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The Effectiveness of a Neighbor-to-Neighbor Get-Out-the-Vote Program: Evidence from the 2017 Virginia State Elections

Published online by Cambridge University Press:  10 May 2021

Cassandra Handan-Nader*
Department of Political Science, Stanford University, Stanford, CA, USA
Daniel E. Ho
Department of Political Science, Stanford University, Stanford, CA, USA Stanford Law School, Stanford, CA, USA, Twitter: @DanHo1
Alison Morantz
Stanford Law School, Stanford, CA, USA, Twitter: @DanHo1
Tom A. Rutter
Department of Economics, London School of Economics, London, UK
*Corresponding author. Email:


We analyze the results of a neighbor-to-neighbor, grassroots get-out-the-vote (GOTV) drive in Virginia, in which unpaid volunteers were encouraged to contact at least three nearby registered voters who were likely co-partisans yet relatively unlikely to vote in the 2017 state election. To measure the campaign’s effectiveness, we used a pairwise randomization design whereby each volunteer was assigned to one randomly selected member of the most geographically proximate pair of voters. Because some volunteers unexpectedly signed up to participate outside their home districts, we analyze the volunteers who adhered to the original hyper-local program design separately from those who did not. We find that the volunteers in the original program design drove a statistically significant 2.3% increase in turnout, which was concentrated in the first voter pair assigned to each volunteer. We discuss implications for the study and design of future GOTV efforts.

Research Article
© The Author(s), 2021. Published by Cambridge University Press on behalf of The Experimental Research Section of the American Political Science Association

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The design of the randomized controlled trial reported herein was developed by D.E.H. and A.M. as unpaid consultants, working in their personal capacity, and by C.H. and T.A.R. in their consulting capacity, independent of Plus3. For full disclosure, A.M. is the spouse of the founder of Plus3, but the evaluation was structured to be independent. The authors otherwise declare no conflicts of interest related to the research described in this paper. We are grateful to David Nickerson, Donald Green and Aaron Strauss for their helpful comments and suggestions. We are also grateful to Plus3 for their willingness to collaborate. The data, code, 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: All errors remain our own.


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