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Back to the future: the identification of market power in the Big Data Sector

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Andrea Lista re-considers the current status quo in relation to the interface between market power and the application of competition law to the Big Data market, with the view to suggesting possible alternative approaches.

Image courtesy of interviewee. July 5, 2024

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This video discusses the relationship between big data and market power, particularly in the context of competition law. It emphasizes that possessing and processing big data can give a company significant market power, potentially leading to anti-competitive behaviors. The author suggests that traditional economic theories used to assess market power in 3D markets may not be directly applicable to the complex, multi-sided nature of big data markets.

Key points include:

1. **Market Power and Competition**: The core inquiry is whether an entity with access to big data can adversely affect competition in its market. The assessment of market power is fundamental to antitrust law, as it can indicate a firm's ability to distort competition.

2. **Big Data's Complexity**: The big data economy is characterized as multi-sided, meaning it involves multiple user groups (e.g., sellers and buyers on a platform), creating feedback loops and network effects that complicate traditional market assessments.

3. **New Paradigms for Assessment**: The author advocates for the creation of new frameworks for assessing market power in the context of big data, rather than merely applying existing ones from traditional markets. This video includes considering factors like data uniqueness and the ability to process data.

4. **Essential Facilities and Contestability**: This video introduces concepts such as the essential facilities doctrine, which examines how control of critical infrastructure (like data) can raise barriers to market entry for competitors, and the theory of contestable markets, which discusses how barriers to entry affect market competitiveness.

5. **Practical Implications**: It emphasizes the need for a holistic approach when assessing market power in the big data sector, which requires understanding the implications of data ownership and processing capabilities on competition, even if the company doesn't operate directly in an advertising space.

In conclusion, the text posits that a deeper understanding and innovative assessment tools are necessary to evaluate the competitive dynamics of the big data economy effectively.

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X-TIMESTAMP-MAP:LOCAL=00:00:00.000,MPEGTS=900000

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In essence, the idea is that,

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and the question is whether an undertaking in possession

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of big data has market power.

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So, and this is the interface between the concept

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of big data and market power.

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So the, the real question is whether an undertaking

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possession of big data

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and with the ability of processing, it

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can have an adverse impact vis-a-vis competition in

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that specific remain in that specific market.

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So this, study,

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and this article came about in relation

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to this specific point.

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And if big data is linked to the idea

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of market power, then that gives rise

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to a competition question, competition law,

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question competition law, problem.

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And, and whether that's specific undertaking then can have,

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adverse impact vis-a-vis competition in

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that specific market.

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And let me emphasize how, in the first place,

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market power lies at the core of any antitrust assessment,

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any competition or assessment is based

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on the ideal market power.

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Why? Because, the idea is then underlying idea is

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that if an undertaking has market power,

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then it could be theoretically in the position

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to distort a market or to end their competition within,

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that specific remit.

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And so that's the reason why the, analysis,

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and delineation of market power is the cornerstone

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of any kind of competition.

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Law assessment and competition law is also one

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to qualify this very important point.

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Competition is a very complex area.

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Law is based on economic assessment,

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so is based on, on a mer merging between,

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competition, law theories and economic assessment.

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And in the past there has been traditional fracture between,

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economists and lawyers.

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And my personal approach in terms

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of researching these are law has always been how to merge.

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And the, these two positions, how the law should embrace

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and the, economic theories will now

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enter the big data economy.

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So now all of a sudden we are parachuted

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and projected into a new reality, so big online

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business world whereby undertaking, starting possession

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of this big data

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and with some of them, with the ability to process them.

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So my question, and underlining

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and underpinning principles of this study,

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were in essence whether we can use

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the traditional neoclassic theories,

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which have been devised in order

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to determine market power within a specific market,

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which are based on what I define a 3D economy.

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So a 3D award award, which was a little bit more linear and,

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and more and simpler if we, if, if you wish,

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not this online, big market.

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So, and there are two options there.

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Either we are going to simply transpose those matrix,

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those, archetypes which were devised for,

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for 3D world economic assessment,

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or we need to devise new, new paradigm

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and new, tools in order to assess

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a market power within, the big data sector.

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My personal, approach was that I do not believe

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that the traditional archetype of of market assessment

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and competition assessment device for a 3, 3, 3 D world

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and a 3D market can be simply transposable onto the

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a big data economy.

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I think that we can still rely upon them,

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but to a limited extent.

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And I, I would advocate for the adoption

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of a new, paradigm.

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And that is the aim of my study and of my article.

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So the next step of my journey into this study was to try

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to identify what are the traits of the big data market.

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Uh, in essence, when we come

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to a competition law assessment, we, never do it on,

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on the contextualized fashion.

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So, a market doesn't work in, in a vacuum, and,

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but it needs to be contextualized.

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So in order to determine, the idea of market power

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and to delineate a competition, low assessment,

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we need in the first place to understand how a market works.

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So the first step of my journey was to try

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to delineate the traits of the big data economy market.

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And what I immediately found the walls that we are dealing

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with a very complex market,

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and we're dealing with a very complex market

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because it's not a linear one-sided market,

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but it is a so-called multi-sided

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platform, multi-sided market.

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What does it mean? It means

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that traditionally we have markets, structures can vary.

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So we can have a very linear market, which could see,

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some, suppliers

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of products selling those products to buyers.

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And that is a one-sided platform,

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or the so-called multi-sided markets, which give rise

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to complexities in terms of structure

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and also in conversion in terms of competition assessment,

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because these kind of markets give rise to,

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feedback loops to network effects,

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and the possibility also to have an impact on the,

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so-called downstream markets.

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In order to visualize all of this, let, let me

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just give you the example of eBay.

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So how, eBay works. So eBay is Ally a multi-site market.

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So it is an online platform, a device in order

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for sellers to

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Sell goods and to, and for buyers to buy goods.

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Now, the way that eBay works is as multi-sided market

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with feedback loops.

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So these feedback loops are dictated by the idea

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that the higher the number

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of the sellers operating within the eBay platform,

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the more it is appealing for buyers

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to buy on their platform.

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At the same time, the second feedback loop is represented

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by the fact that the higher the number of buyers buying in,

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in on the eBay platform, the higher is the appeal

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for sellers to sell on the platform.

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And this two feedback looks are then jointed

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by a third feedback look, which feeds back onto eBay

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to the platform itself.

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Because the higher is the number of the sellers,

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the higher is the number of the buyers,

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the higher is the profit for eBay.

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Also in terms of revenue, in terms of advertising revenue.

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So this is already a complex market

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where we have network effects

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and we have three big feedback loop.

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In addition to that, we need to consider that,

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eBay is capable of collecting big datas.

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All this data stemming out the operations

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between sellers and buyers.

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And now that can have a huge impact in terms

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of advertising.

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So the power that eBay can gather

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by processing these enormous amount of data in relation

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to sellers and buyers can have an impact on advertising.

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And now eBay is not an advertising platform,

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but as a platform can have an impact on the downstream

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market and the downstream market being

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the advertising market.

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So by virtual, these feedback looks, and by different sides,

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and the idea of

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operating in this complex structure, eBay can,

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by obtaining a large amount of data,

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have an impact on the downstream market, meaning, meaning

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that the advertising market.

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Now these of course, changes this paradigm shifting in terms

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of competitional assessment

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because we, we are not talking about the linear structure,

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in terms of the market.

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So we need to consider the network effects.

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We need to consider, the loop, the loop for,

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effects in terms of feedback, loops.

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And we need to consider the possibility of, eBay,

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for instance, of foreclosing the advertising market,

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the downstream market.

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So, let me qualify also point here that traditionally,

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there is no definition

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of multi-side markets in the doctrine

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and in economic theory.

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So these kind of matters,

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markets are already complex per se,

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and being traditionally complex.

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Now to add to, to this complexity,

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there is an additional layer

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of problematic aspects which are dictated by the fact that,

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we are in presence of a big data, element.

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And that is an element which can add serious repercussions

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from an antitrust perspective.

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So the current status quo,

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and this is the next step, which style is very well

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with the two previous points.

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So the co the, the next step was to try to analyze

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how the current status quo competition law works

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at European level.

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And now we can apply this to the big data economy,

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to the online world.

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So, in essence, what competition law,

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does and how the framework, current framework,

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regulatory framework at European level, in, in relation

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to competition law works is not

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to regulate a market ap priori per se,

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but to intervene in case there are distortions within

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that specific market.

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So that is the aim of competition law.

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And the structure is based on,

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preventing abuses of dominant positions.

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So abuses of, extreme market power, collusions

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between undertakings having market powers

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or collusions in terms of, mergers, giving rise

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to a consolidation of market power,

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which could have potentially adverse

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effects vis-a-vis competition.

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So, and then state aid, which, is something

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that doesn't necessarily apply

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to this specific issue of big data.

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So, in order to,

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assess competition

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and assess whether we are, we are in presence

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of possible distortions, as I said

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before, we key element is the determination of market power.

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Traditionally, these,

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ideal market power is based on the identification

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of the market and the structure of the market itself,

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and the identification of the,

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so-called relevant product market, with the idea being that,

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if we need to, assess what is the market power

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of specific undertaking within the context

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of a specific market, we need to identify

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what is the product market, in the first place.

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And this assessment is based on the concept

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of interchangeability, of the possibility of products

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to be interchangeable with one another.

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We, the underlining idea being

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that the more products are interchangeable with one another,

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the more is likely that they belong to the same market.

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And that is a very important prerequisite for the assessment

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of market power because, if we,

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identify product market whereby the,

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specific undertaking has a lion share of the market, then

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that specific undertaking can be in the position

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of distorting the market, for instance, as a semi case,

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united brands, versus commission.

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And in that case, the commission, was, was asked

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to assess whether bananas represent a separate market,

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separate product market, or whether they belong

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to the market, general market of foods,

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considered on a general level.

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So the idea was, are bananas interchangeable

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with apple spares and oranges?

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If so, they belong to the same market.

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If not, they represent a, a, a, a relevant product market.

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And in order to answer this question, we need to ask,

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basically to theorize

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and devise a counterfactual scenario whereby,

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consumers do not, do not have access to bananas.

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So what are they going to do?

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Are they going to buy, apple apples,

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oranges or pears instead?

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If so, it means that, apples purse

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and oranges are interchangeable with bananas

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and they belong to the relevant product.

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On the contrary, if that is not the case,

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it means the bananas are separate product market and that

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and, and the latter war.

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So the conclusion reached by the European courts in

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that case, and this changes everything drastically.

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Why? And the competition law assessment drastically,

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because if bananas were separate product market,

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united brands,

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involved in the case at something like more than 70% shares

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of the market of banana, instead if we talking,

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we were talking about the market

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of fruit on a general level,

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they had something like less than 10% of the market.

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And that it comes. And that brings me to the next level

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of assessment of market power.

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So we start with the idea of identifying the,

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the relevant product market based on the concept

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of interchangeability, and then we move on

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to analyze the market shares.

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So what are the market shares within this relevant product

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market of that specific undertaking?

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So with the idea of, you know, having more than 50%, 60, 70%

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of the market being an indicator of possible dominance

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and the position of, great market power

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and all of these, denominators

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and these archetypes were devi for 3D economy

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and, used by competition authorities all over the world,

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not by, the competition,

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the European competition authorities,

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but all, all over the world in order

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to determine the assessment of market power.

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So, and this is also needs to be read in conjunction

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with other possible ancillaries, regulatory framework,

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which can have an impact with, with the big data.

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So another economies.

14:05.165 --> 14:09.805
So another, indicator is the, the,

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directive on databases.

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So the database directive

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and that directive conference protection onto,

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undertakings, which have compiled a specific database in

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terms of intellectual property, in terms of

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how they devis the organization,

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from an intellectual property perspective,

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all those specific data.

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And also in terms of investments of, made in order

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to collect the data.

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And that is directly applicable to the big data industry

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in conjunction with also another important theory,

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which is a controversial theory,

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but, very important, which is the theory

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of contestable markets.

14:48.395 --> 14:52.445
According to this economic theory, the idea is that,

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a market, in order to be contestable, it needs to be,

14:56.245 --> 14:58.725
a market where there are no barriers to entry.

14:58.745 --> 15:01.405
So where, other undertakings can freely

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access the market.

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If that is the case, even in presence of consolidation,

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very few players, it doesn't necessarily mean that those

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few players operating within the specific market add market

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power because the barrier of entry are completely open.

15:16.385 --> 15:19.285
So in any moment, other under ations can come

15:19.285 --> 15:20.365
in and compete.

15:20.545 --> 15:24.325
So, that is in, in essence, those are the,

15:24.395 --> 15:27.405
this is the rationale of the contest ability theory,

15:27.415 --> 15:30.885
which brings me to the last point that which was relevant

15:30.885 --> 15:32.205
to my study, which is the,

15:32.205 --> 15:34.245
so-called Essential Facility Doctrine.

15:34.425 --> 15:36.885
So the central facility doctrine entails some

15:36.885 --> 15:39.845
that sometimes we can be in presence

15:40.185 --> 15:41.365
of an essential facility,

15:41.505 --> 15:44.445
so an infrastructure which is necessary for in order

15:44.465 --> 15:48.605
to produce a, some specific, products

15:48.865 --> 15:52.445
or to provide some specific, services, for instance,

15:52.545 --> 15:54.365
when it comes to mobile services,

15:54.365 --> 15:58.165
the infrastructure, in, in terms of, antennas

15:58.585 --> 16:01.245
and so forth in order to provide,

16:01.295 --> 16:04.765
these services could be defined as an essential facility in,

16:05.025 --> 16:08.805
in terms of other undertakings wanted to provide mobile

16:09.325 --> 16:12.605
services having to rely upon this infrastructure which was

16:13.015 --> 16:15.605
built, by a, a different undertaking.

16:15.665 --> 16:17.485
So that undertaking, which is in possession

16:17.485 --> 16:20.885
of the essential facility, essential infrastructure, is,

16:21.265 --> 16:25.045
under, scrutiny from a competition law perspective,

16:25.045 --> 16:28.005
because they essentially determine the market.

16:28.545 --> 16:31.605
And this essential facility doctrine is strictly linked

16:31.665 --> 16:33.725
to the idea of accessibility in a sense

16:33.725 --> 16:35.685
that if you are in possessor

16:35.685 --> 16:38.525
or essential facility, then there's the highest possible

16:38.525 --> 16:40.885
barrier to entry that you can possibly devise for a market.

16:40.955 --> 16:44.205
Because if you don't give access to other players to

16:44.205 --> 16:45.285
that essential facility,

16:45.595 --> 16:48.405
then other players cannot operate within

16:48.755 --> 16:49.805
that specific market.

16:50.185 --> 16:52.285
All of this is relevant when it comes

16:52.285 --> 16:53.565
to the big data sector,

16:53.905 --> 16:58.285
and the idea would be how to, devise a market assessment

16:58.865 --> 17:02.885
of big data taking into account all these theories which

17:02.885 --> 17:06.005
have been elaborated for three, for 3D markets

17:06.145 --> 17:07.205
and traditional markets.

17:07.585 --> 17:10.525
In my opinion, and this is actually I think the most

17:10.845 --> 17:14.325
fascinating, outcome of my, of my research.

17:14.885 --> 17:17.805
I approach all of this with a very open mind and,

17:18.225 --> 17:22.605
and without knowing where all of my research was going

17:22.865 --> 17:26.445
to limit to, but I think the most fascinating aspect is,

17:26.585 --> 17:29.325
and now that we are dealing with the online ward

17:29.355 --> 17:32.365
with the bi dimen, bi dimensional world, with the metaverse,

17:32.685 --> 17:37.005
whatever we want to define, do we need to modify

17:37.585 --> 17:40.925
the, our traditional legal archetypes, which were devised

17:41.065 --> 17:43.605
for a normal and traditional 3D environment?

17:43.955 --> 17:46.525
That to me is very fascinating. Uh, why?

17:46.525 --> 17:50.285
Because, I think that, this new dimension

17:50.825 --> 17:53.285
is in need of new neutralization.

17:53.305 --> 17:56.525
So, so I personally believe there is not possible simply

17:56.625 --> 18:00.965
to transpose, the traditional know classical paradigms

18:01.265 --> 18:03.085
and apply them to the online world

18:03.195 --> 18:05.445
because it is a, it is a new reality.

18:05.865 --> 18:08.325
It is a new reality which has different traits

18:08.585 --> 18:09.725
and different connotations

18:10.185 --> 18:13.365
and requires a different kind of assessment.

18:13.745 --> 18:17.085
For instance, the idea of big data

18:17.505 --> 18:20.645
and how do we assess market power when it comes to big data?

18:20.665 --> 18:23.365
So we cannot just use the concept of interchangeability.

18:23.825 --> 18:28.205
So we can of course, consider whether the data is unique

18:28.225 --> 18:31.125
or not, or whether it can be easily replicated

18:31.705 --> 18:33.965
or whether it can be easily accessible

18:34.225 --> 18:35.405
by other undertakings,

18:35.625 --> 18:38.525
but big data per se, they need

18:38.525 --> 18:40.405
to be processed in order to have any value.

18:40.785 --> 18:43.965
So all of a sudden they interchangeability,

18:44.835 --> 18:46.565
test is not enough anymore.

18:46.825 --> 18:51.685
Why? Because we cannot only stop in considering

18:51.945 --> 18:53.765
the possibility of replicating this data.

18:53.765 --> 18:56.085
These data can be perhaps easily replicated,

18:56.585 --> 19:00.205
but are all the undertakings operating within the market?

19:01.705 --> 19:03.565
Do they have the ability to process it?

19:03.755 --> 19:05.685
Because if you have the ability to process it,

19:05.755 --> 19:08.445
then you have a different market position

19:08.745 --> 19:10.485
and you have a different market power.

19:10.615 --> 19:13.205
Conversely, if you don't have that ability,

19:13.205 --> 19:15.685
perhaps you are in possession of, data,

19:15.735 --> 19:17.885
which doesn't have much of a value.

19:18.305 --> 19:22.445
And then, conversely we have as a consequence the idea

19:22.465 --> 19:25.565
of not having a huge impact in terms

19:25.585 --> 19:26.765
of computational assessment.

19:27.025 --> 19:30.605
So my idea would be to devise a new, assessment,

19:30.705 --> 19:33.885
and this is what I did in terms of market power assessment

19:34.225 --> 19:37.685
for the, big data sector and the big data economy.

19:37.905 --> 19:40.405
So the starting point would be, the identification

19:40.485 --> 19:41.885
of the relevant product market,

19:42.745 --> 19:45.565
but not just based on the concept of interchangeability,

19:45.625 --> 19:49.565
as I just said, but also based on the concept of uniqueness

19:50.025 --> 19:51.045
of the datas

19:51.225 --> 19:54.005
and the idea of the possibility to process the data,

19:54.265 --> 19:56.405
and also the idea of being in presence

19:56.405 --> 19:57.525
of an essential facility.

19:57.755 --> 20:01.805
Because in, if only one undertaking is in position

20:01.805 --> 20:04.125
of the specific data with the ability of processing it,

20:04.675 --> 20:08.245
then those are indicators that we might be in presence

20:08.245 --> 20:09.285
of an essential facility.

20:09.515 --> 20:11.965
That would be the starting point of my,

20:12.105 --> 20:13.765
device assessment.

20:14.105 --> 20:17.485
The second, point would be to try to determine,

20:17.625 --> 20:19.845
and then whether an undertaking position

20:19.845 --> 20:21.765
of big data has market power,

20:22.125 --> 20:24.365
relevant market power appreciable in terms

20:24.465 --> 20:27.725
of having an impact on the competition,

20:27.785 --> 20:30.805
on in the specific, stream, in the specific market.

20:31.225 --> 20:34.165
So my test, my, the test that I devi in order

20:34.165 --> 20:36.965
to identify market power will be based on three steps.

20:37.465 --> 20:41.485
The first step would be to analyze the natural effects.

20:41.785 --> 20:43.445
So we're talking about multis,

20:43.445 --> 20:45.685
Saudi PLA platforms having natural effects.

20:46.145 --> 20:49.285
So, and more specifically, my idea would be

20:49.285 --> 20:50.765
to focus on the number of end users,

20:51.235 --> 20:53.565
because the more users we have,

20:53.905 --> 20:55.645
the more data would be available.

20:56.465 --> 20:59.405
And the feedback loops, like the example

20:59.425 --> 21:02.125
of eBay I was referring to before, previously,

21:02.505 --> 21:05.685
and also the, the possibility as a consequence

21:05.865 --> 21:09.165
of operating within the specific network effects

21:09.265 --> 21:12.365
to have an impact on the downstream market market,

21:12.615 --> 21:15.565
which could be an advertising market when it comes

21:15.565 --> 21:18.725
to big data as a sort of natural revolution there.

21:18.905 --> 21:23.045
The second step, in my opinion would, would be

21:23.045 --> 21:25.925
that this steps should, should require an assessment

21:26.225 --> 21:28.725
of the economies of scale of the data

21:28.725 --> 21:29.885
that we are considered.

21:30.025 --> 21:33.365
So, are we talking about, this data in terms

21:33.385 --> 21:37.005
of re being, possible to be replicated?

21:37.185 --> 21:39.125
So, a replicability of the data

21:39.545 --> 21:42.245
and again, the possibility to process it.

21:42.385 --> 21:47.165
And these, specific second step of assessment talls

21:47.165 --> 21:49.605
and is linked to the idea of the product market,

21:49.735 --> 21:53.165
which is the first, very first step of analysis in terms

21:53.185 --> 21:54.685
of market market assessment.

21:55.385 --> 21:58.365
Lastly, and thirdly, I think that we also need

21:58.365 --> 22:01.845
to focus on contestability traits of,

22:02.105 --> 22:03.925
of the big data sector.

22:04.145 --> 22:08.645
So are we talking about then, first party big data,

22:08.825 --> 22:12.005
for instance, which are collect, collected

22:12.025 --> 22:13.445
by a single undertaking

22:13.625 --> 22:17.725
and can be in any case, very easily, replicated

22:18.345 --> 22:22.805
and these undertaking has no possibility to process them?

22:23.265 --> 22:26.045
Or are we talking about actually,

22:26.345 --> 22:28.365
data which

22:28.825 --> 22:32.165
Are Very unique in the, in, in the

22:32.165 --> 22:36.325
Lessons and which are basically capable, capable

22:36.385 --> 22:38.685
of being processed only by one

22:39.025 --> 22:41.325
or very limited number of undertakings?

22:41.425 --> 22:45.325
So are we talking about a contestable market in terms of,

22:45.905 --> 22:48.485
being open in terms of to entry

22:48.505 --> 22:51.525
or we talking about a very close market whereby the big data

22:51.965 --> 22:53.045
represented an essential facility

22:53.305 --> 22:56.565
and the only possibility for other players to come into

22:56.565 --> 22:59.925
that market and to operate in that market is to,

23:00.015 --> 23:01.685
share those big datas

23:01.835 --> 23:04.645
With the undertaking In possession of them. So

23:05.275 --> 23:08.925
This is the third and last step of my assessment

23:09.585 --> 23:14.285
And all of this, I would say, my journey led me

23:14.305 --> 23:17.645
to have a more holistic approach, if you want to,

23:17.985 --> 23:19.165
define it in that way.

23:19.625 --> 23:22.765
And with the idea of combining, elements

23:23.185 --> 23:24.845
Of, of no Classical

23:24.845 --> 23:26.485
Elements of assessment of market

23:26.485 --> 23:28.765
Power, traditional device, Uh, for

23:28.765 --> 23:31.005
Traditional economies, Transpose them

23:31.715 --> 23:34.685
Into the online world, into the big data sector,

23:34.745 --> 23:36.725
but at the same time by modifying them,

23:37.525 --> 23:38.725
enjoying them together

23:39.145 --> 23:40.285
So as to have a more

23:40.285 --> 23:42.685
Comprehensive approach, I think,

23:42.815 --> 23:46.525
these are commodity elements they don't need to require

23:46.585 --> 23:50.125
to be fulfilled, in, on a commodity level in order

23:50.225 --> 23:52.445
to give rise to competition concerns.

23:52.585 --> 23:55.725
So it might be that, the first step

23:55.745 --> 23:58.485
of assessment in terms of network effects

23:58.985 --> 24:00.325
and food feedback loops

24:00.325 --> 24:03.165
and number of end users could give,

24:03.285 --> 24:06.085
a conclusive indication of the presence of market power.

24:06.385 --> 24:08.685
But if that is not the case, we can,

24:09.365 --> 24:10.485
progress in into assessment,

24:10.585 --> 24:13.525
taking into account also the idea of the economies of scale

24:13.525 --> 24:16.565
of the data and the sustainability, of the market

24:16.665 --> 24:20.285
and the idea of the big data possibly to be rise

24:20.705 --> 24:23.245
and as essential facility as the case may be.

24:23.425 --> 24:25.605
So in other words, it all comes together,

24:25.705 --> 24:29.245
but I think what is very important is that we need

24:29.245 --> 24:33.605
to consider the ability of players in possession of big data

24:34.025 --> 24:36.325
to have an impact, an adverse

24:37.305 --> 24:39.685
impact vis-a-vis the downstream market.

24:40.265 --> 24:43.685
So we shouldn't stop in analyzing

24:44.285 --> 24:47.445
a specific area of, for instance, of a merger

24:47.745 --> 24:50.325
or when it comes to the assessment of market power

24:50.785 --> 24:54.125
of one undertaking, considering what is the object

24:54.125 --> 24:57.085
of the platform that is selling platform.

24:57.515 --> 24:59.725
Fine. That doesn't necessarily mean

25:00.115 --> 25:03.285
that our assessment needs to be constrained

25:03.625 --> 25:05.245
by those specific trade.

25:05.465 --> 25:08.765
So can they, on the basis of the elaboration

25:08.765 --> 25:10.085
and the processing of big data,

25:10.115 --> 25:12.765
have an impact on advertising even if they don't deal

25:13.115 --> 25:14.685
with advertising per se.

25:14.915 --> 25:18.085
That is to me a crucial element that we need

25:18.085 --> 25:21.325
to take into account if we want to conduct,

25:22.195 --> 25:25.685
practical and effective assessment of,

25:26.185 --> 25:29.445
of competition and market power within the big data sector.

00:00 - In essence, the idea is that, and the question is whether an undertaking in possession of big data has market power. So, and this is the interface between the concept of big data and market power. So the, the real question is whether an undertaking

00:17 - possession of big data and with the ability of processing, it can have an adverse impact vis-a-vis competition in that specific remain in that specific market. So this, study, and this article came about in relation to this specific point. And if big data is linked to the idea of market power, then that gives rise to a competition question, competition law,

00:47 - question competition law, problem. And, and whether that's specific undertaking then can have, adverse impact vis-a-vis competition in that specific market. And let me emphasize how, in the first place, market power lies at the core of any antitrust assessment, any competition or assessment is based on the ideal market power. Why? Because, the idea is then underlying idea is that if an undertaking has market power,

01:17 - then it could be theoretically in the position to distort a market or to end their competition within, that specific remit. And so that's the reason why the, analysis, and delineation of market power is the cornerstone of any kind of competition. Law assessment and competition law is also one to qualify this very important point. Competition is a very complex area. Law is based on economic assessment,

01:46 - so is based on, on a mer merging between, competition, law theories and economic assessment. And in the past there has been traditional fracture between, economists and lawyers. And my personal approach in terms of researching these are law has always been how to merge. And the, these two positions, how the law should embrace and the, economic theories will now enter the big data economy. So now all of a sudden we are parachuted

02:18 - and projected into a new reality, so big online business world whereby undertaking, starting possession of this big data and with some of them, with the ability to process them. So my question, and underlining and underpinning principles of this study, were in essence whether we can use the traditional neoclassic theories, which have been devised in order to determine market power within a specific market,

02:46 - which are based on what I define a 3D economy. So a 3D award award, which was a little bit more linear and, and more and simpler if we, if, if you wish, not this online, big market. So, and there are two options there. Either we are going to simply transpose those matrix, those, archetypes which were devised for, for 3D world economic assessment, or we need to devise new, new paradigm and new, tools in order to assess

03:27 - a market power within, the big data sector. My personal, approach was that I do not believe that the traditional archetype of of market assessment and competition assessment device for a 3, 3, 3 D world and a 3D market can be simply transposable onto the a big data economy. I think that we can still rely upon them, but to a limited extent. And I, I would advocate for the adoption of a new, paradigm.

04:02 - And that is the aim of my study and of my article. So the next step of my journey into this study was to try to identify what are the traits of the big data market. in essence, when we come to a competition law assessment, we, never do it on, on the contextualized fashion. So, a market doesn't work in, in a vacuum, and, but it needs to be contextualized. So in order to determine, the idea of market power and to delineate a competition, low assessment,

04:37 - we need in the first place to understand how a market works. So the first step of my journey was to try to delineate the traits of the big data economy market. And what I immediately found the walls that we are dealing with a very complex market, and we're dealing with a very complex market because it's not a linear one-sided market, but it is a so-called multi-sided platform, multi-sided market. What does it mean? It means

05:06 - that traditionally we have markets, structures can vary. So we can have a very linear market, which could see, some, suppliers of products selling those products to buyers. And that is a one-sided platform, or the so-called multi-sided markets, which give rise to complexities in terms of structure and also in conversion in terms of competition assessment, because these kind of markets give rise to, feedback loops to network effects,

05:38 - and the possibility also to have an impact on the, so-called downstream markets. In order to visualize all of this, let, let me just give you the example of eBay. So how, eBay works. So eBay is Ally a multi-site market. So it is an online platform, a device in order for sellers to Sell goods and to, and for buyers to buy goods. Now, the way that eBay works is as multi-sided market with feedback loops.

06:06 - So these feedback loops are dictated by the idea that the higher the number of the sellers operating within the eBay platform, the more it is appealing for buyers to buy on their platform. At the same time, the second feedback loop is represented by the fact that the higher the number of buyers buying in, in on the eBay platform, the higher is the appeal for sellers to sell on the platform. And this two feedback looks are then jointed

06:37 - by a third feedback look, which feeds back onto eBay to the platform itself. Because the higher is the number of the sellers, the higher is the number of the buyers, the higher is the profit for eBay. Also in terms of revenue, in terms of advertising revenue. So this is already a complex market where we have network effects and we have three big feedback loop. In addition to that, we need to consider that,

07:05 - eBay is capable of collecting big datas. All this data stemming out the operations between sellers and buyers. And now that can have a huge impact in terms of advertising. So the power that eBay can gather by processing these enormous amount of data in relation to sellers and buyers can have an impact on advertising. And now eBay is not an advertising platform, but as a platform can have an impact on the downstream

07:36 - market and the downstream market being the advertising market. So by virtual, these feedback looks, and by different sides, and the idea of operating in this complex structure, eBay can, by obtaining a large amount of data, have an impact on the downstream market, meaning, meaning that the advertising market. Now these of course, changes this paradigm shifting in terms of competitional assessment

08:04 - because we, we are not talking about the linear structure, in terms of the market. So we need to consider the network effects. We need to consider, the loop, the loop for, effects in terms of feedback, loops. And we need to consider the possibility of, eBay, for instance, of foreclosing the advertising market, the downstream market. So, let me qualify also point here that traditionally, there is no definition

08:31 - of multi-side markets in the doctrine and in economic theory. So these kind of matters, markets are already complex per se, and being traditionally complex. Now to add to, to this complexity, there is an additional layer of problematic aspects which are dictated by the fact that, we are in presence of a big data, element. And that is an element which can add serious repercussions

08:59 - from an antitrust perspective. So the current status quo, and this is the next step, which style is very well with the two previous points. So the co the, the next step was to try to analyze how the current status quo competition law works at European level. And now we can apply this to the big data economy, to the online world. So, in essence, what competition law,

09:25 - does and how the framework, current framework, regulatory framework at European level, in, in relation to competition law works is not to regulate a market ap priori per se, but to intervene in case there are distortions within that specific market. So that is the aim of competition law. And the structure is based on, preventing abuses of dominant positions. So abuses of, extreme market power, collusions

09:54 - between undertakings having market powers or collusions in terms of, mergers, giving rise to a consolidation of market power, which could have potentially adverse effects vis-a-vis competition. So, and then state aid, which, is something that doesn't necessarily apply to this specific issue of big data. So, in order to, assess competition

10:21 - and assess whether we are, we are in presence of possible distortions, as I said before, we key element is the determination of market power. Traditionally, these, ideal market power is based on the identification of the market and the structure of the market itself, and the identification of the, so-called relevant product market, with the idea being that, if we need to, assess what is the market power of specific undertaking within the context

10:49 - of a specific market, we need to identify what is the product market, in the first place. And this assessment is based on the concept of interchangeability, of the possibility of products to be interchangeable with one another. We, the underlining idea being that the more products are interchangeable with one another, the more is likely that they belong to the same market. And that is a very important prerequisite for the assessment of market power because, if we,

11:19 - identify product market whereby the, specific undertaking has a lion share of the market, then that specific undertaking can be in the position of distorting the market, for instance, as a semi case, united brands, versus commission. And in that case, the commission, was, was asked to assess whether bananas represent a separate market, separate product market, or whether they belong to the market, general market of foods, considered on a general level.

11:48 - So the idea was, are bananas interchangeable with apple spares and oranges? If so, they belong to the same market. If not, they represent a, a, a, a relevant product market. And in order to answer this question, we need to ask, basically to theorize and devise a counterfactual scenario whereby, consumers do not, do not have access to bananas. So what are they going to do? Are they going to buy, apple apples,

12:14 - oranges or pears instead? If so, it means that, apples purse and oranges are interchangeable with bananas and they belong to the relevant product. On the contrary, if that is not the case, it means the bananas are separate product market and that and, and the latter war. So the conclusion reached by the European courts in that case, and this changes everything drastically. Why? And the competition law assessment drastically,

12:41 - because if bananas were separate product market, united brands, involved in the case at something like more than 70% shares of the market of banana, instead if we talking, we were talking about the market of fruit on a general level, they had something like less than 10% of the market. And that it comes. And that brings me to the next level of assessment of market power. So we start with the idea of identifying the,

13:07 - the relevant product market based on the concept of interchangeability, and then we move on to analyze the market shares. So what are the market shares within this relevant product market of that specific undertaking? So with the idea of, you know, having more than 50%, 60, 70% of the market being an indicator of possible dominance and the position of, great market power and all of these, denominators and these archetypes were devi for 3D economy

13:38 - and, used by competition authorities all over the world, not by, the competition, the European competition authorities, but all, all over the world in order to determine the assessment of market power. So, and this is also needs to be read in conjunction with other possible ancillaries, regulatory framework, which can have an impact with, with the big data. So another economies. So another, indicator is the, the,

14:09 - directive on databases. So the database directive and that directive conference protection onto, undertakings, which have compiled a specific database in terms of intellectual property, in terms of how they devis the organization, from an intellectual property perspective, all those specific data. And also in terms of investments of, made in order to collect the data.

14:34 - And that is directly applicable to the big data industry in conjunction with also another important theory, which is a controversial theory, but, very important, which is the theory of contestable markets. According to this economic theory, the idea is that, a market, in order to be contestable, it needs to be, a market where there are no barriers to entry. So where, other undertakings can freely access the market.

15:03 - If that is the case, even in presence of consolidation, very few players, it doesn't necessarily mean that those few players operating within the specific market add market power because the barrier of entry are completely open. So in any moment, other under ations can come in and compete. So, that is in, in essence, those are the, this is the rationale of the contest ability theory, which brings me to the last point that which was relevant to my study, which is the,

15:32 - so-called Essential Facility Doctrine. So the central facility doctrine entails some that sometimes we can be in presence of an essential facility, so an infrastructure which is necessary for in order to produce a, some specific, products or to provide some specific, services, for instance, when it comes to mobile services, the infrastructure, in, in terms of, antennas and so forth in order to provide,

16:01 - these services could be defined as an essential facility in, in terms of other undertakings wanted to provide mobile services having to rely upon this infrastructure which was built, by a, a different undertaking. So that undertaking, which is in possession of the essential facility, essential infrastructure, is, under, scrutiny from a competition law perspective, because they essentially determine the market. And this essential facility doctrine is strictly linked to the idea of accessibility in a sense

16:33 - that if you are in possessor or essential facility, then there's the highest possible barrier to entry that you can possibly devise for a market. Because if you don't give access to other players to that essential facility, then other players cannot operate within that specific market. All of this is relevant when it comes to the big data sector, and the idea would be how to, devise a market assessment

16:58 - of big data taking into account all these theories which have been elaborated for three, for 3D markets and traditional markets. In my opinion, and this is actually I think the most fascinating, outcome of my, of my research. I approach all of this with a very open mind and, and without knowing where all of my research was going to limit to, but I think the most fascinating aspect is, and now that we are dealing with the online ward with the bi dimen, bi dimensional world, with the metaverse,

17:32 - whatever we want to define, do we need to modify the, our traditional legal archetypes, which were devised for a normal and traditional 3D environment? That to me is very fascinating. why? Because, I think that, this new dimension is in need of new neutralization. So, so I personally believe there is not possible simply to transpose, the traditional know classical paradigms and apply them to the online world because it is a, it is a new reality.

18:05 - It is a new reality which has different traits and different connotations and requires a different kind of assessment. For instance, the idea of big data and how do we assess market power when it comes to big data? So we cannot just use the concept of interchangeability. So we can of course, consider whether the data is unique or not, or whether it can be easily replicated or whether it can be easily accessible by other undertakings,

18:35 - but big data per se, they need to be processed in order to have any value. So all of a sudden they interchangeability, test is not enough anymore. Why? Because we cannot only stop in considering the possibility of replicating this data. These data can be perhaps easily replicated, but are all the undertakings operating within the market? Do they have the ability to process it? Because if you have the ability to process it,

19:05 - then you have a different market position and you have a different market power. Conversely, if you don't have that ability, perhaps you are in possession of, data, which doesn't have much of a value. And then, conversely we have as a consequence the idea of not having a huge impact in terms of computational assessment. So my idea would be to devise a new, assessment, and this is what I did in terms of market power assessment

19:34 - for the, big data sector and the big data economy. So the starting point would be, the identification of the relevant product market, but not just based on the concept of interchangeability, as I just said, but also based on the concept of uniqueness of the datas and the idea of the possibility to process the data, and also the idea of being in presence of an essential facility. Because in, if only one undertaking is in position

20:01 - of the specific data with the ability of processing it, then those are indicators that we might be in presence of an essential facility. That would be the starting point of my, device assessment. The second, point would be to try to determine, and then whether an undertaking position of big data has market power, relevant market power appreciable in terms of having an impact on the competition,

20:27 - on in the specific, stream, in the specific market. So my test, my, the test that I devi in order to identify market power will be based on three steps. The first step would be to analyze the natural effects. So we're talking about multis, Saudi PLA platforms having natural effects. So, and more specifically, my idea would be to focus on the number of end users, because the more users we have, the more data would be available.

20:56 - And the feedback loops, like the example of eBay I was referring to before, previously, and also the, the possibility as a consequence of operating within the specific network effects to have an impact on the downstream market market, which could be an advertising market when it comes to big data as a sort of natural revolution there. The second step, in my opinion would, would be that this steps should, should require an assessment of the economies of scale of the data

21:28 - that we are considered. So, are we talking about, this data in terms of re being, possible to be replicated? So, a replicability of the data and again, the possibility to process it. And these, specific second step of assessment talls and is linked to the idea of the product market, which is the first, very first step of analysis in terms of market market assessment. Lastly, and thirdly, I think that we also need

21:58 - to focus on contestability traits of, of the big data sector. So are we talking about then, first party big data, for instance, which are collect, collected by a single undertaking and can be in any case, very easily, replicated and these undertaking has no possibility to process them? Or are we talking about actually, data which Are Very unique in the, in, in the

22:32 - Lessons and which are basically capable, capable of being processed only by one or very limited number of undertakings? So are we talking about a contestable market in terms of, being open in terms of to entry or we talking about a very close market whereby the big data represented an essential facility and the only possibility for other players to come into that market and to operate in that market is to, share those big datas

23:01 - With the undertaking In possession of them. So This is the third and last step of my assessment And all of this, I would say, my journey led me to have a more holistic approach, if you want to, define it in that way. And with the idea of combining, elements Of, of no Classical Elements of assessment of market Power, traditional device, for Traditional economies, Transpose them

23:31 - Into the online world, into the big data sector, but at the same time by modifying them, enjoying them together So as to have a more Comprehensive approach, I think, these are commodity elements they don't need to require to be fulfilled, in, on a commodity level in order to give rise to competition concerns. So it might be that, the first step of assessment in terms of network effects

23:58 - and food feedback loops and number of end users could give, a conclusive indication of the presence of market power. But if that is not the case, we can, progress in into assessment, taking into account also the idea of the economies of scale of the data and the sustainability, of the market and the idea of the big data possibly to be rise and as essential facility as the case may be. So in other words, it all comes together,

24:25 - but I think what is very important is that we need to consider the ability of players in possession of big data to have an impact, an adverse impact vis-a-vis the downstream market. So we shouldn't stop in analyzing a specific area of, for instance, of a merger or when it comes to the assessment of market power of one undertaking, considering what is the object of the platform that is selling platform. Fine. That doesn't necessarily mean

25:00 - that our assessment needs to be constrained by those specific trade. So can they, on the basis of the elaboration and the processing of big data, have an impact on advertising even if they don't deal with advertising per se. That is to me a crucial element that we need to take into account if we want to conduct, practical and effective assessment of, of competition and market power within the big data sector.

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