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We analyze text data in all the articles published in the top five (T5) economics journals between 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 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 females are unevenly distributed over the estimated latent topics. This and other findings rely 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).

Gender distribution across topics in the top five economics journals: a machine learning approach