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Big Data and Competition Policy by Stucke, Maurice; Grunes, Allen (1st June 2016)

Part V Advancing a Research Agenda for the Agencies and Academics, 19 Understanding and Assessing Data-Driven Efficiencies Claims

Maurice E. Stucke, Allen P. Grunes

From: Big Data and Competition Policy

Maurice Stucke, Allen Grunes

From: Oxford Competition Law (http://oxcat.ouplaw.com). (c) Oxford University Press, 2015. All Rights Reserved.date: 19 July 2019

Subject(s):
Barriers to entry — Market power — Rights — Internet — Technology — National merger control

This chapter analyses the significant procompetitive efficiencies that emerge from data-driven mergers. As companies undertake data-driven business strategies, one might expect them to raise data-driven efficiencies. Big Data and data-driven network effects can be characterized as entry barriers or efficiencies. An enforcement agency may characterize the need to continually update a significant volume and variety of data as an entry barrier. The merging parties on the other hand may characterize the combining of the volume and variety of their data, and the increase in the velocity in processing data as a procompetitive efficiency, enabling them to deliver value to consumers, such as better quality products. The chapter evaluates several potential data-driven efficiencies, using the three criteria employed in the US and EU, namely whether the efficiencies benefit consumers, are merger-specific, and are verifiable.

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