"Is the Digital Economy Much Less Competitive Than We Think It Is?"

From Chicago Booth's Stigler Center blog, Sept. 23:

Maurice E. Stucke from the University of Tennessee Knoxville and Ariel Ezrachi of Oxford University explain how big data and artificial intelligence can be used to facilitate collusion and potentially harm consumers. The first part of a two-part interview.

The Economist devoted its cover page last week to a remarkable project on the dark side of the superstar economy: namely, the rapid rise in concentration and anti-competitive conduct, particularly in the US.

The tech sector, the magazine noted, is at the forefront of this rise in concentration. Once a harbinger of a new kind of capitalism, Silicon Valley has since become a place where “a handful of winner-takes-most companies have taken over the world’s most vibrant innovation centre, while the region’s (admittedly numerous) startups compete to provide the big league with services or, if they are lucky, with their next acquisition.” 

Powerful tech companies, it warned, have used economies of scale to become giants, shifting their focus “from the supply side (production efficiencies) to the demand side (network effects).” “The superstars are admirable in many ways,” the magazine cautioned, “but they have two big faults. They are squashing competition, and they are using the darker arts of management to stay ahead.”

  

A lot of attention has been devoted in recent years to the growing monopolization of online markets like search, web advertising, mobile operating systems, and online retail. Google is currently facing three antitrust cases against it in Europe—where it holds a 90 percent market share in online search—after being accused by the European Commission of abusing its dominance in mobile and search to favor its own products over rival services. 

What if this isn’t the case of a single company misbehaving but a fundamental and lasting change in the way competition works in the digital economy? In a series of papers from the last two years, Maurice E. Stucke from the University of Tennessee Knoxville and Ariel Ezrachi of the University of Oxford argue that in the world of big data and artificial intelligence, network effects can raise barriers to entry, enabling big platforms to engage in behaviors such as collusion, tacit collusion, and price discrimination, to the detriment of consumers.

In the first part of a two-part interview with ProMarket, Stucke—a former antitrust prosecutor at the Department of Justice—and Ezrachi elaborate on the changing dynamics of what they call the “digitized hand” and explain how the market may in fact appear to be more competitive than it really is.

Q: In your recent papers, you argue that the digital economy, which is typically thought of as innovative and highly competitive, is in fact a lot less competitive than we typically assume it to be. Can you explain?

Ariel Ezrachi: The Internet, big data and big analytics, provide us with extremely valuable benefits that often promote a competitive online environment. This is achieved through the increase in number of sellers, the availability of information, improved market transparency, reduced barriers to entry, etc. However, we cannot uncritically assume that we will always benefit. When we critically examine the complex algorithm-driven environment, we can witness imperfections that result in the new market realities being less competitive than one would expect.

In many ways, the new market dynamic might have the characteristics of competition as we know it. But it’s a much more complex environment, in which the invisible hand that we all rely upon has been pushed aside by what we refer to as the “digitized hand.” This hand is controlled by corporations and can be manipulated. It has the capacity to be selective, to generate different levels of competitive pressures on the players, and that results in an environment that operates using different rules from the ones we assume in the theoretical models.

Q: In your upcoming book Virtual Competition, you make the case that big data, algorithms, and artificial intelligence can all be used to potentially harm competition and consumers. How so? 

AE: We identify three main areas of harm–collusion, behavioral discrimination and frenemy dynamic.

The first–collusion–includes both express or tacit collusion through algorithms. As pricing shifts from humans to computers, so too will the types of collusion in which companies may engage. Take for example the possibility that as part of dynamic pricing, smart algorithms with artificial intelligence are used to monitor the market and stabilize price competition. Under certain market conditions, each algorithm can adopt a strategy which fosters interdependence between operators – following price increases by competitors and punishing deviations from the new equilibrium.

Another collusive example concerns the possible use of a single algorithm by numerous competitors to establish a hub-and-spoke alignment of price. To illustrate, consider the use of a single pricing algorithm by Uber and other similar ride providers. To clarify, we have nothing against Uber.

But we use Uber to illustrate how a hub-and-spoke cartel can develop over time. Here you have independent drivers, all of whom rely on a single algorithm to determine the fare. Moreover when Uber’s algorithm decides, perhaps because it’s raining, that there is a lack of supply, it then determines to raise prices for a specific time period and area. The Uber drivers cannot discount from this algorithm-determined price. As Uber’s market power increases, and as more drivers in the market use the same algorithm, you’re likely to witness an alignment of pricing across the industry.

Our second theory of harm concerns behavioral discrimination, which differs from price discrimination in several important respects. The strategy involves firms harvesting our personal data to identify which emotion (or bias) will prompt us to buy a product, and what’s the most we are willing to pay.  Here sellers track us and collect data about us in order to tailor their advertising and marketing to target us at critical moments with the right price and emotional pitch. So behavioral discrimination increases profits by increasing overall consumption (by shifting the demand curve to the right and price discriminating) and reducing consumer surplus.

Our third theory of harm concerns the unique “frenemy” dynamic between the “super-platforms” and independent apps. A relationship of both competition and cooperation exists between the super-platforms and independent apps. One example involves the operating systems for mobile phones. Two super-platforms—Apple’s iOS and Google’s Android mobile software platforms—dominate. Each super-platform, like a coral reef, attracts to its ecosystem software developers, apps, and accessory makers.

One anticompetitive risk is when the frenemies cooperate to extract data from individuals and promote asymmetrical information flows to foster behavioral exploitation, while simultaneously competing among themselves over the consumer surplus. Another risk is when the super-platforms, as the gatekeepers, can exclude or hinder the independent apps. When the super-platform vertically integrates, its incentives can change. It can engage in unfair practices to favor its own app over rival apps. We see these issues currently in Europe, where there are already three Statements of Objections against Google.

Within that dynamic, perhaps the next frontier will be how those super-platforms will actually control the interface. As internet search is changing and with the rise of digital personal assistants, we are distancing ourselves from the junctions of decision-making and basically putting our trust in those platforms.

Q: What role do network effects play in the ability of super-platforms to subvert competition?

Maurice Stucke: They can play a significant role. Some argue that Big Data does not lend itself to entry barriers. Others go even further. They claim that most online markets are notable for their low entry barriers. If one used the traditional factors for assessing entry barriers, one might agree. Many online industries are dynamic and fast-growing. Data-driven mergers often involve free products, where customers seemingly are not locked-in. Consumers could easily switch to other free products or services. Finally launching a competing app may not require a lot of time and investment. And the requisite technology to enter may be standardized.

Take a look at search engines, like Google, Bing, Yahoo!, and DuckDuckGo. They are free and easy to use. Users can easily switch from one search engine to another. Seemingly users are not locked-in by any data portability issues. Moreover, search engines do not display the classic direct network effects that the courts and agencies have identified. So under antitrust’s traditional factors, the entry barriers appear low, obviating the need for antitrust intervention.

In Big Data and Competition Policy, Allen Grunes and I identify four different types of network effects that can be at play in these online markets. We want to be careful here: these network effects are not necessarily bad. They can be actually quite good and benefit consumers with higher quality products and services. But the data-driven network effects also have the potential to raise entry barriers and enable the big firms to become even bigger, until they dominate the industry....MUCH MORE 

HT: Economist's View