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Accounting for risk in productivity analysis: an application to Norwegian dairy farming

Author

Listed:
  • Gudbrand Lien

    (Norwegian Institute for Bioeconomy Research
    Lillehammer University College)

  • Subal C. Kumbhakar

    (Norwegian Institute for Bioeconomy Research
    State University of New York)

  • J. Brian Hardaker

    (University of New England)

Abstract

Empirical studies have often shown wide differences in productivity among firms. Although several studies have sought to identify factors causing such differences, only a few studies have examined the effects of risk and risk aversion on productivity. In this study, using Norwegian dairy farming data for 2009, we examined the effects of different aspects of risk on productivity. We used a range of variables to construct indices of risk taking, risk perception and risk management. These indices were then included as arguments in an input distance function which represents the production technology. Our results show that these risk indices did affect productivity. Regional differences in productivity, though small, were also found to exist, suggesting that unobserved edaphic factors that differ between regions also affected productivity.

Suggested Citation

  • Gudbrand Lien & Subal C. Kumbhakar & J. Brian Hardaker, 2017. "Accounting for risk in productivity analysis: an application to Norwegian dairy farming," Journal of Productivity Analysis, Springer, vol. 47(3), pages 247-257, June.
  • Handle: RePEc:kap:jproda:v:47:y:2017:i:3:d:10.1007_s11123-016-0482-2
    DOI: 10.1007/s11123-016-0482-2
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    References listed on IDEAS

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    Cited by:

    1. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2022. "Stochastic Frontier Analysis: Foundations and Advances I," Springer Books, in: Subhash C. Ray & Robert G. Chambers & Subal C. Kumbhakar (ed.), Handbook of Production Economics, chapter 8, pages 331-370, Springer.
    2. Almas Heshmati & Masoomeh Rashidghalam, 2020. "Estimation of technical change and TFP growth based on observable technology shifters," Journal of Productivity Analysis, Springer, vol. 53(1), pages 21-36, February.
    3. Zheng, Yu & Alexandre, Gohin, 2018. "Agricultural productivity and price volatility in France: a dynamic stochastic partial equilibrium approach," 2018 Annual Meeting, August 5-7, Washington, D.C. 274354, Agricultural and Applied Economics Association.
    4. Mamonov Mikhail E. & Parmeter Christopher F. & Prokhorov Artem B., 2022. "Dependence modeling in stochastic frontier analysis," Dependence Modeling, De Gruyter, vol. 10(1), pages 123-144, January.
    5. Frank Asche & Andreea L. Cojocaru & Ruth B. M. Pincinato & Kristin H. Roll, 2020. "Production Risk in the Norwegian Fisheries," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 75(1), pages 137-149, January.
    6. Muhamad Zahid Muhamad & Mad Nasir Shamsudin & Nitty Hirawaty Kamarulzaman & Nolila Mohd Nawi & Jamaliah Laham, 2022. "Investigating Yield Variability and Technical Efficiency of Smallholders Pineapple Production in Johor," Sustainability, MDPI, vol. 14(22), pages 1-18, November.
    7. Samira Shayanmehr & Shida Rastegari Henneberry & Mahmood Sabouhi Sabouni & Naser Shahnoushi Foroushani, 2020. "Drought, Climate Change, and Dryland Wheat Yield Response: An Econometric Approach," IJERPH, MDPI, vol. 17(14), pages 1-18, July.
    8. Kabir, Md. Jahangir & Cramb, Rob & Alauddin, Mohammad & Gaydon, Donald S., 2019. "Farmers’ perceptions and management of risk in rice-based farming systems of south-west coastal Bangladesh," Land Use Policy, Elsevier, vol. 86(C), pages 177-188.
    9. Lien, Gudbrand & Kumbhakar, Subal C. & Mishra, Ashok K. & Hardaker, J. Brian, 2022. "Does risk management affect productivity of organic rice farmers in India? Evidence from a semiparametric production model," European Journal of Operational Research, Elsevier, vol. 303(3), pages 1392-1402.

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