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Life cycle effects of technology on revenue in the music recording industry 1973–2017

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  • Ivan L. Pitt

    (Independent Senior Economist and Data Scientist)

Abstract

Revenue in the recording music industry is driven by exogenous technology inputs and revisions to the Copyright Act. Each new technology and regulatory change had a financial impact that altered the life cycle patterns in the industry, that in turn led to innovative marketing applications that transformed the production, sale, and distribution of music. The premise that technological change follows exactly the theoretical S-curve in all cases may be misleading and this paper provides an alternative measure. We analyze the life cycle effects of technology on revenue in the music industry using an unbalanced panel instead of a logistic growth model when life cycle curves may be ‘irregular’ and the mathematical approximation is often difficult. When the error terms are corrected for heteroscedasticity and serial correlation, the model measures the increased marginal effects of digital technologies (physical, downloads, digital subscriptions, streaming, and synchronization) on music industry revenue for the years 1973–2017. This paper adds to the growing literature of advanced econometric modeling, machine learning, and artificial intelligence analysis in the music and creative industries.

Suggested Citation

  • Ivan L. Pitt, 2021. "Life cycle effects of technology on revenue in the music recording industry 1973–2017," SN Business & Economics, Springer, vol. 1(1), pages 1-29, January.
    • Handle: RePEc:spr:snbeco:v:1:y:2021:i:1:d:10.1007_s43546-020-00004-x
    DOI: 10.1007/s43546-020-00004-x
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    References listed on IDEAS

    as
    1. Samuel Cameron, 2016. "Past, present and future: music economics at the crossroads," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 40(1), pages 1-12, February.
    2. Ivan L. Pitt, 2013. "Power Laws and Skew Distributions: An Application to Performance Royalty Income," Journal of Income Distribution, Ad libros publications inc., vol. 22(2), pages 148-159, June.
    3. Ivan L. Pitt, 2015. "Direct Licensing and the Music Industry," Springer Books, Springer, number 978-3-319-17653-6, February.
    4. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    5. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    6. T. S. Breusch & A. R. Pagan, 1980. "The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(1), pages 239-253.
    7. Croissant, Yves & Millo, Giovanni, 2008. "Panel Data Econometrics in R: The plm Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i02).
    8. Ivan L. Pitt, 2010. "Economic Analysis of Music Copyright," Springer Books, Springer, number 978-1-4419-6318-5, February.
    9. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-434, November.
    10. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
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    Cited by:

    1. Ivan L. Pitt, 2022. "The system-wide effects of dispatch, response and operational performance on emergency medical services during Covid-19," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-12, December.

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    More about this item

    Keywords

    Life cycle of music; Physical music segment; Digital permanent downloads; Digital subscriptions; Digital streaming; Digital performance royalty; Pooled time-series cross-section model and RIAA data;
    All these keywords.

    JEL classification:

    • D23 - Microeconomics - - Production and Organizations - - - Organizational Behavior; Transaction Costs; Property Rights
    • L43 - Industrial Organization - - Antitrust Issues and Policies - - - Legal Monopolies and Regulation or Deregulation
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O34 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Intellectual Property and Intellectual Capital
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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