Agricultural Adoption and Behavioral Economics: Bridging the Gap

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Research on the adoption of new practices and new behaviors in agriculture continues to grow and evolve, and its relevance to policy remains high. This special issue presents 10 papers that provide overviews of important aspects of the recent adoption literature, or identify gaps and opportunities in the literature. Adoption research has been innovative in a number of ways, including its recognition of adoption decision making as a process, not an event; its emphases on heterogeneity and learning; and its strong multidisciplinary flavor. There is scope for further innovation through efforts to enhance predictions of adoption, better understanding the balance between profit and non‐profit motivations, and better recognizing the influence on adoption of characteristics of the innovations, not just characteristics of the potential adopters and their contexts. Opportunities for policy include effort to support women farmers' adoption of beneficial innovations in developing countries, and use of tools and approaches from marketing in public extension programs

Section: Methodological Issuesmentioning

confidence: 99%

Section: Methodological Issuesmentioning

confidence: 99%

Section: Innovative Aspects Of the Adoption Literaturementioning

confidence: 99%

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Understanding Adoption of Innovations and Behavior Change to Improve Agricultural Policy

Pannell¹,

Zilberman²

2020

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“…Finally, behavioral and psychological factors are more and more frequently considered by economists as potential factors driving adoption of innovations (Streletskaya et al ), with a focus on internal constraints such as self‐control problems, time‐inconsistent preferences, and high discounting. It has long been recognized that the adoption of innovations is a complex intertemporal choice problem which may be difficult to solve for some individuals.…”

Section: Uncertainty and Learning In Technology Adoptionmentioning

confidence: 99%

Uncertainty, Learning, and Technology Adoption in Agriculture

Chavas¹,

Nauges²

2020

176

103

Adoption of agricultural innovations has been an important factor affecting the welfare of farmers, the productivity of agriculture and the economics of the food sector. This paper reviews the literature on technology adoption in agriculture with a focus on the role of uncertainty and learning. It examines the factors affecting adoption benefits for farmers and their linkages with the innovation process. It also discusses the welfare implications of innovation and adoption for farmers and consumers.

“…The information from the trial reduces the uncertainty surrounding the net benefits, which is particularly important for risk‐averse individuals. Social networks play a significant role in the evaluation stage due to the ability to observe others in the network either conducting small‐scale trials on divisible technologies or implementing large‐scale complex innovations that cannot be trialed (Streletskaya et al ).…”

Section: Review Of Factors Affecting Adoptionmentioning

confidence: 99%

Limits to Profit Maximization as a Guide to Behavior Change

Weersink¹,

Fulton²

2020

Economists have long been interested in why farmers decide to adopt new technologies. Modeling decision making requires modeling behavior; the key behavioral assumption is profit maximization. In addition, most empirical studies assume a binary decision—adopt or not. This paper argues that adoption should be understood as a process with multiple stages in which the final decision to use the new technology only occurs if the previous stages are completed. While profit considerations are clearly important, particularly in the later stages of the process, they need to be supplemented with other social and cognitive considerations, particularly in the early stages. Understood this way, profit maximization assumptions can provide predictions for the upper or lower bounds of adoption. The adoption rate suggested by profit maximization will be an upper bound if noneconomic factors are expected to either slow down or deter adoption, while that rate will be a lower bound if noneconomic factors are expected to encourage adoption. To capture these elements, econometric models need to pay attention to the timing of decisions and use techniques that condition later‐stage decisions on previous stage outcomes. In addition, the paper suggests the use of expert systems that have economic, learning and social aspects explicitly built into them would be valuable.