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“Selling less of more?” The impact of digitization on record companies


In this paper, we use data from a survey of 151 French record companies to test the “long-tail” hypothesis at the level of the firm. More specifically, we test whether, following the “selling less of more” principle coined by Anderson (2006), record companies that have adapted to digitization (at various levels: artists’ scouting, distribution, and promotion) release more new albums without having higher overall sales. We construct a production function in which the output is produced from conventional inputs of labor and capital, as well as inputs that are more specific to the recorded music industry. We consider two types of output: a commercial output (albums sales) and a creative output (number of new albums released). We show that labels that have adapted to digitization are more efficient in respect of creative output, but that there is no effect of adaptation to digitization on the commercial output, which is consistent with the predictions of the long-tail hypothesis.

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  1. See also Belleflamme and Peitz (2010) for a recent survey on the economics of digital piracy.

  2. See Crain and Tollison (2002) and Giles (2006) for recent contributions on the economics of superstars in popular music. Crain and Tollison (2002) argue that the superstardom phenomenon originates from the existence of an opportunity cost of time for searching for new music, which is inversely proportional to the number of fans of an artist. Giles (2006) provides some empirical evidence that the rise of an artist as a “superstar” in the popular music industry does not stem from pure luck.

  3. Besides, we do not have any satisfactory measure for the quality of label’s creative output.

  4. Baker (1991) also stresses that the number of new releases is an important decision of record labels.

  5. For a general discussion on the effects of digitization, see for example Peitz and Waelbroeck (2005), Regner (2003), Easley et al. (2003), Premkumar (2003), and Belleflamme and Peitz (2010).

  6. Noteworthy references include: Throsby (1977), who estimated a production function for the Australian performing arts, Gapinski (1980, 1984) who used US and British data to study the efficiency of performing arts firms (theater, opera, symphony, ballet), Taalas (1997) who studied the production structure in Finnish theaters, and Bishop and Brand (2003) who built a production function for British museums in order to study their efficiency.

  7. This extensive list of French record companies was compiled from the following professional directories: “L’Officiel de la Musique” (IRMA 2006) and “Le Réseau” (IRMA 2005) for popular genres of music; “Jazz de France” (IRMA 2006) for jazz music; “PlanèteMusique” (IRMA 2005) for traditional and world music; “Le guide du disqueclassique” (Editions Cité de la Musique 2005) for classical music. This list corresponds—more or less—to the total population of French record companies (only some very small labels might be missing).

  8. Unfortunately, we cannot test whether our sample is representative of the population it was extracted from, as no variable (such as the number of employees) was available for the full population. However, the market share of independent labels in our sample (compared with the market share of the major) is similar to the market share of independent labels in the French market in 2006. The extrapolation of the number of albums sold in 2005 to the full population is moreover in line with the total number of albums sold that year in France.

  9. We have data on physical sales only. However, in 2005 and 2006, in France, digital sales were negligible relative to physical sales (their share in total sales was 1.1 % in 2005 and 1.7 % in 2006, according to the SNEP).

  10. The ALBUMS variable gives the label's number of “new releases.” According to the terminology used in the record music industry, it corresponds to material that is not older than 18 months. However, we cannot know if the music material is brand new or consists of a compilation of old material.

  11. By including staff and adjuvants (promotional personnel, etc.) in the labor inputs, we follow Gapinski (1980, 1984).

  12. In our dataset, when a label has no employees, this actually means that only the entrepreneur is involved in the label’s activity.

  13. See, for example, Godes and Mayzlin (2004) and Chevalier and Mayzlin (2006).

  14. In particular, we find that for-profit labels are available on 1.5 platforms on average compared with 0.5 for not-for-profit labels (the difference is significant at less than 1 %). Classical and jazz labels are available on 0.5 platforms against 1.5 platforms for labels from other genres of music (the difference is also significant at less than 1 %). In other words, the distribution on digital platforms seems strongly related to the objective function of the label and its main genre of music.

  15. This could be either because the artist had sent a digital demo file by email to the label or the record company had discovered the artist on a specialized website like MySpace. Note that the name of our variable “A&R” stands for “Artist&Repertoires.” This is how the scouting activity is usually referred to by record companies.

  16. We reject the null hypothesis that the coefficients of the three adaptation variables are all equal to zero, at the 1 % level.

  17. The same robustness checks for the negative binomial regressions yield the same results, and are available upon request from the authors.

  18. For the non-profit labels, a test of joint exclusion yields χ2 = 2.24, and p value = 0.5234. Hence, we do not reject the null hypothesis that the coefficients of the three adaptation variables are all equal to zero.

  19. See our discussion on the representativeness of our sample, in Footnote no. 6.

  20. At the aggregate level, Waldfogel (2011) uses critics’ reviews and audiences as indicators of quality, and argues that the quality of music has not decreased since digitization started (i.e., in the late 1990s, when Napster was released).


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We thank two anonymous referees for valuable remarks and suggestions.

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Correspondence to Marc Bourreau.


Appendix 1

See Table 3.

Table 3 List of variables

Appendix 2

See Table 4.

Table 4 Summary statistics (N = 151)

Appendix 3

See Tables 5 and 6.

Table 5 Robustness checks for the commercial output—LOG(SALES)
Table 6 Robustness checks for the creative output (ALBUMS)

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Bourreau, M., Gensollen, M., Moreau, F. et al. “Selling less of more?” The impact of digitization on record companies. J Cult Econ 37, 327–346 (2013).

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  • Recorded music industry
  • Digitization
  • Long tail
  • Innovation

JEL classification

  • Z11
  • O33
  • L2
  • D2