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Page 1 of 1 THE VALUE OF REPUTATION ON EBAY: A CONTROLLED EXPERIMENT∗ Paul Resnick Richard Zeckhauser John Swanson Kate Lockwood1 The latest version of this working paper can be found at http://www.si.umich.edu/~presnick/papers/postcards/ March 12, 2004 Abstract We conducted the first randomized controlled field experiment of an Internetreputation mechanism. A high-reputation, established eBay dealer sold matched pairs oflots -- batches of vintage postcards -- under his regular identity and under new selleridentities (also operated by him). As predicted, the established identity fared better. Thedifference in buyers' willingness-to-pay was 8.1% of the selling price. A subsidiaryexperiment followed the same format, but compared sales by relatively new sellers withand without negative feedback. Surprisingly, one or two negative feedbacks for our newsellers did not affect buyers' willingness-to-pay.
Page 2 of 2 I Introduction As the Internet grows as a means of executing transactions, each buyer's array ofpossible vendors is mushrooming. On auction sites, like eBay, users already buy and sellthings from others across the nation and around the world. Despite opening many newvenues, this electronic bazaar puts stress on some of the foundations of the traditionalmarket place. In traditional markets, a buyer can usually \"squeeze the orange\", e.g.,inspect the vintage plate, before buying. Beyond this, the hostage of a seller's reputation,possibly built over many years, including the cost of her physical facility and herstanding in the community, together with repeat transactions, keeps her honest anddiligent. Sales over the Internet lack these tools of reputation. No seller has long been in theelectronic market. A seller's physical facility may be her kitchen, and virtually no buyerknows a seller's standing in the community. To be sure, some sellers, such as Dell andL.L. Bean, borrow reputations from elsewhere. However, for tens of thousands of sellers,there is no outside instrument of reputation. In such circumstances, the temptation tosellers to misrepresent products, e.g., exaggerate their quality or misrepresent theirprovenance, are great. So too is the temptation to sloth, to ship slowly or sloppily afterreceiving payment.2 This should lower the price that buyers are willing to pay, since theyare forced to assume some risk for the quality and utility of the good being traded. Unlesssellers can provide sufficient information about product quality and their own quality intransaction fulfillment, low-quality products and sellers will drive out those of highquality and the market will shrivel (Akerlof 1970).
Page 3 of 3 Internet marketplaces have been forced to find a substitute for traditional sellerreputations. Important systems have been introduced to enable the systematic elicitationand distribution of reputational information. These systems collect information on thepast behavior of a seller, or for that matter of a buyer, and then make that informationavailable to potential future transaction partners. Because people know that theirbehavior today will affect their ability to transact in the future, not only with their currentpartner but with unknown others as well, opportunistic behavior is deterred. Moreover,less reliable players are discouraged from joining the marketplace. Reputation systemsseek to inform buyers about whether potential trading partners are trustworthy, andthereby to make chiseling and cheating rare and losing propositions. Though disadvantaged in the respects described above, Internet markets also havesignificant advantages in establishing reputations. First, any information that is gleanedcan be near costlessly tallied on a continuing basis, and written assessments can readilybe assembled. Second, that information can be near costlessly transmitted to millions ofpotential customers. (By contrast, word of mouth distribution loses vast amounts ofinformation, with different buyers hearing significantly different assessments of the sameseller. It also entails a per-telling cost.) Third, the Internet has the potential, though atpresent not the reality, for sophisticated processing of information, e.g., using Bayesiancalculations, and for using micropayments to induce careful and honest assessments fromtransactors (Avery, Resnick and Zeckhauser 1999; Miller, Resnick and Zeckhauserforthcoming). The same factors that advantage the Internet in establishing reputations make it awonderful place to study the role of reputations. In auction markets, such as eBay, where
Page 4 of 4the vast majority of sellers are otherwise unknown, the researcher can get precisemeasures of current reputation. Moreover, that information is cheaply available to allparticipants in the marketplace. The contrast with traditional markets is stark. A retailerin a community, say, may have a strong reputation with some individuals, a weak onewith others, and his reputation may be unknown to newcomers. Hence, any reputationmeasure would have considerable noise. With Internet auction sites, reputations arecommon knowledge, or at least commonly available knowledge. This paper assesses thevalue of reputations, capitalizing on the known-reputation feature of the Internet auctionsconducted on eBay, and the ready availability of data on the frequency and price of salesthere. There have been a number of observational studies of eBay reputations and sales.(Fifteen are addressed in section II.B.) Observational studies are limited in two ways.First, they can only examine the impact of reputation in markets for standardized goods,since they depend on naturally occurring variation in reputation for sales either ofidentical goods or of goods whose outside market value can be used as a control. Second,such studies are invariably plagued by an omitted variables problem. In the eBaycontext, for example, we can't tell whether a seller is getting a higher price because of abetter reputation, or alternatively, a more attractive web site, superior presentation ofitems, better answers to inquiries, or superior merchandise. Except in rare instances,observations on these alternative explanations are not immediately detectable by theresearcher. Moreover, both reputation and omitted measures of a seller's skill ormerchandise are likely to be correlated with a seller's experience level. Thus, effects of
Page 5 of 5the omitted variables are likely to be attributed to the reputation, leading to anoverestimate of the true impact of reputation. Laboratory experiments can more clearly isolate the effects of reputation. Keser(2003) utilized an "investment game" where one player's trust increases the total payoffsbut leaves her vulnerable to the other player taking an unfair portion. When subjects whohad not previously interacted with each other were informed of each other's past play,both trust (investment) and trustworthiness (return of profits to the trustor) were higher.Bolton, Katok, and Ockenfels (2003) utilized an analogous two-stage game where buyersdecide whether to send money, and sellers then decide whether to ship the item. In thereputation condition where subjects were informed of each other's past play, trust andtrustworthiness increased as compared to a no reputation condition, but still did not reachthe levels found in a repeated-play condition. In recent years, field experiments have gained favor, as a way of combining thecontrols of lab experiments with the external validity of studying behavior in naturalsettings. For example, Camerer manipulated betting markets (Camerer 1998), List andLucking-Reiley compared different methods of soliciting charitable contributions (Listand Lucking-Reiley 2002) and of auctioning multiple units of a good (List and Lucking-Reiley 2000), and Lucking-Reiley tested the effects of auction formats (Lucking-Reiley1999), minimum bids (Reiley forthcoming), and hidden reserve prices (Katkar andLucking-Reiley 2000).3 We conducted the first randomized controlled study of the value of eBay reputationsin the natural setting of actual eBay auctions. We were fortunate to secure thecollaboration of a highly experienced eBay postcards seller (Swanson). He prepared
Page 6 of 6matched pairs of auction lots, and a random device then determined which lot was soldunder his established, extremely high reputation identity, and which lot was sold throughnewly created, unknown sellers. Roughly similar web sites were prepared for all sellers.Other factors, such as shipping, billing and payment procedures were constant across oursellers. Section II describes the eBay reputation system and examines prior research in moredetail. Section III describes our randomized controlled experiment and its results.Section IV discusses the results and discusses their implication. Section V concludes. II The eBay Reputation System There are many sites with reputation systems of some sort. The eBay system isundoubtedly the biggest and best known. eBay has millions of items available for bid atany time. The eBay reputation system enables users to leave feedback about interactionswith each other. The system is transaction based: to leave feedback for each other, twousers must actually have completed an auction. After the auction ends, the buyer andseller each have the opportunity to rate each other's performance with either a 1(positive), a 0 (neutral), or a –1 (negative). Users also have the opportunity to leave atext comment, and rated individuals can respond to comments that they feel were unfair.Users' net reputation scores are calculated as the count of distinct users who gavepositive feedback minus the count of those who gave negative feedback. The seller's netreputation score -- positives less negatives -- is automatically displayed on the auctionpage for each item she lists. Hence, potential buyers see this rating before bidding.4 Abuyer can choose to click on the net score in order to see a more detailed breakdown intopositive, negative, and neutral over a series of time periods. The buyer can then scroll
Page 7 of 7down to see individual comments, with the most recent ones shown first. A user who isnew to the system starts with a net feedback score of zero and has a sunglasses icondisplayed next to his or her screen name for the first 30 days of membership. Users maychange their eBay identities by registering again, but must then start all over as new userswith a zero reputation score. II.A Expectations About and Experience with Feedback and Reputations on eBay We focus on seller reputations, since those for buyers matter little. (The seller cansimply wait to get paid and thus incurs little risk.) If buyers are uncertain about sellertrustworthiness, they will reward better seller reputations by raising their offers, eventhough each buyer is only concerned about his own welfare. Indeed, if it is costly tomaintain a reputation for high quality, then a good reputation needs to be rewarded by atleast the cost of building one. A bad reputation or a decline in reputation should incur aloss that exceeds the benefit from opportunistic behavior (Shapiro 1983). Thus, inequilibrium, a good reputation must command a price premium.5 Since sellers who getnegative feedback can start over relatively easily, buyers need to impose somedisadvantage on sellers with no feedback at all (Friedman and Resnick 2001).6 Finally,we should expect that buyers will not provide information to help determine sellerreputations, since to do so incurs a cost, and free riding is hard to punish.7 eBay reputations in practice do not illustrate pure rational game-theoretic processesin action. (Resnick and Zeckhauser 2002), hereafter RZ, found that even though theincentive to free-ride is clear, half of the buyers on eBay provided feedback. Thissuggests that a high level of courtesy pertains on eBay. After a satisfactory transaction,
Page 8 of 8you provide a relatively low cost positive feedback just the way you provide a thank youin everyday discourse. The most striking feature about eBay feedback is that it is so positive. Sellersreceived negative feedback only 1% of the time, and buyers 2% (RZ). Given their rarity,negatives should be much more consequential than positives in affecting a seller's overallreputation. The specifics of negatives should be much more informative. However, eBayoffers no search mechanism to find negatives. For a seller, what constitutes a good reputation in the eBay feedback system? Theanswer depends on how buyers behave. There are many possibilities. At one extreme,buyers may in effect be Bayesians, effectively incorporating information not only fromreputation scores but from a seller's product, geographic location, written comments,whether she has an expensive website, etc. Such buyers would be to statistics as Molière'sMonsieur Jourdain was to prose, unknowing but effective users. At the opposite extreme,buyers may employ simple heuristics that are far from optimal, as human decision makersare known to do (Tversky and Kahneman 1974). II.B Prior Empirical Studies In the last few years, a large number of empirical studies of the effects of eBay'sreputation system on sales have been undertaken. We are aware of at least 15, assummarized in Table 1.8 One study, BP, follows the logic of a lab experiment, splicingdifferent seller reputation scores into real auction listings and asking subjects to indicatehow much extra they would pay to different sellers. The remaining studies all follow thelogic of hedonic regression, though their details vary in important ways. Each is anobservational study of a set of items whose sellers had varying reputations. Each
Page 9 of 9correlates the reputations with auction outcomes, while controlling for possibleconfounds. Mean prices for the items studied ranged from $33 to $1621. Generally, theseare relatively high-priced items for eBay: the median selling price for an item in a largedataset from 1999 was less than $15. The results are broadly consistent with the theoretical expectation of buyers payingmore to sellers who have better reputations, but the results do not yield a consistentpicture. While most studies find some effect of positive feedback (or net feedback), threedo not (LPBD, E, CH). At the larger end of effect sizes for positive evaluations, themodel in L finds that sellers with more than 675 positive comments earned a premium of$45.76, more than 10% of the mean selling price, as compared to new sellers with nofeedback. Similarly, while most studies that examined negative feedback found an impacton probability of sale or price, L did not, E found an effect only for established sellers,CH found an effect only after eBay changed its display in 2003 to show the percentagenegative along with the composite score, and MS found that more negatives actuallyincreased the number of bidders. At the larger end of effect sizes for negatives, LBPD,looking at collectible coins, finds that a move from 2 to 3 negatives cuts the price by11%, about $19 from a mean price of $173. These studies all control in some way for variability in underlying product values.9Without such controls, as LBPD demonstrate, omitted variable bias can producemisleading results, including positive reputation scores that appear to lower prices whilenegative reputations raise them. Two methods have been used to control for value of theproduct, so as to correct this omitted variable bias. One is to study auctions of identicalproducts, such as computer processors (HW), specific collectible items (MS, RZ), or
Page 10 of 10coins whose value comes from their gold content rather than their collectible value (MA).An alternative approach is to include book value or market price in regressions to controlfor the differences in item values (LR, BH, DH, BP, KM, JK). Many auctions do not result in transactions, and frequently items do not even receivebids. As expected, reputation affects the probability of sale (E, JK, MA, L) as well asprice.10 It also affects the probability of individual bidders entering the auction (BH), andthe number of bids (BH, MS). It also affects buyers' subjective assessments of a seller'strustworthiness (BP). Many studies include the number of bids as a control variable in regression models.However, LBPD argues, and we agree, that the number of bids is an endogenousindicator of the impact of reputation on price, and should not be an independent variablein a simple regression model. Number of bids or bidders can be treated as an outcomevariable (BH), or may be modeled simultaneously as both an effect of reputation and anindependent contributor to price (MS). The observational studies surveyed here rely on being natural quasi-experiments. Assuch, the danger remains that unknown or otherwise unmeasured covariates of reputationproduce outcome differences that will be mistakenly attributed to the reputation score.Most studies control for covariates that are easily coded from auction listing, includingwhether a picture of the item is displayed, whether credit cards are accepted, time of dayand day of the week that the auction closes, and length of the auction.11 Despite these attempts to control for potential confounds, however, some importantones remain. First, private email communications between buyer and seller during thebidding can influence the buyer's willingness to bid high, but such communication is
Page 11 of 11invisible to the researcher who can monitor only the eBay website. Thus, for example, asellers who are very responsive to email inquiries may have induced buyers to bid higher,and this responsiveness may be correlated with reputation. Second, the completeness andquality of the seller's description page, and the aesthetics of its layout may affect bidding.Since many buyers type in search terms to find auction listings, including the right wordsin an item's title, and spelling it correctly, can make a huge difference (Schemo 2004).More experienced sellers, who have more feedback, are also more likely to have learnedhow best to describe their items. In principle, a researcher could code for the quality oflistings, but in practice it is very difficult to do so. A third and related confound is thatmore experienced sellers may tend to have higher quality goods, and the difference maybe apparent to buyers but not easily coded by researchers. A controlled field experiment, as presented in this paper, offers two main advantagesover observational studies, yet maintains the external validity of observing the behaviorof buyers in a natural market setting.12 First, it automatically controls for confounds. Wevaried the seller's reputation without varying his responsiveness to emails from potentialbidders, his skill at listing items, or the quality of his goods. Second, it makes it possibleto investigate the impact of reputation in markets for non-standardized goods for whichbook values are unavailable. A controlled experiment requires only one matched itemagainst which to compare an auction outcome rather than requiring a comparison againstthe market price data for a large set of similar items.
Page 12 of 12 III The Randomized Controlled Experiment III.A Procedures We worked with an established eBay dealer with a high reputation (net score above2000 as of the beginning of the study, with just one negative). In real life, that reputationbelongs to coauthor John Swanson, who runs a business with Nina Swanson dealingprimarily in vintage postcards. They typically list dozens of items for sale each week oneBay. (They also sell items live at postcard shows and other events.) In addition to selling items using his established identity with a strong reptutation—hereafter often referred to as STRONG--, our dealer created seven new eBay selleridentities. Each new seller started with no feedback. Each of the new sellers will bereferred to generically as NEW. The dealer provided the same great service(communication, packaging, shipping) when listing items under any of the selleridentities, but buyers looking only at the item listings and seller information seen on eBaywould not have known that the same high-quality dealer was behind all the sellers. Our primary experiment compared listings of 200 items by STRONG to listings ofmatched items by one of the new sellers. Information on results was gathered directlyfrom the eBay webpage, using a spider (automated computer program) to collect data. Atthe completion of each auction, detailed information was collected about the bids placed,the selling price of the items, and the feedback of both the buyer and the seller at the timeof the auction. This data was then double checked against the records kept by our seller. Since the postcards were not standard items, the matching, which was done on bothsubject and value, required the dealer's judgment. To avoid possible bias, a randomdevice determined which item in each pair would be listed by STRONG and which by
Page 13 of 13one of the new sellers. By matching items rather than trying to control for variation initem value, the controlled experiment allows us to examine the effects of reputation onsales of unique, used items, in our case vintage postcards. For such items, there is a greatdeal of information asymmetry between seller and buyer about item condition, and noestablished book values to guide buyers. The typical item was a "lot" of vintage postcards, titled something like "VintageValentine Postcards (36)", where the number in parentheses indicated the number ofcards in the lot. The item description indicated the general condition of the cards andprovided photos of one or a few cards in the lot. The dealer followed his usual practicesto determine a minimum starting bid for each item. The range was $4.99-$49.99 with amean starting bid of $13.13 and a median of $9.99. Informal analysis suggests thatlistings of this value are quite typical on eBay, not only in the vintage-postcard category,but overall. Over the course of the experiment, which took twelve weeks, five of the new sellerspresented 20 lots each for sale, and two new sellers presented 50 lots each. This wasdone to allow for accumulation of different amounts of feedback. The lots, which couldinclude multiple items, were divided into five sets, grouped by listing price. Each setcontained 20 pairs (40 lots total) and was listed in two different weeks, which in turnwere separated by a week in the middle to ensure that there would be no overlap in theavailability of the matched items. For half of the pairs in any set, again determined atrandom, STRONG listed its lot in the first week and for the other half, the new sellerlisted its lot first. Two weeks later, the other seller listed the other lot of the pair. In any
Page 14 of 14set, 10 lots each were listed by the two high volume new sellers, and 4 by each of the fivelow volume new sellers.Table 2 illustrates how this counterbalancing worked. For example, the first set of 20pairs had starting (minimum) prices in the $9.99-14.99 range. Half of each pair waslisted in week one and the other half in week 3 of the experiment. For half of the pairs(the 1A group), the new seller listed its lot in week 1 and STRONG listed its matching lotin week 3. For the other half, the order was reversed. The starting bids for the lots werebalanced among the seven new sellers as closely as possible. Several steps were taken to make it difficult for buyers to notice that an experimentwas underway, and we received no communications suggesting that any bidders noticedlot pairings or other elements of an experiment. Lots were listed in a category thattypically has thousands of lots for sale. Each of the new sellers used a slightly differentformat for listing lots (e.g., "36 Valentine Postcards, Vintage"). Each of the new sellershad its own e-mail address from which correspondence emanated. Finally, the twohalves of each matched pair were listed for sale in different weeks rather than at the sametime. Care was taken to assure that each seller listed the lot using the same information(selling price, tax, shipping cost, payment methods and description), while maintaining aunique look and feel to its listings, as illustrated in Figures 1 and 2. The listings werecreated using AuctionHelper, the program our dealer uses to list his lots under hisestablished identity. By giving each seller a unique look, we were able to avoid making itapparent that all sellers were being operated by the same person. However, by includingall the same information, we kept the listings matched as closely as possible.13
Page 15 of 15 Each of the new sellers began with no prior feedback. During the course of theexperiment, feedback from buyers in previous time periods became visible. Our highvolume sellers had 12 and 17 positive feedbacks and our low volume sellers had between5 and 14 positive feedback points, with an average of 9.2 positive points. None of oursellers received any negative feedback from their sales. In a second experiment, we tested the effects of negative feedback. Such feedback,being scarce, carries much more information content. Especially in a brief reputation, itshould hurt sellers if noticed. To prepare for the second experiment, we purchased lotsfrom three of the new sellers in order to give them each one or two negative comments.The experiment, lasting three weeks, compared results for new sellers with and withoutnegative feedback. A previous study analyzed reasons for negative feedbacks at eBay, and foundexplanations ranging from minor (slow shipping) to major (sellers who cashed checks butnever sent lots) (Resnick and Zeckhauser 2002). All of the negative feedback commentswe left indicated either that the item received was not as described or was in worsecondition than in the auction listing text, though we purposefully did not provide anydetails. In all cases the user that gave the feedback was itself a fictitious entity with zerofeedback. The "item not as described" negative highlights one of the big problems withInternet auctions: the inability of potential buyers to decide whether to trust the seller'sdescription of an item. Figure 3 shows the feedback profile for one of our sellers at thebeginning of the second experiment. In all cases, negative feedback was displayed at thetop of the comments at the beginning of the second experiment. Feedback accruing
Page 16 of 16during the second experiment pushed the negative comment(s) down, but never off thefirst page. With our negative feedbacks given, new sellers with similar amounts of positivefeedback were paired, as shown in Table 3. The dealer then listed 35 more matched pairsof lots, with half of each pair listed by a seller with no negative feedback and the otherlisted by a seller with negative feedback. As in the primary experiment, lots were listedin separated weeks, with order of listing counterbalanced between the two sellers.Randomization was used as before. The two high-volume sellers from the primaryexperiment were paired together. The next pair was formed from the two new sellerswith the next most feedback, and the last pair was the two sellers with the next mostfeedback after that. The new seller that ended the primary experiment with the lowestfeedback score (4) was not used in the second experiment. III.B Hypotheses The established seller has a vastly better reputation than any of the new sellers. Themain hypothesis of our study is that buyers will view the established seller as less riskyand thus will pay more. H1: Buyers are willing to pay more to a seller with a strong positive reputation(STRONG) than sellers without such a reputation (NEW). We expected that buyers would give little weight to the one negative feedback of theestablished seller, given the positive feedback from more than 2000 distinct buyers.However, in the second experiment, when sellers with relatively few positive feedbacksalso have negative feedback, we expect buyers to distrust them.14 Our subsidiaryhypothesis is parallel to H1 for the primary experiment.
Page 17 of 17 H2: The new sellers with negative feedback will reap lower profits than thosewithout negative feedback. The new seller with two negative feedbacks will reap lowerprofits than those with just one negative feedback. III.C Results To analyze the impact of reputation on buyers' willingness-to-pay, we compareoutcomes for matched lots, one listed by STRONG and one by a new seller. Ourstatistical tests are conducted putting the results for all the new sellers together. Buyers' collective willingness-to-pay for a lot through an ascending auction isimperfectly observed in eBay auctions.15 When neither STRONG nor NEW sells a lot,i.e., the opening (minimum) bid is above any buyer's willingness-to-pay, we get littleinformation about buyers' relative willingness-to-pay for the lot from the two sellers.When one sells but not the other, the observation provides a lower or upper bound on theratio of buyers' willingness-to-pay. For example, if only STRONG sells a lot, then buyerswere willing to pay somewhat less than the minimum bid to NEW, and hence the ratio ofwillingness-to-pay by the top bidder was at least the price paid to STRONG divided bythe minimum bid. Given this limited information, our tests for significant differences begin withnonparametric tests. We then examine whether there are significant differences in thenumber of non-sales. Finally, we employ censored regression techniques to produceparametric estimates of the magnitudes of differences, taking into account the censoredobservations that result from non-sales.
Page 18 of 18 We expect the ratio of buyers' willingness-to-pay to be lognormally distributed. Thatis, ln STRONG _ price = ln(STRONG _ price) − ln(NEW _ price) should be normally NEW _ pricedistributed. Our hypothesis is that the mean is greater than 0. Test of H1: Table 4 shows the results of a sign test on the paired differencesbetween ln(STRONG _ price) and ln(NEW _ price) . When neither lot sold, we excludethe lot, as no information is available about buyers' relative willingness-to-pay. WhenSTRONG sold a lot but NEW did not sell the matched lot, the sign of the difference waspositive; when the reverse occurred, it was negative. Of course, if both sellers sold a lot,then the observed difference was used. The setup of the auction, with identical startingprices and increments for a pair, tended to produce ties (e.g., if both sales had just onebidder, both sales were exactly at the minimum bid), which worked against anyhypothesis of difference. A one-sided non-parametric sign test was significant:Pr(#positive >= 81 | median of the differences = 0) = Binomial(n = 139, x >= 81, p = 0.5)= 0.0308. There are two ways STRONG can outperform NEW: sell more lots at or above thestarting price, and secure a higher price for lots sold. Table 5 shows the results onfrequency of sale. A chi-square test concludes that the probability of sale was notindependent between the two sellers (p<.001). The NEW sellers were much more likelyto sell when STRONG sold and vice versa; this correlation suggests that our dealer did agood job of pairing lots. That the correlation is far below 1 indicates that other factors,including chance, often determine whether one, both or neither of a matched pair ofpostcard lots gets sold. That some lots sold and some did not indicates that the dealercould not perfectly guess the appropriate starting bid.16
Page 19 of 19 Overall, the strong seller listings sold 63% of the time, the new seller listings 56% ofthe time. A one-sided sign test on the difference between STRONG_sold and NEW_soldapproaches significance, Pr(#positive >= 40 | mean of differences=0) = Binomial(n = 67,x >= 40, p = 1/2) = 0.0710. Next, we employ parametric techniques to estimate the magnitude of the differencebetween buyers' willingness-to-pay for lots listed by STRONG or NEW. Oneconceivable approach would be to consider only lots that sold both times. This wouldhave two disadvantages. First, it would reduce the sample size quite a bit, reducing thestatistical power. Second and more important, it would introduce a truncation bias, sincethe new sellers sold fewer lots. Thus, the observations of sold lots for NEW reflect moreextreme points than for STRONG in their respective distributions of buyers' willingness-to-pay. To avoid this, we employed a censored normal estimation that makes use of the pairswhen either or both of the sellers sold the lot (151 of the 198 cases). Employing thismethod, the estimated mean difference between ln(STRONG) and ln(NEW) is .078, asshown in column one of Table 6. This is positive in accord with H1 and the one-sided testis significant (p=.044).17 We conducted additional censored normal regressions to test for possible confounds.One potential confound is that the difference may depend on the value of the lot beingsold. For example, buyers may be more willing to accept risks on lower priced lots. Asshown in Column 2, however, there was no significant difference due to the minimumbid. Column 3 checks whether the counterbalancing was effective: there was nosignificant difference between lot pairs where STRONG sold first or second. Column 4
Page 20 of 20includes fixed effects of the new sellers (perhaps, despite our efforts to homogenize,some new sellers had more attractive names or formats for listing lots), with one of thenew sellers the omitted variable. None of the individual coefficients is significant and thechi-squared test on the likelihood ratio indicates that the different-attractiveness model asa whole is not significant. Since the large amount of feedback of the established seller made a difference, it isnatural to investigate whether the few positive feedbacks that the new sellers accumulatedduring the primary experiment were sufficient to alter buyers' willingness-to-pay. Theevidence says no. Column 5 of Table 6 compares sales in the last four weeks to those inthe first four weeks. NEW actually fared slightly better relative to STRONG in the firstfour weeks than in the last four, though the difference did not approach significance.Column 6 tests directly whether the amount of prior positive feedback for the NEW sellermattered. Again, the coefficient has a sign opposite to that expected, but again it is notsignificant. We now turn to analysis of the secondary experiment. Surprisingly, H2, which positsthat negatives in a brief reputation will hurt revenues, was not confirmed. The sellerswithout negatives sold 16 of the 35 lots (46%) and those with negatives sold 14 times(40%) of the time. The difference is far from significant given the small sample size.Moreover, the lots listed by the sellers without negatives more often received lowerprices when they did sell: the sign of the difference in willingness-to-pay favored thesellers without negatives 9 times, the sellers with negatives 11 times. A censored normalestimation also shows no significant difference between buyer willingness-to-pay to thetwo types of sellers.
Page 21 of 21 IV Discussion and implications Our basic finding is that, in conformance the theory of reputations and with loreabout eBay, the market rewards (pays more to) a seller who has accumulated a lot ofpositive feedback. The estimated mean difference between ln(STRONG) and ln(NEW) is.078. Exponentiating .078 yields 1.081. Thus, we find that buyers are willing to pay8.1% more for lots sold by STRONG than NEW.18 Given that the logarithm is a concavefunction, the estimated difference at the mean of the distribution may not exactly reflectthe mean difference calculated using straight dollar prices. However, taking logarithmsdoes not affect the median. The actual median of the differences occurred for a pair thatsold under both NEW and STRONG. STRONG received 7.2% more for this lot, which isclose to our 8.1%. Similar findings from previous observational studies could conceivably haveattributed to reputation scores effects that were really due to unmeasured differences inlisting quality, product quality, or seller responsiveness to inquiries of potential bidders.Our experiment controlled for these potential confounds. Before concluding withcertainty that the reputation score itself was responsible for the market's greaterwillingness to pay, however, it is worth considering some threats to validity thatremained even in our controlled experiment. First, there may have been differences in the quality of listings, even though thesame seller created them. To avoid tipping our hand that an experiment was in progress,each seller used a slightly different look and feel. Since there were not statisticallysignificant differences among the new sellers when fixed effects were included in thecensored normal regression, we believe that the dealer was able to present the same
Page 22 of 22information in multiple ways without having a large impact on the market. Extrapolating,it seems unlikely that the difference between the established seller and the new sellerswas not due to the presentation of listings, but we cannot tell for sure. A second threat to validity could come from repeat customers. If some previouscustomers of the seller with the strong reputation were more likely to look at that seller'slistings, or to base their assessments of a lot on their personal experience with that seller,the observed difference in outcomes might be attributable to previous customers ratherthan to the public reputation. We do not have a list of all previous customers ofSTRONG, but we were able to harvest a list of all user ids of previous buyers who leftfeedback. This is sufficient to estimate whether bidders for STRONG were more likelythan bidders for NEW to include previous buyers from STRONG. In fact, 23 ofSTRONG's 137 distinct bidders had given feedback to STRONG before the experimentstarted, accounting for 24% (44 out of 181) of the total distinct lot-bidder combinations.On the other hand, 13 of the NEW sellers' 121 distinct bidders had given feedback toSTRONG, and they accounted for only 11% (15 out of 136) of the total distinct lot-biddercombinations. Thus, we cannot rule out the possibility that the private reputation withspecific customers rather than the public reputation embodied in the sellers' feedbackprofiles was responsible for some or all of the observed difference between STRONGand NEW. A third and related threat to validity could come from multiple purchases. A buyer,having once bid on a lot from a seller, may look for other listings by that seller.19STRONG had more lots listed at any one time than any of the NEW sellers, for tworeasons. First, there were multiple NEW sellers, but all were paired with the same
Page 23 of 23STRONG seller. Second, our dealer conducted additional sales during the time of theexperiment using his regular selling identity, the STRONG seller in the experiment. Ifthere were no such search effect, then bidders on STRONG and NEW lots should havebeen equally likely to bid soon after on a lot listed by STRONG. To test this nullhypothesis, we examined our relatively complete bidding records, restricting our attentiononly to the bidders who had not purchased from our dealer prior to the experiment.20 Foreach bidder's first bid on a lot, on both STRONG and NEW listings in the experiment,we calculated whether the bidder also bid within 3 days on any other STRONG listing(whether part of the experiment or not). For bidders on STRONG lots in the experiment,13 out of 137 (9%) also bid on some other STRONG listing. For bidders on NEW lots, 10out of 121 (8%) also bid on some other STRONG listing.21 Thus, there is little evidenceto suggest that the better market performance for STRONG derived from a larger volumeof listings. Still, an ideal future field experiment should have different sellers list thesame number of lots. Future observational studies of the eBay market should also add asa control variable the number of simultaneous listings for the seller. The results from our second experiment seem somewhat puzzling. We found noimpact of negative feedback. This could merely be a spurious inability to detect a smalleffect with a small sample size, though the trends did not even point toward an effect. Here the threats to validity described above did not come into play, since all sellerswere new, had closely matched previous selling histories, and listed the same number oflots. If the effects in the primary experiment were due mainly to private reputations andvolume of listings, the lack of a measurable difference in the second experiment wouldnot be surprising.
Page 24 of 24 Alternatively it is possible that the public feedback profiles matter, but that buyersdiscounted the negative feedback because it was terse and did not explain exactly whatwas wrong with the lot, or because the feedbacks were given by buyers who themselveshad no feedback. For the seller with two negatives, buyers may have suspected somethingfishy because the two negatives, both from buyers with no feedback themselves, wereboth posted within a minute of each other (in retrospect, an experimenter's error). Another possible explanation along these lines is that the market treats new sellers asuntrustworthy but does not distinguish among feedback profiles of new sellers. Perhapsbuyers are not aware of the predictive value of an early negative on future problematictransactions, think one or two negative feedbacks may be an aberration, and thus payattention to the percentage of negative feedback only for sellers with longer histories.One piece of evidence against this hypothesis is that Dewally and Ederington (2006)found no evidence that buyers give less credence to the percentage negative when it isbased on just a few feedbacks, and that buyers place much more weight on the first fewfeedbacks for a seller than they do on their priors about sellers in general. Our primary conjecture as to why negative feedback had no measurable effect is thatmost buyers simply did not bother to click through to look at the detailed feedback, andinstead merely relied on the overall score (number of positives minus number ofnegatives) that eBay displayed as part of the lot listing. This value barely differed amongthe seller pairs. In support of this conjecture, Cabral and Hortascu (2004) found that thepercentage of negative feedback did not affect market price until after eBay's 2003transition to displaying the percentage of negative feedbacks in addition to the compositescore.
Page 25 of 25 While the field experiment methodology provides a more controlled test of whetherreputation score affected the seller's revenue than can an observational study, it leavesmany questions unanswered. In addition to our lack of control for private reputation andthe search effects of listing volume, it is not clear how well these results would generalizeto other categories of lots on eBay, or to other marketplaces that employ reputationsystems. Even for the vintage postcard market, our experiment provides insufficientinformation to determine whether the market is in an equilibrium that can be explainedwith a reputation model based on some combination of moral hazard and adverseselection, such as that in Diamond (1989) and Cabral and Hortascu (2004). The firstcondition for such equilibria would be that the price premium that buyers pay to sellerswith better reputations should be calibrated to the reduced risks they face. There areseveral difficulties in determining whether that condition is met in eBay's vintagepostcard market. First, since we have estimated only the market's willingness to pay, andnot a structural model of individual willingness to pay, we do not know how individualbuyers are discounting based on the risks they perceive. Second, although we haveestimates of how frequently problems occur, we do not know how much buyers gainfrom successful transactions or lose from problematic transactions. The other conditions for reputation equilibria involve sellers. To deter moral hazard,the cost of building and maintaining a high-quality reputation (including the lost profitsfrom not cheating) should be just enough to balance out the profit premium such areputation secures. If quality is a long-term property of the seller and not a matter ofstrategic control on each sale, then it must be profitable for all the desirable types, and
Page 26 of 26only them, to participate in the marketplace. To assess whether the market is over- orunder-rewarding reputations, however, would require estimates of the profit that could bemade on each transaction from cheating, the number of transactions required to build up astrong reputation, and the number of times a seller could cheat before destroying a strongreputation. None of these is available from the experiments reported here. V Conclusion The eBay Feedback Forum illustrates Yhprum's Law. (Yhprum is Murphy spelledbackward.) Systems that shouldn't work sometimes do, or at least work fairly well. Atleast prior to eBay's recent change toward displaying the percentage of negative feedbackmore prominently, buyers appeared not to pay sufficient attention to negative feedbackfor relatively new sellers. That should have created an opportunity to for sellers to cheatand profit. More generally, it is not clear whether the price premiums reflect a reputationequilibrium. Nonetheless, we find that eBay's public reputation scores play a significant role inthe marketplace, and that virtually all significant sellers have strong reputations. In ourcontrolled experiment, a seller with a strong reputation received a price premium, evenholding constant quality of goods, skill at listing, and responsiveness to inquiries, allpotential confounds in previous observational studies. Looking at matched pairs of lots –batches of vintage postcards – buyers were willing to pay a STRONG-reputation seller8.1% more on average than a NEW seller. While the experiment did not control for thepotential confounds of private reputation information and volume of seller listings, it didrule out several potential confounds for previous observational studies, including seller
Page 27 of 27skill, unmeasured but observable product quality, and seller responsiveness to bidderinquiries. Perhaps the Feedback Forum works because the entry costs in time and skill tobecome an eBay seller discourage low-integrity sellers from joining the market. Perhapsthe skilled yet unscrupulous can make more money elsewhere. Perhaps the pricepremium paid to sellers with strong reputations, while small on any individualtransaction, is sufficient to maintain their good behavior both because it can be enjoyedmany times once a reputation has been built and because it can be destroyed fairlyquickly. Perhaps a fairly effective reputation system is good enough: A reputationsystem that merely reduces the lure of cheating and chiseling without eliminating it maybe sufficient to activate Yhprum's Law.
Page 28 of 28 VI ReferencesAkerlof, G. A. (1970). \"The Market for \"Lemons\": Quality Uncertainty and the Market Mechanism.\" The Quarterly Journal of Economics 84(3): 488-500.Avery, C., P. Resnick and R. Zeckhauser (1999). \"The Market for Evaluations.\" American Economic Review 89(3): 564-584.Ba, S. and P. A. Pavlou (2002). \"Evidence of the Effect of Trust Building Technology in Electronic Markets: Price Premiums and Buyer Behavior.\" MIS Quarterly 26: 243-268.Bajari, P. and A. Hortacsu (2003). \"Winner's Curse, Reserve prices and Endogenous Entry: Empirical Insights from eBay Auctions.\" RAND Journal of Economics 34(2).Bajari, P. and A. Hortacsu (2004). \"Economic Insights from Internet Auctions: A Survey.\" Journal of Economic Literature 42(2): 457-86.Bolton, G., D. Fong and P. Mosquin (2003). \"Bayes Factors with an Application to Experimental Economics.\" Experimental Economics 6(3).Bolton, G., E. Katok and A. Ockenfels (2003). How Effective Are Electronic Reputation Mechanisms? An Experimental Investigation. Available on-line at http://ockenfels.uni-koeln.de/download/papers/BKO2.pdf.Cabral, L. and A. Hortacsu (2004). The Dynamics of Seller Reputation: Theory and Evidence from eBay, NBER. Available on-line at http://www.nber.org/papers/w10363.Camerer, C. (1998). \"Can Asset Markets Be Manipulated? A Field Experiment with Racetrack Betting.\" Journal of Political Economy 106(3): 457-482.Dewally, M. and L. H. Ederington (2006). \"Reputation, Certification, Warranties, and Information as Remedies for Seller-Buyer Information Asymmetries: Lessons from the Online Comic Book Market.\" The Journal of Business 79(2).Dewan, S. and V. Hsu (2004). \"Adverse Selection in Electronic Markets: Evidence from Online Stamp Auctions.\" Journal of Industrial Economics 52(4): 497-516.Diamond, D. W. (1989). \"Repuation Acquisition in Debt Markets.\" Journal of Political Economy 97(4): 828-862.Eaton, D. H. (2002). Valuing Information: Evidence from Guitar Auctions on eBay. Murray, KY, Murray State University: 28. Available on-line at http://campus.murraystate.edu/academic/faculty/david.eaton/workpaper0201.pdf.
Page 29 of 29Friedman, E. and P. Resnick (2001). \"The Social Cost of Cheap Pseudonyms.\" Journal of Economics and Management Strategy 10(2): 173-199.Harrison, G. W. and J. A. List (2004). \"Field Experiments.\" Journal of Economic Literature 42(4).Henrich, B., Bowles, Camerer, Fehr, Gintis, and McElreath (2001). \"Cooperation, Reciprocity and Punishment in Fifteen Small-scale Societies.\" American Economic Review May 2001: 7.Houser, D. and J. Wooders (forthcoming). \"Reputation in Auctions: Theory, and Evidence from eBay.\" Journal of Economics and Management Strategy.Jin, G. and A. Kato (2004). Consumer Frauds and the Misinformed: Evidence From An Online Field Experiment, University of Maryland. Available on-line at http://www.glue.umd.edu/~ginger/research/ebay-Jin-Kato-Feb04.pdf.Kalyanam, K. and S. McIntyre (2001). Return on Reputation in Online Auction Markets. Santa Clara, CA, Santa Clara University. Available on-line at http://business.scu.edu/faculty/research/working_papers/pdf/kalyanam_mcintyre_ wp10.pdf.Katkar, R. and D. Lucking-Reiley (2000). Public Versus Secret Reserve Prices in eBay Auctions:Results from a Pokémon Field Experiment. Available on-line at http://eller.arizona.edu/~reiley/papers/SecretReserves.pdf.Keser, C. (2003). \"Experimental games for the design of reputation management systems.\" IBM Systems Journal 42(3): 498-506.List, J. A. and D. Lucking-Reiley (2000). \"Demand Reduction in Mulitunit Auctions: Evidence from a Sportscard Field Experiment.\" The American Economic Review 90(4): 961-972.List, J. A. and D. Lucking-Reiley (2002). \"The Effects of Seed Money and Refunds on Charitable Giving: Experimental Evidence from a University Capital Campaign.\" Journal of Political Economy 110(8): 215-233.Livingston, J. (2002). How Valuable is a Good Reputation? A Sample Selection Model of Internet Auctions, University of Maryland. Available on-line at http://www.wam.umd.edu/~kth/reputation1.pdf.Lucking-Reiley, D. (1999). \"Using Field Experiments to Test Equivalence Between Auction Formats: Magic on the Internet.\" The American Economic Review: 1063-1080.
Page 30 of 30Lucking-Reiley, D., D. Bryan, N. Prasad and D. Reeves (2000). Pennies from eBay: the Determinants of Price in Online Auctions, Vanderbilt. Available on-line at http://www.vanderbilt.edu/econ/reiley/papers/PenniesFromEBay.pdf.McDonald, C. G. and V. C. Slawson Jr. (2002). \"Reputation in an Internet Auction Market.\" Economic Inquiry 40(4): 533-650.Melnik, M. I. and J. Alm (2002). \"Does a Seller's Reputation Matter? Evidence from eBay Auctions.\" Journal of Industrial Economics 50(September): 337-350.Melnik, M. I. and J. Alm (2003). Reputation, Information Signals, and Willingness to Pay for Heterogeneous Goods in Online Auctions. Available on-line at http://www.gsu.edu/~wwwsps/publications/2003/030101onlineauctions.htm.Miller, N., P. Resnick and R. Zeckhauser (forthcoming). \"Eliciting Honest Feedback in Electronic Markets.\" Management Science.Reiley, D. (forthcoming). \"Field Experiments on the Effects of Reserve Prices in Auctions: More Magic on the Internet.\" RAND Journal of Economics.Resnick, P. and R. Zeckhauser (2002). Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay's Reputation System. The Economics of the Internet and E-Commerce. M. R. Baye. Amsterdam, Elsevier Science. 11: 127-157.Schemo, D. J. (2004). In Online Auctions, Misspelling in Ads Often Spells Cash. New York Times.Shapiro, C. (1983). \"Premiums for High Quality Products as Returns to Reputations.\" Quarterly Journal of Economics 98(4): 659-680.Slonim, R. and A. Roth (1998). \"Learning in High Stakes Ultimatum Games: An Experiment in the Slovak Republic.\" Econometrica 66(3): 569-596.Tversky, A. and D. Kahneman (1974). \"Under Uncertainty: Heuristics and Biases.\" Science 185: 1124-1131.Yin, P.-L. (2002). Information Dispersion and Auction Prices, Stanford University. Available on-line at http://www.stanford.edu/~pyin/info.pdf.
Page 31 of 31 VII AffiliationsResnick, School of Information, University of MichiganZeckhauser, Kennedy School, Harvard University and Harvard Business SchoolSwanson, Johnninaswanson, eBay, and Mission Viejo, CaliforniaLockwood, Northwestern University
Page 32 of 32Table 1. Summary of related studies testing impact of reputation on price and probability of saleInitials Citation Items Sold Mean Remarks Results priceHW (Houser and Pentium $244 Sample of items with at Positive feedback increases price;LBPD Wooders chips least two bids negative feedback reduces itE forthcoming ) Coins $173 Censored normal No effect from positive feedback; (Lucking- regression negative feedback reduces Reiley, Electric willingness to pay Bryan, guitars Prasad et al. $1621 Logit on pr(sale); OLS on No effect from positive feedback. 2000) price for sold items Negative feedback reduces (Eaton probability of sale only for sellers 2002) with >20 feedback; negatve feedback increases price of soldBP (Ba and Music, $15- Lab experiment in the items 2000 field: subjects responded Buyers trust more, and pay more if Pavlou software, with trust level and feedback profile has more positives, 2002) electronics $47 willingness to pay for fewer negatives. Effect is larger for auction listings with higher priced itemsBH (Bajari and Coins different feedback profiles Hortacsu spliced in. Both positive and negative feedback 2003) Structural model of affect probability of modeled buyer bidding based on common entry into the auction, but only values, endogenous entry positive feedback had a significant effect on final price.KM (Kalyanam Palm Pilot $238 Sample of sold items only Positive feedback increases price; and PDAs negative feedback reduces price McIntyre 2001) $208 Sample of sold items only; Higher net score (positives – simultanteous estimation negatives) increases price andMS (McDonald Dolls $33 of price and number of number of bids; % negative increase and Slawson bids number of bids Jr. 2002) $93 Censored normal Positive feedback increases price; $37 regression negative feedback decreases price;MA (Melnik and Gold coins negative feedabck reduces Alm 2002) Censored normal probability of sale only regression Positive feedback increase price;MA2 (Melnik and Circulated Sample of sold items only negative feedback decreases priceDH Alm 2003) coins Higher net score increases priceY (Dewan and Collectible Hsu 2004) stamps $359 Sample of sales with at OLS-- no significant effect of (Yin 2002) Computers least 2 bidders. OLS and reputation; structural model-- more structural model of bidding positive feedback causes higher prices because it increases the credibility of information provided in the auction, thus reducing dispersion of bidder valuations of the product
Page 33 of 33JK (Jin and Sports $166 Probit on pr(sale); OLS on Positive feedback increases Kato 2004) Trading price for sold items probability of sale; negative Cards decreases probability of sale unless card is professionally graded; no significant effects on priceL (Livingston Golf clubs $409 Simultaneous estimation of More positive feedback increases 2002) pr(sale), price if sold probability of sale, high bid; severely decreasing returns to positive feedback; no significant effect of negative feedback on probability of sale or high bidDE (Dewally Collectible $357 Censored normal Better reputation increases and comic books regression and Heckman willingness-to-pay, especially when Ederington estimation not professionally graded 2006)CH (Cabral and Laptops, $15- Sample of sold items only After removing one outlier seller, no Hortacsu coins, $900 impact of positives or negatives. 2004) Beanie Percent negatives has an effect only Babies after eBay starts displaying that statistic in 2003.
Page 34 of 34
Page 35 of 35 Table 2: Sales in the primary experiment.Week STRONG NEW sales Listing prices sales1 1A 1B $9.99-$14.992 2A 2B $9.993 1B 1A $9.99-$14.994 2B 1B $9.995 3A 3B $9.996 - - -7 3B 3A $9.998 4A 4B $14.99-$49.999 5A 5B $4.99-$9.9910 4B 4A $14.99-$49.9911 5B 5A $4.99-$9.99
Page 36 of 36Figure 1. A sample lot description for the dealer using his usual seller identity.
Page 37 of 37 Figure 2. Sample lot descriptions from two of our new sellers, for a matched pairlisted in the second experiment. Note that both have the same information available tothe buyer, but have different layouts to maintain the impression that they are being listedby different sellers.
Page 38 of 38Figure 3. One of the NEW seller's feedback files.
Page 39 of 39
Page 40 of 40Table 3: Feedback profiles of new sellers before the start of the second experiment.Volume in seller with seller withprimary Positives Negativesexperiment OnlyHigh 17+, 0- 12+, 1-Low 11+, 0- 14+, 2-Low 9+, 0- 7+, 1-
Page 41 of 41 Table 4. Observed versus expected values of sign[ln(STRONG _ price) − ln(NEW _ price)].Sign Observed ExpectedPositive 81 69.5Negative 58 69.5Zero 59 59All 198 198
Page 42 of 42 Table 4. Observed versus expected values of sign[ln(STRONG _ price) − ln(NEW _ price)].Sign Observed ExpectedPositive 81 69.5Negative 58 69.5Zero 59 59All 198 198
Page 43 of 43 Table 5. Number of salesSTRONG Not sold NEW Total Sold Not sold Sold 74 Total 47 27 124 40 84 198 87 111
Page 44 of 44 Table 6. Censored normal regression models predictingln(STRONG _ price) − ln(NEW _ price).Const .078* .091 .110 .048 .106* .017 [-.01, 17] [-.14, 32] [-.02, 24] [-.20,.30] [-.01, .22] [-.12, .15]minimum bid -.001 [-.02,.02]STRONG first -.060 [-.24,.12]NEW2 .021 [-.29, .33] -.002NEW3 [-.38, .38]NEW4 .188 [-.12, .5]NEW5 -.004 [-.37, .36]NEW6 .008 [-.35, .39]NEW7 -.197 [-.57,.18]Late round .083 [-.81,.25]Pos. feedback .014 [-.01, .04]N 151 151 151 151 122 151 LR chi-squared .02 .45 5.61 .99 1.38(Pr > chi-squared) (.90) (.50) (.47) (.32) (.24) * means P<.05 (one-sided test used for the constant term, late round, and positivefeedback, which are expected to have an effect in a particular direction; two-sided testused for the other terms). Ranges in square brackets are 95% confidence intervals.
Page 45 of 45 III ∗ ∗ We gratefully acknowledge financial support from the National Science Foundation under grantnumber IIS-9977999. Mihir Mahajan provided valuable research assistance. The participants in seminars atthe University of Michigan, the University of Arizona, and Carnegie Mellon University provided usefulfeedback. 1 Resnick, School of Information, University of Michigan; Zeckhauser, Kennedy School, HarvardUniversity and Harvard Business School; Swanson, Johnninaswanson, eBay, and Mission Viejo,California; Lockwood, Northwestern University. 2 At eBay and most other on-line auction sites, the norm is for the buyer to send payment first, thenfor the seller to send the good. Escrow services are available to withhold payment until after acceptance ofthe shipment, but they are used infrequently. 3 Researchers have also taken lab experiments, with abstract tasks, to interesting subject pools in fieldsettings (e.g., Slonim and Roth 1998; Henrich, Bowles, Camerer, Fehr, Gintis, and McElreath 2001).Harrison and List (2004) refer to these as synthetic field experiments 4 In 2003, eBay began providing the % of positive scores just after the net feedback rating. At thetime of this experiment, only the net feedback rating was shown. 5 Brick and mortar retailers may be rewarded in part through esteem in the community, but suchrewards seem unlikely on eBay. Conceivably such rewards such as self-esteem or adherence tointernalized ethics could lead to good behavior despite insufficient direct economic rewards. 6 The system designer could also impose the penalty; e.g., eBay could charge sellers who develop badreputations. At present, apart from pursuing fraud situations, eBay imposes no penalties. 7 If buyers think that sellers can develop reputations as reciprocators, then they could provide positivefeedback to get a positive feedback of their own, to bolster their own reputations. However, there is noneed for a buyer to have a reputation, unless they too are sellers. EBay merely adds together feedbacksecured as a buyer and as a seller. 8 Drawing in part on an earlier version of this paper and this table, Bajari and Hortacsu (2004) alsoprovide a survey of this literature. We, in turn, have benefited from their survey in updating this section ofthe paper. 9 Shipping costs, usually paid by the buyer, will affect bidding, especially for inexpensive items, forwhich shipping costs account for a large proportion of the total expense. The studies account for thesecosts in various ways. 10 If a study truncates the sample by selecting only sold lots or censors the outcome variable bytreating the minimum bid as selling price, but utilize an Ordinary Least Squares regression, it willunderestimate the effect of a positive factor, such as a strong reputation. The intuition is straightforward.The weaker is this positive factor, the more exceptional will be the required positive error to get included inthe sample. Examining only sold lots would thus disproportionately include strong upside outliers whenreputation is weak, leading to a biased underestimate of reputation benefits. Studies have dealt with thisproblem by employing censored normal regression (LBPD, MA, L), or through simultaneous maximumlikelihood estimation of both probability of sale and price contingent on sale (L). 11 In some sense, such controls argue that the seller may not be maximizing, or that the goods soldhave other intrinsic differences, e.g., their properties call for different length auctions. 12 BP secured many of the benefits of a controlled trial by presenting subjects – all real eBay members-- with actual eBay listings spliced together with fake feedback profiles. They then inquired about their
Page 46 of 46IVtrust in the seller, and the price premium they would pay. This framed field experiment in the classificationscheme of Harrison and List (2004) allows for comparison across feedback profiles while holding the itempresentation constant. However, the subjects knew it was an experiment, might not have been interested inthe items presented, and might have made different choices if bidding with their own money. 13 Two lot pairs were discarded from the analysis because the dealer accidentally listed them withdifferent starting prices for the two sellers. This left a total of 198 pairs for analysis from in the primaryexperiment. 14 Intuitively, a single negative in a short history is far more predictive of a problem on the nexttransaction than a single negative in a long history. Outcomes from eBay's transactions of Feb. 20, 1999confirm this intuition. Among sales by sellers with 5-20 positive feedbacks, 0.71% (21 of 2948) led toneutral or negative feedback when the seller had no prior neutral or negative feedback, but 2.50% (7 of280) led to problematic outcomes for those sellers with exactly one previous negative or neutral feedback(chi-sq(1)=9.50, p<.01). Among sales by sellers with 500 or more positive feedbacks, none of the 352 salesby sellers with unblemished records led to neutral feedback, nor did any of the 306 sales by sales withexactly one previous negative or neutral feedback. Whatever difference in risks may exist between thesetwo groups is undetectable in a sample of this size. Alternatively, we can think of a complete seller history as being like an experiment with the nextbuyer interpreting the data from that experiment to update her initial beliefs about the next transaction'soutcome. Starting from any prior, the Bayes' Factor indicates how far the buyer should update (Bolton,Fong, and Mosquin 2003). The magnitude of the Bayes' factor for the short history including one negativefeedback would be much larger than that for the short history without a negative. There is very littleabsolute difference between the Bayes' factors for long histories with zero or one dissatisfied customer. 15 We could have conducted the experiment with $.01 minimum bids in order to avoid or at leastminimize this problem in analysis, but for ecological validity of the experiment, we preferred to have thedealer follow his normal sales practices as closely as possible. 16 If the dealer had perfect knowledge, implying perfect price discrimination, all lots would sell at thestarting price. 17 The hypothesized underlying variable is the difference in logs of the market's willingness-to-pay.When STRONG sells the lot but NEW does not, we use the minimum bid as NEW's price and treat theobservation as right censored. That is, the market was willing to pay somewhat less than the minimum bidto NEW, so the difference in logs is at least as large as what was observed. Conversely, if NEW sells a lotbut not STRONG, the observation is left censored. This approach follows modeling specifications that havebeen commonly used by other researchers (LBPR, MA).This modeling specification does not recognize that eBay effectively follows second-price auction rules,i.e., that the market's willingness–to-pay is really the second highest valuation among bidders. When thereis more than one bidder, this valuation is observed, but when there is only one bidder, he purchases atexactly the minimum bid, and the second highest valuation is below the observed sale price. If we treatthese minimum-bid transactions as censored, they get pooled with lots that did not sell at all, and much lessinformation is available. For example, there were twelve lots that sold at exactly the minimum bid for eachof STRONG and NEW. In the result reported in the text, these are treated as equal selling prices. If bothare treated instead as left-censored observations, however, there is no information available about thedifference between them, and the remaining lots where there is a clear difference have more weight. Acensored normal estimation of the difference between ln(STRONG) and ln(NEW) with lots sold at theminimum bid treated as censored rather than actual observations yields a higher estimated mean difference,.178, and the one-side test remains significant (p=.023). 18 At the median selling price of $14.99, this would correspond to $1.23 per item. 19 The authors wish to thank Sue Fussell for pointing out this potential confound.


text:
Congratulations To Our Good Friday Crawfish Festival Winner Tosha Jones [PHOTO]
Congratulations to our Good Friday Crawfish Boil winner Tosha Jones who won her very own Boiling Rig and more courtesy of Lake Arthur Butane and Hebert's Crawfish Catering.
Tosha was more then surpirsed when I gave her the call on yesterday letting her know that she was the winner of the prize. We had hundreds of entries from our listeners who were all trying to win this huge prize which also includes a free sack of crawfish and more.
All you have to do is keep listening for your chance to win some big prizes and check here as well to enter for the next big contest coming soon. Also Happy Good Friday to everyone out there from all of us here at The Peoples Station 107 JAMZ.

subset: commoncrawl


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During August and September, the Australian Farmlands Fund has direct-seeded another 105ha of native vegetation. There is now 220ha of landscape dedicated to biodiversity, embedding carbon in the landscape and building landscape resilience in the face of climate change.
Direct seeding at Australian Farmlands Fund dedicates 220ha to biodiversity
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text:
This suit is clubs
These are spades
There are four suits ; spades , diamonds ,clubs , hearts.
In a deck / Pack of cards there are 52 cards.
to shuffle the cards
to give out the cards
a person deals the cards clockwise
you cant look at the cards
set of cards
a run of cards
The aim of the game is to put the cards into sets or runs
1. Shuffle the cards
2. Deal out the cards from the left
3. Give each person seven cards
4. Each player takes a card either from the pile or the card which is face up. Then drop a card/ choose a card from his hand to discard.
5.The first person to have a winning combination in her hand is the winner.
5. The first person with a set an a run is the winner to do it is the winner


text:
End of series for Toronto, which sinks Philadelphia | NBA
For the return of Tobias Harris with the Sixers, Fred VanVleet (32 points, 6 rebounds, 7 assists) had decided to play the spoilsport. Imperial from start to finish, the leader of the Raptors guided his team to precious success, after a disjointed meeting which was decided in the last minute (115-109).
Perfectly supported by OG Anunoby (20 points, 4 rebounds, 4 assists) and Gary Trent Jr. (20 points, 5 rebounds, 4 assists), just as decisive as him in the "money-time", on both sides of the floor, "FVV" was clearly the strong man for Toronto, who returned to victory after losing three in a row.
For the Philadelphia players, who are preparing to embark on a perilous six-game road-trip on the West Coast, the crazy atmosphere of the Wells Fargo Center could not prevent them from suffering another setback, the third of following. Despite, also, the umpteenth superb performance of young Tyrese Maxey (33 points, 4 rebounds, 5 assists).
– Fred VanVleet as boss. Quickly in legs, with his 15 points and 3 assists in the first quarter, "FVV" made his defenders dizzy throughout the evening, finishing with 32 points, 6 rebounds and 7 assists at 11/22 on shots and 6/11 at 3 points. A master at playing the Raptors, the point guard confirmed her current good form by first carrying her team on her shoulders, then waking them up as soon as they were in the hard, in the first half or in the last act. The icing on the cake, the 2019 champion ensured in the "money-time", registering a 3-point assassin in the corner, 25 seconds from the end (113-109).
– Philadelphia impales itself on the Toronto zone. Firmly installed in the lead (68-59) in the middle of the third quarter, the Sixers then thwarted, when Nick Nurse released his zone defense. With elements like OG Anunoby, Scottie Barnes, Precious Achiuwa or Chris Boucher, the Raptors made their opponents live a real nightmare, who broke their teeth in this area 2-3, thus conceding a "run" of… 19 -5! Before resuming a small burst (9-2) at the start of the last quarter, then finally adapting a minimum to the Canadian defense, which clearly turned the tide of the game.
– The return of Tobias Harris. Absent for six matches, due to Covid-19, the Sixers winger has finally found his teammates, still deprived of Joel Embiid, Ben Simmons and Matisse Thybulle. With his 19 points, 7 rebounds and 7 assists, the 29-year-old signed an interesting performance, despite the rather logical waste after two weeks of absence (7/18 on shots, 1/6 at 3-points and 4 losses ball). In any case, he started strong in the first half, being particularly involved in the first 10 points of his team, before losing influence over the minutes.
?? Tyrese Maxey. Already brilliant against the Bucks (31 points, 5 rebounds, 4 assists), the "combo-guard" of the Sixers put it back against the Raptors (33 points, 4 rebounds, 5 assists). Always used as much by Doc Rivers (41 minutes on average for five games, another 40 last night), "T-Max" remains very effective and, today, he especially weighed in the last act. At the time when Philadelphia was dropped to the score and needed a spark in attack. Aggressive towards the circle and full of nerve, the 21-year-old therefore took his responsibilities and relaunched his team, before Toronto showered his attempt to "come back" at the end.
?? Chris Boucher. Little in sight since the start of the season (4.8 points and 3.5 rebounds on average), the interior of the Raptors is finally out of its box tonight. Compiling 17 points, 6 rebounds and 2 blocks, 7/12 on shots, and supporting during the big Canadian streak, straddling the third and fourth quarters. Associated with Scottie Barnes, OG Anunoby or even Precious Achiuwa, the original Saint-Lucian has thus made his mobility, his energy, his physique and his fighting spirit speak for the racket. And it could be that this performance definitely kicks off its 2021/22 fiscal year.
Considered by many to be the greatest player in Toronto history, Kyle Lowry was in something at the Wells Fargo Center that night, since his mother is in the audience, to support the Canadian franchise. Originally from Philadelphia, the new Miami menu must have appreciated from Los Angeles the strength of character displayed by his former teammates in Pennsylvania.
Philadelphia (8-5) : travel to Indiana, overnight from Saturday to Sunday (1:00 am).
Toronto (7-6) : Detroit reception, Saturday night to Sunday (1:30 am).


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Direction: On Loop 410 Exit Roosevelt , Go South About A Mile , Turn Left On Rada St. Subject Property On The Rigth.
Courtesy of Legendary Realty
This real estate information comes in part from the Internet Data Exchange/Broker Reciprocity Program .
Information is deemed reliable but is not guaranteed.
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Game Description
Category : Arcade
Description :
This is actually a fairly pleasurable activity that you can easily certainly not lose out on as a result of its own effortless gameplay, which is additionally actually addictive. You will tackle the role of a fat deposits pusher, eating as much meals as you can easily along the course to coming to be a lot more overweight than before. Furthermore, as you increase body fat, go out on the challenger along with a highly effective whack to squash all of them quickly and experience the advantages. You have to be mindful about the foods items you eat along the road given that they could deplete your electricity, permitting your enemy to promptly knock you out along with a ko if you consume too much. So if you desire to become the put or the master master, a practical diet regimen is actually an option for you.
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This City Run 3D have been played 179 time and receive 0 votes, this City Run 3D was released on category Arcade games and can play more related Arcade games such as Kill The Dragon - Bridge Block Puzzle, Car Stunt Racing, Steveminer Home, Prado Car Parking: Car Games, Kick The Dummy FN, Jelly Bros Red and Blue, Space War Plane, Hide and Seek Mouse, Kwak Kwak!, Grandpa And Granny House Escape, on newcrazygames. Release Date: 16 March 2022
Tags :
1player, 3dgames, run, running, unity3d, webgl


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We Now Know Who Wrote (And Maybe Plagiarized) Melania Trump's Speech
The person who helped write the speech offered to resign, but the campaign rejected the request.
After Melania Trump faced more than a day of plagiarism allegations for her RNC speech, Donald Trump's campaign has named the main person responsible.
It's Meredith McIver, a staff writer for The Trump Organization, according to a statement the Trump campaign released Wednesday.
SEE MORE: Did Melania Trump Plagiarize Michelle Obama's Speech? See For Yourself
The statement, written by McIver, said Melania Trump shared some of the people who inspired her. Trump apparently read McIver some quotes from Michelle Obama's 2008 speech.
According to the statement, McIver wrote those quotes down and included them in the final speech but said she never checked Michelle Obama's 2008 speech.
McIver wrote: "This was my mistake, and I feel terrible for the chaos I have caused Melania and the Trumps, as well as to Mrs. Obama. No harm was meant."
According to the statement, McIver offered her resignation to Donald Trump and his family on Tuesday. However, they rejected it, saying people "make innocent mistakes and that we learn and grow from these experiences."
SEE MORE: Trump Jr.'s Plagiarism Accusation Had A Cryptic, Then Clear, Answer
Shortly before the statement was released, Trump tweeted, "Good news is Melania's speech got more publicity than any in the history of politics especially if you believe that all press is good press!"
McIver says she urged the campaign to release the statement because she didn't want the issue to detract from the campaign.
She wrote, "Today, more than ever, I am honored to work for such a great family."
This video includes clips from C-SPAN and Donald J. Trump for President Inc. and images from Getty Images.
Mixed reviews at Trump's Texas campaign rally
Crowds gathered in Waco, Texas, to hear the former president speak as he hits the campaign trail, despite a looming indictment.
What we know and don't know about a possible Trump indictment
Uncertainty about a possible indictment runs parallel with the fluidity of the Manhattan DA's grand jury investigation of Trump. Here's what we know.
Kamala Harris embarks on weeklong Africa trip
After touching down in Ghana, Vice President Kamala Harris said she's excited about the future of Africa.
Top Stories
Just 6 March Madness brackets predicted the highly unlikely Final Four
The odds of correctly predicting this year's Final Four teams: 1 in 600,000.
Prince Harry, Elton John appear at UK court for privacy lawsuit
Prince Harry is among other big names suing the publisher of the Daily Mail accused of phone-tapping and privacy violations.
Why are single women outnumbering single men in homeownership?
One big factor is that women's earnings have steadily increased in the past decade.


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A state prosecution witness testified at the trial of five former Bosnian Serb fighters charged with participating in the Srebrenica genocide. The witness said he transported able-bodied men from Potocari to Bratunac by bus in July 1995. The witness said that he transported women, children and the elderly to Kladanj a day earlier.
The state prosecution has charged Miodrag Josipovic, Branimir Tesic, Dragomir Vasic, Danilo Zoljic and Radomir Pantic with genocide in Srebrenica, which included the forcible resettlement of the local population, as well as the capture and execution of men and boys.
According to the charges, Josipovic was the chief of the public safety station and Tesic was the deputy commander of the police station in Bratunac. Vasic was the commander of the police headquarters in Zvornik and also the chief of the public safety center in Zvornik. Zoljic was the commander of special police units at the Zvornik public safety center, while Pantic was the commander of the First Company of the special police units.
Testifying at today's hearing, state prosecution witness Miladin Mladjenovic said he worked at the "Vihor" company and was told to drive a bus to Potocari. He said he was ordered to transport Bosniaks from Potocari to Kladanj.
"They told me to drive those who were incapable of military service to Kladanj. Those people got on the bus. When the bus was full, I drove it to Kladanj. Most of them were the elderly, women, children. We drove those incapable of serving in the army," Mladjenovic said.
Mladjenovic said "somebody" decided who would get on the bus, but he didn't know who.
On the following day, Mladjenovic said he was told to drive able-bodied men from Potocari to Bratunac. He said the men were dressed in civilian clothing and belonged to all age groups.
Mladjenovic said a military policeman was in the bus acting as an escort. He said the military police told him to drive the men to a school building in Bratunac.
Mladjenovic said he made eight or nine round trips to Bratunac and that 45 men were on the bus each time. After exiting the bus, Mladjenovic said, the men entered the school building's gym.
Mladjenovic said policeman Radomir Eric received the men. He said when he asked what would happen to the men he was told they would be transported to Kladanj.
Responding to questions from the defense, Mladjenovic said Eric used to work with the state border police in Bratunac, which had an office in the vicinity of the school.
In a statement he gave to State Investigation and Protection Agency investigators in 2009, Mladjenovic said Eric worked at the police station in Bratunac. At today's hearing, Mladjenovic said Eric worked with the border police the entire time.
The trial will continue on March 8.
In this article


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The Federal Communications Commission has taken another step that puts General Electric closer to a dedicated radio frequency band for wireless medical devices — technology that the Fairfield, Conn.-based company has spent more than four years creating through its Global Research Center in Niskayuna and its health care division based in Waukesha, Wis.
The FCC published a notice of proposed rulemaking that is available for public comment until Oct. 5. A public notice regarding GE's proposal was published last April.
GE Global Research Radio Frequency and Photonics Laboratory scientist David Davenport explained that the proposal advocates the creation of a separate home for wireless medical devices so that they won't interfere with cellphones and other types of wireless communication devices.
"What we really focus on is how to make the wireless connection highly robust — to get a high rate of data from many patients in a hosptial without losing the data and really provding a higher level quality of service than you would have with a cellphone," Davenport said Tuesday.
According to Davenport, the networks would help reduce the time it takes to administer care.
For each active critical care bed in a standard hospital setting, 40 percent of patient care time is spent manually recording patient monitor information that the networks would provide in a much more efficient, automated approach to monitoring, Davenport said on a blog published by the Global Research Center.
GE Global Research has dedicated a lab to wireless technologies, according to spokesman Todd Alhart.
"At the research lab, we have a fundamental core expertise in key areas that we can leverage and spread across a number of different business applications," Alhart said. "There's a lot of experience that we've brought from our transportation business and our aviation business that we're translating into [our health care division]."
To comment on GE's proposal for creating a radio frequency band for wireless medical devices, go to www.fcc.gov/cgb/ecfs and click on the "Submit a Filing" link on the right. The docket number is 08-59.
To hear GE Global Research scientist David Davenport explain more about GE's wireless medical device technology, visit www.tinyurl.com/kn6jxn.
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Loan Repayment and Forgiveness
Student education loans are financial obligation you need to pay off, also in the event that you don't finish your level. But based on your position and what type of loans you have got, you could be qualified to receive a repayment that is different or even to get the loans forgiven. So when it comes down to qualifying for those programs, there's absolutely absolutely nothing a personal business may do for you which you can't do your self.
Federal Loans when you have federal loans ( federal government loans), the Department of Education has various programs that are free may help, including:
Loan Consolidation
Consolidating your student loans means combining numerous loans into one loan. Typically, people consolidate their loans to simplify monthly obligations or get repayment that is new.
Loan consolidation is not an idea that is good everybody else. Whether consolidating can help you varies according to facets like what kind of loans you've got, their interest prices, whenever you took them away, and whether or not they included advantages you'dn't wish to lose. Once you consolidate your loans, you can get a whole new loan with brand new terms.
Consolidating federal loans with all the authorities is free. Personal loans need to be consolidated with a personal lender. There might be an expense website here, but avoid organizations that tell you straight to spend fees that are up-front. Some debt settlement businesses and loan providers provide to combine federal and personal loans together into one loan that is new decrease your monthly obligations or rate of interest. Recognize that, when they do, you'll lose any advantages and defenses made available from your loans that are federal.
It could mean for your specific situation before you consolidate your loans, find out what. For those who have personal loans, speak to your loan servicer. For federal loans, call the Department of Education's Loan Consolidation Ideas Call Center at (800) 557-7392.
How many people operating into financial obligation through so-called payday advances has quadrupled in 2 years, based on one debt advisory service.
Pay day loans are little, short-term loans that are unsecured to tide individuals over until they manage to get thier salary.
The people guidance Bureau states its too simple to get such credit and it is calling for tighter legislation.
But Consumer Minister Ed Davey stated tougher measures could push individuals to the tactile fingers of unlawful loan sharks.
For most people a quick payday loan is an instant and efficient appropriate way to get your hands on short-term credit.
This type of lending can be cheaper than paying an unauthorised overdraft or a credit card charge if the money is paid back promptly on the next pay day.
But, in the event that loans – some charging you rates of interest in excess of 4,000per cent – are rolled over, debts can very quickly escalate.
'Not appropriate'
Peter Tutton, through the Citizens guidance Bureau, stated ministers have to take action.
"the kind of regulatory regime is not trying to protect people, generally there's work with the us government to accomplish.
"the federal government has to have a look at credit rating to get serious about rendering it far better.
"we truly need better types of communications to businesses that it is maybe perhaps perhaps not appropriate to deal with individuals badly," he stated.
It was said by the government is carrying out research it is uncertain the clear answer is a cap on cash advance rates of interest.
Mr Davey stated: " when you look at the final federal government – the Labour federal government viewed capping rate of interest expenses of credit 3 x.
"together with final Labour government rejected it 3 x that they might push susceptible customers in to the fingers of those unlawful cash loan providers who're truly the nasty associated with nasty. simply because they had been worried"
The top for the Finance and Leasing Association, Stephen Sklaroff, said it really is as much as customers to simply borrow whatever they are able to afford to settle.
"there is a duty regarding the debtor, and on the lending company, to ensure that all the details is up for grabs," he said.
But Marcus Painton, from Aylesbury, whom found myself in debt through pay day loans stated the "temptation" associated with the loans can be "too much".
"It really is the simpleness of it all, actually. You will get in deep actually quickly, that is that which we did.
" They simply simply just take you for every thing they are able to get. They may be perhaps not worried about your basic circumstances. They simply want their funds, which can be reasonable enough, but does not allow you to physically," he said.
Steve Perry, whom took away 64 loans from 12 various businesses over 18 months, leaving him with a financial obligation of Р'Р€22,000, told the BBC it had been the "roll over loan procedure" that must be tackled.
"The perform loans where individuals are returning thirty days after thirty days, either paying down in full and borrowing once again or having to pay the attention just on that loan.
"People may do this for as much as a year, every month that is single this is exactly what's actually trapping individuals," he stated.
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831 N 1st Street, Jacksonville Beach, FL 32250
sale $7,000,000 | Investment
Courtesy Of Berkshire Hathaway HomeServices Florida Network Realty
Unbeatable Waterfront Opportunity Right On The Jacksonville Beach,This Is A Unique Commercial Building,Zoned Commercial Waterfront And Currently Operating As A Rustic But Elegant Multi Concept Restaurant. Total Lot Is 27,878 Square Feet And Other Potential Investment Opportunities Are Possible For This 131 Sqft Of Stunning Waterfront Property!! 831 N 1st Street Is A Few Blocks From The New Margaritaville Hotel,Dolphin Depot And Located In The Ever Growing Jacksonville Market. All Other Feasibility Information About The Property Upon Request.
Read More About This Property Read Less About This Property
Copyright © 2023 St. Augustine & St. John's County Board of Realtors. All rights reserved. All information provided by the listing agent/broker is deemed reliable but is not guaranteed and should be independently verified. Information being provided is for consumers' personal, non-commercial use and may not be used for any purpose other than to identify prospective properties consumers may be interested in purchasing.
Virtual Tour
Property Features
- Amenities: Elevaor
- Amenities: Disabled Access
- Amenities: Security System
- Architecture Style: Other
- Cooling System: Central A/C
- Exterior Type: Stucco
- Floors: 2.0
- Heating Fuel: Electric
- Heating System: Forced Air
- Legal Description: 3-28 33-2S-29E .64 PABLO BEACH NORTH LOTS 1,2, LANDS LYING E THEREOF TO THE BULKHEAD RECD O/R 20286-94 BLK 81
- Roof Type: Unknown
- Rooms: Full Bath
Data powered by Attom Data Solutions. Copyright© 2022. Information deemed reliable but not guaranteed.


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Francesca (Franki) Zani is the assistant food editor for Delish, where she develops recipes, writes, styles recipes & TikToks, and hosts on-camera recipe videos. Her education at the CIA and experience as a producer/culinary producer for PowerHouse Productions have allowed her to travel the globe. She loves collecting ceramic props, sipping tea lattes, and adventuring to highly rated food spots when she's not collaborating with the New York Women's Culinary Alliance. She's often referred to as "Pasta Zani" due to her undying obsession for all things pasta (you need to try testaroli)!
During the annual lenten period, many Christians abstain from meat on Fridays. There are so many dishes you can make when meat is off the table, like sheet pan spanikopita quesadillas (heck yes!), creamy kale gnocchi bake, and paella verdura. In Mexican culture, capirotada, or Mexican bread pudding, is a popular casserole-style dish that evokes religious significance during the Easter holiday. The ingredients are symbolic of Christ, his body, his blood, the cross, the nails on the cross, and the holy shroud.
What are the ingredients in capirotada?
For this recipe, there's no need to break out the eggs and cream. This type of bread pudding is all about the sweet spiced syrup. Combine water, piloncillo or brown sugar, and whole spices in a pot of water and steep. The liquid shouldn't be too thick so the bread can easily absorb it.
This bread pudding is commonly made with bolillo bread, which look like small rolls. French bread can also be used in place of these. You can dry out the bread or use day-old bread. Piloncillo is a form of sugarcane that can be replaced by brown sugar. Some may refer to it as Mexican brown sugar. For the cheese, queso añejo is the popular choice here, but Monterey Jack could be a substitute. If you'd like, raisins, onions, bananas, walnuts, and almonds can be incorporated too.
Can you make this ahead?
You can assemble the casserole and store in the refrigerator overnight. The bread pudding can also be baked ahead of time and stored in the refrigerator for up to 5 days.
Did you make this recipe? Let us know in the comments below!
- Yields:
- 4 - 6 serving(s)
- Prep Time:
- 5 mins
- Total Time:
- 1 hr 10 mins
- Cal/Serv:
- 764
- 2 tbsp.
unsalted butter, plus more for pan
- 4
bolillo rolls or 1 large loaf plain French bread, cut into 1/2"- to 3/4"-thick slices
- 4 c.
- 2 c.
(430 g.) packed piloncilo or dark brown sugar
- 1 1/2 tsp.
ground cinnamon
- 1/4 tsp.
ground cloves
- 1/4 tsp.
ground nutmeg
Pinch of kosher salt
- 1/2 c.
(45 g.) sliced almonds, divided
- 1/4 c.
(45 g.) raisins, divided
- 1/4 c.
(30 g.) dried apricots, thinly sliced, divided
- 3 oz.
crumbled queso añejo or cotija cheese, divided
- Nutrition Information
- Per Serving (Serves 6)
- Calories510 Fat13 gSaturated fat6 gTrans fat0 gCholesterol25 mgSodium354 mgCarbohydrates92 gFiber3 gSugar78 gProtein7 gVitamin D0 mcgCalcium219 mgIron2 mgPotassium311 mg
- Note: The information shown is Edamam's estimate based on available ingredients and preparation. It should not be considered a substitute for a professional nutritionist's advice.Powered by
- Step 1Preheat oven to 400° and grease an 8"-by-8" baking dish with butter. Set a wire rack inside a baking sheet.
- Step 2Arrange bread on rack and bake until crispy and lightly golden brown around the edges, 15 to 17 minutes. Reduce oven temperature to 350°.
- Step 3Meanwhile, in a medium pot over high heat, bring water, piloncilo, cinnamon, cloves, nutmeg, salt, and remaining 2 tablespoons butter to a simmer. Cook, stirring occasionally, until slightly reduced, about 10 minutes. Remove from heat.
- Step 4Layer half of bread in bottom of prepared dish. Ladle syrup over and sprinkle with half of almonds, raisins, apricots, and cheese. Layer remaining bread on top. Ladle syrup over until bread is soaked and no dry spots remain (there will be syrup left over). Sprinkle with remaining almonds, raisins, apricots, and cheese.
- Step 5Bake bread pudding, adding more syrup if pudding looks dry, until set but soft to the touch, about 30 minutes. Let cool 10 minutes before serving.
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