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How Uber uses innovative management tactics to incentivize its drivers: A critical commentary on Noam Scheiber’s “How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons”

Geoffrey Manne —  5 April 2017

In a recent long-form article in the New York Times, reporter Noam Scheiber set out to detail some of the ways Uber (and similar companies, but mainly Uber) are engaged in “an extraordinary experiment in behavioral science to subtly entice an independent work force to maximize its growth.”

That characterization seems innocuous enough, but it is apparent early on that Scheiber’s aim is not only to inform but also, if not primarily, to deride these efforts. The title of the piece, in fact, sets the tone:

How Uber Uses Psychological Tricks to Push Its Drivers’ Buttons

Uber and its relationship with its drivers are variously described by Scheiber in the piece as secretive, coercive, manipulative, dominating, and exploitative, among other things. As Schreiber describes his article, it sets out to reveal how

even as Uber talks up its determination to treat drivers more humanely, it is engaged in an extraordinary behind-the-scenes experiment in behavioral science to manipulate them in the service of its corporate growth — an effort whose dimensions became evident in interviews with several dozen current and former Uber officials, drivers and social scientists, as well as a review of behavioral research.

What’s so galling about the piece is that, if you strip away the biased and frequently misguided framing, it presents a truly engaging picture of some of the ways that Uber sets about solving a massively complex optimization problem, abetted by significant agency costs.

So I did. Strip away the detritus, add essential (but omitted) context, and edit the article to fix the anti-Uber bias, the one-sided presentation, the mischaracterizations, and the fundamentally non-economic presentation of what is, at its core, a fascinating illustration of some basic problems (and solutions) from industrial organization economics. (For what it’s worth, Scheiber should know better. After all, “He holds a master’s degree in economics from the University of Oxford, where he was a Rhodes Scholar, and undergraduate degrees in math and economics from Tulane University.”)

In my retelling, the title becomes:

How Uber Uses Innovative Management Tactics to Incentivize Its Drivers

My transformed version of the piece, with critical commentary in the form of tracked changes to the original, is here (pdf).

It’s a long (and, as I said, fundamentally interesting) piece, with cool interactive graphics, well worth the read (well, at least in my retelling, IMHO). Below is just a taste of the edits and commentary I added.

For example, where Scheiber writes:

Uber exists in a kind of legal and ethical purgatory, however. Because its drivers are independent contractors, they lack most of the protections associated with employment. By mastering their workers’ mental circuitry, Uber and the like may be taking the economy back toward a pre-New Deal era when businesses had enormous power over workers and few checks on their ability to exploit it.

With my commentary (here integrated into final form rather than tracked), that paragraph becomes:

Uber operates under a different set of legal constraints, however, also duly enacted and under which millions of workers have profitably worked for decades. Because its drivers are independent contractors, they receive their compensation largely in dollars rather than government-mandated “benefits” that remove some of the voluntariness from employer/worker relationships. And, in the case of overtime pay, for example, the Uber business model that is built in part on offering flexible incentives to match supply and demand using prices and compensation, would be next to impossible. It is precisely through appealing to drivers’ self-interest that Uber and the like may be moving the economy forward to a new era when businesses and workers have more flexibility, much to the benefit of all.

Elsewhere, Scheiber’s bias is a bit more subtle, but no less real. Thus, he writes:

As he tried to log off at 7:13 a.m. on New Year’s Day last year, Josh Streeter, then an Uber driver in the Tampa, Fla., area, received a message on the company’s driver app with the headline “Make it to $330.” The text then explained: “You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted.

With my edits and commentary, that paragraph becomes:

As he started the process of logging off at 7:13 a.m. on New Year’s Day last year, Josh Streeter, then an Uber driver in the Tampa, Fla., area, received a message on the company’s driver app with the headline “Make it to $330.” The text then explained: “You’re $10 away from making $330 in net earnings. Are you sure you want to go offline?” Below were two prompts: “Go offline” and “Keep driving.” The latter was already highlighted, but the former was listed first. It’s anyone’s guess whether either characteristic — placement or coloring — had any effect on drivers’ likelihood of clicking one button or the other.

And one last example. Scheiber writes:

Consider an algorithm called forward dispatch — Lyft has a similar one — that dispatches a new ride to a driver before the current one ends. Forward dispatch shortens waiting times for passengers, who may no longer have to wait for a driver 10 minutes away when a second driver is dropping off a passenger two minutes away.

Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods — a key goal for both companies.

Uber and Lyft explain this in essentially the same way. “Drivers keep telling us the worst thing is when they’re idle for a long time,” said Kevin Fan, the director of product at Lyft. “If it’s slow, they’re going to go sign off. We want to make sure they’re constantly busy.”

While this is unquestionably true, there is another way to think of the logic of forward dispatch: It overrides self-control.

* * *

Uber officials say the feature initially produced so many rides at times that drivers began to experience a chronic Netflix ailment — the inability to stop for a bathroom break. Amid the uproar, Uber introduced a pause button.

“Drivers were saying: ‘I can never go offline. I’m on just continuous trips. This is a problem.’ So we redesigned it,” said Maya Choksi, a senior Uber official in charge of building products that help drivers. “In the middle of the trip, you can say, ‘Stop giving me requests.’ So you can have more control over when you want to stop driving.”

It is true that drivers can pause the services’ automatic queuing feature if they need to refill their tanks, or empty them, as the case may be. Yet once they log back in and accept their next ride, the feature kicks in again. To disable it, they would have to pause it every time they picked up a new passenger. By contrast, even Netflix allows users to permanently turn off its automatic queuing feature, known as Post-Play.

This pre-emptive hard-wiring can have a huge influence on behavior, said David Laibson, the chairman of the economics department at Harvard and a leading behavioral economist. Perhaps most notably, as Ms. Rosenblat and Luke Stark observed in an influential paper on these practices, Uber’s app does not let drivers see where a passenger is going before accepting the ride, making it hard to judge how profitable a trip will be.

Here’s how I would recast that, and add some much-needed economics:

Consider an algorithm called forward dispatch — Lyft has a similar one — that dispatches a new ride to a driver before the current one ends. Forward dispatch shortens waiting times for passengers, who may no longer have to wait for a driver 10 minutes away when a second driver is dropping off a passenger two minutes away.

Perhaps no less important, forward dispatch causes drivers to stay on the road substantially longer during busy periods — a key goal for both companies — by giving them more income-earning opportunities.

Uber and Lyft explain this in essentially the same way. “Drivers keep telling us the worst thing is when they’re idle for a long time,” said Kevin Fan, the director of product at Lyft. “If it’s slow, they’re going to go sign off. We want to make sure they’re constantly busy.”

While this is unquestionably true, and seems like another win-win, some critics have tried to paint even this means of satisfying both driver and consumer preferences in a negative light by claiming that the forward dispatch algorithm overrides self-control.

* * *

Uber officials say the feature initially produced so many rides at times that drivers began to experience a chronic Netflix ailment — the inability to stop for a bathroom break. Amid the uproar, Uber introduced a pause button.

“Drivers were saying: ‘I can never go offline. I’m on just continuous trips. This is a problem.’ So we redesigned it,” said Maya Choksi, a senior Uber official in charge of building products that help drivers. “In the middle of the trip, you can say, ‘Stop giving me requests.’ So you can have more control over when you want to stop driving.”

Tweaks like these put paid to the arguments that Uber is simply trying to abuse its drivers. And yet, critics continue to make such claims:

It is true that drivers can pause the services’ automatic queuing feature if they need to refill their tanks, or empty them, as the case may be. Yet once they log back in and accept their next ride, the feature kicks in again. To disable it, they would have to pause it every time they picked up a new passenger. By contrast, even Netflix allows users to permanently turn off its automatic queuing feature, known as Post-Play.

It’s difficult to take seriously claims that Uber “abuses” drivers by setting a default that drivers almost certainly prefer; surely drivers seek out another fare following the last fare more often than they seek out another bathroom break. In any case, the difference between one default and the other is a small change in the number of times drivers might have to push a single button; hardly a huge impediment.

But such claims persist, nevertheless. Setting a trivially different default can have a huge influence on behavior, claims David Laibson, the chairman of the economics department at Harvard and a leading behavioral economist. Perhaps most notably — and to change the subject — as Ms. Rosenblat and Luke Stark observed in an influential paper on these practices, Uber’s app does not let drivers see where a passenger is going before accepting the ride, making it hard to judge how profitable a trip will be. But there are any number of defenses of this practice, from both a driver- and consumer-welfare standpoint. Not least, such disclosure could well create isolated scarcity for a huge range of individual ride requests (as opposed to the general scarcity during a “surge”), leading to longer wait times, the need to adjust prices for consumers on the basis of individual rides, and more intense competition among drivers for the most profitable rides. Given these and other explanations, it is extremely unlikely that the practice is actually aimed at “abusing” drivers.

As they say, read the whole thing!

In Corporate & Securities Law , , , , , , , , , ,
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Rationally Defeating Cronyism in the Boston TNC Suit

Kristian Stout —  5 April 2016

On March 31, a federal judge gave the city of Boston six months to rectify the disparities between the way it treats Transportation Network Companies (“TNC”) (such as Uber and Lyft) and taxicab companies. This comes pursuant to an order by US District Court Judge Nathaniel M. Gorton in a suit filed by members of the Boston taxi industry against the city and various officials. The suit is an interesting one because it reveals unusual fault lines in the ongoing struggle between taxi companies, local regulators, and the way that federal law recognizes and respects property and economic rights.

The three chief claims by the Boston taxi medallion holders are that the city had wronged them by by devaluing their medallions in violation of the Fifth Amendment’s prohibition on regulatory takings, by discriminating against them in favor of TNCs under the equal protection clause (“EPC”) of the Fourteenth Amendment, and by violating Massachusetts law under a theory of promissory estoppel.

On the federal claims, the court seems to get it half right, and half wrong.  In sum, Judge Gorton seems to get the takings argument more or less correct. He notes:

The exclusivity of medallion owners’ access to the market prior to the arrival of TNCs existed by virtue of the City’s regulatory structure rather than the medallion owners’ property rights.  Medallion owners have no property interest in the enforcement of Rule 403 against others  … If a person who wishes to operate a taxicab without a medallion is prevented from doing so, it is because he or she would violate municipal regulations, not because he or she would violate medallion owners’ property rights.

Indeed. The plaintiff’s takings argument essentially amounts to a claim that the government, by virtue of creating the medallion system, is thereby disabled from ever regulating in a way that disrupts medallion owners from making a profit. Efficiency concerns, consumer safety concerns, and the like be damned! takings can be a fairly complicated body of law, but it seems highly unlikely that the plaintiff’s view is right—for one thing, a medallion is much more like a business license subject to health and safety considerations than it is like a property right— and Judge Gorton handily disposes of the plaintiff’s claims.

However, on the EPC analysis Judge Morton’s analysis goes off the rails. He first properly notes that, as an economic rights claim, the EPC analysis is controlled by rational basis review. As the legally trained reader will already know,  “[r]ational basis review simply requires that there be “any reasonably conceivable set of facts justifying the disparate treatment.”

According to the Supreme Court:

[B]ecause we never require a legislature to articulate its reasons for enacting a statute, it is entirely irrelevant for constitutional purposes whether the conceived reason for the challenged distinction actually motivated the legislature.

And as Clark Neily, a constitutional litigator from the Institute for Justice, has noted: “Not only is the government invited to dream up entirely post hoc rationalizations for challenged legislation, it has “no obligation to produce evidence” in support of those rationalizations either.” (citing Heller v. Doe).

In short, rational basis review is an exceedingly easy burden for the government to meet when one of its regulations is challenged.

In this case, Boston offered a number of reasons that it decided to regulate TNCs and taxi companies differently, including a very strong one that doing so “enhances the city’s interest in increasing the availability and accessibility of cost-effective transportation[.]” Nonetheless, Judge Morton disagreed, holding that

[T]he Court finds persuasive plaintiffs’ argument that many of the obvious differences between taxis from TNCs, such as the kind of vehicle used and the fact that taxicabs must be clearly labeled, are caused by the City’s application of the requirements of Rule 403 to taxi operators but not to TNCs.  The City may not treat the two groups unequally and then argue that the results of that unequal treatment render the two groups dissimilarly situated and, consequently, not subject to equal protection analysis.  Such circular logic is unavailing.

The judge pegged his opinion to the fact that Rule 403 — which regulates “hackney carriages” — defines the subject of its regulations as “used or designed to be used for the conveyance of persons for hire from place to place within the city of Boston.” Both TNCs and taxi cabs arguably fit into this definition, thus for Judge Morton, despite the fact that the city offered at least two policy goals for its differential regulations, “[n]either objective is … rationally related to any distinction between taxi operators and TNCs.”

This just has to be wrong under current federal law. As I noted above, rational basis review requires “any reasonably conceivable set of facts”  and, even though the city created the distinctions itself through its regulations, the reasons it states for doing so — including increasing availability of transportation for its citizens — are definitely rationally related to its distinction between the two types of consumer carriers. Sure, Rule 403 provides a scope of regulatory power for the city that sweeps in both TNCs and taxicabs, but within that regulatory scope the City then has the power to “rationally” assign rules as it sees fit (unless someone comes up with a fundamental right here that is more important than economic interests, of course).

I get it, rational basis review of economic regulations is frustrating and often just provides a free pass to protectionist regulators. Nevertheless, it is the law, and I think that Judge Morton got the equal protection claim wrong.

The real lesson here? Don’t get into bed with government and expect a virtual monopoly to protect you indefinitely. It’s no secret that federal law provides scant little protection for economic liberty, so when the government decides it wants to do something that harms the industry that it was previously cozy with it’s just too bad. Maybe there is a future world in which courts will recognize the right to earn a living is as deeply important as the right to speak or practice your religion or vote — but that is not the world we live in today.

Moreover, when an industry depends upon the government to explicitly protect it from competitors it is the worst kind of cronyism, and, at least in this case, represents an economic mindset that is badly aging. As upstart competitors like Uber and Lyft discover new ways to deploy cost-effective (and generally just more effective) technology to manage different industries, the fig leaf of legitimate government intervention is stripped away and revealed for what it often is: protectionism.

So to some extent, I sympathize with  Judge Gorton’s instinct in the equal protection claim: it should be the case that the government is not allowed to pick winners and losers in the economy based on its own taking of the political temperature. But the larger lesson is the opposite of the plaintiff’s intention, in my opinion. The government should roll back the regulations that created the medallion industry in the first place, and find a way to strike a politically feasible deal that eases the taxi companies out of their well-painted corner. We need more competition and more service in pursuit of consumer choice, and we need much less industry control guided in a top-down manner by state fiat.

In Constitutional Law, Regulation, Truth on the Market , , , , , , , ,
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Debunking the Myth of a Data Barrier to Entry for Online Services

Geoffrey Manne & Ben Sperry —  26 March 2015

Recent years have seen an increasing interest in incorporating privacy into antitrust analysis. The FTC and regulators in Europe have rejected these calls so far, but certain scholars and activists continue their attempts to breathe life into this novel concept. Elsewhere we have written at length on the scholarship addressing the issue and found the case for incorporation wanting. Among the errors proponents make is a persistent (and woefully unsubstantiated) assertion that online data can amount to a barrier to entry, insulating incumbent services from competition and ensuring that only the largest providers thrive. This data barrier to entry, it is alleged, can then allow firms with monopoly power to harm consumers, either directly through “bad acts” like price discrimination, or indirectly by raising the costs of advertising, which then get passed on to consumers.

A case in point was on display at last week’s George Mason Law & Economics Center Briefing on Big Data, Privacy, and Antitrust. Building on their growing body of advocacy work, Nathan Newman and Allen Grunes argued that this hypothesized data barrier to entry actually exists, and that it prevents effective competition from search engines and social networks that are interested in offering services with heightened privacy protections.

According to Newman and Grunes, network effects and economies of scale ensure that dominant companies in search and social networking (they specifically named Google and Facebook — implying that they are in separate markets) operate without effective competition. This results in antitrust harm, they assert, because it precludes competition on the non-price factor of privacy protection.

In other words, according to Newman and Grunes, even though Google and Facebook offer their services for a price of $0 and constantly innovate and upgrade their products, consumers are nevertheless harmed because the business models of less-privacy-invasive alternatives are foreclosed by insufficient access to data (an almost self-contradicting and silly narrative for many reasons, including the big question of whether consumers prefer greater privacy protection to free stuff). Without access to, and use of, copious amounts of data, Newman and Grunes argue, the algorithms underlying search and targeted advertising are necessarily less effective and thus the search product without such access is less useful to consumers. And even more importantly to Newman, the value to advertisers of the resulting consumer profiles is diminished.

Newman has put forth a number of other possible antitrust harms that purportedly result from this alleged data barrier to entry, as well. Among these is the increased cost of advertising to those who wish to reach consumers. Presumably this would harm end users who have to pay more for goods and services because the costs of advertising are passed on to them. On top of that, Newman argues that ad networks inherently facilitate price discrimination, an outcome that he asserts amounts to antitrust harm.

FTC Commissioner Maureen Ohlhausen (who also spoke at the George Mason event) recently made the case that antitrust law is not well-suited to handling privacy problems. She argues — convincingly — that competition policy and consumer protection should be kept separate to preserve doctrinal stability. Antitrust law deals with harms to competition through the lens of economic analysis. Consumer protection law is tailored to deal with broader societal harms and aims at protecting the “sanctity” of consumer transactions. Antitrust law can, in theory, deal with privacy as a non-price factor of competition, but this is an uneasy fit because of the difficulties of balancing quality over two dimensions: Privacy may be something some consumers want, but others would prefer a better algorithm for search and social networks, and targeted ads with free content, for instance.

In fact, there is general agreement with Commissioner Ohlhausen on her basic points, even among critics like Newman and Grunes. But, as mentioned above, views diverge over whether there are some privacy harms that should nevertheless factor into competition analysis, and on whether there is in fact  a data barrier to entry that makes these harms possible.

As we explain below, however, the notion of data as an antitrust-relevant barrier to entry is simply a myth. And, because all of the theories of “privacy as an antitrust harm” are essentially predicated on this, they are meritless.

First, data is useful to all industries — this is not some new phenomenon particular to online companies

It bears repeating (because critics seem to forget it in their rush to embrace “online exceptionalism”) that offline retailers also receive substantial benefit from, and greatly benefit consumers by, knowing more about what consumers want and when they want it. Through devices like coupons and loyalty cards (to say nothing of targeted mailing lists and the age-old practice of data mining check-out receipts), brick-and-mortar retailers can track purchase data and better serve consumers. Not only do consumers receive better deals for using them, but retailers know what products to stock and advertise and when and on what products to run sales. For instance:

  • Macy’s analyzes tens of millions of terabytes of data every day to gain insights from social media and store transactions. Over the past three years, the use of big data analytics alone has helped Macy’s boost its revenue growth by 4 percent annually.
  • Following its acquisition of Kosmix in 2011, Walmart established @WalmartLabs, which created its own product search engine for online shoppers. In the first year of its use alone, the number of customers buying a product on Walmart.com after researching a purchase increased by 20 percent. According to Ron Bensen, the vice president of engineering at @WalmartLabs, the combination of in-store and online data could give brick-and-mortar retailers like Walmart an advantage over strictly online stores.
  • Panera and a whole host of restaurants, grocery stores, drug stores and retailers use loyalty cards to advertise and learn about consumer preferences.

And of course there is a host of others uses for data, as well, including security, fraud prevention, product optimization, risk reduction to the insured, knowing what content is most interesting to readers, etc. The importance of data stretches far beyond the online world, and far beyond mere retail uses more generally. To describe even online giants like Amazon, Apple, Microsoft, Facebook and Google as having a monopoly on data is silly.

Second, it’s not the amount of data that leads to success but building a better mousetrap

The value of knowing someone’s birthday, for example, is not in that tidbit itself, but in the fact that you know this is a good day to give that person a present. Most of the data that supports the advertising networks underlying the Internet ecosphere is of this sort: Information is important to companies because of the value that can be drawn from it, not for the inherent value of the data itself. Companies don’t collect information about you to stalk you, but to better provide goods and services to you.

Moreover, data itself is not only less important than what can be drawn from it, but data is also less important than the underlying product it informs. For instance, Snapchat created a challenger to  Facebook so successfully (and in such short time) that Facebook attempted to buy it for $3 billion (Google offered $4 billion). But Facebook’s interest in Snapchat wasn’t about its data. Instead, Snapchat was valuable — and a competitive challenge to Facebook — because it cleverly incorporated the (apparently novel) insight that many people wanted to share information in a more private way.

Relatedly, Twitter, Instagram, LinkedIn, Yelp, Pinterest (and Facebook itself) all started with little (or no) data and they have had a lot of success. Meanwhile, despite its supposed data advantages, Google’s attempts at social networking — Google+ — have never caught up to Facebook in terms of popularity to users (and thus not to advertisers either). And scrappy social network Ello is starting to build a significant base without data collection for advertising at all.

At the same time it’s simply not the case that the alleged data giants — the ones supposedly insulating themselves behind data barriers to entry — actually have the type of data most relevant to startups anyway. As Andres Lerner has argued, if you wanted to start a travel business, the data from Kayak or Priceline would be far more relevant. Or if you wanted to start a ride-sharing business, data from cab companies would be more useful than the broad, market-cross-cutting profiles Google and Facebook have. Consider companies like Uber, Lyft and Sidecar that had no customer data when they began to challenge established cab companies that did possess such data. If data were really so significant, they could never have competed successfully. But Uber, Lyft and Sidecar have been able to effectively compete because they built products that users wanted to use — they came up with an idea for a better mousetrap.The data they have accrued came after they innovated, entered the market and mounted their successful challenges — not before.

In reality, those who complain about data facilitating unassailable competitive advantages have it exactly backwards. Companies need to innovate to attract consumer data, otherwise consumers will switch to competitors (including both new entrants and established incumbents). As a result, the desire to make use of more and better data drives competitive innovation, with manifestly impressive results: The continued explosion of new products, services and other apps is evidence that data is not a bottleneck to competition but a spur to drive it.

Third, competition online is one click or thumb swipe away; that is, barriers to entry and switching costs are low

Somehow, in the face of alleged data barriers to entry, competition online continues to soar, with newcomers constantly emerging and triumphing. This suggests that the barriers to entry are not so high as to prevent robust competition.

Again, despite the supposed data-based monopolies of Facebook, Google, Amazon, Apple and others, there exist powerful competitors in the marketplaces they compete in:

  • If consumers want to make a purchase, they are more likely to do their research on Amazon than Google.
  • Google flight search has failed to seriously challenge — let alone displace —  its competitors, as critics feared. Kayak, Expedia and the like remain the most prominent travel search sites — despite Google having literally purchased ITA’s trove of flight data and data-processing acumen.
  • People looking for local reviews go to Yelp and TripAdvisor (and, increasingly, Facebook) as often as Google.
  • Pinterest, one of the most highly valued startups today, is now a serious challenger to traditional search engines when people want to discover new products.
  • With its recent acquisition of the shopping search engine, TheFind, and test-run of a “buy” button, Facebook is also gearing up to become a major competitor in the realm of e-commerce, challenging Amazon.
  • Likewise, Amazon recently launched its own ad network, “Amazon Sponsored Links,” to challenge other advertising players.

Even assuming for the sake of argument that data creates a barrier to entry, there is little evidence that consumers cannot easily switch to a competitor. While there are sometimes network effects online, like with social networking, history still shows that people will switch. MySpace was considered a dominant network until it made a series of bad business decisions and everyone ended up on Facebook instead. Similarly, Internet users can and do use Bing, DuckDuckGo, Yahoo, and a plethora of more specialized search engines on top of and instead of Google. And don’t forget that Google itself was once an upstart new entrant that replaced once-household names like Yahoo and AltaVista.

Fourth, access to data is not exclusive

Critics like Newman have compared Google to Standard Oil and argued that government authorities need to step in to limit Google’s control over data. But to say data is like oil is a complete misnomer. If Exxon drills and extracts oil from the ground, that oil is no longer available to BP. Data is not finite in the same way. To use an earlier example, Google knowing my birthday doesn’t limit the ability of Facebook to know my birthday, as well. While databases may be proprietary, the underlying data is not. And what matters more than the data itself is how well it is analyzed.

This is especially important when discussing data online, where multi-homing is ubiquitous, meaning many competitors end up voluntarily sharing access to data. For instance, I can use the friend-finder feature on WordPress to find Facebook friends, Google connections, and people I’m following on Twitter who also use the site for blogging. Using this feature allows WordPress to access your contact list on these major online players.

Friend-Finder

Further, it is not apparent that Google’s competitors have less data available to them. Microsoft, for instance, has admitted that it may actually have more data. And, importantly for this discussion, Microsoft may have actually garnered some of its data for Bing from Google.

If Google has a high cost per click, then perhaps it’s because it is worth it to advertisers: There are more eyes on Google because of its superior search product. Contra Newman and Grunes, Google may just be more popular for consumers and advertisers alike because the algorithm makes it more useful, not because it has more data than everyone else.

Fifth, the data barrier to entry argument does not have workable antitrust remedies

The misguided logic of data barrier to entry arguments leaves a lot of questions unanswered. Perhaps most important among these is the question of remedies. What remedy would apply to a company found guilty of leveraging its market power with data?

It’s actually quite difficult to conceive of a practical means for a competition authority to craft remedies that would address the stated concerns without imposing enormous social costs. In the unilateral conduct context, the most obvious remedy would involve the forced sharing of data.

On the one hand, as we’ve noted, it’s not clear this would actually accomplish much. If competitors can’t actually make good use of data, simply having more of it isn’t going to change things. At the same time, such a result would reduce the incentive to build data networks to begin with. In their startup stage, companies like Uber and Facebook required several months and hundreds of thousands, if not millions, of dollars to design and develop just the first iteration of the products consumers love. Would any of them have done it if they had to share their insights? In fact, it may well be that access to these free insights is what competitors actually want; it’s not the data they’re lacking, but the vision or engineering acumen to use it.

Other remedies limiting collection and use of data are not only outside of the normal scope of antitrust remedies, they would also involve extremely costly court supervision and may entail problematic “collisions between new technologies and privacy rights,” as the last year’s White House Report on Big Data and Privacy put it.

It is equally unclear what an antitrust enforcer could do in the merger context. As Commissioner Ohlhausen has argued, blocking specific transactions does not necessarily stop data transfer or promote privacy interests. Parties could simply house data in a standalone entity and enter into licensing arrangements. And conditioning transactions with forced data sharing requirements would lead to the same problems described above.

If antitrust doesn’t provide a remedy, then it is not clear why it should apply at all. The absence of workable remedies is in fact a strong indication that data and privacy issues are not suitable for antitrust. Instead, such concerns would be better dealt with under consumer protection law or by targeted legislation.

In Amazon, Antitrust & Competition, Apple, Barriers to Entry, Data Privacy & Security, Facebook, Google, Monopolization, Platforms, Price Discrimination, Twitter, Vertical Restraints , , , , , , , , , , , , , , , , , , ,
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