IDEAS home Printed from https://ideas.repec.org/a/spr/empeco/v60y2021i5d10.1007_s00181-020-01842-2.html
   My bibliography  Save this article

An analysis of risk aversion in biotechnology adoption: the case of US genetically modified corn

Author

Listed:
  • Do-il Yoo

    (Seoul National University)

  • Jean-Paul Chavas

    (University of Wisconsin-Madison)

Abstract

This paper analyzes the role of risk aversion in biotechnology adoption with the application on the US genetically modified corn. A dynamic programming analysis of technology adoption is conducted using the Kalman filter as a representation of the associated learning process. The relevant Bellman equation is jointly solved within model estimation using a minimum-distance estimator. Results show that the more the risk-averse farmers are, the later they adopt GM technology, such effects being statistically significant. Sensitivity analysis results examine the factors affecting farmers’ behavior. They document the role of risk aversion in technology adoption. They also uncover the presence of farm heterogeneity in the process of adopting a new technology.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:empeco:v:60:y:2021:i:5:d:10.1007_s00181-020-01842-2
    DOI: 10.1007/s00181-020-01842-2
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s00181-020-01842-2
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s00181-020-01842-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. David Zilberman & Doug Parker, 1996. "Explaining Irrigation Technology Choices: A Microparameter Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(4), pages 1064-1072.
    2. Matin Qaim & Alain de Janvry, 2003. "Genetically Modified Crops, Corporate Pricing Strategies, and Farmers' Adoption: The Case of Bt Cotton in Argentina," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 814-828.
    3. 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.
    4. Alexander, Corinne E. & Fernandez-Cornejo, Jorge & Goodhue, Rachael E., 2003. "Effects of the GM Controversy on Iowa Corn-Soybean Farmers' Acreage Allocation Decisions," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 28(3), pages 1-16, December.
    5. repec:pri:rpdevs:besley_case_diffusion.pdf is not listed on IDEAS
    6. Xingliang Ma & Guanming Shi, 2015. "A dynamic adoption model with Bayesian learning: an application to U.S. soybean farmers," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 25-38, January.
    7. L. Dean Hiebert, 1974. "Risk, Learning, and the Adoption of Fertilizer Responsive Seed Varieties," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 56(4), pages 764-768.
    8. Anderson, Jock R. & Dillon, John L. & Hardaker, Brian, 1977. "Agricultural Decision Analysis," Monographs: Applied Economics, AgEcon Search, number 288652, November.
    9. 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.
    10. Munshi, Kaivan, 2004. "Social learning in a heterogeneous population: technology diffusion in the Indian Green Revolution," Journal of Development Economics, Elsevier, vol. 73(1), pages 185-213, February.
    11. Timothy Besley & Anne Case, 1994. "Diffusion as a Learning Process: Evidence from HYV Cotton," Working Papers 228, Princeton University, Woodrow Wilson School of Public and International Affairs, Research Program in Development Studies..
    12. Timothy G. Conley & Christopher R. Udry, 2010. "Learning about a New Technology: Pineapple in Ghana," American Economic Review, American Economic Association, vol. 100(1), pages 35-69, March.
    13. Zellner, Arnold, 1970. "Estimation of Regression Relationships Containing Unobservable Independent Variables," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 11(3), pages 441-454, October.
    14. Lars Ljungqvist & Thomas J. Sargent, 2004. "Recursive Macroeconomic Theory, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026212274x, April.
    15. Elaine M. Liu, 2013. "Time to Change What to Sow: Risk Preferences and Technology Adoption Decisions of Cotton Farmers in China," The Review of Economics and Statistics, MIT Press, vol. 95(4), pages 1386-1403, October.
    16. repec:pri:rpdevs:besley_case_diffusion is not listed on IDEAS
    17. Kenneth A. Baerenklau, 2005. "Toward an Understanding of Technology Adoption: Risk, Learning, and Neighborhood Effects," Land Economics, University of Wisconsin Press, vol. 81(1).
    18. Gershon Feder & Gerald T. O'Mara, 1982. "On Information and Innovation Diffusion: A Bayesian Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 64(1), pages 145-147.
    19. Tülin Erdem & Michael P. Keane, 1996. "Decision-Making Under Uncertainty: Capturing Dynamic Brand Choice Processes in Turbulent Consumer Goods Markets," Marketing Science, INFORMS, vol. 15(1), pages 1-20.
    20. Goldberger, Arthur S, 1972. "Structural Equation Methods in the Social Sciences," Econometrica, Econometric Society, vol. 40(6), pages 979-1001, November.
    21. Chavas, Jean-Paul, 2004. "Risk Analysis in Theory and Practice," Elsevier Monographs, Elsevier, edition 1, number 9780121706210.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Maolong Chen & Chaoran Hu & Robert J. Myers, 2022. "Understanding transient technology use among smallholder farmers in Africa: A dynamic programming approach," Agricultural Economics, International Association of Agricultural Economists, vol. 53(S1), pages 91-107, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yoo, Do-il, "undated". "Farm Heterogeneity in Biotechnology Adoption with Risk and Learning: an Application to U.S. Corn," 2014 Annual Meeting, July 27-29, 2014, Minneapolis, Minnesota 170656, Agricultural and Applied Economics Association.
    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. Khushbu Mishra & Abdoul G. Sam & Gracious M. Diiro & Mario J. Miranda, 2020. "Gender and the dynamics of technology adoption: Empirical evidence from a household‐level panel data," Agricultural Economics, International Association of Agricultural Economists, vol. 51(6), pages 857-870, November.
    4. Yoo, Do-il, 2012. "Individual and Social Learning in Bio-technology Adoption: The Case of GM Corn in the U.S," 2012 Annual Meeting, August 12-14, 2012, Seattle, Washington 124975, Agricultural and Applied Economics Association.
    5. Haluk Gedikoglu & Sansel Tandogan & Joseph Parcell, 2023. "Neighbor effects on adoption of conservation practices: cases of grass filter systems and injecting manure," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 70(3), pages 723-756, June.
    6. Ma, Xingliang & Shi, Guanming, 2011. "A Dynamic Adoption Model with Bayesian Learning: Application to the U.S. Soybean Market," 2011 Annual Meeting, July 24-26, 2011, Pittsburgh, Pennsylvania 104577, Agricultural and Applied Economics Association.
    7. Gine, Xavier & Klonner, Stefan, 2005. "Credit constraints as a barrier to technology adoption by the poor : lessons from South Indian small-scale fishery," Policy Research Working Paper Series 3665, The World Bank.
    8. Xingliang Ma & Guanming Shi, 2015. "A dynamic adoption model with Bayesian learning: an application to U.S. soybean farmers," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 25-38, January.
    9. B Kelsey Jack, "undated". "Market Inefficiencies and the Adoption of Agricultural Technologies in Developing Countries," CID Working Papers 50, Center for International Development at Harvard University.
    10. Magnan, Nicholas & Spielman, David J. & Lybbert, Travis J. & Gulati, Kajal, 2015. "Leveling with friends: Social networks and Indian farmers' demand for a technology with heterogeneous benefits," Journal of Development Economics, Elsevier, vol. 116(C), pages 223-251.
    11. Christopher B. Busch & Colin Vance, 2011. "The Diffusion of Cattle Ranching and Deforestation: Prospects for a Hollow Frontier in Mexico’s Yucatán," Land Economics, University of Wisconsin Press, vol. 87(4), pages 682-698.
    12. Kondylis, Florence & Mueller, Valerie, 2012. "Seeing is Believing? Evidence from a Demonstration Plot Experiment in Mozambique:," MSSP working papers 1, International Food Policy Research Institute (IFPRI).
    13. Gin, Xavier & Yang, Dean, 2009. "Insurance, credit, and technology adoption: Field experimental evidencefrom Malawi," Journal of Development Economics, Elsevier, vol. 89(1), pages 1-11, May.
    14. Che, Yuyuan & Feng, Hongli & Hennessy, David, 2021. "Assessing Peer Effects and Subsidy Impacts in Technology Adoption: Application to Grazing Management Choices with Farm Survey Data," 2021 Conference, August 17-31, 2021, Virtual 315123, International Association of Agricultural Economists.
    15. Mamadou Sissoko & Melinda Smale & Annick Castiaux & Veronique Theriault, 2019. "Adoption of New Sorghum Varieties in Mali Through a Participatory Approach," Sustainability, MDPI, vol. 11(17), pages 1-15, September.
    16. Joshua D. Detre & Hiroki Uematsu & Ashok K. Mishra, 2011. "The influence of GM crop adoption on the profitability of farms operated by young and beginning farmers," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 71(1), pages 41-61, May.
    17. Oliver Musshoff & Norbert Hirschauer, 2011. "A behavioral economic analysis of bounded rationality in farm financing decisions," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 71(1), pages 62-83, May.
    18. Wang, Honglin & Yu, Fan & Reardon, Thomas & Huang, Jikun & Rozelle, Scott, 2013. "Social learning and parameter uncertainty in irreversible investments: Evidence from greenhouse adoption in northern China," China Economic Review, Elsevier, vol. 27(C), pages 104-120.
    19. Ayu Pratiwi & Aya Suzuki, 2020. "Does training location matter? Evidence from a randomized field experiment in Rural Indonesia," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 8(1), pages 1-23, December.

    More about this item

    Keywords

    Biotechnology adoption; Kalman filter algorithm; Minimum-distance estimator (MDE); Risk aversion;
    All these keywords.

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • Q16 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - R&D; Agricultural Technology; Biofuels; Agricultural Extension Services

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:empeco:v:60:y:2021:i:5:d:10.1007_s00181-020-01842-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.