How the Uber & Lyft Case Provides an Impetus to Re-Examine Buyer Power in the World of Big Data and Algorithms
Lund University Legal Research Paper Series Lund Comp Working Paper 01/2017
10 Pages Posted: 14 Jul 2017 Last revised: 3 Aug 2017
Date Written: July 7, 2017
For competition lawyers, Uber is an interesting subject to study. Not only does Uber change the dynamics of the transportation market but it also raises interesting competition law questions. Last year for example, a class action suit against Uber in New York raised the question whether Uber is possibly arranging a hub and spoke cartel amongst the drivers by coordinating their selling prices. 2017 has continued to be litigious and interesting.
One of these new class action lawsuits might also raise thought-provoking antitrust issues related to big data and buyer power. Uber, the maverick firm that revolutionized passenger transportation services across the world has been now sued over its alleged use of its “Hell” software before the U.S. District Court for the Northern District of California filed on April 24th, 2017. The suit alleges a breach of privacy laws due to interception of private communications and unfair competition.
This software apparently allowed Uber to track Lyft drivers, its main competitor, create fake Lyft accounts, determine which drivers drove for both companies, and “execut[e] a plan meant to entice double-appers to drive exclusively for them”.
In this paper we explore such behaviour from a different perspective, the antitrust one. The focus of this paper is on exploring relevant behavior from a buyer power-oriented focusing on reverse rebates and overbuying, while not engaging in a concrete analysis of Uber’s conduct. This analysis provides us with the opportunity to re-explore traditional antitrust concepts, anchored on the purchasing of raw material, in the data and algorithm driven world, in particular, how companies can use big data in anticompetitive strategies, such as granting supra-competitive bonuses, overbuying, and raising rival’s costs through overbuying input.
Keywords: buyer power, big data, Uber, Lyft, predation, competition, antitrust, reverse rebates
JEL Classification: K21, K22, K23, H57
Suggested Citation: Suggested Citation