Tuesday, February 13, 2018

Network Effects, Big Data, and Antitrust Issues For Big Tech

You don't need to be a weatherman to see that the antitrust winds are blowing toward the big tech companies like Amazon, Facebook, Google, Apple, and others. But an immediate problem arises. At least under modern US law, being a monopoly (or a near-monopoly) is not illegal. Nor is making high profits illegal, especially when it is accomplished by providing services that are free to consumers and making money through advertising. Antitrust kicks in when anticompetitive behavior is involved: that is, a situation in which a firm takes actions which have the effect of blocking actual or potential competitors./

For example, the antitrust case against Microsoft that was settled back in 2001 wasn't that the firm was big or successful, bur rather that the firm was engaged in an anticompetitive practice of "tying" together separate products, and in this way trying to use its near-monopoly position in the operating systems that run personal computers to gain a similar monopoly position for its internet browser--and in this way to drive off potential competitors. .

In the case of big tech companies, a common theory is that they hold a monopoly position because of what economists call "network effects." The economic theory of network effects started with the observation that certain products are only valuable if other people also own the same product--think of a telephone or fax machine. Moreover, the product becomes more valuable as the network gets bigger. When "platform" companies like Amazon or Facebook came along, network effects got a new twist. The idea became that if a website managed to gain a leadership position in attracting buyers and sellers (like Amazon, OpenTable, or Uber), or users and providers of content (like Facebook, YouTube, or Twitter), then others would be attracted to the website as well. Any potentially competing website might have a hard time building up its own critical mass of users, in which case network effects are acting as an anticompetitive barrier. 

Of course, the idea that an already-popular meeting place has an advantage isn't limited to the virtual world: many shopping malls and downtown areas rely on a version of network effects, too, as to stock markets, flea markets, and bazaars.

But while it's easy to sketch in the air an argument about network effects,  the question of how network effects work in reality isn't a simple one.  David S. Evans and Richard Schmalensee offer a short essay on "Debunking the `Network Effects' Bogeyman: Policymakers need to march to the evidence, not to slogans," in Regulation magazine Winter 2017-18, pp. 36-39).

As they point out, lots of companies that might at the time seemed to have an advantage of "network effects"  have faltered: for example, eBay looked like the network Goliath back in 2001, but it was soon overtaken by Amazon. They write:
"The flaw in that reasoning is that people can use multiple online communications platforms, what economists call `multihoming.'   A few people in a social network try a new platform. If enough do so and like it, then eventually all network members could use it and even drop their initial platform. This process has happened repeatedly. AOL, MSN Messenger, Friendster, MySpace, and Orkut all rose to great heights and then rapidly declined, while Facebook, Snap, WhatsApp, Line, and others quickly rose. ...
"Systematic research on online platforms by several authors, including one of us, shows considerable churn in leadership for online platforms over periods shorter than a decade. Then there is the collection of dead or withered platforms that dot this sector, including Blackberry and Windows in smartphone operating systems, AOL in messaging, Orkut in social networking, and Yahoo in mass online media ... 
"The winner-take-all slogan also ignores the fact that many online platforms make their money from advertising. As many of the firms that died in the dot-com crash learned, winning the opportunity to provide services for free doesn’t pay the bills. When it comes to micro-blogging, Twitter has apparently won it all. But it is still losing money because it hasn’t been very successful at attracting advertisers, which are its main source of income. Ignoring the advertising side of these platforms is a mistake. Google is still the leading platform for conducting searches for free, but when it comes to product searches—which is where Google makes all its money—it faces serious competition from Amazon. Consumers are roughly as likely to start product searches on Amazon.com, the leading e-commerce firm, as on Google, the leading search-engine firm."
It should also be noted that if network effects are large and block new competition, they pose a problem for antitrust enforcement, too. Imagine that Amazon or Facebook was required by law to split into multiple pieces, with the idea that the pieces would compete with each other. But if network effects really are large, then one or another of the pieces will grow to critical mass and crowd out the others--until the status quo re-emerges.

A related argument is that big tech firms have access to Big Data from many players in a given market, which gives them an advantage. Evans and Schmalensee are skeptical of this point, too. They write:
"Like the simple theory of network effects, the “big data is bad” theory, which is often asserted in competition policy circles as well as the media, is falsified by not one, but many counterexamples. AOL, Friendster, MySpace, Orkut, Yahoo, and many other attention platforms had data on their many users. So did Blackberry and Microsoft in mobile. As did numerous search engines, including AltaVista, Infoseek, and Lycos. Microsoft did in browsers. Yet in these and other categories, data didn’t give the incumbents the power to prevent competition. Nor is there any evidence that their data increased the network effects for these firms in any way that gave them a substantial advantage over challengers.
"In fact, firms that at their inception had no data whatsoever sometimes displaced the leaders. When Facebook launched its social network in India in 2006 in competition with Orkut, it had no data on Indian users since it didn’t have any Indian users. That same year Orkut was the most popular social network in India, with millions of users and detailed data on them. Four years later, Facebook was the leading social network in India. Spotify provides a similar counterexample. When Spotify entered the United States in 2011, Apple had more than 50 million iTunes users and was selling downloaded music at a rate of one billion songs every four months. It had data on all those people and what they downloaded. Spotify had no users and no data when it started. Yet it has been able to grow to become the leading source of digital music in the world. In all these and many other cases the entrants provided a compelling product, got users, obtained data on those users, and grew.
"The point isn’t that big data couldn’t provide a barrier to entry or even grease network effects. As far as we know, there is no way to rule that out entirely. But at this point there is no empirical support that this is anything more than a possibility, which one might explore in particular cases."
Evans and Schmalensee are careful to note that they are not suggesting that online platform companies should be exempt from antitrust scrutiny, and perhaps in some cases the network and data arguments might carry weight. As they write:
"Nothing we’ve said here is intended to endorse a “go-easy” policy toward online platforms when it comes to antitrust enforcement. ... There’s no particular reason to believe these firms are going to behave like angels. Whether they benefit from network effects or not, competition authorities ought to scrutinize dominant firms when it looks like they are breaking the rules and harming consumers. As always, the authorities should use evidence-based analysis grounded in sound economics. The new economics of multisided platforms provides insights into strategies these firms may engage in as well as cautioning against the rote application of antitrust analysis designed for single-sided firms to multisided ones.

"It is time to retire the simple network effects theory—which is older than the fax machine—in place of deeper theories, with empirical support, of platform competition. And it is not too soon to ask for supporting evidence before accepting any version of the “big data is bad” theory. Competition policy should march to the evidence, not to the slogans."
For an introduction to the economics of multi-sided "platform" markets, a useful starting point is Marc Rysman's "The Economics of Two-Sided Markets" in the Summer 2009 issue of the Journal of Economic Perspectives (23:3, 125-43). 

For an economic analysis of policy, the underlying reasons matter a lot, because they set a precedent that will affect future actions by regulators and firms. Thus, it's not enough to rave against the size of Big Tech. It's necessary to get specific: for example, about how public policy should view network effects or online buyer-and-seller platforms, and about the collection, use, sharing, and privacy protections for data. We certainly don't want the current big tech companies to stifle new competition or abuse consumers. But in pushing back against the existing firms, we don't want regulators to set rules that could close off new competitors, either.