Specific Knowledge and Input - vs . Output - Based Incentives

This paper examines optimal incentive contracts in which an agent’s compensation can be based on both “input” measures closely related to an agent’s effort, and “output” measures closely related to the principal’s payoff. I argue that when the agent has specific knowledge (i.e. private information that is difficult to communicate) about how his actions contribute to the principal’s payoff, output-based pay encourages the agent to use his knowledge while input-based pay does not. I show within a two-task agency model that partially output-based compensation is optimal even when the agent’s effort on each task can be measured perfectly. I also study how changes in different parameters affect the optimal balance of inputvs. output-based pay. In particular, an increase in the variance of measured output that is unrelated to the agent’s productivity leads to less output-based pay, consistent with standard agency models. In contrast, an increase in uncertainty about the productivities of the tasks leads to more output-based pay, similar to the predictions of recent models of delegation.