economic insights from...

Journal of Economic LiteratureVol XLII (June 2004) pp 457ndash486

Economic Insights from InternetAuctions

PATRICK BAJARI and ALI HORTACcedilSU1

457

1 Bajari Duke University and NBER HortacsuUniversity of Chicago and NBER We thank the editortwo anonymous referees Ginger Jin Axel OckenfelsDavid Reiley Paul Resnick and Alvin Roth for their verydetailed and insightful comments on various drafts of thisdocument We thank the National Science Foundation forpartial research support grants SES-0012106 (Bajari) andSES-0242031 (Hortaccedilsu)

2 There are several interesting accounts of the earlydevelopment of internet auctions Cohen (2002) chroni-cles the development of eBay through in-depth interviewsof eBay founders executives and users David Lucking-Reiley (2000b) provides an insightful description of inter-net auction sites and auction mechanisms across differentmarket segments

1 Introduction

Electronic commerce continues to growat an impressive pace despite widely

publicized failures by prominent onlineretailers According to the Department ofCommerce total retail e-commerce in theUnited States in 2002 exceeded $45 billiona 27-percent increase over the previous yearOnline auctions are one of the most success-ful forms of electronic commerce In 2002more than 632 million items were listed forsale on the web behemoth eBay alone a 51-percent increase over the previous year Thisgenerated gross merchandise sales of morethan $15 billion

The rapid development of these marketsis usually attributed to three factors2

The first is that online auctions provide a

3 Beanie Babies are stuffed dolls that are popularamong collectors

less-costly way for buyers and sellers onlocally thin markets such as specialized col-lectibles to meet Adam Cohen (2002 p 45)states ldquoIt would be an exaggeration to saythat eBay was built on Beanie Babies butnot by muchrdquo3 In May 1997 nearly$500000 worth of Beanie Babies was sold oneBay totaling 66 percent of overall salesWhile it may be difficult to find a particularBeanie Baby locally such as Splash theWhale or Chocolate the Moose you have agood chance of finding it online Collectiblessuch as Beanie Babies first edition booksGolden Age comics and Elvis paraphernaliaare among the thousands of categoriesactively traded in online auctions

The second factor is that online auctionsites substitute for more traditional marketintermediaries such as specialty dealers inantiques sports cards and other collectiblesFor instance an antique dealer from Seattleexplained on an eBay message board why heclosed his store

A couple of years or so ago my best buyers start-ed spending their money at eBay Then mypickers started selling on eBay instead of sellingto me Then when I went to the flea market andasked how much an item was I got quoted what

jn04_Article 3 6104 1053 AM Page 457

458 Journal of Economic Literature Vol XLII (June 2004)

one sold for on eBay not what the seller want-ed for the item I have a toy show that sold outfor years but nowadays all my vendors sell oneBay and all the buyers are spending theirmoney on eBay I used to buy and sell a lot inthe toy magazines before they got reduced tomere pamphlet-sized ragshellip Get my drift(Cohen p 110)

Online auctions have extensive listingsand powerful search technologies that createliquid markets for specialized product cate-gories The resulting reduction in transac-tion costs forced some intermediaries likethe antique dealer above to exit the market

Finally online auctions can be fun Manybidders clearly enjoy contemplating thesubtleties of strategic bidding and sharingtheir insights with others Most online auc-tion sites have active message boardswhere one can learn the fine points of col-lecting The boards also provide a sense ofcommunity for diehard collectors

In this paper we survey recent researchconcerning online auctions First wedescribe the mechanics of the auction rulesused on the most popular sites and someempirical regularities An especially interest-ing regularity is that bidders frequentlysnipe that is they strategically submit theirbids at the last seconds of an auction thatlasts several days Several authors haveempirically examined sniping and proposedexplanations

We then survey a growing literature inwhich researchers have attempted to docu-ment and quantify distortions from asym-metric information in online auctionmarkets As pointed out by EichiroKazumori and John McMillan (2003) theldquoinformation asymmetryrdquo problem consti-tutes perhaps the biggest limitation posed tothe impressive growth of online auctions Inonline auctions transactions take placebetween complete strangers who may notlive in the same state or the same countrymaking it very difficult for buyers to directlyinspect the good or to make sure that thegood will be delivered at all This creates

4 A number of high-profile incidents of fraud haveoccurred on online auctions For instance the FBIlaunched an investigation called ldquoOperation Bullpenrdquo thatled to the indictment of 25 persons for selling tens of mil-lions of dollars of forged collectibles such as forged signa-tures from Babe Ruth and Lou Gehrig (Cohen p 308)

5 Another survey article that provides a more in-depthsurvey of theoretical models of reputation is ChrysanthosDellarocas (2002) See also the survey by David Baron(2002)

opportunities for misrepresentation ofobjects and fraudulent behavior by sellerswhich may limit trade in these markets4

The informational asymmetry may mani-fest itself as a ldquowinnerrsquos curserdquo problem inwhich bidders recognize that winning anauction is conditional on being the mostoptimistic bidder about the itemrsquos worthAuction theory then predicts that bidderswill respond strategically to the winnerrsquoscurse by lowering their bids thus leading tolower prices and volumes in these marketsIn section 4 we will survey empirical workthat uses detailed data from online auctionsites to test whether bidders indeed actstrategically in the face of a possible winnerrsquoscurse and whether this strategic response islarge enough to prevent fraudulent sellersfrom extracting (short-term) rents

Given the above discussion a very impor-tant component of the online auction busi-ness is to decrease the informationalasymmetries between market participants Aparticularly popular method pioneered byeBay is the use of feedback mechanisms thatallow buyers and sellers to leave publiclyavailable comments about each other Insection 5 we will survey a rapidly growingempirical literature that utilizes data fromthe feedback mechanisms of online auctionsites to quantify the market value of onlinereputations5

Auction theory with its sharp predictionsabout the optimal way to design and conduct auctions has become one of themost successful and applicable branches ofmicroeconomic theory Internet auctionsites with their abundance of detailed dataon bidding and selling behavior provide


Bajari and Hortaccedilsu Economic Insights from Internet Auctions 459

fertile ground to test some of these theoriesOn many internet auction sites sellers areallowed to fine-tune their auctions byexperimenting with minimum bids or secretreserve prices Several sites also allow thesellers to make choices regarding whichauction format to use Thus empiricalresearchers can utilize these sources of vari-ation along with a newfound ability to setup randomized ldquofield experimentsrdquo to testpredictions of auction theory In section 6we discuss several strands of researchaddressing this purpose We first discuss theuse of internet auction sites as a medium forrandomized field experiments and surveyempirical work that has utilized thismethodology to compare bidder behavioracross alternative auction formats Secondwe discuss the use of both field experimen-tation and structural econometric modelingtechniques to investigate how sellers shouldset reserve prices for their auctions In par-ticular we focus on the question of whyreserve prices are kept secret in many inter-net auctions Third we discuss the preva-lence of ascending auctions on the internetWe consider alternative theoretical explana-tions along with a discussion of new exper-imental evidence motivated by thisempirical observation Finally we discussthe importance of taking endogenous par-ticipation decisions into account when conducting theoretical and empirical com-parisons of different auction rules sinceonline auction sites typically feature multi-ple auctions that are taking place at thesame time (and the sites themselves can bethought of as competing with each otherthrough their site designs) We concludewith a brief discussion of open researchquestions that remain to be explored

2 Buying and Selling in Online Auctions

On any given day sellers list millions ofitems in online auctions These sites includebusiness-to-business (B2B) sites6 and siteswhere the typical buyer is a consumer We

6 See David Lucking-Reiley and Daniel Spulber (2001)Examples are MuniAuctioncom which conducts auctionsof municipal bonds ChemConnectcom for trade inchemicals Wexchcom for electricity contracts

7 Lucking-Reiley (2000b) surveyed 142 online auctionsites that were in operation in autumn of 1998 Exact esti-mates of the number of auction listings are few and farbetween Lucking-Reiley (2000b) reported that in summer1999 340000 auctions closed on eBay every day asopposed to 88000 on Yahoo and 10000 on Amazon(most auctions last seven days) Sangin Park (2002)reports based on NielsenNetRatings surveys that eBayhad 56 million listings and Yahoo 40 million listings asof autumn 2000 When we counted the number of activelistings on January 25 2004 we found 145 million listingson eBay and only 193800 listings on Yahoo Our countsof Amazon listings were much more inexact since thewebsite does not divulge this information as visibly as eBayor Yahoo However our estimate is between 500000 and15 million listings

will focus on the latter category since this hasbeen the focus of most empirical researchOn the largest sites such as eBay Amazonand Yahoo bidders can choose from amind-boggling array of listings7

These sites facilitate search in two waysFirst the sites have a carefully designed setof categories and subcategories to organizethe listings For instance eBayrsquos main pagelists general categories that include automo-biles real estate art antiques collectiblesbooks and music Within a subcategorythere are multiple layers of additional cate-gories For instance the subcategory ofpaintings includes antique American andmodern European These categories arecarefully designed by the auction site tofacilitate buyersrsquo searches Secondly userscan search the millions of listings by key-words category price range and completeditems This allows users to gather consider-able information about similar productswhich is useful in forming a bid

In figure 1 we display a listing from eBayThe web page describes the item for salewhich in this case is a collection of signa-tures of fourteen Nobel laureates in eco-nomics The user can see the current highbid the time left in the auction the identityof the seller and the highest bidder Theweb page describes the item for sale and



Figure 1 Sample eBay Listing



8 Similar mechanisms are used on Yahoo andAmazon Yahoorsquos feedback pages look almost exactly likeeBayrsquos Amazon uses a slightly different five-star systemto summarize the comments

9 The fees that eBay charges have been updated sev-eral times with vocal dissent from eBay sellers in certaininstances such as when eBay decided to charge a $1 fee touse the secret reserve option (Troy Wolverton 1999) Alsosee Park (2002) for some evidence on the impact of net-work effects on the competition (in listing-fees) betweeneBay and Yahoo

10 Yahoo also allows ldquostraight biddingrdquo ie biddingprecisely the price you want to pay

sometimes displays a seller-supplied pic-ture The listing also displays the sellerrsquosfeedback to the right of the sellerrsquos identityUsers on eBay can leave each other feed-back in the form of positive neutral or neg-ative comments The total feedback is thesum of positive comments minus the num-ber of negative comments eBay also com-putes the fraction of positive feedbackreceived by the seller8

The listing displays the minimum bid andwhether the seller is using a secret reserveThe minimum bid is analogous to a reserveprice in auction theory that is the sellerwill not release the item for less than theminimum bid A secret reserve price func-tions similarly except that it is not publiclydisplayed to the bidders Bidders can onlysee whether or not the secret reserve ismet Sellers on eBay pay an insertion feeand a final value fee The insertion fee isbased on the minimum bid set by the sellerFor instance if the minimum bid isbetween $25 and $50 the insertion fee iscurrently $110 The final sale fee is a non-linear function of the final sale price Foritems with a final sale price of less than $25the fee is 525 percent of the final saleprice Higher sale prices are discounted atthe margin Also eBay has additional fees ifa secret reserve is used or if more than onepicture is used9

At the bottom of the listing buyers cansubmit their bids All three major onlineauction sites use some variant of proxy bid-ding10 Herersquos how it works Suppose that aseller lists an Indian-head penny for sale

11 eBay has a sliding scale of bid increments based onthe current price level in an auction

12 There is a 10-cent charge for running a ten-dayauction on eBay

with a minimum bid of $1500 At this pricelevel eBay requires a bid increment of $50to outbid a competitor11 If bidder A places aproxy bid of $20 the eBay computer willsubmit a bid of $1500 just enough to makebidder A the highest bidder Suppose thatbidder B comes along and submits a bid of$1800 Then the eBay computer will updateArsquos bid to $1850 (the second-highest bidplus one bid increment) The proxy biddingsystem updates Arsquos bid automatically until Ais outbid by another bidder If this occursthe bidder will be notified by email andgiven a chance to update their bid

Although all three sites use the proxy-bid-ding mechanism they differ in how theyend the auction eBay auctions have a fixedending time set by the seller who chooses aduration between one three five sevenand ten days and the auction closes exactlyafter this duration has passed12 OnAmazon if a bid is submitted within the lastten minutes of the previously fixed endingtime the auction is automatically extendedfor ten additional minutes Furthermorefor every subsequent bid the ten-minuteextension still applies Hence the auctionends only if there is no bidding activity with-in the last ten minutes Interestingly Yahootakes the middle-ground between these twoending rules and allows sellers to choosewhich one to apply to their auction As wewill see in the next section this seeminglyinnocuous difference in ending rules is asso-ciated with divergent bidding behaviorsacross auction sites

3 Last-Minute Bidding

Bids commonly arrive during the last sec-onds of an internet auction that lasts as longas several days For instance Alvin Rothand Axel Ockenfels (2002) and Ockenfelsand Roth (2003) find in a sample of 240



antique auctions on eBay that 89 had bidsin the last minute and 29 in the last ten sec-onds Other researchers including RonaldWilcox (2000) Bajari and Hortaccedilsu (2003)and Julia Schindler (2003) have document-ed similar patterns In this section we sum-marize some of the proposed explanationsfor last-minute bidding and the relatedempirical work

Last-minute bidding is difficult to explainusing standard auction theory Proxy biddingbears a strong similarity to the second-pricesealed-bid auctions since in both cases thepayment by the winning bidder is equal tothe second highest bid William Vickrey(1961) observed that in a second-pricesealed-bid auction with private values it is aweakly dominant strategy for a bidder to bidtheir reservation value The intuition is sim-ple If the bid is less than their private valuethere is a probability that she will lose theauction However the payment the winningbidder makes only depends on the second-highest bid As a result bidding onersquos valua-tion weakly increases onersquos payoff Thus atfirst glance it appears that bidders can leavetheir proxy agents to do their bidding anddo not need to wait until the last seconds ofthe auction

However this is clearly not what happensin practice and therefore several explana-tions for late bidding have been proposed inthe literature A first explanation whichdeparts only slightly from the independentprivate values environment is proposed byOckenfels and Roth (2003) They argue thatlate bidding may be a form of ldquotacit collusionrdquoby the bidders against the seller In theirmodel bidders can choose to bid early or lateA late bid however might not be successful-ly transmitted due to network traffic Thereare (at least) two possible equilibria in theirmodel In the first agents bid early in theauction and in the second agents only bid atthe last second Bidding late is a risk becausethe bids may not be successfully transmittedOn the other hand late bidding softens com-petition compared to the first equilibrium

Ockenfels and Roth (2003) demonstratethat this ldquotacit collusionrdquo explanation of last-minute bidding equilibrium hinges on theassumption that there is a hard deadline forsubmitting bids As mentioned in the lastsection however while eBay auctions have ahard deadline Amazon auctions are auto-matically extended if a late bid arrivesOckenfels and Roth (2003) then demon-strate that late bidding is no longer an equi-librium in Amazon-style auctions Theyconclude that there are more powerfulincentives for late bidding in eBay auctionsthan in Amazon auctions

As a test of this theory Roth and Ockenfels(2002) and Ockenfels and Roth (2003) com-pare the timing of bids for computers andantiques on Amazon and eBay They find thatlate bidding appears to be more prevalent inthe eBay auctions On eBay bids are submit-ted within the last five minutes in 9 percentof the computer auctions and 16 percent ofthe antique auctions On Amazon about 1percent of the auctions in these categoriesreceive bids in the last five minutes Biddersurveys reveal that late bidding on eBay is adeliberate strategy meant to avoid a biddingwar Similar evidence comes from Schindler(2003) who studies bidding in Yahoo auc-tions for computers art and cars In theseauctions the sellers can choose to have a hardclose or an automatic extension similar toAmazon auctions if late bids arrive She findsthat consistent with the Ockenfels and Roththeory the winning bidder tends to arrivelater in the auctions with a hard ending forall three product categories

We should note that the empirical findingby Roth and Ockenfels (2002) Ockenfelsand Roth (2003) and Schindler (2003)mdashthatthere is less sniping in flexible-ending ruleauctionsmdashis not confirmed in all studiesGillian Ku Deepak Malhotra and J KeithMurnighan (2003) study bidding in onlineauctions in several US cities for art that hasbeen publicly displayed The proceeds of theauctions were donated in part to charitablecauses The authors observe online auctions



13 However this revenue ranking may be reversed in anaffiliated interdependent values environment

with both hard endings and flexible endingsThey observe substantially less late biddingthan the previously mentioned studies Only16 percent of the bids arrive in the last fiveminutes of the auctions with hard deadlinesand 05 percent in the auctions with flexibleendings Also Ku et al find that for auctionswith flexible endings a greater percentageof the bids arrive in the last hour and the lastday than for auctions with hard endings Thisis not qualitatively consistent with Ockenfelsand Rothrsquos prediction However it is impor-tant to note that the authors have limitedcontrols for heterogeneity across auctionsthat are held in different cities

Several other empirical researchers haveexamined the tacit collusion hypothesis ofOckenfels and Roth further Kevin HaskerRaul Gonzalez and Robin Sickles (2003) testthe Ockenfels and Roth theory by examiningbids for computer monitors on eBay If latebids soften competition and lower the proba-bility of price wars then the distribution ofthe winning bids conditional upon a snipeshould not equal the distribution of the win-ning bids if no snipe occurs Hasker et al findthat in most of the specifications they exam-ine they are unable to reject the equality ofthese two distributions They argue that thisis inconsistent with the ldquotacit collusionrdquo theo-ry Similarly in a data set of bidding for eBaycoin auctions Bajari and Hortaccedilsu (2003)find that reduced form regressions suggestthat early bidding activity is not correlatedwith increased final sales prices Schindlerrsquos(2003) study also casts some doubt on thetacit collusion hypothesis on the revenuefront Under private values the Ockenfelsand Roth model predicts that a hard closewill decrease revenues for the seller13

Schindler finds sellers more frequently useauctions with automatic extensions for art (82 percent vs 18 percent) and cars (73 per-cent vs 27 percent) but not for computers(91 percent vs 9 percent) Therefore under

14 Based on survey data Ku et al (2003) also find thatsome bidders may be driven by emotional factors whichthey label as ldquocompetitive arousalrdquo For instance one sur-vey respondent from Cincinnati who purchased a ceramicpig explained that she ldquoreally wanted the pig and probablyalso got caught up in the competitive nature of the auc-tionrdquo Another respondent explained her behavior by stat-ing ldquoAuction fever took overrdquo

the Ockenfels and Roth theory the first twocategories are consistent with seller revenuemaximization while the last category is not

A second theoretical explanation alsooffered by Ockenfels and Roth (2003) is thepresence of naiumlve bidders on eBay who donot understand the proxy-bidding mecha-nism and hence bid incrementally inresponse to competitorsrsquo bids14 Ockenfelsand Roth (2003) demonstrate that last-minute bidding is a best-response by ration-al bidders against such naiumlve bidders Theypresent empirical evidence that more expe-rienced bidders are less likely to place mul-tiple incremental bids on eBay Thisevidence is complemented by survey evi-dence reported in Roth and Ockenfels(2002) Furthermore Dan Ariely Ockenfelsand Roth (2003) conduct a controlled labo-ratory experiment in which one of the exper-imental treatments is an eBay-typefixed-deadline auction where the probabili-ty of ldquolosingrdquo a bid due to transmission erroris zero Notice that this feature rules out thetacit collusion explanation however it is stilla best-response against naiumlve incrementalbidders to bid at the last secondAccordingly Ariely et al (2003) find a signif-icant amount of late-bidding activity in thisexperimental treatment

A third explanation is based on a commonvalue In a common-value auction such asRobert Wilsonrsquos (1977) mineral-rights modelthe item up for sale has a true value V that isnot directly observed by the bidders Forexample the common value V could be theresale value of a collectible Each bidderreceives an imperfect signal x of V which isprivate information By bidding early a bid-der may signal information about x to otherbidders and cause them to update their



beliefs about V Conditional on winning thismay increase the price that a bidder has topay for the item Bajari and Hortaccedilsu (2003)formalize this intuition and demonstrate thatlast-minute bidding occurs in models ofonline auctions with a common valuemdashinfact they show that in a symmetric common-value environment the eBay auction can bemodeled as a sealed-bid second-price auc-tion A similar result is also provided inOckenfels and Roth (2003) in a simpler set-ting This explanation also finds some sup-port in the data Roth and Ockenfels (2002)and Ockenfels and Roth (2003) report thatthere is more last-minute bidding on eBayantiques auctions than in eBay computerauctions They argue that antiques auctionsare more likely to possess a common valueelement than computer auctions and hencethe observed pattern is consistent with thetheoretical prediction

A fourth explanation for late bidding is pro-posed by Joseph Wang (2003) who studies amodel in which identical items are simultane-ously listed as opposed to the usual assump-tion that only a single unit of the item is up forsale Last-minute bidding is part of the uniqueequilibrium to his model Michael Peters andSergei Severinov (2001) argue that theirmodel of bidding for simultaneous listings ofidentical objects is also qualitatively consistentwith late bidding Taken together these twopapers suggest that the multiplicity of listingsis another explanation for late biddingHowever it is not clear whether the Wang(2003) and Peters and Severinov (2001) mod-els predict that last-minute bidding should beless prevalent on Amazon as opposed to eBay

A fifth explanation is given by EricRasmusen (2001) who considers a model inwhich bidders have uncertainty about theirprivate valuation for an item As in themodel of Dan Levin and James Smith(1994) he assumes that some bidders mustpay a fixed fee to learn their private infor-mation This is not an unreasonable assump-tion in online auctions In order to learntheir private valuations bidders will inspect

the item and may search for sales prices ofpreviously listed items The fixed fee can bethought of as the opportunity cost of timerequired to do this research Rasmusendemonstrates that late bidding can occurbecause bidders wish to economize on thecosts of acquiring information

The multiplicity of explanations providedin the literature regarding the causes of aseemingly innocuous phenomenon like last-minute bidding is a great example of how theanalysis of online auctions enables us toappreciate the richness and complexity ofstrategic interaction in markets The preced-ing discussion also illustrates how a seeming-ly simple empirical regularity can havemultiple explanations and that it is not easyto discern between these explanations with-out creative exploitation of sources of varia-tion in the data In this regard theexperimental study of Ariely Ockenfels andRoth (2003) is a good demonstration of howlaboratory experiments can complementdata obtained from real markets to helpexplain complex strategic interactions

4 The Winnerrsquos Curse

In online markets buyers are not able toperfectly observe the characteristics of thegoods for sale In the market for collectiblesand other used goods buyers value objectsthat appear new Scratches blemishes andother damage will lower collectorsrsquo valua-tions Since a buyer cannot directly touch orsee the object over the internet it may behard to assess its condition This introducesa common-value component into the auc-tion and therefore bidders should accountfor the winnerrsquos curse

The winnerrsquos curse can be illustrated bythe following experiment discussed in PeterCoy (2000)

Paul Klemperer hellip illustrates the winnerrsquoscurse to his students by auctioning off a jar withan undisclosed number of pennies The stu-dents bid a little below their estimate of thejarrsquos contents to leave a profit Every time



though the hapless winner of the jar is the stu-dent who overestimates the number of penniesby the greatest amount and therefore overpaysby the most

The winnerrsquos curse occurs when biddersdo not condition on the fact that they willonly win the auction when they have thehighest estimate If there is a large numberof bidders the highest estimate may bemuch larger than the average estimateTherefore if the winner bids naively hemay overpay for the item In addition to theclassroom experiment above a number ofexperimental studies find that inexperi-enced bidders frequently are subject to thewinnerrsquos curse (see John Kagel and AlvinRoth 1995 for a survey of the experimentalliterature)

Several authors empirically examinewhether bidders are subject to the winnerrsquoscurse and more generally measure distortionsfrom asymmetric information in online mar-kets Two main methodologies have been uti-lized to answer this question so far The firstmethod utilized by Ginger Jin and AndrewKato (2002) is to sample goods in an actualmarket and determine whether the pricesreflect the ex-post quality of the goods theybuy Online auctions present perhaps a uniqueopportunity in the utilization of this methodol-ogy since items sold in these markets are inex-pensive enough to allow researchers to ldquoshoprdquoout of their own pockets or research grants

In particular Jin and Kato (2002) studyfraudulent seller behavior in an internetmarket for baseball cards Fraud on theinternet is a problem The Internet FraudCenter states that 48 percent of the 16775complaints lodged in 2001 were from onlineauctions In this market Jin and Kato findthat sellers frequently misrepresent howbaseball cards will be graded

Professional grading services are com-monly used for baseball cards Gradingservices produce a ranking from 1 to 10based on the condition of the card Cardsthat are scratched or have bent corners areassigned lower grades than cards that are in

15 They attempted to bid just enough to win the auc-tion and to have a minimal effect on the actions of otherplayers

mint condition Jin and Kato bid on eBayauctions for ungraded cards and then sub-mitted the cards to a professional gradingservice15 Their sample contained 100ungraded baseball cards Of these sellersof nineteen claimed that they had a rankingof 10 (gem mint) 47 claimed mint (9 or95) sixteen near-mint to mint (8 or 85)seven near-mint (7 or 75) eleven cards hadno claim

Many sellers of ungraded cards misrepre-sented the card quality Among sellers whoclaimed that their cards were grade 9 to 10the average grade was 634 In comparisonthe average grade of cards claiming 85 orbelow was 687 The price differencebetween a grade 10 and a grade 65 for somecards could be hundreds or thousands ofdollars Jin and Kato find evidence that somebuyers were misled by these claims Buyerswere willing to pay 27 percent more forcards that were seller-reported as 9 to 95and 47 percent more for cards that werereported as 10 They conclude that somebuyers have underestimated the probabilityof fraud and therefore have fallen prey to theldquowinnerrsquos curserdquo

To establish whether the winnerrsquos curseproblem is more of an issue in online asopposed to offline markets the authorsemployed male agents between 25ndash35 yearsof age to purchase the same types of cardsfrom retail collectibles stores in eleven metro-politan districts and found that the fraud ratethere (32 percent) was much lower thanonline (11 percent) Moreover in their retailpurchases the authors found that retail sellerswere more reluctant to quote the likely gradeof the cards when one was not available

Does this mean that online auctions forbaseball cards are fraught with fraudulentsellers and naiumlve buyersmdasha market that coulduse further regulation and intervention bythird parties to operate more efficiently



Before we jump to this conclusion webelieve that a cautious interpretation of Jinand Katorsquos findings is in order In our opinionthe main finding of this paper is that ldquowhileonline bidders account for the possibility ofmisrepresentation they might make mistakeswhen assessing the probability of fraudulentclaimsrdquo It should be pointed out that theldquomistakesrdquo that buyers make are not likely tolead to very large monetary losses since mostitems in this market are mundanely priced(between $60 and $150) Even for more valu-able items the estimated losses from buyernaivete are not very large For instance Jinand Kato report that the average eBay pricefor a grade-10 Ken Griffey Jrrsquos 1989 UpperDeck Card (the most actively traded card oneBay) is $1450 An ungraded Griffey with aself-claim of 10 only sold for an average of$9426 Jin and Katorsquos estimates imply thatthis self claim generated an extra $30 in rev-enue for a fraudulent seller If bidders naive-ly took the claims at face value this wouldhave generated a mistake of over $1300mdashhence the Ken Griffey example shows thatbidders by and large do correct for thewinnerrsquos curse The contention is whetherthe bidders correct enough for thisFurthermore the authors did manage to pur-chase a card of mint quality (grade 9) fromtheir ungraded group Apparently taking achance with ungraded cards sometimes doesin fact pay off

Regardless of how one may interpret Jinand Katorsquos (2002) findings their methodolo-gy to assess the prevalence of fraud and themagnitude of the corresponding ldquowinnerrsquoscurserdquo suffered by buyers is a very direct andeffective way to get precise measurementsUnfortunately it would be difficult if onlydue to budget constraints to replicate thismethodology to examine markets for moreexpensive items like computers or automo-biles (which are actively traded on eBay)Hence the second set of papers we will sur-vey utilizes more ldquoindirectrdquo methods thatrely on testing the implications of strategicbidding in common-value auction models (in

16 We should note however that Paarsch (1992) imple-mented his idea in the context of first-price auction wherethe comparative static with respect to the number of bid-ders in the auction is ambiguous See Susan Athey andPhilip Haile (2002) and Haile Han Hong and MatthewShum (2003) for more rigorous derivations of this compar-ative static result in the context of second-price andascending (button) auctions

which bidders account for the presence of awinnerrsquos curse)

The first study we will summarize in thisregard is Bajari and Hortaccedilsu (2003) whoexamine the effect of a common value onbidding for collectible coins in eBay auc-tions The authors first argue that bidding inan eBay auction in the presence of a com-mon-value element can be modeled as bid-ding in a second-price sealed-bid auctionwith common values (and an uncertain num-ber of opponents) The reasoning is basedon the observation of sniping in these auc-tions the presence of a common-value ele-ment in an eBay auction suggests thatbidders should wait until the last minute inorder to avoid revealing their private infor-mation If all bids arrive at the last minute abidder will not be able to update his beliefsabout the common value V using the bids ofothersmdashhence his bidding decision will beequivalent to that of a bidder in a sealed-bidsecond-price auction

Given this argument the authors then testfor the presence of a common-value elementusing an idea first proposed by HarryPaarsch (1992)16 If the auction environmentis one with purely private values it is a dom-inant strategy for bidders to bid their privatevalues independent of the number of com-petitors that they face If there is a common-value element however the bids will dependon the number of bidders present This isbecause when there are more bidders thepossibility of suffering a winnerrsquos curse con-ditional on winning the auction is greaterfurther cautioning the bidders to tempertheir bids to avoid the curse

Bajari and Hortaccedilsu (2003) implementthis using cross-sectional regressions of bidson the number of bidders and apply various



instruments to account for the endogeneityof the number of bidders in the auctionConsistent with the presence of a common-value element they find that bids declinewith the number of competing biddersThey test whether bidders with little eBayexperience tend to bid systematically higherby regressing individual bids on total eBayfeedback While they find a statistically sig-nificant effect on bids it is very small inmagnitude This is not consistent with exper-imental evidence that suggests inexperi-enced participants overbid and hence aremore likely to suffer from a winnerrsquos curse

Next the authors attempt to directlymeasure the distortions from asymmetricinformation by estimating a structuralmodel In their structural model the datagenerating process is a Bayes-Nash equilib-rium where 1) there is a common value 2)bidders have to pay a fixed cost to learn theirsignal x and 3) entry is endogenously deter-mined by a zero profit condition Given thisstructural model they estimate the parame-ters for the distribution of the common valueV the distribution of bidderrsquos private infor-mation x and the parameters that governentry decisions

The authors find that biddersrsquo prior infor-mation about V is quite diffuse In theirmodel ex ante beliefs about V are normallydistributed The standard deviation is 056times the book value of the coin if there is noblemish and 059 times the book value if thereis a blemish For instance the standard devia-tion of V for a coin with a book value of $100with no blemish is $56 However the distri-bution of x has much lower variance than VFor instance the distribution for x for the coindescribed above would be normally distrib-uted with a mean equal to V and a standarddeviation of $28 Apparently after a buyer hasspent the time and effort to research a coinhis estimate x is considerably more precisethan his prior information about V

Given their parameter estimates theauthors simulate their structural model toquantify the distortions from asymmetric

information First they compute the equilibrium bidding strategies for an auctionwhere all of the covariates are set equal totheir sample averages (most importantly thebook value is equal to $47) The bid func-tions are roughly linear In this auction bid-ders shade their bids to be $550 less thantheir signals x Given their uncertainty aboutV the buyers bid 10-percent less than theirsignals to compensate for the winnerrsquos curseSecond they simulate the impact of addingan extra bidder on expectation (since biddersarrive at random) When the expected num-ber of bidders increases the winnerrsquos curseis more severe Bajari and Hortaccedilsu find thatfor an auction with all characteristics setequal to their sample averages adding anadditional bidder reduces equilibrium bidsby 32 percent as a function of the bidderrsquossignal x Finally they study the effect of low-ering the variance of V from 052 times thebook value to 01 They find that the bidfunction shifts upwards by $250

There are two main limitations to theiranalysis First the results depend on cor-rectly specifying both the game and the para-metric assumptions used in the structuralmodel The common value model that theyuse is fairly strict All players are perfectlyrational symmetric and have normally dis-tributed private information However theynote that it is not computationally tractablewith current methods to generalize themodel since computing the equilibriuminvolves high dimensional numerical integra-tion Second unlike Pai-Ling Yin (2003) or Jinand Kato (2002) the authors do not have richex-post information about the realization ofthe common value or for bidder uncertainty

In a related study Yin (2003) utilizes com-parative static predictions of the common-value second-price auction model to test forthe presence of common values in eBay auc-tions for used computers Based on numeri-cal simulations Yin establishes that biddingstrategies in the common-value second-priceauction model respond strongly to changesin the variance of x a bidderrsquos private signal



0

01

02

03

04

05

06

07

08

03 04 05 06 07 08 09 1 11 12 13 14 15 16 17 18

Normalized Dispersion SD (=sdV)

Nor

mal

ized

Pri

ces

= P

V

Figure 2 Bidder Uncertainty and Winning Bids

about V Holding V fixed if the variance of xincreases the bidder is more likely to besubject to the winnerrsquos curse Conditional onwinning the value of x should be largerholding V fixed Therefore bidders shouldbehave more conservatively

Yin supplements her data set with surveyinformation in order to understand howchanging the variance of the signal x influ-enced bidding To do this she downloadedthe web pages for 223 completed auctionsand asked survey respondents from theinternet to reveal their estimates x purginginformation about the bids and the reputa-tion of the seller from the web page so thatthe survey responses would primarily reflectbiddersrsquo ex-ante beliefs about the item forsale and not the seller characteristics Bycomputing the variance of an average of 46such responses per auction she constructeda proxy for the variance of x The highestvariance occurred in the auctions where theseller poorly designed the web page orwhere the object for sale had inherentlyambiguous characteristics

Yin finds that the winning bid is negative-ly correlated with the normalized variance ofthe survey responses This is illustrated infigure 2 The vertical axis is the winning biddivided by the average survey response Thehorizontal axis is the variance of the respons-es divided by the average response For anauction where the normalized variance was04 the expected value of the normalizedwinning bid was 07 As the varianceincreased to 13 the expected value of thenormalized winning bid was less than 02Given that the auctions were for computersworth hundreds of dollars these are fairlylarge magnitudes

One interpretation of these results is thatbidders act as if they understand the win-nerrsquos curse The bidders are reluctant to sub-mit high bids when they are uncertain aboutthe condition of the used computer asreflected by a high variance in x On theother hand if they are more certain aboutwhat they are purchasing they will bid withmore confidence These findings suggestthat asymmetric information can generate



significant distortions and therefore sellershave a strong incentive to communicatewhat they are selling by designing a clearweb page to reduce bidder uncertainty

A potential limitation to Yinrsquos analysis isthat the variance of x for survey respondentsmay be an imperfect measure of the varianceof x for the real bidders The survey respon-dentsrsquo reservation values are roughly twicethe final sales price on average Perhaps theyare not well informed about used-computerprices On the other hand if the winnerrsquoscurse is a strong possibility it may be anequilibrium for a bidder to shade his bid tobe one half of his estimate x

Conditional on their caveats all threepapers surveyed in this section suggest thatthe winnerrsquos curse is an important concern inthe eBay markets analyzed (baseball cardscollectible coins used computers) Two ofthe three papers Bajari and Hortaccedilsu (2003)and Yin (2003) find evidence that biddersstrategically respond to the presence of awinnerrsquos curse Jin and Kato (2002) also pres-ent similar evidence of strategic ldquobid-shad-ingrdquo but through their ex-post appraisal ofcards sold on eBay they argue that theamount of bid-shading by bidders in thebaseball cards market is not large enough

As pointed out in the introduction animportant implication of these results notedby Kazumori and McMillan (2003) is thatdepressed prices due to the winnerrsquos cursemay limit the use of online auctions by sell-ers to items for which informational asym-metries do not play a very large roleKazumori and McMillan (2003) report thatafter several years of experimentation withrunning art auctions on the internet in part-nership with eBay Sothebyrsquos decided to dis-continue selling art on the internet as of May2003 As the authors note there may havebeen several confounding factors that led tothe failure of Sothebyscom Fortunatelythis still leaves the investigation of the ques-tion ldquowhich goods can be sold using onlineauctionsrdquo open for further empirical andtheoretical analysis

17 Sellers are also required to list a valid credit-cardnumber

18 Resnick and Zeckhauser (2001) had data on transac-tions conducted on eBay in a five-month period hencethey cannot track transactions preceding this periodWithin this time period however repeat transactions ifthey occurred at all happened in a very short time period

19 Other auction sites such as Amazon allow for morenuanced feedback Amazon for example utilizes a scaleof 1 to 5

20 In March 2003 eBay also began displaying the per-centage of positive feedbacks along with the total score

5 Reputation Mechanisms

Perhaps the most important source ofinformation asymmetry on online auctionsaside from the inability to physically inspectgoods is the anonymity of the sellers Forinstance eBay does not require its users todivulge their actual names or addresses allthat is revealed is an eBay ID17 There arealso very few repeat transactions betweenbuyers and sellers For instance Paul Resnickand Richard Zeckhauser (2001) using a largedata set from eBay report that fewer than 20percent of transactions are between repeatedbuyer-seller pairs within a five-month peri-od18 This obviously limits a buyerrsquos informa-tion about the sellerrsquos reliability and honesty

To ensure honest behavior online auctionsites rely on voluntary feedback mecha-nisms in which buyers and sellers alike canpost reviews of each othersrsquo performanceOn eBay a buyer can rate a seller (and viceversa) by giving them a positive (+1) neutral(0) or negative (-1) score along with a textcomment19 eBay records and displays all ofthese comments including the ID of theperson making the comment eBay also dis-plays some summary statistics of usersrsquo feed-back The most prominently displayedsummary statistic which accompanies everymention of a user ID on eBayrsquos web pages isthe number of positives that a particular userhas received from other unique users minusthe number of negatives20 eBay also computes and reports the number of posi-tivesneutralnegatives that a seller hasreceived in their lifetime along with the lastweek last month and last six months



21 Resnick and Zeckhauser (2001) also report in a small-er sample that in 18 out of 87 cases (20 percent) where abuyer left negative feedback about a seller the sellerresponded with a negative for the buyer There is also thepossibility that some buyers are more critical than othersmdashalthough eBay allows users to observe the feedback that auser left about others it does not provide summary statis-tics of left feedbackmdashhence making it quite costly for aprospective buyer to gauge the attitude of a particularcommentor (other sites that provide user reviews such asAmazon allow readers of a comment to rate that commenton the basis of its usefulness)

A prospective buyer on eBay therefore hasaccess to a considerable amount of informa-tion about the reputation of a seller Forinstance almost all of the feedback on eBayis positive Resnick and Zeckhauser (2001)report that only 06 percent of feedbackcomments left on eBay by buyers about sell-ers was negative or neutral One interpreta-tion of this result is that most users arecompletely satisfied with their transactionAnother interpretation however is thatusers are hesitant to leave negative feedbackfor fear of retaliation Luis Cabral and AliHortaccedilsu (2003) report that a buyer wholeaves a negative comment about a seller hasa 40-percent chance of getting a negativeback from the seller (whereas a neutral com-ment has a 10-percent chance of being retal-iated against)21 Another factor limiting thepotential usefulness of feedback reportedby Resnick and Zeckhauser is that feedbackprovision is an arguably costly activity that iscompletely voluntary and that not all buyers(521 percent) actually provide reviewsabout their sellers

51 Empirical Assessment of FeedbackMechanisms

Do online reputation mechanisms workThe fact that we observe a large volume oftrade on sites like eBay Yahoo and Amazonmay suggest that the answer to this questionis affirmative However there have also beena number of attempts to answer this ques-tion in a more direct manner by estimatingthe market price of reputation in online auc-tions through hedonic regressions

Table 1 which is adapted from Resnick etal (2003) summarizes these empirical stud-ies The first ten studies on this list used dataon completed auctions on eBay to run cross-sectional hedonic regressions of the saleprice or sale probabilities of similar objectson sellersrsquo observable feedback characteris-tics The (log) number of positive and nega-tive comments is used as a right-hand sidevariable in almost all of these studies Someof these studies also attempted to accountfor nonlinearities in the functional relation-ship by putting in dummy variables for theexistence of negatives or dummy variablesfor different ranges of feedback Some stud-ies also explicitly recognize the truncationproblem caused by auctions that did not endin a sale and estimated a ldquoprobability ofsalerdquo equation either separately or jointlywith the (log) price regression

As can be seen in table 1 although thesigns and statistical significance of theregression coefficients are mostly of theexpected kind the empirical results fromthese studies are not easily comparablesince some of them are reported in absoluteterms and some are reported in percentageterms We should also note that the absolutenumber of feedback comments received bysellers follows a highly skewed distributionreflecting a Gibratrsquos Law type effect Giventhis however the starkest differencesappear to be between sellers who have nofeedback records and those with a verylarge number of positive comments Forexample Mikhail Melnik and James Alm(2002) report that the difference between452 and 1 positive comment is $159 for$33 items implying a price premium of 5percent Jeffrey Livingstonrsquos (2002) esti-mates imply a more than 10-percent pricepremium for sellers with more than 675positive comments as opposed to those withno feedback Similarly Kirthi Kalyanamand Shelby McIntyre (2001) report that aseller with 3000 total feedbacks and nonegatives is estimated to get a 12-per-cent higher price than a seller with ten total



22 Standard models of dynamic industry equilibriumsuch as Hugo Hopenhayn (1992) and Richard Ericson andAriel Pakes (1998) suggest that survival is positively corre-lated with measures of seller productivity

comments and four negatives The presenceof an up to 10ndash12 percent price premiumbetween an ldquoestablishedrdquo eBay seller (withhundreds or thousands of feedback com-ments) and a seller with no track recordappears to be the most robust result amongthese studies especially in light of the 81-percent price premium uncovered by arecent ldquofield experimentrdquo conducted byResnick et al (2003) to account for some ofthe more obvious confounding factorsinherent in the hedonic regressions whichwe shall discuss now

The first confounding factor is that the esti-mate of reputation may be subject to an omit-ted variable bias Since there is very littlenegative feedback the number of positives isessentially equal to the number of transactionsthat a seller has completed on eBay Moreexperienced eBayers are on average moreadept at constructing a well-designed webpage and responding to buyer questions Thework of Yin (2003) suggests that a well-designed web page has a sizeable effect onfinal sale prices by reducing buyer uncertain-ty about the object for sale As a result moreexperienced sellers (as measured by positivefeedback) will have higher sales prices for rea-sons that have nothing to do with reputation22

Obviously these factors could lead to biasedestimates that overstate the market value ofreputation scores obtained by sellers on eBay

Sulin Ba and Paul Pavlou (2002) Cabraland Hortaccedilsu (2003) and Resnick et al(2003) attempt to reduce this confoundingfactor by manipulating feedback indicatorsindependently of other characteristics ofauction listings For example Resnick et al(2003) conduct a ldquofield experimentrdquo with thehelp of an established eBay seller (2000 pos-itives 1 negative) by selling matched pairsof postcards both under the sellerrsquos realname and under newly created identitiesThey report that the established seller

23 There may have been some other confounding fac-tors that Resnick et al were not able to remove with theirexperimental design In particular some buyers may havebeen repeat customers of the established seller ID andsome buyers may even have searched (or even had auto-matic watches) for sales by that seller and not even lookedat the matched listings from the new buyer We thank PaulResnick for pointing this out

received after correcting for non-sales 81percent higher prices than his newly formedidentity Ba and Pavlou (2002) on the otherhand asked a sample of eBay bidders tostate their willingness to pay for eBay auc-tion listings obtained from the web site withthe twist that seller feedback characteristicswere manipulated by the experimenters23

Cabral and Hortaccedilsu (2003) exploited achange in eBay feedback reporting policy asmentioned before eBay began displayingthe percentage of positives starting in March2003 Cabral and Hortaccedilsu (2003) run ahedonic regression where they interact dif-ferent feedback summary statistics with adummy variable for the policy change andshow that the negative correlation betweenprices and the percentage of negatives islarger after the policy change whereas thecorrelation between prices and total feed-back score and the sellerrsquos age in days issmaller

A second limitation of hedonic studies isthat there is very little variation in certainindependent variables particularly negativefeedback For instance in Dan Houser andJohn Wooders (2003) the maximum numberof negative feedbacks in their data set istwelve Similarly in Melnik and Alm (2002)the maximum number of negatives is thirteenIn the data set analyzed in Bajari and Hortaccedilsu(2003) we found that only very large sellerswith hundreds if not thousands of positivefeedbacks had more than a handful of nega-tive feedbacks Obviously it is hard to learnabout the value of negative feedback whenthere is not much variation in this variable

A third limitation is that the objects ana-lyzed in the hedonic studies mentionedabove are fairly inexpensive and standard-ized Researchers prefer using standardized


TABLE 1 EMPIRICAL STUDIES ON THE VALUE OF REPUTATION ON EBAY

(ADAPTED FROM RESNICK ET AL 2003)

Source Items Sold Mean Type of Study Covariates ( indicates dummy price variable)

Dewan and Hsu2001

Eaton 2002

Houser andWooders 2000

Jin and Kato2002

Kalyanam andMcIntyre 2001

Livingston 2002

Lucking-Reileyet al 2000

McDonald andSlawson 2000

Melnik and Alm2002

Cabral andHortacsu 2003

Ba and Pavlou2002

Resnick et al2003


Collectible stamps

Electric guitars

Pentium chips

Sports tradingcards

Palm Pilot PDAs

Golf clubs

coins

collectible Dollsnew

gold coins in mintcondition

IBM Thinkpadnotebooks goldcoins mint coinsets Beanie Babies

music CD modemWindows Serversoftware CDdigital camcorder

vintage postcards


$37

$1621

$244

$166

$238

$409

$173

$208

$33

$15-$900

$15- $1200

$13

$15-$900

hedonic regression compareauction prices on eBay withspecialty site (MichaelRogersInc)

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

lab experiment in the fieldsubjects responded with trustlevel and willingness to pay forauction listings with differentfeedback profiles spliced in

field experiment

panel regression usingbackward-looking feedbackprofiles of a cross-section ofseller

book value internationaltransaction number of bids full set close on weekendbuyers net rating

guitar type credit card escrowpictures interaction terms

auction length credit card bookvalue used processor type

same as (Melnik and Alm 2002)

product type picture number of bids

product type minimum bid bookvalue secret reserve credit cardday and time of auction closelength inexperienced bidder

book value minimum bid auction length end on weekendHidden reserve

month

gold price closing time and dayauction length credit cardacceptance images shipping charges

dummy variable for eBay formatchange dateclosing time and dayauction length credit cardacceptance PayPal imageswhether item is refurbished

showed identical listings withdifferent feedback profiles

showed matched information formatched items in different displayformat

control for seller fixed effects age in days reviewer profile


TABLE 1 (cont)

Modeling Specification Results regarding market value of reputation

OLS for sold items only ln(price) vs ln(positive-negative)

reduced form logit on probability of sale OLS onitems sold positives (number) negatives (dummyor number)

model of bidder distributions based on privatevalues motivates reduced-form GLS (on sold itemsonly) with expectation of more bidders for longerauctions ln(high-bid) vs ln(pos) ln(non-positive)

probit predicts probability of sale OLS for solditems ln(pricebook) vs ln(net score) has negatives

OLS for sold items only price against positivesnegatives interaction terms

probit predicts probability of sale simultaneousML estimation of price if observed probability ofsale reputation specifed by dummies for quartilesof positive feedback fraction of negative

reduced form censored normal regression ln(high-bid) vs ln(pos) ln(neg)

Simultaneous regression for sold items only priceagainst bids reputation bids against minimum bidsecret reserve reputation Various specifications ofreputation

reduced form censored normal regression ln(high-bid) vs ln(pos) ln(neg) ln(neutral)

OLS ln(highbid) vs of negatives total numberof feedbacks age of seller in days

OLS ANOVA of trust price premium againstln(positive) ln(negative)

censored normal ln(ratio of prices) dep variablewith no independent variables

look at the impact of first vs subsequent negativeson sales growth where sales levels are proxied bythe number of feedbacks received look at thetiming between negatives

20 additional seller rating points translate into a 5 cent increase inauction price on eBay Average bids higher on specialty site butseller revenues on specialty site (after 15 commission) are samewith revenues on eBay

No robust statistically significant relation between negativefeedback and probability of sale and price of sold items

10 increase in positive feedback increases price 017 10increase in negative feedback reduces it by 024 Increasingpositive comments from 0 to 15 increase price by 5 or $12

Positive feedback increases probability of sale negative decreasesprobability of sale unless card is professionally grade no significanteffects on price

Seller with 3000 total feedback and 0 negatives gets 12 higherprice than a seller with 10 total feedback and 4 negatives

Sellers with 1 to 25 positive comments receive $21 more thansellers with no reports Sellers with more than 675 positivecomments receives $46 more The first 11 good reports increaseprobability of receiving a bid 4 subsequent reports do notappear to have a statistically significant effect on sale probability

No statistically significant effect from positive feedback 1increase in negative feedback reduces price by 011

High reputation seller (90th percentile of eBay rating) gets 5higher price than low reputation (10th percentile) and gets more bids

Decline of positive ratings from 452 to 1 decreases price by $159for $32 items halving negative feedback from 096 comments to048 comments increases price by 28 cents

Effect of negatives increases after eBay begins to reportpercentages in March 2003 (8 price premium if negativesdeclines 1 point) Effect of total no of feedbacks and age of sellerdeclines after format change Effect most visible for Thinkpads

Buyers willing to pay 036 more if feedback profile has 1 morepositives -063 less with 1 more negatives (Table 8) Effect islarger for higher priced items

Seller with 2000 positive comments and 1 negative fetched 8 higherprices for matched items sold by newly created seller identities with10 positives on average Sale probability of large seller 7 higherthan new sellers One or two negatives for a relative newcomer hadno statistically detectable effect over other newcomers

In 4 week window after first negative sales growth rate is 30 lessthan in 4 week window before negative second negatives arrivefaster than first negatives



objects because it is fairly easy to collect bookvalues which are an important independentvariable in hedonic regressions for suchitems Fairly inexpensive items are typicallyused because they are representative of theobjects bought and sold in online markets

It is arguable however that the role ofreputation is most important when a buyer isconsidering the purchase of a very expensiveitem of potentially dubious quality When abuyer is purchasing a new branded andstandardized object (eg a new Palm Pilot)there is probably little uncertainty about theitem up for sale When the item is expensiveused and has uncertain quality such as ahard-to-appraise antique reputation mightplay a more important role A buyer in suchan auction might be reluctant to bid thou-sands of dollars when the seller has only lim-ited feedback Such items however havetypically not been included in previous stud-ies since it is difficult to construct appropri-ate controls for the value of such an item Inparticular book values for such items areprobably not very meaningful

A final limitation which applies equally toboth the hedonic regressions and the field-experiment studies is that it is difficult tointerpret the ldquoimplicit pricesrdquo as buyer valua-tions or some other primitive economicobject In the applied econometric literatureon hedonics such as Sherwin Rosen (1974)and Dennis Epple (1987) there is a fairly sim-ple mapping from ldquoimplicit pricesrdquo to buyerpreferences The implicit price can typicallybe interpreted as the market price for a char-acteristic Hence the implicit price is equal tothe marginal rate of substitution between thischaracteristic and a composite commodityThis straightforward mapping breaks downwhen there is asymmetric information

The winning bid in an auction is a compli-cated function of the underlying privateinformation of all of the bidders Only fairlystylized models of auctions would let theeconomist directly interpret the coefficienton feedback as a marginal rate of substitu-tion between reputation and a composite

commodity In the framework of L Rezende(2003) if the economist makes the followingassumptions then the standard hedonicinterpretation of implicit prices is valid1 There are private values and there is no

asymmetric information among the bid-ders about the marginal value of theobserved product characteristics

2 There are no minimum bids or reserveprices

3 All bidders are ex ante symmetric4 There are no product characteristics

observed by the bidders but not the economist

5 Entry is exogenous and a dummy variablefor the number of bidders is included inthe regression

Clearly these may be strong assumptionsin many applications In particular a largefraction of online auctions uses minimumbids or secret reserve prices Also it couldbe argued that uncertainty about quality nat-urally induces a common-value componentinto the auction

Many of the papers in the literature do notarticulate a primitive set of assumptionsunder which the regression coefficients canbe interpreted as a measure of buyersrsquo will-ingness to pay for characteristics (such asreputation) or some other primitive econom-ic parameter While this limits the generalityof the conclusions it is nonetheless interest-ing to know the conditional mean of the saleprice as a function of characteristics

52 Other Tests of the Theory

In addition to measuring the marketprice of a reputation several other empiri-cal observations have been made aboutfeedback mechanisms For example Baand Pavlou (2002) report that the impact ofvariation in feedback statistics is largerwhen the value of the object being sold ishigher This is consistent with economicintuitionmdashthe value of a reputation is moreimportant for ldquobig ticketrdquo items Jin andKato (2002) and Louis Ederington andMichael Dewally (2003) find that for



collectible objects for which professionalgrading is an option sale prices of ungrad-ed objects respond more to eBayrsquos feedbackstatistics than graded objects This is con-sistent with our conjecture abovemdashreputa-tion is more important the less certain thebuyer is about the quality of the item that isfor sale

Cabral and Hortaccedilsu (2003) investigatewhether sellers respond to the feedbackmechanism They use the feedback profilesof a cross-section of active sellers to con-struct a backward-looking panel data set thattracks the comments received by the sellersThey find that on average the number ofpositive comments received by a seller untiltheir first negative is much larger than thenumber of positive comments receivedbetween their first and second negativesThey investigate a number of alternativehypotheses for this phenomenon in particu-lar the possibility that buyers may be reluc-tant (possibly due to altruistic reasons) to bethe first one to ldquotarnishrdquo a sellerrsquos reputationThey find that buyers who place the firstnegative are not on average more likely togive negative comments than the buyerswho gave the subsequent negativesMoreover they do not find an observabledifference between the textual content offirst vs subsequent comments

53 Do Reputation Mechanisms Work

Given the various results in the literatureit is natural to attempt to reach a judgmentas to whether reputation mechanismsachieve their purpose of reducing tradingfrictions on the internet The robust growthin the number of users and transactions oneBay could be regarded as a testament to thefact that fraud is not perceived as a hugedeterrent in these markets However a num-ber of authors express skepticism about theeffectiveness of the feedback systems usedin online auctions For example the study byJin and Kato (2002) which we discussed insection 4 takes the stance that the observedpatterns of trade cannot be explained by

24 A pioneering attempt in this direction is a laboratoryexperiment by Gary Bolton Elena Katok and AxelOckenfels (2003) which shows that while reputationmechanisms induce a substantial improvement in transac-tional efficiency they also exhibit a kind of public goodsproblem in that the reporting of honest or dishonestbehavior have external benefits to the community that cannot be internalized by the individual making the report

rational buyers The conclusion they reach intheir study is that the prices ungraded cardswere fetching on eBay were higher thancould be rationalized by the frequency offraudulent claims in their graded sampleThey also found that although reputable sell-ers were less likely to make fraudulentclaims and were also less likely to default ordeliver counterfeits the premium that buy-ers pay for reputation (after correcting forsales probability) is much lower than thepremium that buyers pay for self claimsHence they conclude that ldquoIn the currentonline market at least some buyers drasti-cally underestimate the risk of tradingonlinerdquo and that ldquohellip some buyers have diffi-culty interpreting the signals from seller rep-utationrdquo The study by Resnick et al (2003)also concludes with the statement thatldquoNevertheless it is hardly obvious that thisreputation system would work sufficientlywell to induce reliable seller behaviorrdquo

We believe that the jury is still out on theeffectiveness of the reputation systemsimplemented by eBay and other onlineauction sites There is still plenty of work tobe done to understand how market partici-pants utilize the information contained inthe feedback forum system and whethersome of the seemingly obvious deficienciesof these systems such as the free-ridingproblem inherent in the harvesting of userreviews and the presence of seller retalia-tion are large enough to hamper the effec-tiveness of these systems As in the analysisof the ldquolate-biddingrdquo phenomenon per-haps controlled laboratory experiments canhelp shed more light into how differentcomponents of this complex problem workin isolation from each other24



25 See the survey articles by R Preston McAfee andJohn McMillan (1987) Paul Klemperer (1999) and therecent textbook by Vijay Krishna (2002)

26 For a comprehensive survey of experimental work onauctions see Kagel and Roth (1995 ch 7)

6 Auction Design Insights from Internet Auctions

Perhaps the most central question thatauction theorists try to answer is ldquoWhat kindof an auction should I use to sell my goodsrdquoSince Vickreyrsquos (1961) seminal paper a largebody of theoretical literature has investigat-ed how various informational and strategicfactors in the auction environment affectthe decision of how one should design auc-tions25 Economic theorists studying auc-tions have also been influential in thedesign of important high-profile auctionssuch as wireless spectrum auctions and thedesign of deregulated energy marketsacross the world

The dramatic development of our theoret-ical understanding of auction theory and theincreasing scope for real-world applicationsalso drive a natural need for empiricalassessment of how these theories perform inthe real world To this date most empiricalresearch on auctions has been conducted inlaboratory experiments Laboratory experi-ments are well-suited for this purpose sincethe empirical researcher has control over thedesign of auction rules and the flow of infor-mation to participants26 However most lab-oratory experiments employ college studentsas their subjects who are typically not expe-rienced bidders and are given relativelymodest incentives (by the yardstick of corpo-rate wages) Hence empirical researchershailing from nonexperimental branches ofeconomics have frequently questioned theextent to which laboratory experiments canreplicate ldquoreal-worldrdquo incentives

There are important difficulties howeverwith the use of data from nonexperimentalreal-world auctions for empirical testing oftheories First in most field settings of inter-est it is very costly to conduct controlled 27 According to Krishna (2002) page 4 footnote 2

ldquoTwo games are strategically equivalent if they have thesame normal form except for duplicate strategies Roughlythis means that for each strategy in one game a player hasa strategy in the other game which results in the same out-comesrdquo In this context strategic equivalence means thatfixing the valuation constant bids should be the sameacross the two auctions

randomized trials hence statistical infer-ences based on nonexperimental data aretypically subject to much stronger assump-tions than inferences from experimentaldata Second detailed transactions datafrom real markets is typically not easy to getsince such data is sensitive and confidentialinformation in many industries

Internet auctions provide a very interest-ing and promising middle ground betweencontrolled laboratory experiments conduct-ed with student subject pools and large-scalefield applications such as wireless spectrumauctions or energy auctions First data onbids are readily available from online auctionsites lowering this important ldquobarrier-to-entryrdquo for many empirical researchersSecond as discussed in section 3 there aredifferences across the auction rules utilizedacross different auction sites or even withinan auction site and these differences can beexploited to empirically assess whether onemechanism works better than anotherThird empirical researchers can actuallyconduct their own auctions on these siteswhich allows them to run randomized ldquofieldexperimentsrdquo with experienced real-worldsubject pools

We now survey several examples of howthe study of internet auctions has providednovel and potentially useful insightsregarding economic theories of auctiondesign

61 Field Experiments on the Internet

In a set of ldquofield experimentsrdquo DavidLucking-Reiley (1999a) tests two basichypotheses derived from auction theory 1)the strategic equivalence27 between theopen descending price (Dutch) and sealed-bid first-price auction and 2) the strategic



28 Briefly the auction formats are the following in thesealed-bid first-price auction the highest bidder wins theauction and pays the price bid In the sealed-bid second-price auction the highest bidder wins but pays the sec-ond-highest bid In the Dutch auction price decreasescontinuously from a high starting value and the first bid-der to claim the object wins the auction and pays the priceat the instant she called in In the English auction consid-ered here price rises continuously and all bidders indicate(by pressing a button for example) whether they are stillin the auction or not The auction ends when the second-to-last bidder drops out and the winner pays the price atwhich this final drop-out occurs Bidders cannot rejoin theauction once they have dropped out

equivalence between second-price andEnglish auctions28 As discussed in PaulMilgrom and Robert Weber (1982) thestrategic equivalence between Dutch andsealed-bid first-price auctions follows quitegenerally since the only piece of informa-tion available to bidders in a Dutch auctionthat is not available to bidders in a sealed-bid first-price auction is whether a bidderhas claimed the object But this informa-tion cannot be incorporated into biddingstrategies since its revelation causes theauction to end

On the other hand the strategic equiva-lence between second-price and Englishauctions only applies in a private-valuesenvironment where bidders would notmodify their personal willingness to pay forthe object even if they knew how muchother bidders were willing to pay It can beshown that with private values biddingonersquos value or staying in the auction untilthe price reaches onersquos valuation is a (weak-ly) dominant strategy in both the second-price auction and the English auction Inan interdependent-values or common-value environment however seeing anoth-er bidderrsquos willingness to pay may affect theinference one makes regarding the value ofthe object and hence changes their willing-ness to pay As shown by Milgrom andWeber (1982) bidding strategies may bevery different in an English auction inwhich drop-out points of rival bidders arerevealed as opposed to sealed-bid second-price auctions where bidders do not

29 This finding does have an alternative explanationin that bidders have decreasing marginal valuations forthe cards

observe each othersrsquo actions and hencecannot update how much they are willing-to-pay for the object during the course ofthe auction

Lucking-Reileyrsquos experiment used theabove auction formats to sell trading cardsfor the role-playing game Magic TheGathering on an internet newsgroup thatwas organized as an online marketplace forenthusiasts in ldquopre-eBayrdquo days Lucking-Reiley invited participants to his auctionsusing e-mail invitations and postings on thenewsgroup To minimize differences acrossparticipantsrsquo distribution of values for theauctioned cards Lucking-Reiley used aldquomatched-pairrdquo design For example in thecomparison of first-price and Dutch auc-tions he first auctioned a set of cards usingthe first-price auction a few days after hisfirst set of auctions ended he sold an identi-cal set of cards using a Dutch auction Toaccount for temporal differences in biddersrsquodemand for these cards Lucking-Reileyrepeated the paired experiment about fourmonths later but this time selling the firstset using Dutch auctions and the second setusing first-price auctions

Lucking-Reiley found that Dutch auc-tions yielded 30-percent higher average rev-enue than the first-price auctions andrejected the strategic equivalence of the twoauction formats On the other hand in thesecond-price vs English auction experi-ment he found that the auction formatsyielded statistically similar revenuesHowever he found less convincing evidencefor the hypothesis that the two auctionswere strategically equivalent In particularhe found that in 81 out of 231 cases wherethe same bidder placed a bid for the samecard on both an English auction and a sec-ond-price auction the bidderrsquos highestEnglish-auction bid exceeded their bid onthe second-price auction29



30 Lucking-Reiley also discusses whether the speed atwhich prices decline in Dutch auctions may have led to adifference between his results and laboratory results inhis field experiment prices declined much slower than inthe laboratory Interestingly Elena Katok and AnthonyKwasnica (2000) find in a laboratory setting that slowerprice declines in Dutch auctions led to increased rev-enues as compared to sealed-bid auctions suggesting thatbidder impatience may have played a role in the fieldexperiment setting

We should note that previous studiesusing laboratory experiments by VickiCoppinger Vernon Smith and Jon Titus(1980) and James Cox Bruce RobersonandVernon Smith (1982) also rejected thestrategic equivalence of first-price auctionswith descending auctions However theseexperiments reported higher prices in first-price auctions than Dutch auctions withDutch auctions yielding 5-percent lowerrevenues on average Similarly John KagelRonald Harstad and Dan Levin (1987)report the failure of strategic equivalenceof the second-price and English auctions inthe laboratory setting In particular theyreport a tendency for subjects to bid abovetheir valuations in the second-price auc-tion whereas they rapidly converge to bid-ding their values in the English auctionyielding 11 percent higher revenue for thesecond-price auction

What explains the differences acrossLucking-Reileyrsquos results and results fromlaboratory experiments First of all asLucking-Reiley notes his field experimentcannot control for the entry decisions of thebidders he reports that Dutch auctionsattracted almost double the number of bid-ders as his first-price auctions and it is nothard to see that an increase in the number ofbidders would increase revenues The caus-es of the higher participation in the Dutchauction remains a mystery Lucking-Reileyargues that this is not solely due to the nov-elty of the Dutch auction mechanism sincemarket participants had been exposed toDutch auctions before One wonders how-ever whether a ldquotaste for noveltyrdquo effect maypersevere over longer periods of time30

Second Lucking-Reileyrsquos experiment can-not control for the informational structure ofthe auction In the laboratory the researchercan choose whether to run a commoninter-dependent value or a private-value auctionhowever Lucking-Reiley cannot predeter-mine whether bidders will regard his tradingcards as private-value or common-valueobjects This may affect the interpretation ofhis second-price vs ascending auctionresults in a common-value environment thesecond-price auction is predicted to yieldlower average revenues This factor com-pounded with the experimentally reportedbias of the bidders to overbid may result inthe observed revenue equivalence result

On the other hand one may think that theonline marketplace utilized by Lucking-Reiley is populated by veterans of previousauctions who are experienced enough not tooverbid in a second-price auctionmdashhencethe theoretical prediction of revenue equiv-alence is more likely to be borne out in thefield than in the laboratory Lucking-Reileybriefly mentions this possibility thoughmore direct evidence comes from else-where In a recent paper Rod Garratt MarkWalker and John Wooders (2002) invitedexperienced bidders on eBay to take part insecond-price auction experiments conduct-ed in a laboratory setup In contrast to previ-ous experimental findings Garratt Walkerand Wooders found that bidders experi-enced on eBay do not overbid in second-price private-value auctions and very oftengive the correct reasoning to their actionHence field-experiments conducted ononline auction sites may indeed provide amore qualified subject pool for auctionexperiments

Another argument for the use of fieldexperiment methodology as pointed out byLucking-Reiley (1999b) is that in practicalsettings where a seller is trying to assess theproper auction mechanism to use controlsfor endogenous entry decisions and on theinformational environment will typically notbe present For example the fact that a



31 Lucking-Reiley quotes Robert Hansenrsquos (1986) find-ing of systematic differences in timber lots sold usingsealed-bid vs ascending auction by the US ForestService

32 Lucking-Reiley notes that the marketplace was activeenough that his 80 auctions would not have a ldquomarket-widerdquo impact

Dutch auction may lead to higher bidderparticipation may be an outcome variable ofinterest to a seller considering switching tothis auction format especially since it leadsto higher revenues Hence Lucking-Reileyargues that for practical or policy applica-tions results of field experiments may pro-vide more insights regarding the outcome ofa possible policy change

The field experiment methodology alsohas advantages over nonexperimentalanalyses of outcomes across different auc-tion formats As Lucking-Reiley notes anonexperimental analysis of whether a first-price or a second-price auction yields higherrevenues may have to worry about why someauctions were conducted using a first-priceauction and others using a second-priceauction31 On the other hand we shouldnote that the field experimentation method-ology as used by Lucking-Reiley can onlyshed light onto partial-equilibrium respons-es to changes in mechanism design Forexample his finding that Dutch auctionsyield 30-percent higher revenues than first-price auctions may not apply if every selleron an auction site decides to use a Dutchauction as opposed to a first-price auction32

Whatever its pros and cons Lucking-Reileyrsquos paper is a good example of howonline auction sites can be used as a labora-tory for creative field experiments Last butnot least we should point out that a veryimportant advantage of this ldquofield labora-toryrdquo is that it was relatively inexpensive touse Lucking-Reileyrsquos experiment had an ini-tial cost of $2000 to buy about 400 tradingcards which he claims to have recoupedwith a profit (in addition to a published the-sis chapter) after selling them Moreover atthe cost of some lack of customizability

online auction sites make it easy for theresearcher to set up and track experimentsinstead of requiring them to spend a signifi-cant amount of programming time to writesoftware for their own experiments

62 Should Reserve Prices Be Kept Secret

Auction design is not only about choosingbetween formats such as first-price second-price English or Dutch As first shown byRoger Myerson (1981) a seller may signifi-cantly increase their revenues by optimallysetting a publicly observable reserve priceor equivalently a minimum bid

A look at sellersrsquo practices of setting reserveprices on eBay reveals an interesting regular-ity as noted by Bajari and Hortaccedilsu (2003)many sellers on eBay especially those sellinghigher-value items choose to keep theirreserve prices secret as opposed to publiclyannouncing it This immediately brings upthe question ldquoWhen if ever should one use asecret reserve price as opposed to a publiclyobservable minimum bidrdquo

Auction theory has been relatively silentregarding this question with two notableexceptions Li and Tan (2000) show that withrisk-aversion secret reserve prices mayincrease the auctioneerrsquos revenue in an inde-pendent private value first-price auction butin second-price and English auctions regard-less of the biddersrsquo risk preferences the auc-tioneer should be indifferent between settinga secret vs observable reserve price DanielVincent (1995) provides an example in whichsetting a secret reserve price in an interde-pendent value second-price auction canincrease the auctioneerrsquos revenues Vincentrsquosbasic intuition is that the minimum bid cen-sors some bidders and the inference drawnby participants in the auction from this cen-sored distribution may lead to lower bids thaninference from the uncensored distribution

Given the above theoretical results espe-cially that of Vincent (1995) Bajari andHortaccedilsu (2003) estimate biddersrsquo common-value and private signal distributions in asymmetric common-value second-price



auction model of eBay coin auctions anduse these parameter values to numericallycompute optimal minimum bid and secretreserve price levels to compare the rev-enues expected from these two pricing poli-cies They find that at its optimal level asecret reserve price can yield the seller 1percent higher expected revenue

Rama Katkar and David Lucking-Reiley(2000) question the validity of the behavioralassumptions used in Bajari and Hortaccedilsursquos(2003) structural econometric model andattempt to answer the same question using afield experiment In their experiment theauthors bought and sold fifty matched pairsof Pokemon trading cards on eBay auction-ing one card in the pair using a publiclyannounced minimum bid and the otherusing a secret reserve price that was setequal to the minimum bid They found thatthe secret reserve price auctions yielded 60cents less revenue on average (average cardvalue was approximately $7) They alsoreported that secret reserve price auctionswere less likely to end in a sale

Although Katkar and Lucking-Reiley(2000) have a valid point in questioning thestructural econometric approach of Bajariand Hortaccedilsu (2003) especially with regardto its imposition of fully rational behavior bythe bidders the field-experiment approachthey utilize is also subject to importantcaveats For example an important featureof Katkar and Lucking-Reileyrsquos experimentis that the minimum bids and the secretreserves were assigned arbitrarily and werekept constant across treatments Under theassumption of seller rationality a moreappropriate comparison should be betweenthe ex-ante revenue-maximizing values ofthe minimum bid and the secret reserveprice which might not necessarily be thesame Unfortunately deriving ex-ante rev-enue-maximizing values of the choice variables above is actually not a verystraightforward exercise since as firstderived by Myerson (1981) a calculation ofthe revenue-maximizing minimum bid

depends on the distribution of biddersrsquo val-uationsmdashwhich must be estimated frombidding data

The previous discussion underlines someof the subtleties underlying the analysis andinterpretation of experimental or nonexperi-mental data from online auction sites for thepurpose of evaluating mechanism designalternatives The treatment effects estimatedusing a randomized field experiment suchas Katkar and Lucking-Reiley (2000) maynot always have a clear interpretation withinthe context of a theoretical model Structuraleconometric models as utilized in Bajariand Hortaccedilsu (2003) have the advantagethat their estimates can be readily interpret-ed within the context of a theoretical modelbut their results may not always be robust tospecification error or problems associatedwith the nonexperimental nature of the dataThe user of either approach should be veryclear about the shortcomings of the respec-tive methods and if possible use the twomethodologies in a complementary manner

Coming back to the question of whetherempirical studies have taught us anythingabout the use of reserve prices we shouldnote that both of the papers surveyed hereare unable to provide a very satisfactoryanswer to the question ldquoIf one reserveprice mechanism revenue-dominates theother why do sellers persist in using thedominated mechanismrdquo Katkar andLucking-Reiley quote an additional ldquobene-fitrdquo of the secret reserve price strategy thattheir experiment does not account formdashbysetting a very high secret reserve a sellermay first screen out the bidders with thehighest valuations for the object and latercontact them away from eBay to run a pri-vate transaction for which he does not haveto pay commission to eBay On the flipside Bajari and Hortaccedilsu (2003) mentionan additional ldquocostrdquo of using a secretreserve price auction some buyers espe-cially new participants who do not quiteunderstand the rules of eBay may getangry upon not winning an auction due to a



33 We should note that this is no longer possible oneBay since feedback is restricted to being transaction spe-cific

34 We should note that although first-price sealed bidauctions are quite common in offline contexts such as pro-curement second-price auctions were quite rare For ahistorical account of the use of second-price or Vickreyauctions see Lucking-Reiley (2000a)

secret reserve and place a negative com-ment on the sellerrsquos record33 Howeverneither paper provides a satisfactory recon-ciliation of these various costs and benefitsto explain the patterns of usage of differentreserve-price strategies on eBay Webelieve this may be an interesting avenuefor future research

63 The Prevalence of Ascending Auctions

A casual observer of online auction siteswill immediately observe the following pat-tern all three major online auction sites(eBay Yahoo Amazon) use the proxy-bid-ding format (albeit with differing endingrules as discussed in section 3) In a muchmore comprehensive survey Lucking-Reiley (2000b) found that 121 of the 142internet auction sites he surveyed in 1998used an ascending auction format Sevensites used a first-price sealed-bid auctionand eight used a second-price sealed-bidauction34

What is so special about the proxy-bid-ding mechanism or open-ascending auc-tion formats in general A similar questionwas posed in an earlier survey article byMcAfee and McMillan (1987) regardingthe prevalence of ascending auction for-mats in the ldquobrick-and-mortarrdquo era preced-ing the internet McAfee and McMillan(1987) suggest an explanation based on thetheoretical results of Milgrom and Weber(1982) As discussed in section 4 in manyof the auctions conducted on these sites acommon value or interdependent valueselement may be present As shown byMilgrom and Weber (1982) the open-ascending English auction yields higherexpected revenues than its sealed-bid

35 We should note however that the use of a hard-deadline ending rule can nullify this benefit of using anascending auction since rampant sniping leads to muchless information revelation during the auction

36 We should note that in Peters and Severinovrsquos modelall auctions end at the same time

counterpartsmdashintuitively due to the factthat a lot of information about other bid-dersrsquo valuations is revealed during thecourse of an English auction and hencebidders are not as compelled to shade theirbids to combat the winnerrsquos curse Thisdoes not automatically mean however thatex ante bidders expect to gain less surplusfrom participating in an English auction asopposed to a sealed-bid auction TheEnglish auction can yield more revenue tothe seller because more information isrevealed during this auction as opposed toin a sealed-bid auction Left to theirdevices bidders in a sealed-bid auctionmight have chosen to buy some of thisinformation Hence from the perspectiveof sellers buyers and the site operatoralike the use of an open-ascending auctionformat such as the English auction yieldsbenefits to all the sellers gains higher revenues the buyers avoid the winnerrsquoscurse and the site operator gains highercommissions35

Several alternative explanations for theprevalence of ascending auctions havebeen suggested in the literature TheMilgrom and Weber (1982) explanationapplies to isolated single-unit auctionsHowever as Lucking-Reiley (2000b) pointsout on sites like eBay there are many sell-ers trying to sell the same type of goodsimultaneously Hence one intuition sug-gested by Lucking-Reiley is that with openascending auctions bidders may have aneasier time deciding which auction to bidon Peters and Severinov (2001) make thisintuition more rigorous In an independentprivate-value setting where there are manysimultaneous English auctions of the samegood they show that the following is a per-fect Bayesian equilibrium strategy for a



37 This is not to say that there arenrsquot mixed strategyequilibria in the game constructed by Peters andSeverinov however in many plausible versions of thesimultaneous sealed-bid auction game there are no sym-metric pure strategy equilibria

38 This may explain why sealed-bid auctions are veryprevalent in procurement contexts where there is repeat-ed interaction within a small group of bidding firms Oninternet auction sites free-entry geographic dispersionand anonymity of bidders may make collusion much hard-er to sustain

bidder place a bid in the auction with thelowest current price and raise your bid asslowly as possible until you reach your val-uation36 The resulting equilibrium willthen lead to all bidders paying a uniformmarket-clearing price which is equal to thevaluation of the highest losing bidderThus there is no need for an active market-maker to solicit bids and offers from buyersand sellers and match them using a rulesuch as Walrasian market-clearingmdashthedecentralized equilibrium leads to an ex-post efficient allocation (one in which nobuyer regrets the price at which theybought) Observe that although Peters andSeverinovrsquos prescribed equilibrium strategyrequires bidders to be very attentive (orutilize automated bidding software ver-sions of which are available on the inter-net) the decision problem of a bidderfacing multiple simultaneous sealed-bidauctions is considerably more complicatedleading in many cases to randomizedentry decisions37

However there are also several reasonswhy sealed-bid auctions may be preferred toopen-ascending auctions As shown byCharles Holt (1980) and Matthews (1995) ifbidders have decreasing absolute risk aver-sion (DARA) preferences equilibrium bidsand hence the sellerrsquos expected revenue inthe sealed-bid first-price auction are higherthan in the English auction Moreover asshown by Marc Robinson (1985) andMcAfee and McMillan (1992) collusion maybe more difficult to sustain in sealed-bidfirst-price auctions as opposed to Englishauctions38

39 The experiments were independent private valueauctions with the distribution of valuations kept constantacross auction formats The subjects did not observe theirvaluations prior to choosing the auction format to partici-pate in

40 Two notable exceptions are McAfee (1993) andPeters and Severinov (1997)

Motivated by this empirical patternRadosveta Ivanova-Stenzel and Tim Salmon(2003) ran an experiment in which theyallowed bidders to choose for an entry feebetween a sealed-bid first-price auction andan English auction39 The authors reportthat when entry prices for the two auctionformats are the same the subjects over-whelmingly preferred the English auctionBy varying the entry prices across the for-mats the authors attempted to measure bid-dersrsquo willingness to pay for the Englishauction The observed willingness to pay forthe English auction however was muchhigher than the profit differential impliedby the risk-aversion explanation which ledthe authors to conclude that there is a yet-unexplained ldquodemandrdquo component thatdrives biddersrsquo revealed preference for theEnglish auction

We believe that Ivanova-Stenzel andSalmonrsquos (2003) study is an important firststep towards understanding the economicforces shaping the demand for differenttypes of trading mechanisms Auction theoryis almost exclusively couched in a ldquopartial-equilibriumrdquo framework where competitionbetween different trading mechanisms is sel-dom studied40 Studying demand and supplypatterns on the internet may prove quitefruitful in future research in this area sincedata on prices and quantities is relativelyeasy to obtain and wide variation acrossmarketstypes of goods can be observed

64 Endogenous Entry Decisions of Bidders

Another theoretical question that hasbeen put under empirical scrutiny by bothfield experimentation and structural econo-metric modeling is the endogenous entrydecisions of bidders As noted by Levin and



Smith (1994) most theoretical revenuecomparisons between auction formats takethe number of participants in the auction asgiven Even on eBay however bidders mayincur costs to bid in an auction albeit thesemay be as the time spent searching for theright auction or the time spent watchingthe bidding come to a close (though somemay also derive enjoyment from theprocess) Hence a more realistic model ofbidding in an auction should endogenize thenumber of participants where the biddersweigh the expected benefit from winningthe auction against the cost of participatingThis in turn means that auction mecha-nisms that offer different expected surplus-es to the bidders will attract differentnumbers of bidders and hence the rev-enues generated by the alternative auctiondesigns cannot be compared under theassumption that the same number of bid-ders participate in both In fact Levin andSmith (1994) argue that this may lead to amore general revenue equivalence princi-ple since expected payments to biddersshould be equalized across two auctionmechanisms when the choice is presentand the expected revenues of the sellersshould also equalize

The analysis of online auctions suggeststhat entry costs and hence the endogenousentry decisions of bidders are indeed quiteimportant and should be taken intoaccount when modeling the performance ofalternative trading rules in such environ-ments Lucking-Reiley (1999b) once againuses the field-experiment method to assessthe entry costs of bidders in online auctionsHe notes that when he auctioned severaltrading cards simultaneously with zero min-imum bids on each very few bidders placedbids on every item and argues that this isconsistent with the presence of biddingcosts He also finds not very surprisinglythat higher minimum bids on otherwisecomparable auctions resulted in fewer par-ticipants (a finding also corroborated bycorrelations reported by McAfee Quan

41 A quantification of the magnitude of these entrycosts however requires the imposition of a model of entryto calculate the trade-off between the expected benefitfrom participation and the cost of bidding Bajari andHortaccedilsu (2003) use their structural model to estimate theimplied cost of bidding on eBay coin auctions to be around$3 a significant cost given that the average object in theirsample was worth $47 It is interesting to note that severalinternet services like eSnipe and AuctionWatch along witheBay itself have invested in developing technologies tomake it easier for bidders to search listed auctions to mon-itor progress in simultaneous auctions by creating watch-lists and to place ldquosniperdquo bids in the auctions they areparticipating in

and Vincent 2002 Bajari and Hortaccedilsu2003 Lucking-Reiley et al 2000) 41

7 Conclusion

Internet auctions are the subject of a rap-idly growing body of research Interest inthese markets stems from two factors Firstinternet auctions are an inexpensive sourcefor high-quality data In empirical econom-ics our data is often a very incomplete rep-resentation of the markets that we studyTypically the researcher cannot measureimportant product characteristics that deter-mine consumersrsquo choices Also we cannotalways observe all of the relevant actions ofbuyers and sellers In online auctions theeconomist is able to observe almost all of theproduct information that is available to thebidders Also the actions of buyers and sell-ers are recorded in minute detail The exacttime and amount of the proxy bids and thesellersrsquo reserve price policies can easily bedownloaded Second online auctions are anatural testing ground for auction theory Asubstantial body of economic theory studieshow to optimally design an auction In onlineauctions we can see how the mechanismsexamined in theory perform in the field

Given the easy access to high-quality dataand constant evolution of these tradingmechanisms we predict that the study ofinternet auction markets will continue togenerate interest among many researchersIn particular we believe that the study ofthese markets provides a very exciting inter-face for experimental theoretical and



42 In fact as discussed in section 51 and in Cabral andHortaccedilsu (2003) eBay has already changed the way it cal-culates and reports feedback statistics The most recentsite change in response to user comments and internaldevelopment efforts was implemented in late 2003 Forthe details of this change see httppagesebaycomservicesforumnewfbhtml

econometric researchers to bring theirmethodological toolkits together Someimportant research areas remain to beexplored For example we believe that cur-rent research on the design of reputationmechanisms and rating systems to helpresolve information asymmetries in onlinetrading environments is still in its infancySuch a research program will undoubtedlyopen new questions regarding how peopleprocess and transmit information and howincentives affect this we believe there aremany interesting insights to be gained byobserving the experimentation of internetauction sites with different ldquoreputationrdquomechanisms The design of reliable feed-back and quality-enforcement systems is ofcentral importance to internet auction busi-nesses and insights from this research pro-gram can have a lasting influence on the waytheir web sites are designed42 We alsoobserve many businesses selling their goodsthrough eBay and other auction sites oftenusing fixed-price mechanisms (such as theldquoBuy-It-Nowrdquo feature on eBay) rather thanauctions In our opinion the economictrade-offs between fixed-prices versus auc-tions are still largely unexplored and theanalysis of this question will benefit verymuch from careful observation of what hap-pens on the internet We also think thatimportant theoretical and econometric chal-lenges exist in the analysis of markets withmultiple simultaneous auctionsmdashwhich is amore accurate characterization of marketclearing on large internet auction sites likeeBay Pioneering attempts like the work ofPeters and Severinov (2001) exist but webelieve that much more needs to be done inthis area

REFERENCES

Ariely Dan Axel Ockenfels and Alvin Roth 2003 ldquoAnExperimental Analysis of Ending Rules in InternetAuctionsrdquo work paper Harvard Bus School

Athey Susan and Philip Haile 2002 ldquoIdentification inStandard Auction Modelsrdquo Econometrica 706 pp2107ndash40

Ba Sulin and Paul Pavlou 2002 ldquoEvidence of theEffect of Trust Building Technology in ElectronicMarkets Price Premiums and Buyer Behaviorrdquo MISQuart 263 pp 243ndash68

Bajari Patrick and Ali Hortaccedilsu 2003 ldquoThe WinnerrsquosCurse Reserve Prices and Endogenous EntryEmpirical Insights from eBay Auctionsrdquo Rand JEcon 32 pp 329ndash55

Baron David P 2002 ldquoPrivate Ordering on theInternet The EBay Community of Tradersrdquo BusPolitics (online) 43

Bolton Gary E Elena Katok and Axel Ockenfels2003 ldquoHow Effective Are Electronic ReputationMechanisms An Experimental Investigationrdquo forth-coming Manage Sci

Bulow Jeremy and John Roberts 1989 ldquoThe SimpleEconomics of Optimal Auctionsrdquo J Polit Econ 975pp1060ndash90

Cabral Luis and Ali Hortaccedilsu 2003 ldquoDynamics ofSeller Reputation Theory and Evidence from eBayrdquomimeo U Chicago

Camerer Colin 1998 ldquoCan Asset Markets BeManipulated A Field Experiment with RacetrackBettingrdquo J Polit Econ 1063 pp 457ndash82

Cohen Adam 2002 The Perfect Store Inside eBayLittle Brown amp Co

Coppinger Vicki Vernon Smith and Jon Titus 1980ldquoIncentives and Behavior in English Dutch andSealed-Bid Auctionsrdquo Econ Inquiry 43 pp 1ndash22

Cox James C Bruce Roberson and Vernon Smith1982 ldquoTheory and Behavior of single object auctionsrdquoRes Exper Econ Vernon L Smith ed JAI Press

Coy Peter 2002 ldquoGoing Going GonehellipSuckerrdquo BusWeek March 20

Dellarocas Chrysanthos 2002 ldquoThe Digitization ofWord-of-Mouth Promise and Challenges of OnlineReputation Mechanismsrdquo Manage Sci

Dewan Sanjeev and Vernon Hsu 2001 ldquoTrust inElectronic Markets Price Discovery in GeneralistVersus Specialty Online Auctionsrdquo mimeo UWashington Bus School

Eaton David H 2002 ldquoValuing InformationEvidence from Guitar Auction on eBayrdquo mimeoMurray State U

Ederington Louis H and Michael Dewally 2003 ldquoAComparison of Reputation CertificationWarranties and Information Disclosure as Remediesfor Information Asymmetries Lessons from the On-Line Comic Book Marketrdquo work paper PriceCollege Bus U Oklahoma

Elyakime Bernard Jean-Jacques Laffont PatriceLoisel and Quang Vuong 1994 ldquoFirst price sealedbid auctions with secret reservation pricerdquo AnnalesdrsquoEcon Statist 34 pp 115ndash41

Epple Dennis 1987 ldquoHedonic Prices and Implicit



Markets Estimating Demand and Supply Functionsfor Differentiated Productsrdquo J Polit Econ 951pp 59ndash80

Ericson Richard and Ariel Pakes ldquoMarkov PerfectIndustry Dynamics A Framework for EmpiricalWorkrdquo Rev Econ Stud 62 pp 53ndash82

Garratt Rod Mark Walker and John Wooders 2002ldquoExperienced Bidders in Online Second-PriceAuctionsrdquo work paper U Arizona

Haile Philip Han Hong and Matthew Shum 2000ldquoNonparametric Tests for Common Values in First-Price Auctionsrdquo mimeo Yale U

Hansen Robert 1986 ldquoSealed Bid Versus OpenAuctions The Evidencerdquo Econ Inquiry 24 pp125ndash14

Harstad Ronald John Kagel and Dan Levin 1990ldquoEquilibrium Bid Functions for Auctions with anUncertain Number of Biddersrdquo Econ Letters 33pp 35ndash40

Hasker Kevin Raul Gonzalez and Robin Sickles 2003ldquoAn Analysis of Strategic Behavior and ConsumerSurplus in eBay Auctionsrdquo work paper Rice U

Holt Charles 1980 ldquoCompetitive Bidding forContracts under Alternative Auction Proceduresrdquo JPolit Econ 88 pp 433ndash45

Hopenhayn Hugo 1992 ldquoEntry Exit and FirmDynamics in Long Run Equilibriumrdquo Econometrica60 pp 1127ndash50

Houser Dan and John Wooders 2003 ldquoReputation inAuctions Theory and Evidence from eBayrdquo workpaper U Arizona

Ivanova-Stenzel Radosveta and Tim Salmon 2003ldquoBidder Preferences Among Auction InstitutionsrdquoEcon Inquiry

Jin Ginger Z and Andrew Kato 2002 ldquoBlind TrustOnline Experimental Evidence from BaseballCardsrdquo work paper U Maryland

Kagel John H Ronald M Harstad and Dan Levin1987 ldquoInformation Impact and Allocation Rules inAuctions with Affiliated Private Values A LaboratoryStudyrdquo Econometrica 55 pp 1275ndash304

Kagel John and Alvin Roth eds 1995 The Handbookof Experimental Economics Princeton PrincetonU Press

Kalyanam Kirthi and Shelby McIntyre 2001 ldquoReturnsto Reputation in Online Auction Marketsrdquo mimeoLeavey School Bus Santa Clara U

Katkar Rama and David Lucking-Reiley 2000 ldquoPublicversus Secret Reserve Prices in eBay AuctionsResults from a Pokeacutemon Field Experimentrdquo workpaper U Arizona

Katok Elena and Anthony Kwasnica 2000 ldquoTime IsMoney The Effect of Clock Speed and SellerrsquosRevenue in Dutch Auctionsrdquo work paper PennState U

Kazumori Eichiro and John McMillan 2003 ldquoSellingOnline Versus Liverdquo work paper CalTech andStanford GSB

Klemperer Paul 1999 ldquoAuction Theoryrdquo J EconSurveys 133 pp 227ndash86

Krishna Vijay 2002 Auction Theory San DiegoAcademic Press

Ku Gillian Deepak Malhotra and J Keith Murnighan

2003 ldquoCompetitive Arousal in Live and InternetAuctionsrdquo work paper Kellogg Grad SchoolManage Northwestern U

Levin Dan and James L Smith J L 1994ldquoEquilibrium in Auctions with Entryrdquo Amer EconRev 54 pp 585ndash99

Li Huagang and Goufu Tan 2000 ldquoHidden ReservePrices with Risk Averse Biddersrdquo mimeo PennState U

Livingston Jeffrey 2002 ldquoHow Valuable is a GoodReputation A Sample Selection Model of InternetAuctionsrdquo mimeo U Maryland

Lucking-Reiley David 1999a ldquoUsing Field Experimentsto Test Equivalence Between Auction Formats Magicon the Internetrdquo Amer Econ Rev 895 pp 1063ndash80

mdashmdashmdash 1999b ldquoExperimental Evidence on theEndogenous Entry of Bidders in Internet Auctionsrdquowork paper U Arizona

mdashmdashmdash 2000a ldquoVickrey Auctions in Practice FromNineteenth-Century Philately to Twenty-First-Century E-Commercerdquo J Econ Perspect 143 pp183ndash92

mdashmdashmdash 2000b ldquoAuctions on the Internet WhatrsquosBeing Auctioned and Howrdquo J Indust Econ 483pp 227ndash52

Lucking-Reiley David D Bryan N Prasad and DReeves 2000 ldquoPennies from eBay the Determinantsof Price in Online Auctionsrdquo work paper U Arizona

Lucking-Reiley David and Daniel F Spulber 2001ldquoBusiness-to-Business Electronic Commercerdquo JEcon Perspect 151 pp 55ndash68

Matthews Steven 1995 ldquoComparing Auctions for RiskAverse Buyers A Buyerrsquos Point of ViewrdquoEconometrica 55 pp 633ndash46

McAfee R Preston 1993 ldquoMechanism Design byCompeting Sellersrdquo Econometrica 61 pp 1281ndash312

McAfee R Preston and John McMillan 1987 ldquoAuctionsand Biddingrdquo J Econ Lit 252 pp 699ndash738

mdashmdashmdash 1992 ldquoBidding Ringsrdquo Amer Econ Rev 82pp 579ndash99

McAfee R Preston D Quan and D Vincent 2002ldquoHow to Set Minimum Acceptable Bids with anApplication to Real Estate Auctionsrdquo J Ind Econ50 pp 391ndash416

McDonald Cynthia G and V Carlos Slawson 2002ldquoReputation in an Internet Auction Marketrdquo EconInquiry 40 pp 633ndash50

Melnik Mikhail I and James Alm 2002 ldquoDoes aSellerrsquos eCommerce Reputation Matter Evidencefrom eBay Auctionsrdquo J Ind Econ 50 pp 337ndash50

Milgrom Paul and Robert Weber 1982 ldquoA Theory ofAuctions and Competitive Biddingrdquo Econometrica50 pp 1089ndash122

Morgan John and Tanjim Hossain 2003 ldquoA Test of theRevenue Equivalence Theorem using FieldExperiments on eBayrdquo work paper Haas Bus School

Myerson Roger 1981 ldquoOptimal Auction DesignrdquoMath Operation Res 6 pp 58ndash73

Ockenfels Axel and Alvin E Roth 2003 ldquoLate andMultiple Bidding in Second Price InternetAuctions Theory and Evidence ConcerningDifferent Rules for Ending an Auctionrdquo forthcom-ing Games Econ Behav



Paarsch Harry 1992 ldquoDeciding between the Commonand Private Value Paradigms in Empirical Models ofAuctionsrdquo J Econometrics 51 pp 191ndash215

Park Sangin 2002 ldquoWebsite Usage and Sellerrsquos Listing inInternet Auctionsrdquo work paper SUNY Stony Brook

Peters Michael and Sergei Severinov 1997ldquoCompetition among Sellers Who Offer AuctionsInstead of Pricesrdquo J Econ Theory 75 pp 141ndash79

mdashmdashmdash 2001 ldquoInternet Auctions with Many Tradersrdquowork paper U Wisconsin

Rasmusen Eric 2001 ldquoStrategic Implications ofUncertainty over Onersquos Own Private Value inAuctionsrdquo work paper Indiana U

Resnick Paul and Richard Zeckhauser 2001 ldquoTrustAmong Strangers in Internet TransactionsEmpirical Analysis of eBayrsquos Reputation Systemrdquowork paper Harvard Kennedy School

Resnick Paul Richard Zeckhauser John Swanson andKate Lockwood 2003 ldquoThe Value of Reputation oneBay A Controlled Experimentrdquo work paperHarvard Kennedy School Gov

Rezende L 2003 ldquoAuction Econometrics by LeastSquaresrdquo mimeo U Illinois Urbana-Champlain

Riley John and William Samuelson 1981 ldquoOptimalAuctionsrdquo Amer Econ Rev 71 pp 381ndash92

Robinson Marc 1985 ldquoCollusion and the Choice ofAuctionrdquo Rand J Econ 16 pp 141ndash45

Rosen Sherwin 1974 ldquoHedonic Prices and ImplicitMarkets Product Differentiation in PureCompetitionrdquo J Polit Econ 821 pp 34ndash55

Roth Alvin E and Axel Ockenfels 2002 ldquoLast-MinuteBidding and the Rules for Ending Second-PriceAuctions Evidence from eBay and Amazon Auctionson the Internetrdquo Amer Econ Rev 924 pp1093ndash103

Schindler Julia 2003 ldquoLate Bidding on the Internetrdquomimeo Vienna U Econ and Bus Admin

Vickrey William 1961 ldquoCounterspeculation Auctionsand Competitive Sealed Tendersrdquo J Finance 16pp 8ndash37

Vincent Daniel 1995 ldquoBidding Off the Wall WhyReserve Prices May Be Kept Secretrdquo J EconTheory 65 pp 75ndash84

Wang Joseph 2003 ldquoIs Last Minute Bidding Badrdquowork paper UCLA

Wilcox Ronald T 2000 ldquoExperts and Amateurs TheRole of Experience in Internet Auctionsrdquo MarkeingLetters 114 pp 363ndash74

Wilson Robert 1977 ldquoA Bidding Model of PerfectCompetitionrdquo Rev Econ Stud 44 pp 511ndash18

Wolverton Troy 1999 ldquoeBay Users Miffed by NewFeesrdquo CNET Newscom Aug 20

Yin Pai-Ling 2003 ldquoInformation Dispersion andAuction Pricesrdquo work paper Harvard Bus School



one sold for on eBay not what the seller want-ed for the item I have a toy show that sold outfor years but nowadays all my vendors sell oneBay and all the buyers are spending theirmoney on eBay I used to buy and sell a lot inthe toy magazines before they got reduced tomere pamphlet-sized ragshellip Get my drift(Cohen p 110)

Online auctions have extensive listingsand powerful search technologies that createliquid markets for specialized product cate-gories The resulting reduction in transac-tion costs forced some intermediaries likethe antique dealer above to exit the market

Finally online auctions can be fun Manybidders clearly enjoy contemplating thesubtleties of strategic bidding and sharingtheir insights with others Most online auc-tion sites have active message boardswhere one can learn the fine points of col-lecting The boards also provide a sense ofcommunity for diehard collectors

In this paper we survey recent researchconcerning online auctions First wedescribe the mechanics of the auction rulesused on the most popular sites and someempirical regularities An especially interest-ing regularity is that bidders frequentlysnipe that is they strategically submit theirbids at the last seconds of an auction thatlasts several days Several authors haveempirically examined sniping and proposedexplanations

We then survey a growing literature inwhich researchers have attempted to docu-ment and quantify distortions from asym-metric information in online auctionmarkets As pointed out by EichiroKazumori and John McMillan (2003) theldquoinformation asymmetryrdquo problem consti-tutes perhaps the biggest limitation posed tothe impressive growth of online auctions Inonline auctions transactions take placebetween complete strangers who may notlive in the same state or the same countrymaking it very difficult for buyers to directlyinspect the good or to make sure that thegood will be delivered at all This creates

4 A number of high-profile incidents of fraud haveoccurred on online auctions For instance the FBIlaunched an investigation called ldquoOperation Bullpenrdquo thatled to the indictment of 25 persons for selling tens of mil-lions of dollars of forged collectibles such as forged signa-tures from Babe Ruth and Lou Gehrig (Cohen p 308)

5 Another survey article that provides a more in-depthsurvey of theoretical models of reputation is ChrysanthosDellarocas (2002) See also the survey by David Baron(2002)

opportunities for misrepresentation ofobjects and fraudulent behavior by sellerswhich may limit trade in these markets4

The informational asymmetry may mani-fest itself as a ldquowinnerrsquos curserdquo problem inwhich bidders recognize that winning anauction is conditional on being the mostoptimistic bidder about the itemrsquos worthAuction theory then predicts that bidderswill respond strategically to the winnerrsquoscurse by lowering their bids thus leading tolower prices and volumes in these marketsIn section 4 we will survey empirical workthat uses detailed data from online auctionsites to test whether bidders indeed actstrategically in the face of a possible winnerrsquoscurse and whether this strategic response islarge enough to prevent fraudulent sellersfrom extracting (short-term) rents

Given the above discussion a very impor-tant component of the online auction busi-ness is to decrease the informationalasymmetries between market participants Aparticularly popular method pioneered byeBay is the use of feedback mechanisms thatallow buyers and sellers to leave publiclyavailable comments about each other Insection 5 we will survey a rapidly growingempirical literature that utilizes data fromthe feedback mechanisms of online auctionsites to quantify the market value of onlinereputations5

Auction theory with its sharp predictionsabout the optimal way to design and conduct auctions has become one of themost successful and applicable branches ofmicroeconomic theory Internet auctionsites with their abundance of detailed dataon bidding and selling behavior provide

























































































0

01

02

03

04

05

06

07

08

03 04 05 06 07 08 09 1 11 12 13 14 15 16 17 18


Nor

mal

ized

Pri

ces

= P

V











































Dewan and Hsu2001

Eaton 2002


Jin and Kato2002


Livingston 2002



Melnik and Alm2002


Ba and Pavlou2002

Resnick et al2003


Collectible stamps

Electric guitars

Pentium chips

Sports tradingcards

Palm Pilot PDAs

Golf clubs

coins





vintage postcards


$37

$1621

$244

$166

$238

$409

$173

$208

$33

$15-$900

$15- $1200

$13

$15-$900


hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression


field experiment









month







TABLE 1 (cont)







































































































































7 Conclusion







REFERENCES














































































































































































0

01

02

03

04

05

06

07

08

03 04 05 06 07 08 09 1 11 12 13 14 15 16 17 18


Nor

mal

ized

Pri

ces

= P

V











































Dewan and Hsu2001

Eaton 2002


Jin and Kato2002


Livingston 2002



Melnik and Alm2002


Ba and Pavlou2002

Resnick et al2003


Collectible stamps

Electric guitars

Pentium chips

Sports tradingcards

Palm Pilot PDAs

Golf clubs

coins





vintage postcards


$37

$1621

$244

$166

$238

$409

$173

$208

$33

$15-$900

$15- $1200

$13

$15-$900


hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression


field experiment









month







TABLE 1 (cont)







































































































































7 Conclusion







REFERENCES

























































































jn04_Article 3 6104 1053 AM 60











































































0

01

02

03

04

05

06

07

08

03 04 05 06 07 08 09 1 11 12 13 14 15 16 17 18


Nor

mal

ized

Pri

ces

= P

V











































Dewan and Hsu2001

Eaton 2002


Jin and Kato2002


Livingston 2002



Melnik and Alm2002


Ba and Pavlou2002

Resnick et al2003


Collectible stamps

Electric guitars

Pentium chips

Sports tradingcards

Palm Pilot PDAs

Golf clubs

coins





vintage postcards


$37

$1621

$244

$166

$238

$409

$173

$208

$33

$15-$900

$15- $1200

$13

$15-$900


hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression


field experiment









month







TABLE 1 (cont)







































































































































7 Conclusion







REFERENCES

































































































































































0

01

02

03

04

05

06

07

08

03 04 05 06 07 08 09 1 11 12 13 14 15 16 17 18


Nor

mal

ized

Pri

ces

= P

V











































Dewan and Hsu2001

Eaton 2002


Jin and Kato2002


Livingston 2002



Melnik and Alm2002


Ba and Pavlou2002

Resnick et al2003


Collectible stamps

Electric guitars

Pentium chips

Sports tradingcards

Palm Pilot PDAs

Golf clubs

coins





vintage postcards


$37

$1621

$244

$166

$238

$409

$173

$208

$33

$15-$900

$15- $1200

$13

$15-$900


hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression


field experiment









month







TABLE 1 (cont)







































































































































7 Conclusion







REFERENCES



























































































































Dewan and Hsu2001

Eaton 2002


Jin and Kato2002


Livingston 2002



Melnik and Alm2002


Ba and Pavlou2002

Resnick et al2003


Collectible stamps

Electric guitars

Pentium chips

Sports tradingcards

Palm Pilot PDAs

Golf clubs

coins





vintage postcards


$37

$1621

$244

$166

$238

$409

$173

$208

$33

$15-$900

$15- $1200

$13

$15-$900


hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression

hedonic regression


field experiment









month







TABLE 1 (cont)







































































































































7 Conclusion







REFERENCES























































































TABLE 1 (cont)







































































































































7 Conclusion







REFERENCES

































































































































































































7 Conclusion







REFERENCES


























































































REFERENCES























































































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