1 An Online Consumer-to-Consumer Trading Community Presentation is based on Melnik research: “Does a Seller’s eCommerce Reputation Really Matter? Evidence from eBay Auctions” (with James Alm). Journal of Industrial Economics, September “Reputation, Information Signals, and Willingness to Pay for Heterogeneous Goods in Online Auctions”, (with James Alm). Southern Economic Journal, October 2005.

2 eBay: A True Success Story From a simple website in 1995 to being synonymous with online auctions!  1.9 billion listings in 2005  billion in revenues  71.8 million active users  96.2 million accounts listed with PayPal* But what about the economics..... * All information is taken from QIV05 eBay Financial Results report

3 Asymmetry of Information  Akerlof, 1970  Asymmetry of Information on eBay  Buyer’s problem  Uncertainty about delivery of the item (general compliance with the terms of transaction)  Uncertainty about the accuracy in the description of the item  Seller’s problem  Payment/return  Past Reputation as a Signal of Current and Future Behavior  Theoretical support  Klein and Leffler, 1981; Shapiro, 1983; Allen, 1984; Houser and Wooders, 2000  Experimental support  Miller and Plott, 1985; DeJong, Forsythe, and Lundholm, 1985; Camerer and Weigelt, 1988; Holt and Sherman, 1990

4 Reputational Mechanism on eBay  Structure of the mechanism  Quantitative  Positive, negative, neutral rating choices only  Difficult to manipulate through collusive behavior  Rating left by unique registered eBay users  Feedback score = unique positives – unique negatives  Informative  Overall eBay experience of the seller  Past complain history  Does the reputational measure help overcome asymmetries of information?  Is it valued by members of the community?  Is it valued by competing communities?

5 Choice of Data  2002: Homogeneous good study  US $ Gold Coin in Mint Condition  Possibility of encountering a fraudulent seller  2005: Heterogeneous good study  US Morgan Dollars in Almost Uncirculated Condition  Accuracy in the description of item-specific characteristics  Possibility of encountering a fraudulent seller

6 Modeling Reputation  P = f (seller’s reputation, X)  X – a set of auction specific variables  Transaction costs (shipping, insurance)  Time exposure, closing (duration, closing time/date, day of the week)  Supply characteristics (number of available items)  Payment methods

7 Empirical formulation  Censored observations and the use of Tobit model  Fixed price auctions and no-bid auctions 105 price distributions: Huber-White estimation of robust standard errors

8 Estimation Results Mean prices: Certified: $327.50; Non-certified: $ Seller’s reputation impacts buyer’s willingness to pay 2.In heterogeneous goods: A reduction in available information increases the premium to positive reputation and the penalty to negative reputation. 3.Negative feedback effect increases with the value of the item 4.Substantial penalty is imposed on new sellers in non- certified coins auctions

9 Some Previous Findings  - Lucking-Reiley et al. (1999): 1% increase in rating -> 0.03% increase in willingness to pay  - Houser and Wooders (2002): 10% increase in rating -> 0.17% increase in willingness to pay  - Melnik and Alm (2002): Doubling in rating -> 0.55% increase in willingness to pay

10 Conclusions Non-transferable across communities reputational mechanism in online consumer to consumer communities acts as a club good Valued by buyers and sellers Enables a community to overcome asymmetries of information problem Establishes a barrier to entry for a competing community