Working Paper: NBER ID: w23240
Authors: Mitchell Hoffman; Stephen V. Burks
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 accounts 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: overconfidence; employee turnover; returns from training; structural learning model
JEL Codes: D03; J24; J41; M53
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Overconfidence (D83) | Reduced turnover (J63) |
| Eliminating overconfidence (D81) | Improved worker welfare (J89) |
| Eliminating overconfidence (D81) | Decreased firm profits (D21) |
| Overconfidence (D83) | Persistent overprediction of productivity (E27) |
| Higher productivity beliefs (O49) | Overconfidence (D83) |
| Higher productivity beliefs (O49) | Lower likelihood of quitting (J26) |