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Cited by 532

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

An Informational Rationale for Political Parties

Open access
Snyder, James M., Ting, Michael M.·2002·American Journal of Political Science

Abstract

This article studies a model of political parties as informative "brands" to voters. Voters across a large number of constituencies are assumed to be risk averse and incompletely informed about candidate ideal policies, and candidates are unable to commit to a declared policy platform. In this environment, parties can play a critical role by aggregating ideologically similar candidates and signaling their preferences to voters. This signaling is effective because party membership imposes costs, which screen out candidates whose preferences are not sufficiently close to the party's platform. We find that when party labels are very informative, the parties' platforms converge. When party labels are less informative, however, platforms diverge, because taking an extreme position allows a party to reduce the variance of its members' preferences. As parties become less able to impose costs on their members, or less able to screen out certain types of candidates, their platforms move further apart.

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Crossref · 10.2307/3088416

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Metadata

Authors
Snyder, J. M., & Ting, M. M.
Venue
American Journal of Political Science
Publisher
JSTOR
Published
2002-01
Volume
46
Issue
1
Pages
90
ISSN
0092-5853 (ISSN-L)
OpenAlex
W2013115242
Cited by
532
References
30
FWCI
140.96
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: An Informational Rationale for Political Parties. https://app.cochat.ai/papers/10.2307/3088416
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Snyder, J. M., & Ting, M. M. (2002). An Informational Rationale for Political Parties. American Journal of Political Science. https://doi.org/10.2307/3088416

Data sources

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.