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Multiple Comparisons with the Best: Bayesian Precision Measures of Efficiency Rankings

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  • Scott Atkinson
  • Jeffrey Dorfman

Abstract

A large literature measures the allocative and technical efficiency of a set of firms using econometric techniques to estimate stochastic production frontiers or distance functions. Typically, researchers compute only the precision of individual efficiency rankings. Recently, Horrace and Schmidt (Journal of Applied Economics 15, 1–26, 2000) have applied sampling theoretic statistical techniques known as multiple comparisons with a control (MCC) and multiple comparisons with the best (MCB) to make statistical comparisons of efficiency rankings. As an alternative, this paper offers a Bayesian multiple comparison procedure that we argue is simpler to implement, gives the researcher increased flexibility over the type of comparison, and provides greater, and more intuitive, information content. For these methods and a parametric bootstrap technique, we carry out multiple comparisons of technical efficiency rankings for a set of U.S. electric generating firms, estimated using a distance function framework. We find that the Bayesian method provides substantially more precise inferences than obtained using the MCB and MCC methods. Copyright Springer Science+Business Media, Inc. 2005

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  • Scott Atkinson & Jeffrey Dorfman, 2005. "Multiple Comparisons with the Best: Bayesian Precision Measures of Efficiency Rankings," Journal of Productivity Analysis, Springer, vol. 23(3), pages 359-382, July.
    • Handle: RePEc:kap:jproda:v:23:y:2005:i:3:p:359-382
    DOI: 10.1007/s11123-005-2215-9
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    Cited by:

    1. Oum, Tae H. & Yan, Jia & Yu, Chunyan, 2008. "Ownership forms matter for airport efficiency: A stochastic frontier investigation of worldwide airports," Journal of Urban Economics, Elsevier, vol. 64(2), pages 422-435, September.
    2. Gary Koop & Lise Tole, 2008. "What is the environmental performance of firms overseas? An empirical investigation of the global gold mining industry," Journal of Productivity Analysis, Springer, vol. 30(2), pages 129-143, October.
    3. Griffiths, William E. & O'Donnell, Christopher J., 2005. "Estimating variable returns to scale production frontiers with alternative stochastic assumptions," Journal of Econometrics, Elsevier, vol. 126(2), pages 385-409, June.
    4. Alfonso Flores-Lagunes & William C. Horrace & Kurt E. Schnier, 2007. "Identifying technically efficient fishing vessels: a non-empty, minimal subset approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(4), pages 729-745.
    5. Bruce Hollingsworth & Andrew Street, 2006. "The market for efficiency analysis of health care organisations," Health Economics, John Wiley & Sons, Ltd., vol. 15(10), pages 1055-1059, October.

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