Dynamic modeling of biotechnology adoption with individual versus social learning: An application to US corn farmers

Do‐il Yoo and Jean‐Paul Chavas

Agribusiness, 2023, vol. 39, issue 1, 148-166

Abstract: Relative roles of individual versus social learning on biotechnology adoption are investigated with an empirical focus on the adoption of Genetically Modified (GM) corn in the US Corn Belt. Relying on a Kalman filter algorithm, the unobservable learning process is parameterized in a dynamic programming problem, and parameters are estimated using a minimum‐distance estimator. Estimates show that farmers are risk‐averse and that both individual and social learning affect GM technology adoption with more importance on individual learning than social learning, whose statistical significances are confirmed by hypothesis testing. Sensitivity analysis results show that social learning contributes to lower GM adoption rates, reflecting a strategic delay in the presence of information externalities in the early and middle diffusion stages. [EconLit Citations: C61, D83, O33].

Date: 2023
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https://doi.org/10.1002/agr.21772

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Persistent link: https://EconPapers.repec.org/RePEc:wly:agribz:v:39:y:2023:i:1:p:148-166

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