Unfortunately, current difficult economic times are forcing many employers to lay off employees. Employers considering a reduction in force (RIF) can reduce risk of discrimination allegations through relatively inexpensive pre-RIF statistical testing. When compared to the cost of litigation, testing before final conclusions are reached is an inexpensive check on the proposed actions.
A pre-RIF analysis is performed using the same data and calculations that plaintiffs use when establishing a prima facie discrimination case. The data is usually readily available in the employer’s personnel files, often times in electronic form. When terminated employees challenge the selection of those terminated, the same statistical tests can then be used for a quick defense and resolution.
An audit performed prior to litigation is less likely to be viewed as biased. If a trial is necessary, the independent test performed beforehand shows that the employer (i) endeavored to ensure that tentative business decisions did not have any inadvertent impact of hurting protected employee groups, and (ii) attempted to eliminate potential discrimination as part of their decision-making process.
Often, disparate-impact suits change employers discriminated by allowing individual supervisors to make decisions without oversight. Statistical audits address this assertion by providing evidence of centralized control and review.
What are Disparate Impact Claims
Disparate impact refers to the possibility that protected-class members are part of the RIF to a greater extent than the work force as a whole. Disparate impact allegations attempt to show that, based on the outcome, a neutral purpose at the surface level is really discriminatory. Consequently, the disparate impact can occur even when there are other valid reasons for the management action. For example, management might make a decision to retain those having a particular skill, but if the absence of this skill eliminates employees belonging to a protected class to a much greater extent than the exists in the workforce as a whole, then the action may be subject to attack on discrimination grounds. If any protected class is over-represented in the RIF, a statistical test will attempt to ascertain whether this is occurring randomly, or whether the result is “statistically significant”.
In 2008, the Supreme Court determined that a plaintiff must do more than allege a disparate impact in some generalized policy. To prove a prima facie case of disparate impact, a plaintiff must “isolate and identify the specific employment practices that are allegedly responsible for the statistical disparities.” But, once a plaintiff meets that threshold, then the burden of proof shifts to the employer to prove its actions were based on reasonable factors other than the alleged discrimination.
A recent Sixth Circuit case applied this sequential process in a disparate impact allegation. In Shollenbarger vs. Planes Moving and Storage, No. 06-4454 (10/21/08), the Court determined:
“The plaintiffs contend that the statistics do show a disparity and we agree. At this step in the analysis – the prima facie step – Planes’s reasons for selecting certain departments is immaterial; the only questions at this point are whether there was an identifiable disparity and, if so, whether the challenged employment practice (i.e., the selection of certain departments) could have caused the disparity. Based on a rudimentary statistical analysis, we answer both in the affirmative. “
“Thus, the burden shifts and we must consider whether Planes set forth a legitimate business justification. Planes explained that its declining business necessitated the RIF and that some departments were affected more that others; specifically, those employees who dealt most directly with customers were the most affected. In addition, the predominantly male unaffected departments were staffed largely with seasonal workers (typically high school and college students) who had already left at the end of the peak summer season. And, there was no decline in the business being done by the warehouse. We conclude that the challenged employment practice of subjecting only certain departments to the RIF had a legitimate business justification.”
“Because Planes clearly met its burden of showing a legitimate business justification, the burden shifts back to the plaintiffs to show that “other tests or selection devices, without a similarly undesirable . . . effect, would also serve the employer’s legitimate business interest.”
An important distinction occurs between disparate impact (discussed above) and disparate treatment. Disparate treatment involves intentional or deliberate discrimination in a decision. Although intentions are less subject to numerical analysis, statistics can still be used to eliminate unlawful basis for the decision. Usually, this is accomplished through a multiple regression analysis. Such a test can evaluate those factors that impacted the decision in a statistically significant way, and other factors that have no statistical importance in explaining what is being observed.
Best Practices when Conducting a RIF
To avoid litigation losses, businesses need to be able to justify their decisions. If a company can’t document a RIF’s basis and implementation, including reasons for selecting the affected employees, the business