Contextual Evidence of Gender Discrimination: The Ascendance of 'Social Frameworks'

Abstract

In Dukes v. Wal-Mart, the Ninth Circuit upheld the certification of the largest employment discrimination class in history, with more than 1.5 million women employees seeking over $1.5 billion in damages. A crucial piece of evidence supporting class certification came from a sociologist who testified that he performed a social framework analysis to evaluate Wal-Mart against what social science research shows to be factors that create and sustain bias and found the company wanting. As authority for introducing this analysis, the expert and the Ninth Circuit relied on our prior work introducing the concept of social framework to refer to the use of general social science research to provide a context for the determination of specific factual issues in litigation. In this article, we review and recast the procedures originally proposed for apprising juries of general research results to assist in resolving the case before them. We then apply these updated procedures to the expert testimony in Dukes v. Wal-Mart, which promises to be a template for future employment discrimination litigation.

Experience over the past 20 years has shown that that courts will typically allow general contextual information from social science research to be conveyed to the jury by expert witnesses rather than via instructions, as originally envisioned. Where this occurs, we believe it essential that courts limit expert testimony to a description of the findings of relevant and reliable research and of the methodologies that produced those findings, and preclude the witness from linking the general research findings to alleged policies and practices of a specific firm. The landmark class action of Dukes v. Wal-Mart illustrates the centrality of social framework evidence to modern employment litigation, as well as the need for courts to clarify and circumscribe the role of the experts who introduce them.

Keywords: social framework, employment discrimination, expert, Daubert, stereotype, bias