Econometrics in Litigation: Challenges at Class Certification

Gareth Macartney ()
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Gareth Macartney: OnPoint Analytics

A chapter in Modern Agricultural and Resource Economics and Policy, 2022, pp 311-346 from Springer

Abstract: Abstract This chapter describes the increasing challenges faced by econometrics in antitrust class actions. The main hurdle for plaintiffs in any class action in the United States is to get the court to approve that the case be tried as a class in the first place. This phase of the case is colloquially referred to as “class cert.” Much of the battle is fought by economic experts, analyzing whether common evidence can be used to prove that “all or virtually all” class members were injured by the alleged antitrust violation (known as “common impact”) and whether common evidence can be used to estimate aggregate class-wide damages. In the last decade or so, through several landmark rulings, the legal standard for plaintiffs to prevail at class cert has increased substantially. We discuss the repercussions of these rising standards, including a decrease in class actions in favor of direct actions, the new burden placed on regression models, and the erroneous methods increasingly used by defendants’ experts to impugn plaintiffs’ experts’ models. Traditional econometric models can struggle to meet this burden, as the emphasis shifts from measuring average effects on the class, in the direction of measuring individual effects specific to each class member, but using a common econometric model to do so. Specifically, whereas in the past econometric models were tasked with estimating an average price increase to prove aggregate class-wide damages, they are increasingly also being asked to predict what prices each individual class member would have paid absent the antitrust violation, in order to prove common impact. We describe the challenges econometric models have in achieving this. We explore hypothesis testing of common impact and how individual prediction might be improved, including through machine learning techniques. We also describe the challenges for such techniques to be accepted by courts.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:nrmchp:978-3-030-77760-9_14

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DOI: 10.1007/978-3-030-77760-9_14

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