An Empirical Investigation of Diversity in U.S. Arbitration
Posted: 13 Aug 2021 Last revised: 11 Oct 2021
Date Written: March 1, 2021
For decades, the United States system of arbitration has been subject to nearly constant public criticism. Calling arbitration a rigged judicial system, consumer and employee rights groups have voiced opposition to the practice of "forced arbitration" whereby millions of Americans are contractually required to settle disputes in arbitration rather than in litigation. Just when it seemed that things couldn't possibly get any more controversial, arbitration is in the hot seat again, this time over the lack of diversity amongst U.S. arbitrators. In the wake of a national racial reckoning and high profile cases that have called attention to the issue, arbitration has been called the place "where white men rule." Despite this national attention, the number of rigorous empirical studies investigating diversity in arbitration is limited. Because much of the diversity conversation has been based on anecdotal information and survey data which doesn't cover the full population of U.S. arbitrators, basic facts about the demographic profile of U.S. arbitrators are still unknown: What percent of arbitrators are diverse (i.e.—Black, Hispanic, Asian, or female)? Are diverse arbitrators under-selected for arbitration cases compared to their white male counterparts? Most importantly, to what extent do the race and gender of the arbitrator impact the outcome of the arbitration?
This paper contributes to the literature by using an originally-collected data set of arbitrator race, ethnicity and gender from the two largest arbitration firms in the U.S., Judicial Arbitration and Mediation Services (“JAMS”) and the American Arbitration Association (“AAA”). The data were collected using public data sources and cutting-edge machine learning techniques. This is the first-ever empirical effort to estimate the race and ethnicity of arbitrators for both the JAMS and AAA populations. The analysis presents estimates of the demographic profile of the supply of U.S. arbitrators, the demographic profile of the subset of arbitrators that are actually selected to arbitrate, as well as regression analyses examining the relationship between arbitrator diversity and arbitration outcomes.
The study has four main findings. First, women and people of color are underrepresented amongst U.S. arbitrators, both relative to the U.S. population and relative to the population of American lawyers and judges. The extent of the underrepresentation for both groups is significant, though it is more severe for arbitrators of color than for female arbitrators.
Second, the rate at which arbitrators of color and women are selected to arbitrate is generally proportional to their (very low) representation in arbitration organizations.
Third, with respect to arbitration outcomes, there is no statistically significant relationship between an arbitrator's race/ethnicity and how they decide cases. However, the reason for this non-finding is that the study is statistically under-powered. In other words, with less than 4% of arbitrators being Black, Asian, or Hispanic, there are simply not enough arbitrators of color to even conclusively study the relationship between racial / ethnic diversity and arbitration outcomes. This, in and of itself, is a powerful statement about the lack of diversity in US arbitration.
Fourth, as has been found in some previous studies, the evidence suggests that female arbitrators are less likely than their male counterparts to rule in favor of plaintiffs. The likely reason for this is differential case selection; female arbitrators disproportionately decide cases that are harder for plaintiffs to win. This gender effect exists in both JAMS and AAA arbitrations, though the results vary from statistically significant in some models (p<0.05) to weakly statistically significant in others (p<0.10).
The study concludes by offering policy observations for the road forward.
Keywords: machine learning, race and the law, arbitration, empirical legal studies, alternative dispute resolution, diversity, empirical, AAA, American Arbitration Association, JAMS
JEL Classification: K00, J7, J52, C55
Suggested Citation: Suggested Citation