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Society & Natural Resources
An International Journal
Volume 31, 2018 - Issue 6
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ARTICLES

Robustness and the Paradox of Bridging Organizations: The Exit Problem in Regional Water Governance Networks in Central America

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Pages 683-697 | Received 16 May 2017, Accepted 13 Dec 2017, Published online: 01 Feb 2018
 

ABSTRACT

Bridging organizations facilitate a range of governance processes, including cooperation and social learning, and are theorized to be a key component of robust governance systems. In this article, we use node removal simulations to test structural hypotheses of robustness in a regional water governance network in Central America. We investigate the response of network measures supporting core governance processes to the targeted removal of bridging organizations and other actors, which we compare to random and centrality-based simulations. The results indicate removing bridging organizations has a greater impact on the network than any other type of actor, suggesting bridging organizations are critical to the robustness of the governance system. Furthermore, network structures supporting cooperation may be less robust than structures facilitating social learning. We conclude with policy implications of the research findings as they relate to the exit problem in governance systems with a large presence of international development actors.

Acknowledgments

An early version of this paper was presented at the 2016 Political Networks Conference at Washington University in St. Louis, Missouri. The authors thank Matthew Hamilton, Michael Levy, the Center for Environmental Policy and Behavior at UC Davis, and the anonymous reviewers for helpful comments and suggestions made.

Notes

Formalized by Hastie and Tibshirani (Citation1986), GAMs use multiple smoothing functions to estimate the relationship between dependent and independent variables. With GAMs it is not necessary to know the relationship between variables from the outset, which enables the observation of any hidden patterns or structures within the data.

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