Data-Driven Contract Design
Abstract
This paper proposes a prior-free model of incentive contracting wherein the principal’s beliefsabout the agent’s production technology are characterized by revealed preference data. The
principal and the agent are each financially risk neutral and the agent’s preferences are understood to be quasilinear in effort. Prior to contracting with the agent, the principal observes the outcome of a finite number of observations, each of which consists of (1) a contract and (2) the distribution of output (but not the effort cost) associated with the agent’s best response to that contract. She views any technology that rationalizes this data as plausible, and evaluates contracts according to their guaranteed expected payoff against the set of rationalizable technologies. We show that robustly optimal contracts are equity bonus contracts that supplement the contracts in the data with equity payments. Our model makes no assumptions about the agent’s technology beyond rationalizability of the revealed preference data, which itself is characterized by straightforward textbook criteria.