Designing Informative Rating Systems: Evidence from an Online Labor Market

Abstract

Platforms critically rely on rating systems to learn the quality of market participants. In practice, however, these ratings are often highly inflated, and therefore not very informative. In this paper, we investigate whether the platform can obtain less inflated ratings by altering the meaning and relative importance of the levels in the rating system. We then seek a principled approach to make these choices in the design of the rating system.

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