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Cited by 448

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

Power to the Parties: Cohesion and Competition in the European Parliament, 1979–2001

Open access
HIX, SIMON, NOURY, ABDUL, ROLAND, GÉRARD·2005·British Journal of Political Science

Abstract

How cohesive are political parties in the European Parliament? What coalitions form and why? The answers to these questions are central for understanding the impact of the European Parliament on European Union policies. These questions are also central in the study of legislative behaviour in general. We collected the total population of roll-call votes in the European Parliament, from the first elections in 1979 to the end of 2001 (over 11,500 votes). The data show growing party cohesion despite growing internal national and ideological diversity within the European party groups. We also find that the distance between parties on the left-right dimension is the strongest predictor of coalition patterns. We conclude that increased power of the European Parliament has meant increased power for the transnational parties, via increased internal party cohesion and inter-party competition.

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Metadata

Authors
HIX, S., NOURY, A., & ROLAND, G.
Venue
British Journal of Political Science
Published
2005-02-21
Volume
35
Issue
2
Pages
209-234
ISSN
  • 0007-1234 (ISSN-L)
  • 1469-2112
OpenAlex
W2261326549
Cited by
448
References
14
FWCI
166.21
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Power to the Parties: Cohesion and Competition in the European Parliament, 1979–2001. https://app.cochat.ai/papers/10.1017/s0007123405000128
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HIX, S., NOURY, A., & ROLAND, G. (2005). Power to the Parties: Cohesion and Competition in the European Parliament, 1979–2001. British Journal of Political Science. https://doi.org/10.1017/s0007123405000128

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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.