Data and Code for: "A Discrimination Report Card"

We develop an empirical Bayes ranking procedure that assigns ordinal grades to noisy measurements, balancing the information content of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the race and gender contact gaps of 97 U.S. employers, the identities of which we disclose for the first time. The grades are presented alongside measures of uncertainty about each firm's contact gap in an accessible report card that is easily adaptable to other settings where ranks and levels are of simultaneous interest.

我们提出一种经验贝叶斯排序方法（empirical Bayes ranking procedure），可为含噪测量结果分配序数等级，在已分配等级的信息含量与预期排序误差频率之间实现平衡。将该方法应用于一项大规模通信实验（correspondence experiment），我们对97家美国雇主的种族与性别接触差距进行了评级，并首次披露了这些雇主的身份。这些评级与各企业接触差距的不确定性度量一同呈现在一份易读的报告卡中，该报告卡可轻松适配其他同时关注排名与水平的场景。