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

J. Ignacio Conde-Ruiz, Juan José Ganuza, Manu García and Luis Puch

No 2021-07, Working Papers from FEDEA

Abstract: We analyze all the articles published in Top 5 economic journals between 2002 and 2019 in order to find gender di↵erences 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.

Date: 2021-03
New Economics Papers: this item is included in nep-big, nep-cwa and nep-sog
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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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