Incentivizing Agents through Ratings

TL;DR

This paper explores how to design optimal rating schemes to motivate agents to invest in quality, especially when transfers are not possible, by analyzing deterministic and stochastic ratings and their impact on incentives.

Contribution

It characterizes optimal rating schemes under various ability distributions and compares the effectiveness of deterministic versus stochastic ratings in incentivizing quality.

Findings

Pass/fail tests are optimal when ability is high.

Lower censorship is optimal when ability is moderate.

Stochastic ratings can outperform deterministic ones under certain conditions.

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.