IDEAS home Printed from https://ideas.repec.org/p/hhs/nhheco/2019_007.html
   My bibliography  Save this paper

Paywalls and the demand for online news

Author

Listed:
  • Skjeret, Frode

    (SNF)

  • Steen, Frode

    (Dept. of Economics, Norwegian School of Economics and Business Administration)

  • Wyndham, Timothy G.A.

    (Dept. of Economics, Norwegian School of Economics and Business Administration)

Abstract

The digitisation of society has posed a challenge to news outlets. Seeking advertising revenues and facing competition for the attention of their readers, many news outlets entered the digital era with unrestricted access to their online content. More recently, news outlets have sought to restrict the amount of content available for free. We quantify the impact of introducing a paywall on the demand for news in Norway. The short-run average impact of a paywall is negative and between 3 and 4%, in the long run the effect increases to between 9 and 11%. We find heterogeneity in the response to paywalls. The largest news outlet within its market experiences larger effects than the other news outlets. After introducing a paywall, the largest news outlets face a long-run reduction in demand between 13 and 15%, as compared to the others who experience a decrease of between 8 and 11%. The timing of introducing a paywall does not seem to affect the demand response very much.

Suggested Citation

  • Skjeret, Frode & Steen, Frode & Wyndham, Timothy G.A., 2019. "Paywalls and the demand for online news," Discussion Paper Series in Economics 7/2019, Norwegian School of Economics, Department of Economics.
    • Handle: RePEc:hhs:nhheco:2019_007
    as

    Download full text from publisher

    File URL: https://brage.bibsys.no/xmlui/bitstream/handle/11250/2593049/DP%2007.pdf?sequence=1&isAllowed=y
    File Function: Full text
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Chiou, Lesley & Tucker, Catherine, 2013. "Paywalls and the demand for news," Information Economics and Policy, Elsevier, vol. 25(2), pages 61-69.
    2. Ramon Casadesus-Masanell & Feng Zhu, 2010. "Strategies to Fight Ad-Sponsored Rivals," Management Science, INFORMS, vol. 56(9), pages 1484-1499, September.
    3. Matthew Gentzkow & Jesse M. Shapiro, 2010. "What Drives Media Slant? Evidence From U.S. Daily Newspapers," Econometrica, Econometric Society, vol. 78(1), pages 35-71, January.
    4. Mehmet Gümüş & Philip Kaminsky & Sameer Mathur, 2016. "The impact of product substitution and retail capacity on the timing and depth of price promotions: theory and evidence," International Journal of Production Research, Taylor & Francis Journals, vol. 54(7), pages 2108-2135, April.
    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. Havrda, Marek & Klocek, Adam, 2023. "Well-being impact assessment of artificial intelligence – A search for causality and proposal for an open platform for well-being impact assessment of AI systems," Evaluation and Program Planning, Elsevier, vol. 99(C).
    2. Sparacino, Daniele, 2019. "Funktionsweise und Möglichkeiten von Google Shopping mit einem Fokus auf Showcase Ads," Working Papers for Marketing & Management 42, Offenburg University, Department of Media and Information.
    3. Kim, Woojung & Wang, Xiaokun Cara, 2022. "The adoption of alternative delivery locations in New York City: Who and how far?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 127-140.
    4. Grodeck, Ben & Schoenegger, Philipp, 2023. "Demanding the morally demanding: Experimental evidence on the effects of moral arguments and moral demandingness on charitable giving," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 103(C).

    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. Charles Angelucci & Julia Cagé, 2019. "Newspapers in Times of Low Advertising Revenues," American Economic Journal: Microeconomics, American Economic Association, vol. 11(3), pages 319-364, August.
    2. Bertin Martens & Luis Aguiar & Estrella Gomez Herrera & Frank Muller, 2018. "The digital transformation of news media and the rise of disinformation and fake news," JRC Working Papers on Digital Economy 2018-02, Joint Research Centre.
    3. Anderson, Simon & Waldfogel, Joel, 2015. "Preference Externalities in Media Markets," CEPR Discussion Papers 10835, C.E.P.R. Discussion Papers.
    4. repec:hal:spmain:info:hdl:2441/4edekc99or8n2bu86nu4ua8adl is not listed on IDEAS
    5. repec:hal:spmain:info:hdl:2441/26t617gatp86qree1dejcpchbr is not listed on IDEAS
    6. Robert Seamans & Feng Zhu, 2014. "Responses to Entry in Multi-Sided Markets: The Impact of Craigslist on Local Newspapers," Management Science, INFORMS, vol. 60(2), pages 476-493, February.
    7. Robert Seamans & Feng Zhu, 2017. "Repositioning and Cost-Cutting: The Impact of Competition on Platform Strategies," Strategy Science, INFORMS, vol. 2(2), pages 83-99, June.
    8. Kim, Ho & Song, Reo & Kim, Youngsoo, 2020. "Newspapers' Content Policy and the Effect of Paywalls on Pageviews," Journal of Interactive Marketing, Elsevier, vol. 49(C), pages 54-69.
    9. Chutian Wang & Bo Zhou & Yogesh V. Joshi, 2024. "Endogenous Consumption and Metered Paywalls," Marketing Science, INFORMS, vol. 43(1), pages 158-177, January.
    10. Xiaojie Sun, 2023. "Strategy analysis for a digital content platform considering perishability," Annals of Operations Research, Springer, vol. 320(1), pages 415-439, January.
    11. Monic Sun & Feng Zhu, 2013. "Ad Revenue and Content Commercialization: Evidence from Blogs," Management Science, INFORMS, vol. 59(10), pages 2314-2331, October.
    12. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2020. "Measuring partisan media bias in US Newscasts from 2001-2012," Working Paper 183/2020, Helmut Schmidt University, Hamburg, revised 15 Nov 2022.
    13. Mueller, Hannes & Rauh, Christopher, 2018. "Reading Between the Lines: Prediction of Political Violence Using Newspaper Text," American Political Science Review, Cambridge University Press, vol. 112(2), pages 358-375, May.
    14. Thorsten Schoormann & Julia Schweihoff & Ilka Jussen & Frederik Möller, 2023. "Classification tools for business models: Status quo, comparison, and agenda," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-36, December.
    15. Bennani, Hamza, 2018. "Media coverage and ECB policy-making: Evidence from an augmented Taylor rule," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 26-38.
    16. McCannon, Bryan & Zhou, Yang & Hall, Joshua, 2021. "Measuring a Contract’s Breadth: A Text Analysis," Working Papers 11013, George Mason University, Mercatus Center.
    17. Giovanni Facchini & Anna Maria Mayda & Riccardo Puglisi, 2017. "Illegal immigration and media exposure: evidence on individual attitudes," IZA Journal of Migration and Development, Springer;Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 7(1), pages 1-36, December.
    18. Pal Sudeshna, 2011. "Media Freedom and Socio-Political Instability," Peace Economics, Peace Science, and Public Policy, De Gruyter, vol. 17(1), pages 1-23, March.
    19. Munday, Tim & Brookes, James, 2021. "Mark my words: the transmission of central bank communication to the general public via the print media," Bank of England working papers 944, Bank of England.
    20. Federico Boffa & Amedeo Piolatto & Giacomo A. M. Ponzetto, 2016. "Political Centralization and Government Accountability," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(1), pages 381-422.
    21. Shane Greenstein & Yuan Gu & Feng Zhu, 2016. "Ideological Segregation among Online Collaborators: Evidence from Wikipedians," Harvard Business School Working Papers 17-028, Harvard Business School, revised Mar 2017.
    22. Hong, T., 2021. "Revisiting the Trade Policy Uncertainty Index," Cambridge Working Papers in Economics 2174, Faculty of Economics, University of Cambridge.

    More about this item

    Keywords

    Online news; paywalls; business models; two-sided markets;
    All these keywords.

    JEL classification:

    • D40 - Microeconomics - - Market Structure, Pricing, and Design - - - General
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:hhs:nhheco:2019_007. 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: Karen Reed-Larsen (email available below). General contact details of provider: https://edirc.repec.org/data/sonhhno.html .

    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.