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The Impact of the Agency Model on E-book Prices: Evidence from the UK

Author

Listed:
  • Maximilian Maurice Gail

    (Justus Liebig University Giessen)

  • Phil-Adrian Klotz

    (Justus Liebig University Giessen)

Abstract
This paper empirically analyzes the effect of the widely used agency model on retail prices of e-books sold in the United Kingdom. Using an unique cross-sectional dataset of e-book prices for a large number of book titles across all major publishing houses, we exploit cross-genre and cross-publisher variation to identify the effect of the agency model on e-book prices. Since the genre information is ambiguous and even missing for some titles in our original dataset, we also apply a Latent Dirichlet Allocation (LDA) approach to determine detailed book genres based on the book’s descriptions. We find that retail prices for e-books sold under the agency model are on average 18% cheaper than book titles sold under the wholesale model. Our results are robust to different regression specifications, an instrumental variable approach, and double machine learning techniques.

Suggested Citation

  • Maximilian Maurice Gail & Phil-Adrian Klotz, 2021. "The Impact of the Agency Model on E-book Prices: Evidence from the UK," MAGKS Papers on Economics 202111, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    • Handle: RePEc:mar:magkse:202111
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    File URL: https://www.uni-marburg.de/en/fb02/research-groups/economics/macroeconomics/research/magks-joint-discussion-papers-in-economics/papers/2021-papers/11-2021_gail.pdf
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    References listed on IDEAS

    as
    1. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    2. Rey, Patrick & Stiglitz, Joseph, 1988. "Vertical restraints and producers' competition," European Economic Review, Elsevier, vol. 32(2-3), pages 561-568, March.
    3. Johnson, Justin P., 2020. "The agency and wholesale models in electronic content markets," International Journal of Industrial Organization, Elsevier, vol. 69(C).
    4. Erik Brynjolfsson & Yu (Jeffrey) Hu & Mohammad S. Rahman, 2009. "Battle of the Retail Channels: How Product Selection and Geography Drive Cross-Channel Competition," Management Science, INFORMS, vol. 55(11), pages 1755-1765, November.
    5. Jonah Berger & Alan T. Sorensen & Scott J. Rasmussen, 2010. "Positive Effects of Negative Publicity: When Negative Reviews Increase Sales," Marketing Science, INFORMS, vol. 29(5), pages 815-827, 09-10.
    6. Gilbert, Richard J, 2015. "E-books: A Tale of Digital Disruption," Department of Economics, Working Paper Series qt0wg6v2r6, Department of Economics, Institute for Business and Economic Research, UC Berkeley.
    7. Vibhanshu Abhishek & Kinshuk Jerath & Z. John Zhang, 2016. "Agency Selling or Reselling? Channel Structures in Electronic Retailing," Management Science, INFORMS, vol. 62(8), pages 2259-2280, August.
    8. Daniele Condorelli & Andrea Galeotti & Vasiliki Skreta, 2018. "Selling through referrals," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 27(4), pages 669-685, October.
    9. Nelson, Phillip, 1970. "Information and Consumer Behavior," Journal of Political Economy, University of Chicago Press, vol. 78(2), pages 311-329, March-Apr.
    10. Matthew Gentzkow & Bryan Kelly & Matt Taddy, 2019. "Text as Data," Journal of Economic Literature, American Economic Association, vol. 57(3), pages 535-574, September.
    11. Hailiang Chen & Yu Jeffrey Hu & Michael D. Smith, 2019. "The Impact of E-book Distribution on Print Sales: Analysis of a Natural Experiment," Management Science, INFORMS, vol. 65(1), pages 19-31, January.
    12. Ippolito, Pauline M, 1991. "Resale Price Maintenance: Empirical Evidence from Litigation," Journal of Law and Economics, University of Chicago Press, vol. 34(2), pages 263-294, October.
    13. Helmers, Christian & Krishnan, Pramila & Patnam, Manasa, 2019. "Attention and saliency on the internet: Evidence from an online recommendation system," Journal of Economic Behavior & Organization, Elsevier, vol. 161(C), pages 216-242.
    14. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    15. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey, 2017. "Double/Debiased/Neyman Machine Learning of Treatment Effects," American Economic Review, American Economic Association, vol. 107(5), pages 261-265, May.
    16. Patrick Rey & Joseph Stiglitz, 1995. "The Role of Exclusive Territories in Producers' Competition," RAND Journal of Economics, The RAND Corporation, vol. 26(3), pages 431-451, Autumn.
    17. Joseph J. Spengler, 1950. "Vertical Integration and Antitrust Policy," Journal of Political Economy, University of Chicago Press, vol. 58(4), pages 347-347.
    18. Alan T. Sorensen, 2007. "Bestseller Lists And Product Variety," Journal of Industrial Economics, Wiley Blackwell, vol. 55(4), pages 715-738, December.
    19. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    20. Hui Li, 2019. "Intertemporal Price Discrimination with Complementary Products: E-Books and E-Readers," Management Science, INFORMS, vol. 67(6), pages 2665-2694, June.
    21. Francis Fishwick, 2008. "Book Prices in the UK Since the End of Resale Price Maintenance," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 15(3), pages 359-377.
    22. Gaudin, Germain & White, Alexander, 2014. "On the antitrust economics of the electronic books industry," DICE Discussion Papers 147 [rev.], Heinrich Heine University Düsseldorf, Düsseldorf Institute for Competition Economics (DICE).
    23. Babur De los Santos & Matthijs R. Wildenbeest, 2017. "E-book pricing and vertical restraints," Quantitative Marketing and Economics (QME), Springer, vol. 15(2), pages 85-122, June.
    24. Imke Reimers & Joel Waldfogel, 2021. "Digitization and Pre-purchase Information: The Causal and Welfare Impacts of Reviews and Crowd Ratings," American Economic Review, American Economic Association, vol. 111(6), pages 1944-1971, June.
    25. Arthur Lewbel, 2012. "Using Heteroscedasticity to Identify and Estimate Mismeasured and Endogenous Regressor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 67-80.
    26. Robinson, Peter M, 1988. "Root- N-Consistent Semiparametric Regression," Econometrica, Econometric Society, vol. 56(4), pages 931-954, July.
    27. David A. Reinstein & Christopher M. Snyder, 2005. "The Influence Of Expert Reviews On Consumer Demand For Experience Goods: A Case Study Of Movie Critics," Journal of Industrial Economics, Wiley Blackwell, vol. 53(1), pages 27-51, March.
    28. G.F. Mathewson & R.A. Winter, 1984. "An Economic Theory of Vertical Restraints," RAND Journal of Economics, The RAND Corporation, vol. 15(1), pages 27-38, Spring.
    29. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    30. Susan Athey, 2018. "The Impact of Machine Learning on Economics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 507-547, National Bureau of Economic Research, Inc.
    31. Gans, Joshua S., 2012. "Mobile application pricing," Information Economics and Policy, Elsevier, vol. 24(1), pages 52-59.
    32. Richard J. Gilbert, 2015. "E-Books: A Tale of Digital Disruption," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 165-184, Summer.
    33. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2016. "Double/Debiased Machine Learning for Treatment and Causal Parameters," Papers 1608.00060, arXiv.org, revised Nov 2024.
    34. Andre Boik & Kenneth S. Corts, 2016. "The Effects of Platform Most-Favored-Nation Clauses on Competition and Entry," Journal of Law and Economics, University of Chicago Press, vol. 59(1), pages 105-134.
    35. Craig L. Garthwaite, 2014. "Demand Spillovers, Combative Advertising, and Celebrity Endorsements," American Economic Journal: Applied Economics, American Economic Association, vol. 6(2), pages 76-104, April.
    36. Alexander MacKay & David A. Smith, 2017. "Challenges for Empirical Research on RPM," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 50(2), pages 209-220, March.
    37. Georg Goetz & Daniel Herold & Phil-Adrian Klotz & Jan Thomas Schaefer, 2020. "The Substitutability between Brick-and-Mortar Stores and e-Commerce - The Case of Books," MAGKS Papers on Economics 202011, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
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    More about this item

    Keywords

    e-books; agency; resale price maintenance; Amazon; double machine learning; Latent Dirichlet allocation;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L42 - Industrial Organization - - Antitrust Issues and Policies - - - Vertical Restraints; Resale Price Maintenance; Quantity Discounts
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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