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Revisiting the Impact of Bt Corn Adoption by U.S. Farmers

Author

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  • Fernandez-Cornejo, Jorge
  • Wechsler, Seth James

Abstract

This study examines the impact of adopting Bt corn on farm profits, yields, and insecticide use. The study employs an econometric model that corrects for self-selection and simultaneity. The model is estimated using nationwide farm-level survey data for 2005. Regression analysis confirms that Bt adoption is associated with increased variable profits, yields, and seed demand. However, the results of this analysis suggest that Bt adoption is not significantly related to insecticide use. This result may be due to the fact that insect infestation levels were lower in 2005 than they were in previous years.

Suggested Citation

  • Fernandez-Cornejo, Jorge & Wechsler, Seth James, 2012. "Revisiting the Impact of Bt Corn Adoption by U.S. Farmers," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 41(3), December.
  • Handle: RePEc:ags:arerjl:141671
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    File URL: http://purl.umn.edu/141671
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    References listed on IDEAS

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    1. Geweke, John, 1989. "Bayesian Inference in Econometric Models Using Monte Carlo Integration," Econometrica, Econometric Society, vol. 57(6), pages 1317-1339, November.
    2. Fernandez-Cornejo, Jorge & Li, Jiayi, 2005. "The Impacts of Adopting Genetically Engineered Crops in the USA: The Case of Bt Corn," 2005 Annual meeting, July 24-27, Providence, RI 19318, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    3. Jeffrey Hyde & Marshall A. Martin & Paul V. Preckel & C. Richard Edwards, 1999. "The Economics of Bt Corn: Valuing Protection from the European Corn Borer," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 21(2), pages 442-454.
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    5. Ira Matuschke & Matin Qaim, 2009. "The impact of social networks on hybrid seed adoption in India," Agricultural Economics, International Association of Agricultural Economists, vol. 40(5), pages 493-505, September.
    6. David Roodman, 2009. "Estimating Fully Observed Recursive Mixed-Process Models with cmp," Working Papers 168, Center for Global Development.
    7. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Publishing House "SINERGIA PRESS", pages 129-137.
    8. Keane, Michael P, 1992. "A Note on Identification in the Multinomial Probit Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 10(2), pages 193-200, April.
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    10. Lorenzo Cappellari & Stephen P. Jenkins, 2003. "Multivariate probit regression using simulated maximum likelihood," Stata Journal, StataCorp LP, pages 278-294.
    11. 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.
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    Citations

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    Cited by:

    1. Greene, Catherine & Wechsler, Seth J. & Adalja, Aaron & Hanson, James, 2016. "Economic Issues in the Coexistence of Organic, Genetically Engineered (GE), and Non-GE Crops," Economic Information Bulletin 232929, United States Department of Agriculture, Economic Research Service.
    2. Jonas Kathage & Manuel Gómez-Barbero & Emilio Rodríguez-Cerezo, 2016. "Framework for assessing the socio-economic impacts of Bt maize cultivation," JRC Working Papers JRC103197, Joint Research Centre (Seville site).
    3. Sung, Jae-hoon & Miranowski, John A., 2016. "Information technologies and field-level chemical use for corn production," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235858, Agricultural and Applied Economics Association.
    4. Nugzar Todua & Teona Gogitidze, 2017. "Marketing Research Of Attitudes Towards Genetically Modified Crops By Georgian Farmers," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 1, pages 69-76, February.
    5. Skevas, T. & Swinton, S.M. & Meehan, T.D. & Kim, T.N. & Gratton, C. & Egbendewe-Mondzozo, A., 2014. "Integrating agricultural pest biocontrol into forecasts of energy biomass production," Ecological Economics, Elsevier, vol. 106(C), pages 195-203.
    6. Fernandez-Cornejo, Jorge & Nehring, Richard & Osteen, Craig & Wechsler, Seth James & Martin, Andrew & Vialou, Alex, 2014. "Pesticide Use in U.S. Agriculture: 21 Selected Crops, 1960-2008," Economic Information Bulletin 178462, United States Department of Agriculture, Economic Research Service.
    7. Hudak, Michael, 2015. "Three studies on applying Positive Mathematical Programming and Bayesian Analysis to model US crop supply," ISU General Staff Papers 201501010800005844, Iowa State University, Department of Economics.
    8. Brown, Zachary, 2016. "Voluntary programs to encourage compliance with refuge regulations for pesticide resistance management: results from a quasi-experiment," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 237333, Agricultural and Applied Economics Association.

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