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Nonparametric Technical Efficiency With K Firms, N Inputs, And M Outputs: A Simulation

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  • Tauer, Loren W.
  • Hanchar, John J.

Abstract

Monte-Carlo simulation of nonparametric efficiency shows that even when the number of firms is large, defining ten or more inputs results in most firms being measured as efficient. Comparison of the simulated results with any empirical results may suggest that the dimension of the problem, rather than actual efficiencies, determines computed efficiencies.

Suggested Citation

  • Tauer, Loren W. & Hanchar, John J., 1995. "Nonparametric Technical Efficiency With K Firms, N Inputs, And M Outputs: A Simulation," Agricultural and Resource Economics Review, Northeastern Agricultural and Resource Economics Association, vol. 24(2), pages 1-5, October.
    • Handle: RePEc:ags:arerjl:31590
    DOI: 10.22004/ag.econ.31590
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    References listed on IDEAS

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    1. Thompson, Russell G. & Langemeier, Larry N. & Lee, Chih-Tah & Lee, Euntaik & Thrall, Robert M., 1990. "The role of multiplier bounds in efficiency analysis with application to Kansas farming," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 93-108.
    2. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    3. Button, Kenneth J & Weyman-Jones, Thomas G, 1992. "Ownership Structure, Institutional Organization and Measured X-Efficiency," American Economic Review, American Economic Association, vol. 82(2), pages 439-445, May.
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    Cited by:

    1. Avkiran, Necmi K., 2007. "Stability and integrity tests in data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 41(3), pages 224-234, September.
    2. Hughes, Andrew & Yaisawarng, Suthathip, 2004. "Sensitivity and dimensionality tests of DEA efficiency scores," European Journal of Operational Research, Elsevier, vol. 154(2), pages 410-422, April.
    3. Wu, Shunxiang & Prato, Anthony A., 2006. "Cost Efficiency and Scope Economies of Crop and Livestock Farms in Missouri," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 38(3), pages 1-15, December.
    4. Byma, Justin P. & Tauer, Loren W., 2007. "Farm Inefficiency Resulting from the Missing Management Input," Working Papers 127008, Cornell University, Department of Applied Economics and Management.
    5. Holland, D. S. & Lee, S. T., 2002. "Impacts of random noise and specification on estimates of capacity derived from data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 137(1), pages 10-21, February.
    6. Edward Jaenicke, 2000. "Testing for Intermediate Outputs in Dynamic DEA Models: Accounting for Soil Capital in Rotational Crop Production and Productivity Measures," Journal of Productivity Analysis, Springer, vol. 14(3), pages 247-266, November.
    7. Uvarovsky, Vladimir & Voigt, Peter, 2000. "Russia'S Agriculture: Eight Years In Transition - Convergence Or Divergence Of Regional Efficiency," IAMO Discussion Papers 14891, Institute of Agricultural Development in Transition Economies (IAMO).
    8. Jaenicke, Edward C. & Drinkwater, Laurie E., 1999. "Sources of Productivity Growth During the Transition to Alternative Cropping Systems," Agricultural and Resource Economics Review, Cambridge University Press, vol. 28(2), pages 169-181, October.
    9. Shunxiang Wu & Stephen Devadoss & Yaochi Lu, 2003. "Estimation and decomposition of technical efficiency for sugarbeet farms," Applied Economics, Taylor & Francis Journals, vol. 35(4), pages 471-484.
    10. Andreu, Monica Lopez & Featherstone, Allen M. & Langemeier, Michael R. & Grunewald, Orlen C., 2006. "Impact of Financial Variables on Production in Kansas Farms Efficiencies," 2006 Annual meeting, July 23-26, Long Beach, CA 21406, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).

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