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The Editor vs. the Algorithm: Returns to Data and Externalities in Online News

Author

Listed:
  • Jörg Claussen
  • Christian Peukert
  • Ananya Sen

Abstract

We run a field experiment to quantify the economic returns to data and informational ex-ternalities associated with algorithmic recommendation relative to human curation in the context of online news. Our results show that personalized recommendation can outperform human curation in terms of user engagement, though this crucially depends on the amount of personal data. Limited individual data or breaking news leads the editor to outperform the algorithm. Additional data helps algorithmic performance but diminishing economic returns set in rapidly. Investigating informational externalities highlights that personalized recommendation reduces consumption diversity. Moreover, users associated with lower levels of digital literacy and more extreme political views engage more with algorithmic recommendations.

Suggested Citation

  • Jörg Claussen & Christian Peukert & Ananya Sen, 2019. "The Editor vs. the Algorithm: Returns to Data and Externalities in Online News," CESifo Working Paper Series 8012, CESifo.
    • Handle: RePEc:ces:ceswps:_8012
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Dirk Bergemann & Marco Ottaviani, 2021. "Information Markets and Nonmarkets," Cowles Foundation Discussion Papers 2296, Cowles Foundation for Research in Economics, Yale University.
    2. Garz, Marcel & Sörensen, Jil & Stone, Daniel F., 2020. "Partisan selective engagement: Evidence from Facebook," Journal of Economic Behavior & Organization, Elsevier, vol. 177(C), pages 91-108.
    3. Molaie, Mir Majid & Lee, Wonjae, 2022. "Economic corollaries of personalized recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 68(C).
    4. Laura Abrardi & Carlo Cambini & Laura Rondi, 2022. "Artificial intelligence, firms and consumer behavior: A survey," Journal of Economic Surveys, Wiley Blackwell, vol. 36(4), pages 969-991, September.
    5. MARTENS Bertin, 2020. "An economic perspective on data and platform market power," JRC Working Papers on Digital Economy 2020-09, Joint Research Centre.
    6. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2022. "Data brokers co-opetition [The impact of big data on firm performance: an empirical investigation]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 820-839.
    7. de Cornière, Alexandre & Taylor, Greg, 2022. "Data and Competition: a Simple Framework with Applications to Mergers and Market Structure," CEPR Discussion Papers 14446, C.E.P.R. Discussion Papers.
    8. Kesler, Reinhold & Kummer, Michael E. & Schulte, Patrick, 2019. "Competition and privacy in online markets: Evidence from the mobile app industry," ZEW Discussion Papers 19-064, ZEW - Leibniz Centre for European Economic Research.
    9. Hemant K. Bhargava & Olivier Rubel & Elizabeth J. Altman & Ramnik Arora & Jörn Boehnke & Kaitlin Daniels & Timothy Derdenger & Bryan Kirschner & Darin LaFramboise & Pantelis Loupos & Geoffrey Parker &, 2020. "Platform data strategy," Marketing Letters, Springer, vol. 31(4), pages 323-334, December.
    10. Sylvain Dejean & Marianne Lumeau & Stéphanie Peltier, 2021. "Partisan selective exposure in news consumption," Working Papers hal-03295625, HAL.
    11. Joan Calzada & Nestor Duch-Brown & Ricard Gil, 2021. "Do search engines increase concentration in media markets?," UB School of Economics Working Papers 2021/415, University of Barcelona School of Economics.
    12. de Cornière, Alexandre & Taylor, Greg, 2020. "Data and Competition: a General Framework with Applications to Mergers, Market Structure, and Privacy Policy," TSE Working Papers 20-1076, Toulouse School of Economics (TSE).
    13. Maximilian Schäfer & Geza Sapi, 2020. "Learning from Data and Network Effects: The Example of Internet Search," Discussion Papers of DIW Berlin 1894, DIW Berlin, German Institute for Economic Research.
    14. Georgios Petropoulos & Bertin Martens & Geoffrey Parker & Marshall Van Alstyne, 2023. "Platform Competition and Information Sharing," CESifo Working Paper Series 10663, CESifo.
    15. Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.

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    More about this item

    Keywords

    field experiment; economics of AI; returns to data; filter bubbles;
    All these keywords.

    JEL classification:

    • L82 - Industrial Organization - - Industry Studies: Services - - - Entertainment; Media
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity

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