Platform mergers and antitrust

Abstract

Should internet era merger policy differ from industrial era merger policy? Platform ecosystems rely on economies of scale, data-driven economies of scope, high-quality algorithmic systems, and strong network effects that frequently promote winner-take-most markets. Their market dominance has generated competition concerns that appear difficult to assess with traditional merger policy tools. This paper examines the acquisition strategies of the five major U.S. platforms—Google, Amazon, Facebook, Apple, and Microsoft—since their inception. We discuss the main merger and acquisition theories of harm and how these operate differently than in the past. To address merger and acquisition concerns of multi-sided platforms, we develop four proposals that incorporate (i) a new ex ante regulatory framework, (ii) an update of the conditions under which the notification of mergers should be compulsory and the burden of proof should be reversed, (iii) differential regulatory priorities in investigating horizontal versus vertical acquisitions, and (iv) an update of competition enforcement tools to increase visibility into market data and trends.

M&A and early investment decisions by digital platforms

“…Merger policies on digital markets and digital platform ecosystems have been the focus of a recent and growing literature in economics, which highlights that guidance for competition authorities is particularly urgent in this sector. Most importantly, as highlighted in Parker et al ( 2021 ) and Argentesi et al ( 2021 ) among others, analyses that study antitrust policy from an ex-post perspective are at high risk of falling short in the fast-evolving digital platform ecosystem, where large network effects and big data advantage enhance concentration remarkably quickly. On the other hand, Cabral ( 2021 ) notes that “firm acquisition may be the simplest path for an incumbent to acquire the technology created by an entrant”: part of his paper, which cautions against the abuse of preemptive merger measures in digital ecosystems, highlights the important role of acquisitions as a form of technology transfer in digital industries and “when the entrant’s technology is a complement with respect to the incumbent’s assets, anticipated acquisition provides a significant innovation incentive”.…”

“…Successful platforms grow fast 1 and a remarkable volume of merger and acquisition (M&A) activities is observed in digital platform ecosystems (Motta, 2021 ; Cabral, 2021 ; Parker et al, 2021 ); most of these processes are initiated by large multisided platforms and target young and innovative startups (Gautier, 2021 ; Argentesi, 2021 ; Fons-Rosen, 2021 ). The intensity of M&A operations in this sector has been attracting the attention of academic literature and antitrust authorities alike, given that such activities can enhance efficiency but also be a threat to competition, so that predicting the overall effect on consumer welfare is far from straightforward.…”

“… 1 The causes of this phenomenon have been the object of several recent studies and found to be due to a combination of network effects, economies of scale and to the efficiency of algorithms growing with data volume (Parker et al 2021 ; Costa et al 2021 ; van der Aalst et al 2019 ; Parker, Petropoulos, and Van Alstyne (forthcoming) ) …”

Regulating Algorithmic Learning in Digital Platform Ecosystems through Data Sharing and Data Siloing: Consequences for Innovation and Welfare

“…Algorithmic learning gives rise to data-driven network effects (Argenton and Prüfer, 2012;Prüfer and Schottmüller, 2020;Gregory et al, 2020), which have now taken a central role in the debate on the regulation of dominant digital platforms (Kraemer and Schnurr, 2021;Parker et al, 2021;European Commission, 2020;Cennamo and Sokol, 2021). Data-driven network effects are an indirect network effect that constitutes a virtuous cycle as follows: The use of a data-driven service (or product) generates more data, which is the basis for improved algorithmic learning and data analytics, which then allows to further improve the data-driven service, and which then ultimately increases demand, which in turn generates even more data, and so on and so forth.…”

“…By contrast, mandated data sharing allows the regulated firm to use its data acquired in the primary market also in the secondary market, but requires to share this data with rivals so that they compete on more equal footing. Mandated data sharing has been suggested as a promising policy intervention by several scholars (e.g., Argenton and Prüfer, 2012;Kraemer and Schnurr, 2021;Prüfer and Schottmüller, 2020;Parker et al, 2021), as it immediately exploits the non-rivalry of data, which is the main economic property that makes data inputs distinct from physical inputs. Thus, in contrast to data siloing, which limits the extent of data externalities and algorithmic learning, as well as its associated efficiencies, data sharing permits these efficiency spillovers to the secondary market at the cost of increased competition arising from sharing these efficiencies with the rival.…”