Economics of Songwriters’ Performance Royalty Income: Tenure, Age, and Titles

  • Ivan L. Pitt


This chapter examines the dynamic of ‘superstar’ effects of age, length in membership in a PRO, and number of song titles registered on songwriter’s income when publishers are excluded. We found that royalty income distribution is still highly skewed and a key determinant of performance royalty income is due to the number of registered titles. This skewness of a relatively small number of songwriters earning more royalty payments than other members can be explained, in part, by successful members having a larger catalog of songs that are performed more frequently by radio and television stations, and other users of music. The standard skew-t distribution model generalized with location, scale, and a degree of freedom parameter (ν) is used to analyze royalty income.


Copyright Holder Music Industry Royalty Payment Record Label Large Catalog 
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  1. Azzalini, A. (1985). A class of distribution which includes the normal ones. Scandinavian Journal of Statistics, 12:171–178.Google Scholar
  2. Azzalini, A. (1986). Further results on a class of distribution which includes the normal ones. Statistica, 46:199–208.Google Scholar
  3. Azzalini, A. and Capitanio, A. (2003). Distributions generated by perturbation of symmetry with emphasis on a multivariate skew-t distribution. Journal of the Royal Statistical Society, B65:367–389.Google Scholar
  4. Azzalini, A., DalCappello, T., and Kotz, S. (2003). Log-skew-normal and log-skew-t distributions as models for family income data. Journal of Income Distribution, 11(3–4):12–20.Google Scholar
  5. Azzalini, A. and Genton, M. (2008). Robust likelihood methods based on the skew-t and related distributions. International Statistical Review, 76:106–129.CrossRefGoogle Scholar
  6. Dalla-Valle, A. (2007),). A test for the hypothesis of skew-normality in a population. Journal of Statistical Computation and Simulation, 77(1):63–77.CrossRefGoogle Scholar
  7. Galenson, D. (May 2000). The careers of modern artists. Journal of Cultural Economics, 24(2):87–112.CrossRefGoogle Scholar
  8. Galenson, D. (2003). The two life cycles of human creativity. NBER Reporter, Fall:12–15.Google Scholar
  9. Galenson, D. (2005). Literary life cycles: Measuring the careers of modern american poets. Historical Methods, 38(2):45–60.CrossRefGoogle Scholar
  10. Ginsburgh, V. and Weyers, S. (2006). Creativity and life cycles of artists. Journal of Cultural Economics, 30:91–107.CrossRefGoogle Scholar
  11. Halvorsen, R. and Palmquist, R. (1980). The interpretation of dummy variables in semi-logarithmic equations. The American Economic Review, 70(4):474–475.Google Scholar
  12. Kennedy, P. (1981). Estimation with correctly interpreted dummy variables in semilogarithmic equations. The American Economic Review, 71(4):801.Google Scholar
  13. Pitt, I. (2010a). Superstar effects on royalty income in a performance rights organization. Journal of Cultural Economics (forthcoming).Google Scholar
  14. Simonton, D. (1988). Age and outstanding achievement: What do we know after a century of research? Psychological Bulletin, 104:251–267.CrossRefGoogle Scholar
  15. Steinblatt, J. (2006). ‘Wonder Woman:The Amazing, True Life Adventures of One Of America’sGreatest Hitmakers’. Playback magazine. Fall, pp. 40–52.Google Scholar
  16. Walker, J. (2008). This Business of Urban Music: A Practical Guide to Achieving Success in the Industry, From Gospel to Funk to R&B to Hip-Hop. Billboard Books.Google Scholar
  17. Walls, W. (2005). Modeling heavy tails and skewness in film returns. Applied Financial Economics, 15:1181–1188.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.American Society of Composers, Authors and PublishersNew YorkUSA

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