Abstract
Extant research has used the COVID-19 pandemic as a context to test the “women leadership advantage during crisis” hypothesis. An influential paper reported that women U.S. governors were associated with fewer COVID-19 deaths. Building on this work, we demonstrate that methodological assumptions play a critical role in our interpretation of findings. First, we conduct a literal replication (Study 1) of the original study to validate our dataset. Second, a series of constructive replications (Studies 2A-D) shows the results rely on methodological assumptions that are not fully supported. Without these assumptions, we find no evidence for the “women leadership advantage during crisis” hypothesis. Third, in two constructive replications focusing on U.S. counties and Brazilian municipalities, we causally test the relationship between strategic leader gender and COVID-19 deaths using a geographic matching design (Study 3A) and a regression discontinuity design (Study 3B). Again, we find no evidence for the “women leadership advantage during crisis” hypothesis. Collectively, we demonstrate that when following the methodological precedent of extant research, we were able to replicate previously identified relationships between gender and leadership outcomes, but after accounting for endogeneity and basic assumptions of linear models, we were no longer able to replicate these effects. In all our constructive replications, we found no significant difference in the effectiveness of women and men strategic leaders in crises.
Original language | English |
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Article number | 101812 |
Number of pages | 14 |
Journal | The Leadership Quarterly |
Volume | 35 |
Issue number | 6 |
DOIs | |
Publication status | Published - Dec 2024 |
Bibliographical note
Publisher Copyright:© 2024 Elsevier Inc.
Keywords
- COVID-19
- Crisis
- Gender
- Replication
- Strategic leadership
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Women leaders and COVID-19 deaths
Sieweke, J. (Creator), Obenauer, W. (Creator) & Arvate, P. (Creator), OSF, 2024
https://osf.io/8uaws/?view_only=0a2fe73c69994a68b106da221ff0adb6
Dataset / Software: Dataset