Archives For externalities

[TOTM: The following is part of a symposium by TOTM guests and authors marking the release of Nicolas Petit’s “Big Tech and the Digital Economy: The Moligopoly Scenario.” The entire series of posts is available here.

This post is authored by Nicolas Petit himself, the Joint Chair in Competition Law at the Department of Law at European University Institute in Fiesole, Italy, and at EUI’s Robert Schuman Centre for Advanced Studies. He is also invited professor at the College of Europe in Bruges
.]

A lot of water has gone under the bridge since my book was published last year. To close this symposium, I thought I would discuss the new phase of antirust statutorification taking place before our eyes. In the United States, Congress is working on five antitrust bills that propose to subject platforms to stringent obligations, including a ban on mergers and acquisitions, required data portability and interoperability, and line-of-business restrictions. In the European Union (EU), lawmakers are examining the proposed Digital Markets Act (“DMA”) that sets out a complicated regulatory system for digital “gatekeepers,” with per se behavioral limitations of their freedom over contractual terms, technological design, monetization, and ecosystem leadership.

Proponents of legislative reform on both sides of the Atlantic appear to share the common view that ongoing antitrust adjudication efforts are both instrumental and irrelevant. They are instrumental because government (or plaintiff) losses build the evidence needed to support the view that antitrust doctrine is exceedingly conservative, and that legal reform is needed. Two weeks ago, antitrust reform activists ran to Twitter to point out that the U.S. District Court dismissal of the Federal Trade Commission’s (FTC) complaint against Facebook was one more piece of evidence supporting the view that the antitrust pendulum needed to swing. They are instrumental because, again, government (or plaintiffs) wins will support scaling antitrust enforcement in the marginal case by adoption of governmental regulation. In the EU, antitrust cases follow each other almost like night the day, lending credence to the view that regulation will bring much needed coordination and economies of scale.

But both instrumentalities are, at the end of the line, irrelevant, because they lead to the same conclusion: legislative reform is long overdue. With this in mind, the logic of lawmakers is that they need not await the courts, and they can advance with haste and confidence toward the promulgation of new antitrust statutes.

The antitrust reform process that is unfolding is a cause for questioning. The issue is not legal reform in itself. There is no suggestion here that statutory reform is necessarily inferior, and no correlative reification of the judge-made-law method. Legislative intervention can occur for good reason, like when it breaks judicial inertia caused by ideological logjam.

The issue is rather one of precipitation. There is a lot of learning in the cases. The point, simply put, is that a supplementary court-legislative dialogue would yield additional information—or what Guido Calabresi has called “starting points” for regulation—that premature legislative intervention is sweeping under the rug. This issue is important because specification errors (see Doug Melamed’s symposium piece on this) in statutory legislation are not uncommon. Feedback from court cases create a factual record that will often be missing when lawmakers act too precipitously.

Moreover, a court-legislative iteration is useful when the issues in discussion are cross-cutting. The digital economy brings an abundance of them. As tech analysist Ben Evans has observed, data-sharing obligations raise tradeoffs between contestability and privacy. Chapter VI of my book shows that breakups of social networks or search engines might promote rivalry and, at the same time, increase the leverage of advertisers to extract more user data and conduct more targeted advertising. In such cases, Calabresi said, judges who know the legal topography are well-placed to elicit the preferences of society. He added that they are better placed than government agencies’ officials or delegated experts, who often attend to the immediate problem without the big picture in mind (all the more when officials are denied opportunities to engage with civil society and the press, as per the policy announced by the new FTC leadership).

Of course, there are three objections to this. The first consists of arguing that statutes are needed now because courts are too slow to deal with problems. The argument is not dissimilar to Frank Easterbrook’s concerns about irreversible harms to the economy, though with a tweak. Where Easterbook’s concern was one of ossification of Type I errors due to stare decisis, the concern here is one of entrenchment of durable monopoly power in the digital sector due to Type II errors. The concern, however, fails the test of evidence. The available data in both the United States and Europe shows unprecedented vitality in the digital sector. Venture capital funding cruises at historical heights, fueling new firm entry, business creation, and economic dynamism in the U.S. and EU digital sectors, topping all other industries. Unless we require higher levels of entry from digital markets than from other industries—or discount the social value of entry in the digital sector—this should give us reason to push pause on lawmaking efforts.

The second objection is that following an incremental process of updating the law through the courts creates intolerable uncertainty. But this objection, too, is unconvincing, at best. One may ask which of an abrupt legislative change of the law after decades of legal stability or of an experimental process of judicial renovation brings more uncertainty.

Besides, ad hoc statutes, such as the ones in discussion, are likely to pose quickly and dramatically the problem of their own legal obsolescence. Detailed and technical statutes specify rights, requirements, and procedures that often do not stand the test of time. For example, the DMA likely captures Windows as a core platform service subject to gatekeeping. But is the market power of Microsoft over Windows still relevant today, and isn’t it constrained in effect by existing antitrust rules?  In antitrust, vagueness in critical statutory terms allows room for change.[1] The best way to give meaning to buzzwords like “smart” or “future-proof” regulation consists of building in first principles, not in creating discretionary opportunities for permanent adaptation of the law. In reality, it is hard to see how the methods of future-proof regulation currently discussed in the EU creates less uncertainty than a court process.

The third objection is that we do not need more information, because we now benefit from economic knowledge showing that existing antitrust laws are too permissive of anticompetitive business conduct. But is the economic literature actually supportive of stricter rules against defendants than the rule-of-reason framework that applies in many unilateral conduct cases and in merger law? The answer is surely no. The theoretical economic literature has travelled a lot in the past 50 years. Of particular interest are works on network externalities, switching costs, and multi-sided markets. But the progress achieved in the economic understanding of markets is more descriptive than normative.

Take the celebrated multi-sided market theory. The main contribution of the theory is its advice to decision-makers to take the periscope out, so as to consider all possible welfare tradeoffs, not to be more or less defendant friendly. Payment cards provide a good example. Economic research suggests that any antitrust or regulatory intervention on prices affect tradeoffs between, and payoffs to, cardholders and merchants, cardholders and cash users, cardholders and banks, and banks and card systems. Equally numerous tradeoffs arise in many sectors of the digital economy, like ridesharing, targeted advertisement, or social networks. Multi-sided market theory renders these tradeoffs visible. But it does not come with a clear recipe for how to solve them. For that, one needs to follow first principles. A system of measurement that is flexible and welfare-based helps, as Kelly Fayne observed in her critical symposium piece on the book.

Another example might be worth considering. The theory of increasing returns suggests that markets subject to network effects tend to converge around the selection of a single technology standard, and it is not a given that the selected technology is the best one. One policy implication is that social planners might be justified in keeping a second option on the table. As I discuss in Chapter V of my book, the theory may support an M&A ban against platforms in tipped markets, on the conjecture that the assets of fringe firms might be efficiently repositioned to offer product differentiation to consumers. But the theory of increasing returns does not say under what conditions we can know that the selected technology is suboptimal. Moreover, if the selected technology is the optimal one, or if the suboptimal technology quickly obsolesces, are policy efforts at all needed?

Last, as Bo Heiden’s thought provoking symposium piece argues, it is not a given that antitrust enforcement of rivalry in markets is the best way to maintain an alternative technology alive, let alone to supply the innovation needed to deliver economic prosperity. Government procurement, science and technology policy, and intellectual-property policy might be equally effective (note that the fathers of the theory, like Brian Arthur or Paul David, have been very silent on antitrust reform).

There are, of course, exceptions to the limited normative content of modern economic theory. In some areas, economic theory is more predictive of consumer harms, like in relation to algorithmic collusion, interlocking directorates, or “killer” acquisitions. But the applications are discrete and industry-specific. All are insufficient to declare that the antitrust apparatus is dated and that it requires a full overhaul. When modern economic research turns normative, it is often way more subtle in its implications than some wild policy claims derived from it. For example, the emerging studies that claim to identify broad patterns of rising market power in the economy in no way lead to an implication that there are no pro-competitive mergers.

Similarly, the empirical picture of digital markets is incomplete. The past few years have seen a proliferation of qualitative research reports on industry structure in the digital sectors. Most suggest that industry concentration has risen, particularly in the digital sector. As with any research exercise, these reports’ findings deserve to be subject to critical examination before they can be deemed supportive of a claim of “sufficient experience.” Moreover, there is no reason to subject these reports to a lower standard of accountability on grounds that they have often been drafted by experts upon demand from antitrust agencies. After all, we academics are ethically obliged to be at least equally exacting with policy-based research as we are with science-based research.

Now, with healthy skepticism at the back of one’s mind, one can see immediately that the findings of expert reports to date have tended to downplay behavioral observations that counterbalance findings of monopoly power—such as intense business anxiety, technological innovation, and demand-expansion investments in digital markets. This was, I believe, the main takeaway from Chapter IV of my book. And less than six months ago, The Economist ran its leading story on the new marketplace reality of “Tech’s Big Dust-Up.”

More importantly, the findings of the various expert reports never seriously contemplate the possibility of competition by differentiation in business models among the platforms. Take privacy, for example. As Peter Klein reasonably writes in his symposium article, we should not be quick to assume market failure. After all, we might have more choice than meets the eye, with Google free but ad-based, and Apple pricy but less-targeted. More generally, Richard Langlois makes a very convincing point that diversification is at the heart of competition between the large digital gatekeepers. We might just be too short-termist—here, digital communications technology might help create a false sense of urgency—to wait for the end state of the Big Tech moligopoly.

Similarly, the expert reports did not really question the real possibility of competition for the purchase of regulation. As in the classic George Stigler paper, where the railroad industry fought motor-trucking competition with state regulation, the businesses that stand to lose most from the digital transformation might be rationally jockeying to convince lawmakers that not all business models are equal, and to steer regulation toward specific business models. Again, though we do not know how to consider this issue, there are signs that a coalition of large news corporations and the publishing oligopoly are behind many antitrust initiatives against digital firms.

Now, as is now clear from these few lines, my cautionary note against antitrust statutorification might be more relevant to the U.S. market. In the EU, sunk investments have been made, expectations have been created, and regulation has now become inevitable. The United States, however, has a chance to get this right. Court cases are the way to go. And unlike what the popular coverage suggests, the recent District Court dismissal of the FTC case far from ruled out the applicability of U.S. antitrust laws to Facebook’s alleged killer acquisitions. On the contrary, the ruling actually contains an invitation to rework a rushed complaint. Perhaps, as Shane Greenstein observed in his retrospective analysis of the U.S. Microsoft case, we would all benefit if we studied more carefully the learning that lies in the cases, rather than haste to produce instant antitrust analysis on Twitter that fits within 280 characters.


[1] But some threshold conditions like agreement or dominance might also become dated. 

Interrogations concerning the role that economic theory should play in policy decisions are nothing new. Milton Friedman famously drew a distinction between “positive” and “normative” economics, notably arguing that theoretical models were valuable, despite their unrealistic assumptions. Kenneth Arrow and Gerard Debreu’s highly theoretical work on General Equilibrium Theory is widely acknowledged as one of the most important achievements of modern economics.

But for all their intellectual value and academic merit, the use of models to inform policy decisions is not uncontroversial. There is indeed a long and unfortunate history of influential economic models turning out to be poor depictions (and predictors) of real-world outcomes.

This raises a key question: should policymakers use economic models to inform their decisions and, if so, how? This post uses the economics of externalities to illustrate both the virtues and pitfalls of economic modeling. Throughout economic history, externalities have routinely been cited to support claims of market failure and calls for government intervention. However, as explained below, these fears have frequently failed to withstand empirical scrutiny.

Today, similar models are touted to support government intervention in digital industries. Externalities are notably said to prevent consumers from switching between platforms, allegedly leading to unassailable barriers to entry and deficient venture-capital investment. Unfortunately, as explained below, the models that underpin these fears are highly abstracted and far removed from underlying market realities.

Ultimately, this post argues that, while models provide a powerful way of thinking about the world, naïvely transposing them to real-world settings is misguided. This is not to say that models are useless—quite the contrary. Indeed, “falsified” models can shed powerful light on economic behavior that would otherwise prove hard to understand.

Bees

Fears surrounding economic externalities are as old as modern economics. For example, in the 1950s, economists routinely cited bee pollination as a source of externalities and, ultimately, market failure.

The basic argument was straightforward: Bees and orchards provide each other with positive externalities. Bees cross-pollinate flowers and orchards contain vast amounts of nectar upon which bees feed, thus improving honey yields. Accordingly, several famous economists argued that there was a market failure; bees fly where they please and farmers cannot prevent bees from feeding on their blossoming flowers—allegedly causing underinvestment in both. This led James Meade to conclude:

[T]he apple-farmer provides to the beekeeper some of his factors free of charge. The apple-farmer is paid less than the value of his marginal social net product, and the beekeeper receives more than the value of his marginal social net product.

A finding echoed by Francis Bator:

If, then, apple producers are unable to protect their equity in apple-nectar and markets do not impute to apple blossoms their correct shadow value, profit-maximizing decisions will fail correctly to allocate resources at the margin. There will be failure “by enforcement.” This is what I would call an ownership externality. It is essentially Meade’s “unpaid factor” case.

It took more than 20 years and painstaking research by Steven Cheung to conclusively debunk these assertions. So how did economic agents overcome this “insurmountable” market failure?

The answer, it turns out, was extremely simple. While bees do fly where they please, the relative placement of beehives and orchards has a tremendous impact on both fruit and honey yields. This is partly because bees have a very limited mean foraging range (roughly 2-3km). This left economic agents with ample scope to prevent free-riding.

Using these natural sources of excludability, they built a web of complex agreements that internalize the symbiotic virtues of beehives and fruit orchards. To cite Steven Cheung’s research

Pollination contracts usually include stipulations regarding the number and strength of the colonies, the rental fee per hive, the time of delivery and removal of hives, the protection of bees from pesticide sprays, and the strategic placing of hives. Apiary lease contracts differ from pollination contracts in two essential aspects. One is, predictably, that the amount of apiary rent seldom depends on the number of colonies, since the farmer is interested only in obtaining the rent per apiary offered by the highest bidder. Second, the amount of apiary rent is not necessarily fixed. Paid mostly in honey, it may vary according to either the current honey yield or the honey yield of the preceding year.

But what of neighboring orchards? Wouldn’t these entail a more complex externality (i.e., could one orchard free-ride on agreements concluded between other orchards and neighboring apiaries)? Apparently not:

Acknowledging the complication, beekeepers and farmers are quick to point out that a social rule, or custom of the orchards, takes the place of explicit contracting: during the pollination period the owner of an orchard either keeps bees himself or hires as many hives per area as are employed in neighboring orchards of the same type. One failing to comply would be rated as a “bad neighbor,” it is said, and could expect a number of inconveniences imposed on him by other orchard owners. This customary matching of hive densities involves the exchange of gifts of the same kind, which apparently entails lower transaction costs than would be incurred under explicit contracting, where farmers would have to negotiate and make money payments to one another for the bee spillover.

In short, not only did the bee/orchard externality model fail, but it failed to account for extremely obvious counter-evidence. Even a rapid flip through the Yellow Pages (or, today, a search on Google) would have revealed a vibrant market for bee pollination. In short, the bee externalities, at least as presented in economic textbooks, were merely an economic “fable.” Unfortunately, they would not be the last.

The Lighthouse

Lighthouses provide another cautionary tale. Indeed, Henry Sidgwick, A.C. Pigou, John Stuart Mill, and Paul Samuelson all cited the externalities involved in the provision of lighthouse services as a source of market failure.

Here, too, the problem was allegedly straightforward. A lighthouse cannot prevent ships from free-riding on its services when they sail by it (i.e., it is mostly impossible to determine whether a ship has paid fees and to turn off the lighthouse if that is not the case). Hence there can be no efficient market for light dues (lighthouses were seen as a “public good”). As Paul Samuelson famously put it:

Take our earlier case of a lighthouse to warn against rocks. Its beam helps everyone in sight. A businessman could not build it for a profit, since he cannot claim a price from each user. This certainly is the kind of activity that governments would naturally undertake.

He added that:

[E]ven if the operators were able—say, by radar reconnaissance—to claim a toll from every nearby user, that fact would not necessarily make it socially optimal for this service to be provided like a private good at a market-determined individual price. Why not? Because it costs society zero extra cost to let one extra ship use the service; hence any ships discouraged from those waters by the requirement to pay a positive price will represent a social economic loss—even if the price charged to all is no more than enough to pay the long-run expenses of the lighthouse.

More than a century after it was first mentioned in economics textbooks, Ronald Coase finally laid the lighthouse myth to rest—rebutting Samuelson’s second claim in the process.

What piece of evidence had eluded economists for all those years? As Coase observed, contemporary economists had somehow overlooked the fact that large parts of the British lighthouse system were privately operated, and had been for centuries:

[T]he right to operate a lighthouse and to levy tolls was granted to individuals by Acts of Parliament. The tolls were collected at the ports by agents (who might act for several lighthouses), who might be private individuals but were commonly customs officials. The toll varied with the lighthouse and ships paid a toll, varying with the size of the vessel, for each lighthouse passed. It was normally a rate per ton (say 1/4d or 1/2d) for each voyage. Later, books were published setting out the lighthouses passed on different voyages and the charges that would be made.

In other words, lighthouses used a simple physical feature to create “excludability” and prevent free-riding. The main reason ships require lighthouses is to avoid hitting rocks when they make their way to a port. By tying port fees and light dues, lighthouse owners—aided by mild government-enforced property rights—could easily earn a return on their investments, thus disproving the lighthouse free-riding myth.

Ultimately, this meant that a large share of the British lighthouse system was privately operated throughout the 19th century, and this share would presumably have been more pronounced if government-run “Trinity House” lighthouses had not crowded out private investment:

The position in 1820 was that there were 24 lighthouses operated by Trinity House and 22 by private individuals or organizations. But many of the Trinity House lighthouses had not been built originally by them but had been acquired by purchase or as the result of the expiration of a lease.

Of course, this system was not perfect. Some ships (notably foreign ones that did not dock in the United Kingdom) might free-ride on this arrangement. It also entailed some level of market power. The ability to charge light dues meant that prices were higher than the “socially optimal” baseline of zero (the marginal cost of providing light is close to zero). Though it is worth noting that tying port fees and light dues might also have decreased double marginalization, to the benefit of sailors.

Samuelson was particularly weary of this market power that went hand in hand with the private provision of public goods, including lighthouses:

Being able to limit a public good’s consumption does not make it a true-blue private good. For what, after all, are the true marginal costs of having one extra family tune in on the program? They are literally zero. Why then prevent any family which would receive positive pleasure from tuning in on the program from doing so?

However, as Coase explained, light fees represented only a tiny fraction of a ship’s costs. In practice, they were thus unlikely to affect market output meaningfully:

[W]hat is the gain which Samuelson sees as coming from this change in the way in which the lighthouse service is financed? It is that some ships which are now discouraged from making a voyage to Britain because of the light dues would in future do so. As it happens, the form of the toll and the exemptions mean that for most ships the number of voyages will not be affected by the fact that light dues are paid. There may be some ships somewhere which are laid up or broken up because of the light dues, but the number cannot be great, if indeed there are any ships in this category.

Samuelson’s critique also falls prey to the Nirvana Fallacy pointed out by Harold Demsetz: markets might not be perfect, but neither is government intervention. Market power and imperfect appropriability are the two (paradoxical) pitfalls of the first; “white elephants,” underinvestment, and lack of competition (and the information it generates) tend to stem from the latter.

Which of these solutions is superior, in each case, is an empirical question that early economists had simply failed to consider—assuming instead that market failure was systematic in markets that present prima facie externalities. In other words, models were taken as gospel without any circumspection about their relevance to real-world settings.

The Tragedy of the Commons

Externalities were also said to undermine the efficient use of “common pool resources,” such grazing lands, common irrigation systems, and fisheries—resources where one agent’s use diminishes that of others, and where exclusion is either difficult or impossible.

The most famous formulation of this problem is Garret Hardin’s highly influential (over 47,000 cites) “tragedy of the commons.” Hardin cited the example of multiple herdsmen occupying the same grazing ground:

The rational herdsman concludes that the only sensible course for him to pursue is to add another animal to his herd. And another; and another … But this is the conclusion reached by each and every rational herdsman sharing a commons. Therein is the tragedy. Each man is locked into a system that compels him to increase his herd without limit—in a world that is limited. Ruin is the destination toward which all men rush, each pursuing his own best interest in a society that believes in the freedom of the commons.

In more technical terms, each economic agent purportedly exerts an unpriced negative externality on the others, thus leading to the premature depletion of common pool resources. Hardin extended this reasoning to other problems, such as pollution and allegations of global overpopulation.

Although Hardin hardly documented any real-world occurrences of this so-called tragedy, his policy prescriptions were unequivocal:

The most important aspect of necessity that we must now recognize, is the necessity of abandoning the commons in breeding. No technical solution can rescue us from the misery of overpopulation. Freedom to breed will bring ruin to all.

As with many other theoretical externalities, empirical scrutiny revealed that these fears were greatly overblown. In her Nobel-winning work, Elinor Ostrom showed that economic agents often found ways to mitigate these potential externalities markedly. For example, mountain villages often implement rules and norms that limit the use of grazing grounds and wooded areas. Likewise, landowners across the world often set up “irrigation communities” that prevent agents from overusing water.

Along similar lines, Julian Morris and I conjecture that informal arrangements and reputational effects might mitigate opportunistic behavior in the standard essential patent industry.

These bottom-up solutions are certainly not perfect. Many common institutions fail—for example, Elinor Ostrom documents several problematic fisheries, groundwater basins and forests, although it is worth noting that government intervention was sometimes behind these failures. To cite but one example:

Several scholars have documented what occurred when the Government of Nepal passed the “Private Forest Nationalization Act” […]. Whereas the law was officially proclaimed to “protect, manage and conserve the forest for the benefit of the entire country”, it actually disrupted previously established communal control over the local forests. Messerschmidt (1986, p.458) reports what happened immediately after the law came into effect:

Nepalese villagers began freeriding — systematically overexploiting their forest resources on a large scale.

In any case, the question is not so much whether private institutions fail, but whether they do so more often than government intervention. be it regulation or property rights. In short, the “tragedy of the commons” is ultimately an empirical question: what works better in each case, government intervention, propertization, or emergent rules and norms?

More broadly, the key lesson is that it is wrong to blindly apply models while ignoring real-world outcomes. As Elinor Ostrom herself put it:

The intellectual trap in relying entirely on models to provide the foundation for policy analysis is that scholars then presume that they are omniscient observers able to comprehend the essentials of how complex, dynamic systems work by creating stylized descriptions of some aspects of those systems.

Dvorak Keyboards

In 1985, Paul David published an influential paper arguing that market failures undermined competition between the QWERTY and Dvorak keyboard layouts. This version of history then became a dominant narrative in the field of network economics, including works by Joseph Farrell & Garth Saloner, and Jean Tirole.

The basic claim was that QWERTY users’ reluctance to switch toward the putatively superior Dvorak layout exerted a negative externality on the rest of the ecosystem (and a positive externality on other QWERTY users), thus preventing the adoption of a more efficient standard. As Paul David put it:

Although the initial lead acquired by QWERTY through its association with the Remington was quantitatively very slender, when magnified by expectations it may well have been quite sufficient to guarantee that the industry eventually would lock in to a de facto QWERTY standard. […]

Competition in the absence of perfect futures markets drove the industry prematurely into standardization on the wrong system — where decentralized decision making subsequently has sufficed to hold it.

Unfortunately, many of the above papers paid little to no attention to actual market conditions in the typewriter and keyboard layout industries. Years later, Stan Liebowitz and Stephen Margolis undertook a detailed analysis of the keyboard layout market. They almost entirely rejected any notion that QWERTY prevailed despite it being the inferior standard:

Yet there are many aspects of the QWERTY-versus-Dvorak fable that do not survive scrutiny. First, the claim that Dvorak is a better keyboard is supported only by evidence that is both scant and suspect. Second, studies in the ergonomics literature find no significant advantage for Dvorak that can be deemed scientifically reliable. Third, the competition among producers of typewriters, out of which the standard emerged, was far more vigorous than is commonly reported. Fourth, there were far more typing contests than just the single Cincinnati contest. These contests provided ample opportunity to demonstrate the superiority of alternative keyboard arrangements. That QWERTY survived significant challenges early in the history of typewriting demonstrates that it is at least among the reasonably fit, even if not the fittest that can be imagined.

In short, there was little to no evidence supporting the view that QWERTY inefficiently prevailed because of network effects. The falsification of this narrative also weakens broader claims that network effects systematically lead to either excess momentum or excess inertia in standardization. Indeed, it is tempting to characterize all network industries with heavily skewed market shares as resulting from market failure. Yet the QWERTY/Dvorak story suggests that such a conclusion would be premature.

Killzones, Zoom, and TikTok

If you are still reading at this point, you might think that contemporary scholars would know better than to base calls for policy intervention on theoretical externalities. Alas, nothing could be further from the truth.

For instance, a recent paper by Sai Kamepalli, Raghuram Rajan and Luigi Zingales conjectures that the interplay between mergers and network externalities discourages the adoption of superior independent platforms:

If techies expect two platforms to merge, they will be reluctant to pay the switching costs and adopt the new platform early on, unless the new platform significantly outperforms the incumbent one. After all, they know that if the entering platform’s technology is a net improvement over the existing technology, it will be adopted by the incumbent after merger, with new features melded with old features so that the techies’ adjustment costs are minimized. Thus, the prospect of a merger will dissuade many techies from trying the new technology.

Although this key behavioral assumption drives the results of the theoretical model, the paper presents no evidence to support the contention that it occurs in real-world settings. Admittedly, the paper does present evidence of reduced venture capital investments after mergers involving large tech firms. But even on their own terms, this data simply does not support the authors’ behavioral assumption.

And this is no isolated example. Over the past couple of years, several scholars have called for more muscular antitrust intervention in networked industries. A common theme is that network externalities, switching costs, and data-related increasing returns to scale lead to inefficient consumer lock-in, thus raising barriers to entry for potential rivals (here, here, here).

But there are also countless counterexamples, where firms have easily overcome potential barriers to entry and network externalities, ultimately disrupting incumbents.

Zoom is one of the most salient instances. As I have written previously:

To get to where it is today, Zoom had to compete against long-established firms with vast client bases and far deeper pockets. These include the likes of Microsoft, Cisco, and Google. Further complicating matters, the video communications market exhibits some prima facie traits that are typically associated with the existence of network effects.

Along similar lines, Geoffrey Manne and Alec Stapp have put forward a multitude of other examples. These include: The demise of Yahoo; the disruption of early instant-messaging applications and websites; MySpace’s rapid decline; etc. In all these cases, outcomes do not match the predictions of theoretical models.

More recently, TikTok’s rapid rise offers perhaps the greatest example of a potentially superior social-networking platform taking significant market share away from incumbents. According to the Financial Times, TikTok’s video-sharing capabilities and its powerful algorithm are the most likely explanations for its success.

While these developments certainly do not disprove network effects theory, they eviscerate the common belief in antitrust circles that superior rivals are unable to overthrow incumbents in digital markets. Of course, this will not always be the case. As in the previous examples, the question is ultimately one of comparing institutions—i.e., do markets lead to more or fewer error costs than government intervention? Yet this question is systematically omitted from most policy discussions.

In Conclusion

My argument is not that models are without value. To the contrary, framing problems in economic terms—and simplifying them in ways that make them cognizable—enables scholars and policymakers to better understand where market failures might arise, and how these problems can be anticipated and solved by private actors. In other words, models alone cannot tell us that markets will fail, but they can direct inquiries and help us to understand why firms behave the way they do, and why markets (including digital ones) are organized in a given way.

In that respect, both the theoretical and empirical research cited throughout this post offer valuable insights for today’s policymakers.

For a start, as Ronald Coase famously argued in what is perhaps his most famous work, externalities (and market failure more generally) are a function of transaction costs. When these are low (relative to the value of a good), market failures are unlikely. This is perhaps clearest in the “Fable of the Bees” example. Given bees’ short foraging range, there were ultimately few real-world obstacles to writing contracts that internalized the mutual benefits of bees and orchards.

Perhaps more importantly, economic research sheds light on behavior that might otherwise be seen as anticompetitive. The rules and norms that bind farming/beekeeping communities, as well as users of common pool resources, could easily be analyzed as a cartel by naïve antitrust authorities. Yet externality theory suggests they play a key role in preventing market failure.

Along similar lines, mergers and acquisitions (as well as vertical integration, more generally) can reduce opportunism and other externalities that might otherwise undermine collaboration between firms (here, here and here). And much of the same is true for certain types of unilateral behavior. Tying video games to consoles (and pricing the console below cost) can help entrants overcome network externalities that might otherwise shield incumbents. Likewise, Google tying its proprietary apps to the open source Android operating system arguably enabled it to earn a return on its investments, thus overcoming the externality problem that plagues open source software.

All of this raises a tantalizing prospect that deserves far more attention than it is currently given in policy circles: authorities around the world are seeking to regulate the tech space. Draft legislation has notably been tabled in the United States, European Union and the United Kingdom. These draft bills would all make it harder for large tech firms to implement various economic hierarchies, including mergers and certain contractual arrangements.

This is highly paradoxical. If digital markets are indeed plagued by network externalities and high transaction costs, as critics allege, then preventing firms from adopting complex hierarchies—which have traditionally been seen as a way to solve externalities—is just as likely to exacerbate problems. In other words, like the economists of old cited above, today’s policymakers appear to be focusing too heavily on simple models that predict market failure, and far too little on the mechanisms that firms have put in place to thrive within this complex environment.

The bigger picture is that far more circumspection is required when using theoretical models in real-world policy settings. Indeed, as Harold Demsetz famously put it, the purpose of normative economics is not so much to identify market failures, but to help policymakers determine which of several alternative institutions will deliver the best outcomes for consumers:

This nirvana approach differs considerably from a comparative institution approach in which the relevant choice is between alternative real institutional arrangements. In practice, those who adopt the nirvana viewpoint seek to discover discrepancies between the ideal and the real and if discrepancies are found, they deduce that the real is inefficient. Users of the comparative institution approach attempt to assess which alternative real institutional arrangement seems best able to cope with the economic problem […].

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.

[TOTM: The following is part of a digital symposium by TOTM guests and authors on the law, economics, and policy of the antitrust lawsuits against Google. The entire series of posts is available here.]

As one of the few economic theorists in this symposium, I believe my comparative advantage is in that: economic theory. In this post, I want to remind people of the basic economic theories that we have at our disposal, “off the shelf,” to make sense of the U.S. Department of Justice’s lawsuit against Google. I do not mean this to be a proclamation of “what economics has to say about X,” but merely just to help us frame the issue.

In particular, I’m going to focus on the economic concerns of Google paying phone manufacturers (Apple, in particular) to be the default search engine installed on phones. While there is not a large literature on the economic effects of default contracts, there is a large literature on something that I will argue is similar: trade promotions, such as slotting contracts, where a manufacturer pays a retailer for shelf space. Despite all the bells and whistles of the Google case, I will argue that, from an economic point of view, the contracts that Google signed are just trade promotions. No more, no less. And trade promotions are well-established as part of a competitive process that ultimately helps consumers. 

However, it is theoretically possible that such trade promotions hurt customers, so it is theoretically possible that Google’s contracts hurt consumers. Ultimately, the theoretical possibility of anticompetitive behavior that harms consumers does not seem plausible to me in this case.

Default Status

There are two reasons that Google paying Apple to be its default search engine is similar to a trade promotion. First, the deal brings awareness to the product, which nudges certain consumers/users to choose the product when they would not otherwise do so. Second, the deal does not prevent consumers from choosing the other product.

In the case of retail trade promotions, a promotional space given to Coca-Cola makes it marginally easier for consumers to pick Coke, and therefore some consumers will switch from Pepsi to Coke. But it does not reduce any consumer’s choice. The store will still have both items.

This is the same for a default search engine. The marginal searchers, who do not have a strong preference for either search engine, will stick with the default. But anyone can still install a new search engine, install a new browser, etc. It takes a few clicks, just as it takes a few steps to walk down the aisle to get the Pepsi; it is still an available choice.

If we were to stop the analysis there, we could conclude that consumers are worse off (if just a tiny bit). Some customers will have to change the default app. We also need to remember that this contract is part of a more general competitive process. The retail stores are also competing with one another, as are smartphone manufacturers.

Despite popular claims to the contrary, Apple cannot charge anything it wants for its phone. It is competing with Samsung, etc. Therefore, Apple has to pass through some of Google’s payments to customers in order to compete with Samsung. Prices are lower because of this payment. As I phrased it elsewhere, Google is effectively subsidizing the iPhone. This cross-subsidization is a part of the competitive process that ultimately benefits consumers through lower prices.

These contracts lower consumer prices, even if we assume that Apple has market power. Those who recall your Econ 101 know that a monopolist chooses a quantity where the marginal revenue equals marginal cost. With a payment from Google, the marginal cost of producing a phone is lower, therefore Apple will increase the quantity and lower price. This is shown below:

One of the surprising things about markets is that buyers’ and sellers’ incentives can be aligned, even though it seems like they must be adversarial. Companies can indirectly bargain for their consumers. Commenting on Standard Fashion Co. v. Magrane-Houston Co., where a retail store contracted to only carry Standard’s products, Robert Bork (1978, pp. 306–7) summarized this idea as follows:

The store’s decision, made entirely in its own interest, necessarily reflects the balance of competing considerations that determine consumer welfare. Put the matter another way. If no manufacturer used exclusive dealing contracts, and if a local retail monopolist decided unilaterally to carry only Standard’s patterns because the loss in product variety was more than made up in the cost saving, we would recognize that decision was in the consumer interest. We do not want a variety that costs more than it is worth … If Standard finds it worthwhile to purchase exclusivity … the reason is not the barring of entry, but some more sensible goal, such as obtaining the special selling effort of the outlet.

How trade promotions could harm customers

Since Bork’s writing, many theoretical papers have shown exceptions to Bork’s logic. There are times that the retailers’ incentives are not aligned with the customers. And we need to take those possibilities seriously.

The most common way to show the harm of these deals (or more commonly exclusivity deals) is to assume:

  1. There are large, fixed costs so that a firm must acquire a sufficient number of customers in order to enter the market; and
  2. An incumbent can lock in enough customers to prevent the entrant from reaching an efficient size.

Consumers can be locked-in because there is some fixed cost of changing suppliers or because of some coordination problems. If that’s true, customers can be made worse off, on net, because the Google contracts reduce consumer choice.

To understand the logic, let’s simplify the model to just search engines and searchers. Suppose there are two search engines (Google and Bing) and 10 searchers. However, to operate profitably, each search engine needs at least three searchers. If Google can entice eight searchers to use its product, Bing cannot operate profitably, even if Bing provides a better product. This holds even if everyone knows Bing would be a better product. The consumers are stuck in a coordination failure.

We should be skeptical of coordination failure models of inefficient outcomes. The problem with any story of coordination failures is that it is highly sensitive to the exact timing of the model. If Bing can preempt Google and offer customers an even better deal (the new entrant is better by assumption), then the coordination failure does not occur.

To argue that Bing could not execute a similar contract, the most common appeal is that the new entrant does not have the capital to pay upfront for these contracts, since it will only make money from its higher-quality search engine down the road. That makes sense until you remember that we are talking about Microsoft. I’m skeptical that capital is the real constraint. It seems much more likely that Google just has a more popular search engine.

The other problem with coordination failure arguments is that they are almost non-falsifiable. There is no way to tell, in the model, whether Google is used because of a coordination failure or whether it is used because it is a better product. If Google is a better product, then the outcome is efficient. The two outcomes are “observationally equivalent.” Compare this to the standard theory of monopoly, where we can (in principle) establish an inefficiency if the price is greater than marginal cost. While it is difficult to measure marginal cost, it can be done.

There is a general economic idea in these models that we need to pay attention to. If Google takes an action that prevents Bing from reaching efficient size, that may be an externality, sometimes called a network effect, and so that action may hurt consumer welfare.

I’m not sure how seriously to take these network effects. If more searchers allow Bing to make a better product, then literally any action (competitive or not) by Google is an externality. Making a better product that takes away consumers from Bing lowers Bing’s quality. That is, strictly speaking, an externality. Surely, that is not worthy of antitrust scrutiny simply because we find an externality.

And Bing also “takes away” searchers from Google, thus lowering Google’s possible quality. With network effects, bigger is better and it may be efficient to have only one firm. Surely, that’s not an argument we want to put forward as a serious antitrust analysis.

Put more generally, it is not enough to scream “NETWORK EFFECT!” and then have the antitrust authority come in, lawsuits-a-blazing. Well, it shouldn’t be enough.

For me to take the network effect argument seriously from an economic point of view, compared to a legal perspective, I would need to see a real restriction on consumer choice, not just an externality. One needs to argue that:

  1. No competitor can cover their fixed costs to make a reasonable search engine; and
  2. These contracts are what prevent the competing search engines from reaching size.

That’s the challenge I would like to put forward to supporters of the lawsuit. I’m skeptical.

[TOTM: The following is part of a blog series by TOTM guests and authors on the law, economics, and policy of the ongoing COVID-19 pandemic. The entire series of posts is available here.

This post is authored by Dirk Auer, (Senior Fellow of Law & Economics, ICLE); Eric Fruits (Chief Economist, ICLE; Adjunct Professor of Economics, Portland State University); and Kristian Stout (Associate Director, ICLE

The COVID-19 pandemic is changing the way consumers shop and the way businesses sell. These shifts in behavior, designed to “flatten the curve” of infection through social distancing, are happening across many (if not all) markets. But in many cases, it’s impossible to know now whether these new habits are actually achieving the desired effect. 

Take a seemingly silly example from Oregon. The state is one of only two in the U.S. that prohibits self-serve gas. In response to COVID-19, the state fire marshall announced it would temporarily suspend its enforcement of the prohibition. Public opinion fell into two broad groups. Those who want the option to pump their own gas argue that self-serve reduces the interaction between station attendants and consumers, thereby potentially reducing the spread of coronavirus. On the other hand, those who support the prohibition on self-serve have blasted the fire marshall’s announcement, arguing that all those dirty fingers pressing keypads and all those grubby hands on fuel pumps will likely increase the spread of the virus. 

Both groups may be right, but no one yet knows the net effect. We can only speculate. This picture becomes even more complex when considering other, alternative policies. For instance, would it be more effective for the state of Oregon to curtail gas station visits by forcing the closure of stations? Probably not. Would it be more effective to reduce visits through some form of rationing? Maybe. Maybe not. 

Policymakers will certainly struggle to efficiently decide how firms and consumers should minimize the spread of COVID-19. That struggle is an extension of Hayek’s knowledge problem: policymakers don’t have adequate knowledge of alternatives, preferences, and the associated risks. 

A Hayekian approach — relying on bottom-up rather than top-down solutions to the problem — may be the most appropriate solution. Allowing firms to experiment and iteratively find solutions that work for their consumers and employees (potentially adjusting prices and wages in the process) may be the best that policymakers can do.

The case of online retail platforms

One area where these complex tradeoffs are particularly acute is that of online retail. In response to the pandemic, many firms have significantly boosted their online retail capacity. 

These initiatives have been met with a mix of enthusiasm and disapproval. On the one hand online retail enables consumers to purchase “essential” goods with a significantly reduced risk of COVID-19 contamination. It also allows “non-essential” goods to be sold, despite the closure of their brick and mortar stores. At first blush, this seems like a win-win situation for both consumers and retailers of all sizes, with large retailers ramping up their online operations and independent retailers switching to online platforms such as Amazon.

But there is a potential downside. Even contactless deliveries do present some danger, notably for warehouse workers who run the risk of being infected and subsequently passing the virus on to others. This risk is amplified by the fact that many major retailers, including Walmart, Kroger, CVS, and Albertsons, are hiring more warehouse and delivery workers to meet an increase in online orders. 

This has led some to question whether sales of “non-essential” goods (though the term is almost impossible to define) should be halted. The reasoning is that continuing to supply such goods needlessly puts lives at risk and reduces overall efforts to slow the virus.

Once again, these are incredibly complex questions. It is hard to gauge the overall risk of infection that is produced by the online retail industry’s warehousing and distribution infrastructure. In particular, it is not clear how effective social distancing policies, widely imposed within these workplaces, will be at achieving distancing and, in turn, reducing infections. 

More fundamentally, whatever this risk turns out to be, it is almost impossible to weigh it against an appropriate counterfactual. 

Online retail is not the only area where this complex tradeoff arises. An analogous reasoning could, for instance, also be applied to food delivery platforms. Ordering a meal on UberEats does carry some risk, but so does repeated trips to the grocery store. And there are legitimate concerns about the safety of food handlers working in close proximity to each other.  These considerations make it hard for policymakers to strike the appropriate balance. 

The good news: at least some COVID-related risks are being internalized

But there is also some good news. Firms, consumers and employees all have some incentive to mitigate these risks. 

Consumers want to purchase goods without getting contaminated; employees want to work in safe environments; and firms need to attract both consumers and employees, while minimizing potential liability. These (partially) aligned incentives will almost certainly cause these economic agents to take at least some steps that mitigate the spread of COVID-19. This might notably explain why many firms imposed social distancing measures well before governments started to take notice (here, here, and here). 

For example, one first-order effect of COVID-19 is that it has become more expensive for firms to hire warehouse workers. Not only have firms moved up along the supply curve (by hiring more workers), but the curve itself has likely shifted upwards reflecting the increased opportunity cost of warehouse work. Predictably, this has resulted in higher wages for workers. For example, Amazon and Walmart recently increased the wages they were paying warehouse workers, as have brick and mortar retailers, such as Kroger, who have implemented similar policies.

Along similar lines, firms and employees will predictably bargain — through various channels — over the appropriate level of protection for those workers who must continue to work in-person.

For example, some companies have found ways to reduce risk while continuing operations:

  • CNBC reports Tyson Foods is using walk-through infrared body temperature scanners to check employees’ temperatures as they enter three of the company’s meat processing plants. Other companies planning to use scanners include Goldman Sachs, UPS, Ford, and Carnival Cruise Lines.
  • Kroger’s Fred Meyer chain of supermarkets is limiting the number of customers in each of its stores to half the occupancy allowed under international building codes. Kroger will use infrared sensors and predictive analytics to monitor the new capacity limits. The company already uses the technology to estimate how many checkout lanes are needed at any given time.
  • Trader Joe’s limits occupancy in its store. Customers waiting to enter are asked to stand six feet apart using marked off Trader Joe’s logos on the sidewalk. Shopping carts are separated into groups of “sanitized” and “to be cleaned.” Each cart is thoroughly sprayed with disinfectant and wiped down with a clean cloth.

In other cases, bargaining over the right level of risk-mitigation has been pursued through more coercive channels, such as litigation and lobbying:

  • A recently filed lawsuit alleges that managers at an Illinois Walmart store failed to alert workers after several employees began showing symptoms of COVID-19. The suit claims Walmart “had a duty to exercise reasonable care in keeping the store in a safe and healthy environment and, in particular, to protect employees, customers and other individuals within the store from contracting COVID-19 when it knew or should have known that individuals at the store were at a very high risk of infection and exposure.” 
  • According to CNBC, a group of legislators, unions and Amazon employees in New York wrote a letter to CEO Jeff Bezos calling on him to enact greater protections for warehouse employees who continue to work during the coronavirus outbreak. The Financial Times reports worker protests at Amazon warehouse in the US, France, and Italy. Worker protests have been reported at a Barnes & Noble warehouse. Several McDonald’s locations have been hit with strikes.
  • In many cases, worker concerns about health and safety have been conflated with long-simmering issues of unionization, minimum wage, flexible scheduling, and paid time-off. For example, several McDonald’s strikes were reported to have been organized by “Fight for $15.”

Sometimes, there is simply no mutually-advantageous solution. And businesses are thus left with no other option than temporarily suspending their activities: 

  • For instance, McDonalds and Burger King have spontaneously closed their restaurants — including drive-thru and deliveries — in many European countries (here and here).
  • In Portland, Oregon, ChefStable a restaurant group behind some of the city’s best-known restaurants, closed all 20 of its bars and restaurants for at least four weeks. In what he called a “crisis of conscience,” owner Kurt Huffman concluded it would be impossible to maintain safe social distancing for customers and staff.

This is certainly not to say that all is perfect. Employers, employees and consumers may have very strong disagreements about what constitutes the appropriate level of risk mitigation.

Moreover, the questions of balancing worker health and safety with that of consumers become all the more complex when we recognize that consumers and businesses are operating in a dynamic environment, making sometimes fundamental changes to reduce risk at many levels of the supply chain.

Likewise, not all businesses will be able to implement measures that mitigate the risk of COVID-19. For instance, “Big Business” might be in a better position to reduce risks to its workforce than smaller businesses. 

Larger firms tend to have the resources and economies of scale to make capital investments in temperature scanners or sensors. They have larger workforces where employees can, say, shift from stocking shelves to sanitizing shopping carts. Several large employers, including Amazon, Kroger, and CVS have offered higher wages to employees who are more likely to be exposed to the coronavirus. Smaller firms are less likely to have the resources to offer such wage premiums.

For example, Amazon recently announced that it would implement mandatory temperature checks, that it would provide employees with protective equipment, and that it would increase the frequency and intensity of cleaning for all its sites. And, as already mentioned above, Tyson Foods announced that they would install temperature scanners at a number of sites. It is not clear whether smaller businesses are in a position to implement similar measures. 

That’s not to say that small businesses can’t adjust. It’s just more difficult. For example, a small paint-your-own ceramics shop, Mimosa Studios, had to stop offering painting parties because of government mandated social distancing. One way it’s mitigating the loss of business is with a paint-at-home package. Customers place an order online, and the studio delivers the ceramic piece, paints, and loaner brushes. When the customer is finished painting, Mimosa picks up the piece, fires it, and delivers the finished product. The approach doesn’t solve the problem, but it helps mitigate the losses.

Conclusion

In all likelihood, we can’t actually avoid all bad outcomes. There is, of course, some risk associated with even well-resourced large businesses continuing to operate, even though some of them play a crucial role in coronavirus-related lockdowns. 

Currently, market actors are working within the broad outlines of lockdowns deemed necessary by policymakers. Given the intensely complicated risk calculation necessary to determine if any given individual truly needs an “essential” (or even a “nonessential”) good or service, the best thing that lawmakers can do for now is let properly motivated private actors continue to seek optimal outcomes together within the imposed constraints. 

So far, most individuals and the firms serving them are at least partially internalizing Covid-related risks. The right approach for lawmakers would be to watch this process and determine where it breaks down. Measures targeted to fix those breaches will almost inevitably outperform interventionist planning to determine exactly what is essential, what is nonessential, and who should be allowed to serve consumers in their time of need.