nep-sog New Economics Papers
on Sociology of Economics
Issue of 2023‒03‒20
two papers chosen by
Jonas Holmström
Axventure AB

  1. Home Bias in Top Economics Journals By Bethmann, Dirk; Bransch, Felix; Kvasnicka, Michael; Sadrieh, Abdolkarim
  2. Gender differences in reference letters: Evidence from the Economics job market By Markus Eberhardt; Giovanni Facchini; Valeria Rueda

  1. By: Bethmann, Dirk (Otto-von-Guericke University Magdeburg); Bransch, Felix (Otto-von-Guericke University Magdeburg); Kvasnicka, Michael (Otto-von-Guericke University Magdeburg); Sadrieh, Abdolkarim (Tilburg University)
    Abstract: Two of the top economics journals have institutional ties to a specific university, the Quarterly Journal of Economics (QJE) to Harvard University and the Journal of Political Economy (JPE) to the University of Chicago. Researchers from Harvard, but also nearby Massachusetts Institute of Technology (MIT), and from Chicago (co-)author a disproportionate share of articles in their respective home journal. Such home ties and publication bias may harm, but also benefit, article quality. We study this question in a difference-in-differences framework, using data on both current and past author affiliations and cumulative citation counts for articles published between 1995 and 2015 in the QJE, JPE, and American Economic Review (AER), which serves as a benchmark. We find that median article quality is lower in the QJE if authors have ties to Harvard and/or MIT than if authors are from other top-10 universities, but higher in the JPE if authors have ties to Chicago. We also find that home ties matter for the odds of journals to publish highly influential and low impact papers. Again, the JPE appears to benefit, if anything, from its home ties, while the QJE does not.
    Keywords: publishing process, institutional ties, citations, home bias
    JEL: A11 I2 J24
    Date: 2023–02
  2. By: Markus Eberhardt; Giovanni Facchini; Valeria Rueda
    Abstract: Academia, and economics in particular, faces increased scrutiny because of gender imbalance. This paper studies the job market for entry-level faculty positions. We employ machine learning methods to analyze gendered patterns in the text of 12, 000 reference letters written in support of over 3, 700 candidates. Using both supervised and unsupervised techniques, we document widespread differences in the attributes emphasized. Women are systematically more likely to be described using ‘grindstone’ terms and at times less likely to be praised for their ability. Using information on initial placement we highlight the mplications of these gendered descriptors for the quality of academic placement.
    Keywords: gender; natural language processing; diversity
    Date: 2023

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