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Updated: Dec 24, 2025

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Published on: September 10, 2018
Mechanisms and impacts of an incentive-based conservation program with evidence from a randomized control trial
Emma Wiik1, Julia P G Jones1, Edwin Pynegar1,2
1School of Natural Sciences, Deiniol Road, Bangor University, Bangor, LL57 2UW, U.K.
Abstract:
Conservation science needs more high-quality impact evaluations, especially ones that explore mechanisms of success or failure. Randomized control trials (RCTs) provide particularly robust evidence of the effectiveness of interventions (although they have been criticized as reductionist and unable to provide insights into mechanisms), but there have been few such experiments investigating conservation at the landscape scale. We explored the impact of Watershared, an incentive-based conservation program in the Bolivian Andes, with one of the few RCTs of landscape-scale conservation in existence. There is strong interest in such incentive-based conservation approaches as some argue they can avoid negative social impacts sometimes associated with protected areas. We focused on social and environmental outcomes based on responses from a household survey in 129 communities randomly allocated to control or treatment (conducted both at the baseline in 2010 and repeated in 2015-2016). We controlled for incomplete program uptake by combining standard RCT analysis with matching methods and investigated mechanisms by exploring intermediate and ultimate outcomes according to the underlying theory of change. Previous analyses, focused on single biophysical outcomes, showed that over its first 5 years Watershared did not slow deforestation or improve water quality at the landscape scale. We found that Watershared influenced some outcomes measured using the survey, but the effects were complex, and some were unexpected. We thus demonstrated how RCTs can provide insights into the pathways of impact, as well as whether an intervention has impact. This paper, one of the first registered reports in conservation science, demonstrates how preregistration can help make complex research designs more transparent, avoid cherry picking, and reduce publication bias.
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