The Big Tech Competition Dilemma

Nov 21st '18

Advances in technology raise fundamental questions for competition in financial services – and pose a challenge to economists.


In 1899, Charles H Duell, then director of the U.S. Patent Office, memorably predicted that “everything that can be invented, has already been invented.”


It is not a forecast that has aged well. But Duell’s unfortunate (and much mocked) quote does illustrate an important characteristic of technology. The speed of its adoption tends to catch people off-guard. A point that is emphatically true today.


Ten years ago, only two tech companies appeared in the top ten US firms by market cap. Today, Apple looks down on a list spearheaded by Google, Microsoft, Amazon and Facebook. And herein lies an important conundrum for today’s competition economists.


Tech has brought a lot of benefits and in some cases it’s disrupted the status quo to increase competition. As recently noted by the Competition and Markets Authority: “algorithms can reduce transaction costs for firms, reduce frictions in markets, and give consumers greater information on which to base their decisions.”


But technology is also creating important competition and regulatory issues. There have been ongoing discussions, for example, at regulatory organisations around the world about the impact of artificial intelligence (AI) and machine learning on price collusion.


At the same time, firms are getting better at extracting commercial value from our information. And there is mounting concern that the business world’s reliance on data is creating significant network effects, barriers to entry and expansion, and advantages for those with access to more (and better) data.


Firms need a critical mass of data before this value can be extracted. But once operational, incumbents benefit from a cycle of superior products and service offerings (based on existing data) leading to increased data, which can be used to improve their products and services. A search engine being improved by the number of searches that are conducted is a classic example of this effect.


In financial services, the same principle could well apply. A large insurer or bank might use the data they have to get a competitive advantage over new entrants, posing a barrier to entry. Alternatively, firms may have advantages in the data they hold simply by virtue of their scale (without the need for the reinforcing mechanism described above). Arrangements that introduce data sharing, such as Open Banking, offer one method of mitigating these potential barriers, regardless of the source.


However, the pace at which markets are evolving means that policy makers will have to stay on their toes to understand if these solutions become effective or give rise to new problems.


For example, we have seen big tech firms enter financial services in other jurisdictions (Amazon in the US, Alibaba in China). They have complementary consumer data and will know much more about consumers than their rivals who are only operating in financial services.


We don’t yet know what, if any, effect this will have on the dynamics of competition. But we do know that this is a trend that raises some fundamental questions for competition policy makers. Are they adequately equipped to assess if competition is working effectively? And can they respond quickly enough to problems in fast moving markets, without making mistakes that prevent developments that would have brought significant benefits to consumers?


At the moment, economists struggle to accurately define the value of data, or explain the point at which accumulated data can lead to market power. Probably because many of the more traditional measures of market power – like market share, price trends, and margins – have little to say about it.


This is an important gap because it can influence the speed at which (or whether) markets tip, going from effective competition to ineffective competition. If we do not fully understand how (or when) data contributes to market power it will be harder to identify this tipping point using traditional competition frameworks.


A hypothetical (but not especially unlikely) example could be a free platform that provides customers access to a range of financial services. This business might follow a Facebook lifecycle, where it leaps from being a minnow – with no serious market power – to dominance based on the data it collected while offering its free service.


At face value, competition policy has not evolved at anything like the same pace as technology. How different does today’s approach look from twenty years ago? The answer is not a lot at a high level.


Certainly the fundamental legal questions we wrestle with are pretty much the same. I started my career working on mergers using a framework that basically involved defining markets. Seeing if the merging firms overlapped in any of those markets. Using a range of indicators to assess whether the merging firms held (or would be likely to gain) market power on those markets. And assessing the likely effect of the merger on competition.


Of course, it was never straightforward. We considered issues such as whether mergers might increase the risk of coordination or stifle innovation. And it has always been hard to demonstrate, on the basis of balance of probabilities, that a merger will damage long-term innovation, or indeed lead to a market tipping.


The important point though is that if we want to make sure customers continue to enjoy dynamic, competitive markets, it is essential that policy makers’ approach keeps up.


If elements of traditional analysis are no longer fit for purpose, then they should be adapted. Some of the analytical tools economists use to analyse data have improved significantly since I first started, and we are exploring ways of using data science in our analysis of markets, as well the implications for its use by the firms in those markets.


But equally, we must not throw the baby out with the bathwater. Understanding the sources of market power are as important today as ever. We shouldn’t be afraid to challenge ourselves as to how best to identify those sources. Potentially using different indicators, including metrics relating to the importance/quality and quantity of data held exclusively by a firm, and then tackle them appropriately.


Source: FCA Insight. Author: Robin Finer


Robin Finer is the FCA’s Acting Chief Economist and Director of Competition. Prior to joining the FCA in 2014, Robin was a Director of Economic Analysis at the Competition Commission, where he worked on mergers, market investigations and regulatory inquiries across a wide range of sectors.


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