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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 wheat farmers. Our results highlight the need for acknowledging stochastic elements in efficiency analysis.

Suggested Citation

  • Chambers, Robert G. & Hailu, Atakelty & Quiggin, John, 2005. "Event-specific Data Envelopment Models and Efficiency Analysis," Risk and Sustainable Management Group Working Papers 151185, University of Queensland, School of Economics.
    • Handle: RePEc:ags:uqsers:151185
    DOI: 10.22004/ag.econ.151185
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    8. 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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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.

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