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Tech, Regulatory Arbitrage, and Limits

Abstract

Regulatory arbitrage refers to structuring activity to take advantage of gaps or differences in regulations or laws. Examples include Facebook modifying its terms and conditions to reduce the exposure of its user data to strict European privacy laws, and Uber and other platform companies organizing their affairs to categorize workers as non-employees. This article explores the constraints and limits on regulatory arbitrage through the lens of the technology industry, known for its adaptiveness and access to strategic resources. Specifically, the article explores social license and the bundling of laws and resources as constraining forces on regulatory arbitrage, and the legal mismatch that can arise from new business models and innovations as a key area in which the limits of regulatory arbitrage can be observed.

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Notes

  1. 1.

    Barlow (1996).

  2. 2.

    Partnoy (1997), p 227. For additional discussion of the definition of regulatory arbitrage, see Sect. 2.1.

  3. 3.

    Fleischer (2010), p 227.

  4. 4.

    Regulatory arbitrage may or may not be socially optimal, depending on the circumstances, but it is generally discussed in the literature as a problem. See García (2019), p 207 (‘[A]t its core, regulatory arbitrage is distortionary behavior that can thwart regulatory intent and disadvantage actors who play by the rules.’); Fleischer (2010), p 230 (‘[A] more precise understanding of when and how gamesmanship occurs allows us to address the problem in a targeted fashion that avoids sweeping, overbroad reforms that do more harm than good.’).

  5. 5.

    See, e.g., Weadon (2012) (discussing lack of international harmonization in OTC derivatives regulation).

  6. 6.

    Riles (2014); see also Freeman and Rossi (2012) (discussing interagency coordination as a mechanism to reduce regulatory arbitrage).

  7. 7.

    See, e.g., Barry (2011), p 73 (‘[R]egulatory arbitrage can be eliminated by crafting legal rules that accurately track the economic substance of transactions… If there is no gap to take advantage of, there is no risk of regulatory arbitrage. When seen in this light, regulatory arbitrage is a phenomenon that follows from having regulations that fail to take economic reality into account.’).

  8. 8.

    See Katz (1996), p 4 (coining the term ‘avoision’ to refer to cases in which it is unclear whether conduct should be considered lawful avoidance or illegal evasion); Wu (2017); Burk (2016).

  9. 9.

    See Wu (2018) (describing the economic and political power of large technology companies); Fleischer (2010), p 230 (‘[T]he rich, sophisticated, well-advised, and politically connected avoid regulatory burdens the rest of us comply with.’).

  10. 10.

    See Partnoy (2019), pp 1030–1031 (‘If regulatory costs are suboptimally high, regulatory arbitrage can be viewed as socially optimal; if regulatory costs are high for valid social purposes (for example, to internalize the costs of externalities), regulatory arbitrage can be viewed as socially suboptimal’); see also O’Hara and Ribstein (2009), pp 20–21 (noting ‘party choice’ in a ‘market for law’ can encourage states to provide desirable laws, but can ‘impose net costs where a party is forced or manipulated into agreeing that a state’s law should apply or where the chosen law harms others not party to the contract’); Choudhury and Petrin (2018), p 34 (‘In short, international arbitrage highlights the ability of corporations to affect the public on a multitude of issues while similarly being able to thwart accountability.’).

  11. 11.

    Schumpeter (1942).

  12. 12.

    For a discussion of the rise of the intangible economy and implications of ‘capitalism without capital’, see Haskel and Westlake (2018), pp 8–11.

  13. 13.

    Pollman (2019b) (discussing tech culture and permissionless innovation); Stemler (2017) (discussing rhetorical devices and techniques used by ‘sharing economy’ companies to avoid legal rules and obligations).

  14. 14.

    See Cohen (2017), p 199 (‘Although such [platform] corporations are nominally headquartered in particular countries and have physical assets in many other countries that are amenable to control in varying degrees, their great economic power translates into correspondingly powerful capacity for regulatory arbitrage.’).

  15. 15.

    Marian (2017), p 7, fn. 44.

  16. 16.

    Drucker and Bowers (2017).

  17. 17.

    Khan (2017), fn. 204.

  18. 18.

    ‘Facebook to exclude billions from European privacy laws’, BBC (19 April 2018), https://www.bbc.com/news/technology-43822184.

  19. 19.

    See Cui (2018).

  20. 20.

    See, e.g., Wu (2003) (discussing how code is used to avoid the law and seek legal advantage); Elert and Henrekson (2016), p 96 (discussing ‘profit-driven business activity in the market aimed at circumventing the existing institutional framework by using innovations to exploit contradictions in that framework’); Brummer (2015), p 980 (noting that some ‘innovations may have appeal or be popular precisely because of their ability to engage, undermine, or elide existing regulatory and market systems’).

  21. 21.

    Social license refers to the ‘social approval of those affected by a certain business activity’. Melé and Armengou (2016); see also Sale (2019). The concept draws upon the notion of business as a social institution that requires legitimacy from stakeholders. For further discussion, see Sect. 3.1infra.

  22. 22.

    Partnoy (1997), p 227; see also Riles (2014), p 69 (‘[A]rbitrage is about exploiting formal differences, despite the functional similarity of products across different markets owing to the interrelationship of markets.’).

  23. 23.

    Partnoy (1997), p 227; see also Partnoy (2019), pp 1017-1021 (discussing the ‘difference between the effects of financial and regulatory arbitrage’).

  24. 24.

    Fleischer (2010), p 230.

  25. 25.

    Ibid., p 231; Bruner (2016), pp 30-38 (discussing capital mobility and regulatory competition); cf. Talley (2015), p 1653 (arguing that ‘[f]ederal law’s creeping displacement of state law has consequently ‘unbundled’ domestic tax law from domestic corporate governance regulation’ in the US). For a discussion of ‘how offshore jurisdictions enable business entities to opt out of otherwise mandatory domestic regulatory laws’, see Moon (2019), pp 1–2.

  26. 26.

    Fleischer (2010), p 231.

  27. 27.

    Ibid., pp 232–234; see also Burk (2016), pp 6–7 (‘[Exploitation of loopholes] happens routinely, in all areas of social activity, producing unexpected and often undesired outcomes as regulation changes behavior in unanticipated ways.’); Wu (2003), pp 682–683 (describing code as a technological mechanism to minimize the burden of laws).

  28. 28.

    Riles (2014), p 71.

  29. 29.

    Ibid.

  30. 30.

    Ibid., p 72.

  31. 31.

    Ibid.

  32. 32.

    García (2019), p 203; see also Burk (2016), pp 15–16 (discussing technological and ontological avoision).

  33. 33.

    García (2019), p 203.

  34. 34.

    Ibid., pp 203, 237–238.

  35. 35.

    See Katz (2010) (discussing circumvention of law and related other phrases such as evasion, avoidance, and loophole exploitation, as well as more context-specific examples such as tax shelters, bootstrapping, and forum-shopping); see also Moon (2019), fn. 7 (‘Scholars typically distinguish tax evasion, a set of illicit activities aimed at reducing taxes, from tax avoidance, which includes various forms of legal maneuvering.’).

  36. 36.

    In some instances, regulatory arbitrage or legal avoidance may result in an unexpected benefit or may mitigate regulation driven by special interests. For example, some regulatory arbitrage ‘may serve as an impetus for technological innovation, or may signal to Congress an extant imbalance in statutory treatment of similarly situated entities, potentially resulting in societal benefit on balance.’ García (2019), p 203; see also Burk (2016), pp 3–5 (describing serendipitous technology as ‘perverse innovation’ that is a byproduct of regulatory avoidance such as the development of mutagenic crops to avoid strict regulation of genetically modified organisms (GMOs)); Ayres and Kapczynski (2015), pp 1812–1827 (describing innovation to avoid or lessen the impact of a penalty such as the development of energy-efficient cars and light bulbs).

  37. 37.

    García (2019), p 202; Litman (2002), pp 350–354.

  38. 38.

    Wu (2003), p 731.

  39. 39.

    Ibid., p 732.

  40. 40.

    Ibid., p 736; Wu (2017), p 10.

  41. 41.

    Oei (2018).

  42. 42.

    Ibid., pp 109, 120–121.

  43. 43.

    Prassl (2018), pp 19–20; Lobel (2016).

  44. 44.

    Lobel (2016), p 106.

  45. 45.

    Ibid., p 156 (‘[D]efinitional defiance is central to the business model of the platform.’); Tomassetti (2016), p 78 (‘[C]laims about technological sophistication and the knowledge economy can be euphemisms for profit seeking not through productive enterprise, but through regulatory arbitrage, speculation, and other forms of asset manipulation.’). This process is dynamic and the law responds in turn, such as by developing new statutes and case law on the issue of employee status.

  46. 46.

    See Katz (2010).

  47. 47.

    For a classic work that considers the complexity and limits of financial arbitrage in the real world given that it requires capital and entails risk, particularly in the agency context, see Shleifer and Vishny (1997).

  48. 48.

    Fleischer (2010), p 230.

  49. 49.

    Ibid., pp 231–232.

  50. 50.

    Ibid., pp 252–253; see also Buell (2011) (describing good faith doctrines as serving a similar anti-avoidance function); Blank and Staudt (2012), p 1645 (discussing anti-abuse standards and examining judicial responses to ‘technically legal activities that may be perceived as shams’).

  51. 51.

    Fleischer (2010), p 258.

  52. 52.

    Ibid., pp 262–274.

  53. 53.

    Ibid., pp 252, 264; cf. Barry (2011), p 71 (noting that ‘professional constraints are by no means a perfect prevention mechanism’, but they ‘do shift deals between different structures and [this] can affect the degree of regulatory arbitrage’).

  54. 54.

    Fleischer (2010), p 273.

  55. 55.

    Ibid., pp 283–288 (discussing ‘the politics of the deal’).

  56. 56.

    Sale (2019).

  57. 57.

    See, e.g., Melé and Armengou (2016); Demuijnck and Fasterling (2016); Wilburn and Wilburn (2011).

  58. 58.

    Sale (2019).

  59. 59.

    Sale (2019).

  60. 60.

    See Berle (1959), pp 90–91, 114; see also Cheffins (2018–2019) (discussing mid-twentieth century notions of ‘countervailing power’ on corporations); Pollman (2019a), pp 634–639 (discussing Adolf Berle’s concept of ‘inchoate law’ that arises when corporations fail to self-regulate within expected social norms of responsibility).

  61. 61.

    See Volpicelli (2018); Satariano (2018).

  62. 62.

    Lobel (2016), p 157 (‘[T]he economic and social logic of the platform, pushing down transaction costs in all stages of the deal, as well as creating new markets that map onto new preferences and lifestyles, is the primary raison d’être of the rise of the platform.’); Stone (2017) (describing Uber’s philosophy, ‘our product is so superior to the status quo that if we give people the opportunity to see it or try it, in any place in the world where government has to be at least somewhat responsive to the people, they will demand it and defend its right to exist’).

  63. 63.

    MacMillan (2016).

  64. 64.

    Siddiqui (2017).

  65. 65.

    Ibid.

  66. 66.

    Ram and Kazmin (2017).

  67. 67.

    The Greyball tool was part of an Uber program called VTOS, short for ‘violation of terms of service’. It used data collected from the Uber app and other techniques to identify riders that it viewed as using or targeting its service improperly, such as public officials who were posing as customers to investigate or gather evidence that Uber was operating illegally. The tool showed such riders a fake version of the app and blocked them from booking rides. Isaac (2017a, b).

  68. 68.

    Duhigg (2018).

  69. 69.

    Dowd (2017).

  70. 70.

    Transport for London, 22 September, 2017, https://www.ltda.co.uk/assets/files/downloads/TfL%20licensing%20decision%20letter.pdf.

  71. 71.

    Ibid.

  72. 72.

    Ibid.

  73. 73.

    Crerar (2017). The number of controversies and scandals plaguing Uber, along with the company’s own statements, suggested that corporate culture was a contributing factor to the actions that prompted a loss of social license. See Partnoy (2019), pp 1033–1036 (noting that cultural and psychological factors could influence regulatory arbitrage); Langevoort (2017) (discussing the role of corporate culture in compliance regimes).

  74. 74.

    Ibid.

  75. 75.

    Satariano (2018).

  76. 76.

    Ibid.

  77. 77.

    Ibid.

  78. 78.

    Wolfe and Levine (2018).

  79. 79.

    Ibid.

  80. 80.

    Somerville (2018).

  81. 81.

    For example, thousands of taxi drivers filed one of the largest class actions in Australian history against Uber for ‘conspiracy to act unlawfully’, seeking hundreds of millions of dollars in damages. Jacks (2018).

  82. 82.

    For a related discussion of ‘publicness’ as a mechanism by which citizens, media, and other outside actors control public perception and can push for information and public accountability, see Sale (2011, 2013); Langevoort and Thompson (2013).

  83. 83.

    Armour, Enriques, Ezrachi and Vella (2018).

  84. 84.

    Ibid.

  85. 85.

    For a discussion of the history of the relationship between corporate law and social welfare, see Bratton (2017).

  86. 86.

    See Cohen (2017), p 176 (describing how ‘[p]latform companies are encountering legal systems worldwide at a time of crisis’).

  87. 87.

    Larry Fink’s 2019 letter to CEOs: purpose and profit, BlackRock, https://www.blackrock.com/corporate/investor-relations/larry-fink-ceo-letter.

  88. 88.

    Larry Fink’s 2018 letter to CEOs: a sense of purpose, BlackRock, https://www.blackrock.com/corporate/investor-relations/2018-larry-fink-ceo-letter.

  89. 89.

    Ibid.; see also Sorkin (2018).

  90. 90.

    See, e.g., McNamee (2019); Zuboff (2019); Wu (2018); Khan (2017); Galloway (2017).

  91. 91.

    See Osnos (2018); Harvard Business Review (2019).

  92. 92.

    For a discussion of this point in the context of economist Charles Tiebout’s model of jurisdictional competition, see Bratton and McCahery (1997). For a discussion of the tax ‘frictions’ literature, see Fleischer (2010), pp 232–233, 258. Gloukhovtsev et al. (2018) provide a ‘marketing systems’ perspective and a case study of regulatory arbitrage and alcohol policy in Finland.

  93. 93.

    Andreessen (2014); see also Thierer (2016) (discussing innovation and regulatory competition).

  94. 94.

    Levin and Downes (2018).

  95. 95.

    Ibid. (‘[G]oogle’s own interest in fiber stemmed from a conviction that faster speeds would eventually generate more revenue and services for the broader Alphabet enterprise, making the investment justifiable if not profitable.’).

  96. 96.

    Ibid.

  97. 97.

    Andreessen (2014).

  98. 98.

    Levin and Downes (2018). For a discussion of ‘how smart governments are learning to compete on the basis of their openness to innovation, often through flexibility around regulation’, see Burfield (2018), pp 100–105.

  99. 99.

    Levin and Downes (2018).

  100. 100.

    Chung (2015); Murgia (2017).

  101. 101.

    O’Sullivan (2018).

  102. 102.

    Allen (2019).

  103. 103.

    See ibid.; see also Van Loo (2018) (arguing that the US lags in consumer financial technology because of a lack of an agency with the authority and expertise to promote consumer financial competition).

  104. 104.

    Synced (2018); see also Campo-Flores (2017).

  105. 105.

    Budds (2017); Levy (2017). Many of the company’s products have been labeled ‘Designed by Apple in California. Assembled in China.’—pointing to issues of both brand and labor availability. Rawson (2012).

  106. 106.

    See Bratton and McCahery (1997), pp 222–223 (‘[I]ndividual sorting proves difficult to effect because of the complex packaging of public goods and regulations. Although private goods tend to be produced and sold separately, public goods tend to be jointly produced and made available on a bundled basis.’).

  107. 107.

    Chander (2014); Gilson (1999); Lobel (2013).

  108. 108.

    See, e.g., Rodrigues and Schleicher (2012) (discussing how ‘location decisions are valuable because of the “agglomeration” benefits they provide’); Porter (1998) (explaining that ‘[b]eing a part of a cluster allows companies to operate more productively in sourcing inputs; accessing information, technology, and needed institutions; coordinating with related companies; and measuring and motivating improvement’); Ibrahim (2010), p 717 (‘Silicon Valley’s success has led other regions to attempt their own high-tech transformations, yet most imitators have failed.’).

  109. 109.

    See Florida and Mellander (2016), p 32 (describing the preferences of tech executives, workers, and venture capitalists to be in urban environments). These patterns give rise to other social issues such as concerning the connection between inequality and geography. See, e.g., Florida (2014).

  110. 110.

    See Goshen and Hamdani (2016) (discussing entrepreneurs’ pursuit of private benefits and idiosyncratic vision); see also Bratton and McCahery (1997), p 234 (‘Family, community, and cultural ties also may make movement an undesirable response to dissatisfaction with public goods, taxes, or regulation.’).

  111. 111.

    Levy (2019).

  112. 112.

    Reints (2018).

  113. 113.

    See, e.g., Zetlin (2018).

  114. 114.

    For a discussion of business location tax incentives, see Enrich (1996); Schragger (2009), pp 491–497.

  115. 115.

    See Streitfeld (2018).

  116. 116.

    De Blasio (2019).

  117. 117.

    Weise et al. (2019).

  118. 118.

    As discussed above, this path has not been without cost and friction. Uber’s business model has notably incurred billions of dollars of losses and arbitraging employee status has led to lawsuits, settlements, and other expenses, leading to speculation that the company is engaged in a big bet or long game to driverless vehicles. See, e.g., Sherman (2017).

  119. 119.

    See Pollman (2019c); Coolican and Jin (2018).

  120. 120.

    Pollman and Barry (2017), p 383.

  121. 121.

    Ibid. Not all companies succeed at regulatory entrepreneurship. See, e.g., Tusk (2018), p 9 (discussing startup companies that failed or encountered prolonged difficulty with regulatory entrepreneurship).

  122. 122.

    Pollman and Barry (2017), pp 398–408 (discussing how regulatory entrepreneurs may break the law or operate in legal gray areas while trying to change the law, grow too big to ban, mobilize users and other stakeholders for political gain, and use traditional political techniques such as lobbying).

  123. 123.

    See, e.g., Mancuso (2019).

  124. 124.

    See, e.g., Holley (2018).

  125. 125.

    See Tusk (2018).

  126. 126.

    Pollman and Barry (2017), pp 410–424.

  127. 127.

    Ibid., pp 419–421.

  128. 128.

    Ibid., pp 411–412, 421. Strong support from large numbers of users and stakeholders can give regulatory entrepreneurs bargaining leverage with relevant regulators. See, e.g., Burfield (2018), pp 114, 246–258 (advising companies engaged in regulatory entrepreneurship to know their ‘power map’ and to mobilize ‘citizen power’ for leverage with regulators); Tusk (2018), pp 156–157, 169–172, 212–213 (discussing how regulatory entrepreneurs might use both ‘honey and vinegar’ with regulators, such as by creating a positive narrative, threatening jurisdictional exit, and mobilizing customers as political advocates).

  129. 129.

    See Partnoy (2019), p 1035; Fleischer (2010), p 227.

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Acknowledgements

For helpful conversations and comments, thanks to Jordan Barry, Vic Fleischer, Kristelia García, Frank Partnoy, Christine Riefa, Arad Reisberg, Hillary Sale, Adam Thierer, and the participants of the Third Annual Oxford Business Law Blog Conference at the University of Oxford. Special thanks to Luca Enriques for excellent discussion and suggestions.

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Pollman, E. Tech, Regulatory Arbitrage, and Limits. Eur Bus Org Law Rev 20, 567–590 (2019). https://doi.org/10.1007/s40804-019-00155-x

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Keywords

  • Regulatory arbitrage
  • Technology
  • Innovation
  • Regulation
  • Competition
  • Social license
  • Social contract
  • Social responsibility
  • Bundling
  • Innovation cluster
  • Regulatory entrepreneurship

JEL Classification

  • K2
  • K4
  • L5