Adverse Selection or Moral Hazard, An Empirical Study

Abstract

Markets prone to asymmetric information employ reputation mechanisms to address adverse selection and moral hazard. In this paper, we use a change in such a reputation mechanism to examine its effect on improving adverse selection and moral hazard. In May, 2008, eBay changed its reputation mechanism to prevent sellers from giving negative feedback to buyers. This change was intended to prevent sellers from retaliating against buyers who gave them negative feedback. We observe an improvement in the overall quality of the marketplace as a result of this change. We attribute 49%-77% of this improvement to reduced adverse selection as low-quality sellers exit the market or their market share drops, and the rest to a reduction in moral hazard as sellers improve the quality of their service.

Reciprocity and Unveiling in Two-Sided Reputation Systems: Evidence from an Experiment on Airbnb

“…We also test for heterogeneous effects across seller types and find no evidence that ex ante worse hosts are hurt by the treatment. Our findings contrast with those of Bolton et al (2012) and Hui et al (2018), who find that similar changes to reputation systems decreased demand for low-quality sellers. We attribute this contrast to a number of factors, which we discuss in greater detail in Section 2.…”

Properties of feedback mechanisms on digital platforms: an exploratory study

“…Feedback helps users to receive information about the conditions of previous transactions, such as product or service quality, and about potential opportunistic behavior of users (Nosko and Tadelis 2015). Therefore, feedback mechanisms aim to reduce information asymmetries between users (McDonald and Slawson 2002), increase trust between users (Ba and Pavlou 2002), and avoid the problem of adverse selection and moral hazard (Hui et al 2019). Furthermore, feedback mechanisms enable users to be ranked and compared.…”

Certification, Reputation and Entry: An Empirical Analysis

“…Our paper joins a growing literature that uses rich online marketplace data to understand how to foster trade and alleviate asymmetric information in markets. The closest papers to ours are Elfenbein et al [2015], Klein et al [2016], and Hui et al [2018], which also used eBay data to study the effects of different information policies on market structure. In particular, Elfenbein et al [2015] studied the value of a certification badge across different markets and show that certification provides more value when the number of certified sellers is low and when markets are more competitive.…”

“…They did not study the impact of certification on the dynamics of entry and changes in market structure. Klein et al [2016] and Hui et al [2018] exploited a different policy change on eBay after which sellers could no longer leave negative feedback for buyers, reducing the costs for buyers of leaving negative feedback. Both studies found an improvement in buyers' experience after the policy change.…”

“…Using scraped data, Klein et al [2016] cleverly take advantage of the evolution of both the public feedback and the anonymous feedback of Detailed Seller Ratings to show that the improvement in transaction quality is not due to exits from low-quality sellers. Using internal data from eBay, Hui et al [2018] complement Klein et al [2016] and investigate changes in the size of incumbents. They show that although low-quality sellers do not exit after the policy change, their size shrinks dramatically, which accounts for 49%-77% of the quality improvement.…”