Katherine Milkman, "The When and Where of Discrimination: An Audit Study in Academia"
Thursday, October 11, 2012, Noon-1:15pm Porter Hall 223D
We report on the results of an audit experiment set in academia including a sample of 6,548 professors at top U.S. universities drawn from 89 disciplines and 258 institutions. We find that decisions about distant-future events are more likely to generate discrimination against women and minorities (relative to Caucasian males) than are decisions about near-future events. In our study, faculty members received e-mails from fictional prospective doctoral students seeking to schedule a meeting either that day or in 1 week; students' names signaled their race (Caucasian, African American, Hispanic, Indian, or Chinese) and gender. When the requests were to meet in 1 week, Caucasian males were granted access to faculty members 26% more often than were women and minorities; also, compared with women and minorities, Caucasian males received more and faster responses. However, these patterns were essentially eliminated when prospective students requested a meeting that same day. Our identification of a "temporal discrimination effect" is consistent with the predictions of construal-level theory and implies that subtle contextual shifts can alter patterns of race- and gender-based discrimination. In addition to exploring when discrimination occurs, we also examine where in academia discrimination is most pronounced. We find that discrimination varies meaningfully by discipline and is more extreme in higher paying disciplines and at private (higher paying) institutions. These findings raise important questions for future research about how and why pay and institutional characteristics may relate to the manifestation of bias. They also suggest that past audit studies may have underestimated the prevalence of discrimination in the United States. Finally, our documentation of heterogeneity in discrimination suggests where targeted efforts to reduce discrimination in academia are most needed and highlights that similar research may help identify areas in other industries where efforts to reduce bias should focus.