based. With such knowledge and data, it becomes much easier to specify an appropriate statistical model with which to estimate racial discrimination.

Conclusion: Despite limitations, natural experiments—in which a legal change or some other change forces a reduction in or the complete elimination of discrimination against some groups—can provide useful data for measuring discrimination prior to the change and for groups not affected by the change.

Recommendation 7.1. Public and private funding agencies should support focused studies of decision processes, such as the behavior of firms in hiring, training, and promoting employees. The results of such studies can guide the development of improved models and data for statistical analysis of differential outcomes for racial and ethnic groups in employment and other areas.

Recommendation 7.2. Public agencies should assist in the evaluation of natural experiments by collecting data that can be used to evaluate the effect of antidiscrimination policy changes on groups covered by the changes, as well as groups not covered.


We discuss here ways to detect adverse impact discrimination; that is, discrimination by using factors that correlate with race. A firm may not use race directly, but it may weight variables in hiring decisions in a way that is not proportionate to their influence on productivity. For example, suppose the firm uses

as its productivity rating rather than the correct index

and hires accordingly. In this case, y will be determined by


where α′ is (1 –α) as before. It is quite possible for α to be 0 even though the firm’s hiring rule has an adverse impact on R that is not justified by

The National Academies of Sciences, Engineering, and Medicine
500 Fifth St. N.W. | Washington, D.C. 20001

Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement