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A Bootstrap-Regression Procedure to Capture Unit Specific Effects In Data Envelopment Analysis

Author

Listed:
  • Evangelia Desli

    (Lloydí°«s of London, London EC3M 7HA, UK.)

  • Subhash C. Ray

    (University of Connecticut, Storrs CT 06269-1063, USA.)

Abstract

The Data Envelopment Analysis (DEA) efficiency score obtained for an individual firm is a point estimate without any confidence interval around it. In recent years, researchers have resorted to bootstrapping in order to generate empirical distributions of efficiency scores. This procedure assumes that all firms have the same probability of getting an efficiency score from any specified interval within the [0,1] range. We propose a bootstrap procedure that empirically generates the conditional distribution of efficiency for each individual firm given systematic factors that influence its efficiency. Instead of resampling directly from the pooled DEA scores, we first regress these scores on a set of explanatory variables not included at the DEA stage and bootstrap the residuals from this regression. These pseudo-efficiency scores incorporate the systematic effects of unit-specific factors along with the contribution of the randomly drawn residual. Data from the U.S. airline industry are utilized in an empirical application.

Suggested Citation

  • Evangelia Desli & Subhash C. Ray, 2004. "A Bootstrap-Regression Procedure to Capture Unit Specific Effects In Data Envelopment Analysis," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 39(1), pages 89-110, January.
    • Handle: RePEc:dse:indecr:v:39:y:2004:i:1:p:89-110
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    Cited by:

    1. R. Amy Puenpatom & Robert Rosenman, 2006. "Efficiency of Thai provincial public hospitals after the introduction of National Health Insurance Program," Working Papers 2006-2, School of Economic Sciences, Washington State University.
    2. Greer, Mark, 2009. "Is it the labor unions' fault? Dissecting the causes of the impaired technical efficiencies of the legacy carriers in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(9-10), pages 779-789, November.
    3. Sengupta, Atanu & Kundu, Subrata, 2006. "Scale Efficiency of Indian Farmers: A Non- Parametric Approach," Indian Journal of Agricultural Economics, Indian Society of Agricultural Economics, vol. 61(4), pages 1-11.
    4. Petridis, Konstantinos & Malesios, Chrisovalantis & Arabatzis, Garyfallos & Thanassoulis, Emmanuel, 2013. "Efficiency analysis of forestry journals: Suggestions for improving journals’ quality," Journal of Informetrics, Elsevier, vol. 7(2), pages 505-521.
    5. Greer, Mark R., 2008. "Nothing focuses the mind on productivity quite like the fear of liquidation: Changes in airline productivity in the United States, 2000-2004," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(2), pages 414-426, February.
    6. Mainak Mazumdar & Meenakshi Rajeev & Subhash C. Ray, 2012. "Sources of Heterogeneity in the Efficiency of Indian Pharmaceutical Firms," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 47(2), pages 191-221.
    7. Rajitkanok Puenpatom & Robert Rosenman, 2008. "Efficiency of Thai provincial public hospitals during the introduction of universal health coverage using capitation," Health Care Management Science, Springer, vol. 11(4), pages 319-338, December.

    More about this item

    Keywords

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    JEL classification:

    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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