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Behavioral and Experimental Agri-Environmental Research: Methodological Challenges, Literature Gaps, and Recommendations

Leah H. Palm-Forster (), Paul Ferraro (), Nicholas Janusch, Christian Vossler and Kent D. Messer
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Leah H. Palm-Forster: University of Delaware
Nicholas Janusch: California Energy Commission
Kent D. Messer: University of Delaware

Environmental & Resource Economics, 2019, vol. 73, issue 3, 719-742

Abstract: Abstract Insights from behavioral and experimental economics research can inform the design of evidence-based, cost-effective agri-environmental programs that mitigate environmental damages and promote the supply of environmental benefits from agricultural landscapes. To enhance future research on agri-environmental program design and to increase the speed at which credible scientific knowledge is accumulated, we highlight methodological challenges, identify important gaps in the existing literature, and make key recommendations for both researchers and those evaluating research. We first report on four key methodological challenges—underpowered designs, multiple hypothesis testing, interpretation issues, and choosing appropriate econometric methods—and suggest strategies to overcome these challenges. Specifically, we emphasize the need for more detailed planning during the experimental design stage, including power analyses and publishing a pre-analysis plan. Greater use of replication studies and meta-analyses will also help address these challenges and strengthen the quality of the evidence base. In the second part of this paper, we discuss how insights from behavioral and experimental economics can be applied to improve the design of agri-environmental programs. We summarize key insights using the MINDSPACE framework, which categorizes nine behavioral effects that influence decision-making (messenger, incentives, norms, defaults, salience, priming, affect, commitment, and ego), and we highlight recent research that tests these effects in agri-environmental contexts. We also propose a framework for prioritizing policy-relevant research in this domain.

Keywords: Behavioral insights; Conservation; Effect size; Environmental economics; Experimental design; Power analysis; Subject pools (search for similar items in EconPapers)
JEL-codes: C9 D9 Q58 Q52 (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10640-019-00342-x

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