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Incentivizing Agents through Ratings

Author

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  • Peiran Xiao

Abstract

I study the optimal design of ratings to motivate agent investment in quality when transfers are unavailable. The principal designs a rating scheme that maps the agent's quality to a (possibly stochastic) score. The agent has private information about his ability, which determines his cost of investment, and chooses the quality level. The market observes the score and offers a wage equal to the agent's expected quality. For example, a school incentivizes learning through a grading policy that discloses the student's quality to the job market. When restricted to deterministic ratings, I provide necessary and sufficient conditions for the optimality of simple pass/fail tests and lower censorship. In particular, when the principal's objective is expected quality, pass/fail tests are optimal if the agent's ability distribution is concentrated towards the top, while lower censorship is optimal if the ability distribution is concentrated towards the mode. The results also generalize existing results in optimal delegation with an outside option, as pass/fail tests (lower censorship) correspond to take-it-or-leave-it offers (threshold delegation). Additionally, I provide sufficient conditions under which stochastic ratings outperform deterministic ratings and under which they do not.

Suggested Citation

  • Peiran Xiao, 2024. "Incentivizing Agents through Ratings," Papers 2407.10525, arXiv.org, revised Aug 2025.
    • Handle: RePEc:arx:papers:2407.10525
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    References listed on IDEAS

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    1. S Nageeb Ali & Nima Haghpanah & Xiao Lin & Ron Siegel, 2022. "How to Sell Hard Information," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(1), pages 619-678.
    2. Martin F. Hellwig, 2010. "Incentive Problems With Unidimensional Hidden Characteristics: A Unified Approach," Econometrica, Econometric Society, vol. 78(4), pages 1201-1237, July.
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    Cited by:

    1. Jiaming Wei & Dihan Zou, 2025. "Regulating a Monopolist without Subsidy," Papers 2512.06525, arXiv.org, revised Feb 2026.

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