Snipe Bidding Behaviour in eBay Auctions Füsun F. Gönül (*) School of Business, Slippery Rock University Slippery Rock PA 16057-1399
[email protected] (412) 638-7841
Peter T. L. Popkowski Leszczyc School of Business, University of Alberta Edmonton AB, Canada, T6G 2R6
[email protected]
December 2009
(*) Corresponding author. We gratefully acknowledge research support from the Social Sciences and Humanities Research Council of Canada and the University of Alberta, GRA Rice Faculty Fellowship. The usual disclaimer applies.
Snipe Bidding Behaviour in eBay Auctions Abstract Our research investigates what factors make snipe bidding more likely, whether sniping pays, and how an auction can be designed to minimize sniping. We estimate sniping probability in eBay auctions using multivariate models to examine novel datasets compiled from auctions of the Norelco electric razor and the Sony PlayStation2. Our main results reveal that the winner of an auction is more likely to be a snipe bidder and that a lower ending price increases the likelihood that the winner of an auction is a snipe bidder, everything else held constant. We find that experienced bidders are more likely to engage in sniping behavior. In addition, we find that an auction designer or a seller can discourage snipe bidding by setting a longer duration and/or a lower (or no) reserve price.
Keywords: choice; data collection; estimation; online auctions; probability; snipe bidding.
2
1 Introduction Sniping is a bidding strategy that relies on the timing of the bid,1 in that the bidder does not reveal his or her valuation until the end of an auction and bids at the last minute. In this study, we employ multivariate models in an online auction environment to carefully delineate what factors make snipe bidding more likely, whether sniping pays (in terms of both winning an auction and paying a lower ending price), and how an auction can be designed to minimize sniping, if so desired. Our results have implications not only for bidders but for sellers and auction designers. Theory suggests that in the case of a private value auction, where bids of other bidders are uninformative regarding a bidder‟s valuation for an item, a bidder should place a single bid equal to her or his maximum willingness to pay. However, in cases where bidders have common but uncertain values, bids of other bidders may be informative about the value of an item. Under these conditions, a bidder may be best off not to reveal her or his strategy and wait until the end of the auction to place a (snipe) bid. Although waiting until the end of an auction to reveal one‟s preference is a plausible approach to bidding, we do not observe widespread sniping behaviour. For example, when sniping is defined as submitting a bid in the last 5 minutes of an auction our datasets reveal that, on average, sniping incidence is about 4%. (The definition of „last minute‟ is arbitrary and up to the researcher. For example, Roth and Ockenfels (2002) observe a heavy concentration of „last minute‟ bids in the last 5 minutes of eBay and Amazon auctions.) Furthermore, raw data calculations show that 18% of winning bids are snipe bids, and that snipe bids win 63% of the time. At the end of an auction, the average price paid by sniper winners is about the same as the average price paid by 1
We use the terms sniping and snipe bidding interchangeably in this study.
3
nonsniper winners. However, raw statistics, which are essentially univariate or at best bivariate results, are subject to omitted variables bias. A multivariate model has the advantage of controlling for multiple factors and allowing for the identification and estimation of the individual impact of each variable on the sniping probability. For this reason, we employ a multivariate logit choice model to estimate the probability of sniping. Our findings have both strategic and managerial implications. Bidders who want to win an auction and pay the lowest possible price for the item may want to use a sniping strategy, and sellers and auction designers may want to increase bidding activity (i.e., minimize the incidence of sniping) through factors they can control, such as the duration of an auction or the level of the reserve price. However, a factor complicating the use of sniping is frequent bidder reliance on computerized proxy bids. The scope of the Internet expands the capacity of auctions to match buyers and sellers worldwide as they seek to make informational and product transactions, especially through the use of proxy bidding systems. A computerized proxy bidding system, which places bids on behalf of bidders, allows buyers to privately specify the maximum amount they are willing to pay for an item and let the computer bid on their behalf. The system will place a bid one increment over the next highest bid, up to the maximum specified by the bidder. Snipers do not necessarily bid high, because all that is needed to win the auction is a bid just one increment above the current high bid. However, snipers do not always win the auction even though they come in the last minute, because their bid may not beat the winning proxy bid.
4
Several reasons have been advanced for using a sniping strategy in an auction that involves proxy bidding and a fixed ending time (Roth and Ockenfels 2002). For example, snipers may be responding strategically to bidders engaged in incremental bidding by not revealing their valuation of the product. Snipe bidding may be an improvement over more naïve bidding strategies, such as incremental bidding (Ariely, Ockenfels, and Roth 2005). Additionally, sniping helps avoid bidding wars in that if a substantial proportion of bidders are engaged in sniping, the seller‟s profit could potentially be lowered. Bidders who have a reputation for being knowledgeable about the true value of the product may choose to snipe in order not to give other bidders a signal about the quality of the product. A seller may enter fake bids throughout the auction and unethically increase the ending price (Stern and Stafford 2006). Finally, sniping may be an effective weapon to fight against shill (false) bidders. Possible nonstrategic reasons for sniping include lack of knowledge about the proxy bidding system, a personal desire to avoid getting caught in a bidding frenzy, and procrastination, among others. If self-control is an issue, the bidder may choose to snipe in order not to get caught up in a bidding frenzy. In addition, some snipers may view the end of an auction as a deadline. That is, they may simply be procrastinators or people who enjoy the rush of an approaching deadline, rather than being bidders engaged in a rational strategic play. In this vein, Roth, Murnighan, and Schoumaker (1988) refer to the „deadline effect‟ – a concentration of bargaining agreements reached in the last seconds before the deadline. Some studies assume bidders obey rational consumer behaviour as dictated by economic theory (McAfee and McMillan, 1987; Bajari and Hortaçsu, 2003, 2004). Other
5
empirical studies document how consumers behave during an auction (for example, Wilcox, 2000; Roth and Ockenfels, 2002; Dholakia and Simonson, 2005; Ely and Hossain 2009) or touch upon snipe bids as part of a more general study of bidding (Hayne, Smith, and Vijayasarathy, 2003; Dholakia and Simonson, 2005). While it is beyond the scope of our study to summarise the literature on auctions, valuable surveys can be found in Chakravarti et al. (2002) and Ockenfels, Reiley, and Sadrieh (2006). In the remainder of the paper, we present the data in Section 2 and the model and expected results in Section 3. We discuss the implications of our results in Section 4, and conclude with a summary and discussion of future research directions in Section 5.
2 Data We employed novel datasets from eBay auctions for two products from August 2002 to June 2003: the Norelco electric razor (henceforth, Norelco) and the Sony PlayStation2 (henceforth, PS2). We hired a programmer to write a spider in the programming language Perl to collect bidding history data over this period, as web spiders increase the efficiency and accuracy of data collection compared with manual methods (Borle, Boatwright, and Kadane, 2006), We selected the two products for our analysis on the basis of price and product supply considerations. Price had to be high enough that bidding strategy would play an important role, and price also had to vary over time (i.e., be not too predictable). We also selected products with a supply sufficient for a large number of observations. The products within each dataset are new and identical to each other. The Norelco sample has 287 auctions and 987 distinct bidders with a total of 2,953 observations (or
6
bids). The PS2 sample has 411 auctions and 3,715 distinct bidders with a total of 9,215 observations (or bids). Definitions of the variables are given below and descriptive statistics are presented in Tables 1 and 2. 2.1
Dependent variable
If a bid is submitted in the last 5 minutes of the auction, we assign the value 1 to the variable snipe and 0 otherwise. Note that we create the sniping variable as a binary variable that measures whether this bid is the bidder‟s first (and last) bid in the auction. As we note above, the classification of a bid as „last minute‟ is arbitrary. We experiment with both the last 5 minutes and the last 10 minutes, and find no qualitative difference in the results. We present findings from the last 5 minutes specification to sharpen the definition of sniping; other results are available upon request. We observe that sniping activity (as defined by snipe) is on average about 5% in the Norelco data and about 3% in the PS2 data. As an aside, 18% of winning bids are snipe bids in both datasets, and snipe bids win 72% of the time in the Norelco data and 54% of the time in the PS2 data. There appears no substantial difference between the average ending price for winners of an auction who sniped and the overall average – the sniper winners pay on average $55 for Norelco (overall average is $51) and $171 for PS2 (overall average is $171). However, one should not put too much faith in raw statistics as they are subject to omitted variables bias. A multivariate model has the advantage of controlling for multiple factors so the quick conclusions reached by raw data statistics (that are essentially univariate or at best bivariate results) can drastically change as the individual impact of each variable on the
7
sniping probability is identified and estimated. We discuss the explanatory variables in our model next.
2.2
Explanatory variables
We cull several explanatory variables from the data to describe bidder characteristics, auction environment attributes, bid characteristics, seller characteristics, and product characteristics. 1. Winner: Whether the bidder is the winner of the auction. About 21% of the bids in the Norelco data are winner bids compared to about 9% in the PS2 data. 2. Ending price: What the winner of the auction pays to own the item. The ending price is, on average, $51 for Norelco and $171 for PS2. 3. Bidder rating: Historical data on the number of feedbacks on the bidder – a higher number may be viewed as an indicator of bidder expertise or experience. The average bidder rating is 64 in the Norelco data and 24 in the PS2 data. 4. Duration: How long the auction lasts (measured in seconds). Auction durations are 3, 5, 7, or 10 days at the time the data are collected. On average, a Norelco auction lasts about 96 hours (4 days) whereas a PS2 auction lasts about 113 hours (about 5 days). 5. What the bidder sees: The dollar amount the bidder sees when placing a bid, equal to the current winning bid. This amount may be different from the data in the bidding history, which orders bids from small to large, especially when a bidder places an early high proxy bid. The dollar amount the bidder sees is about $26 in the Norelco data and about $90 in the PS2 data.
8
6. Bid amount: The submitted bid amount by the bidder. The average submitted bid amount is almost $35 for Norelco and about $103 for PS2. 7. Seller rating: Historical data on the number of evaluations on the seller – usually the understanding is that the higher the rating the more reputable the seller. On average, the seller rating is about 2,044 in Norelco and about 199 in PS2. 8. Reserve price: About 6% of the auctions in the Norelco data do not have a reserve price compared to 39% in the PS2 data. On average, the reserve price is about $16 for Norelco and about $22 for PS2. Note that, consistent with observations by Bajari and Hortaçsu (2003), on average the reserve price appears to be considerably smaller than the ending price. 9. Description: The variable ranges from 1 to 3, where a higher number indicates a better description, usually accompanied by a picture. In our study, two research assistants rated all product descriptions on the basis of the completeness of the product descriptions. The rate of consistency between the ratings was over 98%, and any inconsistent scores were discussed until the raters agreed upon a rating. 10. Shipping cost: The dollar amount of the shipping cost. The average values are consistent with the value of the items – about $8 for Norelco and $17 for PS2. In the next section we summarize the model and expected results of our estimation based on the prior literature.
3 Model and expected results Since the dependent variable is binary, we employ a logit specification where the predicted value of the dependent variable has a strict probability interpretation. Table 3 summarises the expected impact of the explanatory variables listed above on the sniping
9
probability based on the existing literature. Some coefficients have not been tested before in the literature and hence we let the data reveal the sign of the related coefficient. In the spirit of the multivariate framework, each expected result should be read as accompanied by the phrase ceteris paribus (everything else held constant) . Estimation results are provided in Tables 4 and 5. The model is statistically significant for both datasets, ascertained by the 2 tests, p < 0.001.
4 Discussion We summarise the results and discuss them in two parts: strategic implications for bidders and managerial implications for sellers and/or auction designers.
4.1 Strategic implications for bidders The results are numbered as presented in Table 3 and hence are not in numerical order. 1. Sniping and winning move together. If a bidder wins an auction, s/he is more likely to be a sniper since the coefficient is positive and statistically significant in both datasets (see Tables 4 and 5). Our results are consistent with those of Hayne, Smith, and Vijayasarathy (2003), who find that sniping is more likely to result in winning an auction than not. Our results are also consistent with those of Ely and Hossain (2009), who find that snipe bidding increases the probability of winning an auction for DVD movies from 47.6% to 52.6%. We present the estimated value of the dependent variable under various scenarios in the top two lines of Table 6. The values conditional on the winner variable are obtained from the logit model as follows:
10
n
Pˆ ( Snipe 1 | Winner ) (1 /(1 exp( [ X 1'i ˆ1 Winner * ˆWinner ]))) / n i 1
(1)
where Pˆ () is the predicted probability, X1i is the vector of explanatory variables for observation i excluding the variable winner, ˆ1 is the corresponding coefficient vector, and n is the number of observations. 2. Sniping and ending price move in opposite directions. A bidder paying a relatively lower price upon winning the auction is more likely to be a sniper, since the corresponding coefficient is negative and statistically significant in both datasets. The estimated probabilities conditional on selected values of the auction ending price are obtained as follows, from the logit model:
n
Pˆ ( Snipe 1| Ending price) (1/ (1 exp([ X 2' i ˆ2 ˆEnding price * Ending price]))) / n i 1
(2)
where X2i is the vector of explanatory variables for observation i excluding ending price, and ˆ2 is the corresponding coefficient vector. The movement of ending price with respect to the estimated probability of sniping is shown in the second half of Table 6 and in Figures 1 and 2. This movement suggests a larger probability of sniping for auctions with lower ending prices. In the literature, Bajari and Hortaçsu (2003) do not find a significant effect of snipe bidding on final auction prices. Ariely, Ockenfels, and Roth (2005) find some support in laboratory experiments that sniping significantly improves bidders‟ surplus. Hou (2007b) and Ely and Hossain (2009) provide empirical evidence
11
from eBay auctions that late bidding decreases the ending price. In our case as well, lower price outcomes of auctions are more likely to be associated with snipers than not. 3. Experienced bidders are more likely to submit snipe bids. The results suggest that auctions with more experienced bidders tend to have more snipe bidding behaviour, since the coefficients of bidder rating are positive (although significant only in one dataset).2 However, ratings are imperfect measures of experience, and users may change IDs and hide their true experience. The result is consistent with Wilcox (2000), Borle, Boatwright, and Kadane (2006), and Ockenfels and Roth (2006). 5. The effect of what the bidder sees as the current winning bid on sniping probability is left as an empirical question. Given the positive and significant coefficients of what the bidder sees, we find that bidders are more likely to not reveal their (high) valuation and strategically wait until the end of the auction to bid if the amount they see as the current winning bid is higher. We plan to investigate the impact of the variable more fully in a dynamic choice model in future research. 6. The effect of the bidder‟s own bid amount on sniping probability is left as an empirical question. The positive and significant coefficients of bid amount indicate that if bidders are going to bid a relatively high amount, they prefer to submit a snipe bid. We plan to investigate the variable bid amount in a choice model in future research where we model both the timing of bids and the amount of bids.
2
PS2 dataset overall seems to have lower standard errors (hence, more statistical significance), possibly owing to its larger number of observations.
12
4.2 Managerial implications for sellers and/or auction designers The results are numbered as presented in Table 3 and hence are not in numerical order. 4. The effect of auction duration on sniping probability is negative. We find that auctions with longer duration are less likely to have snipe bids as the corresponding coefficients are negative (although insignificant in one dataset). Auctions with longer duration tend to attract more bidders, and therefore have greater bidding activity (Haruvy and Popkowski Leszczyc, 2009). Additionally, increased bidding activity accelerates prices, and hence provides less opportunity for snipe bids (Jank and Shmueli, 2007). Haruvy and Popkowski Leszczyc (2009) also find a negative effect in their study of the impact of duration on jump bids. Although a snipe bid is not necessarily a jump bid, our result appears to be consistent with theirs. 7. The effect of a seller‟s reputation on sniping probability is left as an empirical question. Auctions with more reputable sellers tend to have more snipe bidding behaviour, since the coefficients of seller rating are positive (although significant in only one of the datasets). This result suggests that snipe bidders are more likely to select reputable sellers that they trust. 8. A higher reserve price increases the probability of sniping. We find that reserve price has a strong positive impact on snipe bidding behaviour since both coefficients are significant. A reserve price can be an important deterrent to entry, as setting a high reserve price attracts fewer bidder to enter an auction, leading to less bidding activity (e.g., Häubl and Popkowski Leszczyc, 2003; Ariely and
13
Simonson, 2003). Therefore, prices will accelerate at a slower rate, which may provide greater opportunity for snipe bids. Also, Dholakia and Simonson (2005) find a positive effect of reference point on sniping. If we interpret reserve price as a reference3 point, then their finding from field experiments is consistent with our finding. 9. Description does not affect the probability of sniping. We find that description is insignificant. This result is consistent with the study by Hou (2007a) that finds that a seller's quality claim has no effect on the auction outcome (measured in terms of probability of a sale and price), even though our definition of description is not based on the seller‟s claim and even though Hou‟s dependent variable differs from the one in our study. 10. The effect of shipping cost on sniping probability is left as an empirical question. That shipping cost is insignificant for the products studied is perhaps encouraging news for sellers and auction designers. However, we can only speculate. In sum, we find empirical support for the expected results posited earlier on the basis of prior literature. Our results are consistent across the product categories studied. We also find empirical answers to questions that were relatively unexplored in the prior literature.
3
Findings by Häubl and Popkowski Leszczyc (2003) and Ariely and Simonson (2003) suggest that reserve prices function as reference points to bidders in auctions.
14
5 Conclusion We examine snipe bidding behaviour using novel datasets from eBay auctions for new and identical products. Using multivariate models, we control for omitted variables bias and estimate the effect of several factors on the sniping probability. Although the incidence of sniping, or bidding in the last 5 minutes of an auction, is low – it varies from 3% to 5% across our datasets – we find that sniping appears to pay for a bidder in terms of winning an auction and paying a lower price for the product, ceteris paribus. Estimated sniping probabilities for alternative ending prices suggest that snipe bidders are opportunistic and that they actively aspire to decrease price outcomes of auctions. Thus, although some uncertainty exists in submitting a snipe bid (since it may not beat the winning proxy bid), bidders are encouraged to employ snipe bidding since it appears to pay in terms of both winning the auction and increasing the bidder‟s surplus. We find that both higher bidder rating and higher seller rating increase the probability of sniping. We also find that if an auctioneer or a seller wants to discourage sniping behaviour and to encourage more bidding early on to make more profits, the auction should be longer in duration and have a lower or no reserve price. Higher reserve prices appear to deter casual bidding activity in auctions and cause an increase in sniping behaviour. We find the description and shipping cost to be insignificant factors in influencing the decision to snipe. No study is without limitations, and our study examines only the two products described here as offered in eBay auctions. Our models can be tested further in different types of auctions with more products to generalise our empirical findings.
15
In our current study, we find substantial evidence for the impact of what the bidder sees as the current winning bid on the sniping probability – a variable that is relatively unexplored in the literature. What the bidder sees is a summary of the bidding history until that point, and speaks to the likely impact of that and other similar variables on a bidder‟s choice in a dynamic framework. A natural extension of our study would be investigation of the dynamic decision-making process of bidders using an estimable dynamic programming model; a survey of estimable dynamic programming models can be found in Magnac and Thesmar, 2002. Such a model enables a fuller description of decisions bidders make in each time period, including whether to snipe. We also find the bid amount – another relatively unexplored variable in the snipe bidding literature – to have a significant impact on sniping probability. Thus, future research should investigate both the timing of bids and the amount of bids jointly in an estimable dynamic programming framework. Having been convinced of the potential of these two variables in the decision-making process by our current empirical work we leave these issues for future research.
16
References Ariely, D., A. Ockenfels, and A. E. Roth (2005), “An Experimental Analysis of Ending Rules in Internet Auctions,” RAND Journal of Economics, 36(4), 890-907. Ariely, D. and I. Simonson (2003), “Buying, Bidding, Playing, or Competing? Value Assessment and Decision Dynamics in Internet Auctions,” Journal of Consumer Psychology, 13(2), 113-123. Bajari, P. and A. Hortaçsu (2003), "Winner‟s Curse, Reserve Prices, and Endogenous Entry: Empirical Insights from eBay Auctions," RAND Journal of Economics, 34 (2), 329-355. Bajari, P. and A. Hortaçsu (2004), "Economic Insights from Internet Auctions," Journal of Economic Literature, June, 457-486. Borle, S., P. Boatwright, and J. B. Kadane (2006), “The Timing of Bid Placement and Extent of Multiple Bidding: An Empirical Investigation Using eBay Online Auctions,” Statistical Science, 21(2), 194-205. Chakravarti, D., A. Sinha, A. Cheema, J. C. Cox, D. Friedman, T. H. Ho, R. M. Isaac, A. A. Mitchell, A. Rapoport, M. H. Rothkopf, J. Srivastava, R. Zwick (2002), “Auctions: Research Opportunities in Marketing,” Marketing Letters 13 (3), 281-296. Dholakia, U. and I. Simonson (2005), “The Effect of Explicit Reference Points on Consumer Choice and Online Bidding Behavior,” Marketing Science 24 (2), 206-217. Ely, J. C. and T. Hossain (2009), "Sniping and Squatting in Auction Markets," American Economic Journal: Microeconomics, 1(2), 68-94. Haruvy, E. and P.T.L. Popkowski Leszczyc (2009), “The Impact of Online Auction Duration,” Decision Analysis, doi10.1287/deca.1090.0149. Häubl, G. and P.T.L. Popkowski Leszczyc (2003), “Minimum Prices and Product Valuations in Auctions,” Marketing Science Institute Report, 3(03-117), 115-41. Hayne, S. C., C. A. P. Smith, and L. R. Vijayasarathy (2003), “Who Wins on eBay: An Analysis of Bidders and Their Bid Behaviours,” Electronic Markets 13 (4), 282-293. Hou, J. (2007a), “Sellers‟ Quality Claims in Online Auctions: Evidence from eBay,” International Journal of E-marketing and Retailing, 1 (4), 355-369. Hou, J. (2007b), “Late Bidding and the Auction Price: Evidence from eBay,” Journal of Product and Brand Management, 16 (6), 422-428.
17
Jank, W. and G. Shmueli (2007), “Modeling Concurrency of Events in Online Auctions via Spatio-Temporal Semiparametric Models,” Journal of the Royal Statistical Society – Series C, 56(1), 1-27. Magnac, T. and D. Thesmar (2002), “Identifying Dynamic Discrete Decision Processes,” Econometrica 70 (2), 801-816. McAfee, R. P. and J. McMillan (1987), “Auctions and Bidding,” Journal of Economic Literature, June, 699-738. Ockenfels, A., D. Reiley, and A. Sadrieh (2006), “Online Auctions,” NBER Working Paper. Ockenfels, A. and A. E. Roth (2006), “Late and Multiple Bidding in Second Price Internet Auctions: Theory and Evidence Concerning Different Rules for Ending An Auction,” Games and Economic Behavior, 55, 297-230. Roth, A. E., J. K. Murnighan, and F. Schoumaker (1988), “The Deadline Effect in Bargaining: Some Experimental Evidence,” American Economic Review, September, 806 – 823. Roth, A. E. and A. Ockenfels (2002), “Last Minute Bidding and the Rules for Ending Second-Price Auctions: Evidence from eBay and Amazon Auctions on the Internet,” American Economic Review 92 (4), 1093-1103. Stern, B. B. and M. R. Stafford (2006), “Individual and Social Determinants of Winning Bids in Online Auctions,” Journal of Consumer Behavior 5, 43-55. Wilcox, R. T. (2000), “Experts and Amateurs: The Role of Experience in Internet Auctions,” Marketing Letters, 11(4), 363-374.
18
Table 1 Descriptive statistics: Norelco Variable
Mean
Std. Dev.
Minimum
Maximum
Snipe
0.0508
0
1
Winner
0.2062
0
1
Ending price
51.2708
10.03
15.50
75.95
Bidder rating
64.3082
219.45
0
4630
344,355.77
148,239.63
93,302
864,000
What the bidder sees
26.1796
18.17
0
70.99
Bid amount
34.7581
16.16
0.06
75.95
Seller rating Reserve price
2043.92 15.0146 15.9828
1622.80 20.63 20.91
1 0.00 1.00
7709 75.95 75.95
Description
2.6072
0.68
1
3
Shipping cost
8.2857
1.10
3.95
10
Duration (seconds)
Note: Number of observations = 2,953. Standard deviations of binary variables are not reported, by convention. The first row in reserve price includes all observations, whereas the second row shows descriptive statistics for cases where there is a positive reserve price. The models we estimate use all observations.
19
Table 2 Descriptive statistics: PS2 Variable
Mean
Std. Dev.
Minimum
Maximum
Snipe
0.02973
0
1
Winner
0.09181
0
1
Ending price
170.8992
36.76
100.01
300
Bidder rating
24.3537
93.86
0
3605
407,729.18
174,286.45
12,845
867,600
What the bidder sees
90.0300
58.22
0
290
Bid amount
102.8493
56.60
0.01
300
Seller rating
198.5786 13.1966 21.7432
828.29 30.43 36.56
0 0.00 0.84
8951 199.95 199.95
Description
2.3588
0.60
1
3
Shipping cost
17.0980
2.83
5
30
Duration (seconds)
Reserve price
Note: Number of observations = 9,215. Standard deviations of binary variables are not reported, by convention. The first row in reserve price includes all observations, whereas the second row shows descriptive statistics for cases where there is a positive reserve price. The models we estimate use all observations.
20
Table 3 Expected results ceteris paribus Expected relationship of explanatory variable with sniping behaviour 1. Sniping and winning move together.4
Variable and expected sign of its coefficient Winner Positive
Related literature
Hayne, Smith, and Vijayasarathy (2003) Ely and Hossain (2009)
2. Sniping and ending price move in opposite directions.
Ending price Negative
Ariely, Ockenfels, and Roth (2005) Hou (2007b) Ely and Hossain (2009)
3. Experienced bidders are more likely to submit snipe bids.
Bidder rating Positive
Wilcox (2000) Borle, Boatwright, and Kadane (2006) Ockenfels and Roth (2006)
4. The effect of auction duration on sniping probability is negative.
Duration Negative
Haruvy and Popkowski Leszczyc (2009)
5. The effect of what the bidder sees as the current winning bid on sniping probability is left as an empirical question.
What the bidder sees ?
6. The effect of the bidder‟s own bid amount on sniping probability is left as an empirical question.
Bid amount ?
7. The effect of a seller‟s reputation on sniping probability is left as an empirical question.
Seller rating ?
8. A higher reserve price increases the probability of sniping.
Reserve price Positive
Dholakia and Simonson (2005)
9. Description does not affect the probability of sniping.
Description No impact
Hou (2007a)
10. The effect of shipping cost on sniping probability is left as an empirical question.
Shipping cost ?
4
In the econometrics literature, co-movement is defined as movement in a correlated fashion.
21
Table 4 Logit model results: Norelco (dependent variable = snipe)
Intercept
Coefficient -3.9089***
Std. error 1.1279
Winner
0.8775***
0.2189
Ending price
-0.1874***
0.0306
Bidder rating
8.2 x 10-5
2.5 x 10-4
Duration
-6.3 x 10-7
7.4 x 10-7
What the bidder sees
0.0169**
0.0078
Bid amount
0.1913***
0.0290
Seller rating
2.5 x 10-5
7.4 x 10-5
Reserve price
0.0236***
0.0050
Description
-0.0245
0.1765
Shipping cost
0.0788
0.0996
lnL = -416.83, (210) 352.58
Notes: Statistical significance at 10% is denoted by (*), 5% by (**), and 1% by (***). The 2 test rejects the null model where the coefficients of all explanatory variables are restricted to be zero (lnL0= -593.12), p < 0.001.
22
Table 5
Logit model results: PS2 (dependent variable = snipe)
Intercept
Coefficient -1.7424***
Std. error 0.5878
Winner
0.4155***
0.1510
Ending price
-0.1356***
0.0121
Bidder rating
0.0009**
0.0005
-1.5 x 10 ***
4.6 x 10-7
What the bidder sees
0.0110***
0.0040
Bid amount
0.1288***
0.0126
Seller rating
0.0002***
0.0001
Reserve price
0.0035**
0.0017
Description
0.1277
0.1293
Shipping cost
-0.0065
0.0269
Duration
-6
lnL = -756.59, (210) 953.07 Notes: Statistical significance at 10% is denoted by (*), 5% by (**), and 1% by (***). The 2 test rejects the null model where the coefficients of all explanatory variables are restricted to be zero (lnL0= -1233.12), p < 0.001.
23
Table 6 Expected probability of sniping under various scenarios
Scenario
Norelco
PS2
Winner = 0
0.0325
0.0257
Winner = 1
0.0680
0.0360
Ending price = Minimum
0.6190
0.4450
Ending price =Sample mean
0.0861
0.0840
Ending price = Maximum
0.0039
0.0001
Sample mean of sniping
0.0508
0.0297
24
Figure 1
Estimated sniping probability for the Norelco dataset for alternative ending prices 0.7 0.6 0.5
Predicted 0.4 Sniping Probability 0.3 0.2 0.1 0 0
20
40
60
80
Ending Price
25
Figure 2
Estimated sniping probability for the PS2 dataset for alternative ending prices 0.5
0.4
0.3
Predicted Sniping Probability 0.2 0.1
0 50
100
150
200
250
300
350
Ending Price
26
