IDEAS home Printed from https://ideas.repec.org/a/kap/jproda/v33y2010i1p1-17.html
   My bibliography  Save this article

Efficiency analysis in the presence of uncertainty

Author

Listed:
  • Christopher O’Donnell
  • Robert Chambers
  • John Quiggin

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.
(This abstract was borrowed from another version of this item.)

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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11123-009-0143-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11123-009-0143-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    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(1), pages 1-19, 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.
    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.
    12. Howard Doran, 1993. "Testing Nonnested Models," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 75(1), pages 95-103.
    13. Rajiv D. Banker, 1993. "Maximum Likelihood, Consistency and Data Envelopment Analysis: A Statistical Foundation," Management Science, INFORMS, vol. 39(10), pages 1265-1273, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cherchye, L. & Post, G.T., 2001. "Methodological Advances in Dea," ERIM Report Series Research in Management ERS-2001-53-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. 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.
    3. Céline Nauges & Christopher J. O'Donnell & John Quiggin, 2011. "Uncertainty and technical efficiency in Finnish agriculture: a state-contingent approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 38(4), pages 449-467, October.
    4. Serra, Teresa & Zilberman, David & Gil, Jose Maria, 2008. "Farms' technical inefficiencies in the presence of government programs," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 52(1), pages 1-20.
    5. 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.
    6. Robert Chambers & Teresa Serra & Spiro Stefanou, 2015. "Using ex ante output elicitation to model state-contingent technologies," Journal of Productivity Analysis, Springer, vol. 43(1), pages 75-83, February.
    7. Bouali Guesmi & Teresa Serra & Allen Featherstone, 2015. "Technical efficiency of Kansas arable crop farms: a local maximum likelihood approach," Agricultural Economics, International Association of Agricultural Economists, vol. 46(6), pages 703-713, November.
    8. Sriram Shankar & John Quiggin, 2013. "Production under uncertainty: a simulation study," Journal of Productivity Analysis, Springer, vol. 39(3), pages 207-215, June.
    9. 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.
    10. Fleming, Euan M. & Farrell, Terence C. & Villano, Renato A. & Fleming, Pauline, 2006. "Is farm benchmarking the new acceptable face of comparative analysis?," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 14.
    11. Thierry Post & Laurens Cherchye & Timo Kuosmanen, 2002. "Nonparametric Efficiency Estimation In Stochastic Environments," Operations Research, INFORMS, vol. 50(4), pages 645-655, August.
    12. Orea, Luis, 2019. "The Econometric Measurement of Firms’ Efficiency," Efficiency Series Papers 2019/02, University of Oviedo, Department of Economics, Oviedo Efficiency Group (OEG).
    13. Serra, Teresa & Chambers, Robert G. & Oude Lansink, Alfons, 2014. "Measuring technical and environmental efficiency in a state-contingent technology," European Journal of Operational Research, Elsevier, vol. 236(2), pages 706-717.
    14. Taylan G. Topcu & Konstantinos Triantis, 2022. "An ex-ante DEA method for representing contextual uncertainties and stakeholder risk preferences," Annals of Operations Research, Springer, vol. 309(1), pages 395-423, February.
    15. Quiggin, John & Chambers, Robert G., 2005. "The state-contingent approach to production and uncertainty," Risk and Sustainable Management Group Working Papers 151168, University of Queensland, School of Economics.
    16. John Quiggin & Robert G. Chambers, 2006. "The state-contingent approach to production under uncertainty ," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 50(2), pages 153-169, June.
    17. Theodoros Skevas & Teresa Serra, 2016. "The role of pest pressure in technical and environmental inefficiency analysis of Dutch arable farms: an event-specific data envelopment approach," Journal of Productivity Analysis, Springer, vol. 46(2), pages 139-153, December.
    18. Hardaker, J. Brian & Lien, Gudbrand, 2010. "Probabilities for decision analysis in agriculture and rural resource economics: The need for a paradigm change," Agricultural Systems, Elsevier, vol. 103(6), pages 345-350, July.
    19. Jean-Paul Chavas, 2012. "On learning and the economics of firm efficiency: a state-contingent approach," Journal of Productivity Analysis, Springer, vol. 38(1), pages 53-62, August.
    20. Chien-Ming Chen & Magali A. Delmas, 2012. "Measuring Eco-Inefficiency: A New Frontier Approach," Operations Research, INFORMS, vol. 60(5), pages 1064-1079, October.

    More about this item

    Keywords

    Uncertainty; Efficiency measurement; Data envelopment analysis; Stochastic frontier analysis; C21; D21; D24; D81;
    All these keywords.

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:jproda:v:33:y:2010:i:1:p:1-17. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.