Archives For Barriers to Entry

The Biden Administration’s July 9 Executive Order on Promoting Competition in the American Economy is very much a mixed bag—some positive aspects, but many negative ones.

It will have some positive effects on economic welfare, to the extent it succeeds in lifting artificial barriers to competition that harm consumers and workers—such as allowing direct sales of hearing aids in drug stores—and helping to eliminate unnecessary occupational licensing restrictions, to name just two of several examples.

But it will likely have substantial negative effects on economic welfare as well. Many aspects of the order appear to emphasize new regulation—such as Net Neutrality requirements that may reduce investment in broadband by internet service providers—and imposing new regulatory requirements on airlines, pharmaceutical companies, digital platforms, banks, railways, shipping, and meat packers, among others. Arbitrarily imposing new rules in these areas, without a cost-beneficial appraisal and a showing of a market failure, threatens to reduce innovation and slow economic growth, hurting producers and consumer. (A careful review of specific regulatory proposals may shed greater light on the justifications for particular regulations.)

Antitrust-related proposals to challenge previously cleared mergers, and to impose new antitrust rulemaking, are likely to raise costly business uncertainty, to the detriment of businesses and consumers. They are a recipe for slower economic growth, not for vibrant competition.

An underlying problem with the order is that it is based on the false premise that competition has diminished significantly in recent decades and that “big is bad.” Economic analysis found in the February 2020 Economic Report of the President, and in other economic studies, debunks this flawed assumption.

In short, the order commits the fundamental mistake of proposing intrusive regulatory solutions for a largely nonexistent problem. Competitive issues are best handled through traditional well-accepted antitrust analysis, which centers on promoting consumer welfare and on weighing procompetitive efficiencies against anticompetitive harm on a case-by-case basis. This approach:

  1. Deals effectively with serious competitive problems; while at the same time
  2. Cabining error costs by taking into account all economically relevant considerations on a case-specific basis.

Rather than using an executive order to direct very specific regulatory approaches without a strong economic and factual basis, the Biden administration would have been better served by raising a host of competitive issues that merit possible study and investigation by expert agencies. Such an approach would have avoided imposing the costs of unwarranted regulation that unfortunately are likely to stem from the new order.

Finally, the order’s call for new regulations and the elimination of various existing legal policies will spawn matter-specific legal challenges, and may, in many cases, not succeed in court. This will impose unnecessary business uncertainty in addition to public and private resources wasted on litigation.

This is the fourth, and last, in a series of TOTM blog posts discussing the Commission’s recently published Google Android decision (the first post can be found here, and the second here, and the third here). It draws on research from a soon-to-be published ICLE white paper.

The previous parts of this series have mostly focused on the Commission’s factual and legal conclusions. However, as this blog post points out, the case’s economic underpinnings also suffer from important weaknesses.

Two problems are particularly salient: First, the economic models cited by the Commission (discussed in an official paper, but not directly in the decision) poorly match the underlying facts. Second, the Commission’s conclusions on innovation harms are out of touch with the abundant economic literature regarding the potential link between market structure and innovation.

The wrong economic models

The Commission’s Chief Economist team outlined its economic reasoning in an article published shortly after the Android decision was published. The article reveals that the Commission relied upon three economic papers to support its conclusion that Google’s tying harmed consumer welfare.

Each of these three papers attempts to address the same basic problem. Ever since the rise of the Chicago-School, it is widely accepted that a monopolist cannot automatically raise its profits by entering an adjacent market (i.e. leveraging its monopoly position), for instance through tying. This has sometimes been called the single-monopoly-profit theory. In more recent years, various scholars have refined this Chicago-School intuition, and identified instances where the theory fails.

While the single monopoly profit theory has been criticized in academic circles, it is important to note that the three papers cited by the Commission accept its basic premise. They thus attempt to show why the theory fails in the context of the Google Android case. 

Unfortunately, the assumptions upon which they rely to reach this conclusion markedly differ from the case’s fact pattern. These papers thus offer little support to the Commission’s economic conclusions.

For a start, the authors of the first paper cited by the Commission concede that their own model does not apply to the Google case:

Actual antitrust cases are fact-intensive and our model does not perfectly fit with the current Google case in one important aspect.

The authors thus rely on important modifications, lifted from a paper by Frederico Etro and Cristina Caffara (the second paper cited by the Commission), to support their conclusion that Google’s tying was anticompetitive. 

The second paper cited by the Commission, however, is equally problematic

The authors’ underlying intuition is relatively straightforward: because Google bundles its suite of Google Apps (including Search) with the Play Store, a rival search engine would have to pay a premium in order to be pre-installed and placed on the home screen, because OEMs would have to entirely forgo Google’s suite of applications. The key assumption here is that OEMs cannot obtain the Google Play app and pre-install and place favorably a rival search app

But this is simply not true of Google’s contractual terms. The best evidence is that rivals search apps have indeed concluded deals with OEMs to pre-install their search apps, without these OEMs losing access to Google’s suite of proprietary apps. Google’s contractual terms simply do not force OEMs to choose between the Google Play app and the pre-installation of a rival search app. Etro and Caffara’s model thus falls flat.

More fundamentally, even if Google’s contractual terms did prevent OEMs from pre-loading rival apps, the paper’s conclusions would still be deeply flawed. The authors essentially assume that the only way for consumers to obtain a rival app is through pre-installation. But this is a severe misreading of the prevailing market conditions. 

Users remain free to independently download rival search apps. If Google did indeed purchase exclusive pre-installation, users would not have to choose between a “full Android” device and one with a rival search app but none of Google’s apps. Instead, they could download the rival app and place it alongside Google’s applications. 

A more efficient rival could even provide side payments, of some sort, to encourage consumers to download its app. Exclusive pre-installation thus generates a much smaller advantage than Etro and Caffara assume, and their model fails to reflect this.

Finally, the third paper by Alexandre de Cornière and Greg Taylor, suffers from the exact same problem. The authors clearly acknowledge that their findings only hold if OEMs (and consumers) are effectively prevented from (pre-)installing applications that compete with Google’s apps. In their own words:

Upstream firms offer contracts to the downstream firm, who chooses which component(s) to use and then sells to consumers. For our theory to apply, the following three conditions need to hold: (i) substitutability between the two versions of B leads the downstream firm to install at most one version.

The upshot is that all three of the economic models cited by the Commission cease to be relevant in the specific context of the Google Android decision. The Commission is thus left with little to no economic evidence to support its finding of anticompetitive effects.

Critics might argue that direct downloads by consumers are but a theoretical possibility. Yet nothing could be further from the truth. Take the web browser market: The Samsung Internet Browser has more than 1 Billion downloads on Google’s Play Store. The Opera, Opera Mini and Firefox browsers each have over a 100 million downloads. The Brave browser has more than 10 million downloads, but is growing rapidly.

In short the economic papers on which the Commission relies are based on a world that does not exist. They thus fail to support the Commission’s economic findings.

An incorrect view of innovation

In its decision, the Commission repeatedly claimed that Google’s behavior stifled innovation because it prevented rivals from entering the market. However, the Commission offered no evidence to support its assumption that reduced market entry on would lead to a decrease in innovation:

(858) For the reasons set out in this Section, the Commission concludes that the tying of the Play Store and the Google Search app helps Google to maintain and strengthen its dominant position in each national market for general search services, increases barriers to entry, deters innovation and tends to harm, directly or indirectly, consumers.

(859) First, Google’s conduct makes it harder for competing general search services to gain search queries and the respective revenues and data needed to improve their services.

(861) Second, Google’s conduct increases barriers to entry by shielding Google from competition from general search services that could challenge its dominant position in the national markets for general search services:

(862) Third, by making it harder for competing general search services to gain search queries including the respective revenues and data needed to improve their services, Google’s conduct reduces the incentives of competing general search services to invest in developing innovative features, such as innovation in algorithm and user experience design.

In a nutshell, the Commission’s findings rest on the assumption that barriers to entry and more concentrated market structures necessarily reduce innovation. But this assertion is not supported by the empirical economic literature on the topic.

For example, a 2006 paper published by Richard Gilbert surveys 24 empirical studies on the topic. These studies examine the link between market structure (or firm size) and innovation. Though earlier studies tended to identify a positive relationship between concentration, as well as firm size, and innovation, more recent empirical techniques found no significant relationship. Gilbert thus suggests that:

These econometric studies suggest that whatever relationship exists at a general economy-wide level between industry structure and R&D is masked by differences across industries in technological opportunities, demand, and the appropriability of inventions.

This intuition is confirmed by another high-profile empirical paper by Aghion, Bloom, Blundell, Griffith, and Howitt. The authors identify an inverted-U relationship between competition and innovation. Perhaps more importantly, they point out that this relationship is affected by a number of sector-specific factors.

Finally, reviewing fifty years of research on innovation and market structure, Wesley Cohen concludes that:

Even before one controls for industry effects, the variance in R&D intensity explained by market concentration is small. Moreover, whatever relationship that exists in cross sections becomes imperceptible with the inclusion of controls for industry characteristics, whether expressed as industry fixed effects or in the form of survey-based and other measures of industry characteristics such as technological opportunity, appropriability conditions, and demand. In parallel to a decades-long accumulation of mixed results, theorists have also spawned an almost equally voluminous and equivocal literature on the link between market structure and innovation.[16]

The Commission’s stance is further weakened by the fact that investments in the Android operating system are likely affected by a weak appropriability regime. In other words, because of its open source nature, it is hard for Google to earn a return on investments in the Android OS (anyone can copy, modify and offer their own version of the OS). 

Loosely tying Google’s proprietary applications to the OS is arguably one way to solve this appropriability problem. Unfortunately, the Commission brushed these considerations aside. It argued that Google could earn some revenue from the Google Play app, as well as other potential venues. However, the Commission did not question whether these sources of income were even comparable to the sums invested by Google in the Android OS. It is thus possible that the Commission’s decision will prevent Google from earning a positive return on some future investments in the Android OS, ultimately causing it to cut back its investments and slowing innovation.

The upshot is that the Commission was simply wrong to assume that barriers to entry and more concentrated market structures would necessarily reduce innovation. This is especially true, given that Google may struggle to earn a return on its investments, absent the contractual provisions challenged by the Commission.


In short, the Commission’s economic analysis was severely lacking. It relied on economic models that had little to say about the market it which Google and its rivals operated. Its decisions thus reveals the inherent risk of basing antitrust decisions upon overfitted economic models. 

As if that were not enough, the Android decision also misrepresents the economic literature concerning the link (or absence thereof) between market structure and innovation. As a result, there is no reason to believe that Google’s behavior reduced innovation.

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.

As the Google antitrust discussion heats up on its way toward some culmination at the FTC, I thought it would be helpful to address some of the major issues raised in the case by taking a look at what’s going on in the market(s) in which Google operates. To this end, I have penned a lengthy document — The Market Realities that Undermine the Antitrust Case Against Google — highlighting some of the most salient aspects of current market conditions and explaining how they fit into the putative antitrust case against Google.

While not dispositive, these “realities on the ground” do strongly challenge the logic and thus the relevance of many of the claims put forth by Google’s critics. The case against Google rests on certain assumptions about how the markets in which it operates function. But these are tech markets, constantly evolving and complex; most assumptions (and even “conclusions” based on data) are imperfect at best. In this case, the conventional wisdom with respect to Google’s alleged exclusionary conduct, the market in which it operates (and allegedly monopolizes), and the claimed market characteristics that operate to protect its position (among other things) should be questioned.

The reality is far more complex, and, properly understood, paints a picture that undermines the basic, essential elements of an antitrust case against the company.

The document first assesses the implications for Market Definition and Monopoly Power of these competitive realities. Of note:

  • Users use Google because they are looking for information — but there are lots of ways to do that, and “search” is not so distinct that a “search market” instead of, say, an “online information market” (or something similar) makes sense.
  • Google competes in the market for targeted eyeballs: a market aimed to offer up targeted ads to interested users. Search is important in this, but it is by no means alone, and there are myriad (and growing) other mechanisms to access consumers online.
  • To define the relevant market in terms of the particular mechanism that prevails to accomplish the matching of consumers and advertisers does not reflect the substitutability of other mechanisms that do the same thing but simply aren’t called “search.”
  • In a world where what prevails today won’t — not “might not,” but won’t — prevail tomorrow, it is the height of folly (and a serious threat to innovation and consumer welfare) to constrain the activities of firms competing in such an environment by pigeonholing the market.
  • In other words, in a proper market, Google looks significantly less dominant. More important, perhaps, as search itself evolves, and as Facebook, Amazon and others get into the search advertising game, Google’s strong position even in the overly narrow “search” market looks far from unassailable.

Next I address Anticompetitive Harm — how the legal standard for antitrust harm is undermined by a proper understanding of market conditions:

  • Antitrust law doesn’t require that Google or any other large firm make life easier for competitors or others seeking to access resources owned by these firms.
  • Advertisers are increasingly targeting not paid search but rather social media to reach their target audiences.
  • But even for those firms that get much or most of their traffic from “organic” search, this fact isn’t an inevitable relic of a natural condition over which only the alleged monopolist has control; it’s a business decision, and neither sensible policy nor antitrust law is set up to protect the failed or faulty competitor from himself.
  • Although it often goes unremarked, paid search’s biggest competitor is almost certainly organic search (and vice versa). Nextag may complain about spending money on paid ads when it prefers organic, but the real lesson here is that the two are substitutes — along with social sites and good old-fashioned email, too.
  • It is incumbent upon critics to accurately assess the “but for” world without the access point in question. Here, Nextag can and does use paid ads to reach its audience (and, it is important to note, did so even before it claims it was foreclosed from Google’s users). But there are innumerable other avenues of access, as well. Some may be “better” than others; some that may be “better” now won’t be next year (think how links by friends on Facebook to price comparisons on Nextag pages could come to dominate its readership).
  • This is progress — creative destruction — not regress, and such changes should not be penalized.

Next I take on the perennial issue of Error Costs and the Risks of Erroneous Enforcement arising from an incomplete and inaccurate understanding of Google’s market:

  • Microsoft’s market position was unassailable . . . until it wasn’t — and even at the time, many could have told you that its perceived dominance was fleeting (and many did).
  • Apple’s success (and the consumer value it has created), while built in no small part on its direct competition with Microsoft and the desktop PCs which run it, was primarily built on a business model that deviated from its once-dominant rival’s — and not on a business model that the DOJ’s antitrust case against the company either facilitated or anticipated.
  • Microsoft and Google’s other critic-competitors have more avenues to access users than ever before. Who cares if users get to these Google-alternatives through their devices instead of a URL? Access is access.
  • It isn’t just monopolists who prefer not to innovate: their competitors do, too. To the extent that Nextag’s difficulties arise from Google innovating, it is Nextag, not Google, that’s working to thwart innovation and fighting against dynamism.
  • Recall the furor around Google’s purchase of ITA, a powerful cautionary tale. As of September 2012, Google ranks 7th in visits among metasearch travel sites, with a paltry 1.4% of such visits. Residing at number one? FairSearch founding member, Kayak, with a whopping 61%. And how about FairSearch member Expedia? Currently, it’s the largest travel company in the world, and it has only grown in recent years.

The next section addresses the essential issue of Barriers to Entry and their absence:

  • One common refrain from Google’s critics is that Google’s access to immense amounts of data used to increase the quality of its targeting presents a barrier to competition that no one else can match, thus protecting Google’s unassailable monopoly. But scale comes in lots of ways.
  • It’s never been the case that a firm has to generate its own inputs into every product it produces — and there is no reason to suggest search/advertising is any different.
  • Meanwhile, Google’s chief competitor, Microsoft, is hardly hurting for data (even, quite creatively, culling data directly from Google itself), despite its claims to the contrary. And while regulators and critics may be looking narrowly and statically at search data, Microsoft is meanwhile sitting on top of copious data from unorthodox — and possibly even more valuable — sources.
  • To defend a claim of monopolization, it is generally required to show that the alleged monopolist enjoys protection from competition through barriers to entry. In Google’s case, the barriers alleged are illusory.

The next section takes on recent claims revolving around The Mobile Market and Google’s position (and conduct) there:

  • If obtaining or preserving dominance is simply a function of cash, Microsoft is sitting on some $58 billion of it that it can devote to that end. And JP Morgan Chase would be happy to help out if it could be guaranteed monopoly returns just by throwing its money at Bing. Like data, capital is widely available, and, also like data, it doesn’t matter if a company gets it from selling search advertising or from selling cars.
  • Advertisers don’t care whether the right (targeted) user sees their ads while playing Angry Birds or while surfing the web on their phone, and users can (and do) seek information online (and thus reveal their preferences) just as well (or perhaps better) through Wikipedia’s app as via a Google search in a mobile browser.
  • Moreover, mobile is already (and increasingly) a substitute for the desktop. Distinguishing mobile search from desktop search is meaningless when users use their tablets at home, perform activities that they would have performed at home away from home on mobile devices simply because they can, and where users sometimes search for places to go (for example) on mobile devices while out and sometimes on their computers before they leave.
  • Whatever gains Google may have made in search from its spread into the mobile world is likely to be undermined by the massive growth in social connectivity it has also wrought.
  • Mobile is part of the competitive landscape. All of the innovations in mobile present opportunities for Google and its competitors to best each other, and all present avenues of access for Google and its competitors to reach consumers.

The final section Concludes.

The lessons from all of this? There are two. First, these are dynamic markets, and it is a fool’s errand to identify the power or significance of any player in these markets based on data available today — data that is already out of date between the time it is collected and the time it is analyzed.

Second, each of these developments has presented different, novel and shifting opportunities and challenges for firms interested in attracting eyeballs, selling ad space and data, earning revenue and obtaining market share. To say that Google dominates “search” or “online advertising” misses the mark precisely because there is simply nothing especially antitrust-relevant about either search or online advertising. Because of their own unique products, innovations, data sources, business models, entrepreneurship and organizations, all of these companies have challenged and will continue to challenge the dominant company — and the dominant paradigm — in a shifting and evolving range of markets.

Perhaps most important is this:

Competition with Google may not and need not look exactly like Google itself, and some of this competition will usher in innovations that Google itself won’t be able to replicate. But this doesn’t make it any less competitive.  

Competition need not look identical to be competitive — that’s what innovation is all about. Just ask those famous buggy whip manufacturers.