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A slacks-based inefficiency measure for a two-stage system with bad outputs

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  • Fukuyama, Hirofumi
  • Weber, William L.
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    Abstract

    We model the performance of DMUs (decision-making units) using a two-stage network model. In the first stage of production DMUs use inputs to produce an intermediate output that becomes an input to a second stage where final outputs are produced. Previous black box DEA models allowed for non-radial scaling of outputs and inputs and accounted for slacks in the constraints that define the technology. We extend these models and build a performance measure that accounts for a network structure of production. We use our method to estimate the performance of Japanese banks, which use labor, physical capital, and financial equity capital in a first stage to produce an intermediate output of deposits. In the second stage, those deposits become an input in the production of loans and securities investments. The network estimates reveal greater bank inefficiency than do the estimates that treat the bank production process as a black box with all production taking place in a single stage.

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    Bibliographic Info

    Article provided by Elsevier in its journal Omega.

    Volume (Year): 38 (2010)
    Issue (Month): 5 (October)
    Pages: 398-409

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    Handle: RePEc:eee:jomega:v:38:y:2010:i:5:p:398-409

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    Keywords: Two-stage DEA Slacks-based inefficiency Japanese banking Bad outputs;

    References

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    Cited by:
    1. Akther, Syed & Fukuyama, Hirofumi & Weber, William L., 2013. "Estimating two-stage network Slacks-based inefficiency: An application to Bangladesh banking," Omega, Elsevier, vol. 41(1), pages 88-96.
    2. Cook, Wade D. & Liang, Liang & Zhu, Joe, 2010. "Measuring performance of two-stage network structures by DEA: A review and future perspective," Omega, Elsevier, vol. 38(6), pages 423-430, December.
    3. AGRELL, Per & HATAMI-MARBINI, Adel, 2011. "Frontier-based performance analysis models for supply chain management; state of the art and research directions," CORE Discussion Papers 2011069, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Mircea Epure & Esteban Lafuente, 2012. "Monitoring bank performance in the presence of risk," Economics Working Papers 1310, Department of Economics and Business, Universitat Pompeu Fabra, revised Mar 2014.
    5. Zhang, Ning & Kong, Fanbin & Choi, Yongrok & Zhou, P., 2014. "The effect of size-control policy on unified energy and carbon efficiency for Chinese fossil fuel power plants," Energy Policy, Elsevier, vol. 70(C), pages 193-200.
    6. Wang, Ke & Huang, Wei & Wu, Jie & Liu, Ying-Nan, 2014. "Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA," Omega, Elsevier, vol. 44(C), pages 5-20.
    7. Barros, Carlos Pestana & Wanke, Peter, 2014. "Banking efficiency in Brazil," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 28(C), pages 54-65.
    8. Amado, Carla A.F. & Santos, Sérgio P. & Marques, Pedro M., 2012. "Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment," Omega, Elsevier, vol. 40(3), pages 390-403.
    9. Paradi, Joseph C. & Zhu, Haiyan, 2013. "A survey on bank branch efficiency and performance research with data envelopment analysis," Omega, Elsevier, vol. 41(1), pages 61-79.
    10. Sahoo, Biresh K. & Tone, Kaoru, 2013. "Non-parametric measurement of economies of scale and scope in non-competitive environment with price uncertainty," Omega, Elsevier, vol. 41(1), pages 97-111.
    11. Fukuyama, Hirofumi & Mirdehghan, S.M., 2012. "Identifying the efficiency status in network DEA," European Journal of Operational Research, Elsevier, vol. 220(1), pages 85-92.
    12. Wang, Chun-Hsien & Lu, Yung-Hsiang & Huang, Chin-Wei & Lee, Jun-Yen, 2013. "R&D, productivity, and market value: An empirical study from high-technology firms," Omega, Elsevier, vol. 41(1), pages 143-155.
    13. Hirofumi Fukuyama & Yuichiro Yoshida & Shunsuke Managi, 2011. "Modal choice between air and rail: a social efficiency benchmarking analysis that considers CO 2 emissions," Environmental Economics and Policy Studies, Society for Environmental Economics and Policy Studies - SEEPS, vol. 13(2), pages 89-102, June.
    14. Hall, Maximilian J.B. & Liu, Wenbin B. & Simper, Richard & Zhou, Zhongbao, 2013. "The economic efficiency of rehabilitative management in young offender institutions in England and Wales," Socio-Economic Planning Sciences, Elsevier, vol. 47(1), pages 38-49.
    15. Paradi, Joseph C. & Rouatt, Stephen & Zhu, Haiyan, 2011. "Two-stage evaluation of bank branch efficiency using data envelopment analysis," Omega, Elsevier, vol. 39(1), pages 99-109, January.
    16. Zhou, P. & Ang, B.W. & Wang, H., 2012. "Energy and CO2 emission performance in electricity generation: A non-radial directional distance function approach," European Journal of Operational Research, Elsevier, vol. 221(3), pages 625-635.
    17. Fukuyama, Hirofumi & Matousek, Roman, 2011. "Efficiency of Turkish banking: Two-stage network system. Variable returns to scale model," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(1), pages 75-91, February.
    18. Barros, Carlos Pestana & Managi, Shunsuke & Matousek, Roman, 2012. "The technical efficiency of the Japanese banks: Non-radial directional performance measurement with undesirable output," Omega, Elsevier, vol. 40(1), pages 1-8, January.
    19. Kao, Chiang, 2014. "Efficiency decomposition in network data envelopment analysis with slacks-based measures," Omega, Elsevier, vol. 45(C), pages 1-6.

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