Meritocracy in the Face of Group Inequality

Rajiv Sethi, R. Somanathan
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引用次数: 2

Abstract

Meritocratic systems are commonly understood as those that assign tasks to individuals who can best perform them. But future performance cannot be known prior to assignment, and must be inferred from other traits. We consider a model in which performance depends on two attributes --- ability and training --- where ability is endowed and unobserved and training is acquired and observed. The potential to acquire training depends on ability and resource access, so ability affects performance through two channels: indirectly through training and directly through the performance function. The population consists of two identity groups, each with the same ability distribution, but with differential access to resources. We characterize the sets of training levels that maximize expected performance. An allocation is monotonic if, for each group, there is a threshold value of training such that all those above this value (and none below) are selected. It is group-blind if assignment is independent of group identity, and psuedomeritocratic if it is both monotonic and group-blind. We show that performance-maximizing allocations are not generally monotonic or group-blind, and are pseudomeritocratic under only very special conditions. This is true even when individuals can respond to non-monotonic policies by underinvesting in training, or when commitment to selection policies is possible.
面对群体不平等的精英管理
精英制度通常被理解为那些将任务分配给最能完成任务的人的制度。但在分配任务之前,未来的表现是无法预知的,必须从其他特征中推断出来。我们考虑一个模型,在这个模型中,表现取决于两个属性——能力和训练——能力是被赋予的,不被观察,而训练是获得的,被观察。获得培训的潜力取决于能力和资源的获取,因此能力通过两个渠道影响绩效:间接通过培训和直接通过绩效功能。人口由两个身份群体组成,每个群体的能力分布相同,但获得资源的途径不同。我们描述了最大化预期性能的训练水平集。如果对于每一组,都有一个训练的阈值,使得所有高于这个值的都被选中(低于这个值的都不被选中),那么分配是单调的。如果分配与群体身份无关,则它是群体盲的;如果分配既单调又群体盲,则它是伪重商的。我们证明了性能最大化分配通常不是单调的或群盲的,并且只有在非常特殊的条件下才具有伪唯一性。即使个人可以通过在培训上投资不足来应对非单调政策,或者当承诺选择政策是可能的时候,情况也是如此。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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