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

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Evidence from eBay Auctions' (with James Alm). Journal of Industrial Economics, September 2002. ... Rating left by unique registered eBay users ... – PowerPoint PPT presentation

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Title: 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
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