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不准确的统计性歧视:一个识别问题

Inaccurate Statistical Discrimination: An Identification Problem
Review of Economics and Statistics · 2023 · J. Aislinn Bohren、Kareem Haggag、Alex Imas、Devin G. Pope

中文摘要

我们研究不准确信念作为歧视来源的作用。经济学家通常将歧视刻画为源于偏好型(偏好)或准确的统计型(信念)来源。尽管个体可能对相关特征(如生产率、信号)与群体身份如何相关持有不准确的信念,但经济学中不到7%的实证歧视研究论文考虑了这种不准确统计性歧视的可能性。我们运用理论和一个劳动力市场实验表明,未能考虑不准确信念会导致对来源的错误归类。我们概述了三种识别来源的方法:改变可观测信号、信念诱导以及针对不准确信念的干预。

Abstract

Abstract We study inaccurate beliefs as a source of discrimination. Economists typically characterize discrimination as stemming from a taste-based (preference) or accurate statistical (belief-based) source. Although individuals may have inaccurate beliefs about how relevant characteristics (e.g., productivity, signals) are correlated with group identity, fewer than 7% of empirical discrimination papers in economics consider the possibility of such inaccurate statistical discrimination. Using theory and a labor market experiment, we show that failing to account for inaccurate beliefs leads to a misclassification of source. We outline three methods to identify source: varying observed signals, belief elicitation, and an intervention to target inaccurate beliefs.
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