Gender Differences in Reference Letters: Evidence from the Economics Job Market

Markus Eberhardt, Giovanni Facchini and Valeria Rueda

No 16960, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: Academia, and economics in particular, faces increased scrutiny because of gender imbalance. This paper studies the job market for entry-level faculty positions. We employ machine learning methods to analyze gendered patterns in the text of 12,000 reference letters written in support of over 3,700 candidates. Using both supervised and unsupervised techniques, we document widespread differences in the attributes emphasized. Women are systematically more likely to be described using ‘grindstone’ terms and at times less likely to be praised for their ability. Using information on initial placement we highlight the implications of these gendered descriptors for the quality of academic placement.

Keywords: Gender; Natural language processing; Gender stereotypes; Diversity (search for similar items in EconPapers)
JEL-codes: A11 J16 (search for similar items in EconPapers)
Date: 2022-01
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Journal Article: Gender Differences in Reference Letters: Evidence from the Economics Job Market (2023)
Working Paper: Gender differences in reference letters: Evidence from the Economics job market (2023)
Working Paper: Gender Differences in Reference Letters: Evidence from the Economics Job Market (2022)
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