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Technology Adoption and Learning-by-Doing: The Case of Bt Cotton Adoption in China

Author

Listed:
  • Guang Tian

    (Department of Agricultural and Applied Economics, University of Wisconsin-Madison, Madison, WI 53705, USA)

  • Xiaoxue Du

    (Department of Agricultural Economics and Rural Sociology, University of Idaho, Moscow, ID 83843, USA)

  • Fangbin Qiao

    (China Economics and Management Academy, Central University of Finance and Economics, Beijing 100081, China)

  • Andres Trujillo-Barrera

    (Department of Agricultural Economics and Rural Sociology, University of Idaho, Moscow, ID 83843, USA)

Abstract

Although the benefits of genetically modified (GM) crops have been well documented, how do farmers manage the risk of new technology in the early stages of technology adoption has received less attention. We compare the total factor productivity (TFP) of cotton to other major crops (wheat, rice, and corn) in China between 1990 and 2015, showing that the TFP growth of cotton production is significantly different from all other crops. In particular, the TFP of cotton production increased rapidly in the early 1990s then declined slightly around 2000 and rose again. This pattern coincides with the adoption of Bt cotton process in China. To further investigate the decline of TFP in the early stages of Bt cotton adoption, using aggregate provincial-level data, we implement a TFP decomposition and show that the productivity of GM technology is higher, whereas the technical efficiency of GM technology is lower than that of traditional technologies. Especially, Bt cotton exhibited lower technical efficiency because farmers did not reduce the use of pesticide when they first started to adopt Bt cotton. In addition, we illustrate the occurrence of a learning process as GM technology diffuses throughout China: after farmers gain knowledge of Bt cotton, pesticide use declines and technical efficiency improves.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jjrfmx:v:14:y:2021:i:11:p:524-:d:670331
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    References listed on IDEAS

    as
    1. David Atkin & Azam Chaudhry & Shamyla Chaudry & Amit K. Khandelwal & Eric Verhoogen, 2017. "Organizational Barriers to Technology Adoption: Evidence from Soccer-Ball Producers in Pakistan," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 132(3), pages 1101-1164.
    2. Pardey, Philip G. & Andrade, Robert S. & Hurley, Terrance M. & Rao, Xudong & Liebenberg, Frikkie G., 2016. "Returns to food and agricultural R&D investments in Sub-Saharan Africa, 1975–2014," Food Policy, Elsevier, vol. 65(C), pages 1-8.
    3. Alexander Bilson Darku & Stavroula Malla & Kien C. Tran, 2016. "Sources and Measurement of Agricultural Productivity and Efficiency in Canadian Provinces: Crops and Livestock," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 64(1), pages 49-70, March.
    4. Liang Lu & Ruby Nguyen & Md Mamunur Rahman & Jason Winfree, 2021. "Demand Shocks and Supply Chain Resilience: An Agent-Based Modelling Approach and Application to the Potato Supply Chain," NBER Chapters, in: Risks in Agricultural Supply Chains, National Bureau of Economic Research, Inc.
    5. Sidartha Gordon & Emeric Henry & Pauli Murto, 2021. "Waiting for my neighbors," RAND Journal of Economics, RAND Corporation, vol. 52(2), pages 251-282, June.
    6. Liang Lu & Thomas Reardon & David Zilberman, 2016. "Supply Chain Design and Adoption of Indivisible Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(5), pages 1419-1431.
    7. Huang, Jikun & Hu, Ruifa & Pray, Carl & Qiao, Fangbin & Rozelle, Scott, 2003. "Biotechnology as an alternative to chemical pesticides: a case study of Bt cotton in China," Agricultural Economics, Blackwell, vol. 29(1), pages 55-67, July.
    8. Patrick L. Hatzenbuehler & Xiaoxue Du & Kathleen Painter, 2021. "Price transmission with sparse market information: The case of United States chickpeas," Agribusiness, John Wiley & Sons, Ltd., vol. 37(3), pages 665-682, July.
    9. Pray, Carl & Ma, Danmeng & Huang, Jikun & Qiao, Fangbin, 2001. "Impact of Bt Cotton in China," World Development, Elsevier, vol. 29(5), pages 813-825, May.
    10. Jean‐Paul Chavas & Céline Nauges, 2020. "Uncertainty, Learning, and Technology Adoption in Agriculture," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 42(1), pages 42-53, March.
    11. Rao, Xudong & Hurley, Terrance M. & Pardey, Philip G., 2019. "Are agricultural R&D returns declining and development dependent?," World Development, Elsevier, vol. 122(C), pages 27-37.
    12. Xiaoxue Du & Liang Lu & Thomas Reardon & David Zilberman, 2016. "Economics of Agricultural Supply Chain Design: A Portfolio Selection Approach," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(5), pages 1377-1388.
    13. 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.
    14. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    15. Qiao, Fangbin, 2015. "Fifteen Years of Bt Cotton in China: The Economic Impact and its Dynamics," World Development, Elsevier, vol. 70(C), pages 177-185.
    16. Xiaobing Wang & Futoshi Yamauchi & Jikun Huang, 2016. "Rising wages, mechanization, and the substitution between capital and labor: evidence from small scale farm system in China," Agricultural Economics, International Association of Agricultural Economists, vol. 47(3), pages 309-317, May.
    17. Fangbin Qiao & Jikun Huang & Caiping Zhang, 2016. "The Sustainability of the Farm-level Impact of Bt Cotton in China," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(3), pages 602-618, September.
    18. Rajagopal, 2014. "Technology Diffusion and Adoption," Palgrave Macmillan Books, in: Architecting Enterprise, chapter 6, pages 148-173, Palgrave Macmillan.
    19. Margarita Genius & Phoebe Koundouri & Céline Nauges & Vangelis Tzouvelekas, 2014. "Information Transmission in Irrigation Technology Adoption and Diffusion: Social Learning, Extension Services, and Spatial Effects," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(1), pages 328-344.
    20. Songqing Jin & Hengyun Ma & Jikun Huang & Ruifa Hu & Scott Rozelle, 2010. "Productivity, efficiency and technical change: measuring the performance of China’s transforming agriculture," Journal of Productivity Analysis, Springer, vol. 33(3), pages 191-207, June.
    21. 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.
    22. Liu, Elaine M. & Huang, JiKun, 2013. "Risk preferences and pesticide use by cotton farmers in China," Journal of Development Economics, Elsevier, vol. 103(C), pages 202-215.
    23. Fangbin Qiao & Jikun Huang, 2020. "Technical Efficiency of Bacillus thuringiensis Cotton in China: Results from Household Surveys," Economic Development and Cultural Change, University of Chicago Press, vol. 68(3), pages 947-963.
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