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Experimental Evidence on Adoption and Impact of the System of rice Intensification

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  • Barrett, Christopher B.
  • Islam, Asad
  • Pakrashi, Debayan
  • Ruthbah, Ummul

Abstract

We report the results of a large-scale, multi-year experimental evaluation of the System of Rice Intensification (SRI), an innovation that first emerged in Madagascar in the 1980s and has now diffused to more than 50 countries. Using a randomized training saturation design, we find that greater cross-sectional or intertemporal intensity of direct or indirect training exposure to SRI has a sizable, positive effect on Bangladeshi farmers’ propensity to adopt (and not to disadopt) SRI. We find large, positive and significant impacts of SRI training on rice yields and profits, as well as multiple household well-being indicators, for both trained and untrained farmers in training villages. Despite the significant farm-level impacts on rice productivity and labor costs, we find no evidence of significant general equilibrium effects on rice prices or wage rates. We also find high rates of disadoption, and clear indications of non-random selection into technology adoption conditional on randomized exposure to training, such that adopters and non-adopters within the same treatment arm experience similar outcomes. Rice yields, profits and household well-being outcomes do not, however, vary at the intensive margin with intensity of training exposure, a finding consistent with multi-object learning models.

Suggested Citation

  • Barrett, Christopher B. & Islam, Asad & Pakrashi, Debayan & Ruthbah, Ummul, 2021. "Experimental Evidence on Adoption and Impact of the System of rice Intensification," Applied Economics and Policy Working Paper Series 309950, Cornell University, Department of Applied Economics and Management.
  • Handle: RePEc:ags:cuaepw:309950
    DOI: 10.22004/ag.econ.309950
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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. Kazushi Takahashi & Christopher B. Barrett, 2014. "The System of Rice Intensification and its Impacts on Household Income and Child Schooling: Evidence from Rural Indonesia," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 269-289.
    3. 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.
    4. Christopher B. Barrett & Christine M. Moser & Oloro V. McHugh & Joeli Barison, 2004. "Better Technology, Better Plots, or Better Farmers? Identifying Changes in Productivity and Risk among Malagasy Rice Farmers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 869-888.
    5. Feder, Gershon & Just, Richard E & Zilberman, David, 1985. "Adoption of Agricultural Innovations in Developing Countries: A Survey," Economic Development and Cultural Change, University of Chicago Press, vol. 33(2), pages 255-298, January.
    6. McKenzie, David, 2012. "Beyond baseline and follow-up: The case for more T in experiments," Journal of Development Economics, Elsevier, vol. 99(2), pages 210-221.
    7. Christine M. Moser & Christopher B. Barrett, 2006. "The complex dynamics of smallholder technology adoption: the case of SRI in Madagascar," Agricultural Economics, International Association of Agricultural Economists, vol. 35(3), pages 373-388, November.
    8. Alexander Wolitzky, 2018. "Learning from Others' Outcomes," American Economic Review, American Economic Association, vol. 108(10), pages 2763-2801, October.
    9. Christopher B. Barrett, 2021. "On design-based empirical research and its interpretation and ethics in sustainability science," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 118(29), pages 2023343118-, July.
    10. Jean‐Paul Chavas & Céline Nauges, 2020. "Uncertainty, Learning, and Technology Adoption in Agriculture," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 42-53, March.
    11. Faraz Usmani & Marc Jeuland & Subhrendu Pattanayak, 2018. "NGOs and the effectiveness of interventions," WIDER Working Paper Series 59, World Institute for Development Economic Research (UNU-WIDER).
    12. Stoop, Willem A. & Uphoff, Norman & Kassam, Amir, 2002. "A review of agricultural research issues raised by the system of rice intensification (SRI) from Madagascar: opportunities for improving farming systems for resource-poor farmers," Agricultural Systems, Elsevier, vol. 71(3), pages 249-274, March.
    13. Faraz Usmani & Marc Jeuland & Subhrendu K. Pattanayak, 2018. "NGOs and the effectiveness of interventions," WIDER Working Paper Series wp-2018-59, World Institute for Development Economic Research (UNU-WIDER).
    14. 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.
    15. Hans P. Binswanger, 1980. "Attitudes Toward Risk: Experimental Measurement in Rural India," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 62(3), pages 395-407.
    16. Annemie Maertens & Hope Michelson & Vesall Nourani, 2021. "How Do Farmers Learn from Extension Services? Evidence from Malawi," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 569-595, March.
    17. Christopher B. Barrett & Michael R. Carter, 2010. "The Power and Pitfalls of Experiments in Development Economics: Some Non-random Reflections," Applied Economic Perspectives and Policy, Agricultural and Applied Economics Association, vol. 32(4), pages 515-548.
    18. Noltze, Martin & Schwarze, Stefan & Qaim, Matin, 2013. "Impacts of natural resource management technologies on agricultural yield and household income: The system of rice intensification in Timor Leste," Ecological Economics, Elsevier, vol. 85(C), pages 59-68.
    19. 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.
    20. Robert Wilson, 1975. "Informational Economies of Scale," Bell Journal of Economics, The RAND Corporation, vol. 6(1), pages 184-195, Spring.
    21. Fafchamps, Marcel & Islam, Asad & Malek, Mohammad Abdul & Pakrashi, Debayan, 2020. "Can referral improve targeting? Evidence from an agricultural training experiment," Journal of Development Economics, Elsevier, vol. 144(C).
    22. Ahmed Mushtaque Raza Chowdhury & Andrew Jenkins & Marziana Mahfuz Nandita, 2014. "Measuring the effects of interventions in BRAC, and how this has driven 'development'," Journal of Development Effectiveness, Taylor & Francis Journals, vol. 6(4), pages 407-424, December.
    23. Oriana Bandiera & Robin Burgess & Narayan Das & Selim Gulesci & Imran Rasul & Munshi Sulaiman, 2017. "Labor Markets and Poverty in Village Economies," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(2), pages 811-870.
    24. Marcel Fafchamps & Måns Söderbom & Monique van den Boogart, 2022. "Adoption with Social Learning and Network Externalities," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 84(6), pages 1259-1282, December.
    25. Gathorne-Hardy, Alfred & Reddy, D. Narasimha & Venkatanarayana, M. & Harriss-White, Barbara, 2016. "System of Rice Intensification provides environmental and economic gains but at the expense of social sustainability — A multidisciplinary analysis in India," Agricultural Systems, Elsevier, vol. 143(C), pages 159-168.
    26. Barrett, Christopher B. & Carter, Michael R., 2020. "Finding our balance? Revisiting the randomization revolution in development economics ten years further on," World Development, Elsevier, vol. 127(C).
    27. Xavier Gabaix & David Laibson & Guillermo Moloche & Stephen Weinberg, 2006. "Costly Information Acquisition: Experimental Analysis of a Boundedly Rational Model," American Economic Review, American Economic Association, vol. 96(4), pages 1043-1068, September.
    28. Abhijit Banerjee & Emily Breza & Arun G. Chandrasekhar & Markus Mobius, 2021. "Naïve Learning with Uninformed Agents," American Economic Review, American Economic Association, vol. 111(11), pages 3540-3574, November.
    29. Marup Hossain & Mohammad Abdul Malek & Md Amzad Hossain & Md Hasib Reza & Md Shakil Ahmed, 2019. "Agricultural Microcredit for Tenant Farmers: Evidence from a Field Experiment in Bangladesh," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(3), pages 692-709.
    30. Young, Alwyn, 2019. "Channeling Fisher: randomization tests and the statistical insignificance of seemingly significant experimental results," LSE Research Online Documents on Economics 101401, London School of Economics and Political Science, LSE Library.
    31. Mark R Rosenzweig & Christopher Udry, 2020. "External Validity in a Stochastic World: Evidence from Low-Income Countries," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 87(1), pages 343-381.
    32. Sarah Baird & J. Aislinn Bohren & Craig McIntosh & Berk Özler, 2018. "Optimal Design of Experiments in the Presence of Interference," The Review of Economics and Statistics, MIT Press, vol. 100(5), pages 844-860, December.
    33. Boyan Jovanovic & Yaw Nyarko, 1995. "A Bayesian Learning Model Fitted to a Variety of Empirical Learning Curves," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 26(1995 Micr), pages 247-305.
    34. Alwyn Young, 2019. "Channeling Fisher: Randomization Tests and the Statistical Insignificance of Seemingly Significant Experimental Results," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 134(2), pages 557-598.
    35. Sinha, Shekhar Kumar & Talati, Jayesh, 2007. "Productivity impacts of the system of rice intensification (SRI): A case study in West Bengal, India," Agricultural Water Management, Elsevier, vol. 87(1), pages 55-60, January.
    36. Rema Hanna & Sendhil Mullainathan & Joshua Schwartzstein, 2014. "Learning Through Noticing: Theory and Evidence from a Field Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 129(3), pages 1311-1353.
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    2. Malek, Mohammad Abdul & Kikkawa, Aiko & Azad, Abul Kalam & Sawada, Yasuyuki, 2022. "Rural Development in Bangladesh Over Four Decades: Findings from Mahabub Hossain Panel Data and the Way Forward," ADBI Working Papers 1350, Asian Development Bank Institute.
    3. Islam, Asadul & Ushchev, Philip & Zenou, Yves & Zhang, Xin, 2019. "The Value of Information in Technology Adoption," IZA Discussion Papers 12672, Institute of Labor Economics (IZA).
    4. Abate, Gashaw T. & Bernard, Tanguy & Makhija, Simrin & Spielman, David J., 2023. "Accelerating technical change through ICT: Evidence from a video-mediated extension experiment in Ethiopia," World Development, Elsevier, vol. 161(C).
    5. Lan Anh Tong & Mehmet Ali Ulubaşoğlu & Cahit Guven, 2022. "Growing more Rice with less water: the System of Rice Intensification and water productivity in Vietnam," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(3), pages 581-611, July.
    6. Zenou, Yves & Islam, Asad & Ushchev, Philip & Zhang, Xin, 2018. "The Value of Information in Technology Adoption: Theory and Evidence from Bangladesh," CEPR Discussion Papers 13419, C.E.P.R. Discussion Papers.
    7. Guven, Cahit & Tong, Lan & Ulubasoglu, Mehmet, 2021. "Growing More Rice with Less Water: The System of Rice Intensification and Rice Productivity in Vietnam," MPRA Paper 108768, University Library of Munich, Germany.

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