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The Value of Data:Towards a Framework to Redistribute It

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  • Maria Savona

    (Science Policy Research Unit, University of Sussex)

Abstract

This note attempts a systematisation of different pieces of literature that underpin the recent policy and academic debate on the value of data. It mainly poses foundational questions around the definition, economic nature and measurement of data value, and discusses the opportunity to redistribute it. It then articulates a framework to compare ways of implementing redistribution, distinguishing between data as capital, data as labour or data as an intellectual property. Each of these raises challenges, revolving around the notions of data property and data rights, that are also briefly discussed. The note concludes by indicating areas for policy considerations and a research agenda to shape the future structure of data governance more at large.

Suggested Citation

  • Maria Savona, 2019. "The Value of Data:Towards a Framework to Redistribute It," SPRU Working Paper Series 2019-21, SPRU - Science Policy Research Unit, University of Sussex Business School.
    • Handle: RePEc:sru:ssewps:2019-21
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    References listed on IDEAS

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    6. Erik Brynjolfsson & Daniel Rock & Chad Syverson, 2018. "Artificial Intelligence and the Modern Productivity Paradox: A Clash of Expectations and Statistics," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 23-57, National Bureau of Economic Research, Inc.
    7. Imanol Arrieta-Ibarra & Leonard Goff & Diego Jiménez-Hernández & Jaron Lanier & E. Glen Weyl, 2018. "Should We Treat Data as Labor? Moving beyond "Free"," AEA Papers and Proceedings, American Economic Association, vol. 108, pages 38-42, May.
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    Cited by:

    1. Bianchini, Stefano & Müller, Moritz & Pelletier, Pierre, 2022. "Artificial intelligence in science: An emerging general method of invention," Research Policy, Elsevier, vol. 51(10).
    2. Franz Huber & Alan Ponce & Francesco Rentocchini & Thomas Wainwright, 2020. "The Wealth of (Open Data) Nations? Examining the Interplay of Open Government Data and Country-level Institutions for Entrepreneurial Activity at the Country-level," SPRU Working Paper Series 2020-13, SPRU - Science Policy Research Unit, University of Sussex Business School.
    3. Igna, Ioana & Venturini, Francesco, 2023. "The determinants of AI innovation across European firms," Research Policy, Elsevier, vol. 52(2).
    4. Bessen, James & Impink, Stephen Michael & Reichensperger, Lydia & Seamans, Robert, 2022. "The role of data for AI startup growth," Research Policy, Elsevier, vol. 51(5).
    5. Andrea Borsato & Andre Lorentz, 2022. "Data Production and the coevolving AI trajectories: An attempted evolutionary model," Working Papers of BETA 2022-09, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    6. Simone Vannuccini & Ekaterina Prytkova, 2021. "Artificial Intelligence’s New Clothes? From General Purpose Technology to Large Technical System," SPRU Working Paper Series 2021-02, SPRU - Science Policy Research Unit, University of Sussex Business School.

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