Gender distribution across topics in Top 5 economics journals: A machine learning approach
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Conde-Ruiz, J. Ignacio
- dc.contributor.author Ganuza, Juan José
- dc.contributor.author Garcia, Manu
- dc.contributor.author Puch, Luis A.
- dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
- dc.date.accessioned 2024-11-14T10:09:39Z
- dc.date.available 2024-11-14T10:09:39Z
- dc.date.issued 2021-02-01
- dc.date.modified 2024-11-14T10:07:44Z
- dc.description.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 unsuper vised 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).
- dc.format.mimetype application/pdf*
- dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1771
- dc.identifier.citation
- dc.identifier.uri http://hdl.handle.net/10230/68569
- dc.language.iso eng
- dc.relation.ispartofseries Economics and Business Working Papers Series; 1771
- dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
- dc.subject.keyword machine learning
- dc.subject.keyword structural topic model
- dc.subject.keyword gender
- dc.subject.keyword research fields
- dc.subject.keyword Behavioral and Experimental Economics
- dc.title Gender distribution across topics in Top 5 economics journals: A machine learning approach
- dc.title.alternative
- dc.type info:eu-repo/semantics/workingPaper