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Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach

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  • Céline Nauges
  • Christopher J. O'Donnell
  • John Quiggin

Abstract

In this article, we present one of the first real-world empirical applications of state-contingent production theory. Our state-contingent behavioural model allows us to analyse production under both inefficiency and uncertainty without regard to the nature of producer risk preferences. Using farm data for Finland, we estimate a flexible production model that permits substitutability between state-contingent outputs. We test empirically and reject an assumption that has been implicit in almost all efficiency studies conducted in the last three decades, namely that the production technology is output-cubical, i.e. that outputs are not substitutable between states of nature. , Oxford University Press.

Suggested Citation

  • Céline Nauges & Christopher J. O'Donnell & John Quiggin, 2011. "Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 38(4), pages 449-467, October.
  • Handle: RePEc:oup:erevae:v:38:y:2011:i:4:p:449-467
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    File URL: http://hdl.handle.net/10.1093/erae/jbr014
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    References listed on IDEAS

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    1. Christopher O’Donnell & Robert Chambers & John Quiggin, 2010. "Efficiency analysis in the presence of uncertainty," Journal of Productivity Analysis, Springer, vol. 33(1), pages 1-17, February.
    2. C. J. O'Donnell & W. E. Griffiths, 2006. "Estimating State-Contingent Production Frontiers," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(1), pages 249-266.
    3. Teresa Serra & Spiro Stefanou & Alfons Oude Lansink, 2010. "A dynamic dual model under state-contingent production uncertainty," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 37(3), pages 293-312, September.
    4. G. Battese & A. Rambaldi & G. Wan, 1997. "A Stochastic Frontier Production Function with Flexible Risk Properties," Journal of Productivity Analysis, Springer, vol. 8(3), pages 269-280, August.
    5. G. Karagiannis & V. Tzouvelekas & A. Xepapadeas, 2003. "Measuring Irrigation Water Efficiency with a Stochastic Production Frontier," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 26(1), pages 57-72, September.
    6. Jean-Paul Chavas, 2008. "A Cost Approach to Economic Analysis Under State-Contingent Production Uncertainty," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(2), pages 435-466.
    7. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521622448.
    8. Subal C. Kumbhakar, 2002. "Specification and Estimation of Production Risk, Risk Preferences and Technical Efficiency," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(1), pages 8-22.
    9. Phoebe Koundouri & Marita Laukkanen & Sami Myyrä & Céline Nauges, 2009. "The effects of EU agricultural policy changes on farmers' risk attitudes," European Review of Agricultural Economics, Foundation for the European Review of Agricultural Economics, vol. 36(1), pages 53-77, March.
    10. Xing, Liu & Pietola, Kyosti, 2005. "Forward Hedging Under Price and Production Risk of Wheat," 2005 International Congress, August 23-27, 2005, Copenhagen, Denmark 24467, European Association of Agricultural Economists.
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    Cited by:

    1. Theodoros Skevas & Teresa Serra, 2016. "The role of pest pressure in technical and environmental inefficiency analysis of Dutch arable farms: an event-specific data envelopment approach," Journal of Productivity Analysis, Springer, vol. 46(2), pages 139-153, December.
    2. Carpentier, Alain & Gohin, Alexandre & Sckokai, Paolo & Thomas, Alban, 2015. "Economic modelling of agricultural production: past advances and new challenges," Revue d'Etudes en Agriculture et Environnement, Editions NecPlus, vol. 96(01), pages 131-165, March.
    3. Macedo, Pedro & Scotto, Manuel, 2014. "Cross-entropy estimation in technical efficiency analysis," Journal of Mathematical Economics, Elsevier, vol. 54(C), pages 124-130.
    4. Yang, Sansi & Shumway, C. Richard, 2015. "Asset Fixity under State-Contingent Production Uncertainty," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205256, Agricultural and Applied Economics Association;Western Agricultural Economics Association.
    5. Tomas Baležentis, 2015. "The Sources of the Total Factor Productivity Growth in Lithuanian Family Farms: A Färe-Primont Index Approach," Prague Economic Papers, University of Economics, Prague, vol. 2015(2), pages 225-241.

    More about this item

    JEL classification:

    • Q10 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - General
    • Q24 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Land
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water

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