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An Analysis of Selectivity in the Productivity Evaluation of Biotechnology: An Application to Corn

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  • Guanming Shi
  • Jean-Paul Chavas
  • Joseph Lauer
  • Elizabeth Nolan

Abstract

We investigate selectivity bias in the evaluation of biotech hybrid productivity. The analysis is applied to experimental data on Wisconsin corn yields from 1990 to 2010. Relying on a Heckman-like factor that accounts for selectivity, we find evidence of selection bias, indicating that some of the observed yield advantage associated with GM hybrids can be attributed to their conventional genes. We document how the rising market concentration of biotech firms has contributed to increasing selectivity bias in corn yield. The impact, however, is offset by the negative effect of the rising adoption rate of GM corn on selectivity bias. Copyright 2013, Oxford University Press.

Suggested Citation

  • Guanming Shi & Jean-Paul Chavas & Joseph Lauer & Elizabeth Nolan, 2013. "An Analysis of Selectivity in the Productivity Evaluation of Biotechnology: An Application to Corn," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(3), pages 739-754.
    • Handle: RePEc:oup:ajagec:v:95:y:2013:i:3:p:739-754
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    File URL: http://hdl.handle.net/10.1093/ajae/aas169
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    Cited by:

    1. Fernandez-Cornejo, Jorge & Valle, Karen, 2014. "A Hedonic Model of Corn Seed Prices," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 169667, Agricultural and Applied Economics Association.
    2. Brown, Zachary S., 2017. "Voluntary programs to encourage refuges for pesticide resistance management: lessons from a quasi-experiment," CEnREP Working Papers 264974, North Carolina State University, Department of Agricultural and Resource Economics.
    3. Xingliang Ma & Melinda Smale & David J. Spielman & Patricia Zambrano & Hina Nazli & Fatima Zaidi, 2017. "A Question of Integrity: Variants of Bt Cotton, Pesticides and Productivity in Pakistan," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(2), pages 366-385, June.
    4. Brown, Zachary S. & Connor, Lawson & Rejesus, Roderick M. & Yorobe Jr., Jose M., 2018. "Bioeconomic feedbacks from large-scale adoption of transgenic pesticidal corn in the Philippines," CEnREP Working Papers 272080, North Carolina State University, Department of Agricultural and Resource Economics.
    5. Paul Vincelli, 2016. "Genetic Engineering and Sustainable Crop Disease Management: Opportunities for Case-by-Case Decision-Making," Sustainability, MDPI, vol. 8(5), pages 1-22, May.
    6. 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.
    7. Hutchins, Jared P. & Irwin, Scott H., 2022. "Productivity Growth from Genetic Improvement: Estimates from Soybean Experiment Station Data," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322202, Agricultural and Applied Economics Association.
    8. Fernandez-Cornejo, Jorge & Livingston, Michael J. & Mitchell, Lorraine & Wechsler, Seth, 2014. "Genetically Engineered Crops in the United States," Economic Research Report 164263, United States Department of Agriculture, Economic Research Service.

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