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      Building Credibility and Cooperation in Low-Trust Settings: Persuasion and Source Accountability in Liberia During the 2014–2015 Ebola Crisis

      1 , 1 , 2
      Comparative Political Studies
      SAGE Publications

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          Abstract

          How can governments in low-trust settings overcome their credibility deficit when promoting public welfare? To answer this question, we evaluate the effectiveness of the Liberian government’s door-to-door canvassing campaign during the 2014–2015 Ebola epidemic, which aimed to persuade residents to voluntarily comply with policies for containing the disease. Combining data from an original representative survey of Monrovia during the crisis with variation in the campaign’s reach and using multiple identification strategies, we find that the informational campaign was remarkably effective at increasing adherence to safety precautions, support for contentious control policies, and general trust in government. To uncover the pathways through which the campaign proved so effective, we conducted over 80 in-depth qualitative interviews in 40 randomly sampled communities. This investigation suggests that local intermediaries were effective because their embeddedness in communities subjected them to monitoring and sanctioning, thereby assuring their fellow residents that they were accountable and thus credible.

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            Unobservable Selection and Coefficient Stability: Theory and Evidence

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              The Persuasiveness of Source Credibility: A Critical Review of Five Decades' Evidence

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                Author and article information

                Journal
                Comparative Political Studies
                Comparative Political Studies
                SAGE Publications
                0010-4140
                1552-3829
                January 20 2020
                : 001041401989769
                Affiliations
                [1 ]Massachusetts Institute of Technology, Cambridge, USA
                [2 ]Brown University, Providence, RI, USA
                Article
                10.1177/0010414019897698
                d29cb16f-9761-4f38-8d0a-5c5bd5787005
                © 2020

                http://journals.sagepub.com/page/policies/text-and-data-mining-license

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