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A multistage method to measure efficiency and its application to Japanese banking industry

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  • Liu, Junming
  • Tone, Kaoru

Abstract

When measuring technical efficiency with existing data envelopment analysis (DEA) techniques, mean efficiency scores generally exhibit volatile patterns over time. This appears to be at odds with the general perception of learning-by-doing management, due to Arrow [The economic implications of learning by doing. Review of Economic Studies 1964; 154-73]. Further, this phenomenon is largely attributable to the fundamental assumption of deterministic data maintained in DEA models, and to the difficulty such models have in incorporating environmental influences. This paper proposes a three-stage method to measure DEA efficiency while controlling for the impacts of both statistical noise and environmental factors. Using panel data on Japanese banking over the period 1997-2001, we demonstrate that the proposed approach greatly mitigates these weaknesses of DEA models. We find a stable upward trend in mean measured efficiency, indicating that, on average, the bankers were learning over the sample period. Therefore, we conclude that this new method is a significant improvement relative to those DEA models currently used by researchers, corporate management, and industrial regulatory bodies to evaluate performance of their respective interests.

Suggested Citation

  • Liu, Junming & Tone, Kaoru, 2008. "A multistage method to measure efficiency and its application to Japanese banking industry," Socio-Economic Planning Sciences, Elsevier, vol. 42(2), pages 75-91, June.
  • Handle: RePEc:eee:soceps:v:42:y:2008:i:2:p:75-91
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    References listed on IDEAS

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    1. repec:gam:jsusta:v:9:y:2017:i:6:p:952-:d:100532 is not listed on IDEAS
    2. Galina Besstremyannaya & Jaak Simm & Sergei Golovan, 2017. "Robust estimation of cost efficiency in non-parametric frontier models," Working Papers w0244, Center for Economic and Financial Research (CEFIR).
    3. repec:eee:empfin:v:42:y:2017:i:c:p:66-89 is not listed on IDEAS
    4. Joseph Paradi & Sandra Vela & Haiyan Zhu, 2010. "Adjusting for cultural differences, a new DEA model applied to a merged bank," Journal of Productivity Analysis, Springer, vol. 33(2), pages 109-123, April.
    5. repec:eee:trapol:v:60:y:2017:i:c:p:75-86 is not listed on IDEAS
    6. Chen, Po-Chi & Yu, Ming-Miin & Chang, Ching-Cheng & Managi, Shunsuke, 2014. "Non-Radial Directional Performance Measurement with Undesirable Outputs," MPRA Paper 57189, University Library of Munich, Germany.
    7. Hirofumi Fukuyama & William Weber, 2015. "Measuring Japanese bank performance: a dynamic network DEA approach," Journal of Productivity Analysis, Springer, vol. 44(3), pages 249-264, December.
    8. Wen-Jen Tsay & Cliff J. Huang & Tsu-Tan Fu & I-Lin Ho, 2009. "Maximum Likelihood Estimation of Censored Stochastic Frontier Models: An Application to the Three-Stage DEA Method," IEAS Working Paper : academic research 09-A003, Institute of Economics, Academia Sinica, Taipei, Taiwan.
    9. Huang, Wei & Eling, Martin, 2013. "An efficiency comparison of the non-life insurance industry in the BRIC countries," European Journal of Operational Research, Elsevier, vol. 226(3), pages 577-591.
    10. Mamatzakis, Emmanuel & Matousek, Roman & Vu, Anh Nguyet, 2016. "What is the impact of bankrupt and restructured loans on Japanese bank efficiency?," Journal of Banking & Finance, Elsevier, vol. 72(S), pages 187-202.
    11. Galina Besstremyannaya & Jaak Simm, 2015. "Robust non-parametric estimation of cost efficiency with an application to banking industry," Working Papers w0217, Center for Economic and Financial Research (CEFIR).
    12. Zhou, Yan & Xing, Xinpeng & Fang, Kuangnan & Liang, Dapeng & Xu, Chunlin, 2013. "Environmental efficiency analysis of power industry in China based on an entropy SBM model," Energy Policy, Elsevier, vol. 57(C), pages 68-75.
    13. Jitsuzumi, Toshiya & Nakamura, Akihiro, 2010. "Causes of inefficiency in Japanese railways: Application of DEA for managers and policymakers," Socio-Economic Planning Sciences, Elsevier, vol. 44(3), pages 161-173, September.
    14. Juo, Jia-Ching & Fu, Tsu-Tan & Yu, Ming-Miin & Lin, Yu-Hui, 2016. "Non-radial profit performance: An application to Taiwanese banks," Omega, Elsevier, vol. 65(C), pages 111-121.
    15. Fukuyama, Hirofumi & Matousek, Roman, 2017. "Modelling bank performance: A network DEA approach," European Journal of Operational Research, Elsevier, vol. 259(2), pages 721-732.
    16. Schaper, Philipp, 2017. "Under pressure: how the business environment affects productivity and efficiency of European life insurance companiesAuthor-Name: Eling, Martin," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1082-1094.

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