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Distinguishing Occasional Abstention from Routine Indifference in Models of Vote Choice*

Published online by Cambridge University Press:  13 August 2015


Researchers commonly employ multinomial logit (MNL) models to explain individual-level vote choice while treating “abstention” as the baseline category. Though many view abstainers as a homogeneous group, we argue that these respondents emerge from two distinct sources. Some nonvoters are likely to be “occasional voters” who abstained from a given election owing to temporary factors, such as a distaste for all candidates running in a particular election, poor weather conditions, or other temporary circumstances. On the other hand, many nonvoters are unlikely to vote regardless of the current political climate. This latter population of “routine nonvoters” is consistently disengaged from the political process in a way that is distinct from that of occasional voters. Including both sets of nonvoters within an MNL model can lead to faulty inferences. As a solution, we propose a baseline-inflated MNL estimator that models heterogeneous populations of nonvoters probabilistically, thus accounting for the presence of routine nonvoters within models of vote choice. We demonstrate the utility of this model using replications of existing political behavior research.

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© The European Political Science Association 2015 

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Benjamin E. Bagozzi, Assistant Professor, Department of Political Science and International Relations, University of Delaware (, Address: 347 Smith Hall, 18 Amstel Ave, Newark, DE 19716. Kathleen Marchetti, Assistant Professor, Department of Political Science, Dickinson College (, Address: 12 Denny Hall, Dickinson College, Carlisle, PA 17013. Earlier drafts of this paper presented at the 7th Annual St. Louis Area Methods Meeting (SLAMM!) and the APSA 2013 Annual Meeting. The authors wish to acknowledge the valuable suggestions and feedback that they received from the editors, reviewers, and replication analysts at Political Science Research and Methods, as well as from Justin Esarey, John Freeman, Jeff Gill, Galin Jones, Luke Keele, Ines Levin, James McCann, Jamie Monogan, Burt Monroe, Jacob Montgomery, Bumba Mukherjee, and Chris Zorn. The authors would also like to thank Kevin Arceneaux, Robin Kolodny, David Campbell, and J. Quin Monson for sharing their replication data. To view supplementary material for this article, please visit


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