Splitting Logs: An Empirical Perspective On Employment Discrimination Settlements

27 Pages Posted: 24 Dec 2015

Date Written: May 1, 2011


Most cases settle, in employment discrimination litigation as elsewhere. Unfortunately, empirical knowledge of settlements remains limited. Data scarcity fuels untested perceptions and, all too frequently, misconceptions about how employment disputes are resolved. This Essay exploits a unique data set of successful settlements in the U.S. District Court for the Northern District of Illinois from 1999-2004 that includes for each case the plaintiffs initial monetary demand, the defendant's offer, and the resulting settlement. We find that in raw constant dollars, final settlements are typically Jar closer to defendant offers than plaintiff demands. After converting plaintiff demands, defendant offers, and final settlements into natural logs, however, the typical settlement splits the difference between plaintiff demand and defendant offer. We also find that settlement amounts rise if a trial date is set for a case. Finally, results from three-stage least squares models-that plain­tiff demands influence defendant offers that, in tum, influence final settlement amounts-provide a glimpse into the structure of employment discrimination settlements.

Keywords: employment discrimination, U.S. District Court, settlements

Suggested Citation

Schwab, Stewart Jon and Heise, Michael, Splitting Logs: An Empirical Perspective On Employment Discrimination Settlements (May 1, 2011). Cornell Law Review, Vol. 96, No. 4, 2011, Cornell Legal Studies Research Paper, Available at SSRN: https://ssrn.com/abstract=2706525

Stewart Jon Schwab (Contact Author)

Cornell Law School ( email )

Myron Taylor Hall
Ithaca, NY 14853
United States
607.255.8584 (Phone)
607-255-7193 (Fax)

Michael Heise

Cornell Law School ( email )

308 Myron Taylor Hall
Ithaca, NY 14853-4901
United States
607-255-0069 (Phone)
607-255-7193 (Fax)

Here is the Coronavirus
related research on SSRN

Paper statistics

Abstract Views
PlumX Metrics