Optimal Rating Design under Moral Hazard

Maryam Saeedi and Ali Shourideh

Papers from arXiv.org

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. We characterize optimal ratings via concavification of a gain function. Optimal ratings depends on interaction of effort and risk: for activities that raise tail risk, optimal ratings exhibit lower censorship, pooling poor outcomes to insure and encourage risk-taking; for activities that reduce tail risk, upper censorship increases penalties for negligence. In multi-task environments with window dressing, less informative ratings deter manipulation. In redistributive test design, optimal tests exhibit mid-censorship.

Date: 2020-08, Revised 2026-01
New Economics Papers: this item is included in nep-des and nep-mic
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