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Bad Blood at the FTC

Thom Lambert —  9 June 2021

John Carreyrou’s marvelous book Bad Blood chronicles the rise and fall of Theranos, the one-time Silicon Valley darling that was revealed to be a house of cards.[1] Theranos’s Svengali-like founder, Elizabeth Holmes, convinced scores of savvy business people (mainly older men) that her company was developing a machine that could detect all manner of maladies from a small quantity of a patient’s blood. Turns out it was a fraud. 

I had a couple of recurring thoughts as I read Bad Blood. First, I kept thinking about how Holmes’s fraud might impair future medical innovation. Something like Theranos’s machine would eventually be developed, I figured, but Holmes’s fraud would likely set things back by making investors leery of blood-based, multi-disease diagnostics.

I also had a thought about the causes of Theranos’s spectacular failure. A key problem, it seemed, was that the company tried to do too many things at once: develop diagnostic technologies, design an elegant machine (Holmes was obsessed with Steve Jobs and insisted that Theranos’s machine resemble a sleek Apple device), market the product, obtain regulatory approval, scale the operation by getting Theranos machines in retail chains like Safeway and Walgreens, and secure third-party payment from insurers.

A thought that didn’t occur to me while reading Bad Blood was that a multi-disease blood diagnostic system would soon be developed but would be delayed, or possibly even precluded from getting to market, by an antitrust enforcement action based on things the developers did to avoid the very problems that doomed Theranos. 

Sadly, that’s where we are with the Federal Trade Commission’s misguided challenge to the merger of Illumina and Grail.

Founded in 1998, San Diego-based Illumina is a leading provider of products used in genetic sequencing and genomic analysis. Illumina produces “next generation sequencing” (NGS) platforms that are used for a wide array of applications (genetic tests, etc.) developed by itself and other companies.

In 2015, Illumina founded Grail for the purpose of developing a blood test that could detect cancer in asymptomatic individuals—the “holy grail” of cancer diagnosis. Given the superior efficacy and lower cost of treatments for early- versus late-stage cancers, success by Grail could save millions of lives and billions of dollars.

Illumina created Grail as a separate entity in which it initially held a controlling interest (having provided the bulk of Grail’s $100 million Series A funding). Legally separating Grail in this fashion, rather than running it as an Illumina division, offered a number of benefits. It limited Illumina’s liability for Grail’s activities, enabling Grail to take greater risks. It mitigated the Theranos problem of managers’ being distracted by too many tasks: Grail managers could concentrate exclusively on developing a viable cancer-screening test, while Illumina’s management continued focusing on that company’s core business. It made it easier for Grail to attract talented managers, who would rather come in as corporate officers than as division heads. (Indeed, Grail landed Jeff Huber, a high-profile Google executive, as its initial CEO.) Structuring Grail as a majority-owned subsidiary also allowed Illumina to attract outside capital, with the prospect of raising more money in the future by selling new Grail stock to investors.

In 2017, Grail did exactly that, issuing new shares to investors in exchange for $1 billion. While this capital infusion enabled the company to move forward with its promising technologies, the creation of new shares meant that Illumina no longer held a controlling interest in the firm. Its ownership interest dipped below 20 percent and now stands at about 14.5 percent of Grail’s voting shares.  

Setting up Grail so as to facilitate outside capital formation and attract top managers who could focus single-mindedly on product development has paid off. Grail has now developed a blood test that, when processed on Illumina’s NGS platform, can accurately detect a number of cancers in asymptomatic individuals. Grail predicts that this “liquid biopsy,” called Galleri, will eventually be able to detect up to 50 cancers before physical symptoms manifest. Grail is also developing other blood-based cancer tests, including one that confirms cancer diagnoses in patients suspected to have cancer and another designed to detect cancer recurrence in patients who have undergone treatment.

Grail now faces a host of new challenges. In addition to continuing to develop its tests, Grail needs to:  

  • Engage in widespread testing of its cancer-detection products on up to 50 different cancers;
  • Process and present the information from its extensive testing in formats that will be acceptable to regulators;
  • Navigate the pre-market regulatory approval process in different countries across the globe;
  • Secure commitments from third-party payors (governments and private insurers) to provide coverage for its tests;
  • Develop means of manufacturing its products at scale;
  • Create and implement measures to ensure compliance with FDA’s Quality System Regulation (QSR), which governs virtually all aspects of medical device production (design, testing, production, process controls, quality assurance, labeling, packaging, handling, storage, distribution, installation, servicing, and shipping); and
  • Market its tests to hospitals and health-care professionals.

These steps are all required to secure widespread use of Grail’s tests. And, importantly, such widespread use will actually improve the quality of the tests. Grail’s tests analyze the DNA in a patient’s blood to look for methylation patterns that are known to be associated with cancer. In essence, the tests work by comparing the methylation patterns in a test subject’s DNA against a database of genomic data collected from large clinical studies. With enough comparison data, the tests can indicate not only the presence of cancer but also where in the body the cancer signal is coming from. And because Grail’s tests use machine learning to hone their algorithms in response to new data collected from test usage, the greater the use of Grail’s tests, the more accurate, sensitive, and comprehensive they become.     

To assist with the various tasks needed to achieve speedy and widespread use of its tests, Grail decided to reunite with Illumina. In September 2020, the companies entered a merger agreement under which Illumina would acquire the 85.5 percent of Grail voting shares it does not already own for cash and stock worth $7.1 billion and additional contingent payments of $1.2 billion to Grail’s non-Illumina shareholders.

Recombining with Illumina will allow Grail—which has appropriately focused heretofore solely on product development—to accomplish the tasks now required to get its tests to market. Illumina has substantial laboratory capacity that Grail can access to complete the testing needed to refine its products and establish their effectiveness. As the leading global producer of NGS platforms, Illumina has unparalleled experience in navigating the regulatory process for NGS-related products, producing and marketing those products at scale, and maintaining compliance with complex regulations like FDA’s QSR. With nearly 3,000 international employees located in 26 countries, it has obtained regulatory authorizations for NGS-based tests in more than 50 jurisdictions around the world.  It also has long-standing relationships with third-party payors, health systems, and laboratory customers. Grail, by contrast, has never obtained FDA approval for any products, has never manufactured NGS-based tests at scale, has only a fledgling regulatory affairs team, and has far less extensive contacts with potential payors and customers. By remaining focused on its key objective (unlike Theranos), Grail has achieved product-development success. Recombining with Illumina will now enable it, expeditiously and efficiently, to deploy its products across the globe, generating user data that will help improve the products going forward.

In addition to these benefits, the combination of Illumina and Grail will eliminate a problem that occurs when producers of complementary products each operate in markets that are not fully competitive: double marginalization. When sellers of products that are used together each possess some market power due to a lack of competition, their uncoordinated pricing decisions may result in less surplus for each of them and for consumers of their products. Combining so that they can coordinate pricing will leave them and their customers better off.

Unlike a producer participating in a competitive market, a producer that faces little competition can enhance its profits by raising its price above its incremental cost.[2] But there are limits on its ability to do so. As the well-known monopoly pricing model shows, even a monopolist has a “profit-maximizing price” beyond which any incremental price increase would lose money.[3] Raising price above that level would hurt both consumers and the monopolist.

When consumers are deciding whether to purchase products that must be used together, they assess the final price of the overall bundle. This means that when two sellers of complementary products both have market power, there is an above-cost, profit-maximizing combined price for their products. If the complement sellers individually raise their prices so that the combined price exceeds that level, they will reduce their own aggregate welfare and that of their customers.

This unfortunate situation is likely to occur when market power-possessing complement producers are separate companies that cannot coordinate their pricing. In setting its individual price, each separate firm will attempt to capture as much surplus for itself as possible. This will cause the combined price to rise above the profit-maximizing level. If they could unite, the complement sellers would coordinate their prices so that the combined price was lower and the sellers’ aggregate profits higher.

Here, Grail and Illumina provide complementary products (cancer-detection tests and the NGS platforms on which they are processed), and each faces little competition. If they price separately, their aggregate prices are likely to exceed the profit-maximizing combined price for the cancer test and NGS platform access. If they combine into a single firm, that firm would maximize its profits by lowering prices so that the aggregate test/platform price is the profit-maximizing combined price.  This would obviously benefit consumers.

In light of the social benefits the Grail/Illumina merger offers—speeding up and lowering the cost of getting Grail’s test approved and deployed at scale, enabling improvement of the test with more extensive user data, eliminating double marginalization—one might expect policymakers to cheer the companies’ recombination. The FTC, however, is trying to block it.  In late March, the commission brought an action claiming that the merger would violate Section 7 of the Clayton Act by substantially reducing competition in a line of commerce.

The FTC’s theory is that recombining Illumina and Grail will impair competition in the market for “multi-cancer early detection” (MCED) tests. The commission asserts that the combined company would have both the opportunity and the motivation to injure rival producers of MCED tests.

The opportunity to do so would stem from the fact that MCED tests must be processed on NGS platforms, which are produced exclusively by Illumina. Illumina could charge Grail’s rivals or their customers higher prices for access to its NGS platforms (or perhaps deny access altogether) and could withhold the technical assistance rivals would need to secure both regulatory approval of their tests and coverage by third-party payors.

But why would Illumina take this tack, given that it would be giving up profits on transactions with producers and users of other MCED tests? The commission asserts that the losses a combined Illumina/Grail would suffer in the NGS platform market would be more than offset by gains stemming from reduced competition in the MCED test market. Thus, the combined company would have a motive, as well as an opportunity, to cause anticompetitive harm.

There are multiple problems with the FTC’s theory. As an initial matter, the market the commission claims will be impaired doesn’t exist. There is no MCED test market for the simple reason that there are no commercializable MCED tests. If allowed to proceed, the Illumina/Grail merger may create such a market by facilitating the approval and deployment of the first MCED test. At present, however, there is no such market, and the chances of one ever emerging will be diminished if the FTC succeeds in blocking the recombination of Illumina and Grail.

Because there is no existing market for MCED tests, the FTC’s claim that a combined Illumina/Grail would have a motivation to injure MCED rivals—potential consumers of Illumina’s NGS platforms—is rank speculation. The commission has no idea what profits Illumina would earn from NGS platform sales related to MCED tests, what profits Grail would earn on its own MCED tests, and how the total profits of the combined company would be affected by impairing opportunities for rival MCED test producers.

In the only relevant market that does exist—the cancer-detection market—there can be no question about the competitive effect of an Illumina/Grail merger: It would enhance competition by speeding the creation of a far superior offering that promises to save lives and substantially reduce health-care costs. 

There is yet another problem with the FTC’s theory of anticompetitive harm. The commission’s concern that a recombined Illumina/Grail would foreclose Grail’s rivals from essential NGS platforms and needed technical assistance is obviated by Illumina’s commitments. Specifically, Illumina has irrevocably offered current and prospective oncology customers 12-year contract terms that would guarantee them the same access to Illumina’s sequencing products that they now enjoy, with no price increase. Indeed, the offered terms obligate Illumina not only to refrain from raising prices but also to lower them by at least 43% by 2025 and to provide regulatory and technical assistance requested by Grail’s potential rivals. Illumina’s continued compliance with its firm offer will be subject to regular audits by an independent auditor.

In the end, then, the FTC’s challenge to the Illumina/Grail merger is unjustified. The initial separation of Grail from Illumina encouraged the managerial focus and capital accumulation needed for successful test development. Recombining the two firms will now expedite and lower the costs of the regulatory approval and commercialization processes, permitting Grail’s tests to be widely used, which will enhance their quality. Bringing Grail’s tests and Illumina’s NGS platforms within a single company will also benefit consumers by eliminating double marginalization. Any foreclosure concerns are entirely speculative and are obviated by Illumina’s contractual commitments.

In light of all these considerations, one wonders why the FTC challenged this merger (and on a 4-0 vote) in the first place. Perhaps it was the populist forces from left and right that are pressuring the commission to generally be more aggressive in policing mergers. Some members of the commission may also worry, legitimately, that if they don’t act aggressively on a vertical merger, Congress will amend the antitrust laws in a deleterious fashion. But the commission has picked a poor target. This particular merger promises tremendous benefit and threatens little harm. The FTC should drop its challenge and encourage its European counterparts to do the same. 


[1] If you don’t have time for Carreyrou’s book (and you should make time if you can), HBO’s Theranos documentary is pretty solid.

[2] This ability is market power.  In a perfectly competitive market, any firm that charges an above-cost price will lose sales to rivals, who will vie for business by lowering their prices down to the level of their cost.

[3] Under the model, this is the price that emerges at the output level where the producer’s marginal revenue equals its marginal cost.

Policy discussions about the use of personal data often have “less is more” as a background assumption; that data is overconsumed relative to some hypothetical optimal baseline. This overriding skepticism has been the backdrop for sweeping new privacy regulations, such as the California Consumer Privacy Act (CCPA) and the EU’s General Data Protection Regulation (GDPR).

More recently, as part of the broad pushback against data collection by online firms, some have begun to call for creating property rights in consumers’ personal data or for data to be treated as labor. Prominent backers of the idea include New York City mayoral candidate Andrew Yang and computer scientist Jaron Lanier.

The discussion has escaped the halls of academia and made its way into popular media. During a recent discussion with Tesla founder Elon Musk, comedian and podcast host Joe Rogan argued that Facebook is “one gigantic information-gathering business that’s decided to take all of the data that people didn’t know was valuable and sell it and make f***ing billions of dollars.” Musk appeared to agree.

The animosity exhibited toward data collection might come as a surprise to anyone who has taken Econ 101. Goods ideally end up with those who value them most. A firm finding profitable ways to repurpose unwanted scraps is just the efficient reallocation of resources. This applies as much to personal data as to literal trash.

Unfortunately, in the policy sphere, few are willing to recognize the inherent trade-off between the value of privacy, on the one hand, and the value of various goods and services that rely on consumer data, on the other. Ideally, policymakers would look to markets to find the right balance, which they often can. When the transfer of data is hardwired into an underlying transaction, parties have ample room to bargain.

But this is not always possible. In some cases, transaction costs will prevent parties from bargaining over the use of data. The question is whether such situations are so widespread as to justify the creation of data property rights, with all of the allocative inefficiencies they entail. Critics wrongly assume the solution is both to create data property rights and to allocate them to consumers. But there is no evidence to suggest that, at the margin, heightened user privacy necessarily outweighs the social benefits that new data-reliant goods and services would generate. Recent experience in the worlds of personalized medicine and the fight against COVID-19 help to illustrate this point.

Data Property Rights and Personalized Medicine

The world is on the cusp of a revolution in personalized medicine. Advances such as the improved identification of biomarkers, CRISPR genome editing, and machine learning, could usher a new wave of treatments to markedly improve health outcomes.

Personalized medicine uses information about a person’s own genes or proteins to prevent, diagnose, or treat disease. Genetic-testing companies like 23andMe or Family Tree DNA, with the large troves of genetic information they collect, could play a significant role in helping the scientific community to further medical progress in this area.

However, despite the obvious potential of personalized medicine, many of its real-world applications are still very much hypothetical. While governments could act in any number of ways to accelerate the movement’s progress, recent policy debates have instead focused more on whether to create a system of property rights covering personal genetic data.

Some raise concerns that it is pharmaceutical companies, not consumers, who will reap the monetary benefits of the personalized medicine revolution, and that advances are achieved at the expense of consumers’ and patients’ privacy. They contend that data property rights would ensure that patients earn their “fair” share of personalized medicine’s future profits.

But it’s worth examining the other side of the coin. There are few things people value more than their health. U.S. governmental agencies place the value of a single life at somewhere between $1 million and $10 million. The commonly used quality-adjusted life year metric offers valuations that range from $50,000 to upward of $300,000 per incremental year of life.

It therefore follows that the trivial sums users of genetic-testing kits might derive from a system of data property rights would likely be dwarfed by the value they would enjoy from improved medical treatments. A strong case can be made that policymakers should prioritize advancing the emergence of new treatments, rather than attempting to ensure that consumers share in the profits generated by those potential advances.

These debates drew increased attention last year, when 23andMe signed a strategic agreement with the pharmaceutical company Almirall to license the rights related to an antibody Almirall had developed. Critics pointed out that 23andMe’s customers, whose data had presumably been used to discover the potential treatment, received no monetary benefits from the deal. Journalist Laura Spinney wrote in The Guardian newspaper:

23andMe, for example, asks its customers to waive all claims to a share of the profits arising from such research. But given those profits could be substantial—as evidenced by the interest of big pharma—shouldn’t the company be paying us for our data, rather than charging us to be tested?

In the deal’s wake, some argued that personal health data should be covered by property rights. A cardiologist quoted in Fortune magazine opined: “I strongly believe that everyone should own their medical data—and they have a right to that.” But this strong belief, however widely shared, ignores important lessons that law and economics has to teach about property rights and the role of contractual freedom.

Why Do We Have Property Rights?

Among the many important features of property rights is that they create “excludability,” the ability of economic agents to prevent third parties from using a given item. In the words of law professor Richard Epstein:

[P]roperty is not an individual conception, but is at root a social conception. The social conception is fairly and accurately portrayed, not by what it is I can do with the thing in question, but by who it is that I am entitled to exclude by virtue of my right. Possession becomes exclusive possession against the rest of the world…

Excludability helps to facilitate the trade of goods, offers incentives to create those goods in the first place, and promotes specialization throughout the economy. In short, property rights create a system of exclusion that supports creating and maintaining valuable goods, services, and ideas.

But property rights are not without drawbacks. Physical or intellectual property can lead to a suboptimal allocation of resources, namely market power (though this effect is often outweighed by increased ex ante incentives to create and innovate). Similarly, property rights can give rise to thickets that significantly increase the cost of amassing complementary pieces of property. Often cited are the historic (but contested) examples of tolling on the Rhine River or the airplane patent thicket of the early 20th century. Finally, strong property rights might also lead to holdout behavior, which can be addressed through top-down tools, like eminent domain, or private mechanisms, like contingent contracts.

In short, though property rights—whether they cover physical or information goods—can offer vast benefits, there are cases where they might be counterproductive. This is probably why, throughout history, property laws have evolved to achieve a reasonable balance between incentives to create goods and to ensure their efficient allocation and use.

Personal Health Data: What Are We Trying to Incentivize?

There are at least three critical questions we should ask about proposals to create property rights over personal health data.

  1. What goods or behaviors would these rights incentivize or disincentivize that are currently over- or undersupplied by the market?
  2. Are goods over- or undersupplied because of insufficient excludability?
  3. Could these rights undermine the efficient use of personal health data?

Much of the current debate centers on data obtained from direct-to-consumer genetic-testing kits. In this context, almost by definition, firms only obtain consumers’ genetic data with their consent. In western democracies, the rights to bodily integrity and to privacy generally make it illegal to administer genetic tests against a consumer or patient’s will. This makes genetic information naturally excludable, so consumers already benefit from what is effectively a property right.

When consumers decide to use a genetic-testing kit, the terms set by the testing firm generally stipulate how their personal data will be used. 23andMe has a detailed policy to this effect, as does Family Tree DNA. In the case of 23andMe, consumers can decide whether their personal information can be used for the purpose of scientific research:

You have the choice to participate in 23andMe Research by providing your consent. … 23andMe Research may study a specific group or population, identify potential areas or targets for therapeutics development, conduct or support the development of drugs, diagnostics or devices to diagnose, predict or treat medical or other health conditions, work with public, private and/or nonprofit entities on genetic research initiatives, or otherwise create, commercialize, and apply this new knowledge to improve health care.

Because this transfer of personal information is hardwired into the provision of genetic-testing services, there is space for contractual bargaining over the allocation of this information. The right to use personal health data will go toward the party that values it most, especially if information asymmetries are weeded out by existing regulations or business practices.

Regardless of data property rights, consumers have a choice: they can purchase genetic-testing services and agree to the provider’s data policy, or they can forgo the services. The service provider cannot obtain the data without entering into an agreement with the consumer. While competition between providers will affect parties’ bargaining positions, and thus the price and terms on which these services are provided, data property rights likely will not.

So, why do consumers transfer control over their genetic data? The main reason is that genetic information is inaccessible and worthless without the addition of genetic-testing services. Consumers must pass through the bottleneck of genetic testing for their genetic data to be revealed and transformed into usable information. It therefore makes sense to transfer the information to the service provider, who is in a much stronger position to draw insights from it. From the consumer’s perspective, the data is not even truly “transferred,” as the consumer had no access to it before the genetic-testing service revealed it. The value of this genetic information is then netted out in the price consumers pay for testing kits.

If personal health data were undersupplied by consumers and patients, testing firms could sweeten the deal and offer them more in return for their data. U.S. copyright law covers original compilations of data, while EU law gives 15 years of exclusive protection to the creators of original databases. Legal protections for trade secrets could also play some role. Thus, firms have some incentives to amass valuable health datasets.

But some critics argue that health data is, in fact, oversupplied. Generally, such arguments assert that agents do not account for the negative privacy externalities suffered by third-parties, such as adverse-selection problems in insurance markets. For example, Jay Pil Choi, Doh Shin Jeon, and Byung Cheol Kim argue:

Genetic tests are another example of privacy concerns due to informational externalities. Researchers have found that some subjects’ genetic information can be used to make predictions of others’ genetic disposition among the same racial or ethnic category.  … Because of practical concerns about privacy and/or invidious discrimination based on genetic information, the U.S. federal government has prohibited insurance companies and employers from any misuse of information from genetic tests under the Genetic Information Nondiscrimination Act (GINA).

But if these externalities exist (most of the examples cited by scholars are hypothetical), they are likely dwarfed by the tremendous benefits that could flow from the use of personal health data. Put differently, the assertion that “excessive” data collection may create privacy harms should be weighed against the possibility that the same collection may also lead to socially valuable goods and services that produce positive externalities.

In any case, data property rights would do little to limit these potential negative externalities. Consumers and patients are already free to agree to terms that allow or prevent their data from being resold to insurers. It is not clear how data property rights would alter the picture.

Proponents of data property rights often claim they should be associated with some form of collective bargaining. The idea is that consumers might otherwise fail to receive their “fair share” of genetic-testing firms’ revenue. But what critics portray as asymmetric bargaining power might simply be the market signaling that genetic-testing services are in high demand, with room for competitors to enter the market. Shifting rents from genetic-testing services to consumers would undermine this valuable price signal and, ultimately, diminish the quality of the services.

Perhaps more importantly, to the extent that they limit the supply of genetic information—for example, because firms are forced to pay higher prices for data and thus acquire less of it—data property rights might hinder the emergence of new treatments. If genetic data is a key input to develop personalized medicines, adopting policies that, in effect, ration the supply of that data is likely misguided.

Even if policymakers do not directly put their thumb on the scale, data property rights could still harm pharmaceutical innovation. If existing privacy regulations are any guide—notably, the previously mentioned GDPR and CCPA, as well as the federal Health Insurance Portability and Accountability Act (HIPAA)—such rights might increase red tape for pharmaceutical innovators. Privacy regulations routinely limit firms’ ability to put collected data to new and previously unforeseen uses. They also limit parties’ contractual freedom when it comes to gathering consumers’ consent.

At the margin, data property rights would make it more costly for firms to amass socially valuable datasets. This would effectively move the personalized medicine space further away from a world of permissionless innovation, thus slowing down medical progress.

In short, there is little reason to believe health-care data is misallocated. Proposals to reallocate rights to such data based on idiosyncratic distributional preferences threaten to stifle innovation in the name of privacy harms that remain mostly hypothetical.

Data Property Rights and COVID-19

The trade-off between users’ privacy and the efficient use of data also has important implications for the fight against COVID-19. Since the beginning of the pandemic, several promising initiatives have been thwarted by privacy regulations and concerns about the use of personal data. This has potentially prevented policymakers, firms, and consumers from putting information to its optimal social use. High-profile issues have included:

Each of these cases may involve genuine privacy risks. But to the extent that they do, those risks must be balanced against the potential benefits to society. If privacy concerns prevent us from deploying contact tracing or green passes at scale, we should question whether the privacy benefits are worth the cost. The same is true for rules that prohibit amassing more data than is strictly necessary, as is required by data-minimization obligations included in regulations such as the GDPR.

If our initial question was instead whether the benefits of a given data-collection scheme outweighed its potential costs to privacy, incentives could be set such that competition between firms would reduce the amount of data collected—at least, where minimized data collection is, indeed, valuable to users. Yet these considerations are almost completely absent in the COVID-19-related privacy debates, as they are in the broader privacy debate. Against this backdrop, the case for personal data property rights is dubious.

Conclusion

The key question is whether policymakers should make it easier or harder for firms and public bodies to amass large sets of personal data. This requires asking whether personal data is currently under- or over-provided, and whether the additional excludability that would be created by data property rights would offset their detrimental effect on innovation.

Swaths of personal data currently lie untapped. With the proper incentive mechanisms in place, this idle data could be mobilized to develop personalized medicines and to fight the COVID-19 outbreak, among many other valuable uses. By making such data more onerous to acquire, property rights in personal data might stifle the assembly of novel datasets that could be used to build innovative products and services.

On the other hand, when dealing with diffuse and complementary data sources, transaction costs become a real issue and the initial allocation of rights can matter a great deal. In such cases, unlike the genetic-testing kits example, it is not certain that users will be able to bargain with firms, especially where their personal information is exchanged by third parties.

If optimal reallocation is unlikely, should property rights go to the person covered by the data or to the collectors (potentially subject to user opt-outs)? Proponents of data property rights assume the first option is superior. But if the goal is to produce groundbreaking new goods and services, granting rights to data collectors might be a superior solution. Ultimately, this is an empirical question.

As Richard Epstein puts it, the goal is to “minimize the sum of errors that arise from expropriation and undercompensation, where the two are inversely related.” Rather than approach the problem with the preconceived notion that initial rights should go to users, policymakers should ensure that data flows to those economic agents who can best extract information and knowledge from it.

As things stand, there is little to suggest that the trade-offs favor creating data property rights. This is not an argument for requisitioning personal information or preventing parties from transferring data as they see fit, but simply for letting markets function, unfettered by misguided public policies.