The basic position of this paper – which can be found here – is that: ‘Competition authorities (…) with support from some dismal scientists, saw the dark side of network effects. Firms could rig the race to become the winner and thereby “tip” the market to make themselves monopolies. And even if a firm won fair and square, network effects would result in insurmountable barriers to entry and would be the font of permanent monopoly power. (…) A recent argument in this debate is that online platforms have troves of data that make network effects even more potent. Unfortunately, this view of network effects evolved from a seminal economic contribution to a set of slogans that don’t comport with the facts.

A first section looks at the economics of networks. This covers the origins of theoretical studies on this topic – which focused on telephone networks and fax machines, and standard-tipping (i.e. the VCR-BetaMax war). Theoretical refinements to the theory were pursued following the onset of online markets. From these studies, three fundamental conclusions have emerged:

  1. Network effects are usually indirect, between different kinds of customers rather than direct for the same kind of Customers – i.e. network effects tend to occur in multisided markets);
  2. Network effects result from getting the right customers, and not just more customers – i.e. platforms create value when customers find good matches and enter into exchanges. Density trumps scale for most platforms. That’s because most customers on most platforms are not very good matches for each other.
  3. Network effects can work in reverse – while the early literature on network effects didn’t pay much attention to the potential for this reversal, the development of e-markets and the demise of prior dominant companies provides examples of how network effects can lead to reverse-tipping.

A second section deals with the empirical evidence regarding network effects. It remarks that: ‘The basic empirical flaw in the simple network effects theory, and the associated slogans, is that it focuses on successful firms, at a point in time, observes they benefited from network effects, and concludes that they won it all and won’t be displaced.’ However, systematic research on online platforms shows considerable churn in leadership for online platforms over periods shorter than a decade. There is a collection of dead or withered platforms which dot this sector, including Blackberry and Windows in smartphone operating systems; AOL in messaging; Orkut in social networking; and Yahoo in mass online media. Lastly, there is the fact that a focus on winner-take-all platforms ignores the fact that many online platforms make their money from advertising –and that, 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.

A third section then looks at recent concerns with big data. The authors note that there is a widespread perception that: “With data there are extra network effects. By collecting more data, a firm has more scope to improve its products, which attracts more users, generating even more data, and so on. (…) As far as we know there is no rigorous theoretical or empirical support for these statements.

It is argued that: ‘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 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 there is no empirical support, at this point, that this is anything more than a possibility, which one might explore in particular cases.

In conclusion, while there may be reasons for competition enforcement against network-based businesses, such enforcement must be based on empirical evidence and not on untested theories and slogans.

Comment:

While I have no qualms about this conclusion, I would have liked to see quite a bit more about the operation of reverse network effects, on which the success of the authors’ arguments hinges. The empirical evidence they provide is mainly anecdotal, so I think would be good to obtain evidence – even if it is internal data from investigated or affected firms – before concluding that network effects or data pose insurmountable obstacles to competition.

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