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Efficiency analysis in the presence of uncertainty

Author

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  • Christopher O’Donnell

    ()

  • Robert Chambers

    ()

  • John Quiggin

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Abstract

In a stochastic decision environment, differences in information can lead rational decision makers facing the same stochastic technology and the same markets to make different production choices. Efficiency and productivity measurement in such a setting can be seriously and systematically biased by the manner in which the stochastic technology is represented. For example, conventional production frontiers implicitly impose the restriction that information differences have no effect on the way risk-neutral decision makers utilize the same input bundle. The result is that rational and efficient ex ante production choices can be mistakenly characterized as inefficient -- informational differences are mistaken for differences in technical efficiency. This paper uses simulation methods to illustrate the type and magnitude of empirical errors that can emerge in efficiency analysis as a result of overly restrictive representations of production technologies.
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Suggested Citation

  • 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.
  • Handle: RePEc:kap:jproda:v:33:y:2010:i:1:p:1-17
    DOI: 10.1007/s11123-009-0143-9
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    References listed on IDEAS

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    1. Powell, Alan A. & Gruen, Fred H.G., 1967. "The Estimation Of Production Frontiers: The Australian Livestock/Cereals Complex," Australian Journal of Agricultural Economics, Australian Agricultural and Resource Economics Society, vol. 11(01), June.
    2. Gong, Linguo & Sun, Bruce, 1995. "Efficiency measurement of production operations under uncertainty," International Journal of Production Economics, Elsevier, vol. 39(1-2), pages 55-66, April.
    3. Nauges, Celine & O'Donnell, Christopher J. & Quiggin, John C., 2009. "Uncertainty and technical efficiency in Finnish Agriculture," 2009 Conference (53rd), February 11-13, 2009, Cairns, Australia 48062, Australian Agricultural and Resource Economics Society.
    4. 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.
    5. Thierry Post & Laurens Cherchye & Timo Kuosmanen, 2002. "Nonparametric Efficiency Estimation In Stochastic Environments," Operations Research, INFORMS, vol. 50(4), pages 645-655, August.
    6. Subal C. Kumbhakar, 2002. "Specification and Estimation of Production Risk, Risk Preferences and Technical Efficiency," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 84(1), pages 8-22.
    7. G. Battese & A. Rambaldi & G. Wan, 1997. "A Stochastic Frontier Production Function with Flexible Risk Properties," Journal of Productivity Analysis, Springer, vol. 8(3), pages 269-280, August.
    8. Coelli, Tim & Perelman, Sergio, 1999. "A comparison of parametric and non-parametric distance functions: With application to European railways," European Journal of Operational Research, Elsevier, vol. 117(2), pages 326-339, September.
    9. Chambers,Robert G. & Quiggin,John, 2000. "Uncertainty, Production, Choice, and Agency," Cambridge Books, Cambridge University Press, number 9780521785235, April.
    10. Robert G. Chambers & John Quiggin, 1998. "Cost Functions and Duality for Stochastic Technologies," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(2), pages 288-295.
    11. O. B. Olesen & N. C. Petersen, 1995. "Chance Constrained Efficiency Evaluation," Management Science, INFORMS, vol. 41(3), pages 442-457, March.
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    More about this item

    Keywords

    Uncertainty; Efficiency measurement; Data envelopment analysis; Stochastic frontier analysis; C21; D21; D24; D81;

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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