The intriguing results of a highly-publicized EEOC lawsuit highlight that if the EEOC is to win a judgment against an employer for disparate impact discrimination, it must use valid statistical methodology to prove “disparate impact”.
This case demonstrates why that will not be easy to do.
EEOC v Kaplan
The EEOC recently sued Kaplan Higher Education Corporation for “disparate impact” discrimination, alleging Kaplan’s use of employment credit checks causes it to disproportionately screen out more African-American applicants than white applicants, creating a disparate impact in violation of Title VII of the federal Civil Rights Act. The Commission attempted to establish this fact by using an expert witness analysis of a pool of background checks from one of Kaplan’s vendors.
Among the problems facing EEOC expert Kevin Murphy, was that Kaplan did not have racial identification in the applicant files, and without that, according to the courts, there was no way to demonstrate discrimination by disparate impact. Although he could try to reconstruct the racial identity of some of the applicants from other files, he had to develop a methodology to assign race to many of the applicants. This method, which was apparently invented by Murphy solely for this trial, was the pivot and end point of the case.
In the first trial, the district court threw out the EEOC’s expert research-based testimony on grounds that the methodology was flawed, and the EEOC appealed. After hearing arguments on appeal, a plainly irritated Sixth District Court of Appeals affirmed the lower court’s decision to reject the expert testimony.
Perhaps the comment most indicative of the court’s view on the matter was this introductory paragraph written by Court Judge Raymond Kethledge:
“In this case the EEOC sued the defendants for using the same type of background check that the EEOC itself uses. The EEOC’s personnel handbook recites that “[o]verdue just debts increase temptation to commit illegal or unethical acts as a means of gaining funds to meet financial obligations.” Because of that concern, the EEOC runs credit checks on applicants for 84 of the agency’s 97 positions. The defendants (collectively, “Kaplan”) have the same concern; and thus Kaplan runs credit checks on applicants for positions that provide access to students’ financial-loan information, among other positions. For that practice, the EEOC sued Kaplan.”
Kethledge’s terse conclusion hints at the criteria cases like this one will have to consider in the future:
“We need not belabor the issue further. The EEOC brought this case on the basis of a homemade methodology, crafted by a witness with no particular expertise to craft it, administered by persons with no particular expertise to administer it, tested by no one, and accepted only by the witness himself. The district court did not abuse its discretion in excluding Murphy’s testimony.”
“The district court’s judgment is affirmed.”
Methodology Will Be the Key
As Judge Kethledge notes, most successfully prosecuted disparate impact cases are based on statistical evidence with a sound scientific methodology. The Kaplan case shows that the EEOC cannot roll over employers without demonstrating statistical rigor. Judge Kethledge’s opinion includes references to some specific criteria for expert testimony, including Federal Rules of Evidence 702 and other cases such as Daubert v. Merrell Dow Pharmaceuticals, Inc (509 U.S. 579 (1993)).
Employers are not out of the woods on disparate impact, but this case shows that a carefully designed background screening program can work. Kaplan had good reasons to want to exclude workers who could be a risk if they got access to sensitive student loan information and application fees. And the court affirmed its right, and the right of all employers to do so.