In his working paper entitled “On-line Piracy and Recorded Music Sales”, David Blackburn used a dataset combining weekly album sales data from Nielsen SoundScan with data of file sharing activity on the 5 largest sharing networks in the U.S. (Kazaa, Grokster, eDonkey, iMesh, and Overnet) provided by BigChampagne over more than 60 weeks between September 2002 and November 2003. The results showed that “(…) file sharing is reducing the sales of ex ante popular artists while redistributing some of these lost sales to smaller, less well known artists” (Blackburn 2004: 41). However, “(…) the aggregate effect of file sharing on sales is quite strongly negative” (Blackburn 2004: 6). “[T]he estimates suggest that a 30% across-the-board reduction in the number of files shared would have resulted in an additional 66 million albums sold in 2003, an increase of approximately $330 million in profits” (Blackburn 2004: 6).
In his model Blackburn identified two competing effects of file sharing on record sales: (1) a substitution effect on sales as some consumers download rather than purchased music; (2) a penetration effect, which positively affects record sales, as the spread of musical works helps the artist to become more well-known. This effect is also known as sampling effect. In the following, Blackburn tested both effects and an overall effect of file sharing on record sales.
In his methodology, Blackburn focused on album that were released between September 2002 and September 2003 and appeared for at least one week on the Billboard Hot 200 album charts. Further, only albums with new material, i.e. no re-releases, were included in the sample. In addition, albums by multiple artists, such as movie soundtracks, were eliminated. Finally, so-called “gospel” records were excluded from the sample, since the sales numbers were reported by the Christian Booksellers Association to SoundScan. This left a potential sample of 602 albums, which was matched with file sharing data from BigChampagne in order to get an operative sample of 197 albums for further testing. Since the sample was not randomly chosen, Blackburn had to reweigh observations in order to equalize the distribution of chart success to the full population.
On this data framework, Blackburn estimated in a two stage least squares approach the impact of file sharing on record sales. However, for the omitted variable bias the author used the annoucements of the Recording Industry Association of America (RIAA) to sue individual file sharers as an instrument variable. The announcement had a strong negative effect of -40% on the number of files in the file sharing networks. However, after the implementation of the lawsuits, file sharing activity once again rose by about 10%. As a second instrument variable Blackburn used the Christmas Holidays, a period associated with large reductions in file sharing.
On the aggregate level, the regression results indicate, that file sharing had no effect on record sales. “The estimated elasticity suggests that eliminating 10% of files shared would increase sales of recorded music by only 0.7%” (Blackburn 2004: 26). However, Blackburn, did not stop here, but differentiated between ex ante popular artists, who gained at least one Top-200 chart position in the last 10 years, and “new” artists who did not chart in this time span. The estimates suggest “(…) that new and relatively unknown artists may find file sharing very beneficial, as doubling the amount of file sharing activity for an album from a new artist would increase sales by 38%” (Blackburn 2004: 28). However, if the artist’s ex ante popularity increased, the positive effect vanished and turned into a negative relationship. For artists with a no. 1 album in the last ten years, the doubling of file sharing activity decreased her/his album sales by 54%. In comparison, the estimates revealed that a positive effect of file sharing on record sales (penetration/sampling effect dominates substitution effect) can be observed only for artists whose albums reached no higher than 147 on the Hot 200 charts, whereas a significantly negative impact of file sharing on record sales can be found for artists whose album reached at least 71 on the Hot 200 charts.
In order to capture competition effects between albums, Blackburn used a multinominal logit model, which led to the more or less same results than the OLS-estimates. Overall, no statistically significant effect of file sharing on record sales could be observed, but in differentiating popular and less popular artists, the study revealed that file sharing reduced sales of the former in favor of the latter.
For the music industry this redistribution of sales from popular to less popular artists was not beneficial in the short run. “If file sharing were to be reduced across the board by 30% sales would increase just over 10%.” (Blackburn 2004: 45). If we consider industry-wide sales of 660 million units in 2003, the sales increase would amount to 66 million albums and to US$ 330 million of additional revenue based on an estimate of US$ 5 of variable profit per sale.
David Blackburn, 2004, On-line Piracy and Recorded Music Sales. Working paper, Harvard University.
In part 3 of this series I will discuss Eric S. Boorstin’s study “Music Sales in the Age of File Sharing” from 2004.