| Popis: |
This thesis consists of three chapters. In the first chapter, I explore a two-period economy with a three-tier hierarchy in which the principal without full commitment decides how and when to motivate a productive intermediary (agent one) to privately sub-contract and collaborate with another agent (agent two) on a project with uncertain quality. The dynamic moral hazard problem arises due to the agents’ hidden effort choice and the opportunity for future work. Besides free riding, the agent one’s exclusion and over-investment incentives need to be considered due to his private sub-contract option. Both the dominant incentive constraint in the optimal short term contract and the principal’s investment decision depend on the project’s value-cost ratio, the level of synergy in the partnership and the amount of patience. In general, the principal under-invests and stops earlier compared to the first-best outcome. However, there exist scenarios in which agent one always over-invests when the individual work is motivated, and the principal might compromise to motivate a higher effort level by over-investing relative to the static game, especially if the synergy is positive but small and the project’s value-cost ratio is medium. In a two-tier hierarchy, the principal can be weakly better off, but the inefficiency caused by agent one’s private link to the other agent still exists. In the second chapter, I study how a principal motivates an uninformed agent to learn about, and reveal, his quality through private experiments. The principal commits to a reward scheme and she aims to assign the rewards to correspond as closely as possible to the quality of the agent. To get a high reward, the agent experiments privately and discloses the results selectively. I show that the optimal reward scheme features an increasing step function: the initial steps encourage a potential good type agent to continue experiments after early successes; the later steps are designed to deter a bad type agent from over-experimentation after a failure, and the scheme becomes flat when enough successes are reported. If the agent’s incentives to deviate from the intended path of experimentation are weak, a one-step function is optimal: the agent receives a bonus if he reports enough successes; otherwise, he only gets a non-negative compensation. I characterise the conditions where the principal achieves the same efficiency level relative to a public information environment. The third chapter is an extension of the second chapter. I consider a situation in which an uninformed agent persuades a principal for a high reward through costly private experimentation. I show the existence of three types of equilibria as well as their conditions: no-experiment equilibrium, separating equilibria with learning and pooling equilibria with learning. The participation threshold determines the upper bound of the entire set of equilibria, and the over-experimentation determines the boundary between the separating and pooling equilibria with learning. As the agent’s value-cost ratio or prior belief increases, the set of separating equilibria with learning shrinks but the set of pooling equilibria with learning expands. Moreover, when the agent can pre-commit to report a specific number of successes to prove his quality, he tends to commit to a number that is as small as possible. |
