns from Training (2017)
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