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Additive decomposition in two-stage DEA: An alternative approach

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  • Despotis, Dimitrs
  • Koronakos, Gregory
  • Sotiros, Dimitris

Abstract

Typically, a two-stage production process assumes that the first stage transforms external inputs to a number of intermediate measures, which then are used as inputs to the second stage that produces the final outputs. The three fundamental approaches to efficiency assessment in the context of DEA (two-stage DEA) are the simple (or independent), the multiplicative and the additive. The simple approach does not assume any relationship between the two stages and estimates the overall efficiency and the individual efficiencies for the two stages independently with typical DEA models. The other two approaches assume a series relationship between the two stages and differ in the way they conceptualize the decomposition of the overall efficiency to the efficiencies of the individual stages. This paper presents an alternative approach to additive efficiency decomposition in two-stage DEA. We show that when using the intermediate measures as pivot, it is possible to aggregate the efficiency assessment models of the two individual stages in a single linear program. We test our models with data sets taken from previous studies and we compare the results with those reported in the literature.

Suggested Citation

  • Despotis, Dimitrs & Koronakos, Gregory & Sotiros, Dimitris, 2012. "Additive decomposition in two-stage DEA: An alternative approach," MPRA Paper 41724, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:41724
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    References listed on IDEAS

    as
    1. 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.
    2. Kao, Chiang & Hwang, Shiuh-Nan, 2008. "Efficiency decomposition in two-stage data envelopment analysis: An application to non-life insurance companies in Taiwan," European Journal of Operational Research, Elsevier, vol. 185(1), pages 418-429, February.
    3. Lorenzo Castelli & Raffaele Pesenti & Walter Ukovich, 2010. "A classification of DEA models when the internal structure of the Decision Making Units is considered," Annals of Operations Research, Springer, vol. 173(1), pages 207-235, January.
    4. Chien Wang & Ram Gopal & Stanley Zionts, 1997. "Use of Data Envelopment Analysis in assessing Information Technology impact on firm performance," Annals of Operations Research, Springer, vol. 73(0), pages 191-213, October.
    5. Lawrence M. Seiford & Joe Zhu, 1999. "Profitability and Marketability of the Top 55 U.S. Commercial Banks," Management Science, INFORMS, vol. 45(9), pages 1270-1288, September.
    6. Chen, Yao & Cook, Wade D. & Zhu, Joe, 2010. "Deriving the DEA frontier for two-stage processes," European Journal of Operational Research, Elsevier, vol. 202(1), pages 138-142, April.
    7. Chen, Yao & Cook, Wade D. & Li, Ning & Zhu, Joe, 2009. "Additive efficiency decomposition in two-stage DEA," European Journal of Operational Research, Elsevier, vol. 196(3), pages 1170-1176, August.
    8. Kao, Chiang & Hwang, Shiuh-Nan, 2011. "Decomposition of technical and scale efficiencies in two-stage production systems," European Journal of Operational Research, Elsevier, vol. 211(3), pages 515-519, June.
    9. R. D. Banker & A. Charnes & W. W. Cooper, 1984. "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science, INFORMS, vol. 30(9), pages 1078-1092, September.
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    Cited by:

    1. Papahristodoulou, Christos, 2013. "Evaluating the performance of UEFA Champions League scorers," MPRA Paper 51288, University Library of Munich, Germany.

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    More about this item

    Keywords

    Data envelopment analysis (DEA); Efficiency; Decomposition; Two-stage DEA;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling

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