We develop an Empirical Bayes grading scheme that balances the informativeness of the assigned grades against the expected frequency of ranking errors. Applying the method to a massive correspondence experiment, we grade the racial biases of 97 U.S. employers. A four-grade ranking limits the chances that a randomly selected pair of firms is mis-ranked to 5% while explaining nearly half of the variation in firms' racial contact gaps. The grades are presented alongside measures of uncertainty about each firm's contact gap in an accessible rubric that is easily adapted to other settings where ranks and levels are of simultaneous interest.

翻译：我们开发了一种经验贝叶斯分级方案，该方案在给定分级的可信息性与预期排序错误频率之间取得平衡。将该方法应用于一项大规模对应实验，我们对美国97家雇主的种族偏见进行分级。四级排序将随机选取的一对企业被错误排序的概率限制在5%，同时解释了企业间种族接触差距近一半的变异。每个企业的接触差距等级均附带不确定性度量，并呈现于易于理解的评估表格中，该表格可轻松适用于其他同时关注排序与水平的场景。