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Incentives for Data Sharing as a Case on (Regulating) Knowledge Externalities

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A Critical Mind

Part of the book series: MPI Studies on Intellectual Property and Competition Law ((MSIP,volume 30))

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Abstract

This contribution reflects on the question posed by Professor Hanns Ullrich: Which concepts and principles of intellectual property law inform the design of a regulatory framework for the data economy? Even though data and knowledge do not lend themselves to form a perfect analogy, their governance underlies the same dilemma of whether, and to what extent, resources that are non-rivalrous in use should be treated as excludable or a non-excludable economic goods. The analysis focuses on the ‘access-incentives stalemate’ over device-generated data and takes recourse to literature on economics of knowledge and innovation in search of guidance as to how the trade-off between the broad accessibility of data and the protection of innovation incentives should be resolved.

Dr. Daria Kim (LL.M., M.A., Dr. iur.) is a Senior research fellow at the Max Planck Institute for Innovation and Competition.

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Notes

  1. 1.

    Hanns Ullrich, ‘The Political Foundations of TRIPS Revisited’ in Hanns Ullrich and others (eds), TRIPS plus 20 – From Trade Rules to Market Principles 85, 101-102 (Springer 2016).

  2. 2.

    European Commission, ‘Towards a Thriving Data-Driven Economy’ COM(2014) 442 final (02.07.2014) 4 (stating that data ‘is at the centre of the future knowledge economy and society’).

  3. 3.

    European Commission, ‘Data Policies and Legislation – Timeline’ <https://ec.europa.eu/digital-single-market/en/data-policies-and-legislation> accessed 1 March 2022.

  4. 4.

    COM(2014) 442 final (supra n 2) 6.

  5. 5.

    European Commission, ‘Building a European Data Economy’ COM(2017) 9 final (10.01.2017) 2 (envisaging that ‘if policy and legal framework conditions for the data economy are put in place in time, its value will increase [substantially]’).

  6. 6.

    European Commission, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions ‘A European Strategy for Data’ COM(2020) 66 final (19.02.2020) 4-5.

  7. 7.

    See COM(2017) 9 final (supra n 5) 18 (concluding that, ‘[t]o build the data economy, the EU needs a policy framework that enables data to be used throughout the value chain for scientific, societal and industrial purposes’). See also COM(2020) 66 final (supra n 6) 4, 15 (stating that the EU ‘should create an attractive policy environment [for] a single European data space – a genuine single market for data’ and further announcing that the Commission intends to propose ‘a legislative framework for the governance of common European data spaces [in] Q4 2020’).

  8. 8.

    Hanns Ullrich, ‘Technology Protection and Competition Policy for the Information Economy. From Property Rights for Competition to Competition Without Property Rights’ (2019) Max Planck Institute for Innovation and Competition Research Paper No 19-12 <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3437177> accessed 1 March 2022.

  9. 9.

    Ibid 2.

  10. 10.

    Ibid 29 fn 90.

  11. 11.

    Ibid.

  12. 12.

    Ibid.

  13. 13.

    While in Europe the idea of introducing individual rights in data, by analogy with IP rights (‘data ownership’), has faded away, the World Intellectual Property Organization has recently posed a question of ‘whether IP policy should go further than the classical system and create new rights in data’. World Intellectual Property Organization, ‘WIPO Conversation on Intellectual Property (IP) and Artificial Intelligence (AI)’ (WIPO/IP/AI/2/GE/20/1, 13 December 2019) <https://www.wipo.int/edocs/mdocs/mdocs/en/wipo_ip_ai_ge_20/wipo_ip_ai_2_ge_20_1.pdf> accessed 1 March 2022 para 23. Notably, the document states that ‘reasons for considering such further action would include the encouragement of the development of new and beneficial classes of data; the appropriate allocation of value to the various actors in relation to data […]; and the assurance of fair market competition against acts or behaviour deemed inimical to fair competition’. ibid.

  14. 14.

    See COM(2017) 9 final, (supra n 5) 10 (pointing out that ‘comprehensive policy frameworks do not currently exist at national or Union level in relation to raw machine-generated data which does not qualify as personal data, or to the conditions of their economic exploitation and tradability [and t]he issue is largely left to contractual solutions’).

  15. 15.

    For a synthesis of this debate, see Hanns Ullrich (2019, supra n 8) 22-23; see also below at 2.2.

  16. 16.

    Hanns Ullrich (2019, supra n 8) 19, 26.

  17. 17.

    See eg European Commission, ‘Document on the Free Flow of Data and Emerging Issues of the European Data Economy’SWD(2017) 2 final (10.01.2017) 34 (contemplating that ‘non-personal or anonymised machine-generated data not yet structured in a protected database [are not] safeguarded through intellectual or industrial property rights [c]onsequently, a right in rem could be created on such data’ (emphasis added)).

  18. 18.

    On the object-oriented vs. conduct-oriented protection, see Annette Kur, ‘What to Protect, and How? Unfair Competition, Intellectual Property, or Protection Sui Generis’ (2012) Max Planck Institute for Intellectual Property and Competition Law Research Paper No 13-12.

  19. 19.

    COM(2017) 9 final (supra n 5) 11. See also Hanns Ullrich (2019, supra n 8) 2.

  20. 20.

    See infra n 45 and the accompanying text.

  21. 21.

    On the partial excludability of device-generated data under the existing framework, see infra nn 95-98 and the accompanying text.

  22. 22.

    This is not to belittle the importance of the questions of accessibility and usability of personal data in the context of data-driven innovation. The inclusion of personal data within the scope of the inquiry, however, would require a much more nuanced analysis than the scope of this contribution permits.

  23. 23.

    Hanns Ullrich (2019, supra n 8) 25. The importance of investment in the production of data, especially good quality data, cannot be dismissed altogether. As acknowledged by Hans Ullrich, incentives for the creation and curation of data need to be factored into the equation when designing the framework of access. ibid 2.

  24. 24.

    See eg Organisation of Economic Co-operation and Development, Enhancing Access to and Sharing of Data. Reconciling Risks and Benefits for Data Re-Use Across Societies (OECD 2019) [hereinafter OECD, Enhancing Access to Data] 11 (acknowledging that ‘despite a growing need for data and evidence of the economic and social benefits, data access and sharing has not achieved its potential’). See also Hanns Ullrich (2019, supra n 8) 21 (observing that ‘the problem of the data economy is not so much an insufficient production of data, but an insufficient dissemination of data for (complementary, improving, diversifying or even displacing) follow-on innovation in the digital economy’).

  25. 25.

    André Freitas and Edward Curry, ‘Big Data Curation’ in José María Cavanillas, Edward Curry and Wolfgang Wahlster (eds), New Horizons for a Data-Driven Economy: A Roadmap for Usage and Exploitation of Big Data in Europe (Springer 2016) 89.

  26. 26.

    COM(2020) 66 final (supra n 6) 7-8.

  27. 27.

    Ibid 7.

  28. 28.

    On the insufficiency of evidence regarding the functioning of data markets, see infra nn 130-131.

  29. 29.

    Non-rivalry in use implies that data can be analysed in parallel R&D projects without depreciating its inherent value. See OECD, Enhancing Access to Data (2019, supra n 24) 17, 60 (pointing out that ‘the use of data does not exhaust the supply of data and (therefore) in principle it’s potential to meet the demands of others’ (emphasised in the original), and that data ‘cannot be depleted as it can be re-used for a theoretically unlimited range of purposes’).

  30. 30.

    See William H Oakland, ‘Theory of Public Goods’ in Alan J Auerbach and Martin Feldstein (eds), Handbook of Public Economics (Elsevier BV 1987) 485, at 485 (noting that, since non-rivalrous goods ‘are not used up in the act of consumption of production, the marginal cost of extending service to additional users is zero’; therefore, a fee charged to recover the costs of providing a public good ‘will usually lead some potential users to forgo consumption, creating a deadweight efficiency loss’).

  31. 31.

    Challenges faced by data markets are discussed below at 3.3.

  32. 32.

    For a synthesis of literature on this subject, see Hanns Ullrich (2019, supra n 8) 22-23; for an overview of the European Commission’s initiatives related to ‘data ownership’, see Daria Kim, ‘No One’s Ownership as the Status Quo and a Possible Way Forward: A Note on the Public Consultation on Building a European Data Economy’ (2017) 66 (8/9) GRUR Int 699 ff.

  33. 33.

    See SWD(2017) 2 final (supra n 17) 33-34.

  34. 34.

    For a proposal for a ‘dataright’ that would ‘entitle data producers to block downstream use of data, but not reproduction or distribution’, as a means to ‘promote the disclosure of big data practices’, see Michael Mattioli, ‘Disclosing Big Data’ (2014) 99 MinnLRev 534, 538.

  35. 35.

    The ‘means-end’ relationship, however, has hardly been elaborated. For an overview of proposals, see SWD(2017) 2 final (supra n 17) 30 ff; for an analysis, see Daria Kim (2017, supra n 32).

  36. 36.

    See infra nn 96-98.

  37. 37.

    Hanns Ullrich (2019, supra n 8) (with further references).

  38. 38.

    OECD, Enhancing Access to Data (2019, supra n 24) 97-98.

  39. 39.

    Ibid 97 (cautioning that, ‘while regulation may impose data access, it may also undermine incentives to invest in data in the first place’). According to the report, ‘[t]he risks of enhanced access and sharing go beyond digital security and personal data breaches [and] include most notably risks of violating contractual and socially agreed terms of data re-use’, ibid 81.

  40. 40.

    Hanns Ullrich (2019, supra n 8) 27.

  41. 41.

    With few exceptions under sector regulations. See SWD(2017) 2 final (supra n 17) 21-22, 37-38.

  42. 42.

    In the words of Hanns Ullrich:

    Attribution of the power to control, if at all legally defined, is not so defined with any degree of precision and operates irrespective of any criteria of selection for merit. In short, control over data merely results from factual possession. […] It is difficult to see how these framework conditions of the data market contribute to optimizing trade in and/or sharing of data.

    Hanns Ullrich (2019, supra n 8) 27.

  43. 43.

    In economics of innovation, the notion of innovation incentives is associated with the protection of returns on innovation activity. See Stefano Breschi and Franco Malerba, ‘Sectoral Innovation Systems: Technological Regimes, Schumpeterian Dynamics, and Spatial Boundaries’ in Charles Edquist (ed), Systems of Innovation. Technologies, Institutions and Organizations (Routledge 2005) 130, 135. Needless to say, no straightforward causal relationship between appropriability and innovation can be assumed. See generally Giovanni Dosi, Luigi Marengo and Corrado Pasquali, ‘How much should society fuel the greed of innovators? On the relations between appropriability, opportunities and rates of innovation’ 35(8) Research Policy (2006) 1110-1121.

  44. 44.

    On the underutilisation of knowledge as a social cost of exclusive rights, see infra nn 104-105; 108-114 (and the accompanying text).

  45. 45.

    Joseph E Stiglitz and Scott J Wallsten, ‘Public-Private Technology Partnerships’ (1999) 43(1) American Behavioral Scientist 52, 56; Dominique Foray, Economics of Knowledge (MIT Press 2004) 116-9; David W Barnes, ‘The Incentives/Access Tradeoff’ (2010) 9 NwJTech&IntellProp 96, 101.

  46. 46.

    For instance, plant pollination is a ‘by-product’ of bee-keeping.

  47. 47.

    Pollution and noise are classical examples.

  48. 48.

    For a historical overview of the concept of R&D externalities in economic literature, see Sinclair Davidson and Heath Spong, ‘Positive Externalities and R&D: Two Conflicting Traditions in Economic Theory’ 22(3) Review of Political Economy 355 (2010); Cristiano Antonelli, Endogenous Innovation. The Economics of an Emergent System Property (Edward Elgar 2017) 3-21.

  49. 49.

    See eg Adam B Jaffe, ‘The Importance of ‘Spillovers’ in the Policy Mission of the Advanced Technology Program’ (1998) 23(2) Journal of Technology Transfer 11, 11-12.

  50. 50.

    See eg Jean-Jacques Laffont, ‘Externalities’ in Steven N Durlauf and Lawrence E Blume (eds), The New Palgrave Dictionary of Economics Vol 7 (3rd edn, Palgrave Macmillan 2008) 4318; Jeffrey I Bernstein and M Ishaq Nadiri, ‘Research and Development and Intra-industry Spillovers: An Empirical Application of Dynamic Duality’ (1989) 56 Review of Economic Studies 249; Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen, ‘Measuring the Returns to R&D’ in Bronwyn H Hall and Nathan Rosenberg (eds), Handbook of the Economics of Innovation Vol 2 (Elsevier B V 2010) 1034, 1065; Richard R Nelson, ‘Building Effective ‘Innovation Systems’ Versus Dealing with ‘Market Failures’ as Ways of Thinking about Technology Policy’ in Dominique Foray (ed), The New Economics of Technology Policy (Edward Elgar 2009) 7, 10.

  51. 51.

    John F Duffy, ‘Intellectual Property Isolationism and the Average Cost Thesis’ (2005) 83 TLR 1077, 1086.

  52. 52.

    See Adam B Jaffe, Richard G Newell and Robert N Stavins, ‘A Tale of Two Market Failures: Technology and Environmental Policy’ (2005) 54 Ecological Economics 164, 167; Adam B Jaffe, ‘Technological Opportunity and Spillovers of R&D: Evidence from Firms’ Patents, Profits, and Market Value’ (1986) 76(5) The American Economic Review 984.

  53. 53.

    Knowledge externalities should be distinguished from technology transfer – the latter implies that the value of knowledge is, to some extent, internalised via a market transaction.

  54. 54.

    See eg Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50) 1065; David B Audretsch, Erik E Lehmann and Joshua Hinger, ‘From Knowledge to Innovation. The Role of Knowledge Spillover Entrepreneurship’ in Cristiano Antonelli and Albert N Link (eds), Routledge Handbook of the Economics of Knowledge (Routledge 2014) 20, 23 (noting that the reuse of knowledge accounts for total factor productivity); Robert E Lucas, Lectures on Economic Growth (HUP 2002) 6. For an overview of growth models that incorporate knowledge externalities, see Pontus Braunerhjelm, ‘Entrepreneurship, Innovation and Economic Growth: Interdependencies, Irregularities and Regularities’ in David B Audretsch and others (eds), Handbook of Research on Innovation and Entrepreneurship (Edward Elgar 2011) 161, 180-182; Cristiano Antonelli (2017, supra n 48) 4. See also European Commission, ‘Impact Assessment accompanying the document proposal for a Directive of the European Parliament and of the Council on the Protection of Undisclosed Know-how and Business Information (trade secrets) Against Their Unlawful Acquisition, Use and Disclosure’ SWD(2013) 471 final (28.11.2013) 139 (noting that ‘[s]pillovers and diffusion of knowledge are considered important determinants of dynamic economic efficiency as innovations spread through industries and economies over time’).

  55. 55.

    Michael Spence, ‘Cost Reduction, Competition, and Industry Performance’ 52(1) The Econometric Society 101, 116 (1984); Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50) 1065; Cristiano Antonelli (2017, supra n 48) 4.

  56. 56.

    Sinclair Davidson and Heath Spong (2010, supra n 48) 364.

  57. 57.

    Zvi Griliches, R&D and Productivity: The Econometric Evidence (The Univ of Chicago Press 1998) 258; Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50) 1065.

  58. 58.

    See Wesley M Cohen, ‘Fifty Years of Empirical Studies of Innovative Activity and Performance’ in Bronwyn H Hall and Nathan Rosenberg (eds), Handbook of the Economics of Innovation Vol 1 (Elsevier BV 2010) 129, 186 (pointing out that ‘R&D spillovers are not as much of a public good’, and that ‘the cost of utilising public domain knowledge fruitfully is minimal only for firms which have accumulated sufficient technological capability to absorb external knowledge’).

  59. 59.

    Ibid; Michael Spence (1984, supra n 55) 103; Wesley M Cohen and Daniel A Levinthal, ‘Innovation and Learning: Two Faces of R&D’ (1989) 99 Economic Journal 569, 576.

  60. 60.

    The terminology in the economic literature is not consistent. See eg Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50) 1065 (using the term ‘rent spillovers’ to describe the effect that occurs ‘when a firm or consumer purchases R&D incorporated goods or services at prices that do not reflect their user value, because of [inter alia] imperfect appropriability and imitation’, and the term ‘knowledge spillovers’ – when discussing situations where ‘an R&D project produces knowledge that can be useful to another firm in doing its own research’. See also Adam B Jaffe (1998, supra n 49) 11-12 (referring to these phenomena as ‘market spillovers’ and ‘knowledge spillovers’); Cristiano Antonelli (2017, supra n 48) 16, 18 (distinguishing between ‘imitation externalities’ and ‘knowledge externalities’, the latter being defined as ‘the opportunity to use the knowledge embodied in an innovation to generate new knowledge’).

  61. 61.

    Zvi Griliches (1998, supra n 57) 251-2; Adam B Jaffe (1998, supra n 49) 11.

  62. 62.

    Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50) 1065.

  63. 63.

    Cristiano Antonelli (2017, supra n 48) 21.

  64. 64.

    See supra n 52.

  65. 65.

    See eg Paul David, ‘The Economic Logic of ‘Open Science’ and the Balance between Private Property Rights and the Public Domain in Scientific Data and Information: A Primer’ in Julie M Esanu and Paul F Uhlir (eds), The Role of Scientific and Technical Data and Information in the Public Domain: Proceedings of a Symposium (The National Academies Press 2003) 19, 30; Suzanne Scotchmer, ‘Standing on the Shoulders of Giants: Cumulative Research and the Patent Law’ 5(1) The Journal of Economic Perspectives 295 (1991).

  66. 66.

    Katharine Rockett, ‘Property Rights and Invention’ in Bronwyn H Hall and Nathan Rosenberg (eds), Handbook of the Economics of Innovation Vol 1 (Elsevier BV 2010) 315, 339; Wesley M Cohen (2010, supra n 58) 192; Adam B Jaffe (1998, supra n 49) 11.

  67. 67.

    Vania Sena, ‘The Return of the Prince of Denmark: A Survey on Recent Developments in the Economics of Innovation’ (2004) 114 Economic Journal 312, 324-325.

  68. 68.

    Hanns Ullrich, ‘Intellectual Property: Exclusive Rights for a Purpose’ (2013) Max Planck Institute for Intellectual Property and Competition Law Research Paper No 13-01, 30 ff.

  69. 69.

    Bundesgesetz über die Erfindungspatente vom 25. Juni 1854, Artikel 40(b).

  70. 70.

    See ‘The Disclosure Function of the Patent System (Or Lack Thereof)’ (2005) 118 (6) HarvLRev 2007-2028.

  71. 71.

    Bronwyn H Hall and Dietmar Harhoff, ‘Recent Research on the Economics of Patents’ (2012) 4 AnnuRevEcon 541, 549.

  72. 72.

    Among the fields of IP, the ‘static-dynamic efficiency trade-off’ is characteristic for patents and copyright. See Gideon Parchomovsky and Peter Siegelman, ‘Towards an Integrated Theory of Intellectual Property’ (2002) 88 VaLRev 1455, 1458.

  73. 73.

    It is well-known that there is no straightforward relationship between exclusivity-based incentives, the propensity to innovate, and the social value of innovation. See eg Louis Kaplow, ‘The Patent-Antitrust Intersection: A Reappraisal’ 97 (8) HarvLRev (1984) 1813, 1823-4.

  74. 74.

    See eg Suzanne Scotchmer (1991, supra n 65) 30 (noting that ‘[m]ost economics literature on patenting and patent races has looked at innovations in isolation, without focusing on the externalities or spillovers that early innovators confer on later innovators’, and that ‘the cumulative nature of research poses problems for the optimal design of patent law that are not addressed by that perspective’); James Bessen and Eric Maskin, ‘Sequential Innovation, Patents, and Imitation’ (2009) 40(4) RAND Journal of Economics 611 (arguing that, ‘when innovation is ‘sequential’ (so that each successive invention builds in an essential way on its predecessors) and ‘complementary’ (so that each potential innovator takes a different research line), patent protection is not as useful for encouraging innovation as in a static setting’); Roberto Mazzoleni and Richard R Nelson, ‘The Benefits and Costs of Strong Patent Protection: A Contribution to the Current Debate’ (1998) 27 Research Policy 273, 280 (observing that ‘whenever an invention is understood as contributing to further invention potential as well as creating a new or improved product or process of immediately final use, a question can be raised as to whether strong patents enhance or hinder technical advances in the long run’); Vania Sena (2004, supra n 67) 324.

  75. 75.

    Notably, in literature on innovation economics, the definition of innovation incentives is associated with improved appropriability conditions by means of protection against imitation. See eg Stefano Breschi and Franco Malerba (2005, supra n 43) 135 (defining appropriability as the conditions that determine ‘possibilities to earn profits from an innovative activity [by] protecting innovations from imitation’).

  76. 76.

    Richard A Posner, ‘The Social Costs of Monopoly and Regulation’ (1975) 83(4) Journal of Political Economy 807, 807.

  77. 77.

    Infra nn 104-105 and the accompanying text.

  78. 78.

    Whether supra-competitive prices are an optimal quid pro quo for R&D incentives across all sectors is by no means a settled issue, as the debate over access to affordable medicine may illustrate.

  79. 79.

    Wesley M Cohen (2010, supra n 58) 185-186, 194 (arriving at this conclusion upon the review of empirical economic research on innovative activity over the past 50 years).

  80. 80.

    Zvi Griliches (1998, supra n 57) 251-2.

  81. 81.

    Ibid 262.

  82. 82.

    Adam B Jaffe, Richard G Newell and Robert N Stavins (2005, supra n 52) 167.

  83. 83.

    Jeffrey I Bernstein and M Ishaq Nadiri (1998, supra n 50) 249 (with further references).

  84. 84.

    Wesley M Cohen and Daniel A Levinthal (1989, supra n 59) 592-3.

  85. 85.

    Sinclair Davidson and Heath Spong (2017, supra n 48) 370.

  86. 86.

    Wesley M Cohen (2010, supra n 58)192 (recommending that ‘when considering the impacts of such knowledge flows, one needs to be attentive to the associated tradeoffs for R&D and innovation between the appropriability incentive effects of such flows, on the one hand, and their complementarity and efficiency effects, on the other hand’).

  87. 87.

    Ibid 186 (noting that ‘a key question for understanding R&D incentives and innovation at the industry level is, in addition to considering the efficiency effect of spillovers […], what factors condition the tradeoff between spillovers’ negative appropriability incentive effect and their positive complementarity effects’). See also Hanns Ullrich (2016, supra n 1) 101-102 (pointing out that, in the case of patent rights, the criteria for striking the trade-offs ‘horizontally’ between innovation incentives and effective dissemination of innovation through competition and ‘vertically’ between innovation incentives and ‘enabling followers to use and actually to exploit all opportunities for desirable (or desired) sequential innovation by improvement, variation, etc. [are] derived from the nature of the technologies and the structure of the industries to be promoted, and, more generally, from the state of an economy, its science base, infrastructure, etc.’).

  88. 88.

    OECD, Enhancing Access to Data (2019, supra n 24) 74 (noting that, because ‘data are in principle non-exclusive goods for which the costs of exclusion can be high, there is the possibility that some may ‘free ride’ on others’ investments’).

  89. 89.

    Ibid 60.

  90. 90.

    Ibid 74 n 2. See also Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas, ‘Markets for Data’ (2020) <https://www.researchgate.net/publication/338411973_Markets_for_Data> accessed 1 March 2022 4 (concluding that ‘appropriation regimes for data are weak [and] data are highly likely to be associated with significant knowledge spillovers’). However, the authors review only the applicability of IP rights to data and do not assess the sufficiency of contractual, trade secret and other types of legal protection non-exclusive in nature.

  91. 91.

    OECD, Enhancing Access to Data (2019, supra n 24) 95.

  92. 92.

    Ibid 35 (pointing out that data holders are ‘among the most critical actors for data sharing and re-using because without their active contributions there would be no data available [and t]he effectiveness of incentive mechanisms will depend on the extent to which data holders can benefit from data sharing and be protected from risks’).

  93. 93.

    See eg Nestor Duch-Brown, Bertin Martens and Frank Mueller-Langer, ‘The Economics of Ownership, Access and Trade in Digital Data’ (2017) JRC Digital Economy Working Paper 2017-01 25 (pointing out that data trade ‘leaves little room for free riding’, and that the assumption that free-riding by third-party users ‘would take away incentives for private agents to invest in [data] production [and] result in a market failure because of undersupply of socially useful data […] may be unrealistic in many cases’).

  94. 94.

    Consider the example of drugs: the chemical composition can be reverse-engineered once the drug is commercialised, but not the patient-level clinical trial data generated in the course of the drug development. Neither is it feasible to ‘reverse-engineer’ and reproduce customer data from a service that was personalised based on the customer data analysis.

  95. 95.

    See eg Nestor Duch-Brown, Bertin Martens and Frank Mueller-Langer (2017, supra n 93) 36 (noting that ‘[s]ome data may be more excludable than others, for instance through technical measures’).

  96. 96.

    In the EU, criminal penalties for offences against information systems, such as illegal access to information systems, illegal system interference and illegal data interference, were harmonised by the Directive 2013/40/EU of the European Parliament and of the Council of 12 August 2013 on attacks against information systems and replacing Council Framework Decision 2005/222/JHA, OJ of 14.08.2013, L 218.

  97. 97.

    Sjef van Erp, ‘Ownership of Data and the Numerus Clausus of Legal Objects’ (2017) Maastricht European Private Law Institute Working Paper No 2017/6 13.

  98. 98.

    Hanns Ullrich (2019, supra n 8) 20-21 (pointing out that ‘the legal titles currently relied upon for protecting data are rather crude and hardly ‘access-friendly’, namely the legal protection that is available for trade secrets, the de facto protection that may be achieved by technological protection measures (TPM), and, where pertinent at all, database protection, either genuine or sui generis’).

  99. 99.

    In contrast to ‘pure’ public goods characterised by both non-excludability and non-rivalry in consumption, ‘impure’ public goods are partially rival and/or partially excludable. See Richard Cornes and Todd Sandler, The Theory of Externalities, Public Goods, and Club Goods (CUP 1996) 4; Joseph E Stiglitz, ‘Knowledge as a Global Public Good’ in Inge Kaul, Isabelle Grunberg and Mark A Stern (eds), Global Public Goods: International Cooperation in the 21st Century (OUP 1999) 308, 309-10.

  100. 100.

    See European Commission, ‘Synopsis Report of the Public Consultation on Building a European Data Economy’ Annex <https://ec.europa.eu/information_society/newsroom/image/document/2017-36/annex_to_the_synopsis_report_-_data_economy_A45A375F-ADFF-3778-E8DD2021E5CC883B_46670.pdf> accessed 1 March 2022 22 (reporting that ‘a considerable number of respondents believe that the existing legal framework is sufficient’, and that ‘[o]verall, technical protection mechanisms were considered to be sufficient’). See also Hanns Ullrich (2019, supra n 8) 23.

  101. 101.

    As envisaged by the European Commission, data should be put to use ‘for the public good’ by generating diverse benefits, and data held by the private sector can ‘also make a significant contribution as public goods’. COM(2020) 66 final (supra n 6) 6-7.

  102. 102.

    Laura Razzolini, ‘Public Goods’ in Charles K Rowley and Friedrich Schneider (eds), The Encyclopedia of Public Choice (Springer 2004) 457, 457 (defining public goods as ‘goods with benefits that extend to a group of individuals’).

  103. 103.

    OECD, Enhancing Access to Data (2019, supra n 24) 74.

  104. 104.

    James M Buchanan and Yong J Yoon, ‘Symmetric Tragedies: Commons and Anticommons’ (2000) 43 JLE 1, 4.

  105. 105.

    Francesco Parisi, Norbert Schultz and Ben Depoorter, ‘Simultaneous and Sequential Anticommons’ (2004) 17 European Journal of Economics 175, 183. The inability to affect the decision making of the effect generators by effect recipients is a definitional characteristic of externalities. Jean-Jacques Laffont (2008, supra n 50) 4318.

  106. 106.

    OECD, Enhancing Access to Data (2019, supra n 24) 74.

  107. 107.

    OECD, Data-Driven Innovation: Big Data for Growth and Well-Being (OECD 2015) [hereinafter OECD, Data-Driven Innovation] 184-185.

  108. 108.

    As a social phenomenon, an ‘anticommons’ refers to a cooperation failure – a collective action problem, whereby individually rational, gain maximising behaviour can lead to a collectively suboptimal outcome. See eg Elinor Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action (CUP 2015) 5 ff; Ivan Major, Ronald F King and Cosmin Gabriel Marian, ‘Confusions in the Anticommons’ (2016) 9(7) JPL 64, 67.

  109. 109.

    Ivan Major, Ronald F King and Cosmin Gabriel Marian (2016, supra n 108) 70.

  110. 110.

    Supra nn 96-99 and the accompanying text.

  111. 111.

    Ibid (further arguing that ‘there is no apparent necessity to insert the concept of legal property as a critical element into [the definition of anticommons]’).

  112. 112.

    Ibid.

  113. 113.

    Michael A Heller and Rebecca S Eisenberg, ‘Can Patents Deter Innovation? The Anticommons in Biomedical Research’ (1998) 280 Science 698; Charlotte Hess and Elinor Ostrom, ‘Introduction: An Overview of the Knowledge Commons’ in Charlotte Hess and Elinor Ostrom (eds), Understanding Knowledge as a Commons: From Theory to Practice (MIT Press 2007) 3, 11 (defining the ‘tragedy of the anticommons in the knowledge arena [as] the potential underuse of scarce scientific resources caused by excessive intellectual property rights and over patenting in biomedical research’); Richard L Wang, ‘Biomedical Upstream Patenting and Scientific Research: The Case for Compulsory Licenses Bearing Research-Through Royalties’ (2008) 10(7) YJoLT 251, 253 (‘The core problem with the anticommons is underuse.’); Jetta Frost and Michèle Morner, ‘Overcoming Knowledge Dilemmas: Governing the Creation, Sharing and Use of Knowledge Resources’ (2010) 2(2/3) Int’l J of Strategic Change Management 172, 178 (‘Resources are underused, because too many ‘knowledge empire builders’ have the right to exclude.’); James M Buchanan and Yong J Yoon (2000, supra n 104) 2 (referring to ‘anticommons’ as ‘a useful metaphor for understanding how and why potential economic value may disappear into the “black hole” of resource underutilization’).

  114. 114.

    James M Buchanan and Yong J Yoon (2000, supra n 104) 4.

  115. 115.

    Michael A Heller and Rebecca S Eisenberg (1998, supra n 113) 698 (arguing that the ‘tragedy of anti-commons’ in the case of patents for research tools in the biomedical innovation ‘is distinct from the routine underuse inherent in any well-functioning patent system’); Fiona Murray and Scott Stern, ‘Do Formal Intellectual Property Rights Hinder the Free Flow of Scientific Knowledge? An Empirical Test of the Anti-Commons Hypothesis’ (2007) 63 Journal of Economic Behavior & Organization 648, 654 (noting that, although ‘not limited to life sciences, many of the issues that currently animate the IPR discussion surround the interaction between public and private knowledge exploitation in areas related to biotechnology’ (with further references)).

  116. 116.

    See Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 7 (observing that similar to inventions, ‘data are intermediate goods and need to be further processed and combined with complementary inputs such as analytic technologies in order to become final goods and contribute to utility or productivity’). See also OECD, Enhancing Access to Data (2019, supra n 24) 74.

  117. 117.

    The relevance of these factors in the case of device-generated data is discussed below at 3.3.

  118. 118.

    Yi Zhou, ‘The Tragedy of the Anticommons in Knowledge’ (2005) Review of Radical Political Economics 1, 3. His model forecasts that, even though the society can gain from more private investment in R&D due to IP protection, ‘it loses to a greater extent from a lower rate of knowledge spillover and diffusion’; ibid 14.

  119. 119.

    Sven Vanneste and others, ‘From “Tragedy” to “Disaster”: Welfare Effects of Commons and Anticommons Dilemmas’ (2006) 26 IRLE 104.

  120. 120.

    Franceso Parisi, Norbert Schulz and Ben Depoorter (2004, supra n 105) 185.

  121. 121.

    Ivan Major, Ronald F King and Cosmin Gabriel Marian, ‘Anticommons, the Coase Theorem, and the Problem of Bundling Inefficiency’ (2016) 10 IJC 244; Zhou (2005, supra n 118); Norbert Schulz, Francesco Parisi and Ben Depoorter, ‘Fragmentation in Property: Towards a General Model’ 158 Journal of Institutional and Theoretical Economics 594 (2002); Franceso Parisi, Norbert Schulz and Ben Depoorter (2004, supra n 105).

  122. 122.

    James M Buchanan and Yong J Yoon (2000, supra n 104) 12.

  123. 123.

    Franceso Parisi, Norbert Schulz and Ben Depoorter (2004, supra n 105) 183.

  124. 124.

    Ivan Major, Ronald F King and Cosmin Gabriel Marian (2016, supra n 108) 77 (emphasis added).

  125. 125.

    Ibid.

  126. 126.

    Several empirical studies attempted to test the anticommons hypothesis with regard to patents for research tools. See eg John P Walsh, Ashish Arora and Wesley M Cohen, ‘Effects of Research Tool Patents and Licensing on Biomedical Innovation’ in Wesley M Cohen and Stephen A Merrill (eds), Patents in the Knowledge-Based Economy (National Academies Press 2003) 285, 285-340; John P Walsh, Charlene Cho and Wesley M Cohen, ‘View From the Bench: Patents and Material Transfers’ (2005) 309 (5743) Science 2002, 2002-3. But see Paul David, ‘The Economic Logic of ‘Open Science’ and the Balance Between Private Property Rights and the Public Domain in Scientific Data and Information: A Primer’ in Julie M Esanu and Paul F Uhlir (eds), The Role of Scientific and Technical Data and Information in the Public Domain. Proceedings of a Symposium 19, 31 ff (National Research Council 2003) (taking a critical view on the suitability of surveys as a method of identifying the ‘anticommons’ effects).

  127. 127.

    Louis Kaplow and Steven Shavell, ‘Economic Analysis of Law’ in Alan J Auerbach and Martin Feldstein (eds), Handbook of Public Economics Vol 3 (Elsevier Science 2002) 1665, 1697.

  128. 128.

    The ‘optimal’ way in this context is defined as having a more favourable cost-benefit ratio relative to the non-intervention scenario. See European Commission, ‘Better Regulation ‘Toolbox” supplementing SWD(2015) 111 final (19.05.2015) 338 ff.

  129. 129.

    The position of the European Commission expressed in its 2020 strategy for data reflects this principle. See COM(2020) 66 final (supra n 6) 13 (stating that ‘[t]he general principle shall be to facilitate voluntary data sharing [and] only where specific circumstances so dictate, access to data should be made compulsory’).

  130. 130.

    Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 2.

  131. 131.

    Hanns Ullrich (2019, supra n 8) 23 n 72.

  132. 132.

    In a general sense of the availability of the relevant market information allowing market participants to make an informed decision. Jens Forssbæck and Lars Oxelheim, The Multifaceted Concept of Transparency’ in Jens Forssbæck and Lars Oxelheim (eds), The Oxford Handbook of Economic and Institutional Transparency (OUP 2014) 3, 6.

  133. 133.

    Parties’ knowledge regarding the value and potential benefits of a transaction is an important factor of the efficient allocation of usage rights through contracts. Elizabeth Hoffman, Kevin McCabe and Vernon L Smith, ‘Experimental Law and Economics’ in Peter Newman (ed), The New Palgrave Dictionary of Economics and the Law 116, 120 (Palgrave Macmillan 2002).

  134. 134.

    Sufficiently low transaction costs imply that the cost-benefit ratio makes a transaction worthwhile. As posited by the Coase’s Theorem, ‘the rearrangement of rights will only be undertaken when the increase in the value of production consequent upon the rearrangement is greater than the costs which would be involved in bringing it about’. Ronald Coase, ‘The Problem of Social Cost’ (1960) 3 The Journal of Law & Economics 1, 15-16.

  135. 135.

    See eg COM(2017) 9 final (supra n 5) 6 (observing that ‘the data services market is substantially influenced by lack of transparent rules’); SWD(2017) 2 final (supra n 17) 28 (pointing out ‘the lack of transparency on data flows and data utilisation’ among other challenges that can hinder the exploitation of the data potential).

  136. 136.

    COM(2017) 9 final (supra n 5) 10 (stating that ‘where the negotiation power of the different market participants is unequal, market-based solutions alone might not be sufficient to ensure fair and innovation-friendly results, facilitate easy access for new market entrants and avoid lock-in situations’).

  137. 137.

    Hanns Ullrich (2019, supra n 8) 22 (noting that ‘a time-limited data producer right akin to an intellectual property right [was proposed] to enhance transparency and tradability of data by providing rules on attribution of ownership and on exclusive control of the use of data in their coded (syntactic) form [which] aims at improving the operation of data markets by transforming data into merchandisable private goods in much the same way as do intellectual property rights in regard of their subject matter’ (with further references)).

  138. 138.

    SWD(2017) 2 final (supra n 17) 33 (referring to the proposals ‘envisaged by scholars [for] a new data producer right with the objective of enhancing the tradability of non-personal or anonymised machine-generated data as an economic good’ (with further references)).

  139. 139.

    See Michael Mattioli (2014, supra n 34) 537, 583 (observing that, even though ‘the big data disclosure problem is not inherently an “intellectual property problem”, it raises familiar concerns for intellectual property law, a primary goal of which is to encourage technological disclosure in order to speed innovation’, and arguing that by granting ‘big data producers […] an exclusive right to limit downstream use of their data – this new intellectual property right could encourage valuable technological disclosures that would otherwise remain shrouded in secrecy’).

  140. 140.

    In the words of Hanns Ullrich, ‘the exclusivity approach to rights in data seems to rest on a contradiction in that it seeks to solve a problem of dissemination of and access to data – i.e. broader data sharing and trade – by the means and tools of an incentive mechanism for the creation/production of knowledge’. Hanns Ullrich (2019, supra n 8) 25.

  141. 141.

    See ‘The Disclosure Function of the Patent System (Or Lack Thereof)’ (2005, supra n 69) 2016, 2010 (noting that ‘[m]ost patented inventions can be uncovered through reverse engineering, and the patent system is therefore of limited value in promoting R&D spillovers and cumulative innovations’, and that ‘the disclosure function is still socially desirable to the extent that it reduces duplicative research after a patent has been published’). See also Bronwyn H Hall and Dietmar Harhoff, ‘Recent Research on the Economics of Patents’ 4 Annual Review of Economics 541, 549 Supplemental Appendix 2 (2012).

  142. 142.

    Directive 2016/943/EU of the European Parliament and of the Council of 8 June 2016 on the protection of undisclosed know-how and business information (trade secrets) against their unlawful acquisition, use and disclosure, OJ of 15.06.2016, L 157/1, Rec 14 (stating that the definition of a trade secret ‘should […] be constructed so as to cover know-how, business information and technological information where there is both a legitimate interest in keeping them confidential and a legitimate expectation that such confidentiality will be preserved’).

  143. 143.

    Bronwyn H Hall and Dietmar Harhoff, ‘Recent Research on the Economics of Patents’ (2012) NBER Working Paper No 17773, 1-51 <https://www.nber.org/papers/w17773.pdf> accessed 1 March 2022 16 (observing that ‘if inventors know that rivals would learn a lot from their disclosed patents, secrecy may become the more appealing option for protecting an invention or innovation’, and ‘those inventions which would generate considerable valuable information for third parties via the patent documents alone will be particularly unlikely to be patented’).

  144. 144.

    Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 3.

  145. 145.

    OECD, Data-Driven Innovation (2015, supra n 107) 38-39 (stating that ‘the context dependency of data and the dynamic environment in which some data are used (e.g. research) make it almost impossible to fully evaluate ex ante the potential of data’, and pricing in a data market ‘is challenging mainly due to the fact that data have no intrinsic value, as the value depends on the context of their use’).

  146. 146.

    Martin Wiener, Carol Saunders and Marco Marabelli, ‘Big-Data Business Models: A Critical Literature Review and Multi-Perspective Research Framework’ (2019) <https://www.researchgate.net/publication/337569060_Big-Data_Business_Models_A_Critical_Literature_Review_and_Multi-Perspective_Research_Framework> accessed 1 March 2022 6 (observing that ‘despite the [big data] hype, a deployment gap has been identified [and] that many organizations get stuck in a ‘limbo stage’; that is, while they intend to deploy [big data], they are unable to do so’ (with further references)).

  147. 147.

    Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 9-10.

  148. 148.

    Consider, for example, training data for machine learning-based technologies and applications.

  149. 149.

    Michael A Heller and Rebecca S Eisenberg (1998, supra n 113) 701.

  150. 150.

    OECD, Data-Driven Innovation (2015, supra n 107)177 ff. Infrastructures are generally defined as ‘large-scale indivisible capital goods producing products and services, which become inputs in most or all economic activities on a multi-user basis’. Keith Smith, ‘Economic Infrastructures and Innovation Systems’ in Charles Edquist (ed), Systems of Innovation. Technologies, Institutions and Organizations (Routledge 2005) 86, 86. Knowledge resources such as aggregated data represent a category of non-traditional infrastructures; ibid 87-88.

  151. 151.

    OECD, Enhancing Access to Data (2019, supra n 24) 60.

  152. 152.

    See Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 28 ff. See also Heiko Richter and Peter R Slowinski, ‘The Data Sharing Economy: On the Emergence of New Intermediaries’ (2019) 50 IIC 4 (exploring the analogy between emerging data-sharing platforms and standard-essential patent pools, and the role of the FRAND principles in the design and functioning of data-sharing platforms as self-regulation mechanisms).

  153. 153.

    Michael A Heller and Rebecca S Eisenberg (1998, supra n 113) 698-699; Wesley M Cohen and Stephen A Merrill (eds), ‘To Promote Innovation: The Proper Balance of Competition and Patent Law and Policy’ (US Federal Trade Commission 2003) 24 ff.

  154. 154.

    COM(2019) 66 final (supra n 6) 7. See also von Max v Grafenstein, Alina Wernick and Christopher Olk, ‘Data Governance: Enhancing Innovation and Protecting Against Its Risks’ (2019) 54 Intereconomics 228, 229.

  155. 155.

    Russell Covey, ‘Behavioral Economics and Plea Bargaining’ in Eyal Zamir and Doron Teichman (eds), The Oxford Handbook of Behavioral Economics and the Law (OUP 2014) 643, 647.

  156. 156.

    European Commission (2015, supra n 128) 72 ff.

  157. 157.

    A notable example is a standard licencing agreement for the secondary use of the anonymised clinical trial data used by the Clinical Study Data Request consortium. In particular, it obliges secondary data users to grant to each Study Sponsor, who contributed clinical trial data for secondary analysis, ‘a perpetual, non-exclusive, fully-paid up, royalty-free, irrevocable, worldwide, unrestricted license under any New Intellectual Property for Study Sponsor Uses, with the right to sublicense through multiple tiers’, whereby it defines the term ‘New Intellectual Property’ ‘as any results of secondary data analysis, irrespective of whether they might be eligible for IP protection or not’. See ClinicalStudyDataRequest, ‘Standard Contract Template for Clinical Trial Data Sharing’ (10 April 2017) <https://www.clinicalstudydatarequest.com/Documents/CSDR%20DATA%20SHARING%20AGREEMENT%20Version%201%204.10.2017.pdf> accessed 1 March 2022 paras 4.2, 1.7.

  158. 158.

    Hanns Ullrich (2019, supra n 8) 27.

  159. 159.

    Suzanne Scotchmer (1991, supra n 162) 135.

  160. 160.

    Richard R Nelson (2009, supra n 50) 10.

  161. 161.

    The rate of social returns is considerably higher than the rate of private returns on innovation due to the factors of imperfect excludability of R&D results, cumulativeness of research and innovation, and non-rivalry in use of knowledge. See eg Zvi Griliches (1998, supra n 57) 264; Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50) 1034, 1065; Adam B Jaffe (1998, supra n 49) 12. With regard to data, see OECD, Enhancing Access to Data (2019, supra n 24) 17.

  162. 162.

    In the context of data, see OECD, Enhancing Access to Data (2019, supra n 24) 95 (noting that ‘[t]he root cause of the incentive problems of data access and sharing can be attributed to a positive externality issue: data access and sharing may benefit others more than it may benefit the data holder and controller, who may not be able to privatise all the benefits of data re-use’).

  163. 163.

    Dominique Foray (2004, supra n 45) 114 (noting that ‘social returns may be so substantial that remunerating the inventor accordingly is unthinkable’). See also Suzanne Scotchmer, Innovation and Incentives (MIT Press 2005) 134; Charles I Jones and John C Williams, ‘Too Much of a Good Thing? The Economics of Investment in R&D’ (2020) 5 Journal of Economic Growth 65, 7; Mark A Lemley, ‘Property, Intellectual Property, and Free Riding’ (2005) 83 TLR 1031, 1032.

  164. 164.

    Ibid 127.

  165. 165.

    Ibid.

  166. 166.

    Steven Shavell, Foundations of Economic Analysis of Law (HUP 2009) 91 ff.

  167. 167.

    In the words of Hanns Ullrich, ‘providing for broader access claims will require establishing some minimum position of legal protection of the interests of the addressees of such claims or else granting such claims would likely have a chilling effect on data production and curation’ Hanns Ullrich (2019, supra n 8) 30.

  168. 168.

    See eg OECD, Enhancing Access to Data (2019, supra n 24) 97-98 (stating that, ‘while regulation may impose data access, it may also undermine incentives to invest in data in the first place’, and ‘for organisations and individuals, including researchers, which build their competitive advantage based on data lock-in, mandatory data access and sharing could undermine their ability to compete, to a point where their incentives to invest in data may be too low to enter a particular market’).

  169. 169.

    See eg Hanns Ullrich (2019, supra n 8) 30.

  170. 170.

    OECD, Enhancing Access to Data (2019, supra n 24) 81, 97-98 (noting that, ‘for organisations and individuals, including researchers, which build their competitive advantage based on data lock-in, mandatory data access and sharing could undermine their ability to compete’).

  171. 171.

    Cristiano Antonelli (2017, supra n 48) 97.

  172. 172.

    See eg Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50) 1065; Cristiano Antonelli (2017, supra n 48) 5.

  173. 173.

    Adam B Jaffe (1998, supra n 49) 14.

  174. 174.

    Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 4 (observing that ‘comprehensive provenance can […] partially alleviat[e] appropriation problems’ in data markets). See also Hanns Ullrich (2019, supra n 8) 27 (observing that, ‘[g]iven that even in their digital presentation data are sensitive commercial assets and held in confidence, orientation in this market remains a matter of relationships of trust, and so remain the transactions themselves’).

  175. 175.

    Ioanna D Constantiou and Jannis Kallinikos, ‘New Games, New Rules: Big Data and the Changing Context of Strategy’ (2015) 30(1) Journal of Information Technology 44. But see Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 5 (pointing out that ‘in the shadows of the digital economy, there have always been thriving marketplaces for stolen data, such as credit card numbers or user profile data’ (with further references)).

  176. 176.

    Ionna D Constantiou and Jannis Kallinikos (2015, supra n 175) 44.

  177. 177.

    Consider the example of drugs, the chemical formulation of which can be reverse-engineered without access to patient-level data generated in the course of clinical trials as the process of drug development.

  178. 178.

    OECD and Eurostat, Oslo Manual. Guidelines for Collecting and Interpreting Innovation Data (3rd edn, OECD 2015) 92.

  179. 179.

    Ibid; see also Bronwyn H Hall, Jacques Mairesse and Pierre Mohnen (2010, supra n 50)1035.

  180. 180.

    On this distinction, see supra nn 61-64 and the accompanying text.

  181. 181.

    Supra nn 63-67 and the accompanying text.

  182. 182.

    OECD, Data-Driven Innovation (supra n 108) 108, 181 (noting that ‘in theory there are no limits with regard to the purposes for which data can be used’ and that data ‘will typically be used for different purposes across multi-sided markets’).

  183. 183.

    Supra nn 176-177.

  184. 184.

    In the case of ‘raw’ data, an absorptive capacity can be understood as all resources necessary to put data to use and derive value from it. Dominique Foray, ‘Generation and Distribution of Technological Knowledge: Incentives, Norms, and Institutions’ in Charles Edquist (ed), Systems of Innovation. Technologies, Institutions and Organizations (Routledge 2005) 64, 65.

  185. 185.

    Pantelis Koutroumpis, Aija Leiponen and Llewellyn DW Thomas (2020, supra n 90) 7.

  186. 186.

    Supra nn 144-145.

  187. 187.

    COM(2020) 66 final (supra n 6) 13 (pointing out that ‘[a] data access should only be sector-specific and only given if a market failure in this sector is identified/can be foreseen, which competition law cannot solve’, and that the scope of a data access right ‘should take into account legitimate interests of the data holder and needs to respect the legal framework’).

  188. 188.

    Hanns Ullrich (2019, supra n 8) 2.

  189. 189.

    Ibid 29.

  190. 190.

    Peter Drahos and John Braithwaite, Information Feudalism. Who Owns the Knowledge Economy? (Earthscan Publications 2002) 13 (referring to this task as ‘the difficult trick for any legislature’).

  191. 191.

    Hanns Ullrich (2019, supra n 8) 29 n 90.

  192. 192.

    The idea that IP protection is a matter of a trade-off is one of the leitmotifs in Professor Ullrich’s scholarship. See eg Hanns Ullrich (2016, supra n 1) 101 ff; Hanns Ullrich, ‘Intellectual Property: Exclusive Rights for Purpose – The Case of Technology Protection by Patents and Copyright’ (2013) Max Planck Institute for Intellectual Property and Competition Law Research Paper No 13-01 <https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2179511> accessed 1 March 2022 19 ff.

  193. 193.

    Laura Razzolini (2004, supra n 102) 458.

  194. 194.

    Inge Kaul, ‘Public Goods: A Positive Analysis’ Discussion Draft, UNDP Office of Development Studies 17 (2013).

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Acknowledgements

I would like to thank all participants of the symposium in Ringberg for stimulating discussions and, in particular, Dr. Heiko Richter for valuable feedback on the draft.

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    Daria Kim

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    Christine Godt

  2. Max Planck Institute for Innovation and Competition, Munich, Germany

    Matthias Lamping

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Kim, D. (2023). Incentives for Data Sharing as a Case on (Regulating) Knowledge Externalities. In: Godt, C., Lamping, M. (eds) A Critical Mind. MPI Studies on Intellectual Property and Competition Law, vol 30. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65974-8_16

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