Template-type: ReDIF-Paper 1.0
Author-Name: J.Ignacio Conde-Ruiz
Author-Workplace-Name: Universidad Complutense de Madrid and ICAE (Spain).
Author-Name: Juan-José Ganuza
Author-Email: juanjo.ganuza@gmail.com
Author-Workplace-Name: Universitat Pompeu Fabra and Barcelona GSE.
Author-Name: Manu García
Author-Workplace-Name: Washington University in St. Louis and ICAE. 
Author-Name: Luis A. Puch
Author-Workplace-Name: Universidad Complutense de Madrid and ICAE (Spain).
Title: Gender Distribution across Topics in the Top 5 Economics Journals: A Machine Learning Approach
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).
Classification-JEL: I20, J16, Z13.
Keywords: Machine Learning; Gender Gaps; Structural Topic Model; Gendered Language; Research Fields.
Length: 54 pages 
Creation-Date: 2021-06
Number: 2021-09
X-File-Ref: http://america.sim.ucm.es/repec/ucm/ref/doicae2109.txt
File-URL: https://eprints.ucm.es/id/eprint/67146/1/2109.pdf
File-Format: Application/pdf
Handle: RePEc:ucm:doicae:2109