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RIFs and age bias suits: Understand the power of statistics

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in Discrimination and Harassment,Employment Law,Firing,Human Resources

If you’re like many employers, you offer severance pay when you have to implement a reduction in force. Never pay severance without getting something in return from the employee, namely a release and waiver of liability.

There’s an important catch to understand when you ask for such a release from older workers. The Older Workers Benefit Protection Act (OWBPA) requires all releases and waivers of federal age discrimination claims to include a written disclosure of the job titles and ages of all eligible individuals selected for the program—and everyone not selected for the program.

The EEOC, in its guidance “Understanding Waivers of Discrimination Claims in Employee Severance Agreements,” provides an example of what this disclosure should look like.

Beyond statistics

When the lone 63-year-old assembly worker is going to decide whether to sign the waiver or pursue an age claim, the only fact he and his lawyer will have to go on is that, within his job grouping, seven of the nine oldest employees were terminated, including the oldest employee.

In other words, the raw statistics will probably be the critical piece of information on which the employee will base the decision whether to sue or walk away. How relevant, though, are these raw numbers to an age discrimination claim?

RIFs provide a built-in protection for employers in age discrimination cases, because the legitimate nondiscriminatory reason for the termination—the economic necessity for the workforce reduction—is established from the outset.

Thus, employees challenging a RIF on account of age face a higher burden. When a termination arises as part of a workforce reduction, the plaintiff must provide “additional direct, circumstantial, or statistical evidence tending to indicate that the employer singled out the plaintiff for discharge for impermissible reasons.”

Sample size is critical

In Schoonmaker v. Spartan Graphics Leasing, the plaintiff claimed that the fact that her employer retained younger employees in her position, and that her employer terminated the two oldest employees, satisfied the “additional evidence” necessary to overcome the employer’s economic justification for the RIF.

The 6th Circuit correctly rejected this assertion, and in doing so put a dagger through the heart of the use of raw statistics of small samples in RIF cases. The court wrote:

If the plaintiff’s case-in-chief is viewed as satisfying the requirements for a prima facie case of age discrimination, then every employer who terminates an employee between 40 and 70 years of age under any circumstances, will carry an automatic burden to justify the termination….

[S]tatistical evidence may satisfy the fourth element in a work force reduction case ... [but] such a small statistical sample is not probative of discrimination.


In other words, in RIFs with a small sample size, an employee will have to come up with evidence other than pure statistics to go forward with a discrimination claim—evidence that that terminated employee was objectively more qualified than the younger retained employees.

Expert vs. expert

This standard raises the question of how small of a sample is too small to make raw statistics irrelevant.

If I were defending a RIF, the first thing I’d do is hire a statistical expert to opine that the sample size is too small to be statistically significant. From there, I’d argue that the case should be dismissed under Schoonmaker, unless the plaintiff can come forward with some “plus” evidence of discrimination.

Conversely, if a terminated employee wants to rely on statistics alone, he or she will have to hire an expert to opine that the sample size is large enough to be statistically significant.

If you have competing experts, you very well might have a factual issue over the sample size. Or, the judge could decide as a matter of law that the sample size is too small and toss out the statistics as irrelevant.

Schoonmaker may question the relevance of small sample raw statistics, but because the numbers must be disclosed to the terminated employees, they are nevertheless critical to any workforce reduction decision.

Employers act at their own peril if they ignore statistics. Before you finalize any RIF, analyze the numbers across all key demographics, in addition to comparing the relative qualities and qualifications of the departing workers versus those who will remain.

You may not prevent a lawsuit from being filed (especially if the raw numbers look discriminatory), but it will give you the necessary ammunition to defend any subsequent discrimination lawsuits that are filed.

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