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The Coming of Age of Open Data

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Law and Economics of the Coronavirus Crisis

Part of the book series: Economic Analysis of Law in European Legal Scholarship ((EALELS,volume 13))

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Abstract

In our increasingly digitized economy, a massive amount of data is generated every moment. While data has always been fundamental to innovation and decision making, the current scale, scope, and speed at which data is collected, organized, and analyzed is unprecedented. Yet, having more data is different from harnessing that data to power useful analytics. Can governments and organizations employ data in a manner that results in even greater value and insights to solve complex problems? This issue has come to the forefront with the Covid-19 outbreak and the attendant societal lockdowns. This Article focuses on one aspect of the data debate, that of “open data”, that is, data that is widely available to the public without cost or restrictions. Led by the United States and Europe, there is a growing open data movement and the associated goal of more transparent and open governments. Yet, what incentivizes governments, businesses, and individuals to open data? This Article first approaches this question from an economic perspective focusing on the characteristics of data that distinguish it from other types of inputs. Next, this Article explores potential privacy concerns within a basic framework and details the concept of a data “life cycle”. Further, the Article explicitly considers the incentives that governments, businesses, and individuals have to open data. These incentives fundamentally boil down to the ability to appropriate, to some degree, the value derived from subsequent use of the open data. Finally, perhaps in the same spirit as the open data movement, there have been increasing calls for competition authorities and governments to wield open data and interoperability as remedies within antitrust and competition laws. This Article highlights some concerns with these “open data antitrust” proposals.

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Notes

  1. 1.

    See e.g. Data Never Sleeps 6.0, Domo, https://www.domo.com/learn/data-never-sleeps-6 (last access 30 April 2021) (“By 2020, it’s estimated that for every person on earth, 1.7 MB of data will be created every second.”).

  2. 2.

    In this Article, the words “data” and “information” are used interchangeably. In information science, some have made distinctions between data, information, knowledge, and wisdom (that is, the DIKW hierarchy or pyramid). See e.g. Ackoff (1989). In some respects, the DIKW categorization mirrors the concept of a data “life cycle”, which is discussed in infra Sect. 3.

  3. 3.

    See e.g. Acquisti et al. (2016). Naturally, there are other concepts of “privacy” based more on notions of autonomy and anonymity. See, e.g., Gavison (1980), p. 423 (defining privacy as “the extent to which we are known to others, the extent to which others have physical access to us, and the extent to which we are the subject of others’ attention.”); Hirshleifer (1980), p. 649 (“The central domain of what we mean by ‘privacy’ is, rather, a concept that might be described as autonomy within society.”).

  4. 4.

    The World Bank, Open Data in 60 Seconds, http://opendatatoolkit.worldbank.org/en/open-data-in-60-seconds.html (last access 30 April 2021).

  5. 5.

    See e.g. Johns Hopkins, University of Medicine, Coronavirus Resource Center, https://coronavirus.jhu.edu/us-map (last access 30 April 2021).

  6. 6.

    U.S . Patent & Trademark Office, Search for Patents, https://www.uspto.gov/patents-application-process/search-patents (last access 30 April 2021).

  7. 7.

    See Michigan Technological University, Free Inactive Patent Search, http://freeip.mtu.edu/home (last access 30 April 2021).

  8. 8.

    See U.S. Constitution, Article I, Section 8, Clause 8 (“To promote the progress of science and useful arts, by securing for limited times to authors and inventors the exclusive right to their respective writings and discoveries.”).

  9. 9.

    To that end, a central requirement to obtain a U.S. patent is a full disclosure of the idea to the general public. This disclosure is immediately available and can potentially be used to gain insight into various innovation trends and even by rivals to design around the patent. Along the same lines, Professor Amanda Levendowski has conducted research showing how public trademark filings can be used to learn about developing surveillance technologies. See Levendowski (2021).

  10. 10.

    See e.g. Open Data Handbook, Introduction, http://opendatahandbook.org/guide/en/introduction (last access 30 April 2021) (“The notion of open data and specifically open government data – information, public or otherwise, which anyone is free to access and re-use for any purpose – has been around for some years. In 2009 open data started to become visible in the mainstream, with various governments (such as the USA, UK, Canada and New Zealand) announcing new initiatives towards opening up their public information.”). According to one source, the term “open data” was first introduced in the mid-1990s. See Simon Chignard, A Brief History of Open Data, Paris Innovation Review, Mar. 29, 2013, http://parisinnovationreview.com/articles-en/a-brief-history-of-open-data (last access 30 April 2021) (“The term open data appeared for the first time in 1995, in a document from an American scientific agency. It dealt about the disclosure of geophysical and environmental data.”).

  11. 11.

    See U.S. Census Bureau, Decennial Census Official Publications, https://www.census.gov/programs-surveys/decennial-census/decade/decennial-publications.1790.html (last access 30 April 2021).

  12. 12.

    See e.g. United States Census 2020, Importance of the Data, https://2020census.gov/en/census-data.html (last access 30 April 2021) (“Over the next decade, lawmakers, business owners, and many others will use 2020 Census data to make critical decisions. The results will show where communities need new schools, new clinics, new roads, and more services for families, older adults, and children.”).

  13. 13.

    See e.g. National Institute of Health, Open-Access Data and Computational Resources to Address COVID-19, https://datascience.nih.gov/covid-19-open-access-resources (last access 30 April 2021) (“COVID-19 open-access data and computational resources are being provided by federal agencies, including NIH, public consortia, and private entities. These resources are freely available to researchers, and this page will be updated as more information becomes available.”). See also Larry Dignan, As COVID-19 Data Sets Become More Accessible, Novel Coronavirus Pandemic May Be Most Visualized Ever, ZDNet, Apr. 20, 2020, https://www.zdnet.com/article/as-covid-19-data-sets-become-more-accessible-novel-coronavirus-pandemic-may-be-most-visualized-ever (last access 30 April 2021).

  14. 14.

    In 2018, the International Data Corporation (IDC) estimated the global volume of data to be 33 zettabytes, which is equivalent to 33 trillion gigabytes—forecasting it to grow to 175 zettabytes in 2025. See Reinsel et al. (2018), p. 6.

  15. 15.

    See Sect. 3 for a fuller discussion of the life cycle of data.

  16. 16.

    Examples include the U.S. federal government’s Data.gov website, Europe’s Open Data Europe Portal (ODP), and the European Data Portal (EDP). More local initiatives include New York City’s Open Data project (https://opendata.cityofnewyork.us (last access 30 April 2021)). In the Netherlands, the Rijkswaterstaat, which is a division of the Department of Infrastructure and Environment, manages key areas of infrastructure and publishes data such water height and location of road signs. See Eckartz et al. (2014), pp. 256–257.

  17. 17.

    See e.g. Berends et al. (2020), p. 3 (“Despite the method applied by the studies and the estimates they provide, there is one finding that is beyond dispute: when opened, data can become a force of growth and development for all countries, regardless of geography and level of economic development.”).

  18. 18.

    See e.g. Australian Competition and Consumer Commission (2019), p. 11 (“The ACCC considers that opening up the data, or the routes to data, held by the major digital platforms may reduce the barriers to competition in existing markets and assist competitive innovation in future markets. This could be achieved by requiring leading digital platforms to share the data with potential rivals […]. Another is to require the platforms to provide interoperability with other services.”); Digital Competition Expert Panel (2019), p. 76 (“The digital markets unit should use data openness as a tool to promote competition, where it determines this is necessary and proportionate to achieve its aims […]. One model would be to require a dataset to be shared in a controlled environment, with access granted to approved businesses.”).

  19. 19.

    In order to understand the concept of economic efficiency, it is useful to start with a description of inefficiency. Economic inefficiency results when achievable benefits are not fully realized. Under such a situation, it is possible to make someone better off without making someone worse off. It follows that allocative efficiency is achieved when you cannot make someone better off without making someone worse off, that is, Pareto Efficiency. See e.g. Lawrence B. Solum, Legal Theory Lexicon 060: Efficiency, Pareto, and Kaldor–Hicks, Legal Theory Lexicon, https://lsolum.typepad.com/legal_theory_lexicon/2006/10/legal_theory_le_1.html (last access 30 April 2021).

  20. 20.

    This fundamental insight was brought to the forefront of economic research by Professor George Stigler in the context of market search costs. See Stigler (1961), p. 224 (“The identification of sellers and the discovery of their prices are only one sample of the vast role of the search for information in economic life.”).

  21. 21.

    Advertising, warranties, trademarks, value of signaling, brands, and reputations are just a few topics that cannot be fully analyzed without incorporating the idea of imperfect information. See, e.g., id.; Spence (1973).

  22. 22.

    See e.g. Mankiw (2015), pp. 216–217.

  23. 23.

    See Dorman (2014), p. 317.

  24. 24.

    Note that these examples also highlight that there are degrees of nonexcludability. For instance, a radio station has reception limits based on the type of modulation used, that is, FM or AM. National defense becomes more costly with more people if they are at the outskirts of a nation’s border.

  25. 25.

    See e.g. Alex Winter, The Short History of Napster 1.0, Wired Magazine, Apr. 4, 2013, https://www.wired.com/2013/04/napster (last access 30 April 2021).

  26. 26.

    See e.g. Hong (2013), p. 299 (“These results therefore suggest that file sharing is likely to explain about 20% of the total sales decline during the Napster period, mostly driven by downloading activities of households with children aged 6–17.”).

  27. 27.

    See e.g. Posner (2005), p. 57.

  28. 28.

    Of course, there is a certain type and amount of data that is created from the mere use or provision of a product—that is, “data exhaust”. See e.g. Terry (2012), pp. 389–390 (“[…] ‘exhaust dataʼ, or data created unintentionally as a byproduct of social networks, web searches, smartphones, and other online behaviors.”). Arguably, widespread use of such data, while perhaps undesirable from the perspective of the firm generating the data, would not impact economic efficiency if the widespread use does not change the ex ante incentives to provide the product. That does not mean that all data exhaust should be open and freely available, however, as there are other considerations including trade secrets, privacy, and incentives to change the nature of the exhaust, which can all impact innovation and efficiency.

  29. 29.

    While some have called for “open innovation” in terms of not having an explicit intellectual property rights regime, there are problems with such a move. For a discussion of open innovation, see, e.g., Dreyfuss (2010), p. 1437.

  30. 30.

    See Hardin (1968). See also Hsu (2005), p. 77 (“[A] tragedy of the commons involves resource users overexploiting a resource and imposing mutual externalities upon each other.”).

  31. 31.

    See, e.g., Hazlett and Caliskan (2008), p. 477 (2008) (“Cable modem services held nearly a two-to-one market share advantage when DSL carriers were most heavily obligated to provide ‘open access’ to competing ISPs. Once the FCC eliminated a key provision of that access regime … DSL subscribership increased dramatically [… and] was 65% higher – more than 9 million households – than it would have been under the linear trend established under ‘open access’ regulation.”).

  32. 32.

    See e.g. Easterbrook (1981).

  33. 33.

    See e.g. Tucker (2020), p. 12 (“In general, though data is non-rivalrous, it is possible to exclude access to particular data if the data are not public. Sometimes, the legal treatment of data has focused on the idea of non-rivalry – which is indeed a key component of the definition of a public good – without also acknowledging that much of the time the same digital tools that allow the collection of vast datasets also permit control over who accesses it.”).

  34. 34.

    The term “platforms” (a.k.a. multisided or two-sided markets) describes firms that have developed a system or network where more than one group (e.g., users, merchants, advertisers) all participate in order to engage in mutually beneficial exchange. See e.g. Evans (2003); Hagiu and Wright (2015), p. 163.

  35. 35.

    Dorman (2014), p. 316.

  36. 36.

    See Bridie Pearson-Jones, Make Your Own IKEA Meatballs: Furniture Giant Shares Six-Step Recipe for Its Famous Swedish Dish So Fans Can Cook It in Lockdown, Daily Mail, Apr. 20, 2020, https://www.dailymail.co.uk/femail/food/article-8236433/IKEA-shares-recipe-famous-meat-balls-make-home.html (last access 30 April 2021). Although, the public version of the recipe does not appear to be the same as the original recipe. See Michelle Gant, Ikea Shares Recipe for Swedish Meatballs to Make During Quarantine, Today.com, https://www.today.com/food/ikea-shares-recipe-swedish-meatballs-make-during-quarantine-t179469 (last access 30 April 2021) (“‘Our ‘realʼ meatballs and Swedish cream sauce recipe remains a closely guarded secret, known only to a select few. However, in good conscience we couldn’t deprive the nation from missing out on their meatball fix, so we’ve made an almost-as-delicious alternative that can be easily made at home! We hope that it fills a gap until we can meet again. (…)’ Lorena Lourido, country food manager at Ikea U.K. and Ireland.”).

  37. 37.

    See, e.g., Doubletree Hotel’s chocolate chip cookies (Hilton, For the First Time, DoubleTree by Hilton Reveals Official Chocolate Chip Cookie Recipe so Bakers Can Create the Warm, Welcoming Treat at Home, Apr. 9, 2020, https://newsroom.hilton.com/static-doubletree-reveals-cookie-recipe.htm (last access 30 April 2021)); Canada’s Wonderland’s funnel cake (Canada’s Wonderland, How to Make the Classic Canada’s Wonderland Funnel Cake at Home, Apr. 17, 2020, https://www.canadaswonderland.com/blog/2020/april-2020/how-to-make-canadas-wonderland-classic-funnel-cake-at-home (last access 30 April 2021)); Wagamama’s katsu curry chicken (Aoife Hanna, Wagamama Shared Its Katsu Curry Recipe on IG So You Can Enjoy the Signature Dish at Home, Bustle, Apr. 23, 2020, https://www.bustle.com/p/wagamama-shared-its-katsu-curry-recipe-on-ig-so-you-can-enjoy-the-signature-dish-at-home-22842002 (last access 30 April 2021)).

  38. 38.

    See Sect. 2.1.

  39. 39.

    This type of data can be labeled public sector information (PSI). See, e.g., The National Archives UK, About PSI, https://www.nationalarchives.gov.uk/information-management/re-using-public-sector-information/about-psi (“Any information (content) whatever its medium (form) – including print, digital or electronic, and sound recordings – produced, held or disseminated by a public sector body is considered public sector information. This includes an enormous range: corporate information such as reports and financial data, codes of practice, public records, statistics, still and moving images, press releases, artefacts, publication schemes, and so on.”).

  40. 40.

    See e.g. Acquisti et al. (2016); Baye and Sappington (2020); Cooper (2013).

  41. 41.

    Acquisti et al. (2016), p. 446.

  42. 42.

    See e.g. Acquisti et al. (2016), p. 467 (“[O]ne of the major themes of this article: the consequences and implications of data sharing or data protection vary very much with context – such as what specific type of data is being shared, how, and when.”).

  43. 43.

    The idea is that each sphere involves fundamentally different considerations regarding what type of information Jeannie wishes to share with people. As a whimsical illustration of this idea that different spheres of our lives can involve different considerations of how we “present” ourselves, there recently was a “Dolly Parton Challenge” on social media where individuals posted different profile pictures based on the specific social network, e.g., LinkedIn v. Instagram. Of course, for some, the profile picture might be the same regardless of the network e.g. Chuck Norris (https://imgflip.com/i/3nhqa2 (last access 07 May 2021)).

  44. 44.

    For instance, she could have the same information, such as, education history, in more than one sphere e.g. Family & Friends and Professional.

  45. 45.

    Of course, this is not an “all or nothing” decision as consumers might be willing to share location data while actively using an app but not when the app is running in the background. This is evident by mobile operating systems giving this precise option to users. See e.g. Location Services & Privacy, Apple, https://support.apple.com/en-us/HT207056 (last access 07 May 2021).

  46. 46.

    Again, this is assuming that privacy is more about control rather than autonomy, anonymity, and freedom from outside stimuli.

  47. 47.

    See e.g. Baye and Sappington (2020).

  48. 48.

    See e.g. Spence (1973).

  49. 49.

    The employment, and legal, consequences from negative spillover effects of social media is an increasingly important area of scholarship. See e.g. Papandrea (2012); Ghoshray (2013); Mund (2017).

  50. 50.

    She wishes to reveal grades to employers because not providing that information would be considered a negative signal of her “type,” that is, it would give the impression that Jeannie’s grades are worse than those applicants who did choose to reveal their grades. The result is an information unraveling where everybody—even those with less than stellar grades—would reveal this information.

  51. 51.

    Another example of spillover effects from the use of social media data is its use by law enforcement agencies. See Ferguson (2015), p. 334 (“Social media sites, such as Twitter and Facebook, even disclose what we think. Currently, law enforcement officers may access many of these records without violating the Fourth Amendment, under the theory that there is no reasonable expectation of privacy in information knowingly revealed to third parties.”).

  52. 52.

    See e.g. Thomas C. Redman & Robert M. Waitman, Do You Care About Privacy as Much as Your Customers Do?, Harv. Bus. Rev., Jan. 28, 2020, https://hbr.org/2020/01/do-you-care-about-privacy-as-much-as-your-customers-do (last access 07 May 2021) (“Privacy actives see respect for privacy as core to the brands of the companies with whom they do business: 90% believe the ways their data is treated reflects how they are treated as customers. Not surprisingly, they also say they will not buy from companies if they don’t trust how their data is used.”). Within the realm of privacy, however, there is a consistent finding that there is a wide gulf between what consumers say in surveys and consumers’ actual behaviour, which is called the “privacy paradox.” See e.g. Susan Athey, Christian Catalini, & Catherine Tucker, The Digital Privacy Paradox: Small Money, Small Costs, Small Talk, Stanford Institute for Economic Policy Research (SIEPR), Working Paper No. 17–032 (Sep. 17, 2017), https://siepr.stanford.edu/sites/default/files/publications/17-032.pdf (last access 07 May 2021).

  53. 53.

    See e.g. Hardy and Maurushat (2017), p. 33 (“Even an agency’s best efforts at de-identifying its data may not prevent that data from being combined with other sources of information to re-identify an individual.”). Similarly, there are concerns regarding public access to court records based on privacy considerations along with First Amendment considerations. See Ardia (2017).

  54. 54.

    An example is the Sentinel Initiative. See Sentinel Initiative, GovLab, https://datacollaboratives.org/cases/sentinel-initiative.html (last access 07 May 2021) (“The US Food and Drug Administration established the Sentinel Initiative with operations overseen by the Harvard Pilgrim Health Care Institute. It uses a distributed database through which the FDA can run analytical programs on local databases of health providers, such as Humana, Inc. and Blue Cross Blue Shield […] Sentinel intends to actively monitor adverse reactions of medical products after they are on the market.”).

  55. 55.

    This concept of a “life cycle” of data can be found in various forms in the area of data management and science. See e.g. Wing (2019); National Network of Libraries of Medicine, Data Lifecycle, https://nnlm.gov/data/thesaurus/data-lifecycle (last access 07 May 2021). See also Oliver Bethell & Alexander Waksman, Applying Economics to the Internet: Can Regulators and Competition Authorities Keep Pace?, Dec. 2019, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3492966 (last access 07 May 2021), p. 8 (“At Google, we tend to think of data in terms of four layers. At bottom, we have raw data…At the second layer, we can start analyzing the information […] At the third layer, we obtain insights […] And at the fourth layer, we can use those insights to develop our products.”). Our focus is on data as an input into a larger production process rather than where data itself is the product—e.g., services such as ACNielsen and IRI, but even these data products involve a life cycle.

  56. 56.

    Again, these categorizations are not always so clear cut. Organization and analysis can overlap. Consider the process of cleaning a dataset where observations are dropped if they are missing or outliers.

  57. 57.

    Wing (2019), p. 4.

  58. 58.

    See e.g. Krzepicki et al. (2020); Yun (2019).

  59. 59.

    See e.g. Martin Casado & Peter Lauten, The Empty Promise of Data Moats, Andreessen Horowitz, May 9, 2019, https://a16z.com/2019/05/09/data-network-effects-moats (last access 07 May 2021) (“The point of this is not to make a categorical statement about the utility of data as a defensive moat – our point is that defensibility is not inherent to data itself.” [Emphasis original]).

  60. 60.

    However, this is changing for brick-and-mortar stores as well. For example, Amazon Go stores continually track the movements of shoppers while in they are in the store, which allows the shoppers to just walk out with their items without stopping at a cashier. See https://www.amazon.com/b?ie=UTF8&node=16008589011 (last access 07 May 2021).

  61. 61.

    Caselaw Access Project, About, https://case.law/about (last access 07 May 2021) (“CAP includes all official, book-published United States case law […] includes all state courts, federal courts, and territorial courts for American Samoa, Dakota Territory, Guam, Native American Courts, Navajo Nation, and the Northern Mariana Islands. Our earliest case is from 1658, and our most recent cases are from 2018.”).

  62. 62.

    There are currently a variety of sources that provide free access to various legal cases including FindLaw, Google Scholar, and Court Links.

  63. 63.

    See e.g. Conradie and Choenni (2014), p. S10 (“We have found that important indicators for data release are how the data is stored (distributed/decentralized versus centralized), how the data is obtained, and the way data is used by the organization.”).

  64. 64.

    For example, the Iowa Department of Transportation’s Track a Plow map allows residents to track the location and number of plows—including the ability to “view from the plow.” See https://iowadot.maps.arcgis.com/apps/webappviewer/index.html?id=3d5bc4ec8c474870a19c7e8f44b39c9c (last access 07 May 2021).

  65. 65.

    See e.g. Ben Miller, 7 Ways Local Governments Are Getting Creative with Data Mapping, Government Technology, Jan. 25, 2016, https://www.govtech.com/7-Ways-Local-Governments-Are-Getting-Creative-with-Data-Mapping.html (last access 07 May 2021) (“Bostonʼs platform is open for users to submit their own maps. And submit they have. The city portal offers everything from maps of bus stops to traffic data pulled from the Waze app.”).

  66. 66.

    Examples of initiatives to reduce frictions and costs to open data is the Open Data Policy Lab. See https://opendatapolicylab.org (last access 07 May 2021).

  67. 67.

    See e.g. Takagi (2014), p. 121 (“Open data is not necessarily limited to government, but the open data movement has mainly evolved in the public sector.”).

  68. 68.

    See About Data.gov, Data.gov, https://www.data.gov/about (last access 07 May 2021) (“Under the terms of the 2013 Federal Open Data Policy, newly-generated government data is required to be made available in open, machine-readable formats, while continuing to ensure privacy and security.”). See also Office of the Federal Chief Information Officer, M-13–13 – Memorandum for the Heads of Executive Departments and Agencies, May 13, 2013, https://policy.cio.gov/open-data/ (last access 07 May 2021) (“Making information resources accessible, discoverable, and usable by the public can help fuel entrepreneurship, innovation, and scientific discovery – all of which improve Americans’ lives and contribute significantly to job creation.”).

  69. 69.

    See Data Catalog, Data.gov, https://catalog.data.gov/dataset#sec-organization_type (last access 07 May 2021).

  70. 70.

    See NYC Open Data, https://opendata.cityofnewyork.us (last access 07 May 2021).

  71. 71.

    See European Data Portal, Frequently Asked Questions, https://www.europeandataportal.eu/en/faq (last access 07 May 2021).

  72. 72.

    See e.g. Berends et al. (2020). See also Vickery (2011); James Manyika, Michael Chui, Diana Farrell, Steve Van Kuiken, Peter Groves, & Elizabeth Almasi Doshi, Open Data: Unlocking Innovation and Performance with Liquid Information, McKinsey & Co., Oct. 2013, https://www.mckinsey.com/business-functions/mckinsey-digital/our-insights/open-data-unlocking-innovation-and-performance-with-liquid-information (last access 07 May 2021).

  73. 73.

    See e.g. Centers for Disease Control and Prevention (CDC), Coronavirus Disease 2019 (COVID-19), Cases, Data, and Surveillance, https://www.cdc.gov/coronavirus/2019-ncov/cases-updates/index.html (last access 07 May 2021); EU Open Data Portal, COVID-19 Coronavirus Data, https://data.europa.eu/euodp/en/data/dataset/covid-19-coronavirus-data/resource/55e8f966-d5c8-438e-85bc-c7a5a26f4863 (last access 07 May 2021).

  74. 74.

    See, e.g., Stanford University’s Center for Artificial Intelligence in Medicine & Imaging, COVID-19 + Imaging AI Resources, https://aimi.stanford.edu/resources/covid19 (last access 07 May 2021); Centers for Disease Control and Prevention, Coronavirus Disease 2019 in Children – United States, February 12 – April 2, 2020, https://www.cdc.gov/mmwr/volumes/69/wr/mm6914e4.htm (last access 07 May 2021).

  75. 75.

    See e.g. Open Data Handbook, Why Open Data?, http://opendatahandbook.org/guide/en/why-open-data (last access 07 May 2021) (“Many individuals and organizations collect a broad range of different types of data in order to perform their tasks. Government is particularly significant in this respect, both because of the quantity and centrality of the data it collects, but also because most of that government data is public data by law, and therefore could be made open and made available for others to use.”).

  76. 76.

    See e.g. Alan McQuinn, The Economics of “Opt-Out” Versus “Opt-In” Privacy, ITIF.com, Oct. 6, 2017, https://itif.org/publications/2017/10/06/economics-opt-out-versus-opt-in-privacy-rules (last access 07 May 2021) (“Many uses of data generate positive externalities, and these benefits grow as more parties share the data. For example, health researchers can use data to track diseases, research cures, and accelerate innovation in health care, and the opportunities for these benefits increase as the data becomes available to more parties.”).

  77. 77.

    See e.g. Berends et al. (2020), p. 22 (describing various examples of government efficiency gains from opening data including reduced number of service inquiries and freeing employee resources).

  78. 78.

    See e.g. Stott (2014), para. 24 (“The amount of use of Open Data within government has been one of the unexpected and surprising observations of the last five years: for instance, one third of the data downloads from the Open Data portal of the province of British Columbia in Canada have been observed to be coming from the province’s own internet addresses; and in the Catalonia Region of Spain the cost savings and efficiencies to public institutions themselves of open metadata on geospatial datasets mandated by the EU INSPIRE Directive recovered four years of development costs in just six months.”).

  79. 79.

    See e.g. Berliner et al. (2018), p. 867 (“FOI [Freedom of Information] is now just one good governance tool in an increasingly crowded field of transparency policy areas. Focus is increasingly shifting toward technology-enabled open data reforms.”).

  80. 80.

    Hardy and Maurushat (2017), pp. 30–31.

  81. 81.

    See Zillow, Where does Zillow get information about my property?, https://zillow.zendesk.com/hc/en-us/articles/213218507-Where-does-Zillow-get-information-about-my-property- (last access 07 May 2021) (“Zillow receives information about property sales from the municipal office responsible for recording real estate transactions in your area.”).

  82. 82.

    See Stott (2014), para 24 (“Like other business consumers, public institutions are purchasers of data-rich services. Indeed, in some cases they buy back their own data after it has been aggregated or enriched by data-rich service providers.”).

  83. 83.

    See e.g. Open Data Handbook, Why Open Data?, http://opendatahandbook.org/guide/en/why-open-data (last access 07 May 2021) (“A woman in Denmark built findtoilet.dk, which showed all the Danish public toilets, so that people she knew with bladder problems can now trust themselves to go out more again. In the Netherlands a service, vervuilingsalarm.nl, is available which warns you with a message if the air-quality in your vicinity is going to reach a self-defined threshold tomorrow. In New York you can easily find out where you can walk your dog, as well as find other people who use the same parks. Services like ‘mapumental’ in the UK and ‘mapnificent’ in Germany allow you to find places to live, taking into account the duration of your commute to work, housing prices, and how beautiful an area is. All these examples use open government data.”).

  84. 84.

    There is also a public choice aspect to opening data. To the extent that open data results in new and more efficiently provided government benefits, then this will result in more votes for incumbents providing these services.

  85. 85.

    See Sieber and Johnson (2015), pp. 311–312 (“A participatory model presents open data as a formalized conduit between citizen and government, where citizen contributions are integrated into decision-making […] This bi-directional linkage can also take the form of a co-management framework, with the end goals to encourage the stable provision of open data, improve quality and utility of datasets, and to highlight areas for expanded data collection to support community or private sector needs.”).

  86. 86.

    The Laws that Govern the Securities Industry, U.S . Securities and Exchange Commission, https://www.sec.gov/answers/about-lawsshtml.html#secact1933 (last access 07 May 2021).

  87. 87.

    See e.g. Outages, Dominion Energy, https://www.dominionenergy.com/outages (last access 07 May 2021).

  88. 88.

    See Apple, Mobility Trends Reports, https://www.apple.com/covid19/mobility (last access 07 May 2021) (“Learn about COVID-19 mobility trends. Reports are published daily and reflect requests for directions in Apple Maps. Privacy is one of our core values, so Maps doesn’t associate your data with your Apple ID, and Apple doesn’t keep a history of where you’ve been.”).

  89. 89.

    See Elon Musk, All Our Patent Are Belong to You [sic], Tesla.com, Jun. 12, 2014, https://www.tesla.com/blog/all-our-patent-are-belong-you (last access 07 May 2021).

  90. 90.

    See e.g. Eric Loveday, Tesla Is Now Consumer Reportsʼ Highest Ranked U.S. Automotive Brand, InsideEVs, Feb. 20, 2020, https://insideevs.com/news/399837/tesla-tops-consumer-reports-u-s (last access 07 May 2021).

  91. 91.

    See e.g. Fred Lambert, Tesla Owns More Than Half the US Market, Keeps Electric Car Sales Growing, Electrek, Feb. 4, 2020, https://electrek.co/2020/02/04/tesla-electric-car-sales-us-market-share (last access 07 May 2021).

  92. 92.

    Yelp Dataset Challenge, GovLab, https://datacollaboratives.org/cases/yelp-dataset-challenge.html (last access 07 May 2021). See also Past Winners, Yelp, https://www.yelp.com/dataset/challenge/winners (last access 07 May 2021).

  93. 93.

    See e.g. Shavell and Van Ypersele (2001); William A. Masters & Benoit Delbecq, Accelerating Innovation with Prize Rewards, IFPRI Discussion Paper 00835, Dec. 2008, http://ebrary.ifpri.org/utils/getfile/collection/p15738coll2/id/15644/filename/15645.pdf (last access 07 May 2021).

  94. 94.

    Stefaan Verhulst & David Sangokoya, Data Collaboratives: Exchanging Data to Improve People’s Lives, Medium, Apr. 22, 2015, https://medium.com/@sverhulst/data-collaboratives-exchanging-data-to-improve-people-s-lives-d0fcfc1bdd9a (last access 07 May 2021). For instance, GovLab is an organization based out of New York University’s Tandon School of Engineering that provides resources to encourage data collaboratives. See GovLab, https://www.thegovlab.org (last access 07 May 2021).

  95. 95.

    See e.g. Edward Cherry, Top 17 Social Media Monitoring Vendors for Business, SocialMedia.biz (Jun. 8, 2018) (“Successful companies in the world make huge profits every year because they use the best social media monitoring tools to understand their markets, monitor reach, listen to their customers’ needs, and track engagements.”). See also Sutherland (2021).

  96. 96.

    See e.g. Loukis et al. (2017), p. 99 (“Motivated by the multiple ‘success stories’ of the open innovation paradigm in the private sector, and also by the increasing complexity of social problems and needs, the public sector has started moving in this direction, attempting to exploit the extensive knowledge of citizens (‘citizen-sourcing’), in order to develop innovations in public policies and services […]”).

  97. 97.

    While definitions can vary, platforms primarily serve as an intermediary that attracts and coordinates various groups who derive some value from interacting or transacting with each other. See e.g. Yun (2020).

  98. 98.

    See e.g. Jonathan Shieber, Citizen Raises $17 Million to Give Cancer Patients Better Control Over Their Health Records, Tech Crunch, Jan. 17, 2019, https://techcrunch.com/2019/01/16/ciitizen-raises-17-million-to-give-cancer-patients-better-control-over-their-health-records (last access 07 May 2021) (“Ciitizen, like Gliimpse before it, is an attempt to break down the barriers that keep patients from being able to record, store and share their healthcare information with whomever they want in their quest for treatment.”).

  99. 99.

    For example, the Covid-19 Symptom Study App was developed by the company ZOE in collaboration with individuals at the King’s College of London and the Massachusetts General Hospital. Individuals download the app and volunteer data related to Covid-19 based on a daily one-minute survey. The site reports that, as of April 20, 2021, 4.6 million people are contributors. See Covid Symptom Study, https://covid.joinzoe.com/us (last access 07 May 2021).

  100. 100.

    For instance, in Europe, the Open Data Directive (Directive (EU ) 2019/1024) went into force in 2019, which replaced the earlier Public Sector Information Directive (Directive 2003/98/EC) from 2003. The goal of the Open Data Directive is to provide public access to dynamic data generated by member states for re-use through application programming interfaces (APIs) and other means. See European Commission, European Legislation on Open Data and the Re-Use of Public Sector Information, Mar. 8, 2020, https://ec.europa.eu/digital-single-market/en/european-legislation-reuse-public-sector-information (last access 07 May 2021). In the U.S., in January 2019, President Donald Trump signed the OPEN Government Data Act into law, which is part of the larger Foundations for Evidence-Based Policymaking Act of 2018. The act requires agencies to appoint chief data officers (CDOs) and to release all non-sensitive data to the public in a machine-readable format. Pub. L. No. 115–435, 132 Stat. 5529 (Jan. 14 2019).

  101. 101.

    See e.g. Competition and Markets Authority (2020), p. 24 (recommending “[i]ncreasing consumer control over data, which includes providing choices over the use of data and facilitating consumer-led data mobility; Mandating interoperability to overcome network effects and coordination failures; Mandating third-party access to data where data is valuable in overcoming barriers to entry and expansion and privacy concerns can be effectively managed.”).

  102. 102.

    Data portability is already part of the EU’s General Data Protection Regulation (GDPR). See Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the Protection of Natural Persons with Regard to the Processing of Personal Data and on the Free Movement of Such Data and Repealing Directive 95/46/EC, at 2016 O.J.L. 119, 4.5.2016, Article 20, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32016R0679&from=EN (last access 07 May 2021) (users “shall have the right to receive the personal data concerning him or her, which he or she has provided to a controller, in a structured, commonly used and machine-readable format and have the right to transmit those data to another controller without hindrance.”).

  103. 103.

    Multi-homing is the practice of concurrently using two or more competing platforms.

  104. 104.

    See About Us, Data Transfer Project, https://datatransferproject.dev (last access 07 May 2021) (“The Data Transfer Project was launched in 2018 to create an open-source, service-to-service data portability platform so that all individuals across the web could easily move their data between online service providers whenever they want.” Apple, Facebook, Google, Microsoft, and Twitter have all committed to the project.).

  105. 105.

    See e.g. Swire and Lagos (2013), pp. 373–375.

  106. 106.

    See e.g. OECD, Big Data: Bringing Competition Policy to the Digital Era 3, No. DAF/COMP/M(2016)2/ANN4/FINAL (Apr. 2017), https://one.oecd.org/document/DAF/COMP/M(2016)2/ANN4/FINAL/en/pdf (last access 07 May 2021) (“The control over a large volume of data is a not sufficient factor to establish market power, as nowadays a variety of data can be easily and cheaply collected by small companies – for instance, through point of sale terminals, web logs and sensors – or acquired from the broker industry.”); Tucker (2019), pp. 684–687.

  107. 107.

    The essential facilities doctrine has a long history in U.S. antitrust jurisprudence and involves the recognition that, while firms normally have no duty to help their rivals, there are instances when a monopolist control over an input is so essential to a rival’s ability to compete, that withholding or foreclosing that input is considered an illegal restraint of trade. See e.g. Pitofsky (2002). In Europe, the essential facilities doctrine developed from Article 82 of the EC Treaty. See e.g. Evrard (2004).

  108. 108.

    For details on the types of agreements between competitors that can result in anticompetitive harm, see U.S. Federal Trade Commission and Department of Justice (2000).

  109. 109.

    See, e.g., Hazlett and Caliskan (2008).

  110. 110.

    540 U.S. 398, 407–408 (2004).

  111. 111.

    Although, one burden that courts must face when it imposes a data sharing obligation is the potential administrative costs after the order. See Trinko, 540 U.S. at 414-15 (“Effective remediation of violations of regulatory sharing requirements will ordinarily require continuing supervision of a highly detailed decree.”). See also Hurwitz (2020) (documenting that regulatory duty to deal imposed on AT&T’s telephone network, known today as the Kingsbury Commitment, in the early twentieth century).

  112. 112.

    See Kuhn (1996).

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Acknowledgments

Support for this research from a research grant from Microsoft is gratefully acknowledged. I thank Seth Sacher, Lyanne Elsener, and Philipp Gisler for valuable comments and suggestions.

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    John M. Yun

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Yun, J.M. (2022). The Coming of Age of Open Data. In: Mathis, K., Tor, A. (eds) Law and Economics of the Coronavirus Crisis. Economic Analysis of Law in European Legal Scholarship, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-95876-3_13

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