Economies of scope in data aggregation (ESDA) are generated by the combination of complementary datasets involving the same observations. We estimate ESDA by progressively and randomly adding health and socioeconomic variables (predictors) to the machine-learning models we use to predict health outcomes. We find a positive effect of the number of variables on prediction quality, while holding the number of observations constant. We observe a positive relationship between variable complementarity and ESDA. ESDA show signs of increasing returns followed by decreasing returns. We further observe a long tail of highly contributing predictors in our data. These findings indicate that the nature of returns to scope in data aggregation may depend on the distribution of the predictors’ information content. This underscores the importance of variable characteristics in determining ESDA’s potential to create data barriers to entry. These results can help policymakers in designing data sharing initiatives such as the European Union's Common European Data Spaces.

This paper examines the welfare effects of mergers between complement producers. We use a Hotelling model with vertical differentiation and a captive market (hinterland). Unlike standard Bertrand or vertical differentiation models, which predict unambiguous consumer benefits, our model reveals a richer set of welfare outcomes. Consumer surplus and total welfare may increase or decrease post-merger: This depends on allocative efficiency (how consumers are distributed between firms) and output efficiency (total output achieved). We show that mergers can increase consumer surplus while reducing total welfare: This depends on the interaction between intrinsic value effects, transportation cost effects, and the size of the hinterland. The type of differentiation – whether vertical or in transportation costs – is critical, as it influences the balance between the elimination of double marginalization and raising rivals’ costs. These findings underline the need to account for market-specific factors when assessing the welfare implications of mergers between complement producers.

Despite the popularization of the concept of 'ecosystems' among academics and practitioners, ecosystems have so far failed to become a coherent unit of analysis in competition law. One of the main reasons is that we lack empirical methodologies to define their boundaries. In this article we describe five non-exclusive methodologies that allow to define ecosystems' boundaries ('ecosystem definition') and, in some cases, describe their structure: hierarchical clustering, k-means clustering, network of complementarities, factor analysis and snowball selection. Furthermore, we provide guidance on when each of them is pertinent. We describe the limitations of the substitutability, aftermarkets, and connected markets approaches in cases in which ecosystems are the main unit of analysis. Finally, we provide four examples of how, in such cases, these methodologies presented can be useful to practitioners: i) market inquiries, investigations, and pro-competition interventions; ii) designation of undertakings as having strategic market status; iii) conglomerate mergers and iv) calculation of fines and quantification of harm/effects.  

In today’s shifting global landscape, the long-held view that industrial policy and competition policy are fundamentally at odds is being reconsidered. In an era marked by geopolitical tensions, rapid technological change and vulnerable supply chains, governments are increasingly exploring how these policy tools might reinforce one another. In the European context, this shift has become increasingly urgent as industries face investment stagnation, rising production costs and technological gaps that national measures alone struggle to address. In sectors such as defence, fragmented markets and home-biased procurement are undermining innovation and scale, prompting calls for more integrated approaches. How can the balance between market openness and strategic intervention be maintained? What forms of institutional coordination will support this shift? How can long-term competitiveness be secured without repeating the missteps of past industrial activism? 

​The digital economy has brought new business models that rely on zero-price markets and multi-sided platforms nested in business ecosystems. The traditional concept of market power used by competition authorities cannot engage with this new reality in which (economic) power manifests beyond price and output within a relevant market. These developments have culminated in multiple recent calls for a more multidimensional concept of power. Consequently, suggestions over new concepts of power triggering antitrust/regulatory intervention, such as ‘strategic market status’, ‘conglomerate market power’, ‘intermediation power’, ‘structuring digital platforms’, or ‘gatekeepers’ have proliferated to complete, or even substitute, the archetypical concept of market or monopoly power in competition law. However, a theoretical framework for this multidimensional concept of power that can set the basis for new metrics is missing. This article makes three contributions in that direction. First, we conceptualize different forms of (economic) power that go beyond competition within a single relevant market in terms of competition law and economics. Second, we propose new metrics to measure two forms of power: panopticon power and power based on differential dependency between value co-creators. Third, we test the latter and show how they could reduce false positives and false negatives when assessing dominance.

This paper relates participation in global value chains (GVCs) to development patterns at the country level. It accounts for the diversity and interdependence of development through a cross-country analysis for 51 countries between 1995 and 2008. We identify three patterns of socio-economic development related to various degrees and modes of GVC participation: a social upgrading mirage, the reproduction of the core and unequal growth. This result is achieved thanks to the introduction of two new elements to the literature: first, the introduction of new macroeconomic indicators of GVC participation and economic gains that are explicitly based in a theoretically consistent definition of GVCs; second, the identification of a variety of interdependent development patterns related to GVC participation through the use of principal component analysis and cluster analysis.

Mobility-as-a-service (MaaS) schemes combining information about and access to multiple means of transportation through a single electronic interface require transportation operators sharing data. In doing so, they coopete: they compete over rides while they collaborate to build a service that can bring them all more rides. Building on microeconomic theory and the experiences of the existing MaaS schemes, we show that, although every operator has incentives to share data if a critical mass of coopetitors do, the coopetitive dynamics of data sharing can lead to multiple mixes of operators. In some mixes, certain operators will not decide not to participate.

This paper shows that the irruption of digital platforms is creating winner-takes-all dynamics in ride-hailing markets. We argue that ride-hailing markets are becoming winner-takes-all for two reasons: product differentiation is not very relevant to carve out market niches and indirect network effects are particularly strong in ride-hailing apps, especially because they are enhanced by the user “data snowball effect” to a larger extent than in other digital platforms. We provide an empirical corroboration of these findings that builds on available data from the main cities of the United States. After defining the relevant market of ride-hailing platforms, we compare the existent data to show that, as expected in winner-takes-all markets, the market shares of a single firm (Uber) have been increasing to the detriment of its competitors’. Finally, we show that Uber’s strategy to conquer these markets seems to be based on predatory pricing and we suggest three ways in which Uber might recoup its losses from predation.