Date of Degree
PhD (Doctor of Philosophy)
This dissertation analyzes the contracting problem between a firm and the research employees in its R&D department. The dissertation consists of two chapters. The first chapter addresses a simplified problem in which the R&D unit has only one agent. The second chapter studies a scenario in which the R&D unit consists of a team.
In the first chapter, I look at problem in which a principal hires an agent to do a multi-stage R&D project. The transition from one stage to the next is modeled by a Poisson-type process, whose arrival rate depends on the agents choice of effort. I assume that effort choice is binary and unobservable by the principal. To overcome the repeated moral-hazard problem, the principal offers the agent a long-term contract which specifies a flow of payments based on his observation of the outcome of the project. The optimal contract combines rewards and punishments: the payment to the agent decrease over time in case of failure and jumps up to a higher level after each success. I also show that the optimal contract can be implemented by using a risky security that has some of the features of the stocks of these firms, thereby providing a theoretical justification for the wide-spread use of stock-based compensation in firms that rely on R&D.
In the second chapter, I look at a scenario in which the R&D unit consists of a team, which I assume, for simplicity, comprises two risk-averse agents. Now, the Poisson arrival rate is jointly determined by the actions of both agents with the action of each remaining unobservable by both the principal and the other agent. I assume that when success in a phase occurs the principal can identify the agent who was responsible for it. In this model, incentive compatibility means that each agent is willing to exert effort conditional on his coworker putting in effort, and thus exerting effort continuously is a Nash-equilibrium strategy played by the agents. In this multiagent problem, each agents payment depends not only on his own performance, but is affected by the other agents performance as well. Similar to the single-agent case, an agent is rewarded when he succeeds, and his payment decreases over time when both agents fail. Regarding how an agents payment relates to his coworkers performance, I find that the optimal incentive regime is a function of the way in which agents efforts interact with one another: relative-performance evaluation is used when their efforts are substitutes whereas joint-performance evaluation is used when their efforts are complements. This result sheds new light on the notion of optimal incentive regimes, an issue that has been widely discussed in multi-agent incentive problems.
Dynamic Contract, Employee Compensation, Multi-Agent, R&D, Repeated Moral Hazard
viii, 80 pages
Includes bibliographical references (pages 79-80).
Copyright 2012 Yaping Shan