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Learning Through Noticing: Theory and Experimental Evidence in Farming

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  • Rema Hanna
  • Sendhil Mullainathan
  • Joshua Schwartzstein

Abstract

Existing learning models attribute failures to learn to a lack of data. We model a different barrier. Given the large number of dimensions one could focus on when using a technology, people may fail to learn because they failed to notice important features of the data they possess. We conduct a field experiment with seaweed farmers to test a model of "learning through noticing". We find evidence of a failure to notice: On some dimensions, farmers do not even know the value of their own input. Interestingly, trials show that these dimensions are the ones that farmers fail to optimize. Furthermore, consistent with the model, we find that simply having access to the experimental data does not induce learning. Instead, farmers change behavior only when presented with summaries that highlight the overlooked dimensions. We also draw out the implications of learning through noticing for technology adoption, agricultural extension, and the meaning of human capital.

Suggested Citation

  • Rema Hanna & Sendhil Mullainathan & Joshua Schwartzstein, 2012. "Learning Through Noticing: Theory and Experimental Evidence in Farming," NBER Working Papers 18401, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:18401
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    4. Fishman, Ram & Smith, Stephen C. & Bobic, Vida & Sulaiman, Munshi, 2019. "Can Agricultural Extension and Input Support Be Discontinued? Evidence from a Randomized Phaseout in Uganda," IZA Discussion Papers 12476, Institute of Labor Economics (IZA).
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    8. Fishman, Ram & Smith, Stephen C. & Bobic, Vida & Sulaiman, Munshi, 2017. "How Sustainable Are Benefits from Extension for Smallholder Farmers? Evidence from a Randomized Phase-Out of the BRAC Program in Uganda," IZA Discussion Papers 10641, Institute of Labor Economics (IZA).
    9. Beaman, Lori & Magruder, Jeremy & Robinson, Jonathan, 2014. "Minding small change among small firms in Kenya," Journal of Development Economics, Elsevier, vol. 108(C), pages 69-86.
    10. Fafchamps, Marcel & McKenzie, David & Quinn, Simon & Woodruff, Christopher, 2014. "Microenterprise growth and the flypaper effect: Evidence from a randomized experiment in Ghana," Journal of Development Economics, Elsevier, vol. 106(C), pages 211-226.
    11. Kyle Emerick & Alain de Janvry & Elisabeth Sadoulet & Manzoor H. Dar, 2016. "Technological Innovations, Downside Risk, and the Modernization of Agriculture," American Economic Review, American Economic Association, vol. 106(6), pages 1537-1561, June.
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    14. C. Kirabo Jackson & Henry S. Schneider, 2013. "Reducing Moral Hazard in Employment Relationships: Experimental Evidence on Managerial Control and Performance Pay," NBER Working Papers 19645, National Bureau of Economic Research, Inc.
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    16. Gars, Jared & Ward, Patrick S., 2016. "The role of learning in technology adoption: Evidence on hybrid rice adoption in Bihar, India," IFPRI discussion papers 1591, International Food Policy Research Institute (IFPRI).

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    More about this item

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • J43 - Labor and Demographic Economics - - Particular Labor Markets - - - Agricultural Labor Markets
    • 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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