Archives For network effects

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.  

[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.

Zoom, one of Silicon Valley’s lesser-known unicorns, has just gone public. At the time of writing, its shares are trading at about $65.70, placing the company’s value at $16.84 billion. There are good reasons for this success. According to its Form S-1, Zoom’s revenue rose from about $60 million in 2017 to a projected $330 million in 2019, and the company has already surpassed break-even . This growth was notably fueled by a thriving community of users who collectively spend approximately 5 billion minutes per month in Zoom meetings.

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. For instance, the value of Skype to one user depends – at least to some extent – on the number of other people that might be willing to use the network. In these settings, it is often said that positive feedback loops may cause the market to tip in favor of a single firm that is then left with an unassailable market position. Although Zoom still faces significant competitive challenges, it has nonetheless established a strong position in a market previously dominated by powerful incumbents who could theoretically count on network effects to stymie its growth.

Further complicating matters, Zoom chose to compete head-on with these incumbents. It did not create a new market or a highly differentiated product. Zoom’s Form S-1 is quite revealing. The company cites the quality of its product as its most important competitive strength. Similarly, when listing the main benefits of its platform, Zoom emphasizes that its software is “easy to use”, “easy to deploy and manage”, “reliable”, etc. In its own words, Zoom has thus gained a foothold by offering an existing service that works better than that of its competitors.

And yet, this is precisely the type of story that a literal reading of the network effects literature would suggest is impossible, or at least highly unlikely. For instance, the foundational papers on network effects often cite the example of the DVORAK keyboard (David, 1985; and Farrell & Saloner, 1985). These early scholars argued that, despite it being the superior standard, the DVORAK layout failed to gain traction because of the network effects protecting the QWERTY standard. In other words, consumers failed to adopt the superior DVORAK layout because they were unable to coordinate on their preferred option. It must be noted, however, that the conventional telling of this story was forcefully criticized by Liebowitz & Margolis in their classic 1995 article, The Fable of the Keys.

Despite Liebowitz & Margolis’ critique, the dominance of the underlying network effects story persists in many respects. And in that respect, the emergence of Zoom is something of a cautionary tale. As influential as it may be, the network effects literature has tended to overlook a number of factors that may mitigate, or even eliminate, the likelihood of problematic outcomes. Zoom is yet another illustration that policymakers should be careful when they make normative inferences from positive economics.

A Coasian perspective

It is now widely accepted that multi-homing and the absence of switching costs can significantly curtail the potentially undesirable outcomes that are sometimes associated with network effects. But other possibilities are often overlooked. For instance, almost none of the foundational network effects papers pay any notice to the application of the Coase theorem (though it has been well-recognized in the two-sided markets literature).

Take a purported market failure that is commonly associated with network effects: an installed base of users prevents the market from switching towards a new standard, even if it is superior (this is broadly referred to as “excess inertia,” while the opposite scenario is referred to as “excess momentum”). DVORAK’s failure is often cited as an example.

Astute readers will quickly recognize that this externality problem is not fundamentally different from those discussed in Ronald Coase’s masterpiece, “The Problem of Social Cost,” or Steven Cheung’s “The Fable of the Bees” (to which Liebowitz & Margolis paid homage in their article’s title). In the case at hand, there are at least two sets of externalities at play. First, early adopters of the new technology impose a negative externality on the old network’s installed base (by reducing its network effects), and a positive externality on other early adopters (by growing the new network). Conversely, installed base users impose a negative externality on early adopters and a positive externality on other remaining users.

Describing these situations (with a haughty confidence reminiscent of Paul Samuelson and Arthur Cecil Pigou), Joseph Farrell and Garth Saloner conclude that:

In general, he or she [i.e. the user exerting these externalities] does not appropriately take this into account.

Similarly, Michael Katz and Carl Shapiro assert that:

In terms of the Coase theorem, it is very difficult to design a contract where, say, the (potential) future users of HDTV agree to subsidize today’s buyers of television sets to stop buying NTSC sets and start buying HDTV sets, thereby stimulating the supply of HDTV programming.

And yet it is far from clear that consumers and firms can never come up with solutions that mitigate these problems. As Daniel Spulber has suggested, referral programs offer a case in point. These programs usually allow early adopters to receive rewards in exchange for bringing new users to a network. One salient feature of these programs is that they do not simply charge a lower price to early adopters; instead, in order to obtain a referral fee, there must be some agreement between the early adopter and the user who is referred to the platform. This leaves ample room for the reallocation of rewards. Users might, for instance, choose to split the referral fee. Alternatively, the early adopter might invest time to familiarize the switching user with the new platform, hoping to earn money when the user jumps ship. Both of these arrangements may reduce switching costs and mitigate externalities.

Danial Spulber also argues that users may coordinate spontaneously. For instance, social groups often decide upon the medium they will use to communicate. Families might choose to stay on the same mobile phone network. And larger groups (such as an incoming class of students) may agree upon a social network to share necessary information, etc. In these contexts, there is at least some room to pressure peers into adopting a new platform.

Finally, firms and other forms of governance may also play a significant role. For instance, employees are routinely required to use a series of networked goods. Common examples include office suites, email clients, social media platforms (such as Slack), or video communications applications (Zoom, Skype, Google Hangouts, etc.). In doing so, firms presumably act as islands of top-down decision-making and impose those products that maximize the collective preferences of employers and employees. Similarly, a single firm choosing to join a network (notably by adopting a standard) may generate enough momentum for a network to gain critical mass. Apple’s decisions to adopt USB-C connectors on its laptops and to ditch headphone jacks on its iPhones both spring to mind. Likewise, it has been suggested that distributed ledger technology and initial coin offerings may facilitate the creation of new networks. The intuition is that so-called “utility tokens” may incentivize early adopters to join a platform, despite initially weak network effects, because they expect these tokens to increase in value as the network expands.

A combination of these arrangements might explain how Zoom managed to grow so rapidly, despite the presence of powerful incumbents. In its own words:

Our rapid adoption is driven by a virtuous cycle of positive user experiences. Individuals typically begin using our platform when a colleague or associate invites them to a Zoom meeting. When attendees experience our platform and realize the benefits, they often become paying customers to unlock additional functionality.

All of this is not to say that network effects will always be internalized through private arrangements, but rather that it is equally wrong to assume that transaction costs systematically prevent efficient coordination among users.

Misguided regulatory responses

Over the past couple of months, several antitrust authorities around the globe have released reports concerning competition in digital markets (UK, EU, Australia), or held hearings on this topic (US). A recurring theme throughout their published reports is that network effects almost inevitably weaken competition in digital markets.

For instance, the report commissioned by the European Commission mentions that:

Because of very strong network externalities (especially in multi-sided platforms), incumbency advantage is important and strict scrutiny is appropriate. We believe that any practice aimed at protecting the investment of a dominant platform should be minimal and well targeted.

The Australian Competition & Consumer Commission concludes that:

There are considerable barriers to entry and expansion for search platforms and social media platforms that reinforce and entrench Google and Facebook’s market power. These include barriers arising from same-side and cross-side network effects, branding, consumer inertia and switching costs, economies of scale and sunk costs.

Finally, a panel of experts in the United Kingdom found that:

Today, network effects and returns to scale of data appear to be even more entrenched and the market seems to have stabilised quickly compared to the much larger degree of churn in the early days of the World Wide Web.

To address these issues, these reports suggest far-reaching policy changes. These include shifting the burden of proof in competition cases from authorities to defendants, establishing specialized units to oversee digital markets, and imposing special obligations upon digital platforms.

The story of Zoom’s emergence and the important insights that can be derived from the Coase theorem both suggest that these fears may be somewhat overblown.

Rivals do indeed find ways to overthrow entrenched incumbents with some regularity, even when these incumbents are shielded by network effects. Of course, critics may retort that this is not enough, that competition may sometimes arrive too late (excess inertia, i.e., “ a socially excessive reluctance to switch to a superior new standard”) or too fast (excess momentum, i.e., “the inefficient adoption of a new technology”), and that the problem is not just one of network effects, but also one of economies of scale, information asymmetry, etc. But this comes dangerously close to the Nirvana fallacy. To begin, it assumes that regulators are able to reliably navigate markets toward these optimal outcomes — which is questionable, at best. Moreover, the regulatory cost of imposing perfect competition in every digital market (even if it were possible) may well outweigh the benefits that this achieves. Mandating far-reaching policy changes in order to address sporadic and heterogeneous problems is thus unlikely to be the best solution.

Instead, the optimal policy notably depends on whether, in a given case, users and firms can coordinate their decisions without intervention in order to avoid problematic outcomes. A case-by-case approach thus seems by far the best solution.

And competition authorities need look no further than their own decisional practice. The European Commission’s decision in the Facebook/Whatsapp merger offers a good example (this was before Margrethe Vestager’s appointment at DG Competition). In its decision, the Commission concluded that the fast-moving nature of the social network industry, widespread multi-homing, and the fact that neither Facebook nor Whatsapp controlled any essential infrastructure, prevented network effects from acting as a barrier to entry. Regardless of its ultimate position, this seems like a vastly superior approach to competition issues in digital markets. The Commission adopted a similar reasoning in the Microsoft/Skype merger. Unfortunately, the Commission seems to have departed from this measured attitude in more recent decisions. In the Google Search case, for example, the Commission assumes that the mere existence of network effects necessarily increases barriers to entry:

The existence of positive feedback effects on both sides of the two-sided platform formed by general search services and online search advertising creates an additional barrier to entry.

A better way forward

Although the positive economics of network effects are generally correct and most definitely useful, some of the normative implications that have been derived from them are deeply flawed. Too often, policymakers and commentators conclude that these potential externalities inevitably lead to stagnant markets where competition is unable to flourish. But this does not have to be the case. The emergence of Zoom shows that superior products may prosper despite the presence of strong incumbents and network effects.

Basing antitrust policies on sweeping presumptions about digital competition – such as the idea that network effects are rampant or the suggestion that online platforms necessarily imply “extreme returns to scale” – is thus likely to do more harm than good. Instead, Antitrust authorities should take a leaf out of Ronald Coase’s book, and avoid blackboard economics in favor of a more granular approach.

One of my favorite stories in the ongoing saga over the regulation (and thus the future) of Internet search emerged earlier this week with claims by Google that Microsoft has been copying its answers–using Google search results to bolster the relevance of its own results for certain search terms.  The full story from Internet search journalist extraordinaire, Danny Sullivan, is here, with a follow up discussing Microsoft’s response here.  The New York Times is also on the case with some interesting comments from a former Googler that feed nicely into the Schumpeterian competition angle (discussed below).  And Microsoft consultant (“though on matters unrelated to issues discussed here”)  and Harvard Business prof Ben Edelman coincidentally echoes precisely Microsoft’s response in a blog post here.

What I find so great about this story is how it seems to resolve one of the most significant strands of the ongoing debate–although it does so, from Microsoft’s point of view, unintentionally, to be sure.

Here’s what I mean.  Back when Microsoft first started being publicly identified as a significant instigator of regulatory and antitrust attention paid to Google, the company, via its chief competition counsel, Dave Heiner, defended its stance in large part on the following ground:

All of this is quite important because search is so central to how people navigate the Internet, and because advertising is the main monetization mechanism for a wide range of Web sites and Web services. Both search and online advertising are increasingly controlled by a single firm, Google. That can be a problem because Google’s business is helped along by significant network effects (just like the PC operating system business). Search engine algorithms “learn” by observing how users interact with search results. Google’s algorithms learn less common search terms better than others because many more people are conducting searches on these terms on Google.

These and other network effects make it hard for competing search engines to catch up. Microsoft’s well-received Bing search engine is addressing this challenge by offering innovations in areas that are less dependent on volume. But Bing needs to gain volume too, in order to increase the relevance of search results for less common search terms. That is why Microsoft and Yahoo! are combining their search volumes. And that is why we are concerned about Google business practices that tend to lock in publishers and advertisers and make it harder for Microsoft to gain search volume. (emphasis added).

Claims of “network effects” “increasing returns to scale” and the absence of “minimum viable scale” for competitors run rampant (and unsupported) in the various cases against Google.  The TradeComet complaint, for example, claims that

[t]he primary barrier to entry facing vertical search websites is the inability to draw enough search traffic to reach the critical mass necessary to become independently sustainable.

But now we discover (what we should have known all along) that “learning by doing” is not the only way to obtain the data necessary to generate relevant search results: “Learning by copying” works, as well.  And there’s nothing wrong with it–in fact, the very process of Schumpeterian creative destruction assumes imitation.

As Armen Alchian notes in describing his evolutionary process of competition,

Neither perfect knowledge of the past nor complete awareness of the current state of the arts gives sufficient foresight to indicate profitable action . . . [and] the pervasive effects of uncertainty prevent the ascertainment of actions which are supposed to be optimal in achieving profits.  Now the consequence of this is that modes of behavior replace optimum equilibrium conditions as guiding rules of action. First, wherever successful enterprises are observed, the elements common to these observable successes will be associated with success and copied by others in their pursuit of profits or success. “Nothing succeeds like success.”

So on the one hand, I find the hand wringing about Microsoft’s “copying” Google’s results to be completely misplaced–just as the pejorative connotations of “embrace and extend” deployed against Microsoft itself when it was the target of this sort of scrutiny were bogus.  But, at the same time, I see this dynamic essentially decimating Microsoft’s (and others’) claims that Google has an unassailable position because no competitor can ever hope to match its size, and thus its access to information essential to the quality of search results, particularly when it comes to so-called “long-tail” search terms.

Long-tail search terms are queries that are extremely rare and, thus, for which there is little user history (information about which results searchers found relevant and clicked on) to guide future search results.  As Ben Edelman writes in his blog post (linked above) on this issue (trotting out, even while implicitly undercutting, the “minimum viable scale” canard):

Of course the reality is that Google’s high market share means Google gets far more searches than any other search engine. And Google’s popularity gives it a real advantage: For an obscure search term that gets 100 searches per month at Google, Bing might get just five or 10. Also, for more popular terms, Google can slice its data into smaller groups — which results are most useful to people from Boston versus New York, which results are best during the day versus at night, and so forth. So Google is far better equipped to figure out what results users favor and to tailor its listings accordingly. Meanwhile, Microsoft needs additional data, such as Toolbar and Related Sites data, to attempt to improve its results in a similar way.

But of course the “additional data” that Microsoft has access to here is, to a large extent, the same data that Google has.  Although Danny Sullivan’s follow up story (also linked above) suggests that Bing doesn’t do all it could to make use of Google’s data (for example, Bing does not, it seems, copy Google search results wholesale, nor does it use user behavior as extensively as it could (by, for example, seeing searches in Google and then logging the next page visited, which would give Bing a pretty good idea which sites in Google’s results users found most relevant)), it doesn’t change the fundamental fact that Microsoft and other search engines can overcome a significant amount of the so-called barrier to entry afforded by Google’s impressive scale by simply imitating much of what Google does (and, one hopes, also innovating enough to offer something better).

Perhaps Google is “better equipped to figure out what users favor.”  But it seems to me that only a trivial amount of this advantage is plausibly attributable to Google’s scale instead of its engineering and innovation.  The fact that Microsoft can (because of its own impressive scale in various markets) and does take advantage of accessible data to benefit indirectly from Google’s own prowess in search is a testament to the irrelevance of these unfortunately-pervasive scale and network effect arguments.

We have just uploaded to SSRN a draft of our article assessing the economics and the law of the antitrust case directed at the core of Google’s business:  Its search and search advertising platform.  The article is Google and the Limits of Antitrust: The Case Against the Antitrust Case Against Google.  This is really the first systematic attempt to address both the amorphous and the concrete (as in the TradeComet complaint) claims about Google’s business and its legal and economic importance in its primary market.  It’s giving nothing away to say we’re skeptical of the claims, and, moreover, that an approach to the issues appropriately sensitive to the potential error costs would be extremely deferential.  As we discuss, the economics of search and search advertising are indeterminate and subtle, and the risk of error is high (claims of network effects, for example, are greatly exaggerated, and the pro-competitive justifications for Google’s use of a quality score are legion, despite frequent claims to the contrary).  We welcome comments on the article, and we look forward to the debate.  The abstract is here:

The antitrust landscape has changed dramatically in the last decade.  Within the last two years alone, the United States Department of Justice has held hearings on the appropriate scope of Section 2, issued a comprehensive Report, and then repudiated it; and the European Commission has risen as an aggressive leader in single firm conduct enforcement by bringing abuse of dominance actions and assessing heavy fines against firms including Qualcomm, Intel, and Microsoft.  In the United States, two of the most significant characteristics of the “new” antitrust approach have been a more intense focus on innovative companies in high-tech industries and a weakening of longstanding concerns that erroneous antitrust interventions will hinder economic growth.  But this focus is dangerous, and these concerns should not be dismissed so lightly.  In this article we offer a comprehensive cautionary tale in the context of a detailed factual, legal and economic analysis of the next Microsoft: the theoretical, but perhaps imminent, enforcement action against Google.  Close scrutiny of the complex economics of Google’s technology, market and business practices reveals a range of real but subtle, pro-competitive explanations for features that have been held out instead as anticompetitive.  Application of the relevant case law then reveals a set of concerns where economic complexity and ambiguity, coupled with an insufficiently-deferential approach to innovative technology and pricing practices in the most relevant precedent (the D.C. Circuit’s decision in Microsoft), portend a potentially erroneous—and costly—result.  Our analysis, by contrast, embraces the cautious and evidence-based approach to uncertainty, complexity and dynamic innovation contained within the well-established “error cost framework.”  As we demonstrate, while there is an abundance of error-cost concern in the Supreme Court precedent, there is a real risk that the current, aggressive approach to antitrust error, coupled with the uncertain economics of Google’s innovative conduct, will nevertheless yield a costly intervention.  The point is not that we know that Google’s conduct is procompetitive, but rather that the very uncertainty surrounding it counsels caution, not aggression.