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An Empirical Investigation of Arbitrator Race and Gender in U.S. Arbitration
Chandrasekher, Andrea Cann
Chandrasekher, Andrea Cann
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Abstract
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 resolve disputes in arbitration rather than in litigation.
On top of the concerns over the unfairness of forced arbitration itself, recent
attention has been drawn to the lack of racial and gender diversity within the
arbitrator profession. When women and racially marginalized plaintiffs are
forced to arbitrate their employment discrimination or consumer-based claims
in the arbitral forum, that they may have no meaningful access to arbitrators
that look like them seems additionally problematic.
Scholars in the field have argued back and forth about the root of the
diversity problem. Is it a labor supply problem? In other words, are parties to
arbitration open to hiring marginalized arbitrators but there are just not enough
to choose from? Or is it a labor demand problem? In other words, when women
and arbitrators of color are available, are they chosen at rates consistent with
their white male counterparts? Or, are both supply and demand problems at
work? Because much of the scholarly diversity conversation has been based on
anecdotal information and survey data which don’t cover the full population of
U.S. arbitrators, these basic questions are still unanswered.
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 scholarly effort to empirically 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 and the
demographic profile of the subset of arbitrators that are actually selected to
arbitrate—with a special focus on the extent to which under-selection is
happening.
The study has four main findings. First, along the supply dimension,
women and people of color are underrepresented amongst JAMS 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. For AAA arbitrators, I find an even greater degree of underrepresentation
for Black arbitrators.
Second, along the demand dimension, I find different results for JAMS and
AAA. For JAMS, I find that, conditional on being selected to arbitrate at least
once in the sample period, Asian and Black arbitrators receive fewer cases than
their proportional share, and female arbitrators receive slightly more cases than
their proportional share. Moreover, arbitrators that were formerly judges
receive more cases than their proportional share. For AAA, the selection
analysis is hampered by limited data availability. However, the data that I do
have suggest that diverse neutrals are selected for cases at a rate that is at or
above their proportional share.
Third, given the first two results, my data suggest that diversity issues exist
both along the labor supply dimension and the labor demand dimension within
U.S. arbitration.
Fourth and finally, I find that future empirical diversity work in arbitration
will be severely hindered unless more and better data are available to
researchers.
The study concludes by offering concrete and specific recommendations
for how and why better data should be collected and made available to the
public.