Essays on applied economics


Mechanism in eBay and Summary of Data



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3. Mechanism in eBay and Summary of Data

eBay provides a rich resource for the empirical study of auctions, and since there exists a large quantity of similar auctions at any given time, eBay also provides an excellent resource for the study of competing auctions.


eBay is a list e-commerce site. It provides a central market for buyers and sellers to meet each other by way of auctions. The income eBay earns comes from a fee charged to sellers, which varies from a fixed fee per listing, or a small proportion of the final sale price. Buyers on eBay do not pay anything to participate.

Sellers choose an auction type in which they sell their good.2 They set a starting bid, a bid increment, and the duration of the auction. Sellers also provide a detailed description of the item, which usually also includes the method of delivery and method of payment. Sellers have the option to set a secret reserve price. If they do so, during the process of the auction, eBay will indicate whether the reserve is met or not. At the end of the auction, if the reserve is not met, sellers have the right not to sell the item with the final price.

eBay use the mechanism of a second price auction. At any time, eBay shows the current standing bid of the auction, which is the current second highest bid (if there is no bid or there is only one bid, the current bid is the starting bid). When a bidder submits a bid, he has access to the level of present standing bid, the identity of the seller (with the seller’s “feedback,” discussed below), the starting and ending time and the description of the item. He also has access to information as to how many bids have already been submitted, the identity of the bidders, and the time of the bids. However, the exact amount of each bid is not revealed until the end of the auction. The final price is the second highest bid plus the bid increment.

At the end of an auction, eBay does not intervene in the actual transaction between the seller and the winner of the auction; the seller and the winner contact each other themselves to complete the transaction. Since bidders cannot inspect the good directly, sellers have incentives to provide false information; winners may regret the high price they have to pay and may not contact the seller to finish the purchase. To promote the faithful implementation of the transaction, eBay uses a feedback system. After each transaction (whether successful or not), the seller and the bidder can send feedback about the other party to eBay, marked as positive, neutral and negative, with values of +1, 0, -1 respectively, plus brief comments. A trader is given a feedback number, which is the sum of value of each feedback. The feedback information is public, and always associated with the trader, though eBay cannot prevent the traders from changing identity. Indeed, traders with negative feedbacks have a strong incentive to change their identities in subsequent trades, while traders with high feedbacks and good comments own an asset of good reputation. There is some evidence that the reputation associated with the feedbacks of sellers have an effect on the final price of auctions (see Houser and Wooders (2001)).

When an auction has ended, eBay provides detailed information about the bid history.3 Figure 1 is an example of an auction history extracted from eBay’s website. The first half page shows the basic information about the auction. It is a three-day auction, started at Oct-31-01 22:51:27 PST and ended at Nov-03-01 22:51:27 PST. The seller has feedback with value 11. The starting bid set by the seller is $10 and the increment is set to $0.50. The auction received 10 bids from 4 different bidders. There was a shipping cost of $5 and optional shipping insurance $5.

The second half page shows the detailed bidding history. The bid history is sorted by the amount of each bid. The auction received its first bid of $17.50 by planetorb around 23 hours after the start of the auction. Then 4 hours before the end of the auction, bidder raheem112 started to bid. He could observe that there was already a bidder, but would not know the exact amount of bid. Since there was only one bidder at that time, the standing bid was still $10. Bidder raheem112 first bid $11, and found that the standing bid increased to $11 and he was not the highest bidder. Then he increased his bid subsequently in 2 minutes to $13, $14, $15, until at last he became the highest bidder with bid $20. The standing bid became $17.50. In the last hour of the auction, the bidder iteachcomputers submitted a bid $20, increasing the standing bid to $20. Bidder raheem increased his bid to $21, followed by another bidder who bid $23.99. Bidder planetorb finally won the auction with an unknown bid. Since the minimum increment is $0.50, the final price was $24.49, which is the second highest bid $23.99 plus the bid increment $0.50. There was no bid retraction or cancellation for this auction. eBay keeps the information of completed auctions public for one month.

Competing auctions in our sample should satisfy the following conditions: 1. they should be reasonably homogeneous in quality (including warranty) and have similar delivery method and shipping cost, 2. they should end at approximately the same time. As documented by Roth and Ockenfels (2000) and Bajari and Hortacsu (2000), bids on eBay tend to be clustered towards the end of the auction.

We use eBay CPU auctions data from a one-month period - September 20 to October 19. These are drawn for the category of “Computer, Component, CPUs” in eBay, which includes subcategories “AMD”, “Cyrix”, “Intel” and “Others”, and each subcategory includes further subcategories. At the time of writing in November 2001, there are 800 to 900 new CPU auctions every day. We choose those auctions with only one CPU for sale, and only those auctions with the method of standard auction.4 Most of the CPUs are second hand. Table 1 reports the basic statistics of the sample.

In the sample, there are 7910 auctions involving a single CPU for sale. Not all items were sold. Among all auctions, 1452 of them did not receive any bids. 899 of them, which is more than 10% of the auctions, have a secret reserve price. In all 899 auctions with secret reserve price, 515 auctions had the reserve price met.

The CPUs in the sample are very different. The mean final price is $60.60, with standard deviation of $82.34. The number of bids received is 6.66 per auction, with standard deviation of 6.91. The maximum number of bids in the sample is 49. Also the starting bids of these auctions are very different, with a mean of $29.25 and standard deviation of $61.60.

Though the conditions of properly working CPUs in the same specific category (such as Pentium III 800 retail box) are largely the same, they may be very different in many respects: some are new and never opened, others are used for several months or years; some are still under warranty, others aren’t; some are with both box and complete manual, others without. And the method of the delivery, the shipping cost and the method of payment can differ.

To overcome this complication in obtaining the competing auctions, we use a sample consisting of groups of auctions with the same product description, the same delivery method and the same shipping cost. This is done by choosing the auctions sold by the same sellers.5 Some big second CPU sellers sell many items with the same description, the same delivery method and the same shipping cost. Without considering the different ending time, these auctions are completely indistinguishable to buyers.

Such auctions with identical items started and ended at different time. Auctions with almost the same ending time compete directly against each other, and auctions with large differences in ending time compete less directly. We get three different samples for competing auctions. Each observation in the samples is a group of auctions, which consists of 2 or more competing auctions. In the first sample (the daily sample), each group of competing auctions consists of homogenous auctions ending on the same day. In the second sample (the hourly sample), each observation is a group of competing auctions consisting of homogenous auctions with the difference of ending time being less than 1 hour. In the third sample (the minute sample), each observation is a group of competing auctions consisting of homogenous auctions with the difference of ending time being less than 1 minute.

Auctions appearing in the minute sample must appear in the hourly sample and auctions appear in the hourly sample must appear in the daily sample. The minute sample consists of groups of auctions that compete directly against each other, and the hourly and daily data consists of groups of auctions that compete against each other less directly. Table 2 is a sample description of the three samples.

In the daily sample, there are 550 groups of competing auctions, consisting of 1247 different auctions. Among all auctions, 305 (24%) of them do not receive any bids and 106 auctions have secret reserve price (40 of them with the reserve price not met). The hourly sample has a relatively smaller size, with 321 groups of competing auctions, consisting of 748 different auctions. Among these auctions, 196 (26%) of them do not receive any bids and 66 of them have secret reserve price (30 of them with reserve price not met). The minute sample is less than half of the size of the hourly data, with 139 groups of competing auctions, consisting of 346 different auctions. Among them 115 auctions (33% of the sample) do not receive any bids and 24 have secret reserve price (19 of them with reserve price not met).

We find that in each sample, there are bidders who are winners for more than one auction in a group of competing auctions. In the daily sample, there are 24 groups of competing auctions with bidders winning more than one auction, representing 4% of the total groups. In the hourly sample and minute sample, the number of the groups with a bidder winning more than one auctions is 18 and 10, representing 6% and 7% of the total groups.

These bidders may happen to need more than one item for themselves, or they can be professional dealers. Buyers with multiple demands can bid across more than one auction at the same time. If we consider all bidders with single unit demand, the existence of buyers with multiple demands will exaggerate the result that bidders bid across auctions. However, the proportion of such bidders is low, and the number of group in which such bidders win more than one auctions is low. In the following, we will address this problem in more detail and distinguish true cross bidders from the multiple demand bidders. Our results are not affected significantly by the existence of those multiple demand buyers.

One may be concerned that bidders, especially the novice, may not fully understand the mechanism in eBay auction. They might not use optimized strategies. We use the feedback as an indicator of traders’ experience in eBay6. eBay also uses heavily the number of feedback in daily trade. For example, to use the Buy It Now feature, sellers must have a feedback greater than 10 or be ID verified7. Figure 2 is the distribution of bidders’ number of feedbacks. In the daily sample, there are 2286 bidders and 21 bidders with negative feedback (1 bidder with feedback –4, 2 bidders with feedback –3, 8 bidders with feedback –2, the rest 10 with feedback –1). 60% of the bidders have feedback greater than 8. Most of the bidders have history of transactions in eBay. We can be confident that the observed behavior is not very different from the optimal one for most individuals.




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