G2TT
来源类型Discussion paper
规范类型论文
来源IDDP16960
DP16960 Gender Differences in Reference Letters: Evidence from the Economics Job Market
Markus Eberhardt; Giovanni Facchini; Valeria Rueda
发表日期2022-01-28
出版年2022
语种英语
摘要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 9,000 reference letters written in support of 2,800 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. Given the time and effort letter writers devote to supporting their students, this gender stereotyping is likely due to unconscious biases.
主题Labour Economics ; Public Economics
关键词Gender Natural language processing Gender stereotypes Diversity
URLhttps://cepr.org/publications/dp16960
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/545903
推荐引用方式
GB/T 7714
Markus Eberhardt,Giovanni Facchini,Valeria Rueda. DP16960 Gender Differences in Reference Letters: Evidence from the Economics Job Market. 2022.
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