Indicator Choice in Pay-for-Performance
Paper joint with Majid Mahzoon, Ali Shourideh
Abstract: We study the classic principal-agent model when the signal observed by the principal is chosen by the agent. We fully characterize the optimal information structure from an agent’s perspective in a general moral hazard setting. Unlike standard information design, the full distribution of beliefs is relevant to the principal. We show that the problem can be mapped into a geometrical game between the principal and the agent in the space of likelihood ratios. We use this representation result to show that coarse contracts are sufficient: The agent can achieve her best with binary signals. Additionally, we characterize conditions under which the agent is able to extract all the surplus and implement the efficient allocation under full information. Finally, we show that when effort and performance are one-dimensional, under a general class of models, threshold signals are optimal.