Optimal Rating Design under Moral Hazard

Abstract

We study optimal rating design under moral hazard and strategic manipulation. An intermediary observes a noisy indicator of effort and commits to a rating policy that shapes market beliefs and pay. Whether optimal ratings reveal or censor information depends on how effort shifts the outcome distribution: when effort increases tail risk, optimal ratings use lower censorship, pooling poor outcomes to encourage risk-taking; when effort reduces tail risk, upper censorship discourages negligence. In multi-task settings with window dressing, a monotone relative informativeness property again delivers upper- or lower-censorship ratings. In an application to redistributive test design, optimal tests can feature mid-censorship. These results follow from a general characterization of optimal ratings via concavification of a gain function, accommodating violations of monotone likelihood ratios and distributional concerns.