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Quantitative Marketing and Economics

Erschienen in:

01.06.2011

Vettes and lemons on eBay

verfasst von: Christopher P. Adams, Laura Hosken, Peter W. Newberry

Erschienen in: Quantitative Marketing and Economics | Ausgabe 2/2011

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Abstract

This paper empirically tests whether there are asymmetric information problems in the market for used Chevrolet Corvettes sold on eBay. The first test is based on a result from Akerlof (Quarterly Journal of Economics, 1970). If there is asymmetric information in the used market, bidders should place a premium on a new Corvette, all else equal. The paper finds little systematic evidence of a new car premium. The second test is based on a result from Bajari and Hortacsu (Rand Journal of Economics 34(2):329–355, 2003). If there is asymmetric information, then bidders will prefer to bid late in the auction in order to conceal their private information from other bidders. The paper fails to find evidence of more late bidding on used Corvettes relative to new Corvettes. The third test is based on the idea that if there is asymmetric information then there may be a winner’s curse problem in these auctions. The paper fails to find evidence consistent with bidders avoiding the winner’s curse. The results are not consistent with asymmetric information problems in the market for used Corvettes sold on eBay. It is not clear however, whether this result generalizes to other cars sold on eBay or cars sold in the off-line market.

Nächster Artikel Local competition, entry, and agglomeration

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According to the data approximately 300,000 cars were sold through eBay in 2003 with an average price of $8,000. The store generated sales of approximately $2.4 billion. By 2008, this had increased to $13.5 billion.

Genesove (1993) finds some evidence supporting Akerlof’s lemons hypothesis by using data on dealer behavior and preferences. Emons and Sheldon (2002) analyze used car sales in Switzerland and find some support for the lemons problem but state that it is not a widespread problem.

Most Corvettes in the sample are less than six years old and none is older than 22 years.

Wolf and Muhanna test for evidence of a lemons problem by comparing vehicle prices between online (eBay motors) and “offline” (Kelly Blue Book) sites. They find that age and mileage of cars sold on eBay have significant negative impact on online prices compared to offline prices, indicating an adverse selection problem on eBay Motors. Our paper does not focus on this adverse selection problem. Rather, the paper focuses on whether there is a discrete difference in bids for new versus “newly used” cars when controlling for car characteristics. Our paper does examine whether late bidding occurs more often for older cars (indicating an asymmetric information problem) but we do not find evidence of this.

As discussed further below, there is some empirical evidence of a lemons problem for older used cars, although we find little support in our data.

Note that age is determined by time between the date of the sale and the start of the model year from the seller-entered VIN. The model year is assumed to begin in July (the date is based on the first observed sale of the next model year).

Only authorized Chevrolet dealers are able to sell new Corvettes. We use information provided by eBay and information from public websites such as Yahoo! to determine whether a seller is an authorized Chevrolet dealer.

This statement is robust to t-tests of equal mean of bids on new versus used cars and Wilcoxon rank-sum tests of equal distribution of bids on new versus used cars.

Note the price regressions are at the level of the auction and do not include information about the bidders.

For our preferred specification (specification 3 in Table 2) we also tried an alternative definition of new in which the seller stated the car was new, it was sold by a new car dealer and the car had less than 150 miles. The results are quantitatively similar to those presented.

Note that cars that are neither “new” by the definition stated nor “used” are not included in the specification. This is one of the reasons why the number of observations changes from specification to specification.

In the data there are three main versions of the Corvette, the C3, C4 and C5. These are complete redesigns that occur every few years. There are also special versions that are made contemporaneously with the standard version. For more information on Corvettes see http://www.idavette.net.

Other specifications that we tried included having a quadratic for age, log of mileage, year dummies, measuring age by month instead of year, and only including the “C5” Corvette which is model year 1998 to 2004.

See Bajari and Hortacsu (2004) for the various explanations for late bidding.

Of course the top two bidders may have bid timing that is systematically different from other bidders in the auction. It is not possible to determine whether this is true, but our results do not seem to change much if all bidders are included.

Thanks to Bob Hammond for pointing out this possibility.

Note that an endogenous increase in the number of bidders may lead to higher bids as it may signal that other bidders have a high value for the item.

If prices increase at different rates for different length auctions, then the selection of observed bids may related to the auction length and the test on the first two samples would be invalid.

If the choice of auction length is correlated with car characteristics unobserved by us but not the bidder it may affect our test results. If longer auctions are associated with higher quality cars (and this quality is observed by the bidder but not us) then this may explain why we don’t observe a negative relationship between bids and auction length.

eBay now requires an accurate VIN to be provided.

The cars are located in the U.S. and for the analysis using the bid data we only use information from bidders located in the U.S..

The C5 is the fifth version of the Corvette and has model years 1998 to 2004.

This may lead to some selection bias in the data, although it is unclear how this bias would affect the tests presented.

For the buyer, the Buy-It-Now option goes away once there is a bid above the larger of the starting price or the hidden reserve.

Distance is calculated using zip code centriods on MapInfo.

Akerlof, G. (1970). The market for ‘lemons’. Quarterly Journal of Economics.

Bajari, P., & Hortacsu, A. (2003). The Winner’s curse, reserve prices and endogenous entry: empirical insights from eBay auctions. The Rand Journal of Economics, 34(2), 329–355.CrossRef

Bajari, P., & Hortacsu, A. (2004). Economic insights from internet auctions. Journal of Economic Literature, 152, 457–486.CrossRef

Bond, E. (1982). A direct test of the “lemons” model: the market for used pickup trucks. The American Economic Review, 72(4), 836–840.

Bond, E. (1984). Test of the lemons model: a reply. The American Economic Review, 74(4), 801–804.

Emons, W., Sheldon, G. (2002). The market for used cars: a new test of the lemons model. May, CEPR Discussion Paper No. 3360.

Engers, M., Hartmann, M., & Stern, S. (2009). Are lemons hot potatoes? International Journal of Industrial Organization, 27(2), 250–263.CrossRef

Genesove, D. (1993). Adverse selection in the wholesale used car market. Journal of Political Economy, 101(4), 644–655.CrossRef

Haile, P., Hong, H., Shum, M. (2003). Nonparametric tests for common values at first-price sealed bid auctions. NBER Working Paper #10105, November.

Lacko, J. (1986). Product Quality and Information in the Used Car Market. FTC Bureau of Economics Staff Report.

Lewis, G. (2010). Asymmetric information, adverse selection and online disclosure: The case of eBay motors. American Economic Rewiew, forthcoming.

Milgrom, P., & Weber, R. (1982). A theory of auctions and competitive bidding. Econometrica, 50(5), 1089–1122.CrossRef

Porter, R., Sattler, P. (1999). Patterns of trade in the market for used durables: Theory and evidence, NBER Working Paper #7149.

Song, U. (2005). Nonparametric estimation of an eBay auction model with an unknown number of bidders. Discussion Paper 05–14, University of British Columbia.

Wolf, J. R., Muhanna, W. A. (2005). Adverse selection and reputation systems in online auctions: Evidence from eBay motors. Twenty Sixth International Conference on Information Systems.

Yin, P-L. (2008). Information dispersion and auction prices. Stanford Institute for Economic Policy Research Working Paper #02–024.

Zeithammer, R., Adams, C. (2010). Tests of the sealed-bid abstraction in online auctions. Marketing Science, 29(6), 964–987.

Titel: Vettes and lemons on eBay
verfasst von: Christopher P. Adams
Laura Hosken
Peter W. Newberry
Publikationsdatum: 01.06.2011
Verlag: Springer US
Erschienen in: Quantitative Marketing and Economics / Ausgabe 2/2011
Print ISSN: 1570-7156
Elektronische ISSN: 1573-711X
DOI: https://doi.org/10.1007/s11129-011-9095-2