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An Economic Analysis of Risk, Management, and Agricultural Technology

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  • Chavas, Jean-Paul
  • Shi, Guanming

Abstract

This paper uses conditional quantile regression to analyze the effects of genetically modified (GM) seed technology and management on production risk in agriculture, with an application to the distribution of corn yield in Wisconsin. Using the certainty equivalent (CE) as a welfare measure, our analysis decomposes the welfare effects of risk, management, and agricultural technology into two parts: mean effects and risk premium (measuring the cost of risk). We document how biotechnology and management interact to improve agricultural productivity and reduce farm risk exposure. For corn, we find that GM European Corn Borer (GM-ECB) technology consistently increases CE (the increase ranging from +4.6% to +11.8%) and that a significant part of this increase can come from risk reduction. We also show that the benefits of the GMECB biotechnology are heterogeneous: they vary significantly across regions as well as across management schemes

Suggested Citation

  • Chavas, Jean-Paul & Shi, Guanming, 2015. "An Economic Analysis of Risk, Management, and Agricultural Technology," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(1), pages 1-17.
    • Handle: RePEc:ags:jlaare:197377
    DOI: 10.22004/ag.econ.197377
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    References listed on IDEAS

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    8. Jesse Tack & Ardian Harri & Keith Coble, 2012. "More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1037-1054.
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    Cited by:

    1. Do-il Yoo & Jean-Paul Chavas, 2021. "An analysis of risk aversion in biotechnology adoption: the case of US genetically modified corn," Empirical Economics, Springer, vol. 60(5), pages 2613-2635, May.
    2. Do‐il Yoo & Jean‐Paul Chavas, 2023. "Dynamic modeling of biotechnology adoption with individual versus social learning: An application to US corn farmers," Agribusiness, John Wiley & Sons, Ltd., vol. 39(1), pages 148-166, January.
    3. Bougherara, Douadia & Friesen, Lana & Nauges, Céline, 2022. "Risk-taking and skewness-seeking behavior in a demographically diverse population," Journal of Economic Behavior & Organization, Elsevier, vol. 201(C), pages 83-104.
    4. 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.
    5. Serkan Aglasan & Barry K. Goodwin & Roderick M. Rejesus, 2023. "Risk effects of GM corn: Evidence from crop insurance outcomes and high‐dimensional methods," Agricultural Economics, International Association of Agricultural Economists, vol. 54(1), pages 110-126, January.
    6. Douadia Bougherara & Lana Friesen & Céline Nauges, 2021. "Risk Taking with Left- and Right-Skewed Lotteries," Journal of Risk and Uncertainty, Springer, vol. 62(1), pages 89-112, February.
    7. Mukasa, Adamon N., 2018. "Technology adoption and risk exposure among smallholder farmers: Panel data evidence from Tanzania and Uganda," World Development, Elsevier, vol. 105(C), pages 299-309.
    8. Jutao Zeng & Jie Lyu, 2023. "Simultaneous Decisions to Undertake Off-Farm Work and Straw Return: The Role of Cognitive Ability," Land, MDPI, vol. 12(8), pages 1-21, August.
    9. Bozzola, Martina & Smale, Melinda, 2020. "The welfare effects of crop biodiversity as an adaptation to climate shocks in Kenya," World Development, Elsevier, vol. 135(C).
    10. Aglasan, Serkan & Goodwin, Barry K. & Rejesus, Roderick, 2020. "Genetically Modified Rootworm-Resistant Corn, Risk, and Weather: Evidence from High Dimensional Methods," 2020 Annual Meeting, July 26-28, Kansas City, Missouri 305181, Agricultural and Applied Economics Association.
    11. A Ford Ramsey, 2020. "Probability Distributions of Crop Yields: A Bayesian Spatial Quantile Regression Approach," American Journal of Agricultural Economics, John Wiley & Sons, vol. 102(1), pages 220-239, January.
    12. Luigi Biagini & Simone Severini, 2022. "How Does the Farmer Strike a Balance between Income and Risk across Inputs? An Application in Italian Field Crop Farms," Sustainability, MDPI, vol. 14(23), pages 1-15, December.
    13. Souto, Augusto & Carriquiry, Miguel A. & Rosas, Juan Francisco, 2021. "Assessing the Impact of Agricultural Intensification on Water Pollution: An Integrated Model Assessment of the San Salvador Basin in Uruguay," 2021 Annual Meeting, August 1-3, Austin, Texas 314037, Agricultural and Applied Economics Association.
    14. Zheng Li & Roderick M. Rejesus & Xiaoyong Zheng, 2021. "Nonparametric Estimation and Inference of Production Risk," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(5), pages 1857-1877, October.
    15. Elizabeth Nolan & Paulo Santos, 2019. "Genetic modification and yield risk: A stochastic dominance analysis of corn in the USA," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-10, October.
    16. Nsabimana, Aimable, 2021. "Is change worth it? The effects of adopting modern agricultural inputs on household welfare in Rwanda," African Journal of Agricultural and Resource Economics, African Association of Agricultural Economists, vol. 16(3), September.
    17. Dragos, Cristian Mihai & Dragos, Simona Laura & Mare, Codruta & Muresan, Gabriela Mihaela & Purcel, Alexandra-Anca, 2023. "Does risk assessment and specific knowledge impact crop insurance underwriting? Evidence from Romanian farmers," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 343-358.
    18. Che, Yuyuan & Ghosh, Sujit K. & Rejesus, Roderick M., 2022. "Estimating Production Risk Effects with Inequality Constraints," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322189, Agricultural and Applied Economics Association.
    19. Li, Zheng & Rejesus, Roderick M. & Zheng, Xiaoyong, 2018. "Nonparametric Estimation and Inference of Production Risk with Categorical Variables," 2018 Annual Meeting, August 5-7, Washington, D.C. 274400, Agricultural and Applied Economics Association.
    20. Pi, Lulu & Li, Zheng & Zheng, Xiaoyong & Rejesus, Roderick M., 2023. "Modeling the Relationship between Planting Density and Yield and Yield Risk Using a Flexible Estimation Framework," 2023 Annual Meeting, July 23-25, Washington D.C. 335514, Agricultural and Applied Economics Association.

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