IDEAS home Printed from https://ideas.repec.org/p/ipt/decwpa/202106.html
   My bibliography  Save this paper

To pool or to pull back? An economic analysis of health data pooling

Author

Abstract

We present a novel generic theoretical framework to analyze the incentives agents have to engage in n-way data sharing or ‘data pooling’ and the factors affecting those incentives. Based on the results obtained, we provide policy recommendations aimed at fostering health data pooling. Section 1 develops a baseline framework and multiple variations including zero-sum data pooling games, competing pools and intra-pool negative externalities. The section offers analytical solutions and examples to show under which conditions agents decide to pool data. Section 2 illustrates how different factors can lead to sub-optimal data pooling. Section 3 provides policy recommendations to foster data pooling in the health sector and discusses the conditions under which they can be effective.

Suggested Citation

  • CARBALLA SMICHOWSKI Bruno & DUCH BROWN Nestor & MARTENS Bertin, 2021. "To pool or to pull back? An economic analysis of health data pooling," JRC Working Papers on Digital Economy 2021-06, Joint Research Centre.
    • Handle: RePEc:ipt:decwpa:202106
    as

    Download full text from publisher

    File URL: https://publications.jrc.ec.europa.eu/repository/handle/JRC126961
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bertin Martens & Alexandre de Streel & Inge Graef & Thomas Tombal & Nestor Duch-Brown, 2020. "Business-to-Business data sharing: An economic and legal analysis," JRC Working Papers on Digital Economy 2020-05, Joint Research Centre.
    2. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asu Ozdaglar, 2022. "Too Much Data: Prices and Inefficiencies in Data Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 218-256, November.
    3. Dirk Bergemann & Alessandro Bonatti, 2019. "Markets for Information: An Introduction," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 85-107, August.
    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. Rodrigo Montes & Wilfried Sand-Zantman & Tommaso Valletti, 2019. "The Value of Personal Information in Online Markets with Endogenous Privacy," Management Science, INFORMS, vol. 65(3), pages 1342-1362, March.
    6. Alvin E. Roth, 2007. "Repugnance as a Constraint on Markets," Journal of Economic Perspectives, American Economic Association, vol. 21(3), pages 37-58, Summer.
    7. MARTENS Bertin & DUCH BROWN Nestor, 2020. "The economics of Business-to-Government data sharing," JRC Working Papers on Digital Economy 2020-04, Joint Research Centre.
    8. Kalai, Ehud, 1977. "Proportional Solutions to Bargaining Situations: Interpersonal Utility Comparisons," Econometrica, Econometric Society, vol. 45(7), pages 1623-1630, October.
    9. Bruno Carballa Smichowski, 2018. "The value of data: an analysis of closed-urban-data-based and open-data-based business models," Working Papers hal-01736484, HAL.
    10. Prüfer, Jens & Schottmuller, C., 2017. "Competing with Big Data," Other publications TiSEM b09cad5c-e6eb-4fe7-9184-f, Tilburg University, School of Economics and Management.
    11. Jens Prüfer & Christoph Schottmüller, 2021. "Competing with Big Data," Journal of Industrial Economics, Wiley Blackwell, vol. 69(4), pages 967-1008, December.
    12. Sandro Ambuehl & Muriel Niederle & Alvin E. Roth, 2015. "More Money, More Problems? Can High Pay Be Coercive and Repugnant?," American Economic Review, American Economic Association, vol. 105(5), pages 357-360, May.
    13. Bruno Carballa Smichowski, 2019. "Alternative Data Governance Models: Moving Beyond One-Size-Fits-All Solutions," Intereconomics: Review of European Economic Policy, Springer;ZBW - Leibniz Information Centre for Economics;Centre for European Policy Studies (CEPS), vol. 54(4), pages 222-227, July.
    14. Prüfer, Jens & Schottmuller, C., 2017. "Competing with Big Data," Other publications TiSEM 29de4480-00db-473b-a0ee-b, Tilburg University, School of Economics and Management.
    15. Michael J. Sandel, 2013. "Market Reasoning as Moral Reasoning: Why Economists Should Re-engage with Political Philosophy," Journal of Economic Perspectives, American Economic Association, vol. 27(4), pages 121-140, Fall.
    16. Admati, Anat R. & Pfleiderer, Paul, 1986. "A monopolistic market for information," Journal of Economic Theory, Elsevier, vol. 39(2), pages 400-438, August.
    17. repec:hal:cepnwp:hal-01736484 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

    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. 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. Bertin Martens & Alexandre de Streel & Inge Graef & Thomas Tombal & Nestor Duch-Brown, 2020. "Business-to-Business data sharing: An economic and legal analysis," JRC Working Papers on Digital Economy 2020-05, Joint Research Centre.
    3. Alessandro Bonatti, 2023. "The Platform Dimension of Digital Privacy," NBER Chapters, in: The Economics of Privacy, National Bureau of Economic Research, Inc.
    4. MARTENS Bertin, 2020. "An economic perspective on data and platform market power," JRC Working Papers on Digital Economy 2020-09, Joint Research Centre.
    5. Navarra, Federico & Pino, Flavio & Sandrini, Luca, 2024. "Mandated data-sharing in hybrid marketplaces," ZEW Discussion Papers 24-051, ZEW - Leibniz Centre for European Economic Research.
    6. Andrei Hagiu & Julian Wright, 2023. "Data‐enabled learning, network effects, and competitive advantage," RAND Journal of Economics, RAND Corporation, vol. 54(4), pages 638-667, December.
    7. Elliott, M. & Galeotti., A. & Koh., A. & Li, W., 2021. "Market Segmentation Through Information," Janeway Institute Working Papers 2114, Faculty of Economics, University of Cambridge.
    8. Schaefer, Maximilian & Sapi, Geza, 2023. "Complementarities in learning from data: Insights from general search," Information Economics and Policy, Elsevier, vol. 65(C).
    9. Calvano, Emilio & Polo, Michele, 2021. "Market power, competition and innovation in digital markets: A survey," Information Economics and Policy, Elsevier, vol. 54(C).
    10. Jean-Marc Zogheib & Marc Bourreau, 2021. "Privacy, Competition, and Multi-Homing," EconomiX Working Papers 2021-15, University of Paris Nanterre, EconomiX.
    11. Rockett Katharine, 2023. "Is Data the New Gold? Considering Intellectual Property Protection and Regulation of Data," Economics - The Open-Access, Open-Assessment Journal, De Gruyter, vol. 17(1), pages 1-15, January.
    12. Nathalie Jorzik & Paula Johanna Kirchhof & Frank Mueller-Langer, 2024. "Industrial data sharing and data readiness: a law and economics perspective," European Journal of Law and Economics, Springer, vol. 57(1), pages 181-205, April.
    13. Flavio Pino, 2022. "The microeconomics of data – a survey," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 635-665, September.
    14. World Bank, 2023. "Digital Conglomerates in East Asia," World Bank Publications - Reports 40641, The World Bank Group.
    15. Dengler, Sebastian & Prüfer, Jens, 2021. "Consumers' privacy choices in the era of big data," Games and Economic Behavior, Elsevier, vol. 130(C), pages 499-520.
    16. Daron Acemoglu & Ali Makhdoumi & Azarakhsh Malekian & Asu Ozdaglar, 2022. "Too Much Data: Prices and Inefficiencies in Data Markets," American Economic Journal: Microeconomics, American Economic Association, vol. 14(4), pages 218-256, November.
    17. S Nageeb Ali & Greg Lewis & Shoshana Vasserman, 2023. "Voluntary Disclosure and Personalized Pricing," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 90(2), pages 538-571.
    18. Jens Prüfer & Patricia Prüfer, 2020. "Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands," Small Business Economics, Springer, vol. 55(3), pages 651-672, October.

    More about this item

    Keywords

    data pool; health data; data sharing;
    All these keywords.

    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:ipt:decwpa:202106. 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: Publication Officer (email available below). General contact details of provider: https://edirc.repec.org/data/ipjrces.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.