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Cited by 381

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

Mean voter representation and partisan constituency representation: Do parties respond to the mean voter position or to their supporters?

Open access
Ezrow, Lawrence, De Vries, Catherine, Steenbergen, Marco, Edwards, Erica·2011·Party Politics

Abstract

Do political parties respond to shifts in the preferences of their supporters, which we label the partisan constituency model, or to shifts in the mean voter position (the general electorate model)? Cross-national analyses — based on observations from Eurobarometer surveys and parties’ policy programmes in 15 countries from 1973 to 2002 — suggest that the general electorate model characterizes the policy shifts of mainstream parties. Alternatively, when we analyse the policy shifts of Communist, Green and extreme Nationalist parties (i.e. ‘niche’ parties), we find that these parties respond to shifts in the mean position of their supporters. The findings have implications for spatial theories and political representation.

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Metadata

Authors
Ezrow, L., De Vries, C., Steenbergen, M., & Edwards, E.
Venue
Party Politics
Published
2010-07-29
Volume
17
Issue
3
Pages
275-301
ISSN
  • 1354-0688 (ISSN-L)
  • 1460-3683
OpenAlex
W2083390881
Cited by
381
References
78
FWCI
86.11
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Mean voter representation and partisan constituency representation: Do parties respond to the mean voter position or to their supporters? https://app.cochat.ai/papers/10.1177/1354068810372100
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Ezrow, L., De Vries, C., Steenbergen, M., & Edwards, E. (2011). Mean voter representation and partisan constituency representation: Do parties respond to the mean voter position or to their supporters? Party Politics. https://doi.org/10.1177/1354068810372100

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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.