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

Author

Listed:
  • Celine Nauges

    (School of Economics, University of Queensland)

  • Chris O'Donnell

    (School of Economics, University of Queensland)

  • John Quiggin

    (School of Economics, University of Queensland)

Abstract
In this article, we present one of the first real-world empirical applications of state-contingent production theory. Our state-contingent behavioral model allows us to analyze 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.

Suggested Citation

  • Celine Nauges & Chris O'Donnell & John Quiggin, 2010. "Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach," Risk & Uncertainty Working Papers WPR10_2, Risk and Sustainable Management Group, University of Queensland.
    • Handle: RePEc:rsm:riskun:r10_2
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    File URL: http://www.uq.edu.au/rsmg/WP/WPR10_02.pdf
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    References listed on IDEAS

    as
    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.
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    Cited by:

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    2. 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.
    3. Taylan G. Topcu & Konstantinos Triantis, 2022. "An ex-ante DEA method for representing contextual uncertainties and stakeholder risk preferences," Annals of Operations Research, Springer, vol. 309(1), pages 395-423, February.
    4. Bostian, AJ & Bostian, Moriah & Laukkanen, Marita & Simola, Antti Mikko, 2017. "Assessing The Impact Of Agri-Environmental Management Practices On Farm Productivity When Adoption Is Endogenous," 2017 International Congress, August 28-September 1, 2017, Parma, Italy 261154, European Association of Agricultural Economists.
    5. 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.
    6. Amer Ait Sidhoum, 2023. "Assessing the contribution of farmers’ working conditions to productive efficiency in the presence of uncertainty, a nonparametric approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8601-8622, August.
    7. Nguyen-Anh, Tuan & Hoang-Duc, Chinh & Tiet, Tuyen & Nguyen-Van, Phu & To-The, Nguyen, 2022. "Composite effects of human, natural and social capitals on sustainable food-crop farming in Sub-Saharan Africa," Food Policy, Elsevier, vol. 113(C).
    8. Tomas Baležentis, 2015. "The Sources of the Total Factor Productivity Growth in Lithuanian Family Farms: A Färe-Primont Index Approach," Prague Economic Papers, Prague University of Economics and Business, vol. 2015(2), pages 225-241.
    9. Bouali Guesmi & Teresa Serra & Amr Radwan & José María Gil, 2018. "Efficiency of Egyptian organic agriculture: A local maximum likelihood approach," Agribusiness, John Wiley & Sons, Ltd., vol. 34(2), pages 441-455, March.
    10. Raushan Bokusheva & Lajos Baráth, 2024. "State‐contingent production technology formulation: Identifying states of nature using reduced‐form econometric models of crop yield," American Journal of Agricultural Economics, John Wiley & Sons, vol. 106(2), pages 805-827, March.
    11. Amer Ait Sidhoum, 2023. "Measuring farm productivity under production uncertainty," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 67(4), pages 672-687, October.
    12. Macedo, Pedro & Scotto, Manuel, 2014. "Cross-entropy estimation in technical efficiency analysis," Journal of Mathematical Economics, Elsevier, vol. 54(C), pages 124-130.
    13. 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.
    14. Sansi Yang & C Richard Shumway, 2018. "Asset fixity under state-contingent production uncertainty," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 45(5), pages 831-856.
    15. Ashok K. Mishra & Mike G. Tsionas, 2020. "A Minimax Regret Approach to Decision Making Under Uncertainty," Journal of Agricultural Economics, Wiley Blackwell, vol. 71(3), pages 698-718, September.
    16. 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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    More about this item

    Keywords

    state-contingent; production; uncertainty;
    All these keywords.

    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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