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Estimating State-Contingent Production Frontiers

Chambers and Quiggin (2000) use state-contingent representations of risky production technologies to establish important theoretical results concerning producer behavior 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 conventional 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. Copyright 2006, Oxford University Press.

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File URL: http://hdl.handle.net/10.1111/j.1467-8276.2006.00851.x
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Article provided by Agricultural and Applied Economics Association in its journal American Journal of Agricultural Economics.

Volume (Year): 88 (2006)
Issue (Month): 1 ()
Pages: 249-266

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Handle: RePEc:oup:ajagec:v:88:y:2006:i:1:p:249-266
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  1. 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.
  2. 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 325-32.
  3. Chambers, Robert G & Quiggin, John, 1997. "Separation and Hedging Results with State-Contingent Production," Economica, London School of Economics and Political Science, vol. 64(254), pages 187-209, May.
  4. 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), June.
  5. KOOP, Gary & STEEL, Mark F. & OSIEWALSKI, Jacek, 1994. "Posterior Analysis of Stochastic Frontier Models using Gibbs Sampling," CORE Discussion Papers 1994061, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  6. 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.
  7. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
  8. 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.
  9. 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.
  10. repec:cup:cbooks:9780521785235 is not listed on IDEAS
  11. 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.
  12. Newbery, David M, 1989. "The Theory of Food Price Stabilisation," Economic Journal, Royal Economic Society, vol. 99(398), pages 1065-82, December.
  13. 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.
  14. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-74, October.
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