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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.

Suggested Citation

  • 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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    File URL: http://hdl.handle.net/10.1111/agec.12123
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    References listed on IDEAS

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    Cited by:

    1. Daniel Gregg & John Rolfe, 2017. "Risk Behaviours and Grazing Land Management: A Framed Field Experiment and Linkages to Range Land Condition," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(3), pages 682-709, September.
    2. 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, Foundation for the European Review of Agricultural Economics, vol. 44(5), pages 810-835.
    3. Zhang, Biao & Fu, Zetian & Wang, Jieqiong & Zhang, Lingxian, 2019. "Farmers’ adoption of water-saving irrigation technology alleviates water scarcity in metropolis suburbs: A case study of Beijing, China," Agricultural Water Management, Elsevier, vol. 212(C), pages 349-357.
    4. Kaywana Raeburn & Jim Engle-Warnick & Sonia Laszlo & Jian Li, 2016. "Learning in a Bandit Game and Technology Choice," CIRANO Working Papers 2016s-47, CIRANO.
    5. 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.
    6. 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.
    7. 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.

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