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Access to Clinical Trial Data as a Case on R&D Externalities: A Theoretical Framework

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Part of the Munich Studies on Innovation and Competition book series (MSIC,volume 16)

Abstract

This chapter outlines an analytical framework that can inform the design of the legal rules on access to IPD as a knowledge resource for research and innovation. It starts by characterising data as an economic good and frames the dual policy objective of regulating access to IPD as an ‘access-innovation’ dilemma. The analysis finds that such dilemmas typically arise with the private provisioning of public goods and stem from the dual implications of non-excludability of R&D results and knowledge externalities for innovation. Accordingly, an overview of law-and-economics-of-innovation research on R&D externalities is provided. The ‘access-innovation’ policy dilemma is then restated as a potential trade-off between greater knowledge diffusion and diminished innovation incentives that might arise if data generated in industry-sponsored trials is treated as a non-excludable knowledge resource.

Keywords

  • Access to data
  • Access-incentives paradox
  • Clinical trial data
  • Disclosure
  • Innovation incentives
  • Knowledge externalities
  • Knowledge spillovers
  • Public good
  • R&D externalities
  • Research tools

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Notes

  1. 1.

    Razzolini (2004), p. 457 (defining public goods as ‘goods with benefits that extend to a group of individuals’).

  2. 2.

    Non-rivalry in use means that data can be analysed in parallel research projects without depreciating its inherent value or imposing additional costs on the data producer. On rivalry of benefits of data analysis, see Chap. 8 at Sect. 8.3.4.

  3. 3.

    See e.g. Blumenthal (2010), p. 142 (concluding that ‘large clinical databases have an aspect of a quasi-public good [as they] are definitely excludable’). On the legal determinants of control over and access to clinical trial data under EU framework, see Chap. 4.

  4. 4.

    A ‘pure public good’ is characterised by both non-excludability and non-rivalry in consumption; an ‘impure public goods’ can be partially rival and/or partially excludable. See Cornes and Sandler (1999), p. 4; Stiglitz (1999), pp. 309–310 (referring to knowledge that can be made excludable, e.g. through trade secrets, as an impure public good).

  5. 5.

    See Kaul I (2013) Public goods: a positive analysis. Discussion draft, UNDP Office of Development Studies, p. 17 (pointing out that excludability of an economic good is ‘a social construct’ in a sense that it is determined not only by the inherent properties of a good but also by man-made rules).

  6. 6.

    In the case of clinical trials, several interrelated market failures can be distinguished, namely, the market failure of public goods (suboptimal investment into drug R&D), the market failure of information asymmetry (the risk of inaccurate representation of information regarding the drug effects) and the market failure of insufficient testing. On market failures in the pharmaceutical industry, see Orsenigo et al. (2006), pp. 406–409.

  7. 7.

    See e.g. Nelson (1969), p. 306; Oakland (1987), p. 485 (noting that, since public goods ‘are not used up in the act of consumption […], 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’); Razzolini (2004), p. 458 (explaining that ‘society’s welfare would be maximized when the good is available for consumption at no cost to everyone who places a positive value on it: that is, no individual should be excluded from consumption’).

  8. 8.

    Heal (1999), p. 223 (observing that public goods pose the policy questions of how much should be provided and how their provision should be financed).

  9. 9.

    See generally Frischmann (2012); Frischmann and Lemley (2007); Frischmann (2005).

  10. 10.

    Arrow (1962), p. 618; Dasgupta and David (1987), p. 535; Antonelli (2017), p. 18.

  11. 11.

    As shown in Chap. 3, aggregated IPD has a considerable potential to generate knowledge beyond the benefit-risk assessment of the investigational products.

  12. 12.

    Intermediate public goods are also known as collective intermediate goods or collective factors of production. See e.g. Oakland (1987), pp. 492–493; Sandmo (1972), p. 149.

  13. 13.

    Smith (2005), p. 86.

  14. 14.

    ibid pp. 87–88.

  15. 15.

    Such resources, for instance, include scientific equipment, archives, collections, e-infrastructures, computing systems, and communication networks. See European Commission. About the research infrastructures. http://ec.europa.eu/research/infrastructures/index.cfm?pg=about. Accessed 26 Mar 2021.

  16. 16.

    Reichman et al. (2016), p. 475.

  17. 17.

    Cameron (2001), p. 32.

  18. 18.

    Rose (2003), p. 90.

  19. 19.

    OECD (2015), p. 197.

  20. 20.

    European Commission. About the research infrastructures. http://ec.europa.eu/research/infrastructures/index.cfm?pg=about. Accessed 26 Mar 2021.

  21. 21.

    Lauer (2010), p. 91.

  22. 22.

    Dedeurwaerdere (2009), p. 366 (referring to ‘research commons’ as comprising scientific data, information, materials and research tools).

  23. 23.

    See e.g. van Overwalle (2014), p. 137; Dedeurwaerdere (2009), pp. 376 ff.

  24. 24.

    See e.g. Reichman et al. (2016), p. 475 (arguing that ‘the disaggregated knowledge assets of microbiological research should be combined and strengthened within a contractually constructed research commons to be organised and managed by the public science community itself’).

  25. 25.

    Reichman (2009), p. 57.

  26. 26.

    See e.g. Crown (2010), p. 143.

  27. 27.

    The potential to contribute to academic and commercial research is characteristic of other types of research tools used in molecular biology. See Eisenberg (1997), p. 13; Reichman et al. (2016), p. 255; Murray and Stern (2007), p. 648; Lee (2008), p. 73.

  28. 28.

    Reichman et al. (2016), p. 255.

  29. 29.

    On this issue, Chap. 8 at Sect. 8.2.2.3.

  30. 30.

    See generally Chap. 3.

  31. 31.

    On the role of control over data as a means to protect competitive advantage in competition in drug innovation, see Chap. 8 at Sect. 8.1.4.4, subheading ‘Protection of Exploratory Endpoints as Intermediate Research Results’.

  32. 32.

    Gruber (2016), pp. 197–203.

  33. 33.

    On the inherent tension between appropriability and diffusion, see below (nn 92–95) in this chapter and the accompanying text.

  34. 34.

    Foray (2004), pp. 113–118.

  35. 35.

    Efficiency is defined as rationing scarce resources for the ‘maximum possible satisfaction’. Wadley (2011), p. 97.

  36. 36.

    Oakland (1987), p. 485 (stating that a fee ‘charged by the private firm for the consumption of a public good in order to recover the costs of producing a public good will lead some potential users to forgo consumption [and] would result in a deadweight efficiency loss’).

  37. 37.

    Platteau (2008), p. 22.

  38. 38.

    As shown in Chap. 6, transparency and innovation objectives concerning access to IPD require distinct policy approaches, while erga omnes disclosure of IPD is unlikely to be a proportionate measure in either case.

  39. 39.

    Audretsch et al. (2004), p. 21 (noting that ‘[c]oncepts such as knowledge and innovative activity do not lend themselves to obvious quantification’). See also de la Mano M (2002) For the customer’s sake: the competitive effects of efficiencies on the European merger control. European Commission’s Enterprise Directorate-General, Enterprise Papers no. 11, p. 52 (stating that dynamic efficiency is ‘the least quantifiable form of efficiency but is almost always the most economically significant component of global efficiency gains’); Kerber (2008), p. 98; Kerber and Schwalbe (2008), para 1-8-097 (defining that the economic process is ‘dynamically efficient if [the development and introduction of new goods and production technologies] take place over time at the rate which is socially optimal’); Eagles and Longdin (2011), p. 54 (observing that ‘[i]nnovative efficiency is loved by all but measured by few.’). On measuring innovative activity, see Audretsch et al. (2004), pp. 21–22.

  40. 40.

    See e.g. PhRMA/EFPIA, ‘Principles’ 4 (pointing out ‘the risks to innovation [by] disclosure to competitors of companies’ trade secrets and proprietary information that could allow others to “free ride” off of the substantial investments of innovators’) (emphasis added).

  41. 41.

    Below (nn 75–80) and the accompanying text.

  42. 42.

    Below at Sect. 7.2.1 in this chapter.

  43. 43.

    Davidson and Spong (2010), p. 366. See also below at Sect. 7.2.3.2 in this chapter.

  44. 44.

    Below at Sect. 7.2.3.1 in this chapter.

  45. 45.

    Laffont (2018), p. 4318; Bernstein and Nadiri (1989), p. 249; Schall (1971), p. 983; Hall et al. (2010), p. 1065; Nelson (2009), p. 10.

  46. 46.

    For an account of R&D externalities in economic literature, see Davidson and Spong (2010); Antonelli (2017), pp. 3–21.

  47. 47.

    While the term ‘knowledge spillover’ is widely used in the literature, benefits from others’ R&D do not simply ‘spill over’ unless the knowledge recipient has developed adequate absorptive capacity. See Cohen (2010), p. 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’). See generally Cohen and Levinthal (1989).

  48. 48.

    Above (n 45).

  49. 49.

    Bohm (2018), p. 4314; Laffont (2018), p. 4318.

  50. 50.

    See Laffont (2018), p. 4318; Griliches (1998), p. 258; Bohanon (1985), p. 306 (pointing out that ‘the salient distinction’ between ‘genuine’ and pecuniary externalities is that the latter ‘never enter third parties’ utility (or production) functions, whereas by definition, technical externalities always enter the utility (or production) functions of third parties’).

  51. 51.

    Hall et al. (2010), p. 1065 (emphasis added).

  52. 52.

    ibid p. 1065 (observing that ‘the more knowledge is codified and the higher is the absorptive capacity of other firms, the more knowledge spillover will take place’).

  53. 53.

    Knowledge externalities should be distinguished from technology transfer. While both are the mechanisms of knowledge diffusion, technology transfer implies that externally produced knowledge is acquired under contractual terms, thus, allowing the producer of knowledge to (partially) internalise its value.

  54. 54.

    Nelson (2009), p. 10 (noting that ‘virtually all research and development […] yields externalities, in the sense that some parties not involved in R&D decision-making will be able to learn something useful from its results’).

  55. 55.

    Davidson and Spong (2010), p. 364.

  56. 56.

    Griliches (1998), p. 258.

  57. 57.

    Private returns to R&D refer to profits earned by the firm undertaking research and generating knowledge, while social returns comprise private returns as well as benefits received by customers and other firms. The rate of social returns is considerably higher than the rate of private returns. See e.g. Griliches (1998), p. 264; Hall et al. (2010), pp. 1034, 1065; Jaffe (1998), p. 12.

  58. 58.

    Griliches (1998), pp. 251–252.

  59. 59.

    ibid p. 262.

  60. 60.

    Antonelli (2017), pp. 83–84.

  61. 61.

    For instance, according to Hall, Mairesse, Mohnen, ‘rent spillovers’ 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’, while ‘knowledge spillovers’ happen ‘when an R&D project produces knowledge that can be useful to another firm in doing its own research’. Hall et al. (2010), p. 1065 (emphasis added). Jaffe uses the terms ‘market spillovers’ and ‘knowledge spillovers’ to refer to the same phenomena. Jaffe (1998), pp. 11–12. Antonelli defines imitation externalities as ‘the opportunity for imitators to replicate the innovation introduced by the ‘inventor” and knowledge externalities as ‘the opportunity to use the knowledge embodied in an innovation to generate new knowledge’. Antonelli (2017), pp. 16, 18. See also Martin (2002), pp. 1–2 (referring to the effect the costs of research for the firms-recipients of external knowledge is reduced as ‘input spillovers’ and the effect when diminished appropriability of R&D efforts of the firms generating knowledge as ‘output spillovers’).

  62. 62.

    Griliches (1998), pp. 251–252; Jaffe (1998), p. 11.

  63. 63.

    Hall et al. (2010), p. 1065 (emphasis added).

  64. 64.

    Antonelli (2017), p. 21.

  65. 65.

    Jaffe (1998), pp. 11–12.

  66. 66.

    Hall et al. (2010), p. 1065. See also Audretsch et al. (2004), p. 23 (noting that knowledge re-use accounts for total factor productivity); Lucas (2002), p. 6. For an overview of growth models that incorporate knowledge externalities, see Braunerhjelm (2011), pp. 180–182; Antonelli (2017), p. 4.

  67. 67.

    Henderson and Cockburn (1996), p. 56.

  68. 68.

    Duffy (2005), p. 1086.

  69. 69.

    ibid.

  70. 70.

    Jaffe et al. (2005), p. 167.

  71. 71.

    In other words, the knowledge-producing activity would generate external benefits for other firms within the same or different industries, while the knowledge consumption by an imitating firm can diminish returns on R&D of the knowledge-producing firm. Jaffe (1986).

  72. 72.

    Spence (1984), p. 116; Hall et al. (2010), p. 1065 (noting that knowledge ‘spillovers’ can reduce the production costs of rival firms).

  73. 73.

    Hall et al. (2010), p. 1065.

  74. 74.

    ibid. See also Antonelli (2017), p. 4; Antonelli C, Colombelli A (2017) The locus of knowledge externalities and the cost of knowledge. LEI&BRICK Working Paper 11/2017, pp. 1–2.

  75. 75.

    See e.g. Jones and Williams (2000), p. 70 (referring to the ‘standing on shoulders’ effect as intertemporal knowledge spillovers that occur when firms that create knowledge today cannot appropriate its value for future research); Jaffe (1998), pp. 11–12 (defining ‘knowledge spillovers’ as ‘knowledge created by one agent [that] can be used by another without compensation, or with compensation less than the value of the knowledge’).

  76. 76.

    Sena (2004), pp. 324–325; Greenstein (2010), pp. 500–501.

  77. 77.

    Cockburn IM, Henderson R, Stern S (2018) The impact of artificial intelligence on innovation. NBER Working paper 24449, p. 8 (noting that ‘an increasing body of evidence suggests that research tools and the institutions that support their development and diffusion play an important role in generating intertemporal spillovers’ (with further references)).

  78. 78.

    Newton’s epigram is often evoked in the literature on cumulative innovation and economics of knowledge. See e.g. David (2003), p. 30; Scotchmer (1991), p. 5; Eisenberg (1989), pp. 1055–1056; Merton (1974), p. 275 (observing that Newton’s aphorism expresses ‘a sense of indebtedness to the common heritage and a recognition of the essentially cooperative and selectively cumulative quality of scientific achievement’).

  79. 79.

    Thus, earlier innovation ‘creates the seeds’ for the later innovation and ‘a positive externality’ allows the later innovator to build on the past advances. Rockett (2010), p. 339. On the importance of the dissemination of unsuccessful research results, see e.g. Cohen (2010), p. 192; Jaffe (1998), p. 11 (noting that ‘one firm’s abandonment of a particular research line signals to others that the line is unproductive and hence saves them the expense of learning this themselves’); Callon (1998), p. 245 (observing that knowledge generated and disclosed by one firm ‘may inspire [other firms] to rethink the direction of their own research’).

  80. 80.

    Foray (2004), p. 166.

  81. 81.

    Cohen and Levinthal (1989), pp. 575–576.

  82. 82.

    Cohen (2010), p. 186. See also Spence (1984), p. 103; Cohen and Levinthal (1989), p. 576.

  83. 83.

    Arrow (1962), p. 146; den Hertog J (2010) Review of economic theories of regulation. Utrecht School of Economics Discussion Paper Series No 10-18, p. 16; Bergstrom et al. (1986), p. 25.

  84. 84.

    Gruber (2016), p. 188; den Hertog J (2010) Review of economic theories of regulation. Utrecht School of Economics Discussion Paper Series No 10-18, p. 16.

  85. 85.

    Jaffe (1998), p. 14.

  86. 86.

    Below at Sect. 7.2.3.3, subheading ‘The (Controversial) Role of Patents as a Means to Coordinate Research Efforts’.

  87. 87.

    Cohen (2010), p. 186.

  88. 88.

    Antonelli (2017), p. 4.

  89. 89.

    ibid p. 98 ff.

  90. 90.

    Denicolo and Franzoni (2011), p. 112.

  91. 91.

    ibid.

  92. 92.

    Breschi and Malerba (2005), pp. 135–136.

  93. 93.

    ibid.

  94. 94.

    ibid.

  95. 95.

    Foray (2004), pp. 116–119. See also Stiglitz and Wallsten (1999), p. 56.

  96. 96.

    Among the fields of IP, the static-dynamic efficiency trade-off is characteristic of patents and copyright. See Parchomovsky and Siegelman (2002), p. 1458.

  97. 97.

    European Commission (28 Nov 2013) 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, p. 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’). See also Lemley (2005), p. 1032; Foray (2004), p. 114 (noting that ‘social returns may be so substantial that remunerating the inventor accordingly is unthinkable’).

  98. 98.

    For a literature review on the optimal design of patent protection, see e.g. Rockett (2010), pp. 333–361; Hall (2007), pp. 576–577.

  99. 99.

    Drahos and Braithwaite (2002), p. 13 (referring to this task as a ‘difficult trick for any legislature’).

  100. 100.

    Patent rights can create a market for technological knowledge and allow innovators to internalise R&D benefits through ex ante cooperative research arrangements or ex post licensing agreements. Menell (2000), p. 139.

  101. 101.

    On the incentive-to-disclose theory of patent law, see e.g. Mazzoleni and Nelson (1998a), p. 1038 (arguing that ‘patents encourage disclosure and, more generally, provide a vehicle for a quick and wide diffusion of the technical information underlying new inventions’). See also Landes and Posner (2003), p. 304 (‘Invention is a matter of adding to the stock of useful knowledge and so of reducing uncertainty.’). The qualifier ‘earlier’ should be added to the premise of promoting disclosure. See The disclosure function of the patent system (or lack thereof), p. 2016 (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’). However, even though ‘the patent system as a whole does not reduce the overall level of wasteful research, the disclosure function is still socially desirable to the extent that it reduces duplicative research after a patent has been published’. ibid 2010 (emphasis added). Furthermore, it is worth noting that economic research finds ‘little empirical evidence as to the extent of disclosure and its economic impact’. See Hall and Harhoff (2012), p. 549.

  102. 102.

    Both early disclosure and experimental use exception can be conceptually viewed as promoting the ‘standing-on-shoulders’ effect associated with knowledge externalities. As for the experimental use exception, its scope tend to be rather narrow. For a comparative overview, see e.g. WIPO (2009) Exclusions from patentable subject matter and exceptions and limitations to the rights. SCP/13/3.

  103. 103.

    Landes and Posner (2003), p. 294; Shavell (2004), p. 138.

  104. 104.

    American Bar Association (2015), p. 102. See also Scotchmer (1991), p. 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’); Mazzoleni and Nelson (1998b), p. 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’); Sena (2004), p. 324.

  105. 105.

    For an overview of this debate, see Chap. 8 at Sect. 8.2.2.2.

  106. 106.

    Cahoy (2006), p. 589 (summarising that ‘the degree to which current patent systems promote innovative behavior remains surprisingly unclear’); Posner (2005), p. 59 (noting that, ‘[u]nfortunately, economists do not know whether the existing system of intellectual property rights is, or for that matter whether any other system of intellectual property rights would be, a source of net social utility, given the costs of the system and the existence of alternative sources of incentives to create such property’).

  107. 107.

    Bessen and Maskin (2009), p. 611 (arguing that where ‘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’).

  108. 108.

    Foray (2004), pp. 166, 169.

  109. 109.

    ibid.

  110. 110.

    ibid.

  111. 111.

    Jones and Williams (2000), pp. 66, 69.

  112. 112.

    Cockburn and Henderson (1994), p. 508.

  113. 113.

    ibid

  114. 114.

    Jones and Williams (1998), p. 1125.

  115. 115.

    ibid.

  116. 116.

    Foray (2004), p. 169.

  117. 117.

    ibid.

  118. 118.

    Menell (2000), p. 138 (with further references). For an overview of literature on this subject, see ibid 146–148.

  119. 119.

    ibid 147. This argument is particularly relevant in the case of drug R&D. For a discussion, see Chap. 8 at Sects. 8.2.3.1 and 8.2.3.2.

  120. 120.

    ibid.

  121. 121.

    On uncertainty in drug R&D, see Chap. 8 at Sect. 8.2.3.3.

  122. 122.

    For an overview of the literature on evolutionary innovation economics with a focus on the interrelation between diversity, competition, and technological progress, see Linge (2008), pp. 81–114.

  123. 123.

    Witt (2018), p. 4090.

  124. 124.

    Menell (2000), p. 147; Kerber (2010), p. 184.

  125. 125.

    Linge (2008), pp. 200–202.

  126. 126.

    Merges and Nelson (1990), p. 908.

  127. 127.

    For an overview of the literature on the coordination role of the patent disclosure, see Rockett (2010), pp. 350–354.

  128. 128.

    Rockett (2010), p. 353.

  129. 129.

    Andrade et al. (2016), p. 48 (noting, in the context of drug R&D, that ‘[p]rices and volumes are amongst the main factors that will bring benefits and returns for firms to recover the amount of R&D expended on drug development’).

  130. 130.

    Kitch (1977). See also Grady and Alexander (1992), p. 310 (arguing that the grant of patent rights early in the development process can reduce the likelihood of rent-dissipating patent races, especially ‘a nascent invention that “signals” many different, possibly patentable, improvements should be given a broad scope so as to avoid the possibility of races to patent these improvements’).

  131. 131.

    Kitch (1977), p. 266. A ‘prospect’ is understood as a ‘nascent’ invention presenting a promising technological opportunity, which development requires further investment.

  132. 132.

    ibid p. 276 (noting that ‘the patent owner has an incentive to make investments to maximize the value of the patent without fear that the fruits of the investment will produce unpatentable information appropriable by competitors’).

  133. 133.

    ibid p. 266.

  134. 134.

    ibid p. 278.

  135. 135.

    Merges and Nelson (1990), p. 907 (warning that ‘[h]olders of broad patents would be operating as tollkeepers, not coordinators’ and, even though ‘the ability to charge a toll may add to the incentives facing an inventor, it does not ensure more efficient development’).

  136. 136.

    Menell (2000), pp. 146–147.

  137. 137.

    ibid p. 871 (noting that ‘proprietary control of technology tend[s] to cause “dead weight” costs’).

  138. 138.

    Merges and Nelson (1990), p. 872 (referring to ‘many instances when a firm that thought it had control over a broad technology rested on its laurels until jogged to action by an outside threat’).

  139. 139.

    Mazzoleni and Nelson (1998b), p. 280 (referring to such technologies as ‘cumulative system technologies’).

  140. 140.

    ibid p. 280 (referring to research on genes and gene expression as an example that ‘is also running into systems problems, particularly insofar as patents are being granted piecemeal on various parts of the puzzle’).

  141. 141.

    ibid (with further references).

  142. 142.

    Merges and Nelson (1990), p. 873 (emphasis added). This argument is particularly relevant for the discoveries of generic nature that can be a catalyst for developing multiple technological applications embodied in diverse innovative products.

  143. 143.

    Above (n 122–125) and the accompanying text.

  144. 144.

    Drug R&D is a pertinent example. See below (Chap. 8, nn 87–88) and the accompanying text.

  145. 145.

    Cohen (2010), pp. 185–186. Further, Cohen notes 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’. ibid p. 186 (emphasis added).

  146. 146.

    Economic incentives to business R&D, p. 5. https://europa.eu/epc/sites/epc/files/docs/pages/annexd_en.pdf. Accessed 26 Mar 2021.

  147. 147.

    Jaffe et al. (2005), p. 167 (with further references). See also Cohen and Levinthal (1989), pp. 592–593 (concluding that ‘the positive absorption incentive associated with spillovers may be sufficiently strong in some cases to more than offset the negative appropriability incentive’); Jeffrey I. Bernstein, M. Ishaq Nadiri, ‘Research and Development and Intra-industry Spillovers: An Empirical Application of Dynamic Duality’ p. 249 (noting that ‘the trade-off between the cost-reducing (or productivity) effect and the incentive effect of R&D investment may be exaggerated’ (with further references)).

  148. 148.

    Cohen (2010), p. 194. See also Linge (2008), p. 63 (summarising that ‘the more firms invent successfully and share their R&D results, the more learning opportunities are provided and thus from a total welfare perspective the negative effect of imitation on innovation incentives is outweighed by rapid diffusion’ (with further references)).

  149. 149.

    Kerber (2010), p. 185 (emphasis added) (with further references).

  150. 150.

    Davidson and Spong (2010), p. 370 (recommending that industrial policy ‘should abandon the Pigovian view that spillover benefits from R&D discourage innovation’ and that it would benefit by following the approach of economists Alfred Marshall and Adam Smith and the view of knowledge externalities as ‘the free exchange of ideas’).

  151. 151.

    Cohen (2010), p. 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’).

  152. 152.

    Foray (2009), p. 34 (arguing that it is ‘important to decouple the objective of limiting spillovers and the objective of securing rents from the innovation; the latter being the fundamental appropriability objective while the former is likely to serve this fundamental objective well in certain situations but not so well in others’).

  153. 153.

    ibid pp. 35–36 (emphasis added).

References

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Kim, D. (2021). Access to Clinical Trial Data as a Case on R&D Externalities: A Theoretical Framework. In: Access to Non-Summary Clinical Trial Data for Research Purposes Under EU Law. Munich Studies on Innovation and Competition, vol 16. Springer, Cham. https://doi.org/10.1007/978-3-030-86778-2_7

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