Illegal Music Downloading and its Impact on Legitimate Sales: Australian Empirical Evidence

Transcription

1 Illegal Music Downloading and its Impact on Legitimate Sales: Australian Empirical Evidence Jordi Mckenzie * August 13, 2009 Abstract This paper explores illegal music file-sharing activity and its effect on Australian sales of singles in the physical and digital retail markets. Using fifteen weeks of Australian Recording Industry Association weekly chart rankings of physical and digital sales, combined with a proxy for download activity derived from the popular peer-to-peer (P2P) network Limewire, the evidence suggests no discernible impact of download activity on legitimate sales. Whilst significant negative correlation between chart rank and download activity is observed in the digital market, once download endogeneity is purged from the model and song heterogeneity is controlled for no significant relationship remains. Keywords: P2P, file-sharing, music downloading, physical and digital singles. JEL Classification Numbers: Z11 * This study was in no way supported by the Australian Recorded Industry Association (ARIA) or any other industry organisation. I am grateful to the research assistance of Kamilla Scott-Mckenzie. Author: Jordi McKenzie, Lecturer, Discipline of Economics, University of Sydney, NSW, J.Mckenzie@econ.usyd.edu.au

2 1 Introduction The recorded music industry is currently experiencing one of the biggest transitional changes in its history. Never before have consumers had the ability to acquire music from so many sources both lawfully and unlawfully. The internet file-sharing revolution has brought with it a number of challenges for the record companies seeking to protect their profits and their artists profits. It is extremely difficult to put an exact figure on the cost to the industry from illegal downloading but estimates have put the number of illegally downloaded songs in excess of one billion each week (Oberholzer-Gee and Strumpf, 2007; and Zentner, 2006). In Australia, Music Industry Piracy Investigation (MIPI) estimates that in excess of one billion songs were traded illegally by Australians in The music industry is understandably concerned with such activity and has responded in a variety of ways ranging from legal actions, 1 accommodating such behaviour, 2 through to lobbying governments to impose taxes on blank media devices (such as CDRs) and to use this revenue to compensate copyright holders for their lost income. 3 Largely, however, there has been a push to increase the opportunities for music lovers to access music through legal downloading sites that are increasing in number (e.g. Apple Itunes). In Australia, as in other countries, there is an increasing trend towards digitally purchased music. Although as a percentage of overall market share digital purchases are still comparatively low, there has been significant growth of this sector in recent times as evidenced by the market share of digital music sales increasing from 5.5% in 1 The most famous class action brought by several record labels was A&M Records Inc. vs. Napster Inc., which Napster lost in For example, in 2007 the popular cult band Radiohead released the first of two albums In Rainbows on the internet at a price that was effectively free with a voluntary payment system that allowed the consumer to choose no payment. 3 This tax known as the blank media levy prevails in Canada and some European countries (e.g. Finland, Netherlands and Sweden), but has found little support elsewhere including Australia. 1

3 2006, to 9.4% in 2007, to 14.6% at the end of 2008 (ARIA, 2009) an increase which has been offset by a subsequent decline in the physical market in terms of both volume and value. In terms of sales receipts and sales volumes of singles (the unit of analysis in this study), the value (volume) of physical sales decreased from A$6,712k (2,497k) in 2007 to A$3,570k (1,315k), whereas in the digital market sales receipts (volume) from singles increased from A$18,695k (17,657k) in 2007 to A$26,735k (23,465k) in From this broad evidence, it is clear that the music industry is undergoing significant structural change. Although the music industry is unambiguously of the opinion that illegal downloading has only negative consequences for music sales, there is compelling argument that must be explored suggesting that, in fact, illegal downloading through its word-ofmouth effect may actually help in boosting sales. In a recent study, Oberholzer-Gee and Strumpf (2007) found no relation between download activity and music sales using data on sales from both physical and digital retail sales of albums for users located in the U.S. over the last third of These authors use file-sharing data collected from OpenNap, a centralised peer-to-peer (P2P) network, which they claim gives them a sample capturing 0.01% of the world s downloads, and cross reference these to actual sales of albums through retail and on-line purchasing over a 17 week period. This study takes the same research question but considers it in relation to physical (i.e. traditional brick and mortar retailers) and digital (i.e. online music stores) sales of singles rather than albums. Given that the music industry is experiencing the biggest drop in sales in this area, this presents an opportunity to test whether illegal file-sharing is indeed displacing legitimate purchases of singles, and further what the displacement relationship is when digital and physical sales are 2

4 treated independently. One might consider that these two different products are broadly representative of two different types of consumers, and it would seem reasonable to hypothesise that the digital consumer would be more likely to displace her purchases with illegal substitutes clearly defining one of this study s primary research objectives. Although the study of Oberholzer-Gee and Strumpf (2007) has defined itself as the seminal research of this little investigated (by economists) area, its scope of investigation was limited in certain respects, and moreover, its findings are contentious to many (see Liebowitz, 2007) considering many other studies have reached opposing conclusions. 4 Also, even though the study of Oberholzer-Gee and Strumpf appeared in 2007, the data sample is fast becoming out of date in the rapidly changing industry given that it was compiled in To further note the significance of this research beyond the discussion of academics, the problem of illegal downloading has recently caught the attention of the recently elected Australian Labour Government who are considering a three strike policy to deal with repeat offenders similar to the system recently employed in the U.K. Under this arrangement ISPs would have to police users and take action against those who access pirated material, and would see them warning, suspending, and finally cancelling internet access under the three strike system. It is evident that this is an important issue and one that has many stakeholders to consider. 4 See, for example, Peitz and Waelbereck (2004), Stevans and Sessions (2005), and Zetner (2006). 3

5 Whilst a number of theoretical contributions have been put forward discussing possible implications of illegal downloading, 5 there is no agreed a-priori foundation for suggesting that illegal downloading necessarily helps or hinders legitimate sales and the question subsequently remains an empirical one. In a 2003 survey commissioned by ARIA, it was declared that those who engaged in internet filesharing reported a net reduction of 12% in their CD purchasing behaviour as a direct result of that activity. Indeed, ARIA claim, this finding is consistent with other international survey findings. 6 Whilst surveys are one form of addressing the empirical question, the construction appears necessarily biased towards individuals who engage in file-sharing. It would seem conceivable that the increased availability of songs would filter through to (the ears of) other consumers who may be now inclined to make legitimate purchases that they may have not made previously. It may be the case that these increased purchases subsequently outweigh the displacement effect reported in the survey results. 7 This study therefore follows an approach similar to that of Oberholzer and Strumpf (2007), and takes the question directly to a data set that relates industry sales to P2P file-sharing activity. The data set covers the period 5 th November 2007 to 11 th February 2008 and includes all songs ranked in the weekly ARIA charts of single sales in both physical and digital markets. Downloading activity, however, is not measured directly but is proxied by the corresponding number of users who had each song available for download on the popular P2P file-sharing network Limewire on the Monday of each week prior to the charts release on the Sunday. Limewire is known to be the most popular such network in Australian with Music Industry Piracy Investigation Pty. Ltd. suggesting it 5 See survey of Dejean (2009) for a discussion of these papers. 6 See 7 Not all survey analysis, however, has universally found that illegal file sharing harms sales. A recent study by Andersen and Frenz (2008) on the Canadian industry found that for every 12 P2P downloaded songs music purchases increase by 0.44 CDs. 4

6 represents in excess of 60% of file-sharing activity. The Limewire file-sharing program can be downloaded free of charge from the internet, and can also be extended to a Pro version for a nominal fee of approximately AU$20. The number of users who have each song available for download is shown to be negatively (and significantly) correlated with rank in the digital charts which intuitively suggests common popularity between the two measures recalling higher ranked songs are expressed as smaller numbers, i.e. have more sales. However, there is no significant correlation observed between the numbers of illegal downloads (proxied by the number of Limewire users) and the physical sales chart rankings. This evidence is suggestive of two different types of consumers in the two different markets and a market segmentation effect. Following the study of Oberholzer-Gee and Strumpf (2007) this paper then uses fixed effects and instrumental variable methodology to examine the effect of illegal downloading activity on the ARIA rankings in each market. Specifically, because there is likely to be a positive correlation between illegal downloading activity and legitimate sales due to an unobserved (by the econometrician) taste effect there is a need to find a variable(s) which are correlated with downloading activity, yet uncorrelated with the error term of the regression. In this respect the file size and the bit-rate as recorded on Limewire are used as instruments in the first stage regression to address this problem. The results suggest that once download endogeneity is considered, there is inconclusive evidence of downloading activity affecting sales in either market a finding consistent with Olbeholzer-Gee and Strumpf s conclusion. 5

7 2 Econometric Model The econometric methodology of this study examines the impact of levels of illegal downloading on legitimate sales by investigating the change in weekly rankings of songs in the ARIA Top 40 (digital) and Top 50 (physical) singles charts. 8 Limewire s reported number of sources for music sales provides a proxy for the level of illegal downloading activity on a single song, as well as the ease of availability should a user be inclined to consider downloading the song illegally (Limewire users report unambiguously a favour to download songs when the number of potential sources is higher). To motivate the analysis, the following initial regression model is proposed that pools the two sub-samples, i.e. markets m {Digital, Physical} ln R = α + βti + χ( TI ) + γd + η( D * DU ) + μ + { ϕ WK} + v (1) m m m 2 D m 14 m it, it, 1 it, 1 it, 1 it, 1 i T T= 1 it, In this specification, ln R is the (log) rank of song i in market m in week t. 9 m it, Although it would be preferable to have actual sales numbers (or revenues) as the dependent variable rather than simply a ranking, industry confidentiality prevented such an analysis. In any case, however, it is well known that the rankings are simply based on ARIA s estimated and so the actual level of sales of any particular song is not likely to be known with certainty. Rank is transformed into log form to capture the non-linearity in chart position and reflects that, for example, a drop from rank 37 to 40 is not as significant as a drop 8 Obviously a more desirable measure of illegal downloading activity s impact on legitimate purchases would be to investigate the effect on weekly sales data rather than weekly rankings data, however, ARIA were not accommodating with this request despite repeated enquiries. Given the estimated nature of this variable by ARIA and the assumptions invoked in the empirical analysis (see below) it is believed that this proxy does not alter the results substantially. 9 Throughout this analysis a rank of 1 suggests the most popular song of the week. 6

8 from position 1 to 4 in terms of sales volumes. That is, sales are assumed to be subject to the super-star effect (Rosen, 1981), or winner-take-all effect (Frank and Cook, 1995). The (assumed) convex relationship between ranking and sales has been observed in many cultural industries and there are strong a-priori reasons to believe that this assumption is warranted here. 10 m TIit, 1 is the number of times song i had appeared in the m charts up to, and including, week t-1. There is also a quadratic of this variable to account for rank decreasing (or increasing) in a non-linear way as a song ages. 11 Dit, 1 is the number of people who have the particular song available for download in week t-1. This is the key variable of interest and to investigate its effect between the two markets m, it also appears as an interaction variable with the dummy variable DU D taking the value 1 in the digital sales sub-sample, and 0 in the physical sales sub-sample. μ m i is the time invariant fixed effect, which captures song heterogeneity for song i in market m. There are also 14 weekly dummy variables (WK) to account for weekly fixed effect across the sample period that are estimated relative to the week 15 dummy variable. 12 m Finally, v it, captures the unobserved idiosyncratic error. Equation (1) is then estimated separately for the two markets, and consequently the interaction (dummy) variable is dropped. The following regression describes this ln R = α + βti + χ( TI ) + γd + μ + { ϕ WK} + v. (2) m m m 2 m 14 m it, it, 1 it, 1 it, 1 i T T= 1 it, Of course there is one critical point of concern with equations (1) and (2) relating to the likely endogenous regressor D i,t-1. Specifically, the number downloads is likely to 10 De Vany and Walls (1996), Walls (1997), Hand (2001) and Mckenzie (2008) document this for box office returns, Maddison (2004) for Broadway theatre, and Giles (2007) for the US popular music industry. 11 See the evidence of Table 2 discussed below. 12 The inclusion of the weekly dummy variables effectively gives a two way error components model. 7

9 be endogenous and correlated with unobserved song heterogeneity because the more popular songs will tend to be those that are downloaded more frequently. To adjust for this possibility the variable is instrumented in the standard two-stage least squares manner. 13 The first stage regression can be written D = α + βti + χ( TI ) + γd + φs + ηbr + { ϕ WK} + u. (3) m m 2 14 it, it, it, it, 1 it, it, T T= 1 it, In this specification the additional instruments are the lagged number of downloads in the previous week D i,t-1, the size of the file in kilobytes S i,t, and the bit-rate BR i,t. These last two instruments are chosen to given a measure of the (implicit time) cost and quality of a download and would be expected to be uncorrelated with the error terms in (1) or (2). That is, they would be expected to be uncorrelated with the weekly ranking of sales (physical and digital), yet correlated with (unlawful) download activity. To the extent that music downloaders consider the time taken to download a song (file size) and the quality of the download (bit-rate), these two variables have implications for whether or not the file will be downloaded illegally. These variables, however, are unlikely to directly affect whether or not consumers choose to purchase a song lawfully. Oberholzer-Gee and Strumpf (2007) use a number of instruments in their analysis with the main ones related to whether German school children are on school holidays or not, under the assumption that when they are more file-sharers will be online and consequently downloading activity will be more prevalent and successful. It is worth noting, however, that there has been some discussion as to the quality of this particular instrument in this analysis (Liebowitz, 2007). Although the current download data being considered is admittedly inferior to that of Oberholzer-Gee and 13 See Anderson and Hsiao (1981). 8

10 Strumpf (2007), it would seem that the chosen instruments are, at least in theory, more appropriate to the first stage regression ARIA Weekly Rankings and File-sharing Data The data set is compiled from weekly Top 40 (digital) and Top 50 (physical) singles charts as released each Sunday by the Australian Recording Industry Association (ARIA) covering fifteen weeks from 5 th November 2007, through 11 th February This period covers the Christmas/New Year period where retail sales are highest and many school children aged children (the largest demographic of file-sharers) are on holiday. The Top 40/50 ranks singles by their (estimated) number of units sold from Australian music retailers. 15 The charts also list the ranking of the previous week, the number of times a single has entered the charts, the highest ranking attained to date, as well as the title, artist and record label. Of the full sample of 1,350 observations (600 digital and 750 physical) there were 91 unique songs in the digital sample and 104 in the physical sales sample. Each single had been in the charts an average of 9.5 times (10.4 for digital and 8.8 for physical), the average highest rank was 13.2 (12.2 for digital and 14.0 for physical), there were 120 new entries (60 in each market), and 50 singles re-entered the charts (27 in the digital and 23 in the physical). The average change in rank between adjacent weeks was a drop of 1.3 places in the physical chart and a drop of 0.7 places in the digital 14 It should also be noted that other instruments relating to the file-size and bit-rate were considered. Specifically, treating other songs as rivals meant that their individual and combined properties could also be considered as potential instruments under certain assumptions (see Berry, Levinson, and Pakes, 1995). However, all were shown to give similar results with no improvement on identification. 15 A full description of the collation of ARIA charts can be found at 9

11 chart. Table 1 reports summary statistics for the full sample and the two distinct subsamples. [INSERT TABLE 1 NEAR HERE] On the following Monday of each week of the sample, each single from the Top 40 and Top 50 was cross referenced on the popular P2P file-sharing network Limewire. Music Industry Piracy Investigation (MIPI) unit suggests this network to represent in excess of 60% of all music file-sharing activity in Australia, and this network is therefore chosen to give a proxy for the number of downloads occurring for this song as well as the ease of availability for new potential downloaders. Between 7-10pm, a time when internet usage is highest, each single was individually searched for and the number of users who had the track available for download was recorded along with information relating to the file size, speed of download, and bit-rate. The summary statistics of Table 1 also reveal that in the full sample the average single had users. In the digital sample the average was and in the physical sample there were users on average. The highest number of users for a digital song was 350 for Clumsy by Fergie in both charts, who also tied with Michael Jackson with Akon remix of Wanna be Startin Somethin 2008 in the digital charts. The average file size was 5,348 Kb with the biggest file belonging to Michael Buble s Let it Snow!, which was 9,985 Kb in size. The average bit-rate (reflecting the quality of a download) in the full sample was about 187 bits per second and was not significantly different in the two sub-samples. [INSERT TABLE 2 NEAR HERE] Table 2 provides further information on the movements of songs through the charts as they mature. New songs debuted at a rank of approximately 25 in the physical charts 10

12 and 29 in the digital charts. Songs in their second week of chart appearance had an average ranking of 23 and 18 in the physical and digital charts respectively. This table provides some suggestion that singles may move through the charts in a nonlinear way as they age guiding the specification of this variable in the econometric methodology discussed above. Table 3 provides pair-wise correlation coefficients for the pooled sub-samples and a number of relations are evident. In particular, and not surprisingly, last week s ranking correlates strongly with this week s ranking, which in turn correlates with next week s ranking. There is a significant positive correlation between the time spent in the charts and ranking, which is consistent with the idea that as a single ages its ranking falls (i.e. popularity decreases). The number of Limewire users in the current and previous week correlates (significantly) negative with the week ranking suggesting that more popular songs are downloaded more frequently. Finally, there is a significant and positive relationship between the number of Limewire users and the bit-rate of a download (recalling bitrate is indicative of quality of download), however, there is no significant correlation with file size. The finding of a positive relationship with bit-rate is consistent with a- pirori expectations and validates its inclusion as an instrument in equation (3) Table 4 provides similar correlations for the partitioned sub-samples. The most striking feature of this partition is that the number of Limewire users doesn t show any sign of significant correlation with ranking for the physical sales sub-sample, whereas this relationship does exist for the digital sales sub-sample. This initial evidence is suggestive of different types of consumers in each sub-sample, and that songs which are popular in the digital market are also popular for (illegal) file shares, but songs which are popular in the physical sales market are not popular with file-sharers. 11

13 [INSERT TABLES 3&4 NEAR HERE] 4 Results Table 5 provides the results of the two sub-samples pooled together. To avoid problems of serial correlation which may be introduced into the model owing to the persistence of unobserved time effects, all estimates are done using Newey-West standard errors with one lag. The first basic regression of (log) this week ranking on number of downloads last week reveals that there is no evidence of a relationship between the two in the physical sales market, but that there is a significant (negative) relationship with the digital market. 16 This is consistent with the correlation evidence of Table 4. By admission, however, this regression is overly simplistic in its construction and columns 2 and 3 introduce (market specific) song fixed effects and week effects. Also introduced is the additional regressor(s) that relate to the number of weeks the song has been in the charts. 17 There is no longer significance observed with respect to download activity but there is with respect to the number of weeks the song has been in the charts. The linear interaction with the number of times the song had appeared in the charts reveals (as expected) a positive relation with rank suggesting a fall in popularity as a song ages ceteris paribus. Inclusion of the quadratic variant of this variable suggests this interaction may be non-linear. Of course the most compelling concern of the regressions in columns 1 through 3 is the fact that downloads may be correlated with the error through an unobservable effect. Columns 4 and 5 subsequently consider an instrumental variable modification with the first stage as described in equation (3), where the additional instruments file- 16 The regression of this week ranking on number of (last week) illegal down-loads similarly revealed a statistically significant negative relation. It is therefore evident that this relationship is being driven solely by the relationship in the digital sales market. This is also consistent with the evidence of the partitioned analysis discussed below. 17 The quadratic s inclusion is guided by the non-linearity of rank over song life suggested in Table 2. 12

14 size and bit-rate are included. All estimated coefficients signage remains the same as in the fixed effect regressions, however, there is only weak significance in the regressions at the 10% level for the number of weeks previously spent in the charts. The evidence suggests that at least using this data there is no significant impact on sales in either market because of P2P activity. [INSERT TABLE 5 NEAR HERE] To investigate this relationship further, the two markets are treated separately in Tables 6 and 7. Columns 1 of each table confirm exactly what was detected in the pair-wise correlations, and in the results of Table 5. That is, there is no evident relationship between sales rankings and (illegal) downloads in the physical sales market, yet this does appear in the digital market. However, once additional explanatory variables (times in charts and its quadratic counterpart) are added, and once fixed effects and week effects are purged this (apparent) relationship is no longer observed. This is further confirmed when the problematic variable is instrumented in columns 4 and 5. Once again, however, there is some evidence of the number of times that a song has remained in the charts exerting a positive effect of rank and that (at least in the digital) sample this may not be a linear path. [INSERT TABLES 6&7 NEAR HERE] 5 Conclusions This paper has utlised a unique data set to consider the impact of illegal file-sharing activity on legitimate purchases of singles in both the physical and digital retail markets in Australia over a 15 week period during late 2007 and early Using ARIA charts of weekly sales rankings of singles and cross referencing these with user activity on the popular P2P file-sharing network Limewire, it was observed that 13

15 there was a (significant) negative correlation between weekly rankings and the number of file-sharers in the digital sales sub-sample that was not evident in the physical sales sub-sample. This negative relationship is not surprising and is suggestive of unobserved (by the econometrician) factors relating to a single s popularity (i.e. more popular songs achieve higher rankings in terms of sales, and exhibit high levels of download activity). It was also interesting to note that the negative correlation was only observed in the digital market which suggests some degree of market segmentation. Specifically, digital purchasers and illegal downloaders appear to share more common tastes vis-à-vis physical store purchasers and illegal downloaders. Intuitively it would seem that this market segmentation derives from the new (mostly younger) computer savvy generation, as compared to the more traditional consumer type The econometric results reveal, however, that once the regression methodology accounts for song fixed effects, week fixed effects, and the endogenouus (illegal) download variable no significant negative relationship remains. This finding may be cautiously interpreted as (illegal) downloading activity having no effect on sales in either the physical or digital markets. Of course, this conclusion should be considered with much caution given the numerous studies (many of them industry based) that have found an adverse effect of illegal downloading. The present study therefore finds results similar to those of Olberholzer-Gee and Strumpf in respect to their seminal study. It is unclear to what extent better data is likely to change this result. Obviously data on actual sales would be preferable to the second best solution of using ranking as the dependent variable, but moreover better data on illegal filesharing activity is critical to properly addressing this question. The data used by 14

16 Olberholzer-Gee and Strumpf were extremely rich and no other researchers to date have been able to access data of this quality. Considering the importance of the impact of illegal file-sharing activity for the industry, it is hoped that in time researchers will better be able to access quality data to allow definitive judgements to be made on this important issue. 15

17 References Anderson T. W. and Hsiao, C. (1981) Estimation of Dynamic Models with Error Components Journal of the American Statistical Association, 76, Andersen, B. and Frenz, M. (2008) The Impact of Music Downloads and P2P File- Sharing on the Purchase of Music: A Study for Industry Canada ARIA, various publications, Berry, S., Levinsohn, J. and Pakes, A. (1995) Automobile Prices in Equilibrium Econometrica, 63(4), Dejean, S. (2009) What Can We Learn From Empirical Studies About Piracy CESifo Economic Studies, 55, De Vany, A. and Walls, D. (1996) Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry The Economic Journal, 106, Frank, R. and Cook, P. (2005) The Winner-Take-All Society, The Free Press, New York. Giles, D. (2007) Increasing Returns to Information in the Popular U.S. Music Industry Applied Economics Letters, 14, Hand, C. (2001) Increasing Returns to Information: Further Evidence from the UK Film Market Applied Economics Letters, 8, Oberholzer-Gee, F. and Strumpf, K (2007) The Effect of File-sharing on Record Sales: An Empirical Analysis Journal of Political Economy, 115(1),

18 Liebowitz, S. (2003) Will MP3 Downloads Annihilate the Record Industry? The Evidence So Far, in Libecap, G. (Ed), Advances in the Study of Entrepreneurship, Innovation and Economic Growth, JAI Press. Liebowitz, S. (2006) File-Sharing: Creative Destruction or just Plain Destruction? Journal of Law and Economics, 49, Liebowitz, S. (2007) How Reliable is Oberholzer-Gee and Strumpf s paper on Filesharing, Working Paper, University of Texas at Dallas. Maddison, D. (2004) Increasing Returns to Information and the Survival of Broadway Theatre Productions. Applied Economic Letters, 11 (10), McKenzie, J. (2008) Bayesian Information Transmission and Stable Distributions: Motion Picture Revenues at the Australian Box Office. The Economic Record, 84(266), MIPI, various publications, Peitz, M. and Waelbroeck, P. (2003) The Effect of Internet Piracy on Music Sales: Cross-Section Evidence Review of Economic Research on Copyright Issues, 1(2), Rosen, S. (1981) The Economics of Superstars American Economic Review, 71(5), Stevans, L. and Sessions, D. (2005) An Empirical Investigation into the Effect of Music Downloading on the Consumer Expenditure of Recorded Music: A Time Series Approach, Journal of Consumer Policy, 28, Walls, D. (1997) Increasing Returns to Information: Evidence from the Hong Kong Movie Market. Applied Economics Letters, 4(5),

19 Zentner, M. (2006) Measuring the Effect of Music Download on Music Purchases Journal of Law and Economics, 49(1),

20 Table 1 Summary Statistics: Pooled Sample and by Physical/Digital Sub-Sample Variable Obs. Mean Median Min Max Std. Dev. Skew Kurt Pooled Sample Times in Charts Highest Place Change in Rank No. Limewire Users Size of File Bit-rate Physical Sub-Sample Times in Charts Highest Place Change in Rank No. Limewire Users Size of File Bit-rate Digital Sub-Sample Times in Charts Highest Place Change in Rank No. Limewire Users Size of File Bit-rate

21 Table 2 Weekly Rank Summary Statistics by Physical and Digital Sub-Samples Physical Sub Sample Digital Sub Sample Times in Average Rank Std. Average Rank Std. Obs. Min Rank Max Rank Obs. Charts Rank Dev Rank Dev Min Rank Max Rank

22 Table 3 Pair-wise Correlation Matrix of Key Variables: Pooled Sample This Week Rank Last Week Rank Next Week Rank Times in Charts No. Limewire Users This Weeek No. Limewire Users Last Week Size of File This Week Rank 1 Last Week Rank * 1 Next Week Rank * * 1 Times in Charts * * * 1 No. Limewire Users This Week * * * * 1 No. Limewire Users Last Week * * * * 1 Size of File * Bit-rate * * * * * * * 1 * Denotes pair-wise correlation is significant at 5%. Bit-rate 21

23 Table 4 Pair-wise Correlation Matrix of Key Variables: Physical/Digital Sub-Samples a This Week Rank Last Week Rank Next Week Rank Times in Charts No. Limewire Users This Week No. Limewire Users Last Week Size of File This Week Rank * * * * * Last Week Rank * * * * * Next Week Rank * * * * * Times in Charts * * * * No. Limewire Users This Week * * * No. Limewire Users Last Week * * * Size of File * * Bit-rate * * * * * * * 1 a Numbers in bottom left refer to correlations in physical sales sub-sample and numbers in top right refer to correlations in digital sales sub-sample. * Denotes pair-wise correlation is significant at 5%. Bit-rate 22

24 Table 5 Pooled Sample: Dependent Variable is (log) This Week Ranking OLS FE FE IVFE IVFE D i,t ( ) ( ) ( ) ( ) ( ) D i,t-1 *DU(Digital) *** ( ) ( ) ( ) ( ) ( ) TI i,t *** *** * * ( ) ( ) ( ) ( ) 2 TI i,t *** * ( ) ( ) Constant *** *** *** ( ) ( ) ( ) ( ) ( ) Fixed Effects No Yes Yes Yes Yes Week Effects No Yes Yes Yes Yes Observations No. Groups Max Group Size Min Group Size Avg Group Size F (Prob > F) Notes: All estimations are done using Newey-West standard errors with one lag. FE employs a within fixed effect estimation procedure. IVFE employs a within instrumental variables two-stage least square procedure. Values in parentheses are standard errors. *,**, *** represent significance at 10%, 5% and 1% respectively. 23

25 Table 6 Physical Sales Sample: Dependent Variable is (log) This Week Ranking OLS FE FE IVFE IVFE D i,t ( ) ( ) ( ) ( ) ( ) TI i,t *** *** * ** ( ) ( ) ( ) ( ) 2 TI i,t ( ) ( ) Constant *** *** *** *** ( ) ( ) ( ) ( ) ( ) Fixed Effects No Yes Yes Yes Yes Week Effects No Yes Yes Yes Yes Observations No. Groups Max Group Size Min Group Size Avg Group Size F (Prob > F) Notes: All estimations are done using Newey-West standard errors with one lag. FE employs a within fixed effect estimation procedure. IVFE employs a within instrumental variables two-stage least square procedure. Values in parentheses are standard errors. *,**, *** represent significance at 10%, 5% and 1% respectively. 24

26 Table 7 Digital Sales Sample: Dependent Variable is (log) This Week Ranking OLS FE FE IVFE IVFE D i,t *** ( ) ( ) ( ) ( ) ( ) TI i,t *** ** ** ( ) ( ) ( ) ( ) 2 TI i,t *** ** ( ) ( ) Constant *** *** ** ( ) ( ) ( ) ( ) ( ) Fixed Effects No Yes Yes Yes Yes Week Effects No Yes Yes Yes Yes Observations No. Groups Max Group Size Min Group Size Avg Group Size F (Prob > F) Notes: All estimations are done using Newey-West standard errors with one lag. FE employs a within fixed effect estimation procedure. IVFE employs a within instrumental variables two-stage least square procedure. Values in parentheses are standard errors. *,**, *** represent significance at 10%, 5% and 1% respectively. 25