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Can Network Theory-based Targeting Increase Technology Adoption?

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  • Lori Beaman
  • Ariel BenYishay
  • Jeremy Magruder
  • Ahmed Mushfiq Mobarak

Abstract

In order to induce farmers to adopt a productive new agricultural technology, we apply simple and complex contagion diffusion models on rich social network data from 200 villages in Malawi to identify seed farmers to target and train on the new technology. A randomized controlled trial compares these theory-driven network targeting approaches to simpler strategies that either rely on a government extension worker or an easily measurable proxy for the social network (geographic distance between households) to identify seed farmers. Our results indicate that technology diffusion is characterized by a complex contagion learning environment in which most farmers need to learn from multiple people before they adopt themselves. Network theory based targeting can out-perform traditional approaches to extension, and we identify methods to realize these gains at low cost to policymakers.

Suggested Citation

  • Lori Beaman & Ariel BenYishay & Jeremy Magruder & Ahmed Mushfiq Mobarak, 2018. "Can Network Theory-based Targeting Increase Technology Adoption?," NBER Working Papers 24912, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:24912
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    1. Esther Duflo & Michael Kremer & Jonathan Robinson, 2011. "Nudging Farmers to Use Fertilizer: Theory and Experimental Evidence from Kenya," American Economic Review, American Economic Association, vol. 101(6), pages 2350-2390, October.
    2. 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.
    3. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    4. 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.
    5. Emily Breza & Arun G. Chandrasekhar & Tyler H. McCormick & Mengjie Pan, 2017. "Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data," NBER Working Papers 23491, National Bureau of Economic Research, Inc.
    6. Grant Miller & A. Mushfiq Mobarak, 2015. "Learning About New Technologies Through Social Networks: Experimental Evidence on Nontraditional Stoves in Bangladesh," Marketing Science, INFORMS, vol. 34(4), pages 480-499, July.
    7. Scott E. Carrell & Bruce I. Sacerdote & James E. West, 2013. "From Natural Variation to Optimal Policy? The Importance of Endogenous Peer Group Formation," Econometrica, Econometric Society, vol. 81(3), pages 855-882, May.
    8. Leonardo Bursztyn & Florian Ederer & Bruno Ferman & Noam Yuchtman, 2014. "Understanding Mechanisms Underlying Peer Effects: Evidence From a Field Experiment on Financial Decisions," Econometrica, Econometric Society, vol. 82(4), pages 1273-1301, July.
    9. Jesse Perla & Christopher Tonetti, 2014. "Equilibrium Imitation and Growth," Journal of Political Economy, University of Chicago Press, vol. 122(1), pages 52-76.
    10. Lori A. Beaman, 2012. "Social Networks and the Dynamics of Labour Market Outcomes: Evidence from Refugees Resettled in the U.S," Review of Economic Studies, Oxford University Press, vol. 79(1), pages 128-161.
    11. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 531-542.
    12. Fernando E. Alvarez & Francisco J. Buera & Robert E. Lucas, Jr., 2013. "Idea Flows, Economic Growth, and Trade," NBER Working Papers 19667, National Bureau of Economic Research, Inc.
    13. Jeremy R. Magruder, 2010. "Intergenerational Networks, Unemployment, and Persistent Inequality in South Africa," American Economic Journal: Applied Economics, American Economic Association, vol. 2(1), pages 62-85, January.
    14. 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.
    15. Abhijit Banerjee & Emily Breza & Arun G. Chandrasekhar & Benjamin Golub, 2018. "When Less is More: Experimental Evidence on Information Delivery During India's Demonetization," NBER Working Papers 24679, National Bureau of Economic Research, Inc.
    16. Ariel BenYishay & A. Mushfiq Mobarak, 2014. "Social Learning and Communication," NBER Working Papers 20139, National Bureau of Economic Research, Inc.
    17. Udry, Christopher, 2010. "The economics of agriculture in Africa: Notes toward a research program," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 5(1), pages 1-16, September.
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    More about this item

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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