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A quantile-based approach for relative efficiency measurement

Author

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  • Paul M. Griffin

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA)

  • Paul H. Kvam

    (School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, USA)

Abstract

Two popular approaches for efficiency measurement are a non-stochastic approach called data envelopment analysis (DEA) and a parametric approach called stochastic frontier analysis (SFA). Both approaches have modeling difficulty, particularly for ranking firm efficiencies. In this paper, a new parametric approach using quantile statistics is developed. The quantile statistic relies less on the stochastic model than SFA methods, and accounts for a firm's relationship to the other firms in the study by acknowledging the firm's influence on the empirical model, and its relationship, in terms of similarity of input levels, to the other firms. Copyright © 1999 John Wiley & Sons, Ltd.

Suggested Citation

  • Paul M. Griffin & Paul H. Kvam, 1999. "A quantile-based approach for relative efficiency measurement," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 403-410.
    • Handle: RePEc:wly:mgtdec:v:20:y:1999:i:8:p:403-410
    DOI: 10.1002/1099-1468(199912)20:8<403::AID-MDE956>3.0.CO;2-E
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    References listed on IDEAS

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    1. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    2. Fried, Harold O. & Lovell, C. A. Knox & Schmidt, Shelton S. (ed.), 1993. "The Measurement of Productive Efficiency: Techniques and Applications," OUP Catalogue, Oxford University Press, number 9780195072181.
    3. 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.
    4. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    1. Kejia Hu & Sunil Chopra & Yuche Chen, 2021. "The Effect of Tightening Standards on Automakers’ Non‐compliance," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3094-3115, September.

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