VOL 94 NO. 4 BERTRAND AND MULLAINATHAN: RACE IN THE LABOR MARKET race-specific names we have chosen might also Rouse(2000), for example, examine the effect proxy for social class above and beyond the race of blind auditioning on the hiring process of of the applicant. Using birth certificate data on orchestras. By observing the treatment of fe- mothers education for the different first names male candidates before and after the introdu used in our sample, we find little relationship tion of blind auditions, they try to measure the between social background and the name- amount of sex discrimination. when such pseu specific callback rates. Second, we discuss how do-experiments can be found, the resulting our results map back to the different models of study can be very informative; but finding such discrimination proposed in the economics liter- experiments has proven to be extremely ature. In doing so, we focus on two important challenging results: the lower returns to credentials for a different set of studies, known as audit African-Americans and the relative homogene- studies, attempts to place comparable minority ity of the racial gap across occupations and and White actors into actual social and eco- industries. We conclude that existing models do nomic settings and measure how each group a poor job of explaining the full set of findings. fares in these settings. Labor market audit Section V concludes studies send comparable minority(African- American or Hispanic) and White auditors in I. Previous Research for interviews and measure whether one is more likely to get the job than the other. while the With conventional labor force and household results vary somewhat across studies, minority surveys, it is difficult to study whether differ- auditors tend to perform worse on average: they ential treatment occurs in the labor market. are less likely to get called back for a second Armed only with survey data, researchers usu- interview and, conditional on getting called ally measure differential treatment by compar- back, less likely to get hired ing the labor market performance of Whites and These audit studies provide some of the African-Americans (or men and women) for cleanest nonlaboratory evidence of differential which they observe similar sets of skills. But treatment by race. But they also have weak such comparisons can be quite misleading. nesses, most of which have been highlighted in Standard labor force surveys do not contain all Heckman and Siegelman( 1992)and Heckman the characteristics that employers observe when (1998). First, these studies require that both hiring, promoting, or setting wages. So one can members of the auditor pair are identical in all never be sure that the minority and nonminority dimensions that might affect productivity in workers being compared are truly similar from employers eyes, except for race. To accomplish the employers'perspective. As a consequence, this, researchers typically match auditors on any measured differences in outcomes could be several characteristics(height, weight, age, di attributed to these unobserved (to the re- alect, dressing style, hairdo) and train them for several days to coordinate interviewing styles This difficulty with conventional data has Yet, critics note that this is unlikely to erase the led some authors to instead rely on pseudo- numerous differences that exist between the au experiments. Claudia Goldin and Cecilia ditors in a pair Another weakness of the audit studies is that they are not double-blind. Auditors know the e 6 We also argue that a social class interpretation would purpose of the study. As Turner et al.(1991) ndings, such as why living in a better neighborhood does not increase callback rates ican- American names than for white names. Michael Fix and Marjery A. Turner(1998)provide a See Joseph G, Altonji and Rebecca M. Blank(1999) survey of many such audit studies (1978)and Shelby J. Mclntyre et al. (1980). Three more B William A. Darity, Jr. and Patrick L. escribe an interesting nonexperimental study. Prior to the and Steve w. DelCastillo (1991), and Turner et al.(1991) Civil Rights Act of 1964, ial biases, providing a direct measure of differential and Altonji and Blank (1999)summarize these studies. See treatment, Of course, as ( 998)mentions, discrin also David Neumark(1996)for a labor market audit stud nation was at that time too evident for detection on gender discriminationVOL. 94 NO. 4 BERTRAND AND MULLAINATHAN: RACE IN THE LABOR MARKET 993 race-specific names we have chosen might also proxy for social class above and beyond the race of the applicant. Using birth certificate data on mother's education for the different first names used in our sample, we find little relationship between social back round and the name￾specific callback rates. WSecond, we discuss how our results map back to the different models of discrimination proposed in the economics liter￾ature. In doing so, we focus on two important results: the lower returns to credentials for African-Americans and the relative homogene￾ity of the racial gap across occupations and industries. We conclude that existing models do a poor job of explaining the full set of findings. Section V concludes. 1. Previous Research With conventional labor force and household surveys, it is difficult to study whether differ￾ential treatment occurs in the labor market.7 Armed only with survey data, researchers usu￾ally measure differential treatment by compar￾ing the labor market performance of Whites and African-Americans (or men and women) for which they observe similar sets of skills. But such comparisons can be quite misleading. Standard labor force surveys do not contain all the characteristics that employers observe when hiring, promoting, or setting wages. So one can never be sure that the minority and nonminority workers being compared are truly similar from the employers' perspective. As a consequence, any measured differences in outcomes could be attributed to these unobserved (to the re￾searcher) factors. This difficulty with conventional data has led some authors to instead rely on pseudo￾experiments.* Claudia Goldin and Cecilia We also argue that a social class interpretation would find it hard to explain some of our findings, such as why living in a better neighborhood does not increase callback rates more for African-American names than for White names. 'See Joseph G. Altonji and Rebecca M. Blank (1999) for a detailed review of the existing literature on racial discrimination in the labor market. William A. Darity, Jr. and Patrick L. Mason (1998) describe an interesting nonexperimental study. Prior to the Civil Rights Act of 1964, employment ads would explicitly state racial biases, providing a direct measure of differential treatment. Of course, as Arrow (1998) mentions, discrimi￾nation was at that time "a fact too evident for detection." Rouse (2000), for example, examine the effect of blind auditioning on the hiring process of orchestras. By observing the treatment of fe￾male candidates before and after the introduc￾tion of blind auditions, they try to measure the amount of sex discrimination. When such pseu￾do-experiments can be found, the resulting study can be very informative; but finding such experiments has proven to be extremely challenging. A different set of studies, known as audit studies, attempts to place comparable minority and White actors into actual social and eco￾nomic settings and measure how each group fares in these settings9 Labor market audit studies send comparable minority (African￾American or Hispanic) and White auditors in for interviews and measure whether one is more likely to get the job than the other.'' While the results vary somewhat across studies, minority auditors tend to perform worse on average: they are less likely to get called back for a second interview and, conditional on getting called back, less likely to get hired. These audit studies provide some of the cleanest nonlaboratory evidence of differential treatment by race. But they also have weak￾nesses, most of which have been highlighted in Heckman and Siegelman (1992) and Heckman (1998). First, these studies require that both members of the auditor pair are identical in all dimensions that might affect productivity in employers' eyes, except for race. To accomplish this, researchers typically match auditors on several characteristics (height, weight, age, di￾alect, dressing style, hairdo) and train them for several days to coordinate interviewing styles. Yet, critics note that this is unlikely to erase the numerous differences that exist between the au￾ditors in a pair. Another weakness of the audit studies is that they are not double-blind. Auditors know the purpose of the study. As Turner et al. (1991) Michael Fix and Marjery A. Turner (1998) provide a survey of many such audit studies. lo Earlier hiring audit studies include Jerry M. Newman (1978) and Shelby J. McIntyre et al. (1980). Three more recent studies are Harry Cross et al. (1990), Franklin James and Steve W. DelCastillo (19911, and Turner et al. (1991). Heckman and Peter Siegelman (1992), Heckman (1998), and Altonji and Blank (1999) summarize these studies. See also David Neumark (1996) for a labor market audit study on gender discrimination