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

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Listed:
  • J. Ignacio Conde-Ruiz
  • Juan-José Ganuza
  • Manu García
  • Luis A. Puch

Abstract

We analyze all the articles published in Top 5 economic journals between 2002 and 2019 in order to find gender differences in their research approach. Using an unsupervised machine learning algorithm (Structural Topic Model) developed by Roberts et al. (2019) we characterize jointly the set of latent topics that best fits our data (the set of abstracts) and how the documents/abstracts are allocated in each latent topic. This latent topics are mixtures over words were each word has a probability of belonging to a topic after controlling by year and journal. This latent topics may capture research fields but also other more subtle characteristics related to the way in which the articles are written. We find that females are uneven distributed along these latent topics by using only data driven methods. The differences about gender research approaches we found in this paper, are "automatically" generated given the research articles, without an arbitrary allocation 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 Top 5 Economics Journals: A Machine Learning Approach," Working Papers 1241, Barcelona School of Economics.
    • Handle: RePEc:bge:wpaper:1241
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    References listed on IDEAS

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    1. Leah Boustan & Andrew Langan, 2019. "Variation in Women's Success across PhD Programs in Economics," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 23-42, Winter.
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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; structural topic model; gender; 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

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