Gender and Diversity; Bias; Selection; Hiring Decisions; Computer Simulation;
It is widely acknowledged that subgroup bias can influence hiring evaluations. However, the notion that bias still threatens equitable hiring outcomes in modern employment contexts continues to be debated, even among organizational scholars.In this study, the authors sought to contextualize this debate by estimating the practical impact of bias on real-world hiring outcomes (a) across a wide range of hiring scenarios and (b) in the presence of diversity-oriented staffing practices. Toward this end, the authors conducted a targeted meta-analysis of recent hiring experiments that manipulated both candidate gender and qualifications to couch their investigation within ongoing debates surrounding the impact of small amounts of bias in otherwise meritocratic hiring contexts.Consistent with prior research, the authors found evidence of small gender bias effects (d = −0.30) and large qualification effects (d = 1.61) on hiring managers’ evaluations of candidate hireability. The authors then used these values to inform the starting parameters of a large-scale computer simulation designed to model conventional processes by which candidates are recruited, evaluated, and selected for open positions.Collectively, the authors' simulation findings empirically substantiate assertions that even seemingly trivial amounts of subgroup bias can produce practically significant rates of hiring discrimination and productivity loss. Furthermore, the authors found contextual factors can alter but cannot obviate the consequences of biased evaluations, even within apparently optimal hiring scenarios (e.g., when extremely valid assessments are used).Finally, the authors' results demonstrate residual amounts of subgroup bias can undermine the effectiveness of otherwise successful targeted recruitment efforts. Implications for future research and practice are discussed.