L03 Proving Discrimination in Employment: Statistical Evidence

Many trials involving cases alleging discrimination feature testimony from experts who interpret statistical data reflecting demographics, pools of qualified applicants, and whatever anyone can interpret based on numbers. Experts for plaintiffs and defendants can interpret the same data very differently.

Why the importance of statistical evidence? As previously noted, outcomes say it all. A problem encountered by many who allege discrimination is framing an argument based on outcomes alone. The EEOC is often the proponent in cases alleging discriminatory recruitment practices because those impacted by them are unaware that something was amiss. Most who apply for a position and are not selected move on in their job search. Let’s look at two examples.

EEOC v. Mavis Discount Tire Example

The text includes a case (EEOC v. Mavis Discount Tire, 139 F. Supp. 3d 90 (S.D.N.Y. 2015) where the statistics broke down according to the chart below:

Actual Five-Year Hiring Period

Position	Male	Female
Store Manager	80	0
Assistant Manager	287	1
Mechanics	655	0
Tire Installers	1677	0

It is doubtful that many women applied for positions as mechanics (3.6% are women) or tire installers (5.4% are women), which points to how experts for each side will interpret statistics. Representation for women in store management slots is 48.8%. They hold 61% of assistant manager positions. Does combining every position skew the conclusions? What were the demographics concerning the pools of qualified applicants? If the tire shop’s hiring patters reflected the demographics, it should be as follows:

Five-Year Hiring Period Per Demographics

Position	Male	Female
Store Manager	41	39
Assistant Manager	102	175
Mechanics	631	24
Tire Installers	1597	91

Apart from those categories, the outcomes in selectees for the positions of store managers and assistants; 367 men and one woman, “says it all.” (EEOC v. Mavis Discount Tire, 139 F. Supp. 3d 90 (S.D.N.Y. 2015.)

It is a basic premise; even axiomatic, that output is a function of input. In other words, a lack of diversity is likely the product of a recruiting effort that produced that outcome. There can be exceptions based on demographics and qualified applicants for a particular position in a relevant labor market. Those situations do not apply to many geographic areas or most jobs that become available.

Other Examples

87.7 % of RNs are women. Women comprise 3.5% of licensed plumbers. A lack of applications from males for RN positions or women for jobs as plumbers would not be, without more, evidence of discriminatory recruiting practices. Those situations are not germane to jobs that call for the general skills many possess. If applicant pools lack diversity, employers should consider adjusting the geographic scope of their recruiting effort, based on what is reasonable or attainable. Where does that begin? Some will drive 50 miles for the right job. Few would consider 500 miles a reasonable commute. As with many things, it depends.

Next, let’s look at affirmative action and the implications it has on the hiring process.