Improved Statistical Methods for the Calculation of Damages in Discrimination Lawsuits

Start Page



This paper develops a new method to calculate individual-specific damage payments in discrimination lawsuits using empirical Bayes techniques and a simple random-coefficients model. The method yields payments that can be mathematically proven to be more accurate than existing statistically based approaches, as measured by the mean squared error. This method also provides a natural justification for not playing the members of the noninjured class and substantially reduces the bias caused by the legal restriction on negative payments. We empirically demonstrate our method in the context of mortgage-pricing decisions using detailed loan-level data from a large subprime lender.

Full text not available in ChicagoUnbound.