Behavioral Insights for Agri-Environmental Program and Policy Design

Insights from other behavioral sciences (e.g., psychology, neuroscience) have slowly been infiltrating mainstream economic thought and are now routinely informing the design of programs and policies in multiple domains. The same insights hold promise for designing more effective agri-environmental programs and policies. Motivated by the MINDSPACE categorization of behavioral insights introduced by Dolan et al. (2012), we develop the Ag-E MINDSPACE framework (where “Ag-E” stands for agri-environmental) to organize a review of the experimental literature on behavioral insights within the agri-environmental domain. The mnemonic MINDSPACE categorizes the behavioral impacts of messengers, incentives, norms, defaults, salience, priming, affect, commitments, and ego. Our Ag-E MINDSPACE framework further categorizes these insights as they apply to relevant agri-environmental issues, which are affected by the decisions of producers and consumers. Designed as a practical guide for researchers and an aid to practitioners in deciding which behavioral interventions to embed in their programs, this review summarizes the estimated effect sizes of behavioral interventions that are relevant for agri-environmental applications. We find that, unlike other policy domains, in which one can find dozens of relevant behavioral studies, the agri-environmental domain is characterized by a paucity of behavioral studies that can guide practitioners. Practitioners are thus forced to either (i) assume that results from other domains, which are largely focused on consumer decision-making in contexts such as healthcare, anti-poverty, education, and finance, can be applied to the agri-environmental programs and policies, or (ii) collaborate with researchers to replicate and extend the insights from other domains to important agri-environmental contexts.


Issue Date:
Dec 01 2017
Publication Type:
Conference Paper/ Presentation
Language:
English
Total Pages:
51
JEL Codes:
Q10; Q50; C90




 Record created 2017-12-22, last modified 2018-01-22

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