Date of Award
Doctor of Philosophy (PhD)
George J. Mailath
This dissertation consists of three essays that examine incentive problems within various dynamic environments. In Chapter 1, I study the optimal design of a dynamic regulatory system that encourages regulated agents to monitor their activities and voluntarily report their violations. Self-monitoring is a private and costly process, and comprises the core of the incentive problem. There are no monetary transfers. Instead, the regulator (she) uses future regulatory behavior for incentive provision. When the regulator has full commitment power, she can induce costly self-monitoring and revelation of ``bad news'' in the initial phase of the optimal policy. During this phase, the agent is promised a higher continuation utility (in the form of future regulatory approval) each time he discloses ``bad news''. If the regulator internalizes self-monitoring costs, the agent is either blacklisted or whitelisted in the long run. When she does not internalize these costs, blacklisting is replaced by a temporary probation state, and whitelisting becomes the unique long run outcome. This result suggests that whitelisting, which may appear to be a form of regulatory capture, may instead be a consequence of optimal policy.
In Chapter 2, I study the dynamic pricing problem of a durable good monopolist with commitment power, when a new version of the good is expected at some point in the future. The new version of the good is superior to the existing one, bringing a higher flow utility. The buyers are heterogeneous in terms of their valuations and strategically time their purchases. When the arrival is a stationary stochastic process, the corresponding optimal price path is shown to be constant for both versions of the good, hence there is no delay on purchases and time is not used to discriminate over buyers, which is in line with the literature. However, if the arrival of the new version occurs at a commonly known deterministic date, then the price path may decrease over time, resulting in delayed purchases. For both arrival processes, posted prices is a sub-optimal selling mechanism. The optimal one involves bundling of both versions of the good and selling them only together, which can easily be implemented by selling the initial version of the good with a replacement guarantee.
Finally, Chapter 3 examines the question under what conditions can automation be less desirable compared to human labor. We study a firm that has to decide between a human-human team and a human-machine team for production. The effort choice of a human employee is not observed by the manager, therefore the incentives need to be properly aligned. We argue that, despite the desirable benefits resulting from the partial substitution of labor with automated machines such as less costly machine input and reduced scope of moral hazard, the teams with only human employees can, under some conditions, be more preferred over the human-machine teams. This stems from the fact that, in all-human teams, the principal, through the selection of incentive scheme, can control the interaction among the agents and get benefit from the mutual monitoring capacity between them. The automation, however, eliminates this interaction and shuts down a channel that can potentially help to mitigate the overall agency problem.
Dogan, Mustafa, "Essays On Dynamic Incentive Design" (2017). Publicly Accessible Penn Dissertations. 2259.