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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.

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Bibliographic Info

Article provided by Agricultural and Applied Economics Association in its journal American Journal of Agricultural Economics.

Volume (Year): 84 (2002)
Issue (Month): 2 ()
Pages: 513-526

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Handle: RePEc:oup:ajagec:v:84:y:2002:i:2:p:513-526

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Cited by:
  1. 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.
  2. 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), December.
  3. 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.
  4. 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.
  5. Blancard, Stephane & Boussemart, Jean-Philippe & Crainich, D. & Leleu, Herve, 2008. "How can allocative inefficiency reveal risk preference? An empirical investigation on French wheat farms," 2008 International Congress, August 26-29, 2008, Ghent, Belgium 44208, European Association of Agricultural Economists.
  6. 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.
  7. 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.
  8. 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.
  9. Roosen, Jutta & Hennessy, David A., 2003. "Tests for the Role of Risk Aversion on Input Use," Staff General Research Papers 10071, Iowa State University, Department of Economics.
  10. Chris O'Donnell & W.E. Griffiths, 2004. "Estimating State-Contingent Production Frontiers," CEPA Working Papers Series WP022004, School of Economics, University of Queensland, Australia.
  11. 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(01), April.
  12. 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.

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