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

J. Ignacio Conde-Ruiz, Juan-José Ganuza (), Manu García and Luis Puch
Additional contact information
Juan-José Ganuza: Universitat Pompeu Fabra and Barcelona GSE.

No 2021-09, Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico

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).

Keywords: Machine Learning; Gender Gaps; Structural Topic Model; Gendered Language; Research Fields. (search for similar items in EconPapers)
JEL-codes: I20 J16 Z13 (search for similar items in EconPapers)
Pages: 54 pages
Date: 2021-06
New Economics Papers: this item is included in nep-big, nep-cwa, nep-isf and nep-sog
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://eprints.ucm.es/id/eprint/67146/1/2109.pdf (application/pdf)

Related works:
Working Paper: Gender Distribution across Topics in 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)
Working Paper: Gender distribution across topics in Top 5 economics journals: A machine learning approach (2021)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ucm:doicae:2109

Ordering information: This working paper can be ordered from
Facultad de Ciencias Económicas y Empresariales. Pabellón prefabricado, 1ª Planta, ala norte. Campus de Somosaguas, 28223 - POZUELO DE ALARCÓN (MADRID)
https://www.ucm.es/f ... -de-trabajo-del-icae

Access Statistics for this paper

More papers in Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico Contact information at EDIRC.
Bibliographic data for series maintained by Águeda González Abad ().