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Cited by 295

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Rational Learning and Partisan Attitudes

Gerber, Alan, Green, Donald P.·1998·American Journal of Political Science
DOI 10.2307/2991730
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Abstract

Theory: Characterizing voters as rational actors who update their party affiliations based on a Bayesian assimilation of new information, Achen (1992) showed that a revisionist model of party identification generates, among its empirical implications, stable partisanship among adults. The model further implies that susceptibility to partisan change declines with age. The significance of Achen's model for the study of party identification leads us to examine more closely its underlying assumptions and the empirical ramifications of this and other learning models. Method: This essay develops a more general learning model, based upon the Kalman filter, that encompasses the Achen model as a special case. Results: We show that the Achen assumption of a fixed party benefit level leads to implausible implications about how voters learn from the history of party performance. When party benefit levels are allowed to vary over time, models of voter learning no longer imply that partisan attitudes, even among the older segments of the population, remain stable in the wake of new information about the parties. We conclude by discussing the empirical viability of our revised learning model and its implications for the study of partisan attitudes.

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Metadata

Authors
Gerber, A., & Green, D. P.
Venue
American Journal of Political Science
Publisher
JSTOR
Published
1998-07
Volume
42
Issue
3
Pages
794
ISSN
0092-5853 (ISSN-L)
DOI
10.2307/2991730
OpenAlex
W2028133560
Cited by
295
References
23
FWCI
24.26
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Rational Learning and Partisan Attitudes. https://app.cochat.ai/papers/10.2307/2991730
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Gerber, A., & Green, D. P. (1998). Rational Learning and Partisan Attitudes. American Journal of Political Science. https://doi.org/10.2307/2991730

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

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