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Gender Distribution across Topics in the Top 5 Economics Journals: A Machine Learning Approach

Author

Listed:
  • J.Ignacio Conde-Ruiz

    (Universidad Complutense de Madrid and ICAE (Spain).)

  • Juan-José Ganuza

    (Universitat Pompeu Fabra and Barcelona GSE.)

  • Manu García

    (Washington University in St. Louis and ICAE.)

  • Luis A. Puch

    (Universidad Complutense de Madrid and ICAE (Spain).)

Abstract
We analyze all the articles published in the top five (T5) Economics journals be- tween 2002 and 2019 in order to find gender differences in their research approach. We implement an unsupervised machine learning algorithm: the Structural Topic Model (STM), so as to incorporate gender document-level meta-data into a probabilistic text model. This algorithm characterizes jointly the set of latent topics that best fits our data (the set of abstracts) and how the documents/abstracts are allocated to each latent topic. Latent topics are mixtures over words where each word has a probability of belonging to a topic after controlling by journal name and publication year (the meta-data). Thus, the topics may capture research fields but also other more subtle characteristics related to the way in which the articles are written. We find that fe- males are unevenly distributed along the estimated latent topics, by using only data driven methods. This finding relies on “automatically” generated built-in data given the contents in the abstracts of the articles in the T5 journals, without any arbitrary allocation of texts to particular categories (as JEL codes, or research areas).

Suggested Citation

  • J.Ignacio Conde-Ruiz & Juan-José Ganuza & Manu García & Luis A. Puch, 2021. "Gender Distribution across Topics in the Top 5 Economics Journals: A Machine Learning Approach," Documentos de Trabajo del ICAE 2021-09, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    • Handle: RePEc:ucm:doicae:2109
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    References listed on IDEAS

    as
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    Cited by:

    1. Conde-Ruiz, J. Ignacio & Ganuza, Juan José & Profeta, Paola, 2022. "Statistical discrimination and committees," European Economic Review, Elsevier, vol. 141(C).
    2. Szymon Sacher & Laura Battaglia & Stephen Hansen, 2021. "Hamiltonian Monte Carlo for Regression with High-Dimensional Categorical Data," Papers 2107.08112, arXiv.org, revised Feb 2024.

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    More about this item

    Keywords

    Machine Learning; Gender Gaps; Structural Topic Model; Gendered Language; Research Fields.;
    All these keywords.

    JEL classification:

    • I20 - Health, Education, and Welfare - - Education - - - General
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • Z13 - Other Special Topics - - Cultural Economics - - - Economic Sociology; Economic Anthropology; Language; Social and Economic Stratification

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