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

J. Ignacio Conde-Ruiz, Luis Puch and Juan-José Ganuza
Authors registered in the RePEc Author Service: Manu García

No 1241, Working Papers from Barcelona School of Economics

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).

Keywords: gender; machine learning; structural topic model; research fields (search for similar items in EconPapers)
JEL-codes: I20 J16 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-gen and nep-sog
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Working Paper: Gender Distribution across Topics in Top 5 Economics Journals: A Machine Learning Approach (2021)
Working Paper: Gender Distribution across Topics in the Top 5 Economics Journals: A Machine Learning Approach (2021)
Working Paper: Gender distribution across topics in Top 5 economics journals: A machine learning approach (2021)
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