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

Mitchell Hoffman, Stephen V. Burks

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)315-348
Number of pages34
JournalQuantitative Economics
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2020

Bibliographical note

Publisher Copyright:
Copyright © 2020 The Authors.

Keywords

  • D03
  • J24
  • J41
  • M53
  • Overconfidence
  • biased learning
  • turnover

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