HR leaders and hiring managers shudder at the thought of job candidates claiming unfair discrimination in hiring. The defense that the organization “took what came its way” is a much weaker defense than if the organization took affirmative action to place females, blacks, Hispanics, etc. in the pool in the first place. Today’s organizations aren’t attracting 50 applicants for 10 jobs anymore. The tight labor market makes it even more important to be purposeful and proactive when hiring employees. Creating a balanced employee selection system reduces the chances of costly, time-consuming legal challenges. From a decision-making perspective, the balance of three groups is of utmost importance, because an unbalanced selection system may create an adverse effect and open the door to discrimination claims.
Job applicants fall into one, two, or three specific groups:
1. Infinite assembly
2. Screened applicant pool
3. Selected group
Infinite Assembly
First, let’s look at the infinite assembly. The infinite assembly is determined by the Standard Metropolitan Statistical Area or SMSA.
This statistic from the U.S. Census Bureau shows the percentages of affected class candidates that populate the set for various levels of jobs in the organization.
For example, in a given geographical area, the percentages for intermediate operator jobs in manufacturing organizations may be as follows: 35% for females, 20% for African Americans, 10% for Hispanics, and 5% for veterans. These sets may overlap. An example: One could be an African American female who is also a veteran.
HR managers responsible for their organization’s Affirmative Action program are very familiar with the SMSA. They focus their pool formation strategies on SMSA percentages. Additionally, they take advantage of the Law of Large Numbers. The larger the sample, the more likely it is to be normally distributed around important variables: demographics, abilities, and adaptation history of persons in the assembly.
Screened Applicant Pool
The “infinite” set, by definition, is extensive. The screened applicant pool is much smaller. The smaller sample size makes it even more important to form an applicant pool that is representative. For example, the SMSA might maintain that 35% of people eligible for the organization’s work are females. Thus, HR leaders need to maintain that 35% in the screened applicant pool. If the organization has an improper applicant pool formation policy, female candidates may be adversely affected. If the organization loses the 35% in this group, it will never make it up in the final selections unless it creates a reverse discrimination scenario. There is no way it would ever meet that target when the applicant pool is short on the affected class. So, you see the importance of implementing the right balance in applicant pool policies to create the best outcome.
Selected Group
Most selection systems use a rank-ordering system and apply previously agreed upon rules to make selections. A commonly used rule is “top-down.” Basically, the most qualified candidates with the highest probability of success are selected first, then the next most qualified, and so on. A second commonly used rule uses Equal Employment Opportunity or Affirmative Action directives to break ties.
Affirmative Action Amid Labor Shortages
As you can imagine, the labor shortage presents some Affirmative Action issues. For example, when females leave the workforce in droves like they did in 2021, what does that do to the SMSA percentages? Are they still in the workforce or not? Another issue has to do with retention. Let’s say you hire the appropriate number of females, but can’t keep them in the organization. I would argue that that does not create adverse impact but certainly impacts the workforce.
There are no easy answers to the talent shortage and its subsequent impact on organizations. To avoid legal challenges and to bring the most value to an organization, HR teams still need to focus on Affirmative Action strategies, despite the labor market. Essentially, HR leaders can defend discrimination challenges by proving two things:
1. The hiring system is valid
2. Predictions are systematically related to the learning and performance of the jobs in question
Learn to Avoid Unfair Discrimination in Hiring
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