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

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  • Rueda, Valeria
  • Eberhardt, Markus
  • Facchini, Giovanni

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.

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  • Rueda, Valeria & Eberhardt, Markus & Facchini, Giovanni, 2022. "Gender Differences in Reference Letters: Evidence from the Economics Job Market," CEPR Discussion Papers 16960, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:16960
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    Cited by:

    1. Ahrens, Achim & Hansen, Christian B. & Schaffer, Mark E & Wiemann, Thomas, 2024. "Model Averaging and Double Machine Learning," IZA Discussion Papers 16714, Institute of Labor Economics (IZA).
    2. Sona Badalyan & Darya Korlyakova & Rastislav Rehak, 2023. "Disclosure Discrimination: An Experiment Focusing on Communication in the Hiring Process," CERGE-EI Working Papers wp743, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    3. Ronald B. Davies & Zuzanna Studnicka, 2023. "A review of submissions to International Tax and Public Finance, 2010–2020," International Tax and Public Finance, Springer;International Institute of Public Finance, vol. 30(4), pages 1185-1201, August.
    4. Nicolás Ajzenman & Bruno Ferman & Sant’Anna Pedro C., 2023. "Discrimination in the Formation of Academic Networks: A Field Experiment on #EconTwitter," Working Papers 235, Red Nacional de Investigadores en Economía (RedNIE).
    5. Piera Bello & Alessandra Casarico & Debora Nozza, 2023. "Research Similarity and Women in Academia," CESifo Working Paper Series 10657, CESifo.
    6. Audinga Baltrunaite & Alessandra Casarico & Lucia Rizzica, 2024. "Women in economics: the role of gendered references at entry in the profession," Temi di discussione (Economic working papers) 1438, Bank of Italy, Economic Research and International Relations Area.
    7. Gonzalez, Felipe & Coy, Felipe & Prem, Mounu & von Dessauer, Cristine, 2022. "Uncertainty from dictatorship to democracy: Evidence from business communications," SocArXiv gz934, Center for Open Science.

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    More about this item

    Keywords

    Gender; Natural language processing; Gender stereotypes; Diversity;
    All these keywords.

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists

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