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Performance evaluation using bootstrapping DEA techniques: Evidence from industry ratio analysis

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  • Halkos, George
  • Tzeremes, Nickolaos

Abstract

In Data Envelopment Analysis (DEA) context financial data/ ratios have been used in order to produce a unified measure of performance metric. However, several scholars have indicated that the inclusion of financial ratios create biased efficiency estimates with implications on firms’ and industries’ performance evaluation. There have been several DEA formulations and techniques dealing with this problem including sensitivity analysis, Prior-Ratio-Analysis and DEA/ output–input ratio analysis for the assessment of the efficiency and ranking of the examined units. In addition to these computational approaches this paper in order to overcome these problems applies bootstrap techniques. Moreover it provides an application evaluating the performance of 23 Greek manufacturing sectors with the use of financial data. The results reveal that in the first stage of our sensitivity analysis the efficiencies obtained are biased. However, after applying the bootstrap techniques the sensitivity analysis reveals that the efficiency scores have been significantly improved.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 25072.

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Date of creation: 2010
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Handle: RePEc:pra:mprapa:25072

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Keywords: Performance measurement; Data Envelopment Analysis; Financial ratios; Bootstrap; Bias correction;

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  1. Simar, L. & Wilson, P.W., . "Sensitivity analysis of efficiency scores: how to bootstrap in nonparametric frontier models," CORE Discussion Papers RP -1304, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Leopold Simar & Paul Wilson, 2000. "A general methodology for bootstrapping in non-parametric frontier models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 27(6), pages 779-802.
  3. Valdmanis, Vivian, 1992. "Sensitivity analysis for DEA models : An empirical example using public vs. NFP hospitals," Journal of Public Economics, Elsevier, vol. 48(2), pages 185-205, July.
  4. B. Hollingsworth & P. Smith, 2003. "Use of ratios in data envelopment analysis," Applied Economics Letters, Taylor & Francis Journals, vol. 10(11), pages 733-735.
  5. Thanassoulis, E. & Boussofiane, A. & Dyson, R. G., 1996. "A comparison of data envelopment analysis and ratio analysis as tools for performance assessment," Omega, Elsevier, vol. 24(3), pages 229-244, June.
  6. Simar, L. & Wilson, P.W., 1999. "Statistical Inference in Nonparametric Frontier Models: the State of the Art," Papers 9904, Catholique de Louvain - Institut de statistique.
  7. Dyson, R. G. & Allen, R. & Camanho, A. S. & Podinovski, V. V. & Sarrico, C. S. & Shale, E. A., 2001. "Pitfalls and protocols in DEA," European Journal of Operational Research, Elsevier, vol. 132(2), pages 245-259, July.
  8. Grosskopf, S. & Valdmanis, V., 1987. "Measuring hospital performance : A non-parametric approach," Journal of Health Economics, Elsevier, vol. 6(2), pages 89-107, June.
  9. 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.
  10. George Emmanuel Halkos & Nickolaos Tzeremes, 2010. "Measuring regional economic efficiency: the case of Greek prefectures," The Annals of Regional Science, Springer, vol. 45(3), pages 603-632, December.
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Cited by:
  1. Halkos, George & Tzeremes, Nickolaos, 2011. "A nonparametric analysis of the Greek renewable energy sector," MPRA Paper 30467, University Library of Munich, Germany.

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