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Cited by 371

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Rising to the Top: Gender, Political Performance, and Party Leadership in Parliamentary Democracies

O'Brien, Diana Z.·2015·American Journal of Political Science

Abstract

Party leaders are the main actors controlling campaign strategies, policy agendas, and government formation in advanced parliamentary democracies. Little is known, however, about gender and party leadership. This article examines gendered leadership patterns across 71 political parties in 11 parliamentary democracies between 1965 and 2013. It shows that men and women have different access to, and experiences in, party leadership and that these gendered political opportunity structures are shaped by parties' political performances. Women are more likely to initially come to power in minor opposition parties and those that are losing seat share. Once selected for the position, female leaders are more likely to retain office when their parties gain seats, but they are also more likely to leave the post when faced with an unfavorable trajectory. Together, these results demonstrate that prospective female leaders are playing by a different (and often more demanding) set of rules than their male counterparts.

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Metadata

Authors
O'Brien, D. Z.
Venue
American Journal of Political Science
Publisher
Wiley
Published
2015-02-10
Volume
59
Issue
4
Pages
1022-1039
ISSN
  • 0092-5853 (ISSN-L)
  • 1540-5907
OpenAlex
W1547721553
Cited by
371
References
2
FWCI
44.17
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Rising to the Top: Gender, Political Performance, and Party Leadership in Parliamentary Democracies. https://app.cochat.ai/papers/10.1111/ajps.12173
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O'Brien, D. Z. (2015). Rising to the Top: Gender, Political Performance, and Party Leadership in Parliamentary Democracies. American Journal of Political Science. https://doi.org/10.1111/ajps.12173

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.