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USING REGRESSION ANALYSIS TO COMPUTE BACK PAY

Kevin Gilmartin and Elizabeth Hartka
Jurimetrics
Vol. 31, No. 3 (Spring 1991), pp. 289-317
Published by: American Bar Association
Stable URL: http://www.jstor.org/stable/29762222
Page Count: 29
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
USING REGRESSION ANALYSIS TO COMPUTE BACK PAY
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Abstract

Multiple regression analysis is frequently used to determine liability in employment discrimination lawsuits, yet regression analysis has not been used until recently as a method for computing back pay for eligible class members. Reasons are presented for preferring regression analysis to alternate methods used in the past, and a practical description is given of how regression coefficients can be used to compute relief. Issues addressed when applying the methodology include the functional form of the regression model (e.g., log vs. linear), the types of explanatory variables to include, and how to handle employees' breaks in service with the employer and periods of leave with or with-out pay. Of particular importance are methods for excluding the effects of employment decisions prior to the time period that is legally actionable and methods for excluding the effects of disparities prior to an employee's date of hire when computing relief for that employee.

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