Evidence from eBay Auctions' (with James Alm). Journal o

1
An Online Consumer-to-Consumer Trading Community

• Presentation is based on Melnik research
• Does a Sellers eCommerce Reputation Really
  Matter? Evidence from eBay Auctions (with James
  Alm). Journal of Industrial Economics, September
  2002.
• 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
• 4.552 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
• Buyers problem
• Uncertainty about delivery of the item (general
  compliance with the terms of transaction)
• Uncertainty about the accuracy in the description
  of the item
• Sellers 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
SIMPLE MEASURABLE DIFFICULT TO
MANIPULATE

• 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 5 1999 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 (sellers 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
58.08

• Sellers reputation impacts buyers willingness
  to pay
• In heterogeneous goods A reduction in available
  information increases the premium to positive
  reputation and the penalty to negative
  reputation.
• Negative feedback effect increases with the value
  of the item
• 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 -gt 0.03 increase in willingness to pay
• - Houser and Wooders (2002) 10 increase in
  rating -gt 0.17 increase in willingness to pay
• - Melnik and Alm (2002) Doubling in rating -gt
  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