Meta-Research: Individual-level researcher data confirm the widening gender gap in publishing rates during COVID-19
Abstract
Publications are essential for a successful academic career, and there is evidence that the COVID-19 pandemic has amplified existing gender disparities in the publishing process. We used longitudinal publication data on 431,207 authors in four disciplines - basic medicine, biology, chemistry and clinical medicine - to quantify the differential impact of COVID-19 on the annual publishing rates of men and women. In a difference-in-differences analysis, we estimated that the average gender difference in publication productivity increased from -0.26 in 2019 to -0.35 in 2020; this corresponds to the output of women being 17% lower than the output of men in 2019, and 24% lower in 2020. An age-group comparison showed a widening gender gap for both early-career and mid-career scientists. The increasing gender gap was most pronounced among highly productive authors and in biology and clinical medicine. Our study demonstrates the importance of reinforcing institutional commitments to diversity through policies that support the inclusion and retention of women in research.
Data availability
The current manuscript is a computational study, so no data have been generated for this manuscript. Source data and code will be provided on git-hub for all tables and figures.
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Funding
Samfund og Erhverv, Det Frie Forskningsråd (DFF-0133-00165B)
- Emil Bargmann Madsen
- Mathias Wullum Nielsen
- Josefine Bjørnholm
- Jens Peter Andersen
Aarhus Universitets Forskningsfond (AUFF-F-2018-7-5)
- Jens Peter Andersen
Independent Research Fund Denmark (9130-00029B)
- Mathias Wullum Nielsen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Madsen et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Further reading
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- Computational and Systems Biology
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