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Risk, learning, and technology adoption

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  • Bradford L. Barham
  • Jean-Paul Chavas
  • Dylan Fitz
  • Vanessa Ríos-Salas
  • Laura Schechter

Abstract

This article explores how decision makers learn and use information, with an application to the adoption of biotechnology in agriculture. The empirical analysis relies on experimental and survey data measuring risk preferences, learning processes, and the adoption of genetically modified (GM) seeds among U.S. grain farmers. While controlling for risk aversion, we link individual learning rules with the cognitive abilities of each decision maker and their actual GM adoption decisions. We find evidence that very few individuals are Bayesian learners, and that the population of farmers is quite heterogeneous in terms of learning rules. This suggests that Bayesian learning (as commonly assumed in the analysis of agricultural technology adoption) is not an appropriate characterization. In addition, we do not find a strong relationship between observed learning styles and the timing of GM seed adoption. To the extent that learning is a key part of the process of technology adoption, this suggests the presence of much unobserved heterogeneity in learning among farmers.

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  • Bradford L. Barham & Jean-Paul Chavas & Dylan Fitz & Vanessa Ríos-Salas & Laura Schechter, 2015. "Risk, learning, and technology adoption," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 11-24, January.
    • Handle: RePEc:bla:agecon:v:46:y:2015:i:1:p:11-24
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    Cited by:

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    13. Gars, Jared & Ward, Patrick S., 2019. "Can differences in individual learning explain patterns of technology adoption? Evidence on heterogeneous learning patterns and hybrid rice adoption in Bihar, India," World Development, Elsevier, vol. 115(C), pages 178-189.
    14. Gharib, Mariam H. & Palm-Forster, Leah H. & Lybbert, Travis J. & Messer, Kent D., 2021. "Fear of fraud and willingness to pay for hybrid maize seed in Kenya," Food Policy, Elsevier, vol. 102(C).
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    16. Barham, Bradford L. & Chavas, Jean-Paul & Fitz, Dylan & Schechter, Laura, 2018. "Receptiveness to advice, cognitive ability, and technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 239-268.
    17. Francesco Cavazza & Francesco Galioto & Meri Raggi & Davide Viaggi, 2020. "Digital Irrigated Agriculture: Towards a Framework for Comprehensive Analysis of Decision Processes under Uncertainty," Future Internet, MDPI, vol. 12(11), pages 1-16, October.
    18. Kaywana Raeburn & Jim Engle-Warnick & Sonia Laszlo & Jian Li, 2016. "Learning in a Bandit Game and Technology Choice," CIRANO Working Papers 2016s-47, CIRANO.
    19. Omotuyole Isiaka Ambali & Francisco Jose Areal & Nikolaos Georgantzis, 2021. "Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference," Agriculture, MDPI, vol. 11(8), pages 1-13, July.
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    21. Doris Läpple & Garth Holloway & Donald J Lacombe & Cathal O’Donoghue, 2017. "Sustainable technology adoption: a spatial analysis of the Irish Dairy Sector," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 44(5), pages 810-835.
    22. Scott Kaplan & Ben Gordon & Feras El Zarwi & Joan L. Walker & David Zilberman, 2019. "The Future of Autonomous Vehicles: Lessons from the Literature on Technology Adoption," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(4), pages 583-597, December.
    23. Dougherty, John & Flatnes, Jon Einar & Gallenstein, Richard & Miranda, Mario J. & Sam, Abdoul G., 2017. "Investigating the Impact of Climate Change on the Demand for Index Insurance," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258524, Agricultural and Applied Economics Association.
    24. Damien Jourdain1,2,3 & Juliette Lairez4,5 & Bruno Striffler & François Affholder, 2020. "Farmers’ preference for cropping systems and the development of sustainable intensification: a choice experiment approach," Review of Agricultural, Food and Environmental Studies, INRA Department of Economics, vol. 101(4), pages 417-437.

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