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Experimental evidence on robustness of data envelopment analysis

Author

Listed:
  • D U A Galagedera

    (Monash University)

  • P Silvapulle

    (Monash University)

Abstract

There is an on-going debate about variable selection in data envelopment analysis (DEA) as there are no diagnostic checks for model misspecification. This paper contributes to this debate by investigating the sensitivity of DEA efficiency estimates to including inappropriate and/or omitting several important variables in a large-sample DEA model. Data are simulated from constant, increasing and decreasing returns-to-scale (RS) Cobb–Douglas production processes. For constant and decreasing RS processes with irrelevant inputs, DEA tends to overestimate efficiency in almost all production units. When relevant variables are omitted, variable RS appears to be a safer option. The correct RS specification is vital when the DEA model includes irrelevant variables. The effect of omission of relevant inputs on individual production unit efficiency is more adverse compared to the inclusion of irrelevant ones.

Suggested Citation

  • D U A Galagedera & P Silvapulle, 2003. "Experimental evidence on robustness of data envelopment analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 654-660, June.
    • Handle: RePEc:pal:jorsoc:v:54:y:2003:i:6:d:10.1057_palgrave.jors.2601507
    DOI: 10.1057/palgrave.jors.2601507
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    References listed on IDEAS

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    Cited by:

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    6. Adler, Nicole & Yazhemsky, Ekaterina, 2010. "Improving discrimination in data envelopment analysis: PCA-DEA or variable reduction," European Journal of Operational Research, Elsevier, vol. 202(1), pages 273-284, April.
    7. Adeabah, David & Gyeke-Dako, Agyapomaa & Andoh, Charles, 2018. "Board gender diversity, corporate governance and bank efficiency in Ghana: a two-stage data envelope analysis (DEA) approach," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 19(2), pages 299-320.
    8. Cordero, José Manuel & Santín, Daniel & Sicilia, Gabriela, 2013. "Dealing with the Endogeneity Problem in Data Envelopment Analysis," MPRA Paper 47475, University Library of Munich, Germany.
    9. N. Avkiran, 2010. "Sensitivity analysis of network DEA illustrated in branch banking," CEPA Working Papers Series WP122010, School of Economics, University of Queensland, Australia.

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