Employer Neighborhoods and Racial Discrimination
Using a large field experiment, we show that racial composition of employer neighborhoods predicts employment discrimination patterns in a direction suggesting in-group bias. Our data also show racial disparities in the geographic distribution of job postings. Simulations illustrate how these patterns combine to shape disparities. When jobs are located far from Black neighborhoods, Black applicants are doubly disadvantaged: discrimination patterns disfavor them, and they have fewer nearby opportunities. Finally, building on prior work on Ban-the-Box laws, we show that employers in less Black neighborhoods appear much likelier to stereotype Black applicants as potentially criminal when they lack criminal record information.
Rutgers University and University of Chicago, respectively. Thanks to Sara Heller for helpful comments; research assistants Linfeng Li, Monica Mogollon, Humberto Martinez Beltran, Jason Marquez, Keerthana Nunna, and Yongbo Sim; and workshop participants at the University of Chicago, the University of Michigan, NYU, Georgetown, George Mason University, The University of California-Berkeley, and Wayne State University. We also thank the many research assistants who helped to carry out the underlying experiment, as well as all those who offered feedback on that work (see Agan and Starr 2018 for detailed acknowledgments). The experiment was generously funded by the Princeton University Industrial Relations Section, the University of Michigan Empirical Legal Studies Center, and the University of Michigan Office of Research. The RCT was registered in the American Economic Association Registry for randomized control trials under trial number AEARCTR-0000675. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.