Essays on Optimal Contracts with Overconfidence
Author | : Justin R. Downs |
Publisher | : |
Total Pages | : 102 |
Release | : 2020 |
ISBN-10 | : OCLC:1246321630 |
ISBN-13 | : |
Rating | : 4/5 ( Downloads) |
Download or read book Essays on Optimal Contracts with Overconfidence written by Justin R. Downs and published by . This book was released on 2020 with total page 102 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation studies the effect of overconfidence on markets and organizations with asymmetric information. In the first chapter, I introduce overconfidence into a standard information gathering contracting model. A principal (she) hires an agent (he) to gather information about a project's cost before he implements the project, and the agent overestimates the probability of having a low implementation cost. The agent's overconfidence makes him more willing to sign the contract, but less willing to gather information, and increases in overconfidence may increase or decrease the principal's profit. In the second chapter, I study a labor market where firms hire overconfident workers who have private information about their productivity. I derive the optimal contracts for both a monopsonistic market, where one firm makes take-it-or-leave-it offers to the workers, as well as a competitive market, where many firms compete for the services of workers. Overconfidence causes the optimal contract to be distorted away from the efficient outcome in both markets, but a monopsonistic firm internalizes these distortions while a competitive firm does not. The main result is that monopsonistic markets can be more efficient than competitive markets. In the third chapter, I provide a review of several mathematical definitions of overconfidence used in the contract theory literature and apply them all to a generalized version of the information gathering model from Chapter 1. The effects overconfidence has on the agent's willingness to participate, to gather information, and on the principal's profit are all sensitive to the mathematical definition of overconfidence used in the model.