Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/216484 
Year of Publication: 
2020
Series/Report no.: 
IZA Discussion Papers No. 13172
Publisher: 
Institute of Labor Economics (IZA), Bonn
Abstract: 
Detecting racial discrimination using observational data is challenging because of the presence of unobservables that may be correlated with race. Using data made public in the SFFA v. Harvard case, we estimate discrimination in a setting where this concern is mitigated. Namely, we show that there is a substantial penalty against Asian Americans in admissions with limited scope for omitted variables to overturn the result. This is because (i) Asian Americans are substantially stronger than whites on the observables associated with admissions and (ii) the richness of the data yields a model that predicts admissions extremely well. Our preferred model shows that Asian Americans would be admitted at a rate 19% higher absent this penalty. Controlling for one of the primary channels through which Asian American applicants are discriminated against — the personal rating — cuts the Asian American penalty by less than half, still leaving a substantial penalty.
Subjects: 
higher education
racial discrimination
college admissions
admissions preference
JEL: 
I23
I24
J15
Document Type: 
Working Paper

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