不准确的统计歧视:一个识别问题

J. Aislinn Bohren, Kareem Haggag, Alex Imas, Devin G. Pope
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引用次数: 0

摘要

我们研究不准确的信念作为歧视的来源。经济学家通常将歧视描述为源于品味(偏好)或准确的统计(基于信念)来源。虽然个人可能对相关特征(例如,生产力,信号)与群体身份的相关性有不准确的信念,但经济学中只有不到7%的实证歧视论文考虑了这种不准确统计歧视的可能性。使用理论和劳动力市场实验,我们表明,未能解释不准确的信念导致来源的错误分类。我们概述了三种方法来识别来源:不同的观察信号,信念启发,以及针对不准确信念的干预。
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Inaccurate Statistical Discrimination: An Identification Problem
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. While 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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