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Measuring Racial Discrimination
main, across domains, and over time; and
determining how much of an observed disparity is an effect of discrimination.
Our discussion of these issues is limited by our charge to focus on the measurement of racial discrimination. However, much of the discussion can be readily applied to measurement of closely related topics, such as gender or age discrimination.
There are many different methods for measuring racial discrimination. We review three types of methods that are widely used in various literatures: controlled laboratory experiments and field experiments; analysis of observational data and natural experiments; and measures of reported perceptions and experiences of discrimination from surveys and administrative records. It is important to note that no one method allows researchers to address all of the measurement issues listed above.
For example, laboratory experiments help researchers to identify the mechanisms that may lead to different forms of racial discrimination and the factors that mediate the expression of discriminatory attitudes and behaviors. Because of experimental control over relevant variables, researchers are able to identify whether race or the interaction of race and other factors triggers an expression of racial discrimination. Laboratory experiments are useful for drawing causal inferences at the individual level and important for identifying subtle mechanisms of discrimination; however, they do not directly address disparities in the aggregate. That is, laboratory effects do not often generalize to the broader population and can rarely tell us the extent to which naturally observed disparities are the result of discrimination.
The results of field experiments, on the other hand, are often more generalizable than the results of laboratory experiments. Although field experiments may involve less experimental control, researchers can use them to measure the extent of discrimination in a particular domain, such as the housing or labor market. For instance, audit studies in the housing or employment arena can provide useful information about the possible occurrence of discrimination by real estate agents against homebuyers or by employers against job applicants from disadvantaged racial groups.
Some ability to generalize may also be gained by using nonexperimental approaches. Researchers can use statistical modeling and estimation to analyze observational data and draw causal inferences. Statistical models are useful for identifying associations between race and different outcomes while controlling for other factors that may explain the observed outcomes. Simply identifying an association with race, however, is not equivalent to measuring the magnitude of racial discrimination or its contribution to differential outcomes by race. In most observational settings, the lack of ex-