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Sustained Government Engagement Improves Subsequent Pandemic Risk Reporting In Conflict Zones

Published online by Cambridge University Press:  25 January 2021

DOTAN HAIM*
Affiliation:
Florida State University
NICO RAVANILLA*
Affiliation:
University of California, San Diego
RENARD SEXTON*
Affiliation:
Emory University
*
Dotan Haim, Assistant Professor, Department of Political Science, Florida State University, dhaim@fsu.edu.
Nico Ravanilla, Assistant Professor, School of Global Policy and Strategy, University of California, San Diego, nravanilla@ucsd.edu.
Renard Sexton, Assistant Professor, Department of Political Science, Emory University, renard.sexton@emory.edu.
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Abstract

Community information sharing is crucial to a government’s ability to respond to a disaster or a health emergency, such as a pandemic. In conflict zones, however, citizens and local leaders often lack trust in state institutions and are unwilling to cooperate, risking costly delays and information gaps. We report results from a randomized experiment in the Philippines regarding government efforts to provide services and build trust with rural communities in a conflict-affected region. We find that the outreach program increased the probability that village leaders provide time-sensitive pandemic risk information critical to the regional Covid-19 Task Force by 20%. The effect is largest for leaders who, at baseline, were skeptical about government capacity and fairness and had neutral or positive attitudes towards rebels. A test of mechanisms suggests that treated leaders updated their beliefs about government competence and shows that neither security improvement nor project capture by the rebels are primary drivers. These findings highlight the important role that government efforts to build connections with conflict-affected communities can play in determining public health outcomes during times of national emergencies.

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Letter
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the American Political Science Association
Figure 0

Figure 1. Conflict Activity in Bicol RegionNote: Village-level rebel presence categorized according to Armed Forces of the Philippines intelligence reports. Municipalities are shaded by the percentage of villages with some New People’s Army (NPA) presence in any year during 2009–2015.

Figure 1

Figure 2. Research Design

Figure 2

Figure 3. Effect of Intervention on Response RateNote: Outcome is whether village leader provided COVID-19 risk information with Covid Task Force within five days. Control group mean is 0.51. “Village Effect” compares treated villages with all control villages. “Village Effect 2” compares treated villages with control villages in treated municipalities. “Municipality Effect” compares treated villages in treated municipalities with control villages in control municipalities. “Spillovers Check” compares control villages in treated municipalities with control villages in control municipalities. Standard errors are clustered at the municipal level in all models. Corresponding regression tables are included in the supplementary materials.

Figure 3

Figure 4. Mechanisms: Heterogenous Treatment Effects by Baseline Political AttitudesNote: Outcome is whether village leader provided COVID-19 risk information with Covid Task Force within five days of being requested. Pretreatment attitudes towards rebels based on average of two endorsement experiments. Government trust based on terciles of a 0–10 feeling thermometer. Capacity and patronage responses based on binary survey question responses. Standard errors are clustered at the municipal level in all models. Corresponding regression tables and details of survey questions are included in the supplementary materials. The effects in panels 1 and 3 are statistically significantly different from each other by conventional measures.

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Haim et al. Dataset

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Haim et al. supplementary material

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