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Gender Stereotype in Academia: Evidence from Economics Job Market Rumors Forum

Author

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  • Alice Wu

    (Princeton University)

Abstract

This paper examines whether people in academia portray and judge women and men differently in everyday "conversations" that take place online. I combine methods from text mining, machine learning and econometrics to study the existence and extent of gender stereotyping on the Economics Job Market Rumors forum. I first design a propensity score model to infer the gender a post mainly refers to from text, and simultaneously identify the individual words with the strongest association with gender. The words selected provide a direct look into the gender stereotyped language on this forum. Through a topic analysis of the posts, I find that when women are under discussion, the discourse tends to become significantly less academic or professionally oriented, and more about personal information and physical appearance. Moreover, a panel data analysis reveals the state dependence between the content of posts within a thread. In particular, once women are mentioned in a thread, the topic is likely to shift from academic to personal. Finally, I restrict the analysis to discussions about specific economists, and find that high-profile female economists tend to receive more attention on EJMR than their male counterparts.

Suggested Citation

  • Alice Wu, 2017. "Gender Stereotype in Academia: Evidence from Economics Job Market Rumors Forum," Working Papers 2017-09, Princeton University, Woodrow Wilson School of Public and International Affairs, Center for Health and Wellbeing..
  • Handle: RePEc:pri:cheawb:2017-09
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    Cited by:

    1. Stéphanie Combes & Pauline Givord, 2018. "Selective matching: gender gap and network formation in research," Working Papers 2018-07, Center for Research in Economics and Statistics.
    2. Ductor, L & Goyal, S. & Prummer, A., 2018. "Gender & Collaboration," Cambridge Working Papers in Economics 1820, Faculty of Economics, University of Cambridge.
    3. Mengel, Friederike & Sauermann, Jan & Zölitz, Ulf, 2017. "Gender Bias in Teaching Evaluations," IZA Discussion Papers 11000, Institute for the Study of Labor (IZA).
    4. Valerie K. Bostwick & Bruce A. Weinberg, 2018. "Nevertheless She Persisted? Gender Peer Effects in Doctoral STEM Programs," NBER Working Papers 25028, National Bureau of Economic Research, Inc.
    5. Della Giusta, Marina & Vukadinovic-Greetham, Danica & Jaworska, Sylvia, 2018. "Tweeting Economists: Antisocial in the socials?," MPRA Paper 89527, University Library of Munich, Germany.

    More about this item

    JEL classification:

    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • J23 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Demand
    • M51 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Personnel Economics - - - Firm Employment Decisions; Promotions
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions

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