Worker overconfidence: Field evidence and implications for employee turnover and firm profits

• 成果类型: Article
• 署名作者: Hoffman, Mitchell; Burks, Stephen V.
• 署名单位: University of Toronto; National Bureau of Economic Research; University of Minnesota System; University of Minnesota Morris; IZA Institute Labor Economics
• 刊物名称: QUANTITATIVE ECONOMICS
• ISSN/ISSBN: 1759-7323
• DOI: 10.3982/QE834
• 发表日期: 2020
• 页码: 315-348
• 关键词: Overconfidence; biased learning; turnover; D03; J24; J41; M53

摘要

Combining weekly productivity data with weekly productivity beliefs for a large sample of truckers over 2 years, we show that workers tend to systematically and persistently overpredict 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 accounts for many key features of the data. While worker overconfidence moderately decreases worker welfare, it also substantially increases firm profits.