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Gender bias and statistical discrimination against female instructors in student evaluations of teaching

Shao-Hsun Keng

Labour Economics, 2020, vol. 66, issue C

Abstract: This paper uses administrative data from a public university in Taiwan to examine gender bias in teaching evaluations. We test for statistical discrimination against female instructors using the employer learning model in which the instructor value-added to the grades of the current course and follow-on course is used to measure teaching effectiveness. The results show that statistical discrimination is a significant source of gender bias in teaching evaluations, especially among male students and in STEM departments where female faculty is underrepresented. Gender bias in teaching evaluations was reduced by nearly 50% after ten years of teaching. The results also suggest that the gender gap in teaching evaluations changes over time as male and female students evaluate male and female instructors differentially. Statistical discrimination is closely related to the underrepresentation of women in academia. For female students, the gender gap in evaluation scores narrows when the share of female faculty in the department rises. By contrast, male students are less sensitive to the percentage of female faculty in the department.

Keywords: Student evaluations of teaching; Statistical discrimination; Gender bias; Gender inequality; Value-added (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:labeco:v:66:y:2020:i:c:s0927537120300932

DOI: 10.1016/j.labeco.2020.101889

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