Reputation Mechanism Design in Online Trading Environments with Pure Moral Hazard

This paper offers a systematic exploration of reputation mechanism design in trading environments with opportunistic sellers of commonly known cost and ability parameters, imperfect monitoring of a seller's actions, and two possible seller effort levels, one of which has no value to buyers. The objective of reputation mechanisms in such pure moral hazard settings is to induce sellers to exert high effort as often as possible. I study the impact of various mechanism parameters (such as the granularity of solicited feedback, the format of the public reputation profile, the policy regarding missing feedback, and the rules for admitting new sellers) on the resulting market efficiency. I find that maximum efficiency is bounded away from the hypothetical first-best case where sellers can credibly precommit to full cooperation by a factor that is related to the probability that cooperating sellers may receive "unfair" bad ratings. Furthermore, maximum efficiency is independent of the length of past history summarized in a seller's public reputation profile. I apply my framework to a simplified model of eBay's feedback mechanism and conclude that, in pure moral hazard settings, eBay's simple mechanism is capable of inducing the maximum theoretical efficiency independently of the number of recent ratings that are being summarized in a seller's profile. I derive optimal policies for dealing with missing feedback and easy online identity changes. Finally, I show that if the number of buyers is large, the results obtained in the monopoly case are also approximately valid in settings where multiple sellers of different reputations simultaneously offer auctions for identical goods.

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