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

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  • Do‐il Yoo
  • Jean‐Paul Chavas

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].

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  • Do‐il Yoo & Jean‐Paul Chavas, 2023. "Dynamic modeling of biotechnology adoption with individual versus social learning: An application to US corn farmers," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 148-166, January.
    • Handle: RePEc:wly:agribz:v:39:y:2023:i:1:p:148-166
    DOI: 10.1002/agr.21772
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    1. Zuomiao Xie & Shiqi Yuan & Jinjing Zhu & Weiming Li, 2023. "Contract farming led by a seed enterprise and incentives to produce high quality: Which contract design performs best?," Agribusiness, John Wiley & Sons, Ltd., vol. 39(4), pages 1173-1198, October.
    2. Li, Feng & Du, Timon C. & Wei, Ying, 2023. "This is what’s in store for you: How online social learning affects product positioning," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 179(C).

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