nep-sog New Economics Papers
on Sociology of Economics
Issue of 2024‒03‒18
two papers chosen by
Jonas Holmström, Axventure AB

  1. Scientific Talent Leaks Out of Funding Gaps By Wei Yang Tham; Joseph Staudt; Elisabeth Ruth Perlman; Stephanie D. Cheng
  2. Women in economics: the role of gendered references at entry in the profession By Audinga Baltrunaite; Alessandra Casarico; Lucia Rizzica

  1. By: Wei Yang Tham; Joseph Staudt; Elisabeth Ruth Perlman; Stephanie D. Cheng
    Abstract: We study how delays in NIH grant funding affect the career outcomes of research personnel. Using comprehensive earnings and tax records linked to university transaction data along with a difference-in-differences design, we find that a funding interruption of more than 30 days has a substantial effect on job placements for personnel who work in labs with a single NIH R01 research grant, including a 3 percentage point (40%) increase in the probability of not working in the US. Incorporating information from the full 2020 Decennial Census and data on publications, we find that about half of those induced into nonemployment appear to permanently leave the US and are 90% less likely to publish in a given year, with even larger impacts for trainees (postdocs and graduate students). Among personnel who continue to work in the US, we find that interrupted personnel earn 20% less than their continuously-funded peers, with the largest declines concentrated among trainees and other non-faculty personnel (such as staff and undergraduates). Overall, funding delays account for about 5% of US nonemployment in our data, indicating that they have a meaningful effect on the scientific labor force at the national level.
    Date: 2024–02
  2. By: Audinga Baltrunaite (Bank of Italy and CEPR); Alessandra Casarico (Bocconi University, CESIfo and Dondena); Lucia Rizzica (Bank of Italy)
    Abstract: We study the presence and the extent of gender differences in reference letters for graduate students in economics and finance, and how these differences relate to early labor market outcomes. To these ends, we build a novel rich dataset and combine Natural Language Processing techniques with standard regression analysis. We find that men are described more often as brilliant and women as hardworking and diligent. We show that the former (latter) description relates positively (negatively) with various subsequent career outcomes. We provide evidence that the observed differences in the way candidates are described are driven by implicit gender stereotypes.
    Keywords: gender bias, research institutions, professional labor markets, word embeddings
    JEL: I23 J16 J44
    Date: 2024–02

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