  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.
