Applied Economics Letters, 2012, 19, 711 715 Internet and the Long Tail versus superstar effect debate: evidence from the French book market St ephanie Peltier a and Franc ois Moreau b, * a GRANEM, University of Angers and CEREGE-LRMOS, University of La Rochelle, La Rochelle, France b LIRSA, Conservatoire National des Arts et Métiers, Case 153, 2 rue Conté, 75003 Paris, France From a comprehensive database of monthly sales of comic books and literature books in France over the period 2003 to 2007, we show that (i) bestsellers got smaller market shares online than offline, contrary to medium- and low-sellers; (ii) both online and offline sales shift from the head of the distribution to the tail with increasing magnitude over the period; and (iii) the Long Tail appears to be more than just a short-lived phenomenon caused by the specific preferences of early adopters of e-commerce. These three results suggest that online information and distribution tools, whose use increased over the period 2003 to 2007, do have an impact on book distribution and on consumers purchase decisions. Keywords: Long Tail; concentration of product sales; book market; Internet JEL Classification: D12; L81; L82 I. Introduction Two views exist about the effect of digitization on the concentration of product sales. Internet might reinforce the popularity of products that are already bestsellers following the superstar effect (Rosen, 1981) or winnertake-all phenomenon (Frank and Cook, 1995). Conversely, thanks to lower distribution costs and new ways of connecting demand and supply (blogs, forums, recommender tools), a shift in demand from the most popular products ( hits or the head of the distribution of sales) to niche products might occur. This is the Long Tail effect (Anderson, 2004). Both theoretical works and empirical studies provide conflicting evidence about the existence and the magnitude of the Long Tail (Bakos, 1997; Brynjolfsson et al., 2006, 2011; Elberse and Oberzholzer-Gee, 2008; Fleder and Hosanagar, 2009; Hervas-Drane, 2010; etc.). In this article, we empirically test the merits of these two hypotheses using data on the book market. To our knowledge, none of the previous studies of this industry (Brynjolfsson et al., 2003, 2010; Chevalier and Mayzlin, 2006; Benghozi and Benhamou, 2010; Bounie et al., 2010) is based on such a comprehensive data set than the one we used, which can simultaneously capture the evolution of the distribution of sales over a sufficiently long period of time and compare distributions of online and offline sales, and thus take into account the increasing use of online distribution and information tools. II. Empirical Methodology By examining a series of selected quantiles of the distribution of book sales, we can assess how the sales *Corresponding author. E-mail: francois.moreau@cnam.fr Applied Economics Letters ISSN 1350 4851 print/issn 1466 4291 online# 2012 Taylor & Francis http://www.informaworld.com DOI: 10.1080/13504851.2011.597714 711
712 S. Peltier and F. Moreau distribution changes with various covariates that include time, genre, channel of distribution and so on. It allows us to study especially whether the possible changes in distribution occur in the head or in the tail. However, a decrease in units sold for superstars can reflect either a Long Tail effect or a recession affecting the whole market. To control for this potential bias, we use market shares rather than units sold as the dependent variable. The model we estimate is the following: log Qy mij ¼ xb þ e where Qy mij denotes the yth quantile of the distribution of market shares for a given month m, the distribution channel i (offline or online) and the genre j (literature or comics). x is a vector of covariates, b is the set of coefficients to be estimated and e is an error term. The variables used in the regressions are the following: l ONLINE is a dummy that indicates if the distribution of sales refers to online or offline channels. For a given year, a given month and a given genre, we indeed consider two distributions of sales (online and offline). Hence when a title is sold both online and offline it will appear in both distributions. l Y2003 to Y2007 are dummies for each of the 5 years and M1 to M12 dummies for each month. l COMICS is a dummy that equals 1 if the distribution of sales refers to comic books and 0 if it refers to literature books. l The log of the number of titles (TITLES) that sold at least one copy in the month allows us to control for the fact that an increase in the number of different titles sold mechanically leads to a decrease in the average market share of all other titles. l Since the bulk of sales is made in the first weeks following publication, if the catalogue of available titles is ageing, the head of sales distribution will be eroded in favour of the tail. NEW is the log of the number of new titles that have been released during the month and allows us to control for this bias. To capture the Long Tail effect in the above setting, we make three research hypotheses. H1: The distribution of book sales is expected to be less concentrated online than offline. The impact of online information tools should indeed be stronger when purchasing on the Internet rather than in a conventional shop. Furthermore, the use of Internet as a distribution device that makes the access to niche products much easier only affects the concentration of online sales. H2: Sales should shift from the head of the distribution to the tail with an increasing magnitude over the period. The Long Tail theory assumes that the market share of bestsellers should decrease conversely to that of lowsellers. The wider use of the Internet as a source of information and discovery of books is assumed to play a significant role in this change. Since the use of Internet has increased over the period, the shift in the distribution of sales should become increasingly important. We thus expect Year dummies coefficients associated with the highest quantiles to be more and more negative from 2003 to 2007 conversely to lower quantiles. Note that our regressions capture the specific effect of online sales, and thus the easier distribution effect of Internet. If a decreasing concentration appears over the period, even when controlling for online sales and for the number of different titles sold, this should be related to the nature of Internet as an information device that also affects offline sales. H3: If the Long Tail effect is not temporary, the lower concentration of online sales with regard to offline sales should not vanish over the period. Online purchases could only be due to early adopters who are younger and more educated than the average reader and who thus exhibit stronger tastes for niche products (Brynjolfsson et al., 2010). Since the use of Internet has increased over the period, if the Long Tail effect is not a temporary one, we can expect the lower concentration of online sales in relation to offline sales not to disappear between 2003 and 2007. We thus include in the previous model Year effects that vary by distribution channel with five dummies ONLINE Year. To support hypothesis 3, we should not observe decreasing coefficients for ONLINE Year over the period. III. Data/Results We use a comprehensive database of monthly sales of physical books (pocket editions excluded) over a period of 5 years (January 2003 to December 2007) obtained from the GfK group that tracks all book sales in almost all outlets in France. Data focus on two genres, comic books and literature books which jointly represent about 40% of the whole French book
Internet and the Long Tail versus superstar effect debate 713 Table 1. Regressions on quantiles Model 1 Model 2 Log Q40 Log Q80 Log Q90 Log Q99 Log Q99.9 Log Q40 Log Q80 Log Q90 Log Q99 Log Q99.9 Y2004 0.147 (0.049)** Y2005 0.232 (0.058)** Y2006 0.450 (0.064)** Y2007 0.655 (0.071)** COMICS 0.221 (0.132) NEW 0.044 (0.092) TITLES 2.129 (0.092)** ONLINE 1.156 (0.060)** 0.024 (0.030) 0.015 (0.036) 0.087 (0.040)* 0.142 (0.044)** 0.329 (0.082)** 0.061 (0.057) 1.300 0.329 (0.037)** 0.023 (0.025) 0.052 (0.030) 0.111 (0.033)** 0.132 (0.036)** 0.294 (0.068)** 0.061 (0.047) 1.234 (0.047)** 0.058 (0.031) 0.003 (0.017) 0.044 (0.020)* 0.081 (0.023)** 0.060 (0.025)* 0.130 (0.047)** 0.032 (0.032) 0.950 (0.033)** 0.286 (0.021)** 0.074 (0.043) 0.117 (0.051)* 0.172 (0.056)** 0.229 (0.062)** 0.200 (0.116) 0.111 (0.080) 0.579 (0.081)** 0.270 (0.052)** 0.033 (0.062) 0.076 (0.064) 0.385 (0.068)** 0.685 (0.070)** 0.352 (0.141)* 0.028 2.363 (0.143)** 0.754 (0.151)** ONLINE Y2004 0.322 (0.093)** ONLINE Y2005 0.483 (0.112)** ONLINE Y2006 0.343 (0.118)** ONLINE Y2007 0.200 (0.131) Constant 7.775 (1.036)** 2.375 (0.639)** 2.688 (0.530)** 2.017 (0.366)** 0.089 (0.908) 10.060 (1.455)** 0.000 (0.038) 0.000 (0.040) 0.093 (0.042)* 0.128 (0.043)** 0.126 0.037 (0.054) 1.665 (0.088)** 0.127 (0.093) 0.194 0.299 (0.069)** 0.319 (0.072)** 0.430 (0.080)** 0.007 (0.032) 0.027 (0.033) 0.097 (0.035)** 0.130 (0.036)** 0.127 (0.072) 0.041 (0.045) 1.533 (0.073)** 0.328 (0.077)** 0.180 (0.048)** 0.270 0.298 (0.060)** 0.334 (0.067)** 0.010 (0.023) 0.038 (0.024) 0.077 (0.025)** 0.071 (0.026)** 0.130 (0.052)* 0.032 (0.033) 0.950 (0.053)** 0.280 (0.056)** 0.027 (0.035) 0.011 (0.042) 0.008 (0.044) 0.023 (0.049) 0.086 (0.058) 0.151 (0.060)* 0.162 (0.063)** 0.228 (0.065)** 0.197 (0.130) 0.110 (0.081) 0.574 (0.133)** Number of observations (month level) 240 240 240 240 240 240 240 240 240 240 Number of observations (title level) 3 200 870 3 200 870 3 200 870 3 200 870 3 200 870 3 200 870 3 200 870 3 200 870 3 200 870 3 200 870 Adjusted R 2 0.975 0.977 0.978 0.950 0.581 0.978 0.979 0.980 0.950 0.576 5.854 (0.896)** 5.547 (0.743)** 2.010 (0.543)** 0.278 (0.140)* 0.022 0.064 (0.104) 0.024 (0.109) 0.006 (0.121) 0.129 (1.351) Notes: Y2003 and ONLINE Y2003 dummies are omitted; estimations of Month fixed effect are not reported. * and **Significant at the 5% and 1% levels, respectively.
714 S. Peltier and F. Moreau market in 2007. On a monthly basis, the database includes on average 26 986 different titles of literature books that sold at least one copy and 11 663 titles of comic books. The number of monthly observations is 240 (12 months 5 years 2 genres 2 distribution channels), representing 3 200 870 observations at the title level. Each observation at the monthly level corresponds to a distribution of sales from which we calculated the quantiles. Table 1 displays the estimation results for regressions on selected quantiles from the 40th to the 99.9th. 1 The effect of a covariate on the dependent variable varies freely from quantile to quantile. Model 1 shows a strong and significative impact of the dummy ONLINE on predicted market shares. Bestsellers (the top 1% ) perform less well online, though this effect seems less pronounced for the hits (the 0.1% that sell the most). Conversely, the market share of low-seller books (belonging to the bottom 80% ) is higher online than offline, with the strongest effect for very low-seller books (the bottom 40% ). Hence, online sales exhibit a lower head and a thicker tail. Our results thus support our first hypothesis. Model 1 also shows that when controlling for the number of titles and for online sales, the Year dummies still have a significant impact on market shares. For hits, the decrease in market share is higher over the period and has been significant since 2005. These results support our second hypothesis and suggest that the Long Tail phenomenon stems, at least partially, from a change in consumer behaviour due to the use of Internet as an information tool. Model 2 shows that for bestsellers (99th quantile), the (negative) difference in online market share as compared with offline neither increases nor decreases over the period. For the 80th and 90th quantiles, the magnitude of the positive difference steadily increases over the period. These results support our third hypothesis asserting that the impact of Internet on the change in sales distribution is not temporary. Note that model 2 also gives stronger support to our second hypothesis. The coefficients of Year dummies (without interactions with ONLINE) reflect the impact of time for the distribution of offline sales. The signs and significance of these coefficients confirm that the distribution of sales in conventional shops also shifts from the head to the tail. IV. Conclusion Our results suggest that the use of online information and distribution tools does have an impact on consumers purchase decisions and leads them to shift somewhat from bestsellers to medium- or lowsellers. Of course, this statistical significance does not mean that the Long Tail phenomenon in the French book market is economically very significant yet. In 2007, according to our data, online sales only accounted for 4% of overall sales. However, those sales are experiencing strong growth, which will be undoubtedly reinforced with the advent of the digital book. Acknowledgements This research was supported by a grant from the DEPS/French Ministry of Culture and Communication and by the French National Research Agency (ANR-08-CORD-018). The authors thank Gilbert Laffond and Karim Kilani for useful discussions and insightful suggestions. References Anderson, C. (2004) The long tail, Wired Magazine, 12, 170 7. Bakos, Y. J. (1997) Reducing buyer search costs: implications for electronic market places, Management Science, 43, 1676 92. Benghozi, P. J. and Benhamou, F. (2010) The long tail: Myth or reality?, International Journal of Art Management, 12, 43 53. Bounie, D., Eang, B. and Waelbroeck, P. (2010) March e Internet et r eseaux physiques: comparaison des ventes de livres en France, Revue D economie Politique, 120, 141 62. Brynjolfsson, E., Hu, Y. J. and Simester, D. (2011) Goodbye Pareto principle, hello long tail: the effect of search costs on the concentration of product sales, Management Science. Available at http://ssrn.com/ abstract=953587 (accessed 30 June 2011). Brynjolfsson, E., Hu, Y. J. and Smith, M. D. (2003) Consumer surplus in the digital economy: estimating the value of increased product variety at online booksellers, Management Science, 49, 1580 96. Brynjolfsson, E., Hu, Y. J. and Smith, M. D. (2006) From niches to riches: the anatomy of the long tail, Sloan Management Review, 47, 67 71. Brynjolfsson, E., Hu, Y. J. and Smith, M. D. (2010) The longer tail: the changing shape of Amazon s sales distribution curve, Working Paper. Available at http:// ssrn.com/abstract=1679991 (accessed 30 June 2011). Chevalier, J. A. and Mayzlin, D. (2006) The effect of word of mouth on sales: online book reviews, Journal of Marketing Research, 43, 345 54. Elberse, A. and Oberholzer-Gee, F. (2008) Superstars and underdogs: an examination of the long tail phenomenon in video sales, Harvard Business School Working Paper No. 07 015, Harvard Business School, Boston, MA. 1 Regressions on the comprehensive set of quantiles are available upon request.
Internet and the Long Tail versus superstar effect debate 715 Fleder, D. and Hosanagar, K. (2009) Culture s next rise or fall: the impact of recommender systems on sales diversity, Management Science, 55, 697 712. Frank, R. and Cook, P. (1995) The Winner-Take-All Society, The Free Press, New York. Hervas-Drane, A. (2010) Word of mouth and taste matching: a theory of the long tail, mimeo, University of Pompeu-Fabra, Barcelona. Rosen, S. (1981) The economics of superstars, American Economic Review, 71, 845 58.