0 Measuring prices and price competition online: Amazon and Barnes and Noble Judith Chevalier Yale School of Management and NBER Austan Goolsbee University of Chicago Graduate School of Business, American Bar Foundation, and NBER April 2003 Abstract Despite the interest in measuring price sensitivity of online consumers, most academic work on Internet commerce is hindered by a lack of data on quantity. In this paper we use publicly available data on the sales ranks of about 20,000 books to derive quantity proxies at the two leading online booksellers. Matching this information to prices, we can directly estimate the elasticities of demand facing both merchants as well as create a price index for online books. The results show significant price sensitivity at both merchants but demand at Barnes and Noble is much more price-elastic than is demand at Amazon. The data also allow us to estimate the magnitude of bias in the CPI due to the rise of Internet sales. Acknowledgements: We would like to thank Michael Smith, Scott Schaefer, Joel Waldfogel and seminar participants at Yale, Northwestern, and the AEA meetings for helpful comments. We benefited greatly from expert research assistance by Chip Hunter, Patrik Guggenberger, and Tina Lam. We thank Madeline Schnapp for assistance and rely heavily on her 6/21/2001 presentation at the UCB/SIMS Web Mining workshop for data. Goolsbee would like to thank the National Science Foundation (SES 9984567), the Sloan Foundation and the American Bar Foundation for financial support. We have no reason to thank the University of Chicago Press.1 In the earliest days of Internet commerce, many economists and media observers predicted that competition among Internet retailers would quickly resemble perfect competition.1 After all, the Internet already reduces search costs relative to visiting physical stores and shopbots and comparison sites could be expected to lower search costs still further. Two strands of research have addressed the question of price competition on the Internet. The first set of papers examines patterns of prices for homogeneous goods. Using price dispersion to measure the extent of competition has been used extensively in traditional bricks and mortar retail settings (see Sorensen, 2000, for example). Researchers have examined the degree of price dispersion amongst Internet retailers, as well as between Internet retailers and bricks-and-mortar retailers. The general consensus of these papers is that price dispersion amongst Internet retailers is large, and that online retailers charge prices that are either modestly lower or actually higher than their offline counterparts.2 These results seem incompatible with the idea that the Internet has completely eliminated consumer search costs. An important advantage of this strand of research is that these studies require only publicly available price data. However, a concern with these findings is that, while relatively high prices are posted at some Internet sites, few or no transactions may be taking place at those relatively high prices. Without quantity data, it is impossible to know. 1 See, for example, Kuttner (1998). 2 Work by Lee (1997) for cars and Bailey (1998) for books, CDs, and software suggest that prices were actually higher online than in retail stores. More recent work by Brynjolfsson and Smith (2000) for books and CDs and by Clay et al. (2000) for books has found prices the same or lower online but that online price dispersion is quite high, perhaps greater than in retail stores. Carlton and Chevalier (2001) show, among other things, the existence of price dispersion among online fragrance retailers.2 A second strand of research attempts more direct measures of consumer price sensitivity.3 The general consensus from this work seems to be that Internet markets do seem competitive in the sense that demand for a seller appears to be quite elastic to the seller’s own price or to competitors prices. One important drawback of this research is that all of the papers rely on proprietary information on firm sales or consumer buying patterns. In particular, much of this work focuses on consumers who use Internet shopbots, a group that may not be representative of overall Internet shoppers.4 In general, there has been little overlap in the industries studied by the two approaches. We examine online books, in part because this is the most-studied Internet retail category, but also because it is one of the largest online sales categories. We develop a method to estimate directly the own- and cross-price elasticities of demand at Amazon and Barnes and Noble.com (hereafter, BN.com). We also compute a Fisher-ideal price index for online books. To do these things we need only 2 sources of data: publicly available information on prices and sales ranks at the two leading sites and data from simple experiments which anyone can conduct for less than $50. Our results show several things about prices and competition in the online book industry. First, having sales data matters for the results. The prices of online books, for example, look dramatically different when books are weighted by sales compared to when all books are weighted equally (as assumed in the conventional literature). Also, it is clear that online inflation behaves quite differently in this period than does the CPI for recreational books. Indeed, our best estimates suggest that the CPI misstates the true inflation rate by 3 Goolsbee (2000; 2001) finds a large cross-price elasticity of online retail and online computers with respect to physical retail prices. Ellison and Ellison (2001) find large elasticities for computer memory and motherboards from data on a private computer parts retailer. Brown and Goolsbee (2002) and Scott Morton et. al. (2001) examine the impact of Internet shop-bots on prices of life insurance and for cars respectively and find that the Internet leads to significantly lower prices. Smith and Brynjolfsson (2001) examine customer behavior at a book price comparison site but find that brand still matters a lot for consumers' click through probabilities. 4 See White (2000) for a discussion of the usage of Internet shopbots.3 almost a factor of three in one part of our sample and gets the sign wrong in another. Second, we show that there is significant price sensitivity for online book purchases at both sites. The demand at BN.com,