Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/209749
Authors: 
Dunning, Thad
Grossman, Guy
Humphreys, Macartan
Hyde, Susan D.
McIntosh, Craig
Nellis, Gareth
Adida, Claire L.
Arias, Eric
Bicalho, Clara
Boas, Taylor C.
Buntaine, Mark T.
Chauchard, Simon
Chowdhury, Anirvan
Gottlieb, Jessica
Hidalgo, F. Daniel
Holmlund, Marcus
Jablonski, Ryan
Kramon, Eric
Larreguy, Horacio
Lierl, Malte
Marshall, John
McClendon, Gwyneth
Melo, Marcus A.
Nielson, Daniel L.
Pickering, Paula M.
Platas, Melina R.
Querubín, Pablo
Raffler, Pia
Sircar, Neelanjan
Year of Publication: 
2019
Citation: 
[Journal:] Science Advances [ISSN:] 2375-2548 [Publisher:] American Association for the Advancement of Science [Place:] Washington, DC [Volume:] 5 [Year:] 2019 [Issue:] 7 (Article No.:) eaaw2612 [Pages:] 1-10
Abstract: 
Voters may be unable to hold politicians to account if they lack basic information about their representatives' performance. Civil society groups and international donors therefore advocate using voter information campaigns to improve democratic accountability. Yet, are these campaigns effective? Limited replication, measurement heterogeneity, and publication biases may undermine the reliability of published research. We implemented a new approach to cumulative learning, coordinating the design of seven randomized controlled trials to be fielded in six countries by independent research teams. Uncommon for multisite trials in the social sciences, we jointly preregistered a meta-analysis of results in advance of seeing the data. We find no evidence overall that typical, nonpartisan voter information campaigns shape voter behavior, although exploratory and subgroup analyses suggest conditions under which informational campaigns could be more effective. Such null estimated effects are too seldom published, yet they can be critical for scientific progress and cumulative, policy-relevant learning.
Persistent Identifier of the first edition: 
Creative Commons License: 
https://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
Document Version: 
Published Version

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