A recent paper by Marianne Bertrand and Sendhil Mullainathan has been getting a lot of press in the shadow of the debate over the University of Michigan’s affirmative action policies. The paper, entitled Are Emily and Brendan More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination, is accessible to the non-specialist and well worth reading.
The authors started with a collection of resumes that were downloaded from web sites catering to jobseekers, and modified them to remove identifying information. They then sent out resumes in response to vacancies advertised in Boston and Chicago, after first randomly assigning to each resume either a "white-sounding" or a "black-sounding" name. The names were obtained from historical birth records, and were based on the relative frequencies of names in black and white households. Hence names that were either much more common in white households than in black ones (such as Meredith and Todd) or much more common in black households than in white ones (such as Tanisha and Hakim) were selected for the study. Names such as Michael and Vanessa, which are common in both communities and hence carry little or no racial association, were not used. The authors found that resumes which had been randomly assigned white-sounding names elicited significantly higher callback rates than those assigned black-sounding names.
Many economists will be tempted to interpret these findings through the lens of statistical discrimination theory: if names carry information about the populations from which subjects are drawn, and these populations differ with respect to their productive characteristics, then names will carry statistical information about worker productivity. The authors try to test this interpretation by selectively improving some resumes prior to the random assignment of names. This is done, for instance, by filling in gaps in employment history and adding volunteer work. They find that such improvements significantly increase callback rates for resumes assigned white-sounding names but have negligible effects for those assigned black-sounding names. This flies in the face of the standard statistical discrimination model, in which greater information about candidates should narrow rather than widen discriminatory treatment.
So what’s going on? The authors are careful to leave open the possibility that more sophisticated models of statistical discrimination can account for their findings. But sometimes the simplest explanation really is the right one, and in my own view this is the case here. It seems to me that there is a subset of employers who have a strong negative gut-reaction to a black-sounding name and don’t bother to scan resumes for additional information once this reaction is triggered. The number of such individuals may be small relative to the population of employers, but they must be sufficiently numerous for their behavior to result in statistically discernible aggregate effects.