In this paper we extend the standard model of statistical discrimination to a multidimensional framework where the accuracy of evaluators depends on how knowledgeable they are in each dimension. The model yields two main implications. First, candidates who excel in the same dimensions as the evaluator tend to be preferred. Second, if two equally productive groups of workers differ in their distribution of ability across dimensions group discrimination will arise unless (i) evaluators are well informed about the extent of these differences and (ii) evaluators can take candidates' group belonging into account in their assessments. (C) 2013 Elsevier Inc. All rights reserved.
机构:
Howard Univ, Coll Med, Washington, DC 20059 USA
Georgetown Univ, Med Ctr, Washington, DC 20007 USA
Prison Cells PhD, Baltimore, MD 21224 USAHoward Univ, Coll Med, Washington, DC 20059 USA