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Non-competing Data Intermediaries

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  • Shota Ichihashi

Abstract

I study a model of competing data intermediaries (e.g., online platforms and data brokers) that collect personal data from consumers and sell it to downstream firms. Competition in this market has a limited impact in terms of benefits to consumers: If intermediaries offer high compensation for their data, then consumers may share this data with multiple intermediaries, and this lowers its downstream price and hurts intermediaries. As intermediaries anticipate this problem, they offer low compensation for this data. Competing intermediaries can earn a monopoly profit if and only if firms’ data acquisition unambiguously hurts consumers. I generalize the results to include arbitrary consumer preferences and study the information design of data intermediaries. The results provide new insights into when competition among data intermediaries benefits consumers. It also highlights the limits of competition in terms of improving efficiency in the market for data.

Suggested Citation

  • Shota Ichihashi, 2020. "Non-competing Data Intermediaries," Staff Working Papers 20-28, Bank of Canada.
    • Handle: RePEc:bca:bocawp:20-28
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    References listed on IDEAS

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    1. 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.
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    Cited by:

    1. 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.
    2. Dirk Bergemann & Alessandro Bonatti & Tan Gan, 2022. "The economics of social data," RAND Journal of Economics, RAND Corporation, vol. 53(2), pages 263-296, June.
    3. Lin William Cong & Danxia Xie & Longtian Zhang, 2021. "Knowledge Accumulation, Privacy, and Growth in a Data Economy," Management Science, INFORMS, vol. 67(10), pages 6480-6492, October.
    4. Charles I. Jones & Christopher Tonetti, 2020. "Nonrivalry and the Economics of Data," American Economic Review, American Economic Association, vol. 110(9), pages 2819-2858, September.
    5. 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).
    6. Yiquan Gu & Leonardo Madio & Carlo Reggiani, 2019. "Exclusive Data, Price Manipulation and Market Leadership," CESifo Working Paper Series 7853, CESifo.
    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. DELBONO Flavio & REGGIANI Carlo & SANDRINI Luca, 2021. "Strategic data sales to competing firms," JRC Working Papers on Digital Economy 2021-05, Joint Research Centre.
    9. Cong, Lin William & Wei, Wenshi & Xie, Danxia & Zhang, Longtian, 2022. "Endogenous growth under multiple uses of data," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).
    10. Lu Tan & Jingsong Pei, 2023. "Open Government Data and the Urban–Rural Income Divide in China: An Exploration of Data Inequalities and Their Consequences," Sustainability, MDPI, vol. 15(13), pages 1-18, June.

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    More about this item

    Keywords

    Economic models;

    JEL classification:

    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • L12 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Monopoly; Monopolization Strategies

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