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Valuing Product Innovation: Genetically Engineered Varieties in U.S. Corn and Soybeans

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Abstract

We develop and estimate a discrete-choice model of differentiated products for the corn and soybean seed industry in the United States to assess the welfare impact of genetically engineered (GE) crop varieties. We use a unique dataset, spanning the period 1996-2011, that contains rich information on the adoption of GE traits. Using a two-level nested logit model, we estimate that U.S. farmers are willing to pay a significant premium for GE traits, and this value has increased over time. Over the last five years of the sample, our results imply that farmers' average willingness to pay for glyphosate tolerance in soybeans was $24/acre/year. During the same period, farmers' willingness to pay for a common triple-stack in corn that includes two insect resistance traits and glyphosate tolerance was $35/acre/year. To compute overall welfare estimates, we evaluate counterfactual scenarios in which GE varieties are not available, with counterfactual non-GE seed prices predicted by a hedonic price equation. Counterfactual scenarios are adjusted to account for the fact that GE crop varieties crowded out non-GE varieties by the end of our sample. We estimate that GE innovations increased farmers' welfare by more than $14 billion over the period of study. We also find that the development and diffusion of GE traits increased U.S. corn and soybean seed industry revenues by nearly $23 billion over this period. Thus, seed firms have been able to appropriate the larger share of the ex post value of innovation created by GE technologies. Key Words: Discrete choice, Innovation, Nested logit, Product characteristics, Seed demand, Transgenic crops, Welfare JEL Codes

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  • Federico Ciliberto & GianCarlo Moschini & Edward D. Perry, 2017. "Valuing Product Innovation: Genetically Engineered Varieties in U.S. Corn and Soybeans," Center for Agricultural and Rural Development (CARD) Publications 17-wp576, Center for Agricultural and Rural Development (CARD) at Iowa State University.
  • Handle: RePEc:ias:cpaper:17-wp576
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    Cited by:

    1. Seungki Lee & GianCarlo Moschini, 2020. "Estimating the Value of Innovation and Extension Information: SCNResistant Soybean Varieties," Center for Agricultural and Rural Development (CARD) Publications 20-wp603, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    2. Seungki Lee & GianCarlo Moschini, 2022. "On the value of innovation and extension information: SCN‐resistant soybean varieties," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(4), pages 1177-1202, August.
    3. Edward D. Perry & David A. Hennessy & GianCarlo Moschini, 2022. "Uncertainty and learning in a technologically dynamic industry: Seed density in U.S. maize," American Journal of Agricultural Economics, John Wiley & Sons, vol. 104(4), pages 1388-1410, August.
    4. Jared Hutchins & Brent Hueth, 2023. "100 years of data sovereignty: Cooperative data governance and innovation in US dairy," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(3), pages 1551-1576, September.
    5. Raj Chandra & Gabriel E. Lade & GianCarlo Moschini, 2021. "Geographical Indications and Welfare: Evidence from the US Wine Market," Center for Agricultural and Rural Development (CARD) Publications 21-wp628, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    6. Lee, Yunkyung & Perrin, Richard K. & Fulginiti, Lilyan E., 2022. "Potential Economic Impacts of Gene-edited High-oleic Soybeans," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322392, Agricultural and Applied Economics Association.
    7. Brown, Zachary S. & Connor, Lawson & Rejesus, Roderick M. & Yorobe, Jose M., 2021. "Landscape-level feedbacks in the demand for transgenic pesticidal corn in the Philippines," Ecological Economics, Elsevier, vol. 180(C).
    8. Scheitrum, Daniel & Schaefer, K. Aleks & Nes, Kjersti, 2020. "Realized and potential global production effects from genetic engineering," Food Policy, Elsevier, vol. 93(C).
    9. Du, Xiaodong & Feng, Hongli & Hennessy, David A., 2021. "Implications of WTP-AFP Discrepancy, Premium Subsidy Reduction and Program Changes in U.S. Crop Insurance," 2021 Annual Meeting, August 1-3, Austin, Texas 313881, Agricultural and Applied Economics Association.
    10. MacDonald, James M. & Dong, Xiao & Fuglie, Keith O., 2023. "Concentration and Competition in U.S. Agribusiness," Economic Information Bulletin 337566, United States Department of Agriculture, Economic Research Service.
    11. Seungki Lee & Yongjie Ji & GianCarlo Moschini, 2021. "Agricultural Innovation and Adaptation to Climate Change: Insights from Genetically Engineered Maize," Center for Agricultural and Rural Development (CARD) Publications 21-wp616, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    12. Jennifer Syme & Henry An & Mohammad Torshizi, 2024. "Estimating the effect of time‐invariant characteristics in panel data: wheat adoption in Western Canada," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(2), pages 828-851, March.
    13. Lee, Seungki & Moschini, GianCarlo & Perry, Edward D., 2023. "Genetically engineered varieties and applied pesticide toxicity in U.S. maize and soybeans: Heterogeneous and evolving impacts," Ecological Economics, Elsevier, vol. 211(C).

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    discrete choice; innovation; nested logit; product characteristics; seed demand; transgenic crops; welfare jel codes;
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