The decisions of judges, lenders, journal editors, and other gatekeepers often lead to significant disparities across affected groups. An important question is whether, and to what extent, these group-level disparities are driven by relevant differences in underlying individual characteristics, or by biased decision makers. Becker (1957, 1993) proposed an outcome test of bias based on differences in post-decision outcomes across groups, inspiring a large and growing empirical literature. The goal of our paper is to offer a methodological blueprint for empirical work that seeks to use outcome tests to detect bias. We show that models of decision making underpinning outcome tests can be usefully recast as Roy models, since heterogeneous potential outcomes enter directly into the decision maker’s choice equation. Different members of the Roy model family, however, are distinguished by the tightness of the link between potential outcomes and decisions. We show that these distinctions have important implications for defining bias, deriving logically valid outcome tests of such bias, and identifying the marginal outcomes that the test requires.

中文翻译：

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关于使用结果测试来检测决策中的偏差

法官、贷款人、期刊编辑和其他把关人的决定往往会导致受影响群体之间存在巨大差异。一个重要的问题是，这些群体层面的差异是否以及在多大程度上是由潜在个人特征的相关差异或有偏见的决策者造成的。Becker (1957, 1993) 提出了一种基于不同群体决策后结果差异的偏差结果检验，启发了大量且不断增长的实证文献。我们论文的目标是为实证工作提供方法蓝图，旨在利用结果测试来检测偏见。我们表明，支持结果测试的决策模型可以有效地改写为罗伊模型，因为异质的潜在结果直接进入决策者的选择方程。然而，罗伊模型家族的不同成员的区别在于潜在结果和决策之间联系的紧密程度。我们表明，这些区别对于定义偏差、导出此类偏差的逻辑上有效的结果测试以及确定测试所需的边际结果具有重要意义。