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Efficiency through feedback-contingent fees and rewards in auction marketplaces with adverse selection and moral hazard
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Source Electronic Commerce archive
Proceedings of the 4th ACM conference on Electronic commerce table of contents
San Diego, CA, USA
Pages: 11 - 18  
Year of Publication: 2003
ISBN:1-58113-679-X
Author
Chrysanthos Dellarocas  Massachusetts Institute of Technology, Cambridge, MA
Sponsors
ACM: Association for Computing Machinery
SIGEcom: ACM Special Interest Group on Electronic Commerce
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 3,   Downloads (12 Months): 29,   Citation Count: 5
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ABSTRACT

This paper proposes a novel mechanism for inducing cooperation in online auction settings with noisy monitoring of quality and adverse selection. The mechanism combines the ability of electronic markets to solicit feedback from buyers with the more traditional ability to levy listing fees from sellers. Each period the mechanism charges a listing fee contingent on a seller's announced expected quality. It subsequently pays the seller a reward contingent on both his announced quality and the rating posted for that seller by that period's winning bidder. I show that, in the presence of a continuum of seller types with different cost functions, imperfect private monitoring of a seller's effort level and a simple "binary" feedback mechanism that asks buyers to rate a transaction as "good" or "bad", it is possible to derive a schedule of fees and rewards that induces all seller types to produce at their respective first-best quality levels and to truthfully announce their intended quality levels to buyers. The mechanism maximizes average social welfare for the entire community and is robust to a number of contingencies of particular concern in online environments, such as easy name changes and the existence of inept sellers. On the other hand, the mechanism distorts the resulting payoffs of individual sellers relative to the complete information case, transferring part of the payoffs of more efficient sellers to less efficient sellers. The magnitude of this distortion is proportional to the amount of noise associated with observing and reporting the quality of a good.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
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9
Miller, N., Resnick, P., and Zeckhauser, R. (2002) Eliciting Honest Feedback in Electronic Markets. Harvard Kennedy School Research Working Paper RWP02-039. Available at: http://www.si.umich.edu/$sim$presnick/papers/elicit/index.html.
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Resnick, P., and Zeckhauser, R. (2002) Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay's Reputation System. In The Economics of the Internet and E-Commerce. Michael R. Baye, editor. Volume 11 of Advances in Applied Microeconomics, JAI Press.


Collaborative Colleagues:
Chrysanthos Dellarocas: colleagues