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An Economic Analysis of Risk, Management, and Agricultural Technology

Listed author(s):
  • Chavas, Jean-Paul
  • Shi, Guanming

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

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File URL: http://purl.umn.edu/197377
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Article provided by Western Agricultural Economics Association in its journal Journal of Agricultural and Resource Economics.

Volume (Year): 40 (2015)
Issue (Month): 1 (January)
Pages:

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Handle: RePEc:ags:jlaare:197377
Contact details of provider: Web page: http://waeaonline.org/

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  1. Matin Qaim, 2009. "The Economics of Genetically Modified Crops," Annual Review of Resource Economics, Annual Reviews, vol. 1(1), pages 665-694, September.
  2. Alan P. Ker & Keith Coble, 2003. "Modeling Conditional Yield Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 291-304.
  3. J. M. Antle & W. J. Goodger, 1984. "Measuring Stochastic Technology: The Case of Tulare Milk Production," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(3), pages 342-350.
  4. Menezes, C & Geiss, C & Tressler, J, 1980. "Increasing Downside Risk," American Economic Review, American Economic Association, vol. 70(5), pages 921-932, December.
  5. Antle, John M, 1983. "Testing the Stochastic Structure of Production: A Flexible Moment-based Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 1(3), pages 192-201, July.
  6. Vitor Ozaki & Barry Goodwin & Ricardo Shirota, 2008. "Parametric and nonparametric statistical modelling of crop yield: implications for pricing crop insurance contracts," Applied Economics, Taylor & Francis Journals, vol. 40(9), pages 1151-1164.
  7. Martin L. Weitzman, 2009. "On Modeling and Interpreting the Economics of Catastrophic Climate Change," The Review of Economics and Statistics, MIT Press, vol. 91(1), pages 1-19, February.
  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.
  9. Binswanger, Hans P, 1981. "Attitudes toward Risk: Theoretical Implications of an Experiment in Rural India," Economic Journal, Royal Economic Society, vol. 91(364), pages 867-890, December.
  10. Barry K. Goodwin & Alan P. Ker, 1998. "Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 139-153.
  11. Joseph Atwood & Saleem Shaik & Myles Watts, 2003. "Are Crop Yields Normally Distributed? A Reexamination," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 888-901.
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