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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

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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    1. Vernon W. Ruttan, 2002. "Productivity Growth in World Agriculture: Sources and Constraints," Journal of Economic Perspectives, American Economic Association, vol. 16(4), pages 161-184, Fall.
    2. Hall, Andy & Mytelka, Lynn & Oyelaran-Oyeyinka, Banji, 2006. "Concepts and guidelines for diagnostic assessments of agricultural innovation capacity," MERIT Working Papers 017, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    3. Nicholas Bloom & John Van Reenen, 2007. "Measuring and Explaining Management Practices Across Firms and Countries," The Quarterly Journal of Economics, Oxford University Press, vol. 122(4), pages 1351-1408.
    4. Mark Doms & Eric J. Bartelsman, 2000. "Understanding Productivity: Lessons from Longitudinal Microdata," Journal of Economic Literature, American Economic Association, vol. 38(3), pages 569-594, September.
    5. Kumbhakar, Subal C. & Hjalmarsson, Lennart, 1998. "Relative performance of public and private ownership under yardstick competition: electricity retail distribution," European Economic Review, Elsevier, vol. 42(1), pages 97-122, January.
    6. Hung-Jen Wang, 2002. "Heteroscedasticity and Non-Monotonic Efficiency Effects of a Stochastic Frontier Model," Journal of Productivity Analysis, Springer, vol. 18(3), pages 241-253, November.
    7. Glass, Anthony & Kenjegalieva, Karligash & Paez-Farrell, Juan, 2013. "Productivity growth decomposition using a spatial autoregressive frontier model," Economics Letters, Elsevier, vol. 119(3), pages 291-295.
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    Cited by:

    1. Subal C. Kumbhakar & Christopher F. Parmeter & Valentin Zelenyuk, 2017. "Stochastic Frontier Analysis: Foundations and Advances," Working Papers 2017-10, University of Miami, Department of Economics.
    2. 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.

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