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| Content Provider | Springer Nature Link |
|---|---|
| Author | Goukasian, Levon Wan, Xuhu |
| Copyright Year | 2010 |
| Abstract | We are studying in this article an interplay between workers in organizations under the assumption that workers exhibit behavioral biases: envy, jealousy, or admiration toward the other coworkers’ compensation. We assume workers care about their relative position, and we study the impact of this assumption on their efforts and on their optimal incentive contracts. We explicitly solve for the optimal incentive contract of moral hazard a la Holmstrom and Milgrom (Econometrica 55:303–328, 1987). We model team production by agents in which each agent’s effort generates an observable signal and depends on efforts of other agents. One of the important findings is that an agent’s optimal effort is negatively impacted by the behavioral biases in other agents’ judgments. We also show envious behavior is destructive for organizations. Consistent with Tirole (Econometrica 69(1):1–35, 2001), our findings suggest that in the presence of agency problems induced by envy or jealousy, the optimal compensation exhibits high pay-for-performance sensitivity. |
| Starting Page | 57 |
| Ending Page | 86 |
| Page Count | 30 |
| File Format | |
| ISSN | 18629679 |
| Journal | Mathematics and Financial Economics |
| Volume Number | 4 |
| Issue Number | 1 |
| e-ISSN | 18629660 |
| Language | English |
| Publisher | Springer-Verlag |
| Publisher Date | 2010-11-24 |
| Publisher Place | Berlin, Heidelberg |
| Access Restriction | One Nation One Subscription (ONOS) |
| Subject Keyword | Multi-agent problems Hidden action Envy Jealousy Behavioral Contract Theory Optimal effort Pay-for-performance sensitivity Game Theory/Mathematical Methods Statistics for Business/Economics/Mathematical Finance/Insurance Applications of Mathematics Quantitative Finance Financial Economics Finance /Banking |
| Content Type | Text |
| Resource Type | Article |
| Subject | Finance Statistics and Probability Statistics, Probability and Uncertainty |
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