Ethnic and gender discrimination in academia

Written by: Stephen Hsu

Primary Source: Information Processing

This is the paper whose results (described in the NYTimes) I linked to in the previous post. The researchers are from Wharton, Columbia Business School, and NYU Stern Business School. They emailed the message below to over 6,500 professors at top US universities. Response rates varied strongly by perceived ethnicity of the sender. As you can see from the figure above, anti-Asian discrimination was largest. I suspect, though, that smaller circles (e.g., few percent or smaller effect) may not be statistically significant, nor the results for the smallest disciplines. The overall effect for a particular gender/ethnicity, aggregating over many disciplines, is probably strong enough to be replicable.

HETEROGENEITY IN DISCRIMINATION?: A FIELD EXPERIMENT

ABSTRACT: We provide evidence from the field that levels of discrimination are heterogeneous across contexts in which we might expect to observe bias. We explore how discrimination varies in its extent and source through an audit study including over 6,500 professors at top U.S. universities drawn from 89 disciplines and 258 institutions. Faculty in our field experiment received meeting requests from fictional prospective doctoral students who were randomly assigned identity-signaling names (Caucasian, Black, Hispanic, Indian, Chinese; male, female). Faculty response rates indicate that discrimination against women and minorities is both prevalent and unevenly distributed in academia. Discrimination varies meaningfully by discipline and is more extreme in higher paying disciplines and at private 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.

Here is the email message:

Subject Line: Prospective Doctoral Student (On Campus Today/[Next Monday])

Dear Professor [Surname of Professor Inserted Here],

I am writing you because I am a prospective doctoral student with considerable interest in your research. My plan is to apply to doctoral programs this coming fall, and I am eager to learn as much as I can about research opportunities in the meantime.

I will be on campus today/[next Monday], and although I know it is short notice, I was wondering if you might have 10 minutes when you would be willing to meet with me to briefly talk about your work and any possible opportunities for me to get involved in your research. Any time that would be convenient for you would be fine with me, as meeting with you is my first priority during this campus visit.

Thank you in advance for your consideration.

Sincerely,
[Student’s Full Name Inserted Here]

These are the gender / ethnically identifiable names used in the emails:

Ruh roh, smallest N values — and largest effect sizes — in Human Services, Fine Arts, and Business. 10 emails (2 genders x 5 ethnicities; presumably no professor received more than one of the identical messages) sent to ~200 people means statistically questionable result. Better to aggregate all the data across disciplines to get a reliable result.

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Stephen Hsu
Stephen Hsu is vice president for Research and Graduate Studies at Michigan State University. He also serves as scientific adviser to BGI (formerly Beijing Genomics Institute) and as a member of its Cognitive Genomics Lab. Hsu’s primary work has been in applications of quantum field theory, particularly to problems in quantum chromodynamics, dark energy, black holes, entropy bounds, and particle physics beyond the standard model. He has also made contributions to genomics and bioinformatics, the theory of modern finance, and in encryption and information security. Founder of two Silicon Valley companies—SafeWeb, a pioneer in SSL VPN (Secure Sockets Layer Virtual Private Networks) appliances, which was acquired by Symantec in 2003, and Robot Genius Inc., which developed anti-malware technologies—Hsu has given invited research seminars and colloquia at leading research universities and laboratories around the world.
Stephen Hsu

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