Regression analysis, a common method of attempting to demonstrate raci
al and gender discrimination in hiring and other contexts, suffers fro
m a number of shortcomings that invariably cast doubt on any incrimina
ting results it produces. Most notably, regression models may fail to
account for variables which correlate both with legitimate goals of a
decisionmaker and with race/gender categories. In this article, Profes
sors Ayres and Waldfogel offer an alternative method of measuring disc
rimination that overcomes the limitations of traditional regression te
sts. The authors present a market-based test of unjustified disparate
impact using data from the bail bond market in New Haven, Connecticut,
to demonstrate that New Haven courts systematically ''overdeter'' bla
ck and male Hispanic defendants from fleeing after release on bail by
setting bail at seemingly unjustified high levels for these groups. Pr
ofessors Ayres and Waldfogel discuss the validity of the assumptions u
nderlying their model, and of possible nondiscriminatory explanations
for their finding of disparate impact. They also offer suggestions for
application of their methodology in contexts other than bail setting.