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

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• dc.contributor.author Conde-Ruiz, J. Ignacio
• dc.contributor.author Ganuza, Juan José
• dc.contributor.author García, Manu
• dc.contributor.author Puch, Luis A.
• dc.date.accessioned 2023-06-19T06:18:06Z
• dc.date.available 2023-06-19T06:18:06Z
• dc.date.issued 2021
• dc.description.abstract 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).
• dc.description.sponsorship José Ignacio Conde-Ruiz acknowledges the Spanish Ministry of Science and Innovation for financial support through the project PID2019-105499GB-I00. Manu García and Luis Puch acknowledge the support through the project PID2019-107161GB-C32. Juan-José Ganuza gratefully acknowledges the financial support from the Spanish Agencia Estatal de Investigación, through the Severo Ochoa Programme for Centres of Excellence in R&D (CEX2019-000915-S) and the Spanish Ministry of Science and Innovation through Project PID2020-115044GB-I00.
• dc.format.mimetype application/pdf
• dc.identifier.citation Conde-Ruiz JI, Ganuza JJ, García M, Puch LA. Gender distribution across topics in the top five economics journals: a machine learning approach. SERIEs. 2022;13(1-2):269-308. DOI: 10.1007/s13209-021-00256-2
• dc.identifier.doi http://dx.doi.org/10.1007/s13209-021-00256-2
• dc.identifier.issn 1869-4187
• dc.identifier.uri http://hdl.handle.net/10230/57226
• dc.language.iso eng
• dc.publisher Springer
• dc.relation.ispartof SERIEs. 2022;13(1-2):269-308.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-105499GB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2019-107161GB-C32
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/CEX2019-000915-S
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-115044GB-I00
• dc.rights © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Machine learning
• dc.subject.keyword Gender gaps
• dc.subject.keyword Structural topic model
• dc.subject.keyword Gendered language
• dc.subject.keyword Research fields
• dc.title Gender distribution across topics in the top five economics journals: a machine learning approach
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion