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Estimating State-Contingent Production Frontiers

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Abstract

Chambers and Quiggin (2000) advocate the use of state-contingent production technologies to represent risky production and establish important theoretical results concerning producer behaviour under uncertainty. Unfortunately, perceived problems in the estimation of state-contingent models have limited the usefulness of the approach in policy formulation. We show that fixed and random effects state-contingent production frontiers can be conveniently estimated in a finite mixtures framework. An empirical example is provided. Compared to standard estimation approaches, we find that estimating production frontiers in a state-contingent framework produces significantly different estimates of elasticities, firm technical efficiencies and other quantities of economic interest.

Suggested Citation

  • Chris O'Donnell & W.E. Griffiths, 2004. "Estimating State-Contingent Production Frontiers," CEPA Working Papers Series WP022004, School of Economics, University of Queensland, Australia.
    • Handle: RePEc:qld:uqcepa:07
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    File URL: https://economics.uq.edu.au/files/5346/WP022004.pdf
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    1. Chambers, R.G. & Quiggin, J., 1995. "Separation and Hedging Results with State-Contingent Production," Papers 293, Australian National University - Department of Economics.
    2. 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.
    3. Chambers, Robert G. & Quiggin, John C., 2004. "Technological and financial approaches to risk management in agriculture: an integrate approach," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 48(2), pages 1-25.
    4. Just, Richard E. & Pope, Rulon D., 1978. "Stochastic specification of production functions and economic implications," Journal of Econometrics, Elsevier, vol. 7(1), pages 67-86, February.
    5. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521785235.
    6. Koop, Gary & Steel, Mark F.J. & Osiewalski, Jacek, 1992. "Posterior analysis of stochastic frontier models using Gibbs sampling," DES - Working Papers. Statistics and Econometrics. WS 3677, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    8. Newbery, David M, 1989. "The Theory of Food Price Stabilisation," Economic Journal, Royal Economic Society, vol. 99(398), pages 1065-1082, December.
    9. Robert G. James & John Quiggan, 1997. "Separation and Hedging Results with State‐Contingent Production," Economica, London School of Economics and Political Science, vol. 64(254), pages 187-209, May.
    10. Battese, G E & Coelli, T J, 1995. "A Model for Technical Inefficiency Effects in a Stochastic Frontier Production Function for Panel Data," Empirical Economics, Springer, vol. 20(2), pages 325-332.
    11. 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.
    12. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    13. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    14. Pitt, Mark M. & Lee, Lung-Fei, 1981. "The measurement and sources of technical inefficiency in the Indonesian weaving industry," Journal of Development Economics, Elsevier, vol. 9(1), pages 43-64, August.
    15. Yangseon Kim & Peter Schmidt, 2000. "A Review and Empirical Comparison of Bayesian and Classical Approaches to Inference on Efficiency Levels in Stochastic Frontier Models with Panel Data," Journal of Productivity Analysis, Springer, vol. 14(2), pages 91-118, September.
    16. Chambers, Robert G. & Quiggin, John, 1996. "Non-point-source pollution regulation as a multi-task principal-agent problem," Journal of Public Economics, Elsevier, vol. 59(1), pages 95-116, January.
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