Gender differences in reference letters: Evidence from the Economics job market

Markus Eberhardt, Giovanni Facchini and Valeria Rueda

No 2023-02, Discussion Papers from University of Nottingham, GEP

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 mplications of these gendered descriptors for the quality of academic placement.

Keywords: gender; natural language processing; diversity (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-hrm, nep-lab and nep-sog
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Citations: View citations in EconPapers (11)

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Related works:
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 (2022)
Working Paper: Gender Differences in Reference Letters: Evidence from the Economics Job Market (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:not:notgep:2023-02

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