was discussed by invited experts from
academia and the private sector, followed by contributions provided by BIAC (with a Microsoft representative as ad hoc
speaker), Germany, France, the EU (DGCom and EDPS), the United States and the United Kingdom.
The
Committee Chair, after noticing that big data for competition authorities was a
bit like sex for teenagers (“everybody talks about it, nobody has experienced it, everybody thinks that the others know more about it than this"), warned the distinguished attendees that views on
this subject tended to be quite polarized, also due to the novelty of the
economic and legal questions raised. Despite a few genuinely irreconcilable
views, my understanding was that none of the experts and other contributors would
have gone so far as to deny that big data had at least some implications for
consumers and competition authorities (“too big an issue to be ignored”). The
discussions within the OECD Committee were mainly theoretical, however, as
case-related experience by competition authorities is still much limited. It should also be noted that, while the
attention of the Competition Committee and its invited experts was almost entirely focused
on the online tracking of activities by users of digitally-enabled services, big
data logically comprises also data produced directly by machines (non-personal data). Besides, a series of advances in the area of
artificial intelligence and virtual/advanced reality, as well as the increased
popularity of the Internet of Things, provide strong indications that the competitive significance of
data is reasonably expected to grow in the future. Indeed, we might still be at a
comparatively early stage in the transition towards an increasingly, and
potentially paradigm-shifting, data-based economy.
What follows is a brief account of the some of the discussions that took place at the hearing, for personal memory and sharing with my students and other patient @wavesblog readers.
What is data in economic terms?
Basically, data can be in input or an output. As an input, or
asset, data in digital markets would seem rather on the cheap side in the sense that nowadays there
are inexpensive ways to generate and collect data produced by consumers
while they are connected to the Internet (“web logs, sensors, etc.”). While
firms offering digitally-enabled services get a lot of data almost automatically,
data becomes really helpful and valuable “if it can be turned into information,
knowledge and action”. This type of data analytics requires investment in
complementary assets such as hardware, software, and skilled labour. Arguably, improvements
in terms of value extraction (“information, knowledge and action “) are
directly related to improvements in software and hardware and not only to improvements
in terms of the amount and variety of data that is available.
On the demand side, increasing returns to scale that have attracted
much attention in the case of online platforms such as Google and Facebook are
so called direct and indirect network effects. Here what matters is share: the firm
that has the larger share of users has power, as more users automatically make the
digitally-enabled service more attractive relative to another online platform. On
the supply side, classic returns to scale are often caused by high fixed costs,
entry costs, relatively low marginal costs. In this respect, size is what
matters for supply side economies of scale. The cost per unit decreases as the
quantity produced increases. That said, the digital economy is characterized nowadays
by less fixed costs (thanks to cloud computing, software as a service, freely
available productivity tools, etc.). In this scenario, firms can relatively
easily enter new markets and scale up their business.
The classic demand and supply side returns to scale are produced almost automatically (“positive feedback loop”). A critical aspect in the world of
big data, however, is that it is mainly user generated data which directly helps with
product improvement. This specific “return to scale” has also been called a “data
network effect”. For instance, the consumer uses a digitally enabled service
that is connected to the Internet, like Google Maps. In this case, the user is
not only producing the data while using the service so that the supplier of the
service can learn, but the user directly produces the data that enables the
service and improves it in real time. It could be argued that this is a supply
side phenomenon rather than a demand side phenomenon. More specifically, considered
from the supply side, it’s not really a network effect but rather a matter of learning
by doing, as the cost per unit decreases and quality increases as experience
increases. This continuous improvement on the production side would be reminiscent
of the Japanese Kaizen, with its focus on constant learning on how to optimise
production and how to optimise the characteristics of the product to make it
more attractive to user. Experience
is
particularly relevant for learning by doing: How long firms have been around
and what they have learned. As such, this supply side phenomenon known as
learning by doing wouldn’t be specific to the data economy but is present in
virtually all industries (“companies from all industries have long sought to improve the relevance of their data, in order to inform their decision making, and better serve consumers”). Differently from more traditional returns to scale,
this type of positive feedback would require a serious investment and
commitment. In particular, learning by doing by online platforms requires data,
hardware, software, tools, and expertise.
In this respect, a really intriguing question discussed at the hearing is whether there is something genuinely new going on here. The user is not only producing the data while using the service, from which the online platform can obviously learn, but the user produces the data that directly contributes to the making available of the service and its improvement in real time.
This specific feedback loop might be something peculiar to the data economy, as using the service has an effect not only on the capacity of the firm to improve the service, but on the capacity of the firm to provide it. Thus, using a digitally-enabled service such as Google Maps would have a direct influence on the production of the service, as if by driving a car the user "might make the car production line goes faster". Consumption would thus be intertwined with production, from the demand side to the supply side.
Is there a data barrier to entry?
The fact that data is relatively cheap and available, or that some companies are better than others at utilising data to extract value, would not seem to exclude
per se that there might be serious data-related impediments or even barriers to entry.
Conversely, there is no automatic barrier to entry because of the increasingly importance of data as a parameter of competition.
As seen above, an interesting peculiarity characterizing big data-driven economic sectors is that valuable data is generated by the interaction between the consumer and the digitally-enabled service, that is when the firm is already in the market. In this respect, a distinction should be made between data
for entry and data that can be generated only
after entry. This would raise the question about the minimum quantity and variety of data necessary in order to enter the specific big data-driven market (a sort of "minimum efficient scale").
At any rate, there seemed to be general consensus that the attention of
competition authorities should be primarily focused on sectors in which a certain quantity/variety of data is indispensable for firms and alternative data sources (such as data brokers) cannot be tapped. Future examples of this situation could arise in the world of so called Industry 4.0, i.e. the trend towards the full computerization of manufacturing where machine directly produce and exchange valuable data.
Should privacy be protected as a component of quality competition?
It is clear to anyone who has been around in the “last 45 years” that competition policy has become quite price-centric. Antitrust is parameterized to assessing short-term price effects in narrowly defined markets. But, as Hayek has been
, "unlike the position that exists in the physical sciences, in economics and other disciplines that deal with essentially complex phenomena, the aspects of the events to be accounted for about which we can get quantitative data are necessarily limited and may not include the important ones”.
Last week, on a sunny and frosty November day (“Cold frost and sunshine: day of wonder! -
Мороз и солнце; день чудесный”),
competition authorities gathered at the OECD in Paris with the intention of
figuring out analytically what big data could mean for competition enforcement.
With regard to competition enforcement, it is often the case
that quality and innovation concerns that are non-price-related (and therefore not
easily quantifiable) are what should really matter in terms of this policy’s exclusive goal,
i.e. to make competition processes work best in the interest of consumers. In this respect, big data could be like a
canary in the coalmine, i.e. could serve to expose a crucial weakness in the
current antitrust framework.
