IDEAS home Printed from https://ideas.repec.org/a/bla/agecon/v46y2015i1p11-24.html
   My bibliography  Save this article

Risk, learning, and technology adoption

Author

Listed:
  • Bradford L. Barham
  • Jean-Paul Chavas
  • Dylan Fitz
  • Vanessa Ríos-Salas
  • Laura Schechter

Abstract

This article explores how decision makers learn and use information, with an application to the adoption of biotechnology in agriculture. The empirical analysis relies on experimental and survey data measuring risk preferences, learning processes, and the adoption of genetically modified (GM) seeds among U.S. grain farmers. While controlling for risk aversion, we link individual learning rules with the cognitive abilities of each decision maker and their actual GM adoption decisions. We find evidence that very few individuals are Bayesian learners, and that the population of farmers is quite heterogeneous in terms of learning rules. This suggests that Bayesian learning (as commonly assumed in the analysis of agricultural technology adoption) is not an appropriate characterization. In addition, we do not find a strong relationship between observed learning styles and the timing of GM seed adoption. To the extent that learning is a key part of the process of technology adoption, this suggests the presence of much unobserved heterogeneity in learning among farmers.

Suggested Citation

  • Bradford L. Barham & Jean-Paul Chavas & Dylan Fitz & Vanessa Ríos-Salas & Laura Schechter, 2015. "Risk, learning, and technology adoption," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 11-24, January.
    • Handle: RePEc:bla:agecon:v:46:y:2015:i:1:p:11-24
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/agec.12123
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Andrew D. Foster & Mark R. Rosenzweig, 2010. "Microeconomics of Technology Adoption," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 395-424, September.
    2. Foster, Andrew D & Rosenzweig, Mark R, 1995. "Learning by Doing and Learning from Others: Human Capital and Technical Change in Agriculture," Journal of Political Economy, University of Chicago Press, vol. 103(6), pages 1176-1209, December.
    3. Barham, Bradford L. & Chavas, Jean-Paul & Fitz, Dylan & Salas, Vanessa Ríos & Schechter, Laura, 2014. "The roles of risk and ambiguity in technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 204-218.
    4. Marra, Michele & Pannell, David J. & Abadi Ghadim, Amir, 2003. "The economics of risk, uncertainty and learning in the adoption of new agricultural technologies: where are we on the learning curve?," Agricultural Systems, Elsevier, vol. 75(2-3), pages 215-234.
    5. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
    6. Simeon Djankov & Edward Miguel & Yingyi Qian & Gérard Roland & Ekaterina Zhuravskaya, 2005. "Who are Russia's Entrepreneurs?," Journal of the European Economic Association, MIT Press, vol. 3(2-3), pages 587-597, 04/05.
    7. 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.
    8. 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.
    9. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    10. Nassul Ssentamu Kabunga & Thomas Dubois & Matin Qaim, 2012. "Heterogeneous information exposure and technology adoption: the case of tissue culture bananas in Kenya," Agricultural Economics, International Association of Agricultural Economists, vol. 43(5), pages 473-486, September.
    11. Cheung, Yin-Wong & Friedman, Daniel, 1997. "Individual Learning in Normal Form Games: Some Laboratory Results," Games and Economic Behavior, Elsevier, vol. 19(1), pages 46-76, April.
    12. 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.
    13. Noah Gans & George Knox & Rachel Croson, 2007. "Simple Models of Discrete Choice and Their Performance in Bandit Experiments," Manufacturing & Service Operations Management, INFORMS, vol. 9(4), pages 383-408, December.
    14. Juan Camilo Cardenas & Jeffrey Carpenter, 2008. "Behavioural Development Economics: Lessons from Field Labs in the Developing World," Journal of Development Studies, Taylor & Francis Journals, vol. 44(3), pages 311-338.
    15. Ross, Nicholas & Santos, Paulo & Capon, Timothy, 2012. "Risk, ambiguity and the adoption of new technologies: experimental evidence from a developing economy," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 126492, International Association of Agricultural Economists.
    16. Charles A. Holt & Susan K. Laury, 2002. "Risk Aversion and Incentive Effects," American Economic Review, American Economic Association, vol. 92(5), pages 1644-1655, December.
    17. Epstein Larry G & Noor Jawwad & Sandroni Alvaro, 2010. "Non-Bayesian Learning," The B.E. Journal of Theoretical Economics, De Gruyter, vol. 10(1), pages 1-20, January.
    18. Colin Camerer & Teck-Hua Ho, 1999. "Experience-weighted Attraction Learning in Normal Form Games," Econometrica, Econometric Society, vol. 67(4), pages 827-874, July.
    19. Thea Nielsen & Alwin Keil & Manfred Zeller, 2013. "Assessing farmers’ risk preferences and their determinants in a marginal upland area of Vietnam: a comparison of multiple elicitation techniques," Agricultural Economics, International Association of Agricultural Economists, vol. 44(3), pages 255-273, May.
    20. Gary Charness & Dan Levin, 2005. "When Optimal Choices Feel Wrong: A Laboratory Study of Bayesian Updating, Complexity, and Affect," American Economic Review, American Economic Association, vol. 95(4), pages 1300-1309, September.
    21. Urs Fischbacher, 2007. "z-Tree: Zurich toolbox for ready-made economic experiments," Experimental Economics, Springer;Economic Science Association, vol. 10(2), pages 171-178, June.
    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. Zhang, Biao & Fu, Zetian & Wang, Jieqiong & Zhang, Lingxian, 2019. "Farmers’ adoption of water-saving irrigation technology alleviates water scarcity in metropolis suburbs: A case study of Beijing, China," Agricultural Water Management, Elsevier, vol. 212(C), pages 349-357.
    2. Wang, Anbang & He, Ke & Zhang, Junbiao & Zeng, Yangmei, 2021. "Green Production Technologies and Technical Efficiency of Rice Farmers in China: A Case Study of Straw-Derived Biochar," 2021 Conference, August 17-31, 2021, Virtual 315026, International Association of Agricultural Economists.
    3. 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.
    4. Xiaohui Li & Hang Xiong & Jinghui Hao & Gucheng Li, 2024. "Impacts of internet access and use on grain productivity: evidence from Central China," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-9, December.
    5. Daniel Gregg & John Rolfe, 2017. "Risk Behaviours and Grazing Land Management: A Framed Field Experiment and Linkages to Range Land Condition," Journal of Agricultural Economics, Wiley Blackwell, vol. 68(3), pages 682-709, September.
    6. Dougherty, John P. & Flatnes, Jon Einar & Gallenstein, Richard A. & Miranda, Mario J. & Sam, Abdoul G., 2020. "Climate change and index insurance demand: Evidence from a framed field experiment in Tanzania," Journal of Economic Behavior & Organization, Elsevier, vol. 175(C), pages 155-184.
    7. Gars, Jared & Ward, Patrick S., 2019. "Can differences in individual learning explain patterns of technology adoption? Evidence on heterogeneous learning patterns and hybrid rice adoption in Bihar, India," World Development, Elsevier, vol. 115(C), pages 178-189.
    8. Gharib, Mariam H. & Palm-Forster, Leah H. & Lybbert, Travis J. & Messer, Kent D., 2021. "Fear of fraud and willingness to pay for hybrid maize seed in Kenya," Food Policy, Elsevier, vol. 102(C).
    9. Kaywana Raeburn & Jim Engle-Warnick & Sonia Laszlo & Jian Li, 2016. "Learning in a Bandit Game and Technology Choice," CIRANO Working Papers 2016s-47, CIRANO.
    10. Omotuyole Isiaka Ambali & Francisco Jose Areal & Nikolaos Georgantzis, 2021. "Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference," Agriculture, MDPI, vol. 11(8), pages 1-13, July.
    11. Simone Cerroni, 2020. "Eliciting farmers’ subjective probabilities, risk, and uncertainty preferences using contextualized field experiments," Agricultural Economics, International Association of Agricultural Economists, vol. 51(5), pages 707-724, September.
    12. Doris Läpple & Garth Holloway & Donald J Lacombe & Cathal O’Donoghue, 2017. "Sustainable technology adoption: a spatial analysis of the Irish Dairy Sector," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 44(5), pages 810-835.
    13. Damien Jourdain & Juliette Lairez & Bruno Striffler & François Affholder, 2020. "Farmers’ preference for cropping systems and the development of sustainable intensification: a choice experiment approach," Review of Agricultural, Food and Environmental Studies, Springer, vol. 101(4), pages 417-437, December.
    14. Dakuan Qiao & Lei Luo & Chenyang Zhou & Xinhong Fu, 2023. "The influence of social learning on Chinese farmers’ adoption of green pest control: mediation by environmental literacy and moderation by market conditions," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(11), pages 13305-13330, November.
    15. Guang Tian & Xiaoxue Du & Fangbin Qiao & Andres Trujillo-Barrera, 2021. "Technology Adoption and Learning-by-Doing: The Case of Bt Cotton Adoption in China," JRFM, MDPI, vol. 14(11), pages 1-13, November.
    16. Ghadir Asadi & Mohammad H. Mostafavi-Dehzooei, 2022. "The Role of Learning in Adaptation to Technology: The Case of Groundwater Extraction," Sustainability, MDPI, vol. 14(12), pages 1-37, June.
    17. Hanna Freudenreich & Sindu W. Kebede, 2022. "Experience of shocks, household wealth and expectation formation: Evidence from smallholder farmers in Kenya," Agricultural Economics, International Association of Agricultural Economists, vol. 53(5), pages 756-774, September.
    18. Gonzalo Villa‐Cox & Francesco Cavazza & Cristian Jordan & Mijail Arias‐Hidalgo & Paúl Herrera & Ramon Espinel & Davide Viaggi & Stijn Speelman, 2021. "Understanding constraints on private irrigation adoption decisions under uncertainty in data constrained settings: A novel empirical approach tested on Ecuadorian Cocoa cultivations," Agricultural Economics, International Association of Agricultural Economists, vol. 52(6), pages 985-999, November.
    19. Jutao Zeng & Jie Lyu, 2023. "Simultaneous Decisions to Undertake Off-Farm Work and Straw Return: The Role of Cognitive Ability," Land, MDPI, vol. 12(8), pages 1-21, August.
    20. Barham, Bradford L. & Chavas, Jean-Paul & Fitz, Dylan & Schechter, Laura, 2018. "Receptiveness to advice, cognitive ability, and technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 239-268.
    21. Francesco Cavazza & Francesco Galioto & Meri Raggi & Davide Viaggi, 2020. "Digital Irrigated Agriculture: Towards a Framework for Comprehensive Analysis of Decision Processes under Uncertainty," Future Internet, MDPI, vol. 12(11), pages 1-16, October.
    22. Scott Kaplan & Ben Gordon & Feras El Zarwi & Joan L. Walker & David Zilberman, 2019. "The Future of Autonomous Vehicles: Lessons from the Literature on Technology Adoption," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 41(4), pages 583-597, December.
    23. Dougherty, John & Flatnes, Jon Einar & Gallenstein, Richard & Miranda, Mario J. & Sam, Abdoul G., 2017. "Investigating the Impact of Climate Change on the Demand for Index Insurance," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258524, Agricultural and Applied Economics Association.
    24. Damien Jourdain1,2,3 & Juliette Lairez4,5 & Bruno Striffler & François Affholder, 2020. "Farmers’ preference for cropping systems and the development of sustainable intensification: a choice experiment approach," Review of Agricultural, Food and Environmental Studies, INRA Department of Economics, vol. 101(4), pages 417-437.

    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. Gars, Jared & Ward, Patrick S., 2019. "Can differences in individual learning explain patterns of technology adoption? Evidence on heterogeneous learning patterns and hybrid rice adoption in Bihar, India," World Development, Elsevier, vol. 115(C), pages 178-189.
    2. Kaywana Raeburn & Sonia Laszlo & Jim Warnick, 2023. "Resolving ambiguity as a public good: experimental evidence from Guyana," Theory and Decision, Springer, vol. 95(1), pages 79-107, July.
    3. Barham, Bradford L. & Chavas, Jean-Paul & Fitz, Dylan & Salas, Vanessa Ríos & Schechter, Laura, 2014. "The roles of risk and ambiguity in technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 204-218.
    4. Wu, Haixia & Ge, Yan & Li, Jianping, 2023. "Uncertainty, time preference and households’ adoption of rooftop photovoltaic technology," Energy, Elsevier, vol. 276(C).
    5. Crentsil, Christian & Gschwandtner, Adelina & Wahhaj, Zaki, 2020. "The effects of risk and ambiguity aversion on technology adoption: Evidence from aquaculture in Ghana," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 46-68.
    6. Kaywana Raeburn & Jim Engle-Warnick & Sonia Laszlo & Jian Li, 2016. "Learning in a Bandit Game and Technology Choice," CIRANO Working Papers 2016s-47, CIRANO.
    7. Wu, Haixia & Li, Jianping & Ge, Yan, 2022. "Ambiguity preference, social learning and adoption of soil testing and formula fertilization technology," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    8. Omotuyole Isiaka Ambali & Francisco Jose Areal & Nikolaos Georgantzis, 2021. "Improved Rice Technology Adoption: The Role of Spatially-Dependent Risk Preference," Agriculture, MDPI, vol. 11(8), pages 1-13, July.
    9. Barham, Bradford L. & Chavas, Jean-Paul & Fitz, Dylan & Schechter, Laura, 2018. "Receptiveness to advice, cognitive ability, and technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 149(C), pages 239-268.
    10. Terrance Hurley & Jawoo Koo & Kindie Tesfaye, 2018. "Weather risk: how does it change the yield benefits of nitrogen fertilizer and improved maize varieties in sub‐Saharan Africa?," Agricultural Economics, International Association of Agricultural Economists, vol. 49(6), pages 711-723, November.
    11. 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.
    12. Galarza, Francisco, 2009. "Choices under Risk in Rural Peru," MPRA Paper 17708, University Library of Munich, Germany.
    13. Nadia A. Streletskaya & Samuel D. Bell & Maik Kecinski & Tongzhe Li & Simanti Banerjee & Leah H. Palm‐Forster & David Pannell, 2020. "Agricultural Adoption and Behavioral Economics: Bridging the Gap," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 54-66, March.
    14. Ahsanuzzaman, & Priyo, Asad Karim Khan & Nuzhat, Kanti Ananta, 2022. "Effects of communication, group selection, and social learning on risk and ambiguity attitudes: Experimental evidence from Bangladesh," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 96(C).
    15. Ihli, Hanna Julia & Chiputwa, Brian & Musshoff, Oliver, 2016. "Do Changing Probabilities or Payoffs in Lottery-Choice Experiments Affect Risk Preference Outcomes? Evidence from Rural Uganda," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 41(2), May.
    16. Ward, Patrick S. & Singh, Vartika, 2013. "Risk and Ambiguity Preferences and the Adoption of New Agricultural Technologies: Evidence from Field Experiments in Rural India," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150794, Agricultural and Applied Economics Association.
    17. Chowdhury, Shyamal & Satish, Varun & Sulaiman, Munshi & Sun, Yi, 2022. "Sooner rather than later: Social networks and technology adoption," Journal of Economic Behavior & Organization, Elsevier, vol. 203(C), pages 466-482.
    18. Belissa, Temesgen Keno & Lensink, Robert & van Asseldonk, Marcel, 2020. "Risk and ambiguity aversion behavior in index-based insurance uptake decisions: Experimental evidence from Ethiopia," Journal of Economic Behavior & Organization, Elsevier, vol. 180(C), pages 718-730.
    19. Mekonnen, Daniel Ayalew & Gerber, Nicolas & Matz, Julia Anna, 2018. "Gendered Social Networks, Agricultural Innovations, and Farm Productivity in Ethiopia," World Development, Elsevier, vol. 105(C), pages 321-335.
    20. 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.

    More about this item

    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:bla:agecon:v:46:y:2015:i:1:p:11-24. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/iaaeeea.html .

    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.