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Multiple Comparisons With The Best: Bayesian Precision Measures Of Efficiency Rankings

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  • Dorfman, Jeffrey H.
  • Atkinson, Scott E.
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    Abstract

    A large literature exists on measuring the allocative and technical efficiency of a set of firms. A segment of this literature uses data envelopment analysis (DEA), creating relative efficiency rankings that are nonstochastic and thus cannot be evaluated according to the precision of the rankings. A parallel literature uses econometric techniques to estimate stochastic production frontiers or distance functions, providing at least the possibility of computing the precision of the resulting efficiency rankings. Recently, Horrace and Schmidt (2000) have applied sampling theoretic statistical techniques known as multiple comparisons with control (MCC) and multiple comparisons with the best (MCB) to the issue of measuring the precision of efficiency rankings. This paper offers a Bayesian multiple comparison alternative that we argue is simpler to implement, gives the researcher increased exibility over the type of comparison made, and provides greater, and more in-tuitive, information content. We demonstrate this method on technical efficiency rankings of a set of U.S. electric generating firms derived within a distance function framework.

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    File URL: http://purl.umn.edu/19800
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    Bibliographic Info

    Paper provided by American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association) in its series 2002 Annual meeting, July 28-31, Long Beach, CA with number 19800.

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    Date of creation: 2002
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    Handle: RePEc:ags:aaea02:19800

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    Keywords: Research Methods/ Statistical Methods;

    References

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    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-54, July.
    2. Barbera, Anthony J. & McConnell, Virginia D., 1990. "The impact of environmental regulations on industry productivity: Direct and indirect effects," Journal of Environmental Economics and Management, Elsevier, vol. 18(1), pages 50-65, January.
    3. Chambers, Robert G. & Chung, Yangho & Fare, Rolf, 1996. "Benefit and Distance Functions," Journal of Economic Theory, Elsevier, vol. 70(2), pages 407-419, August.
    4. Atkinson, Scott E & Cornwell, Christopher & Honerkamp, Olaf, 2003. "Measuring and Decomposing Productivity Change: Stochastic Distance Function Estimation versus Data Envelopment Analysis," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(2), pages 284-94, April.
    5. Koop, Gary, 2001. "Cross-Sectoral Patterns of Efficiency and Technical Change in Manufacturing," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 42(1), pages 73-103, February.
    6. Pittman, Russell W, 1983. "Multilateral Productivity Comparisons with Undesirable Outputs," Economic Journal, Royal Economic Society, vol. 93(372), pages 883-91, December.
    7. Cornwell, Christopher & Schmidt, Peter & Sickles, Robin C., 1990. "Production frontiers with cross-sectional and time-series variation in efficiency levels," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 185-200.
    8. Lastrapes, W.D., 2000. "Measuring and Decomposing Productivity Change: Stochastic Distance Function Estimation VS. DEA," Papers 99-478, Georgia - College of Business Administration, Department of Economics.
    9. Zellner, Arnold, 1998. "The finite sample properties of simultaneous equations' estimates and estimators Bayesian and non-Bayesian approaches," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 185-212.
    10. Nelson, Randy A, 1984. "Regulation, Capital Vintage, and Technical Change in the Electric Utility Industry," The Review of Economics and Statistics, MIT Press, vol. 66(1), pages 59-69, February.
    11. William C. Horrace & Peter Schmidt, 2000. "Multiple comparisons with the best, with economic applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(1), pages 1-26.
    12. Fare, Rolf, et al, 1989. "Multilateral Productivity Comparisons When Some Outputs Are Undesirable: A Nonparametric Approach," The Review of Economics and Statistics, MIT Press, vol. 71(1), pages 90-98, February.
    13. Ellerman,A. Denny & Joskow,Paul L. & Schmalensee,Richard & Montero,Juan-Pablo & Bailey,Elizabeth M., 2000. "Markets for Clean Air," Cambridge Books, Cambridge University Press, number 9780521660839, October.
      • Ellerman,A. Denny & Joskow,Paul L. & Schmalensee,Richard & Montero,Juan-Pablo & Bailey,Elizabeth M., 2005. "Markets for Clean Air," Cambridge Books, Cambridge University Press, number 9780521023894, October.
    14. Gollop, Frank M & Roberts, Mark J, 1983. "Environmental Regulations and Productivity Growth: The Case of Fossil-Fueled Electric Power Generation," Journal of Political Economy, University of Chicago Press, vol. 91(4), pages 654-74, August.
    15. Baltagi, Badi H & Griffin, James M, 1988. "A General Index of Technical Change," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 20-41, February.
    16. Gary Koop, 1998. "Carbon dioxide emissions and economic growth: A structural approach," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(4), pages 489-515.
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