IDEAS home Printed from https://ideas.repec.org/a/spr/snbeco/v1y2021i1d10.1007_s43546-020-00004-x.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s43546-020-00004-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s43546-020-00004-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    4. Ivan L. Pitt, 2010. "Economic Analysis of Music Copyright," Springer Books, Springer, number 978-1-4419-6318-5, September.
    5. 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.
    6. Ivan L. Pitt, 2015. "Direct Licensing and the Music Industry," Springer Books, Springer, number 978-3-319-17653-6, September.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huy Quang Doan, 2019. "Trade, Institutional Quality and Income: Empirical Evidence for Sub-Saharan Africa," Economies, MDPI, vol. 7(2), pages 1-23, May.
    2. Vasilis Sarafidis & Tom Wansbeek, 2012. "Cross-Sectional Dependence in Panel Data Analysis," Econometric Reviews, Taylor & Francis Journals, vol. 31(5), pages 483-531, September.
    3. Emirmahmutoglu, Furkan & Kose, Nezir, 2011. "Testing for Granger causality in heterogeneous mixed panels," Economic Modelling, Elsevier, vol. 28(3), pages 870-876, May.
    4. Archi Bhatia & Hans Raj Sharma, 2019. "Financial liberalization and channels of growth: a comparative study of developed and emerging economies," Indian Economic Review, Springer, vol. 54(1), pages 81-119, June.
    5. F. Di Lascio & Simone Giannerini & Antonello Scorcu & Guido Candela, 2011. "Cultural tourism and temporary art exhibitions in Italy: a panel data analysis," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 20(4), pages 519-542, November.
    6. Tuhkuri, Joonas, 2016. "Forecasting Unemployment with Google Searches," ETLA Working Papers 35, The Research Institute of the Finnish Economy.
    7. Uggeldahl, Kennet & Jacobsen, Catrine & Lundhede, Thomas Hedemark & Olsen, Søren Bøye, 2016. "Choice certainty in Discrete Choice Experiments: Will eye tracking provide useful measures?," Journal of choice modelling, Elsevier, vol. 20(C), pages 35-48.
    8. 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).
    9. Cristiana Tudor & Robert Sova, 2021. "On the Impact of GDP per Capita, Carbon Intensity and Innovation for Renewable Energy Consumption: Worldwide Evidence," Energies, MDPI, vol. 14(19), pages 1-25, October.
    10. Roger Bivand & Giovanni Millo & Gianfranco Piras, 2021. "A Review of Software for Spatial Econometrics in R," Mathematics, MDPI, vol. 9(11), pages 1-40, June.
    11. Bolat, C. Kaan & Soytas, Ugur & Akinoglu, Bulent & Nazlioglu, Saban, 2023. "Is there a macroeconomic carbon rebound effect in EU ETS?," Energy Economics, Elsevier, vol. 125(C).
    12. Arellano, Manuel, 2016. "Modelling optimal instrumental variables for dynamic panel data models," Research in Economics, Elsevier, vol. 70(2), pages 238-261.
    13. Chun, Hyunbae & Kim, Jung-Wook & Morck, Randall & Yeung, Bernard, 2008. "Creative destruction and firm-specific performance heterogeneity," Journal of Financial Economics, Elsevier, vol. 89(1), pages 109-135, July.
    14. Nielsen, Thea & Zeller, Manfred, 2013. "The Impact of Shocks on Risk Preference Changes between Seasons for Smallholder Farmers in Vietnam," 53rd Annual Conference, Berlin, Germany, September 25-27, 2013 156101, German Association of Agricultural Economists (GEWISOLA).
    15. Millo, Giovanni, 2014. "Robust standard error estimators for panel models: a unifying approach," MPRA Paper 54954, University Library of Munich, Germany.
    16. Arestis, Philip & Gonzalez-Martinez, Ana Rosa, 2016. "Revisiting the accelerator principle in a world of uncertainty: Some empirical evidence," Economic Modelling, Elsevier, vol. 56(C), pages 35-42.
    17. Peppel-Srebrny, Jemima, 2021. "Not all government budget deficits are created equal: Evidence from advanced economies' sovereign bond markets," Journal of International Money and Finance, Elsevier, vol. 118(C).
    18. Müller, Karsten, 2020. "German forecasters' narratives: How informative are German business cycle forecast reports?," Working Papers 23, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
    19. Kjetil Bjorvatn & Mohammad Reza Farzanegan, 2014. "Resource Rents, Power, and Political Stability," CESifo Working Paper Series 4727, CESifo.
    20. Zhao, Jun & Shahbaz, Muhammad & Dong, Kangyin, 2022. "How does energy poverty eradication promote green growth in China? The role of technological innovation," Technological Forecasting and Social Change, Elsevier, vol. 175(C).

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:snbeco:v:1:y:2021:i:1:d:10.1007_s43546-020-00004-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.