Archives For Transaction cost

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 […].

In current discussions of technology markets, few words are heard more often than “platform.” Initial public offering (IPO) prospectuses use “platform” to describe a service that is bound to dominate a digital market. Antitrust regulators use “platform” to describe a service that dominates a digital market or threatens to do so. In either case, “platform” denotes power over price. For investors, that implies exceptional profits; for regulators, that implies competitive harm.

Conventional wisdom holds that platforms enjoy high market shares, protected by high barriers to entry, which yield high returns. This simple logic drives the market’s attribution of dramatically high valuations to dramatically unprofitable businesses and regulators’ eagerness to intervene in digital platform markets characterized by declining prices, increased convenience, and expanded variety, often at zero out-of-pocket cost. In both cases, “burning cash” today is understood as the path to market dominance and the ability to extract a premium from consumers in the future.

This logic is usually wrong. 

The Overlooked Basics of Platform Economics

To appreciate this perhaps surprising point, it is necessary to go back to the increasingly overlooked basics of platform economics. A platform can refer to any service that matches two complementary populations. A search engine matches advertisers with consumers, an online music service matches performers and labels with listeners, and a food-delivery service matches restaurants with home diners. A platform benefits everyone by facilitating transactions that otherwise might never have occurred.

A platform’s economic value derives from its ability to lower transaction costs by funneling a multitude of individual transactions into a single convenient hub.  In pursuit of minimum costs and maximum gains, users on one side of the platform will tend to favor the most popular platforms that offer the largest number of users on the other side of the platform. (There are partial exceptions to this rule when users value being matched with certain typesof other users, rather than just with more users.) These “network effects” mean that any successful platform market will always converge toward a handful of winners. This positive feedback effect drives investors’ exuberance and regulators’ concerns.

There is a critical point, however, that often seems to be overlooked.

Market share only translates into market power to the extent the incumbent is protected against entry within some reasonable time horizon.  If Warren Buffett’s moat requirement is not met, market share is immaterial. If XYZ.com owns 100% of the online pet food delivery market but entry costs are asymptotic, then market power is negligible. There is another important limiting principle. In platform markets, the depth of the moat depends not only on competitors’ costs to enter the market, but users’ costs in switching from one platform to another or alternating between multiple platforms. If users can easily hop across platforms, then market share cannot confer market power given the continuous threat of user defection. Put differently: churn limits power over price.

Contrary to natural intuitions, this is why a platform market consisting of only a few leaders can still be intensely competitive, keeping prices low (down to and including $0) even if the number of competitors is low. It is often asserted, however, that users are typically locked into the dominant platform and therefore face high switching costs, which therefore implicitly satisfies the moat requirement. If that is true, then the “high churn” scenario is a theoretical curiosity and a leading platform’s high market share would be a reliable signal of market power. In fact, this common assumption likely describes the atypical case. 

AWS and the Cloud Data-Storage Market

This point can be illustrated by considering the cloud data-storage market. This would appear to be an easy case where high switching costs (due to the difficulty in shifting data among storage providers) insulate the market leader against entry threats. Yet the real world does not conform to these expectations. 

While Amazon Web Services pioneered the $100 billion-plus market and is still the clear market leader, it now faces vigorous competition from Microsoft Azure, Google Cloud, and other data-storage or other cloud-related services. This may reflect the fact that the data storage market is far from saturated, so new users are up for grabs and existing customers can mitigate lock-in by diversifying across multiple storage providers. Or it may reflect the fact that the market’s structure is fluid as a function of technological changes, enabling entry at formerly bundled portions of the cloud data-services package. While it is not always technologically feasible, the cloud storage market suggests that users’ resistance to platform capture can represent a competitive opportunity for entrants to challenge dominant vendors on price, quality, and innovation parameters.

The Surprising Instability of Platform Dominance

The instability of leadership positions in the cloud storage market is not exceptional. 

Consider a handful of once-powerful platforms that were rapidly dethroned once challenged by a more efficient or innovative rival: Yahoo and Alta Vista in the search-engine market (displaced by Google); Netscape in the browser market (displaced by Microsoft’s Internet Explorer, then displaced by Google Chrome); Nokia and then BlackBerry in the mobile wireless-device market (displaced by Apple and Samsung); and Friendster in the social-networking market (displaced by Myspace, then displaced by Facebook). AOL was once thought to be indomitable; now it is mostly referenced as a vintage email address. The list could go on.

Overestimating platform dominance—or more precisely, assuming platform dominance without close factual inquiry—matters because it promotes overestimates of market power. That, in turn, cultivates both market and regulatory bubbles: investors inflate stock valuations while regulators inflate the risk of competitive harm. 

DoorDash and the Food-Delivery Services Market

Consider the DoorDash IPO that launched in early December 2020. The market’s current approximately $50 billion valuation of a business that has been almost consistently unprofitable implicitly assumes that DoorDash will maintain and expand its position as the largest U.S. food-delivery platform, which will then yield power over price and exceptional returns for investors. 

There are reasons to be skeptical. Even where DoorDash captures and holds a dominant market share in certain metropolitan areas, it still faces actual and potential competition from other food-delivery services, in-house delivery services (especially by well-resourced national chains), and grocery and other delivery services already offered by regional and national providers. There is already evidence of these expected responses to DoorDash’s perceived high delivery fees, a classic illustration of the disciplinary effect of competitive forces on the pricing choices of an apparently dominant market leader. These “supply-side” constraints imposed by competitors are compounded by “demand-side” constraints imposed by customers. Home diners incur no more than minimal costs when swiping across food-delivery icons on a smartphone interface, casting doubt that high market share is likely to translate in this context into market power.

Deliveroo and the Costs of Regulatory Autopilot

Just as the stock market can suffer from delusions of platform grandeur, so too some competition regulators appear to have fallen prey to the same malady. 

A vivid illustration is provided by the 2019 decision by the Competition Markets Authority (CMA), the British competition regulator, to challenge Amazon’s purchase of a 16% stake in Deliveroo, one of three major competitors in the British food-delivery services market. This intervention provides perhaps the clearest illustration of policy action based on a reflexive assumption of market power, even in the face of little to no indication that the predicate conditions for that assumption could plausibly be satisfied.

Far from being a dominant platform, Deliveroo was (and is) a money-losing venture lagging behind money-losing Just Eat (now Just Eat Takeaway) and Uber Eats in the U.K. food-delivery services market. Even Amazon had previously closed its own food-delivery service in the U.K. due to lack of profitability. Despite Deliveroo’s distressed economic circumstances and the implausibility of any market power arising from Amazon’s investment, the CMA nonetheless elected to pursue the fullest level of investigation. While the transaction was ultimately approved in August 2020, this intervention imposed a 15-month delay and associated costs in connection with an investment that almost certainly bolstered competition in a concentrated market by funding a firm reportedly at risk of insolvency.  This is the equivalent of a competition regulator driving in reverse.

Concluding Thoughts

There seems to be an increasingly common assumption in commentary by the press, policymakers, and even some scholars that apparently dominant platforms usually face little competition and can set, at will, the terms of exchange. For investors, this is a reason to buy; for regulators, this is a reason to intervene. This assumption is sometimes realized, and, in that case, antitrust intervention is appropriate whenever there is reasonable evidence that market power is being secured through something other than “competition on the merits.” However, several conditions must be met to support the market power assumption without which any such inquiry would be imprudent. Contrary to conventional wisdom, the economics and history of platform markets suggest that those conditions are infrequently satisfied.

Without closer scrutiny, reflexively equating market share with market power is prone to lead both investors and regulators astray.  

Not surprisingly, we’ve discussed Coase quite a bit here at Truth on the Market. Follow this link to see our collected thoughts on Coase over the years.

Probably my favorite, and certainly most frequently quoted, of Coase’s many wise words is this:

One important result of this preoccupation with the monopoly problem is that if an economist finds something—a business practice of one sort or other—that he does not understand, he looks for a monopoly explanation. And as in this field we are very ignorant, the number of ununderstandable practices tends to be rather large, and the reliance on a monopoly explanation, frequent.

Of course this, a more generalized statement of the above from The Problem of Social Cost, is the essence of his work:

All solutions have costs, and there is no reason to suppose that governmental regulation is called for simply because the problem is not well handled by the market or the firm. Satisfactory views on policy can only come from a patient study of how, in practice, the market, firms and governments handle the problem of harmful effects…. It is my belief that economists, and policy-makers generally, have tended to over-estimate the advantages which come from governmental regulation. But this belief, even if justified, does not do more than suggest that government regulation should be curtailed. It does not tell us where the boundary line should be drawn. This, it seems to me, has to come from a detailed investigation of the actual results of handling the problem in different ways.