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

  1. J.Ignacio Conde-Ruiza 1
  2. Juan-José Ganuza 2
  3. Manu García 1
  4. Luis A. Puchc 1
  1. 1 Universidad Complutense de Madrid
    info

    Universidad Complutense de Madrid

    Madrid, España

    ROR 02p0gd045

  2. 2 Universitat Pompeu Fabra
    info

    Universitat Pompeu Fabra

    Barcelona, España

    ROR https://ror.org/04n0g0b29

Revista:
Documentos de trabajo ( FEDEA )

ISSN: 1696-7496

Año de publicación: 2021

Número: 7

Páginas: 1-49

Tipo: Documento de Trabajo

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Otras publicaciones en: Documentos de trabajo ( FEDEA )

Resumen

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 unsupervised 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).

Referencias bibliográficas

  • Bagues, Manuel and Pamela Campa, “Can Gender Quotas in Candidate Lists Empower Women? Evidence from a Regression Discontinuity Design,” 2017, (12149).
  • Bayer, Amanda and Cecilia Elena Rouse, “Diversity in the Economics Profession: A New Attack on an Old Problem,” Journal of Economic Perspectives, November 2016, 30 (4), 221–42.
  • Beneito, P., J. E. Bosc´a, J. Ferri, and M. Garc´ıa, “Women across Subfields in Economics: Relative Performance and Beliefs,” Fedea Working Papers, June 2018.
  • Blei, DavidM., Andrew Y. Ng, andMichael I. Jordan, “Latent Dirichlet Allocation,” J. Mach. Learn. Res., March 2003, 3 (null), 9931022.
  • Boustan, Leah and Andrew Langan, “Variation in Women’s Success across PhD Programs in Economics,” Journal of Economic Perspectives, February 2019, 33 (1), 23–42.
  • Buckley, Chris, “Implementation of the SMART Information Retrieval System,” Technical Report, USA 1985.
  • Cabrales, A., M. Garc´ıa, and L. A. Puch, “Gendered Language in the British Press,” Mimeo COSME: Gender at 2018 Meetings of the Spanish Economic Association., 2018.
  • Card, David and Stefano DellaVigna, “Nine Facts about Top Journals in Economics,” Journal of Economic Literature, March 2013, 51 (1), 144–61.
  • Card, David and Stefano DellaVigna, Patricia Funk, Nagore Iriberri, “Are Referees and Editors in Economics Gender Neutral?*,” The Quarterly Journal of Economics, 11 2019, 135 (1), 269–327.
  • Chari, Anusha and Paul Goldsmith-Pinkham, “Gender Representation in Economics Across Topics and Time: Evidence from the NBER Summer Institute,” Working Paper 23953, National Bureau of Economic Research October 2017.
  • Chevalier, Judy, “The 2020 Report of the Committee on the Status of Women in the Economics Profession,” 2020.
  • Conde-Ruiz, J. Ignacio, Juan-Jos´e Ganuza, and Paola Profeta, “Statistical Discrimination and the Efficiency of Quotas,” Fedea Working Papers, 2017.
  • Conde-Ruiz, J. Ignacio, Juan Jos´e Ganuza, and Paola Profeta, “Statistical Discrimination and Committees,” Fedea Working Papers, February 2021, (2021-06).
  • Dolado, Juan, Florentino Felgueroso, and Miguel Almunia, “Are men and womeneconomists evenly distributed across research fields? Some new empirical evidence,” SERIEs: Journal of the Spanish Economic Association, September 2012, 3 (3), 367–393.
  • Hansen, Stephen, Michael McMahon, and Andrea Prat, “Transparency and Deliberation Within the FOMC: A Computational Linguistics Approach,” The Quarterly Journal of Economics, 10 2017, 133 (2), 801–870.
  • Heckman, James J. and Sidharth Moktan, “Publishing and Promotion in Economics: The Tyranny of the Top Five,” Journal of Economic Literature, June 2020, 58 (2), 419–70.
  • Hengel, Erin and Eunyoung Moon, “Gender and quality at top economics journals,” Working Papers 202001, University of Liverpool, Department of Economics February 2020.
  • Lundberg, Shelly and Jenna Stearns, “Women in Economics: Stalled Progress,” Journal of Economic Perspectives, February 2019, 33 (1), 3–22.
  • Mimno, David, Hanna M. Wallach, Edmund Talley, Miriam Leenders, and Andrew McCallum, “Optimizing Semantic Coherence in Topic Models,” 2011, p. 262272.
  • Roberts, Margaret E., Brandon M. Stewart, and Dustin Tingley, “stm: An R Package for Structural Topic Models,” Journal of Statistical Software, Articles, 2019, 91 (2), 1–40.
  • Roberts, Margaret E., Brandon M. Stewart, Christopher Lucas, Jetson Leder-Luis, Shana Kushner Gadarian, Bethany Albertson, and David G. Rand, “Structural Topic Models for Open-Ended Survey Responses,” American Journal of Political Science, 2014, 58 (4), 1064–1082.
  • Siniscalchi, Marciano and Pietro Veronesi, “Self-image Bias and Lost Talent,” December 2020, (28308).
  • Tang, Cong, Keith Ross, Nitesh Saxena, and Ruichuan Chen, “What’s in a Name: A Study of Names, Gender Inference, and Gender Behavior in Facebook,” 2011, pp. 344– 356.
Fuente de los datos: Dialnet