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Combining two approaches to efficiency assessment

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  • C Tofallis

    (University of Hertfordshire Business School)

Abstract

The advent of data envelopment analysis (DEA) enabled the measurement of efficiency to be extended to the case of multiple outputs. Prior to DEA we had the parametric approach based on multiple regression. We highlight some difficulties associated with these two approaches and present a hybrid which overcomes them whilst maintaining the respective advantages of each. This hybrid models the efficient frontier using an algebraic expression; the resulting smooth representation allows all units to be naturally enveloped and hence slacks to be avoided. (Slacks are potential improvements for inefficient units which are not accounted for in the DEA (radial) score, and so have been problematic for DEA.) The approach identifies the DEA-efficient units and fits a smooth model to them using maximum correlation modelling. This new technique extends the method of multiple regression to the case where there are multiple variables on each side of the model equation (eg outputs and inputs). The resulting expression for the frontier permits managers to estimate the effect on their efficiency score of adjustments in one or more input or output levels.

Suggested Citation

  • C Tofallis, 2001. "Combining two approaches to efficiency assessment," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 52(11), pages 1225-1231, November.
    • Handle: RePEc:pal:jorsoc:v:52:y:2001:i:11:d:10.1057_palgrave.jors.2601231
    DOI: 10.1057/palgrave.jors.2601231
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    Citations

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    Cited by:

    1. Kaab, Ali & Sharifi, Mohammad & Mobli, Hossein & Nabavi-Pelesaraei, Ashkan & Chau, Kwok-wing, 2019. "Use of optimization techniques for energy use efficiency and environmental life cycle assessment modification in sugarcane production," Energy, Elsevier, vol. 181(C), pages 1298-1320.
    2. K S Park & K W Lee & M S Park & D Kim, 2009. "Joint use of DEA and constrained canonical correlation analysis for efficiency valuations involving categorical variables," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1775-1785, December.
    3. J M Cordero-Ferrera & F Pedraja-Chaparro & D Santín-González, 2010. "Enhancing the inclusion of non-discretionary inputs in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 61(4), pages 574-584, April.
    4. Fabio Pammolli & Nicola Carmine Salerno, 2011. "Le differenze regionali nella governance della spesa sanitaria - SaniRegio-2011," Working Papers CERM 02-2011, Competitività, Regole, Mercati (CERM).
    5. José Cordero Ferrera & Eva Cebada & Luis Murillo Zamorano, 2014. "The effect of quality and socio-demographic variables on efficiency measures in primary health care," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(3), pages 289-302, April.
    6. Antonio García-Romero & Daniel Santín & Gabriela Sicilia, 2016. "Another brick in the wall: a new ranking of academic journals in Economics using FDH," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 91-101, April.

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