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Risk, learning, and technology adoption

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Listed:
  • 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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