When an employer significantly reduces its workforce, there’s a risk that terminated employees will allege unlawful employment discrimination. Finance and accounting professionals with statistical expertise can help employers structure layoffs in a manner that minimizes the risk of discrimination allegations. And, in the event that a reduction in force (RIF) leads to litigation, experts can help demonstrate that the employer’s actions served a legitimate business purpose — even if they also have a disparate effect on women, minorities, older employees or some other protected class.
Minimize litigation risks
A pre-RIF statistical audit is one of the most effective tools for anticipating discrimination claims and ensuring the employer has a solid defense. That’s because the expert analyzes the same data and makes the same computations as a plaintiff’s expert when he or she evaluates a discrimination claim.
An audit can, for example, lead to recommendations that reduce the likelihood an employer’s RIF plan will be challenged. For example, the expert may conclude that the plan relies too heavily on subjective criteria — such as “quality of work” or informal performance reviews — making it difficult to mount a defense in the event there’s a discriminatory effect. The plan may be more defensible if objective, quantitative criteria — such as years of service or performance ratings — are incorporated.
Correlate terminations with nondiscriminatory variables
A pre-RIF audit can also predict whether implementation of a RIF will disproportionately affect a protected class. If so, the audit can determine whether the results are better explained by a legitimate, nondiscriminatory business purpose.
There are a variety of statistical tools at the expert’s disposal, but one of the most effective is multiple regression analysis. This tool analyzes a set of variables to model relationships between a “response (or dependent) variable” (the result one is attempting to explain) and one or more “explanatory (or independent) variables” — that is, variables that cause or are positively correlated with changes in the response variable.
For example, the plaintiff(s) in a RIF age discrimination case might demonstrate a positive correlation between age (an explanatory variable) and termination rates (the response variable). However, the employer’s expert might use multiple regression analysis to show that there’s a stronger relationship between termination rates and a nondiscriminatory explanatory variable, such as a lack of computer skills. Arguably, such an analysis would support the employer’s position that terminations weren’t age-driven, even though they had a disparate effect on older workers.
Ideally, a financial expert should be consulted long before a contemplated RIF. That gives the expert time to study the employer’s RIF plan, assess its effect on protected classes of employees and recommend adjustments that minimize the risk of discrimination claims.