Archives For antitrust

In 2014, Benedict Evans, a venture capitalist at Andreessen Horowitz, wrote “Why Amazon Has No Profits (And Why It Works),” a blog post in which he tried to explain Amazon’s business model. He began with a chart of Amazon’s revenue and net income that has now become (in)famous:

Source: Benedict Evans

A question inevitably followed in antitrust circles: How can a company that makes so little profit on so much revenue be worth so much money? It must be predatory pricing!

Predatory pricing is a rather rare anticompetitive practice because the “predator” runs the risk of bankrupting itself in the process of trying to drive rivals out of business with below-cost pricing. Furthermore, even if a predator successfully clears the field of competition, in developed markets with deep capital markets, keeping out new entrants is extremely unlikely.

Nonetheless, in those rare cases where plaintiffs can demonstrate that a firm actually has a viable scheme to drive competitors from the market with prices that are “too low” and has the ability to recoup its losses once it has cleared the market of those competitors, plaintiffs (including the DOJ) can prevail in court.

In other words, whoa if true.

Khan’s Predatory Pricing Accusation

In 2017, Lina Khan, then a law student at Yale, published “Amazon’s Antitrust Paradox” in a note for the Yale Law Journal and used Evans’ chart as supporting evidence that Amazon was guilty of predatory pricing. In the abstract she says, “Although Amazon has clocked staggering growth, it generates meager profits, choosing to price below-cost and expand widely instead.”

But if Amazon is selling below-cost, where does the money come from to finance those losses?

In her article, Khan hinted at two potential explanations: (1) Amazon is using profits from the cloud computing division (AWS) to cross-subsidize losses in the retail division or (2) Amazon is using money from investors to subsidize short-term losses:

Recently, Amazon has started reporting consistent profits, largely due to the success of Amazon Web Services, its cloud computing business. Its North America retail business runs on much thinner margins, and its international retail business still runs at a loss. But for the vast majority of its twenty years in business, losses—not profits—were the norm. Through 2013, Amazon had generated a positive net income in just over half of its financial reporting quarters. Even in quarters in which it did enter the black, its margins were razor-thin, despite astounding growth.

Just as striking as Amazon’s lack of interest in generating profit has been investors’ willingness to back the company. With the exception of a few quarters in 2014, Amazon’s shareholders have poured money in despite the company’s penchant for losses.

Revising predatory pricing doctrine to reflect the economics of platform markets, where firms can sink money for years given unlimited investor backing, would require abandoning the recoupment requirement in cases of below-cost pricing by dominant platforms.

Below-Cost Pricing Not Subsidized by Investors

But neither explanation withstands scrutiny. First, the money is not from investors. Amazon has not raised equity financing since 2003. Nor is it debt financing: The company’s net debt position has been near-zero or negative for its entire history (excluding the Whole Foods acquisition):

Source: Benedict Evans

Amazon does not require new outside financing because it has had positive operating cash flow since 2002:

Notably for a piece of analysis attempting to explain Amazon’s business practices, the text of Khan’s 93-page law review article does not include the word “cash” even once.

Below-Cost Pricing Not Cross-Subsidized by AWS

Source: The Information

As Priya Anand observed in a recent piece for The Information, since Amazon started breaking out AWS in its financials, operating income for the North America retail business has been significantly positive:

But [Khan] underplays its retail profits in the U.S., where the antitrust debate is focused. As the above chart shows, its North America operation has been profitable for years, and its operating income has been on the rise in recent quarters. While its North America retail operation has thinner margins than AWS, it still generated $2.84 billion in operating income last year, which isn’t exactly a rounding error compared to its $4.33 billion in AWS operating income.

Below-Cost Pricing in Retail Also Known as “Loss Leader” Pricing

Okay, so maybe Amazon isn’t using below-cost pricing in aggregate in its retail division. But it still could be using profits from some retail products to cross-subsidize below-cost pricing for other retail products (e.g., diapers), with the intention of driving competitors out of business to capture monopoly profits. This is essentially what Khan claims happened in the Diapers.com (Quidsi) case. But in the retail industry, diapers are explicitly cited as a loss leader that help retailers to develop a customer relationship with mothers in the hopes of selling them a higher volume of products over time. This is exactly what the founders of Diapers.com told Inc Magazine in a 2012 interview (emphasis added):

We saw brick-and-mortar stores, the Wal-Marts and Targets of the world, using these products to build relationships with mom and the end consumer, bringing them into the store and selling them everything else. So we thought that was an interesting model and maybe we could replicate that online. And so we started with selling the loss leader product to basically build a relationship with mom. And once they had the passion for the brand and they were shopping with us on a weekly or a monthly basis that they’d start to fall in love with that brand. We were losing money on every box of diapers that we sold. We weren’t able to buy direct from the manufacturers.

An anticompetitive scheme could be built into such bundling, but in many if not the overwhelming majority of these cases, consumers are the beneficiaries of lower prices and expanded output produced by these arrangements. It’s hard to definitively say whether any given firm that discounts its products is actually pricing below average variable cost (“AVC”) without far more granular accounting ledgers than are typically  maintained. This is part of the reason why these cases can be so hard to prove.

A successful predatory pricing strategy also requires blocking market entry when the predator eventually raises prices. But the Diapers.com case is an explicit example of repeated entry that would defeat recoupment. In an article for the American Enterprise Institute, Jeffrey Eisenach shares the rest of the story following Amazon’s acquisition of Diapers.com:

Amazon’s conduct did not result in a diaper-retailing monopoly. Far from it. According to Khan, Amazon had about 43 percent of online sales in 2016 — compared with Walmart at 23 percent and Target with 18 percent — and since many people still buy diapers at the grocery store, real shares are far lower.

In the end, Quidsi proved to be a bad investment for Amazon: After spending $545 million to buy the firm and operating it as a stand-alone business for more than six years, it announced in April 2017 it was shutting down all of Quidsi’s operations, Diapers.com included. In the meantime, Quidsi’s founders poured the proceeds of the Amazon sale into a new online retailer — Jet.com — which was purchased by Walmart in 2016 for $3.3 billion. Jet.com cofounder Marc Lore now runs Walmart’s e-commerce operations and has said publicly that his goal is to surpass Amazon as the top online retailer.

Sussman’s Predatory Pricing Accusation

Earlier this year, Shaoul Sussman, a law student at Fordham University, published “Prime Predator: Amazon and the Rationale of Below Average Variable Cost Pricing Strategies Among Negative-Cash Flow Firms” in the Journal of Antitrust Enforcement. The article, which was written up by David Dayen for In These Times, presents a novel two-part argument for how Amazon might be profitably engaging in predatory pricing without raising prices:

  1. Amazon’s “True” Cash Flow Is Negative

Sussman argues that the company has been inflating its free cash flow numbers by excluding “capital leases.” According to Sussman, “If all of those expenses as detailed in its statements are accounted for, Amazon experienced a negative cash outflow of $1.461 billion in 2017.” Even though it’s not dispositive of predatory pricing on its own, Sussman believes that a negative free cash flow implies the company has been selling below-cost to gain market share.

2. Amazon Recoups Losses By Lowering AVC, Not By Raising Prices

Instead of raising prices to recoup losses from pricing below-cost, Sussman argues that Amazon flies under the antitrust radar by keeping consumer prices low and progressively decreasing AVC, ostensibly through using its monopsony power to offload costs on suppliers and partners (although this point is not fully explored in his piece).

But Sussman’s argument contains errors in both legal reasoning as well as its underlying empirical assumptions.

Below-cost pricing?

While there are many different ways to calculate the “cost” of a product or service, generally speaking, “below-cost pricing” means the price is less than marginal cost or AVC. Typically, courts tend to rely on AVC when dealing with predatory pricing cases. And as Herbert Hovenkamp has noted, proving that a price falls below the AVC is exceedingly difficult, particularly when dealing with firms in dynamic markets that sell a number of differentiated but complementary goods or services. Amazon, the focus of Sussman’s article, is a useful example here.

When products are complements, or can otherwise be bundled, firms may also be able to offer discounts that are unprofitable when selling single items. In business this is known as the “razor and blades model” (i.e., sell the razor handle below-cost one time and recoup losses on future sales of blades — although it’s not clear if this ever actually happens). Printer manufacturers are also an oft-cited example here, where printers are often sold below AVC in the expectation that the profits will be realized on the ongoing sale of ink. Amazon’s Kindle functions similarly: Amazon sells the Kindle around its AVC, ostensibly on the belief that it will realize a profit on selling e-books in the Kindle store.

Yet, even ignoring this common and broadly inoffensive practice, Sussman’s argument is odd. In essence, he claims that Amazon is concealing some of its costs in the form of capital leases in an effort to conceal its below-AVC pricing while it works to simultaneously lower its real AVC below the prices it charges consumers. At the end of this process, once its real AVC is actually sufficiently below consumers prices, it will (so the argument goes) be in the position of a monopolist reaping monopoly profits.

The problem with this argument should be immediately apparent. For the moment, let’s ignore the classic recoupment problem where new entrants will be drawn into the market to win some of those monopoly prices based on the new AVC that is possible. The real problem with his logic is that Sussman basically suggests that if Amazon sharply lowers AVC — that is it makes production massively more efficient — and then does not drop prices, they are a “predator.” But by pricing below its AVC in the first place, consumers in essence were given a loan by Amazon — they were able to enjoy what Sussman believes are radically low prices while Amazon works to actually make those prices possible through creating production efficiencies. It seems rather strange to punish a firm for loaning consumers a large measure of wealth. Its doubly odd when you then re-factor the recoupment problem back in: as soon as other firms figure out that a lower AVC is possible, they will enter the market and bid away any monopoly profits from Amazon.

Sussman’s Technical Analysis Is Flawed

While there are issues with Sussman’s general theory of harm, there are also some specific problems with his technical analysis of Amazon’s financial statements.

Capital Leases Are a Fixed Cost

First, capital leases should be not be included in cost calculations for a predatory pricing case because they are fixed — not variable — costs. Again, “below-cost” claims in predatory pricing cases generally use AVC (and sometimes marginal cost) as relevant cost measures.

Capital Leases Are Mostly for Server Farms

Second, the usual story is that Amazon uses its wildly-profitable Amazon Web Services (AWS) division to subsidize predatory pricing in its retail division. But Amazon’s “capital leases” — Sussman’s hidden costs in the free cash flow calculations — are mostly for AWS capital expenditures (i.e., server farms).

According to the most recent annual report: “Property and equipment acquired under capital leases was $5.7 billion, $9.6 billion, and $10.6 billion in 2016, 2017, and 2018, with the increase reflecting investments in support of continued business growth primarily due to investments in technology infrastructure for AWS, which investments we expect to continue over time.”

In other words, any adjustments to the free cash flow numbers for capital leases would make Amazon Web Services appear less profitable, and would not have a large effect on the accounting for Amazon’s retail operation (the only division thus far accused of predatory pricing).

Look at Operating Cash Flow Instead of Free Cash Flow

Again, while cash flow measures cannot prove or disprove the existence of predatory pricing, a positive cash flow measure should make us more skeptical of such accusations. In the retail sector, operating cash flow is the appropriate metric to consider. As shown above, Amazon has had positive (and increasing) operating cash flow since 2002.

Your Theory of Harm Is Also Known as “Investment”

Third, in general, Sussman’s novel predatory pricing theory is indistinguishable from pro-competitive behavior in an industry with high fixed costs. From the abstract (emphasis added):

[N]egative cash flow firm[s] … can achieve greater market share through predatory pricing strategies that involve long-term below average variable cost prices … By charging prices in the present reflecting future lower costs based on prospective technological and scale efficiencies, these firms are able to rationalize their predatory pricing practices to investors and shareholders.

“’Charging prices in the present reflecting future lower costs based on prospective technological and scale efficiencies” is literally what it means to invest in capex and R&D.

Sussman’s paper presents a clever attempt to work around the doctrinal limitations on predatory pricing. But, if courts seriously adopt an approach like this, they will be putting in place a legal apparatus that quite explicitly focuses on discouraging investment. This is one of the last things we should want antitrust law to be doing.

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.

Last month, the European Commission slapped another fine upon Google for infringing European competition rules (€1.49 billion this time). This brings Google’s contribution to the EU budget to a dizzying total of €8.25 billion (to put this into perspective, the total EU budget for 2019 is €165.8 billion). Given this massive number, and the geographic location of Google’s headquarters, it is perhaps not surprising that some high-profile commentators, including former President Obama and President Trump, have raised concerns about potential protectionism on the Commission’s part.

In a new ICLE Issue Brief, we question whether there is any merit to these claims of protectionism. We show that, since the entry into force of Regulation 1/2003 (the main piece of legislation that implements the competition provisions of the EU treaties), US firms have borne the lion’s share of monetary penalties imposed by the Commission for breaches of competition law.

For instance, US companies have been fined a total of €10.91 billion by the European Commission, compared to €1.17 billion for their European counterparts:

Although this discrepancy seems to point towards protectionism, we believe that the case is not so clear-cut. The large fines paid by US firms are notably driven by a small subset of decisions in the tech sector, where the plaintiffs were also American companies. Tech markets also exhibit various features which tend to inflate the amount of fines.

Despite the plausibility of these potential alternative explanations, there may still be some legitimacy to the allegations of protectionism. The European Commission is, by design, a political body. One may thus question the extent to which Europe’s paucity of tech sector giants is driving the Commission’s ideological preference for tech-sector intervention and the protection of the industry’s small competitors.

Click here to read the full article.

Source: Benedict Evans

[N]ew combinations are, as a rule, embodied, as it were, in new firms which generally do not arise out of the old ones but start producing beside them; … in general it is not the owner of stagecoaches who builds railways. – Joseph Schumpeter, January 1934

Elizabeth Warren wants to break up the tech giants — Facebook, Google, Amazon, and Apple — claiming they have too much power and represent a danger to our democracy. As part of our response to her proposal, we shared a couple of headlines from 2007 claiming that MySpace had an unassailable monopoly in the social media market.

Tommaso Valletti, the chief economist of the Directorate-General for Competition (DG COMP) of the European Commission, said, in what we assume was a reference to our posts, “they go on and on with that single example to claim that [Facebook] and [Google] are not a problem 15 years later … That’s not what I would call an empirical regularity.”

We appreciate the invitation to show that prematurely dubbing companies “unassailable monopolies” is indeed an empirical regularity.

It’s Tough to Make Predictions, Especially About the Future of Competition in Tech

No one is immune to this phenomenon. Antitrust regulators often take a static view of competition, failing to anticipate dynamic technological forces that will upend market structure and competition.

Scientists and academics make a different kind of error. They are driven by the need to satisfy their curiosity rather than shareholders. Upon inventing a new technology or discovering a new scientific truth, academics often fail to see the commercial implications of their findings.

Maybe the titans of industry don’t make these kinds of mistakes because they have skin in the game? The profit and loss statement is certainly a merciless master. But it does not give CEOs the power of premonition. Corporate executives hailed as visionaries in one era often become blinded by their success, failing to see impending threats to their company’s core value propositions.

Furthermore, it’s often hard as outside observers to tell after the fact whether business leaders just didn’t see a tidal wave of disruption coming or, worse, they did see it coming and were unable to steer their bureaucratic, slow-moving ships to safety. Either way, the outcome is the same.

Here’s the pattern we observe over and over: extreme success in one context makes it difficult to predict how and when the next paradigm shift will occur in the market. Incumbents become less innovative as they get lulled into stagnation by high profit margins in established lines of business. (This is essentially the thesis of Clay Christensen’s The Innovator’s Dilemma).

Even if the anti-tech populists are powerless to make predictions, history does offer us some guidance about the future. We have seen time and again that apparently unassailable monopolists are quite effectively assailed by technological forces beyond their control.

PCs

Source: Horace Dediu

Jan 1977: Commodore PET released

Jun 1977: Apple II released

Aug 1977: TRS-80 released

Feb 1978: “I.B.M. Says F.T.C. Has Ended Its Typewriter Monopoly Study” (NYT)

Mobile

Source: Comscore

Mar 2000: Palm Pilot IPO’s at $53 billion

Sep 2006: “Everyone’s always asking me when Apple will come out with a cellphone. My answer is, ‘Probably never.’” – David Pogue (NYT)

Apr 2007: “There’s no chance that the iPhone is going to get any significant market share.” Ballmer (USA TODAY)

Jun 2007: iPhone released

Nov 2007: “Nokia: One Billion Customers—Can Anyone Catch the Cell Phone King?” (Forbes)

Sep 2013: “Microsoft CEO Ballmer Bids Emotional Farewell to Wall Street” (Reuters)

If there’s one thing I regret, there was a period in the early 2000s when we were so focused on what we had to do around Windows that we weren’t able to redeploy talent to the new device form factor called the phone.

Search

Source: Distilled

Mar 1998: “How Yahoo! Won the Search Wars” (Fortune)

Once upon a time, Yahoo! was an Internet search site with mediocre technology. Now it has a market cap of $2.8 billion. Some people say it’s the next America Online.

Sep 1998: Google founded

Instant Messaging

Sep 2000: “AOL Quietly Linking AIM, ICQ” (ZDNet)

AOL’s dominance of instant messaging technology, the kind of real-time e-mail that also lets users know when others are online, has emerged as a major concern of regulators scrutinizing the company’s planned merger with Time Warner Inc. (twx). Competitors to Instant Messenger, such as Microsoft Corp. (msft) and Yahoo! Inc. (yhoo), have been pressing the Federal Communications Commission to force AOL to make its services compatible with competitors’.

Dec 2000: “AOL’s Instant Messaging Monopoly?” (Wired)

Dec 2015: Report for the European Parliament

There have been isolated examples, as in the case of obligations of the merged AOL / Time Warner to make AOL Instant Messenger interoperable with competing messaging services. These obligations on AOL are widely viewed as having been a dismal failure.

Oct 2017: AOL shuts down AIM

Jan 2019: “Zuckerberg Plans to Integrate WhatsApp, Instagram and Facebook Messenger” (NYT)

Retail

Source: Seeking Alpha

May 1997: Amazon IPO

Mar 1998: American Booksellers Association files antitrust suit against Borders, B&N

Feb 2005: Amazon Prime launches

Jul 2006: “Breaking the Chain: The Antitrust Case Against Wal-Mart” (Harper’s)

Feb 2011: “Borders Files for Bankruptcy” (NYT)

Social

Feb 2004: Facebook founded

Jan 2007: “MySpace Is a Natural Monopoly” (TechNewsWorld)

Seventy percent of Yahoo 360 users, for example, also use other social networking sites — MySpace in particular. Ditto for Facebook, Windows Live Spaces and Friendster … This presents an obvious, long-term business challenge to the competitors. If they cannot build up a large base of unique users, they will always be on MySpace’s periphery.

Feb 2007: “Will Myspace Ever Lose Its Monopoly?” (Guardian)

Jun 2011: “Myspace Sold for $35m in Spectacular Fall from $12bn Heyday” (Guardian)

Music

Source: RIAA

Dec 2003: “The subscription model of buying music is bankrupt. I think you could make available the Second Coming in a subscription model, and it might not be successful.” – Steve Jobs (Rolling Stone)

Apr 2006: Spotify founded

Jul 2009: “Apple’s iPhone and iPod Monopolies Must Go” (PC World)

Jun 2015: Apple Music announced

Video

Source: OnlineMBAPrograms

Apr 2003: Netflix reaches one million subscribers for its DVD-by-mail service

Mar 2005: FTC blocks Blockbuster/Hollywood Video merger

Sep 2006: Amazon launches Prime Video

Jan 2007: Netflix streaming launches

Oct 2007: Hulu launches

May 2010: Hollywood Video’s parent company files for bankruptcy

Sep 2010: Blockbuster files for bankruptcy

The Only Winning Move Is Not to Play

Predicting the future of competition in the tech industry is such a fraught endeavor that even articles about how hard it is to make predictions include incorrect predictions. The authors just cannot help themselves. A March 2012 BBC article “The Future of Technology… Who Knows?” derided the naysayers who predicted doom for Apple’s retail store strategy. Its kicker?

And that is why when you read that the Blackberry is doomed, or that Microsoft will never make an impression on mobile phones, or that Apple will soon dominate the connected TV market, you need to take it all with a pinch of salt.

But Blackberry was doomed and Microsoft never made an impression on mobile phones. (Half credit for Apple TV, which currently has a 15% market share).

Nobel Prize-winning economist Paul Krugman wrote a piece for Red Herring magazine (seriously) in June 1998 with the title “Why most economists’ predictions are wrong.” Headline-be-damned, near the end of the article he made the following prediction:

The growth of the Internet will slow drastically, as the flaw in “Metcalfe’s law”—which states that the number of potential connections in a network is proportional to the square of the number of participants—becomes apparent: most people have nothing to say to each other! By 2005 or so, it will become clear that the Internet’s impact on the economy has been no greater than the fax machine’s.

Robert Metcalfe himself predicted in a 1995 column that the Internet would “go spectacularly supernova and in 1996 catastrophically collapse.” After pledging to “eat his words” if the prediction did not come true, “in front of an audience, he put that particular column into a blender, poured in some water, and proceeded to eat the resulting frappe with a spoon.”

A Change Is Gonna Come

Benedict Evans, a venture capitalist at Andreessen Horowitz, has the best summary of why competition in tech is especially difficult to predict:

IBM, Microsoft and Nokia were not beaten by companies doing what they did, but better. They were beaten by companies that moved the playing field and made their core competitive assets irrelevant. The same will apply to Facebook (and Google, Amazon and Apple).

Elsewhere, Evans tried to reassure his audience that we will not be stuck with the current crop of tech giants forever:

With each cycle in tech, companies find ways to build a moat and make a monopoly. Then people look at the moat and think it’s invulnerable. They’re generally right. IBM still dominates mainframes and Microsoft still dominates PC operating systems and productivity software. But… It’s not that someone works out how to cross the moat. It’s that the castle becomes irrelevant. IBM didn’t lose mainframes and Microsoft didn’t lose PC operating systems. Instead, those stopped being ways to dominate tech. PCs made IBM just another big tech company. Mobile and the web made Microsoft just another big tech company. This will happen to Google or Amazon as well. Unless you think tech progress is over and there’ll be no more cycles … It is deeply counter-intuitive to say ‘something we cannot predict is certain to happen’. But this is nonetheless what’s happened to overturn pretty much every tech monopoly so far.

If this time is different — or if there are more false negatives than false positives in the monopoly prediction game — then the advocates for breaking up Big Tech should try to make that argument instead of falling back on “big is bad” rhetoric. As for us, we’ll bet that we have not yet reached the end of history — tech progress is far from over.

 

Will the merger between T-Mobile and Sprint make consumers better or worse off? A central question in the review of this merger—as it is in all merger reviews—is the likely effects that the transaction will have on consumers. In this post, we look at one study that opponents of the merger have been using to support their claim that the merger will harm consumers.

Along with my earlier posts on data problems and public policy (1, 2, 3, 4, 5), this provides an opportunity to explore why seemingly compelling studies can be used to muddy the discussion and fool observers into seeing something that isn’t there.

This merger—between the third and fourth largest mobile wireless providers in the United States—has been characterized as a “4-to-3” merger, on the grounds that it will reduce the number of large, ostensibly national carriers from four to three. This, in turn, has led to concerns that further concentration in the wireless telecommunications industry will harm consumers. Specifically, some opponents of the merger claim that “it’s going to be hard for someone to make a persuasive case that reducing four firms to three is actually going to improve competition for the benefit of American consumers.”

A number of previous mergers around the world can or have also been characterized as 4-to-3 mergers in the wireless telecommunications industry. Several econometric studies have attempted to evaluate the welfare effects of 4-to-3 mergers in other countries, as well as the effects of market concentration in the wireless industry more generally. These studies have been used by both proponents and opponents of the proposed merger of T-Mobile and Sprint to support their respective contentions that the merger will benefit or harm consumer welfare.

One particular study has risen to prominence among opponents of 4-to-3 mergers in telecom in general and the T-Mobile/Sprint merger in specific. This is worrying because the study has several fundamental flaws. 

This study, by Finnish consultancy Rewheel, has been cited by, among others, Phillip Berenbroick of Public Knowledge, who in Senate testimony, asserted that “Rewheel found that consumers in markets with three facilities-based providers paid twice as much per gigabyte as consumers in four firm markets.”

The Rewheel report upon which Mr. Berenbroick relied, is, however, marred by a number of significant flaws, which undermine its usefulness.

The Rewheel report

Rewheel’s report purports to analyze the state of 4G pricing across 41 countries that are either members of the EU or the OECD or both. The report’s conclusions are based mainly on two measures:

  1. Estimates of the maximum number of gigabytes available under each plan for a specific hypothetical monthly price, ranging from €5 to €80 a month. In other words, for each plan, Rewheel asks, “How many 4G gigabytes would X euros buy?” Rewheel then ranks countries by the median amount of gigabytes available at each hypothetical price for all the plans surveyed in each country.
  2. Estimates of what Rewheel describes as “fully allocated gigabyte prices.” This is the monthly retail price (including VAT) divided by the number of gigabytes included in each plan. Rewheel then ranks countries by the median price per gigabyte across all the plans surveyed in each country.

Rewheel’s convoluted calculations

Rewheel’s use of the country median across all plans is problematic. In particular it gives all plans equal weight, regardless of consumers’ use of each plan. For example, a plan targeted for a consumer with a “high” level of usage is included with a plan targeted for a consumer with a “low” level of usage. Even though a “high” user would not purchase a “low” plan (which would be relatively expensive for a “high” user), all plans are included, thereby skewing upward the median estimates.

But even if that approach made sense as a way of measuring consumers’ willingness to pay, in execution Rewheel’s analysis contains the following key defects:

  • The Rewheel report is essentially limited to quantity effects alone (i.e., how many gigabytes available under each plan for a given hypothetical price) or price effects alone (i.e., price per included gigabyte for each plan). These measures can mislead the analysis by missing, among other things, innovation and quality effects.
  • Rewheel’s analysis is not based on an impartial assessment of relevant price data. Rather, it is based on hypothetical measures. Such comparisons say nothing about the plans actually chosen by consumers or the actual prices paid by consumers in those countries, rendering Rewheel’s comparisons virtually meaningless. As Affeldt & Nitsche (2014) note in their assessment of the effects of concentration in mobile telecom markets:

Such approaches are taken by Rewheel (2013) and also the Austrian regulator rtr (when tracking prices over time, see rtr (2014)). Such studies face the following problems: They may pick tariffs that are relatively meaningless in the country. They will have to assume one or more consumption baskets (voice minutes, data volume etc.) in order to compare tariffs. This may drive results. Apart from these difficulties such comparisons require very careful tracking of tariffs and their changes. Even if one assumes studying a sample of tariffs is potentially meaningful, a comparison across countries (or over time) would still require taking into account key differences across countries (or over time) like differences in demand, costs, network quality etc.

  • The Rewheel report bases its comparison on dissimilar service levels by not taking into account, for instance, relevant features like comparable network capacity, service security, and, perhaps most important, overall quality of service.

Rewheel’s unsupported conclusions

Rewheel uses its analysis to come to some strong conclusions, such as the conclusion on the first page of its report declaring the median gigabyte price in countries with three carriers is twice as high as in countries with four carriers.

The figure below is a revised version of the figure on the first page of Rewheel’s report. The yellow blocks (gray dots) show the range of prices in countries with three carriers the blue blocks (pink dots) shows the range of prices in countries with four carriers. The darker blocks show the overlap of the two. The figure makes clear that there is substantial overlap in pricing among three and four carrier countries. Thus, it is not obvious that three carrier countries have significantly higher prices (as measured by Rewheel) than four carrier countries.

Rewheel

A simple “eyeballing” of the data can lead to incorrect conclusions, in which case statistical analysis can provide some more certainty (or, at least, some measure of uncertainty). Yet, Rewheel provides no statistical analysis of its calculations, such as measures of statistical significance. However, information on page 5 of the Rewheel report can be used to perform some rudimentary statistical analysis.

I took the information from the columns for hypothetical monthly prices of €30 a month and €50 a month, and converted data into a price per gigabyte to generate the dependent variable. Following Rewheel’s assumption, “unlimited” is converted to 250 gigabytes per month. Greece was dropped from the analysis because Rewheel indicates that no data is available at either hypothetical price level.

My rudimentary statistical analysis includes the following independent variables:

  • Number of carriers (or mobile network operators, MNOs) reported by Rewheel in each country, ranging from three to five. Israel is the only country with five MNOs.
  • A dummy variable for EU28 countries. Rewheel performs separate analysis for EU28 countries, suggesting they think this is an important distinction.
  • GDP per capita for each country, adjusted for purchasing power parity. Several articles in the literature suggest higher GDP countries would be expected to have higher wireless prices.
  • Population density, measured by persons per square kilometer. Several articles in the literature argue that countries with lower population density would have higher costs of providing wireless service which would, in turn, be reflected in higher prices.

The tables below confirm what an eyeballing of the figure suggest: Rewheel’s data show number of MNOs in a country have no statistically significant relationship with price per gigabyte, at either the €30 a month level or the €50 a month level.

RewheelRegression

While the signs on the MNO coefficient are negative (i.e., more carriers in a country is associated with lower prices), they are not statistically significantly different from zero at any of the traditional levels of statistical significance.

Also, the regressions suffer from relatively low measures of goodness-of-fit. The independent variables in the regression explain approximately five percent of the variation in the price per gigabyte. This is likely because of the cockamamie way Rewheel measures price, but is also due to the known problems with performing cross-sectional analysis of wireless pricing, as noted by Csorba & Pápai (2015):

Many regulatory policies are based on a comparison of prices between European countries, but these simple cross-sectional analyses can lead to misleading conclusions because of at least two reasons. First, the price difference between countries of n and (n + 1) active mobile operators can be due to other factors, and the analyst can never be sure of having solved the omitted variable bias problem. Second and more importantly, the effect of an additional operator estimated from a cross-sectional comparison cannot be equated with the effect of an actual entry that might have a long-lasting effect on a single market.

The Rewheel report cannot be relied upon in assessing consumer benefits or harm associated with the T-Mobile/Sprint merger, or any other merger

Rewheel apparently has a rich dataset of wireless pricing plans. Nevertheless, the analyses presented in its report are fundamentally flawed. Moreover, Rewheel’s conclusions regarding three vs. four carrier countries are not only baseless, but clearly unsupported by closer inspection of the information presented in its report. The Rewheel report cannot be relied upon to inform regulatory oversight of the T-Mobile/Spring merger or any other. This study isn’t unique and it should serve as a caution to be wary of studies that merely eyeball information.

Near the end of her new proposal to break up Facebook, Google, Amazon, and Apple, Senator Warren asks, “So what would the Internet look like after all these reforms?”

It’s a good question, because, as she herself notes, “Twenty-five years ago, Facebook, Google, and Amazon didn’t exist. Now they are among the most valuable and well-known companies in the world.”

To Warren, our most dynamic and innovative companies constitute a problem that needs solving.

She described the details of that solution in a blog post:

First, [my administration would restore competition to the tech sector] by passing legislation that requires large tech platforms to be designated as “Platform Utilities” and broken apart from any participant on that platform.

* * *

For smaller companies…, their platform utilities would be required to meet the same standard of fair, reasonable, and nondiscriminatory dealing with users, but would not be required to structurally separate….

* * *
Second, my administration would appoint regulators committed to reversing illegal and anti-competitive tech mergers….
I will appoint regulators who are committed to… unwind[ing] anti-competitive mergers, including:

– Amazon: Whole Foods; Zappos;
– Facebook: WhatsApp; Instagram;
– Google: Waze; Nest; DoubleClick

Elizabeth Warren’s brave new world

Let’s consider for a moment what this brave new world will look like — not the nirvana imagined by regulators and legislators who believe that decimating a company’s business model will deter only the “bad” aspects of the model while preserving the “good,” as if by magic, but the inevitable reality of antitrust populism.  

Utilities? Are you kidding? For an overview of what the future of tech would look like under Warren’s “Platform Utility” policy, take a look at your water, electricity, and sewage service. Have you noticed any improvement (or reduction in cost) in those services over the past 10 or 15 years? How about the roads? Amtrak? Platform businesses operating under a similar regulatory regime would also similarly stagnate. Enforcing platform “neutrality” necessarily requires meddling in the most minute of business decisions, inevitably creating unintended and costly consequences along the way.

Network companies, like all businesses, differentiate themselves by offering unique bundles of services to customers. By definition, this means vertically integrating with some product markets and not others. Why are digital assistants like Siri bundled into mobile operating systems? Why aren’t the vast majority of third-party apps also bundled into the OS? If you want utilities regulators instead of Google or Apple engineers and designers making these decisions on the margin, then Warren’s “Platform Utility” policy is the way to go.

Grocery Stores. To take one specific case cited by Warren, how much innovation was there in the grocery store industry before Amazon bought Whole Foods? Since the acquisition, large grocery retailers, like Walmart and Kroger, have increased their investment in online services to better compete with the e-commerce champion. Many industry analysts expect grocery stores to use computer vision technology and artificial intelligence to improve the efficiency of check-out in the near future.

Smartphones. Imagine how forced neutrality would play out in the context of iPhones. If Apple can’t sell its own apps, it also can’t pre-install its own apps. A brand new iPhone with no apps — and even more importantly, no App Store — would be, well, just a phone, out of the box. How would users even access a site or app store from which to download independent apps? Would Apple be allowed to pre-install someone else’s apps? That’s discriminatory, too. Maybe it will be forced to offer a menu of all available apps in all categories (like the famously useless browser ballot screen demanded by the European Commission in its Microsoft antitrust case)? It’s hard to see how that benefits consumers — or even app developers.

Source: Free Software Magazine

Internet Search. Or take search. Calls for “search neutrality” have been bandied about for years. But most proponents of search neutrality fail to recognize that all Google’s search results entail bias in favor of its own offerings. As Geoff Manne and Josh Wright noted in 2011 at the height of the search neutrality debate:

[S]earch engines offer up results in the form not only of typical text results, but also maps, travel information, product pages, books, social media and more. To the extent that alleged bias turns on a search engine favoring its own maps, for example, over another firm’s, the allegation fails to appreciate that text results and maps are variants of the same thing, and efforts to restrain a search engine from offering its own maps is no different than preventing it from offering its own search results.

Nevermind that Google with forced non-discrimination likely means Google offering only the antiquated “ten blue links” search results page it started with in 1998 instead of the far more useful “rich” results it offers today; logically it would also mean Google somehow offering the set of links produced by any and all other search engines’ algorithms, in lieu of its own. If you think Google will continue to invest in and maintain the wealth of services it offers today on the strength of the profits derived from those search results, well, Elizabeth Warren is probably already your favorite politician.

Source: Web Design Museum  

And regulatory oversight of algorithmic content won’t just result in an impoverished digital experience; it will inevitably lead to an authoritarian one, as well:

Any agency granted a mandate to undertake such algorithmic oversight, and override or reconfigure the product of online services, thereby controls the content consumers may access…. This sort of control is deeply problematic… [because it saddles users] with a pervasive set of speech controls promulgated by the government. The history of such state censorship is one which has demonstrated strong harms to both social welfare and rule of law, and should not be emulated.

Digital Assistants. Consider also the veritable cage match among the tech giants to offer “digital assistants” and “smart home” devices with ever-more features at ever-lower prices. Today the allegedly non-existent competition among these companies is played out most visibly in this multi-featured market, comprising advanced devices tightly integrated with artificial intelligence, voice recognition, advanced algorithms, and a host of services. Under Warren’s nondiscrimination principle this market disappears. Each device can offer only a connectivity platform (if such a service is even permitted to be bundled with a physical device…) — and nothing more.

But such a world entails not only the end of an entire, promising avenue of consumer-benefiting innovation, it also entails the end of a promising avenue of consumer-benefiting competition. It beggars belief that anyone thinks consumers would benefit by forcing technology companies into their own silos, ensuring that the most powerful sources of competition for each other are confined to their own fiefdoms by order of law.

Breaking business models

Beyond the product-feature dimension, Sen. Warren’s proposal would be devastating for innovative business models. Why is Amazon Prime Video bundled with free shipping? Because the marginal cost of distribution for video is close to zero and bundling it with Amazon Prime increases the value proposition for customers. Why is almost every Google service free to users? Because Google’s business model is supported by ads, not monthly subscription fees. Each of the tech giants has carefully constructed an ecosystem in which every component reinforces the others. Sen. Warren’s plan would not only break up the companies, it would prohibit their business models — the ones that both created and continue to sustain these products. Such an outcome would manifestly harm consumers.

Both of Warren’s policy “solutions” are misguided and will lead to higher prices and less innovation. Her cause for alarm is built on a multitude of mistaken assumptions, but let’s address just a few (Warren in bold):

  • “Nearly half of all e-commerce goes through Amazon.” Yes, but it has only 5% of total retail in the United States. As my colleague Kristian Stout says, “the Internet is not a market; it’s a distribution channel.”
  • “Amazon has used its immense market power to force smaller competitors like Diapers.com to sell at a discounted rate.” The real story, as the founders of Diapers.com freely admitted, is that they sold diapers as what they hoped would be a loss leader, intending to build out sales of other products once they had a base of loyal customers:

And so we started with selling the loss leader product to basically build a relationship with mom. And once they had the passion for the brand and they were shopping with us on a weekly or a monthly basis that they’d start to fall in love with that brand. We were losing money on every box of diapers that we sold. We weren’t able to buy direct from the manufacturers.

Like all entrepreneurs, Diapers.com’s founders took a calculated risk that didn’t pay off as hoped. Amazon subsequently acquired the company (after it had declined a similar buyout offer from Walmart). (Antitrust laws protect consumers, not inefficient competitors). And no, this was not a case of predatory pricing. After many years of trying to make the business profitable as a subsidiary, Amazon shut it down in 2017.

  • “In the 1990s, Microsoft — the tech giant of its time — was trying to parlay its dominance in computer operating systems into dominance in the new area of web browsing. The federal government sued Microsoft for violating anti-monopoly laws and eventually reached a settlement. The government’s antitrust case against Microsoft helped clear a path for Internet companies like Google and Facebook to emerge.” The government’s settlement with Microsoft is not the reason Google and Facebook were able to emerge. Neither company entered the browser market at launch. Instead, they leapfrogged the browser entirely and created new platforms for the web (only later did Google create Chrome).

    Furthermore, if the Microsoft case is responsible for “clearing a path” for Google is it not also responsible for clearing a path for Google’s alleged depredations? If the answer is that antitrust enforcement should be consistently more aggressive in order to rein in Google, too, when it gets out of line, then how can we be sure that that same more-aggressive enforcement standard wouldn’t have curtailed the extent of the Microsoft ecosystem in which it was profitable for Google to become Google? Warren implicitly assumes that only the enforcement decision in Microsoft was relevant to Google’s rise. But Microsoft doesn’t exist in a vacuum. If Microsoft cleared a path for Google, so did every decision not to intervene, which, all combined, created the legal, business, and economic environment in which Google operates.

Warren characterizes Big Tech as a weight on the American economy. In fact, nothing could be further from the truth. These superstar companies are the drivers of productivity growth, all ranking at or near the top for most spending on research and development. And while data may not be the new oil, extracting value from it may require similar levels of capital expenditure. Last year, Big Tech spent as much or more on capex as the world’s largest oil companies:

Source: WSJ

Warren also faults Big Tech for a decline in startups, saying,

The number of tech startups has slumped, there are fewer high-growth young firms typical of the tech industry, and first financing rounds for tech startups have declined 22% since 2012.

But this trend predates the existence of the companies she criticizes, as this chart from Quartz shows:

The exact causes of the decline in business dynamism are still uncertain, but recent research points to a much more mundane explanation: demographics. Labor force growth has been declining, which has led to an increase in average firm age, nudging fewer workers to start their own businesses.

Furthermore, it’s not at all clear whether this is actually a decline in business dynamism, or merely a change in business model. We would expect to see the same pattern, for example, if would-be startup founders were designing their software for acquisition and further development within larger, better-funded enterprises.

Will Rinehart recently looked at the literature to determine whether there is indeed a “kill zone” for startups around Big Tech incumbents. One paper finds that “an increase in fixed costs explains most of the decline in the aggregate entrepreneurship rate.” Another shows an inverse correlation across 50 countries between GDP and entrepreneurship rates. Robert Lucas predicted these trends back in 1978, pointing out that productivity increases would lead to wage increases, pushing marginal entrepreneurs out of startups and into big companies.

It’s notable that many in the venture capital community would rather not have Sen. Warren’s “help”:

Arguably, it is also simply getting harder to innovate. As economists Nick Bloom, Chad Jones, John Van Reenen and Michael Webb argue,

just to sustain constant growth in GDP per person, the U.S. must double the amount of research effort searching for new ideas every 13 years to offset the increased difficulty of finding new ideas.

If this assessment is correct, it may well be that coming up with productive and profitable innovations is simply becoming more expensive, and thus, at the margin, each dollar of venture capital can fund less of it. Ironically, this also implies that larger firms, which can better afford the additional resources required to sustain exponential growth, are a crucial part of the solution, not the problem.

Warren believes that Big Tech is the cause of our social ills. But Americans have more trust in Amazon, Facebook, and Google than in the political institutions that would break them up. It would be wise for her to reflect on why that might be the case. By punishing our most valuable companies for past successes, Warren would chill competition and decrease returns to innovation.

Finally, in what can only be described as tragic irony, the most prominent political figure who shares Warren’s feelings on Big Tech is President Trump. Confirming the horseshoe theory of politics, far-left populism and far-right populism seem less distinguishable by the day. As our colleague Gus Hurwitz put it, with this proposal Warren is explicitly endorsing the unitary executive theory and implicitly endorsing Trump’s authority to direct his DOJ to “investigate specific cases and reach specific outcomes.” Which cases will he want to have investigated and what outcomes will he be seeking? More good questions that Senator Warren should be asking. The notion that competition, consumer welfare, and growth are likely to increase in such an environment is farcical.

The German Bundeskartellamt’s Facebook decision is unsound from either a competition or privacy policy perspective, and will only make the fraught privacy/antitrust relationship worse.

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A recent working paper by Hashmat Khan and Matthew Strathearn attempts to empirically link anticompetitive collusion to the boom and bust cycles of the economy.

The level of collusion is higher during a boom relative to a recession as collusion occurs more frequently when demand is increasing (entering into a collusive arrangement is more profitable and deviating from an existing cartel is less profitable). The model predicts that the number of discovered cartels and hence antitrust filings should be procyclical because the level of collusion is procyclical.

The first sentence—a hypothesis that collusion is more likely during a “boom” than in recession—seems reasonable. At the same time, a case can be made that collusion would be more likely during recession. For example, a reduced risk of entry from competitors would reduce the cost of collusion.

The second sentence, however, seems a stretch. Mainly because it doesn’t recognize the time delay between the collusive activity, the date the collusion is discovered by authorities, and the date the case is filed.

Perhaps, more importantly, it doesn’t acknowledge that many collusive arrangement span months, if not years. That span of time could include times of “boom” and times of recession. Thus, it can be argued that the date of the filing has little (or nothing) to do with the span over which the collusive activity occurred.

I did a very lazy man’s test of my criticisms. I looked at six of the filings cited by Khan and Strathearn for the year 2011, a “boom” year with a high number of horizontal price fixing cases filed.

khanstrathearn

My first suspicion was correct. In these six cases, an average of more than three years passed from the date of the last collusive activity and the date the case was filed. Thus, whether the economy is a boom or bust when the case is filed provides no useful information regarding the state of the economy when the collusion occurred.

Nevertheless, my lazy man’s small sample test provides some interesting—and I hope useful—information regarding Khan and Strathearn’s conclusions.

  1. From July 2001 through September 2009, 24 of the 99 months were in recession. In other words, during this period, there was a 24 percent chance the economy was in recession in any given month.
  2. Five of the six collusive arrangements began when the economy was in recovery. Only one began during a recession. This may seem to support their conclusion that collusive activity is more likely during a recovery. However, even if the arrangements began randomly, there would be a 55 percent chance that that five or more began during a recovery. So, you can’t read too much into the observation that most of the collusive agreements began during a “boom.”
  3. In two of the cases, the collusive activity occurred during a span of time that had no recession. The chances of this happening randomly is less than 1 in 20,000, supporting their conclusion regarding collusive activity and the business cycle.

Khan and Strathearn fall short in linking collusive activity to the business cycle but do a good job of linking antitrust enforcement activities to the business cycle. The information they use from the DOJ website is sufficient to determine when the collusive activity occurred—but it’ll take more vigorous “scrubbing” (their word) of the site to get the relevant data.

The bigger question, however, is the relevance of this research. Naturally, one could argue this line of research indicates that competition authorities should be extra vigilant during a booming economy. Yet, Adam Smith famously noted, “People of the same trade seldom meet together, even for merriment and diversion, but the conversation ends in a conspiracy against the public, or in some contrivance to raise prices.” This suggests that collusive activity—or the temptation to engage in such activity—is always and everywhere present, regardless of the business cycle.

 

A recent NBER working paper by Gutiérrez & Philippon has attracted attention from observers who see oligopoly everywhere and activists who want governments to more actively “manage” competition. The analysis in the paper is fundamentally flawed and should not be relied upon by policymakers, regulators, or anyone else.

As noted in my earlier post, Gutiérrez & Philippon attempt to craft a causal linkage between differences in U.S. and EU antitrust enforcement and product market regulation to differences in market concentration and corporate profits. Their paper’s abstract leads with a bold assertion:

Until the 1990’s, US markets were more competitive than European markets. Today, European markets have lower concentration, lower excess profits, and lower regulatory barriers to entry.

This post focuses on Gutiérrez & Philippon’s claim that EU markets have lower “excess profits.” This is perhaps the most outrageous claim in the paper. If anyone bothers to read the full paper, they’ll see that claims that EU firms have lower excess profits is simply not supported by the paper itself. Aside from a passing mention of someone else’s work in a footnote, the only mention of “excess profits” is in the paper’s headline-grabbing abstract.

What’s even more outrageous is the authors don’t define (or even describe) what they mean by excess profits.

These two factors alone should be enough to toss aside the paper’s assertion about “excess” profits. But, there’s more.

Gutiérrez & Philippon define profit to be gross operating surplus and mixed income (known as “GOPS” in the OECD’s STAN Industrial Analysis dataset). GOPS is not the same thing as gross margin or gross profit as used in business and finance (for example GOPS subtracts wages, but gross margin does not). The EU defines GOPS as (emphasis added):

Operating surplus is the surplus (or deficit) on production activities before account has been taken of the interest, rents or charges paid or received for the use of assets. Mixed income is the remuneration for the work carried out by the owner (or by members of his family) of an unincorporated enterprise. This is referred to as ‘mixed income’ since it cannot be distinguished from the entrepreneurial profit of the owner.

Here’s Figure 1 from Gutiérrez & Philippon plotting GOPS as a share of gross output.

Fig1-GutierrezPhilippon

Look at the huge jump in gross operating surplus for U.S. firms!

Now, look at the scale of the y-axis. Not such a big jump after all.

Over 23 years, from 1992 to 2015, the gross operating surplus rate for U.S. firms grew by 2.5 percentage points. In the EU, the rate increased by about one percentage point.

Using the STAN dataset, I plotted the gross operating surplus rate for each EU country (blue dots) and the U.S. (red dots), along with a time trend. Three takeaways:

  1. There’s not much of a difference between the U.S. and the EU average—they both hover around a gross operating surplus rate of about 19.5 percent; and
  2. There’s a huge variation in gross operating surplus rate across EU countries.
  3. Yes, gross operating surplus is trending slightly upward in the U.S. and slightly downward for the EU average, but there doesn’t appear to be a huge difference in the slope of the trendlines. In fact the slopes of the trendlines are not statistically significantly different from zero and are not statistically significantly different from each other.

GOPSprod

The use of gross profits raises some serious questions. For example, the Stigler Center’s James Traina finds that, after accounting for selling, general, and administrative expenses (SG&A), mark-ups for publicly traded firms in the U.S. have not meaningfully increased since 1980.

The figure below plots net operating surplus (NOPS equals GOPS minus consumption of fixed capital)—which is not the same thing as net income for a business.

Same three takeaways:

  1. There’s not much of a difference between the U.S. and the EU average—they both hover around a net operating surplus rate of a little more than seven percent; and
  2. There’s a huge variation in net operating surplus rate across EU countries.
  3. The slope of the trendlines for net operating surplus in the U.S. and EU are not statistically significantly different from zero and are not statistically significantly different from each other.

NOPSprod

It’s very possible that U.S. firms are achieving higher and growing “excess” profits relative to EU firms. It’s also very possible they’re not. Despite the bold assertions of Gutiérrez & Philippon, the information presented in their paper provides no useful information one way or the other.

 

REGISTER HERE for the much-anticipated 2018 ICLE/Leeds competition law conference, this Friday and Saturday in Washington, DC.

NB: We’ve been approved for 8 credit hours of VA MCLE

The conference agenda is below. We hope to see you there!

ICLE/Leeds 2018 Competition Law Conference: Have We Exceeded the Limits of Antirust?
Agenda Day 1
Agenda Day 2