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Event-specific data envelopment models and efficiency analysis

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  • Chambers, Robert G.
  • Hailu, Atakelty
  • Quiggin, John

Abstract

Most, if not all, production technologies are stochastic. This article demonstrates how data envelopment analysis (DEA) methods can be adapted to accommodate stochastic elements in a state-contingent setting. Specifically, we show how observations on a random input, not under the control of the producer and not known at the time that variable input decisions are made, can be used to partition the state space in a fashion that permits DEA models to approximate an event-specific production technology. The approach proposed in this article uses observed data on random inputs and is easy to implement. After developing the event-specific DEA representation, we apply it to a data set for Western Australian barley production data. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.

Suggested Citation

  • Chambers, Robert G. & Hailu, Atakelty & Quiggin, John, 2011. "Event-specific data envelopment models and efficiency analysis," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 55(1), pages 1-17.
    • Handle: RePEc:ags:aareaj:176734
    DOI: 10.22004/ag.econ.176734
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    1. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    2. John Quiggin & David Adamson & Sarah Chambers & Peggy Schrobback, 2010. "Climate Change, Uncertainty, and Adaptation: The Case of Irrigated Agriculture in the Murray–Darling Basin in Australia," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 58(4), pages 531-554, December.
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    7. Fraser, Iain & Graham, Mary, 2005. "Efficiency Measurement of Australian Dairy Farms: National and Regional Performance," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 13.
    8. Robert G. Chambers & Erik Lichtenberg, 1996. "A Nonparametric Approach to the von Liebig-Paris Technology," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(2), pages 373-386.
    9. Chambers, Christopher P. & Miller, Alan D., "undated". "Inefficiency," Working Papers WP2011/14, University of Haifa, Department of Economics, revised 30 Nov 2011.
    10. Henderson, Benjamin B. & Kingwell, Ross S., 2005. "Rainfall and Farm Efficiency Measurement for Broadacre Agriculture in South-Western Australia," Australasian Agribusiness Review, University of Melbourne, Department of Agriculture and Food Systems, vol. 13.
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    Cited by:

    1. 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.
    2. Kapelko, Magdalena & Oude Lansink, Alfons & Stefanou, Spiro E., 2014. "Assessing dynamic inefficiency of the Spanish construction sector pre- and post-financial crisis," European Journal of Operational Research, Elsevier, vol. 237(1), pages 349-357.
    3. 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.
    4. Boussemart, Jean-Philippe & Crainich, David & Leleu, Hervé, 2015. "A decomposition of profit loss under output price uncertainty," European Journal of Operational Research, Elsevier, vol. 243(3), pages 1016-1027.
    5. Theodoros Skevas & Teresa Serra, 2017. "Derivation of netput shadow prices under different levels of pest pressure," Journal of Productivity Analysis, Springer, vol. 48(1), pages 25-34, August.
    6. Skevas, Theodoros & Stefanou, Spiro E. & Oude Lansink, Alfons, 2014. "Pesticide use, environmental spillovers and efficiency: A DEA risk-adjusted efficiency approach applied to Dutch arable farming," European Journal of Operational Research, Elsevier, vol. 237(2), pages 658-664.
    7. Peggy Schrobback & Sean Pascoe & Louisa Coglan, 2014. "Shape Up or Ship Out: Can We Enhance Productivity in Coastal Aquaculture to Compete with Other Uses?," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-25, December.
    8. 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.
    9. Kevin Schneider & Ioannis Skevas & Alfons Oude Lansink, 2021. "Spatial Spillovers on Input‐specific Inefficiency of Dutch Arable Farms," Journal of Agricultural Economics, Wiley Blackwell, vol. 72(1), pages 224-243, February.
    10. Skevas, Theodoros & Lansink, Alfons Oude & Stefanou, Spiro E., 2012. "Measuring technical efficiency in the presence of pesticide spillovers and production uncertainty: The case of Dutch arable farms," European Journal of Operational Research, Elsevier, vol. 223(2), pages 550-559.
    11. Amer Ait Sidhoum, 2023. "Assessing the contribution of farmers’ working conditions to productive efficiency in the presence of uncertainty, a nonparametric approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(8), pages 8601-8622, August.

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