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The State-Contingent Properties of Stochastic Production Functions

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  • Robert G. Chambers
  • John Quiggin

Abstract

The state-contingent properties of the most frequently used representations of stochastic production in the agricultural-economics literature are examined. Particular attention is paid to the cases of multiplicative uncertainty, additive uncertainty, and the Just-Pope production function. State-contingent technologies and their associated cost functions are reviewed, and that theory is applied to the stochastic production function. A generalization of the Just-Pope technology that has desirable state-contingent characteristics is proposed. Cost functions based upon state-contingent technologies are compared with cost functions based on a parametrized distribution representation of production uncertainty. Copyright 2002, Oxford University Press.

Suggested Citation

  • Robert G. Chambers & John Quiggin, 2002. "The State-Contingent Properties of Stochastic Production Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(2), pages 513-526.
  • Handle: RePEc:oup:ajagec:v:84:y:2002:i:2:p:513-526
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    File URL: http://hdl.handle.net/10.1111/1467-8276.00314
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    Cited by:

    1. Viaggi, Davide & Raggi, Meri & Gomez y Paloma, Sergio, 2011. "Farm-household investment behaviour and the CAP decoupling: Methodological issues in assessing policy impacts," Journal of Policy Modeling, Elsevier, vol. 33(1), pages 127-145, January.
    2. Jutta Roosen & David A. Hennessy, 2003. "Tests for the Role of Risk Aversion on Input Use," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 30-43.
    3. Serra, Teresa & Oude Lansink, Alfons, 2014. "Measuring the impacts of production risk on technical efficiency: A state-contingent conditional order-m approach," European Journal of Operational Research, Elsevier, vol. 239(1), pages 237-242.
    4. Jean-Philippe Boussemart & Walter Briec & Christophe Tavera, 2011. "More evidence on technological catching-up in the manufacturing sector," Applied Economics, Taylor & Francis Journals, vol. 43(18), pages 2321-2330.
    5. Kunimitsu, Yoji, 2006. "Effects of Socio-Institutional and Emotional Factors of Japanese Farmland Rental Transactions," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(1), pages 1-14, April.
    6. Boussemart, Jean-Philippe & Crainich, David & Leleu, Hervé, 2015. "A decomposition of profit loss under output price uncertainty," European Journal of Operational Research, Elsevier, vol. 243(3), pages 1016-1027.
    7. Rasmussen, Svend, 2004. "Optimizing Production under Uncertainty: Generalisation of the State-Contingent Approach and Comparison of Methods for Empirical Application," Unit of Economics Working Papers 24184, Royal Veterinary and Agricultural University, Food and Resource Economic Institute.
    8. Rasmussen, Svend, 2003. "Criteria for optimal production under uncertainty. The state-contingent approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 47(4), pages 1-30.
    9. Adamson, David, 2010. "Climate change, Irrigation and Pests: Examining Heliothis in the Murray Darling Basin," Risk and Sustainable Management Group Working Papers 149879, University of Queensland, School of Economics.
    10. Shankar, Sriram, 2015. "Efficiency analysis under uncertainty: a simulation study," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(2), April.
    11. Abedullah & Mubaraik Ali, 2006. "Quantifying the Extent and Nature of Risk in Alternative Cropping Patterns in Claveria, Philippines," The Pakistan Development Review, Pakistan Institute of Development Economics, vol. 45(2), pages 261-280.
    12. Pedro Macedo & Elvira Silva & Manuel Scotto, 2014. "Technical efficiency with state-contingent production frontiers using maximum entropy estimators," Journal of Productivity Analysis, Springer, vol. 41(1), pages 131-140, February.
    13. Stéphane Blancard & Jean-Philippe Boussemart & David Crainich & Hervé Leleu, 2008. "How can allocative inefficiency reveal risk preference? An empirical investigation on French wheat farms," Working Papers 2008-ECO-02, IESEG School of Management.
    14. Jean-Philippe Boussemart & David Crainich & Hervé Leleu, 2012. "A decomposition of profit inefficiency into price expectation error, preferences towards risk and technical inefficiency," Working Papers 2012-ECO-04, IESEG School of Management.
    15. Kota Minegishi, 2016. "Comparison of production risks in the state-contingent framework: application to balanced panel data," Journal of Productivity Analysis, Springer, vol. 46(2), pages 121-138, December.
    16. Kunimitsu, Yoji, 2005. "Impacts of Paddy-Field Consolidation Projects on Farmland Rental Transactions: Application of Discrete Choice Model," Japanese Journal of Agricultural Economics (formerly Japanese Journal of Rural Economics), Agricultural Economics Society of Japan (AESJ), vol. 7, pages 1-12.
    17. Huettel, Silke & Narayana, Rashmi & Odening, Martin, 2011. "Measuring dynamic efficiency under uncertainty," Structural Change in Agriculture/Strukturwandel im Agrarsektor (SiAg) Working Papers 129062, Humboldt University Berlin, Department of Agricultural Economics.
    18. 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.
    19. Shankar, Sriram, 2012. "Production economics in the presence of risk," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 56(4), pages 1-24, December.

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