Forced distribution rating systems: when does "rank and yank" lead to adverse impact?

Despite widespread use of forced distribution rating systems (FDRSs), the potential for this performance appraisal method to lead to adverse impact (AI) in a layoff context has yet to be examined empirically. Thus, the current study uses a Monte Carlo simulation to examine the likelihood of encountering AI violations when an FDRS is used in the context of layoffs. The primary research questions included an examination of how AI violations change depending on the definition of the employment action (i.e., retention vs. layoff), the length of the repeated layoffs, and whether or not laid off employees are replaced each year. The current study also examined the impact of the size of the organization, the percentage of the workforce laid off, and the type of AI calculation method used on the likelihood of AI violations. Results suggest that defining the employment action as layoffs (rather than as retentions) may result in a greater likelihood of AI violations, and AI violations are likely to peak in the 1st year of use. Further, replacing laid off employees may result in higher levels of AI over time as compared with not replacing layoffs. Additionally, the greatest risk for AI occurs when the organization size is large (i.e., N = 10,000) and when certain AI calculation methods are used. Results are discussed in terms of their practical and legal implications for organizations.