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Believe It or Not? Partisanship, Preferences, and the Credibility of Campaign Promises

Published online by Cambridge University Press:  27 May 2019

Pablo Fernandez-Vazquez
Affiliation:
Department of Political Science, University of Pittsburgh, Pittsburgh, PA15260, USA, e-mail: pablo.fernandezvz@gmail.com; Twitter: @pfernandezvz
Alexander G. Theodoridis
Affiliation:
Department of Political Science, University of California, Merced, CA95343, USA, e-mail: atheodoridis@ucmerced.edu; Twitter: @AGTheodoridis

Abstract

We use a novel survey experiment with a broadly representative sample to reveal an important phenomenon in voter integration of campaign communications: preference-mediated partisan motivation. When evaluating the credibility of candidate position changes on minimum wage policy (a readily quantifiable and salient issue domain), partisans do not take a new stance at face value, apply universal skepticism, or simply afford more credibility to co-partisans. Instead, they process a candidate’s stance through an interaction between the voter’s partisan allegiance and their own policy preference. Partisans update more when a co-partisan moves closer to them than when the candidate shifts away from them. The opposite pattern emerges with the other party’s candidates: partisans tend to be more receptive if the candidate moves away from them. This feature of campaign message acceptance has profound implications for political communication and our understanding of partisan cognition.

Type
Research Article
Copyright
© The Experimental Research Section of the American Political Science Association 2019

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Footnotes

We thank Steve Ansolabehere, Vin Arceneaux, Larry Bartels, Toby Bolsen, Henry Brady, Jack Citrin, Jamie Druckman, Nick Eubank, Stephen Goggin, John Henderson, Travis Johnston, Cindy Kam, Kevin Mullinix, Steve Nicholson, David Nickerson, Eric Schickler, Paul Sniderman, Rob Van Houweling, Julie Wronski and the Vanderbilt University Center for the Study of Democratic Institutions. This research was supported by generous funding from the University of California, Merced, and supported by the National Science Foundation, Award #1559125. The authors have no conflicts of interest pertaining to this research. The data, code, online supplement, and any additional materials required to replicate all analyses in this article are available at the Journal of Experimental Political Science Dataverse within the Harvard Dataverse Network, at: https://doi.org/10.7910/DVN/6JGSZP.

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