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Review of Theories of Learning for Adopting

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  • Elisabeth SADOULET

    (Université de Californie Berkeley)

Abstract

The diffusion of a new agricultural technology requires farmers to learn about the existence and the benefits of the technology. What do they have to learn, how do they learn it, and from whom, is the subject of a large literature, both theoretical and empirical. The purpose of this brief is to review the most prominent learning models, briefly assess recent empirical results derived from these theories, and raise a few important remaining issues not explicitly addressed by the theories.We will focus on the literature that refers to learning from experience, either own or that of others, giving prominence to the network of connections that farmers have. This review is purposefully very selective, with the objective of illustrating concepts and categories of models, rather than providing a genuine literature review. Document de travail préparé pour l'atelier Ferdi - SPIA Innovations agricoles : apprendre pour adopter, organisé à Clermont-Ferrand (France), les 1er et 2 juin 2016.

Suggested Citation

  • Elisabeth SADOULET, 2016. "Review of Theories of Learning for Adopting," Working Papers P163, FERDI.
  • Handle: RePEc:fdi:wpaper:3193
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    References listed on IDEAS

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    1. Joshua Schwartzstein, 2014. "Selective Attention And Learning," Journal of the European Economic Association, European Economic Association, vol. 12(6), pages 1423-1452, December.
    2. Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2021. "Can Network Theory-Based Targeting Increase Technology Adoption?," American Economic Review, American Economic Association, vol. 111(6), pages 1918-1943, June.
    3. Feder, Gershon & Savastano, Sara, 2006. "The role of opinion leaders in the diffusion of new knowledge: The case of integrated pest management," World Development, Elsevier, vol. 34(7), pages 1287-1300, July.
    4. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    5. Szeidl, Adam & Mobius, Markus & Phan, Tuan, 2015. "Treasure Hunt: Social Learning in the Field," CEPR Discussion Papers 10493, C.E.P.R. Discussion Papers.
    6. Oriana Bandiera & Imran Rasul, 2006. "Social Networks and Technology Adoption in Northern Mozambique," Economic Journal, Royal Economic Society, vol. 116(514), pages 869-902, October.
    7. Besley, T. & Case, A., 1994. "Diffusion as a Learning Process: Evidence from HYV Cotton," Papers 174, Princeton, Woodrow Wilson School - Development Studies.
    8. B. Kelsey Jack, 2013. "Private Information and the Allocation of Land Use Subsidies in Malawi," American Economic Journal: Applied Economics, American Economic Association, vol. 5(3), pages 113-135, July.
    9. Jing Cai & Alain De Janvry & Elisabeth Sadoulet, 2015. "Social Networks and the Decision to Insure," American Economic Journal: Applied Economics, American Economic Association, vol. 7(2), pages 81-108, April.
    10. Kondylis, Florence & Mueller, Valerie & Zhu, Jessica, 2017. "Seeing is believing? Evidence from an extension network experiment," Journal of Development Economics, Elsevier, vol. 125(C), pages 1-20.
    11. repec:pri:rpdevs:besley_case_diffusion is not listed on IDEAS
    12. H. Peyton Young, 2009. "Innovation Diffusion in Heterogeneous Populations: Contagion, Social Influence, and Social Learning," American Economic Review, American Economic Association, vol. 99(5), pages 1899-1924, December.
    13. Wang, Honglin & Yu, Fan & Reardon, Thomas & Huang, Jikun & Rozelle, Scott, 2013. "Social learning and parameter uncertainty in irreversible investments: Evidence from greenhouse adoption in northern China," China Economic Review, Elsevier, vol. 27(C), pages 104-120.
    14. repec:pri:rpdevs:besley_case_diffusion.pdf is not listed on IDEAS
    15. Margarita Genius & Phoebe Koundouri & Céline Nauges & Vangelis Tzouvelekas, 2014. "Information Transmission in Irrigation Technology Adoption and Diffusion: Social Learning, Extension Services, and Spatial Effects," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 328-344.
    16. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    17. Magnan, Nicholas & Spielman, David J. & Lybbert, Travis J. & Gulati, Kajal, 2015. "Leveling with friends: Social networks and Indian farmers' demand for a technology with heterogeneous benefits," Journal of Development Economics, Elsevier, vol. 116(C), pages 223-251.
    18. Rema Hanna & Sendhil Mullainathan & Joshua Schwartzstein, 2014. "Learning Through Noticing: Theory and Evidence from a Field Experiment," The Quarterly Journal of Economics, Oxford University Press, vol. 129(3), pages 1311-1353.
    19. Annemie Maertens, 2017. "Who Cares What Others Think (or Do)? Social Learning and Social Pressures in Cotton Farming in India," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(4), pages 988-1007.
    20. Bardhan, Pranab & Udry, Christopher, 1999. "Development Microeconomics," OUP Catalogue, Oxford University Press, number 9780198773719, Decembrie.
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