Buyers pay for and sellers invest in a good reputation: More evidence from eBay

The Journal of Socio-Economics 42 (2013) 31–42

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Buyers pay for and sellers invest in a good reputation: More evidence from eBay夽 Wojtek Przepiorka a,b,∗ a b

ETH Zurich, Sociology, Clausiusstrasse 50, CLU D 4, CH-8092 Zurich, Switzerland University of Oxford, Department of Sociology, Manor Road, Oxford OX1 3UQ, UK

a r t i c l e

i n f o

Article history: Received 20 October 2011 Received in revised form 22 August 2012 Accepted 5 November 2012 JEL classification: C73 D82 L81 C51 Keywords: Trust Trustworthiness Uncertainty Reputation Online markets

a b s t r a c t This article contributes to the research on trust and reputation formation in anonymous online markets. I first give a formal account of the reputation mechanism in anonymous online markets and derive testable hypotheses. Based on the analysis of a large set of process data (N ≈ 176,000), I corroborate a statistically and economically significant seller reputation effect on the probability of sale and the selling price both in auctions and fixed price offers. Moreover, my analysis shows that sellers making fixed price offers invest in a good reputation to a similar extent as buyers pay for it in auctions. Finally, I obtain repeated observations on a considerable subset of the buyer population by including highest non-winning bids in the analysis and show that buyers trade off sellers’ reputations and prices within the set of offered items they choose to bid on. My findings provide further evidence that reputation systems solve trust problems and reduce transaction costs in anonymous online markets by providing incentives for traders’ cooperative behavior. © 2012 Elsevier Inc. All rights reserved.

1. Introduction Over time, more and more social interactions are taking place online. The internet opens up virtually endless opportunities to communicate with people all over the world and thus to buy and sell goods. Online markets, with thousands of anonymous buyers and sellers, are very popular. Online trade has the advantage of efficiently coordinating supply and demand, but lacks the geographical and temporal embeddedness of traditional traders, making the legal enforceability of contracts more difficult. While online transactions are more efficient and often cheaper, sellers can choose not to ship goods at all or to ship inferior quality goods. In contrast, traditional retail allows buyers to inspect goods and to personally interact with sellers. Hence, buying online involves greater uncertainty as to whether sellers will meet buyers’ expectations. Before the internet, the problem of uncertainty and asymmetrically informed traders in markets was addressed in economics.

夽 I would like to thank Stefan Wehrli for his support in the collection of the data, Ben Jann for his valuable advice, and Heiko Rauhut, Debra Hevenstone and an anonymous reviewer for helpful comments and suggestions. This work was partly supported by the Swiss National Science Foundation [grant number 100017 124877]. ∗ Correspondence address: University of Oxford, Department of Sociology, Manor Road, Oxford OX1 3UQ, UK. Tel.: +44 1865 278 656; fax: +44 1865 286 171. E-mail address: [email protected] 1053-5357/$ – see front matter © 2012 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.socec.2012.11.004

Akerlof (1970) was among the first to point out that markets run the risk of failure if buyers are unable to inspect products pre-purchase and remain uncertain as to the marketed products’ quality. Buyers having bad experiences with low-quality sellers decrease their quality expectations and thus their willingness to pay what highquality products cost. Consequently, sellers’ incentives to enter the market with high-quality products decrease, further reducing buyers’ quality expectations and mutually beneficial trade. Most online traders, however, want to win customers, just as traditional store owners do. Given that online traders do not have a physical site, they must find other ways to signal their trustworthiness and reliability to potential customers. Shapiro (1983) suggested that in order to overcome the tradeimpeding information gap between buyers and sellers, high-quality sellers must invest in reputation when entering the market. That is, they must offer their products for a price that makes buyers indifferent between their product and a minimum quality substitute. As soon as they have a reputation for high quality, they can adjust prices such that they are compensated for their initial investment in reputation by buyers’ repeated purchases. However, in anonymous online markets, where mostly durable goods are traded, repeated interactions between the same two traders are rare (Diekmann et al., 2012) and an investment in reputation may therefore not be worthwhile. Most anonymous online markets solve this problem by implementing an electronic reputation system, where buyers and sellers

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W. Przepiorka / The Journal of Socio-Economics 42 (2013) 31–42

ship buy

2 not ship

1

R R S T

not buy P P Fig. 1. The binary one-shot trust game.

can rate one another after a transaction (Kollock, 1999; Resnick and Zeckhauser, 2002; Bajari and Hortacsu, 2004). Ratings can be quantitative (positive and negative scores) and qualitative (written comments) and are made available to all potential customers immediately and free of charge. Clearly, when confronted with two sellers – one with a positive record and the other with no record or a poor record – a rational buyer will choose the seller with the positive record. Sellers, on the other hand, protect themselves from opportunistic buyers by demanding advance payment for the traded item (Diekmann et al., 2009). Thus, according to Shapiro’s argument, sellers entering the market have to give a discount on their products in order to compensate buyers for their uncertainty and establish a reputation for being trustworthy and reliable. The better their reputation becomes, the lower the discount they will have to give. Correspondingly, the prices buyers are willing to pay for an item will increase with sellers’ reputations. Based on a stylized game theoretic model, the next section gives a formal account of the reputation mechanism in anonymous online markets and derives testable hypotheses. Section 3 introduces the data, Section 4 presents the results and Section 5 concludes. The appendix contains additional material referred to throughout the article. 2. Model and hypotheses Güth and Ockenfels (2003) were the first to suggest that the interaction between a buyer and a seller in anonymous online markets can be conceptualized as a binary one-shot trust game (Dasgupta, 1988; Kreps, 1990). In this game (Fig. 1), the buyer (player 1) first decides whether or not to buy a product from the seller. If the buyer decides not to buy, the interaction is finished and both buyer and seller receive payoff P. If, however, the buyer decides to buy, the seller (player 2) can decide whether or not to ship the product the buyer paid for. If the seller ships, both receive payoff R. If the seller does not ship, he or she receives payoff T while the buyer receives S. The payoffs are ordered such that the seller does not ship after having received the buyer’s money (i.e. T > R) and the buyer prefers not to buy rather than losing his or her money (i.e. P > S). However, both the buyer and the seller prefer earning the gains from trade over not trading at all (i.e. R > P). I use the trust game as the stage game in my model. Moreover, my model extends the reputation models suggested by Shapiro (1983), Friedman and Resnick (2001), and Ockenfels (2003) in several respects. First, a seller is active in the market for some time while a buyer visits the market only once. A seller’s probability of being in the market in the next period is ı and this is the seller’s private information. Second, sellers are heterogeneous with respect to ı. Third, after each interaction in which a buyer buys, the buyer gives a positive rating if the seller shipped and a negative rating otherwise. However, the buyer rates the seller only with a certain probability ϕ, which is introduced as an exogenous parameter and

establishes an additional degree of freedom in the model. If the buyer does not buy, he or she cannot rate the seller. Finally, buyers can choose sellers according to their reputation. The model also implies that sellers can re-enter the market under a new identity at no cost but lose their reputation if they do.1 For a buyer, it would be most beneficial to only buy from established sellers, who have received a positive rating. This is because, as I will show below, a positive rating perfectly identifies a seller who ships with certainty if the buyer buys. However, in order for a seller to receive a positive rating, he or she must be able to enter the market in the first place. Therefore, a buyer must be indifferent between a newcomer and an established seller. This is the case if a buyer’s expected payoff from buying from a newcomer, who gives a price discount c, is equal to their expected payoff from buying from an established seller. That is, if ˛R + (1 − ˛)S + c = R, where ˛ denotes the probability that the newcomer ships if the buyer buys. Thus, the discount a newcomer gives is: c = (1 − ˛)(R − S)

(1)

In other words, a newcomer compensates the buyer’s expected loss from an interaction with a newcomer who, with probability 1 − ˛, could turn out to be a cheat. But where does a buyer know ˛ from, and how is it that a rational and self-interested seller has an incentive to ship if a buyer buys? For a seller with time restriction ı, the expected payoff from always shipping if a buyer buys is: 2

=

2

R − c + ı[ϕR + (1 − ϕ)(R − c)] + ı2 [ϕR + (1 − ϕ)ϕR + (1 − ϕ) (R − c)] 2

3

+ı3 [ϕR + (1 − ϕ)ϕR + (1 − ϕ) ϕR + (1 − ϕ) (R − c)] + . . . 2

t−1

+ıt {ϕR[1 + (1 − ϕ) + (1 − ϕ) + . . . + (1 − ϕ)

t

] + (1 − ϕ) (R − c)}

(2)

+... =

∞ 

t

ıt [R − (1 − ϕ) c]

t=0

=

R c − 1−ı 1 − (1 − ϕ)ı

For the same seller, the expected payoff from never shipping if a buyer buys is: 2 = T − c + ı(T − c) + ı2 (T − c) + ı3 (T − c) + · · · =

T −c 1−ı

(3)

Note that in the latter case (Eq. (3)), the seller gives a discount in every interaction because he or she has either not been rated by the buyer or has received a negative rating and re-enters the market anew under a new identity. In the former case (Eq. (2)), a seller gives a discount only until he or she receives a positive rating from a buyer. Thus, a self-interested seller with time restriction ı has a rational incentive to ship if a buyer buys, if 2 > 2 , that is, if: ı > ı∗ =

T −R ϕc + (1 − ϕ)(T − R)

(4)

Eq. (4) implies that there are two types of newcomers: long-term types with ı > ı* , who always ship if the buyer buys, and short-term types with ı < ı* , who never ship.2 While sellers who have received

1 For other approaches to modeling reputation effects in social interaction see, for instance, Raub and Weesie (1990) and Lahno (1995). 2 Alternatively, a newcomer could give a discount and ship (earning R − c) until he or she receives a positive rating, only to take advantage of a buyer that trusts him or her and thus pays the full price (earning T), then re-entering the market under a new identity as soon as he or she receives a negative rating. Assuming ϕ = 1, this   seller’s expected payoff would be 2 = (R − c + ıT )/(1 − ı2 ). However, 2 > 2 or 

2 > 2 only if ı < ı* or ı > ı* , respectively. In other words, neither a long-term nor a short-term newcomer has an incentive to employ this strategy.

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a positive rating are known to be long-term types, buyers are uncertain about the type of newcomers. A buyer’s best guess is to assume that types are uniformly distributed in the population of newcomers, i.e. ı ∼ U[0, 1]. Now, the buyer can derive the probability of interacting with a long-term newcomer, which is 1 − ı* . Plugging 1 − ı* for ˛ into Eq. (1) and re-arranging gives the price discount c* a newcomer gives in equilibrium.3 c∗ =

1 {[(1 − ϕ)2 (T − R)2 + 4ϕ(T − R)R]1/2 − (1 − ϕ)(T − R)} 2ϕ

(5)

Taking the first derivative of Eq. (5) shows that c* is a mono* tonically  decreasing function of ϕ (i.e. ∂c /∂ϕ < 0) if 2R > T, and

c∗

= (T − R)R if ϕ = 1. In other words, as long as a seller’s temptation to abuse a buyer’s trust is not too large relative to the gains from trade, an increase in buyers’ rating propensity decreases the price discount newcomers have to give. Thus, truthful ratings decrease transaction costs in anonymous online markets because they make the reputation system more effective in detecting cheaters. Although the model devised above does not perfectly capture a reputation mechanism implemented in anonymous online markets, it integrates its most important elements and shows that newcomers have to give price discounts in order to establish their reputation.4 The following hypotheses can be derived from it:

H1:

H2:

The better a seller’s reputation in terms of positive and negative ratings, the higher will be the prices the seller sets in a fixed price offer. The better a seller’s reputation in terms of positive and negative ratings, the higher will be the prices buyers will pay for an item both in fixed price offers (H2a) and in auctions (H2b).

If supply exceeds demand and thus the market does not clear, an analogous hypothesis regarding the probability of sale can be derived: H3:

The better a seller’s reputation in terms of positive and negative ratings, the higher will be the probability that an item will be sold both in fixed price offers (H3a) and in auctions (H3b).

3. Material and methods The data were collected by means of a spider program between October 30 and December 31 2006 on the market platform eBay.de.5 The net sample contains 176,391 offers posted during the two months in the category “Foto & Camcorder > Speicherkarten > SD” (see the appendix for details). This category contained offers of new and used SD (Secure Digital) memory cards of different formats, with different memory capacities, and from different producers. Table A.1 in the appendix lists the frequencies of item characteristics in the sample. The items were offered for sale by sellers from all over the world that would ship their items to Germany, Europe, or worldwide. Table A.2 in the appendix lists the 20 most frequent seller and buyer countries of origin. Items were listed by

3 For the market to have a positive proportion of long-term newcomers, it must hold that ı* < 1. Hence, by Eq. (4), it must hold that c > T − R. In other words, the price discount must more than outweigh the difference between a seller’s payoff from not shipping and shipping. Then, by Eq. (5), it must hold that 2R > T. 4 Note that the model is not restricted to cases in which sellers differ in terms of ı; it can be equally applied to cases in which one type of seller prefers not to ship if a buyer buys and another type prefers to ship because of social preferences or internalized social norms, for instance (e.g. Gintis et al., 2005). 5 It should be noted that eBay’s reputation system changed significantly in May 2007 (see Bolton et al., forthcoming).

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Table 1 Buying formats and sales. Buying format

Not sold

Auctioned

Bought

Total

Auction only

26,485 (28.99) (31.05) 29,172 (64.01) (34.20) 29,642 (75.09) (34.75) 85,299 (48.36) (100.00)

64,861 (71.01) (89.63) 7502 (16.46) (10.37) –

–

91,346 (100.00) (51.79) 45,572 (100.00) (25.84) 39,473 (100.00) (22.38) 176,391 (100.00) (100.00)

Auction &Buy-It-Now

Buy-It-Now only

Total

72,363 (41.02) (100.00)

8898 (19.53) (47.51) 9831 (24.91) (52.49) 18,729 (10.62) (100.00)

Notes: Row and column percentages in parentheses.

4875 unique sellers with an average of 36.18 offers per seller, and 78,589 unique buyers participated in the market with an average of 1.14 purchases per buyer. In 49% of the cases transactions were between a buyer and a seller from two different countries. Finally, 7501 (8.23%) transactions took place between a buyer and seller who had traded before, within the time of the data collection (see also Table 2). 3.1. Buying and selling on eBay There are three basic buying formats on eBay.de. A seller can post his or her items in auctions, in auctions with a so-called BuyIt-Now option, or as fixed price offers in the Buy-It-Now format (see Table 1). The majority of offers were posted in auction format (52%). In auctions, the seller must specify an initial price (iprice), i.e. the price below which he or she will not sell the item. Furthermore, the seller sets the duration of the auction to either one, three, five, seven, 10, or 14 days (durset). During this time, potential buyers submit bids. The first bid must be at least as high as the initial price, and every subsequent bid increases the price of the item by a small increment.6 When an auction ends and at least one person has placed a bid on the item, the item is sold to the last (i.e. highest) bidder. The buyer pays the highest bid (eprice) and the shipping costs (scavg). In 29% of the auctions no bids were submitted. There are two main differences between conventional auctions and auctions with a Buy-It-Now option. First, apart from the initial price, the seller must also specify a Buy-It-Now price (bprice). The BuyIt-Now price is always at least as high as the initial price. Second, the offer ends early if either a buyer chooses the Buy-It-Now option or the highest bid reaches the Buy-It-Now price. At this point, the item is sold for the Buy-It-Now price. Otherwise, the offer ends after the specified duration and the item is sold for the highest bid or is not sold. In auctions with a Buy-It-Now option, 64% of the offers were unsuccessful, 16.5% were sold for the highest bid and 19.5% for the Buy-It-Now price. Note that if the Buy-It-Now price was specified to be equal to the initial price, a buyer could acquire the item either by posting a bid or pressing the Buy-It-Now button. Finally, a BuyIt-Now offer does not allow for bidding. A buyer can either purchase the item at the specified Buy-It-Now price or not. The offer ends if the item is purchased or the specified duration elapses. This buying format has the least amount of successful sales (25%). After an item is sold, the actual transaction between the buyer and the seller takes place. First, the buyer pays the highest bid or the Buy-It-Now price and the shipping costs. There are several

6 The bid increment depends on the current price and is relatively small (e.g. EUR 0.5 if the current price is between EUR 1 and EUR 50). Note, moreover, that the German market platform does not implement auctions with secret reserve prices in this product category.

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payment methods which differ with respect to the degree to which they protect the buyer from fraud. Payment by PayPal (paypal), an online payment service, is convenient, traceable, and protects the buyer up to the amount of the purchase, including the standard shipping costs. Hence, in the event of a fraud, the buyer is reimbursed for his or her total expenses. Some sellers accept payment by credit card (pcard), which is equivalent to payment by PayPal. However, where there is fraud, the buyer has to deal with the credit card company, which has its own reimbursement policy. A bank transfer (ptrans) is as convenient as PayPal or a credit card payment, but does not protect the buyer. After receiving payment, the seller ships the item. There is also cash on delivery (pcod), cash on pickup (pcash), and payment by collection-only checks (pcheck). These payment methods are suited for spot transactions, where the buyer receives the item at the moment of payment either from the seller or from the delivery service. Besides payment methods that provide the buyer with some protection against fraud, some sellers require shipping insurance (scinsur) and/or offer a return guarantee (iret). Finally, at the end of a transaction the buyer can rate the seller and the seller can rate the buyer. It is common practice for the buyer to start the transaction with the payment. However, in the first-mover position the buyer is exposed to the moral hazard of the seller. Despite all precautions the buyer can take by choosing the right payment method and mode of shipment, he or she must rely on a third party to insure against fraud. Moreover, even if the buyer is confident of being refunded for his or her losses, there are other costs of a failed transaction. The buyer loses time, possibly money, and is inconvenienced. Therefore, even with the most secure payment methods, a buyer wants to choose an honest and reliable seller. The market platform provides information about sellers that a buyer can consider pre-purchase. First, a buyer can consider a seller’s reputation, i.e. the positive (sepos) and negative (seneg) ratings the seller received from previous customers. Second, the buyer can observe whether the seller invests in self-promotion. The market platform provides several options for self-promotion. A seller can choose whether his or her offer appears at the top of the listing resulting from a search query (ltop); can specify whether the item will be listed with a thumbnail (lpic), a bold title (ltbold), in a bold frame, and/or with a background color (lbg); and can choose whether the offer title has a subtitle (ltst) and/or appears with a picture on the item page (ipic). eBay charges a seller for all these options. A seller can also promote him- or herself by establishing a Me-page, the seller’s homepage on eBay (sehasme), by acquiring a verified identity (seisid),7 or investing time and effort in the design of the offer page (ides). The latter is measured by the number of characters used in the product description. The items traded during this period sold for EUR 14.9 on average and shipping costs were on average EUR 9.6.8 Prices varied by memory capacity and format. For example, a standard memory card with 512 MB sold for about EUR 6.3, a 1 GB card cost EUR 11.1 and a 2 GB card sold for EUR 21 on average. 3.2. Variables and model estimation A seller’s reputation is operationalized by two variables: the number of positive ratings (sepos) and the number of negative ratings (seneg). I assume a decreasing marginal utility (disutility) of

7 The municipal administration or post office at a seller’s residence verifies the seller’s identity. A form containing the seller’s address data is accredited at a charge of about EUR 15. The form is sent to the platform provider whereupon the verified identity icon appears on the seller’s profile page and on every offer page (see Przepiorka, 2011). 8 At the time of the data collection this corresponded to USD 19.4 and USD 12.5, respectively.

positive (negative) ratings and therefore use the natural logarithm of these numbers. Whether or not an item was sold (sold) and the selling price (eprice, i.e. the end price or highest bid in Euros) are the two outcome variables of interest. The binary outcome variable is 1 if the item received at least one bid or was sold in a fixed price offer. The aim of the statistical analysis is to test the hypotheses derived in Section 2. Since my analysis is based on observational data, I need to control for confounding factors in multiple regression models. These variables can be divided into two groups. The first group measures the following product differences: memory capacity (cap), card format (form), brand (brnd), condition (cond), seller country of origin (seorigin), and accessories (acs) like USB card readers offered together with the product (see Tables A.1 and A.2 in the appendix). While it is important to control for these factors in the analysis, they are of secondary substantial interest, and thus I will only reported their joined significance in the estimation tables. The other group of variables (Table 2) are more interesting and I state in what follows my expectations as to the effect they may have on the outcome variables. • The initial price (iprice) will have a negative effect on sales. However, once an item is sold, offers with higher initial prices should, ceteris paribus, reach higher selling prices. • Shipping costs (scavg) will have a negative effect on sales as well as on selling prices. Note, however, that actual shipping costs can only be observed imprecisely and only if an item was sold. The variable scavg is the average price of all shipping and handling options offered by the seller for a particular item. • In auctions with a Buy-It-Now option, the seller is required to specify an initial price and a Buy-It-Now price. In many such offers the initial price equals the Buy-It-Now price and, if the item is sold, would be equal to the selling price. In order to avoid collinearity and endogeneity problems, the variable ipbpdif is the difference between the initial price and the Buy-It-Now price. Since this difference can be either due to a high Buy-It-Now price, a low initial price, or both, I have no expectation as to its effect. • Payment options offered by the seller provide different degrees of buyer protection. Payment by PayPal and credit card (paypal) provide the highest level of buyer protection followed by a bank transfer (ptrans). The two dummy variables stand for the most secure payment methods. Both will thus have a positive effect on sales and selling prices as compared to the reference category of offers lacking both options. • Return guarantees (iret) and shipping insurance (scinsur) also protect buyers from losses. Shipping insurance, on the other hand, is often required by the seller and means the buyer entails an additional cost. Thus, unlike the return guarantee, compulsory shipping insurance will have a negative effect on sales and selling price. • The variable list sums all self-promotion options adopted by the seller (ltop, lpic, ltbold, lbg, ltst, and ipic). Apart from the picture on the item page (ipic), these options increase an offer’s visibility. Therefore, the more options a seller uses, the higher will be the probability of sale. Also, self-promotion will attract more bidders and increase the selling price. • The other options for seller self-promotion (seisid, sehasme, seindus, and ides) will also enhance a buyer’s propensity to buy and his or her willingness to pay for an offered item. • A buyer interacting repeatedly with the same seller (seburep) has likely had a positive past experience. Consequently, secondtime buyers will be willing to pay at least as much as in the previous transaction. • A seller’s market activity (seactiv) is a proxy for size and I expect a larger seller to attain lower prices. Furthermore, the degree of competition (secomp) will reduce the probability of sale and

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Table 2 Descriptive statistics of main variables. Variable

Description

eprice sepos seneg iprice scavg paypal pcard ptrans pcheck pcod pcash scinsur iret list seisid sehasme seindus ides seburep seactiv secomp semsm timeec weekend durset tusbr tcase tadap

Selling price (in EUR) Seller positive feedback (unique) Seller negative feedback (unique) Initial price (set by seller) Avg. price of shipping options Payment method: PayPal Payment method: credit card Payment method: bank transfer Payment method: collection-only check Payment method: cash on delivery Payment method: cash on pickup Seller requires shipping insurance Seller offers return guarantee Proxy for seller’s self-promotion Seller has verified id Seller has me-page Seller is industrial Length of product desc. (in char.) Repeated interaction Seller’s market activity Seller competition at end of offer Seller member since (in months) Offer end time in days (centered) Offer ends on weekend (Fr/Sa/Su) Offer duration (in days, set by seller) Supplement: USB reader Supplement: case Supplement: adapter

N 91,092 176,343 176,343 176,391 174,563 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,391 176,343 176,391 176,391 176,390 176,391 176,391 176,391

N/A 48 48 48 0 1828 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 48 0 0 1 0 0 0

Mean 14.90 30,390 272 10.64 9.61 0.84 0.05 0.41 0.15 0.004 0.02 0.84 0.37 0.90 0.11 0.31 0.75 19,968 0.04 3680 10,223 33.57 −0.06 0.43 2.58 0.02 0.01 0.02

Median 13.29 7251 43.00 7.43 8.99 1.00 0.00 0.00 0.00 0.00 0.00 1.00 0.00 1.00 0.00 0.00 1.00 14,366 0.00 2110 10,657 30.00 1.00 0.00 1.00 0.00 0.00 0.00

S.D. 11.16 58,636 574 11.45 3.98 0.37 0.22 0.49 0.36 0.07 0.13 0.36 0.48 0.40 0.32 0.46 0.43 19,083 0.20 3845 2134 22.56 17.03 0.50 2.25 0.13 0.10 0.15

P5

P95

1.99 67.00 0.00 0.01 3.90 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 3532 0.00 16.00 5850 3.00 −28.00 0.00 1.00 0.00 0.00 0.00

32.68 188,777 1855 30.77 16.96 1.00 1.00 1.00 1.00 0.00 0.00 1.00 1.00 1.00 1.00 1.00 1.00 45,919 0.00 13,432 12,588 79.00 26.00 1.00 7.00 0.00 0.00 0.00

Notes: Variables are labeled in the text by their acronyms. Further variables are described in Tables A.1 and A.2 in the appendix. Descriptive statistics are the number of observations, the number of missing observations, the arithmetic mean, the median, the standard deviation and the 5%- as well as the 95%-quantiles.

selling price. A seller’s membership duration (in months, semsm) captures a seller’s experience in the market, which is independent of the trade volume. The more experience a seller has, the higher will be both the number of sales and selling prices. • The linear term timeec accounts for the fact that commodity prices usually decrease over time. Finally, prior studies have found that sales and prices are lower on weekends (Friday to Sunday, weekend) and also identified a positive effect from the offer duration (in days, durset). To determine buyers’ propensity to bid for or buy an item conditional on item, offer, and seller characteristics, I estimate logistic regression models with sales as the binary outcome variable; I use ordinary least squares (OLS) regression models with selling price as the outcome variable. Statistical significance is set at the 5% level for two-sided tests. I account for the repeated measures obtained on the same traders by calculating cluster-robust standard errors. Additional model estimations accounting for the potential censoring problem in the OLS regressions and model estimations based on data from auctions with a Buy-It-Now option are presented in the appendix. All estimation tables are directly produced from stored estimates by Stata’s esttab command (Jann, 2007). 4. Results 4.1. Auctions The estimation of the first two models in Table 3 is based on auction data only. In the first model (Logit 1, testing H3b), other things being equal, the odds of an offered item being sold increase by 100 × [exp(0.387) − 1] = 47 % if a seller’s number of positive ratings (sepos) increases by a factor of 2.7.9 The odds of an offer

9 The change in the outcome variable is calculated for a unit change in the explanatory variable ln(x). In terms of x, the unit change in ln(x) corresponds to the factor e ≈ 2.7.

being sold change by 100 × [exp(−0.285) − 1] = −25 % if a seller’s number of negative ratings (seneg) increases by a factor of 2.7. Similar results hold with selling price as the outcome variable. In model OLS 1 (testing H2b), a tenfold increase in the number of positive ratings (e.g., from 50 to 500) increases the expected highest bid by 100 × [exp(0.078 × log 10) − 1] = 20 %. Based on the average selling price of about EUR 15, the change due to the increase in a seller’s positive reputation amounts to EUR 2.95. Accordingly, an increase in the number of negative ratings by 100% (e.g., from 10 to 20) changes the expected highest bid by 100 × [exp(−0.065 × log 2) − 1] = −4.4 % or by EUR 0.66, if the average selling price is considered.10 In line with expectations, the odds of an item being sold decrease by 31% or 28% if the initial price (iprice) or the shipping costs (scavg) are increased by EUR 1, respectively. In addition, a EUR 1 increase in shipping costs changes expected selling prices by −8%. This corresponds to EUR 1.2 in relation to the average selling price. Sellers offering payment by PayPal and/or credit card (paypal) or by a bank transfer (paytrans) are more likely to sell their items and attain higher prices than sellers not offering either of these payment methods. The effect of paypal, however, is not larger than the effect of paytrans. Nor does the return guarantee (iret) exhibit a significantly positive effect. On the contrary, if the seller requires shipping insurance (scinsur), the odds of an item being sold decrease by 48%. Seller self-promotion affects sales only if it enhances an offer’s visibility in the listing (list). For an additional measure of self-promotion, the odds of sale increase by 117% and selling prices increase by 15%. The other factors accounting for a seller’s

10 Both estimates for positive and negative ratings in model OLS 1 lie in between the estimates from censored normal (CNR 1: 0 . 104*** , −0 .075** ) and heteroscedastic censored normal regressions (HCNR 1: 0 . 077*** , −0 .056*** ) presented in Table B.1 in the appendix. This indicates that the estimates from the OLS regression may not be much distorted by the selective sample of sold items only.

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W. Przepiorka / The Journal of Socio-Economics 42 (2013) 31–42

Table 3 Model estimations based on auctions or fixed price offers. Logit 1 Coef.

OLS 1 SE

Coef.

(Intercept) 17.073*** 1.506 1.519* Reputation and costs 0.067 0.078*** ln(sepos+1) 0.387*** 0.070 −0.065*** ln(seneg+1) −0.285*** iprice −0.305*** 0.017 0.014*** scavg −0.284*** 0.026 −0.081*** Buyer protection 0.336 0.195* paypal 0.854* 0.387 0.240** ptrans 0.894* 0.160 −0.227 scinsur −0.655*** iret −0.100 0.170 −0.076 Seller self-promotion *** list 0.774 0.131 0.152** seisid −0.077 0.172 −0.062 sehasme 0.186 0.176 0.016 0.149 −0.100 seindus −0.368* ln(ides) −0.093 0.080 −0.006 Seller activity and competition seburep −0.103*** ln(seactiv) −0.058 0.049 −0.008 0.053 −0.023 ln(secomp) −0.107* semsm −0.006 0.004 0.000 Offer time and duration 0.004 −0.004*** timeec −0.008* 0.033 −0.016* weekend −0.201*** 0.025 0.027** durset 0.097*** Control variables (joined significance, 2(df ) or F(df1 , df2 ) statistic) acs cap form brnd cond seorigin N(obs.) N(sel.) adj. R2 Pseudo R2 2(df )

7.01(3) 355(8)*** 90.1(5)*** 168(26)*** 22.6(3)*** 188(15)*** 89,982 3294

Logit 2

OLS 2

OLS 3

SE

Coef.

SE

Coef.

SE

Coef.

SE

0.633

8.643**

2.669

5.085***

0.395

0.812*

0.365

0.018 0.018 0.003 0.008

0.388** −0.337** −0.071*** −0.071**

0.128 0.117 0.013 0.024

0.101*** −0.065**

0.029 0.023

0.084** −0.054*

0.026 0.024

−0.047***

0.007

−0.037***

0.007

0.083 0.079 0.132 0.047

−0.836 0.062 0.314 0.021

0.461 0.511 0.217 0.178

0.402*** 0.325** −0.173*** −0.019

0.097 0.125 0.041 0.038

0.328* 0.365* −0.145*** −0.004

0.129 0.159 0.041 0.043

0.054 0.047 0.056 0.052 0.027

0.299** 0.001 0.370 −0.749*** 0.135

0.107 0.356 0.198 0.190 0.108

0.037 −0.030 −0.098* 0.009 0.020

0.028 0.118 0.048 0.064 0.025

0.017 0.067 −0.109* 0.125 −0.001

0.028 0.121 0.046 0.077 0.020

0.019 0.015 0.018 0.001

0.005 −0.148** −0.006

0.049 0.056 0.005

−0.018 −0.026 −0.020 0.001

0.009 0.014 0.023 0.001

−0.033* 0.010 0.001

0.015 0.019 0.001

0.001 0.007 0.009

−0.004 −0.146*** −0.036

0.004 0.036 0.029

−0.005*** 0.006 0.012

0.001 0.006 0.007

−0.005*** 0.002 0.018*

0.000 0.006 0.008

4.59(3)** 138(9)*** 31.5(5)*** 6.28(23)*** 19.2(3)*** 9.2(14)*** 63,815 3103 0.67

0.49 8755(79)***

5.14(3) 43.9(8)*** 18.9(5)** 115(17)*** 17.2(3)*** 28.1(9)*** 38,985 494

2.12(3) 296(8)*** 31.3(5)*** 8.63(9)*** 4.18(3)** 24.9(7)*** 9767 255 0.89

2.74(3)* 171(9)*** 40.0(5)*** 1164(18)*** 0.36(3) 9.84(9)*** 38,989 498 0.82

0.09 3891(63)***

Notes: The table lists coefficient estimates and cluster-robust standard errors from logistic and OLS regressions. The estimation of models Logit 1 and OLS 1 is based on auction data. The estimation of models Logit 2, OLS 2, and OLS 3 is based on data from fixed price offers. The binary outcome variable is 1 if an item received at least one bid or was sold. The outcome variable in OLS 1 and OLS 2 is the log transformed selling price (in EUR) of auctioned and sold items, respectively. In model OLS 3 the outcome variable is the log transformed selling price (in EUR) of sold and unsold items. * p < 0.05, for two-sided tests. ** p < 0.01, for two-sided tests. *** p < 0.001, for two-sided tests.

self-promotion are either statistically insignificant or point in the wrong direction. Surprisingly, if an interaction between a buyer and seller is repeated, (seburep) an offer reaches a lower selling price. This contradicts the conjecture that the additional information a buyer has about the trustworthiness and reliability of a seller increases his or her willingness to pay for an item.11 Finally, sales and the selling price are smaller if there are more other active offers at end time (secomp), decrease over time (timeec), are smaller on weekends (weekend), and are higher the longer an auction lasts (durset). 4.2. Fixed price offers

data from fixed price offers (Table 3). Sellers’ positive and negative ratings are highly influential determinants of sales (Logit 2, testing H3a) and selling price (OLS 2, testing H2a), although negative ratings tend to have a larger effect on sales and positive ratings affect selling prices to a larger extent than in auctions. That is, for instance, if a seller’s number of negative ratings increases by a factor of 2.7, the odds of an offered item being sold changes by −29% (−25% in auctions) and a tenfold increase in the number of positive ratings increases the selling price by 26% (20% in auctions).12 Except for the variables accounting for measures of buyer protection in model Logit 2, most of the other explanatory variables retain their direction and statistical significance. The outcome variable in the last model in Table 3 (OLS 3, testing H1) is the price in fixed price offers of both sold and unsold

The main results obtained from model estimations based on auction data barely change if model estimations are performed on

11 A closer look at the data reveals that 59% of the repeated interactions had started before the buyers could have received the item from the first interaction. That is, buyers in these interactions could not rely on their own experience with the seller. However, the estimate for seburep hardly changes, if these cases are not coded as repeated interactions (not reported).

12 While the estimate for positive ratings in model OLS 2 lies in between the estimates from censored normal (CNR 2: 0 . 146*** ) and heteroscedastic censored normal regressions (HCNR 2: 0 . 061*** ), the OLS estimate for negative ratings is lowest in absolute terms in comparison with the other two estimates (CNR 2: −0 .129*** , HCNR 2: −0 .090*** ) (see Table B.1 in the appendix). This indicates that the coefficient for negative ratings from the OLS regression of sold items only could be slightly underestimated.

W. Przepiorka / The Journal of Socio-Economics 42 (2013) 31–42

37

Table 4 Model estimations based on highest bids. OLS

FE

Coef.

SE

(Intercept) 0.352** 0.111 Reputation and costs 0.003 ln(sepos+1) 0.053*** ln(seneg+1) −0.045*** 0.003 *** scavg −0.055 0.001 Buying format buyf2 0.384*** 0.008 0.008 buyf3 0.649*** 0.000 iprice* 0.027*** ipbpdif* −0.016*** 0.001 Buyer protection *** paypal 0.291 0.022 ptrans 0.328*** 0.022 0.015 scinsur −0.276*** iret −0.007 0.007 Seller self-promotion 0.006 list 0.083*** seisid −0.004 0.008 ** 0.007 sehasme −0.023 seindus −0.065*** 0.009 ln(ides) 0.003 0.004 Seller activity and competition seburep −0.044*** 0.005 0.002 ln(seactiv) −0.017*** 0.006 ln(secomp) −0.017** *** 0.000 semsm −0.001 Offer time and duration timeec −0.004*** 0.000 weekend −0.013*** 0.003 durset 0.002 0.002 Control variables (joined significance, 2(df ) or F(df1 , df2 ) statistic) acs cap form brnd cond seorigin N(obs.) N(bid.) adj. R2 2(df )

36.4(3)*** 2526(9)*** 905(5)*** 73.3(25)*** 54.2(3)*** 248(16)*** 301,882 137,217 0.47

RE

Coef.

SE

Coef.

SE

4.088***

0.300

3.482***

0.166

0.027*** −0.023*** −0.050***

0.004 0.004 0.002

0.044*** −0.040*** −0.057***

0.002 0.002 0.001

0.168*** 0.355*** 0.019*** −0.006***

0.013 0.018 0.001 0.001

0.317*** 0.560*** 0.024*** −0.013***

0.006 0.007 0.000 0.001

0.138*** 0.154*** −0.157*** 0.007

0.026 0.025 0.029 0.010

0.229*** 0.263*** −0.229*** −0.020***

0.018 0.017 0.015 0.006

0.054*** 0.007 −0.040*** −0.027 −0.010*

0.008 0.012 0.011 0.014 0.005

0.072*** 0.006 −0.030*** −0.035*** 0.008**

0.005 0.007 0.006 0.008 0.003

−0.017*** −0.002 −0.013 0.000

0.004 0.003 0.012 0.000

−0.029*** −0.009*** −0.012* −0.001**

0.004 0.002 0.006 0.000

−0.002** −0.007 0.007***

0.001 0.004 0.002

−0.004*** −0.010** 0.007***

0.000 0.003 0.001

27.9(3)*** 582(9)*** 121(5)*** 14.8(25)*** 19.0(3)*** 27.4(16)*** 301,882 137,217 0.28

117(3)*** 30,864(9)*** 5551(5)*** 1944(25)*** 249(3)*** 4268(16)*** 301,882 137,217 94,969(84)***

Notes: The table lists coefficient estimates and cluster-robust standard errors from OLS, fixed effects, and random effects regressions. The log transformed highest bid (in EUR) is the outcome variable in all models. * p < 0.05, for two-sided tests. ** p < 0.01, for two-sided tests. *** p < 0.001, for two-sided tests.

items. Since prices in fixed price offers are entirely determined by the seller, the estimates allow for inferences about sellers’ behavior. In line with theoretical predictions, sellers with more positive or fewer negative ratings set higher prices for their products. For a tenfold increase in the number of positive ratings a seller charges 21% more on average. Accordingly, for a 100% increase in the number of negative ratings, the seller gives a discount of 3.7%. This corresponds to what buyers pay for sellers’ reputation in auctions, where the respective values are 20% and −4.4%. Moreover, there is evidence that sellers who demand higher shipping costs, require shipping insurance, or are more active in the market set lower prices for their products. Finally, sellers adjust prices over time.13 4.3. Highest bids So far my analysis has been cross-sectional and despite the fact that I control for a great many confounders in my regression

13 Similar results are obtained from model estimations based on auctions with a Buy-It-Now option (see Table B.2 in the appendix).

models, the reputation effect could be biased or even spurious due to unmeasured factors that are correlated with sellers’ reputations (Resnick et al., 2006). Other studies have coped with this problem by taking advantage of the repeated observations obtained on the same sellers and have estimated regression models with seller fixed effects (Diekmann et al., 2012). The advantage of this approach is that the estimation of coefficients is based on within-seller variations in the outcome and explanatory variables. In the course of the estimation, unmeasured time-constant factors are canceled out, making the estimates more resistant to model misspecification (Allison, 2009). However, sellers with only one observation are excluded from the estimation. In the case at hand, 60% of sellers would be excluded, and these sellers have considerably fewer positive ratings (x = 402.6) than the other 40% at their first observation (x = 2500.0). What is more, for most of the remaining sellers, the changes in their reputation during the short period of data collection will be small compared to the betweenseller differences buyers face in the market. Consequently, the estimation of a regression model with seller fixed effects would be based on a selective subsample of sellers and a smaller variation in the explanatory variables, which may lead to biased and imprecise

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W. Przepiorka / The Journal of Socio-Economics 42 (2013) 31–42

estimates of the reputation effects, respectively. I therefore take a somewhat different approach. My data comprise 139,548 unique bidders who placed 306,175 highest bids, of which 91,092 (30%) were winning and 215,083 were non-winning highest bids (i.e. there was another bidder who placed a higher bid). For 35% of the bidder population there is more than one record of a highest bid in the data. Although buyers’ highest bid sets are only subsets of their initial choice sets, the repeated measures obtained on a considerable proportion of the buyer population allows estimating whether buyers trade off sellers’ reputation and prices within the set of offers they choose to bid on (for related approaches, see Livingston, 2003; Snijders and Weesie, 2009). The following analysis is based on the whole sample of highest winning and non-winning bids pooled across all three buying formats. Sales in fixed price offers are treated as winning highest bids.14 Two additional dummy variables (buyf2, buyf3) control for possible differences between the buying formats, and two additional variables account for the fact that initial prices can only be positive in auctions (iprice*) and the difference between the initial and the Buy-It-Now price can only be positive in auctions with the Buy-It-Now option (ipbpdif*). The first model in Table 4 is an OLS regression with robust standard errors accounting for within-bidder clustering. This model is comparable to model OLS 1 in Table 3. The results show that the estimates for sellers’ positive and negative ratings, although still in line with the theoretical predictions, become smaller if highest nonwinning bids are included in the analysis. The second model also accounts for bidder fixed effects, and the third model is a random effects specification. If unmeasured bidder-specific factors are correlated with the regressors, the estimates from the random effects model will be inconsistent. The Hausman test statistic suggests that this is the case (2(79) = 2935.99, p < 0.001), and the fixed effects regression model is therefore more appropriate. Compared to model estimations with selling price as the outcome variable, the effect of sellers’ number of positive and negative ratings on highest bids is considerably smaller. Now a tenfold increase in the number of positive ratings (e.g. from 50 to 500) increases the expected highest bid by 6.4%. This corresponds to an increase by EUR 0.96 in relation to the average selling price of EUR 15. Accordingly, an increase in the number of negative ratings by 100% (e.g. from 10 to 20) changes the expected highest bid by −1.6% or by −0.24 Euros. These effects – still supporting the theoretical predictions – are about three times smaller than the ones reported in Table 3. This result shows that buyers trade sellers’ reputations off against prices within the set of offered items they choose to bid on. However, the actual reputation effect is most likely being underestimated. The model estimation is based on a truncated sample that does not take account of information about offers initially discarded by the bidders. The results of the logistic regressions in Table 3 show that potential buyers discriminate sellers based on their reputation. Hence, a large part of the reputation effect will already be absorbed in their choice of the bid set. Moreover, what I attempt to get at here is what a potential buyer pays for reputation, but the more relevant question is what the maximum is a seller can expect for his or her reputation. Thus, if non-winning bidders value a seller’s reputation to a lesser extent than winning bidders – and this is what

Table A.1 Item characteristics. Brand

Freq.

Percent

Cum.

Sandisk (unknown) Kingston PQI Toshiba (other) Transcend OEM A-Data (multiple) Emtec Dane-Elec Extrememory Kodak Supertalent Panasonic Topram Technaxx Benchip Magu Lexar Geedom PNY Fuji Viking Reekin Kingmax Canon Hama Corsair Sony Nikon Total

80,541 20,906 16,527 13,056 12,493 5191 4145 3512 2940 2932 2278 1678 1130 901 847 816 784 734 704 511 500 433 420 357 357 331 325 271 247 197 168 159 176,391

45.66 11.85 9.37 7.40 7.08 2.94 2.35 1.99 1.67 1.66 1.29 0.95 0.64 0.51 0.48 0.46 0.44 0.42 0.40 0.29 0.28 0.25 0.24 0.20 0.20 0.19 0.18 0.15 0.14 0.11 0.10 0.09 100.00

45.66 57.51 66.88 74.28 81.37 84.31 86.66 88.65 90.32 91.98 93.27 94.22 94.86 95.37 95.85 96.32 96.76 97.18 97.58 97.87 98.15 98.39 98.63 98.83 99.04 99.23 99.41 99.56 99.70 99.81 99.91 100.00

Format

Freq.

Percent

Cum.

standard mini micro (unknown) ultra (multiple) Total

96,723 49,749 19,378 5971 3186 1384 176,391

54.83 28.20 10.99 3.39 1.81 0.78 100.00

54.83 83.04 94.02 97.41 99.22 100.00

Capacity

Freq.

Percent

Cum.

16 MB 32 MB 64 MB 128 MB 256 MB 512 MB 1024 MB 2048 MB 4096 MB 8192 MB N/A Total

169 281 532 3489 3511 21,773 84,429 57,611 4261 63 272 176,391

0.10 0.16 0.30 1.98 1.99 12.34 47.86 32.66 2.42 0.04 0.15 100.00

0.10 0.26 0.56 2.53 4.53 16.87 64.73 97.39 99.81 99.85 100.00

Condition

Freq.

Percent

Cum.

Used New Packed (Unknown) Total

2198 133,561 36,445 4187 176,391

1.25 75.72 20.66 2.37 100.00

1.25 76.96 97.63 100.00

Notes: Item characteristics described in this table are included as factors (brnd, form, cap, cond) in all model estimations. 14 Highest non-winning bids are more accurate measurements of a buyer’s willingness to pay for an item than winning highest bids because the latter are right-censored. With highest non-winning bids, a buyer’s willingness to pay lies somewhere between his or her highest and the next highest bid. In these cases, the outcome variable is the arithmetic mean of these two values.

W. Przepiorka / The Journal of Socio-Economics 42 (2013) 31–42

the results of model OLS in Table 4 suggest – the reputation effect will be underestimated. 5. Discussion and conclusions In online markets, anonymous buyers and sellers trade with each other over large distances, and the sequential nature of their interactions does not allow buyers to inspect products prepurchase. The potential trust problem arising from sellers’ rational incentive to keep the money without sending the good is mitigated by reputation systems implemented in online markets. Buyers rely on information about sellers’ past behavior when choosing a seller. This information is based on feedback voluntarily provided by previous customers. Sellers who enter the market and have not yet established a record of good conduct can build their reputation by giving price discounts to buyers. With an increasing number of positive ratings, they can thus compensate their initial investment by demanding a premium for their good reputation. I formalize this argument in a stylized game-theoretical model and derive testable hypotheses from it. The main theoretical prediction is that in anonymous online markets with a reputation system, sales and selling prices will be correlated with sellers’ reputations both in auctions and in fixed price offers. To test these hypotheses I analyze data collected from the online market platform eBay.de. The net sample comprises 176,391 auctions and fixed price offers of memory cards used in small electronic devices. Half of the offered items were sold and 49% of these transactions involved a buyer and a seller from different countries. Within the time period of the data collection, 8% of transactions took place between a buyer and a seller who had traded before. The variety of model estimations applied in this study corroborate a statistically and substantially significant positive effect of sellers’ positive ratings, and a negative effect of their negative ratings on the probability of sale and the selling price both in auctions and fixed price offers. What is more, the data from fixed price offers provides a unique opportunity to investigate whether sellers too behave according to theoretical predictions. This is an important question because the theoretical argument is based on the assumption that both buyers’ and sellers’ beliefs accord with respect to the significance of reputation. It turns out that sellers making fixed

39

price offers value their own reputation to a similar extent as buyers pay for sellers’ reputations in auctions. That is, the better a seller’s reputation, the higher is the price he or she sets in fixed price offers. The influence of other trust-enhancing factors such as the degree of legal buyer protection or seller self-promotion is rather ambiguous. Surprisingly, if an interaction between a buyer and seller is repeated, an offer reaches a lower selling price. Furthermore, results show that competition decreases sales, selling prices decrease over time, are lower on weekends, and are higher in longer-lasting auctions. The empirical evidence presented here corroborates that reputation systems in anonymous online markets – based on the voluntary contributions of ratings by market participants – solve trust problems and reduce transaction costs by providing incentives for cooperative behavior. It remains an open question what motivates traders to voluntarily provide truthful ratings after a transaction (but see Bolton et al., forthcoming; Diekmann et al., 2012). To address this question in future research is particularly important because the effectiveness of reputation systems in detecting cheaters and thus the stability of online markets crucially depend on truthful ratings. Appendix A. Material and methods Data collection consisted of two steps. First, every 24 h a search query was posted that returned a complete list of active offers in the relevant product category. From this list, the unique item ID and the estimated ending time were extracted. This information was stored in a database and received the status “ongoing”. Second, the program checked whether the current date and time exceeded the estimated end time of ongoing offers. The offers that were completed received the status “closed” and were “collected”. That is, the offer page was accessed, the available information was extracted using regular expressions, and the extracted information (as well as the whole html text) was stored in the database. Furthermore, information from the profile page of the seller and, if available, of the buyer was extracted. In auctions which received at least one bid the information available on the list of bidders was collected. In the first instance, information on a memory card’s condition, format, capacity, and brand was extracted from the standard

Table A.2 Seller and buyer countries of origin. Seller origin Hong Kong Great Britain USA China Germany Singapore Malaysia Canada Australia Italy France Belgium Netherlands Austria Japan Switzerland Thailand Latvia Jersey Ireland other (14) N/A Total

Freq. 61,620 27,139 25,004 20,268 20,003 10,351 4313 2680 1520 1089 998 374 208 207 165 143 77 49 31 30 74 48 176,391

Percent 34.93 15.39 14.18 11.49 11.34 5.87 2.45 1.52 0.86 0.62 0.57 0.21 0.12 0.12 0.09 0.08 0.04 0.03 0.02 0.02 0.04 0.03 100.00

Cum. 34.93 50.32 64.49 75.99 87.33 93.19 95.64 97.16 98.02 98.64 99.20 99.41 99.53 99.65 99.74 99.82 99.87 99.90 99.92 99.94 99.98 100.00

Buyer origin Great Britain USA Germany Australia Canada France Italy Spain Ireland Belgium Netherlands Austria Brazil Greece Portugal Hungary Sweden Switzerland Malta Israel other (71) N/A Total

Notes: Seller countries of origin listed in this table are included as factor (seorigin) in all model estimations.

Freq. 29,452 22,761 18,030 4322 3372 3045 2295 916 857 805 776 556 443 218 201 146 133 117 112 101 1310 1123 91,092

Percent

Cum.

32.33 24.99 19.79 4.74 3.70 3.34 2.52 1.00 0.94 0.88 0.85 0.61 0.49 0.24 0.22 0.16 0.15 0.13 0.12 0.11 1.45 1.23 100.00

32.33 57.32 77.11 81.86 85.56 88.90 91.42 92.42 93.36 94.24 95.09 95.70 96.19 96.43 96.65 96.81 96.96 97.09 97.21 97.32 98.77 100.00

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W. Przepiorka / The Journal of Socio-Economics 42 (2013) 31–42

description on the offer page. While the information on condition and capacity could be extracted from the standard description, information on format and brand was often missing. In this case, the brand and format were extracted from the title. If the information was not in the standard description or the title, it was extracted from the seller’s own description on the offer page. Those cases for which no information about format or brand could be retrieved remain in the sample and are marked as “unknown”. The total sample contains 215,912 offers. In about 28,000 cases the offer comprised more than one item and potential buyers could acquire any subset of the quantity offered. Since it is not obvious what the actual sales and the price paid by the buyer are, these cases were dropped. About 8200 cases were dropped because offers were for USB sticks, USB readers, memory cards of other formats, and plastic cases. Another 1185 cases were excluded because they allowed the buyer to propose a price to the seller and therefore sales and selling prices eventually depended on sellers’ decisions. In about 1100 cases, the seller canceled the offer early or changed the duration of the offer, with the result that these offers were collected before they had ended.

Appendix B. Further model estimations Using observed selling prices from auctions as the outcome variable implies limiting the sample to the subset of cases for which a latent outcome variable, yi∗ , lies above a certain threshold value, yLi . The latent variable yi∗ can be thought of as buyers’ maximum willingness to pay for an item with given characteristics. Since selling prices are only observed if an item is sold, the outcome variable is left-censored at the initial price for unsold items. In addition, Buy-It-Now offers and auctions with a Buy-It-Now option exhibit right-censoring on the outcome variable as the highest bid cannot exceed the Buy-It-Now price. More formally, in the presence of left- and right-censoring at individual levels, the observed outcome variable is:

yi =

⎧ y ⎪ ⎨ Li ⎪ ⎩

yi∗

= xi ˇ + εi

yRi

if

i∈L

if

i∈C

if

i∈R

(6)

where L is the set of unsold items, C the set of auctioned items (which did not reach the Buy-It-Now price), and R the set of items sold for the Buy-It-Now price. Analyzing a truncated sample (i.e.

Table B.1 Additional model estimations based on auctions or fixed price offers. CNR 1

HCNR 1

Coef. (Intercept) 5.562*** Reputation and costs ln(sepos+1) 0.104*** ln(seneg+1) −0.075** iprice scavg −0.130*** Buyer protection paypal 0.360* ptrans 0.335* scinsur −0.509** iret −0.192** Seller self-promotion list 0.347*** seisid −0.035 sehasme 0.040 seindus −0.134 ln(ides) −0.031 Seller activity and competition seburep 0.129** ln(seactiv) 0.010 ln(secomp) −0.064 semsm −0.002 Offer time and duration timeec −0.003* weekend −0.033* durset 0.043** Control variables (joined significance, 2(df ) acs cap form brnd cond seorigin N(obs.) N(sel.) adj. R2 Pseudo R2 2(df )

OLS

CNR 2

HCNR 2

SE

Coef.

SE

Coef.

SE

Coef.

SE

0.747

5.522***

0.279

6.506*

2.604

4.427

.

0.026 0.028 0.016

0.077*** −0.056*** −0.076***

0.014 0.014 0.006

−0.249* 0.366** −0.093**

0.124 0.137 0.035

0.146*** −0.129*** −0.057

0.156 0.143 0.160 0.066

0.103 0.099 −0.203 −0.045

0.075 0.081 0.113 0.042

−0.496 −0.913 0.772 0.126

0.751 0.718 0.454 0.330

0.086 0.091 0.078 0.082 0.040

0.118*** −0.002 0.003 −0.017 0.033

0.033 0.037 0.032 0.047 0.019

−0.356 0.351 −1.052** −0.307 0.005

0.044 0.021 0.033 0.002

−0.021* −0.005 −0.029* −0.003*

0.008 0.012 0.014 0.001 0.001 0.005 0.005

0.001 −0.005*** 0.013 −0.015** 0.013 0.005 or F(df1 , df2 ) statistic)

13.4(3)** 937(9)*** 97.1(5)*** 103(26)*** 30.6(3)*** 77.7(15)*** 89,997 3295

15.8(3)** 3953(9)*** 157(5)*** 109(26)*** 7.55(3) 150(15)*** 89,997 3295

0.34 16,056(80)***

95,941(77)***

Coef.

SE

4.049***

0.497

0.035 0.027 0.000

0.061** −0.090*** −0.043***

0.023 0.025 0.008

−0.058 0.117 −0.079* −0.018

0.000 0.099 0.034 0.046

0.071 0.211 −0.003 −0.098**

0.085 0.132 0.041 0.035

0.251 0.327 0.377 0.352 0.160

0.061 −0.153 −0.015 0.023 0.019

0.031 0.111 0.047 0.056 0.023

0.024 0.012 0.025 0.116 0.033

0.017 0.159 0.046 0.060 0.027

−0.167 0.072 −0.004

0.086 0.126 0.007

0.229*** 0.002 −0.024 −0.001

0.043 0.014 0.015 0.001

0.052** 0.047** −0.036* 0.003

0.017 0.016 0.002

−0.008* −0.019 −0.059

0.003 0.028 0.058

−0.005*** −0.030*** −0.006

0.001 0.009 0.009

−0.006*** −0.005 −0.008

0.001 0.004 0.004

7.14(3)** 3.97(9)*** 2.37(5)* 6.97(26)*** 4.50(3)** 4.08(15)*** 89,997 3295 0.54

5.59(3) 1733(9)*** 122(5)*** 2802(17)*** 9.56(3)* 113(9)*** 38,989 498

4.94(3) 1927(9)*** 636(5)*** 45,837(18)*** 60.6(3)*** 91.6(9)*** 38,989 498

0.48 1,567,654(63)***

14,424(60)***

Notes: The table lists coefficient estimates and cluster-robust standard errors from OLS, censored normal, and heteroscedastic censored normal regressions. The estimation of models CNR 1, HCNR 1 and OLS is based on auction data. The estimation of models CNR 2 and HCNR 2 is based on data from fixed price offers. The outcome variable in CNR and HCNR is the log transformed selling price (in EUR) of auctioned and sold items, respectively. Left-censoring at the initial price for unsold items is accounted for in models CNR and HCNR. 2(df ) at the bottom of model HCNR is from an LR test of CNR nested in HCNR. The log transformed initial price (in EUR) is the outcome variable in model OLS (this model is not referred to in the article). * p < 0.05, for two-sided tests. ** p < 0.01, for two-sided tests. *** p < 0.001, for two-sided tests.

W. Przepiorka / The Journal of Socio-Economics 42 (2013) 31–42

41

Table B.2 Model estimations based on auctions with Buy-It-Now option. Logit

OLS 1

Coef.

SE

Coef.

(Intercept) 16.942*** 3.485 −0.774 Reputation and costs 0.069 0.064** ln(sepos+1) 0.193** ln(seneg+1) −0.062 0.073 −0.073** iprice −0.057** 0.019 ipbpdif scavg −0.033 0.035 −0.081*** Buyer protection 1.584 0.885*** paypal −3.146* ptrans −0.794 1.504 0.922*** scinsur 0.467 0.275 −0.161 iret 0.312* 0.156 −0.014 Seller self-promotion list −0.096 0.146 0.066 seisid −0.055 0.456 0.221 sehasme −0.082 0.158 0.044 seindus −0.318 0.198 0.083 ln(ides) −0.055 0.095 0.031 Seller activity and competition seburep −0.030* ln(seactiv) 0.043 0.060 0.000 ln(secomp) −0.432** 0.162 0.044*** semsm 0.001 0.003 0.001 Offer time and duration timeec −0.000 0.003 −0.006*** 0.027 −0.003 weekend −0.130*** durset −0.110* 0.049 0.002 Control variables (joined significance, 2(df ) or F(df1 , df2 ) statistic) 39.7(3)*** 90.0(8)*** 65.3(5)*** 73.8(15)*** 36.4(3)*** 73.2(12)*** 45,256 376

acs cap form brnd cond seorigin N(obs.) N(sel.) adj. R2 Pseudo R2 2(df )

CNR

HCNR

OLS 2

SE

Coef.

SE

Coef.

SE

Coef.

SE

0.510

4.948***

0.582

3.534***

0.445

5.518***

0.610

0.022 0.022

0.101*** −0.079***

0.020 0.022

0.106*** −0.108***

0.031 0.025

0.074** −0.044

0.024 0.027

0.004 0.013

−0.089***

0.011

−0.090***

0.011

−0.064***

0.014

0.222 0.205 0.143 0.051

0.387 0.779*** −0.055 0.182**

0.215 0.166 0.122 0.063

1.077*** 1.330*** 0.057* 0.012

0.298 0.332 0.027 0.049

0.551** 0.489* −0.340 0.134

0.197 0.195 0.189 0.081

0.042 0.120 0.042 0.064 0.022

0.016 0.028 0.011 0.108* 0.041

0.037 0.113 0.061 0.048 0.029

−0.024 0.245** 0.113 0.106 0.061*

0.029 0.085 0.061 0.081 0.030

0.083 −0.035 −0.049 0.140* 0.019

0.048 0.104 0.062 0.056 0.032

0.012 0.019 0.010 0.002

0.592*** 0.011 −0.077* 0.001

0.049 0.020 0.033 .

0.338*** 0.080 −0.051 0.002

0.075 0.049 0.048 0.003

−0.062*** −0.087*** −0.001 0.001

0.018 0.024 0.017 0.001

0.001 0.007 0.013

−0.006 −0.040** −0.022

0.000 0.014 0.018

−0.008** −0.015 −0.040*

0.003 0.011 0.018

−0.005*** −0.000 0.027

0.001 0.003 0.016

5.82(3)** 282(8)*** 22.6(5)*** 2.97(9)** 1.52(3) 9.81(6)*** 16,307 274 0.88

0.08 4630(64)***

15.3(3)** 1808(8)*** 224(5)*** 151(15)*** 1.52(3) 181(12)*** 45,256 376

2.42(3) 5539(8)*** 170(5)*** 397(15)*** 2.71(3) 201(12)*** 45,256 376

0.27 9,081,679(63)***

3063(62)***

4.86(3)** 128(8)*** 11.5(5)*** 6.26(15)*** 3.42(3)* 4.54(12)*** 43,915 286 0.88

Notes: The table lists coefficient estimates and cluster-robust standard errors from logistic, OLS, censored normal, and heteroscedastic censored normal regression. The binary outcome variable is 1 if an item received at least one bid or was sold. The outcome variable in models OLS 1, CNR, and HCNR is the log transformed selling price (in EUR). Left-censoring at the initial price for unsold items and right-censoring at the Buy-It-Now price for items sold for the Buy-It-Now price are accounted for in models CNR and HCNR. 2(df ) at the bottom of model HCNR is from an LR test of CNR nested in HCNR. The log transformed Buy-It-Now price (in EUR) is the outcome variable in model OLS 2. * ** ***

p < 0.05, for two-sided tests. p < 0.01, for two-sided tests. p < 0.001, for two-sided tests.

sold items only) or censored data yields inconsistent coefficient estimates (Long, 1997, 201–203). However, the model stated in Eq. (6) can be estimated by a censored normal regression (Greene, 2002, 764–768), where the log of the likelihood function is:

ln L =



ln (

yLi − xi ˇ

i∈L

+

 i∈R

ln {1 − (



)+

 i∈C

yRi − xi ˇ 

)}

ln

1 yi − xi ˇ ( )  

explicitly allows one to test for heteroscedasticity and to assess the magnitude of the bias in the estimates. Table B.1 presents additional model estimations based on auctions and fixed price offers. Models CNR and HCNR allow for left-censoring at the initial price for unsold items. Table B.2 presents model estimations based on auctions with a Buy-It-Now option. Both tables provide further evidence for the results discussed in the main part of the article. Some of the coefficients listed in these two tables are referred to in the text.

(7)

Coefficients estimated in a censored normal regression can be interpreted in terms of the latent variable y* . Unlike in OLS, estimates of the censored normal regression are inconsistent, if the assumptions of homoscedasticity and normally distributed errors do not hold. These problems can partly be accounted for by estimating a heteroscedastic censored normal regression. That is, instead of assuming constant error variance, i2 =  2 exp(xi ˛) is specified in Eq. (7) where xi is the vector of explanatory variables also contained in the model for the mean. Modeling the error variance

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