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A Network DEA Model with Super Efficiency and Undesirable Outputs: An Application to Bank Efficiency in China

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  • Jianhuan Huang
  • Juanjuan Chen
  • Zhujia Yin

Abstract

There are two typical subprocesses in bank production—deposit generation and loan generation. Aiming to open the black box of input-output production of banks and provide comprehensive and accurate assessment on the efficiency of each stage, this paper proposes a two-stage network model with bad outputs and supper efficiency (US-NSBM). Empirical comparisons show that the US-NSBM may be promising and practical for taking the nonperforming loans into account and being able to rank all samples. Applying it to measure the efficiency of Chinese commercial banks from 2008 to 2012, this paper explores the characteristics of overall and divisional efficiency, as well as the determinants of them. Some interesting results are discovered. The polarization of efficiency occurs in the bank level and deposit generation, yet does not in the loan generation. Five hypotheses work as expected in the bank level, but not all of them are supported in the stage level. Our results extend and complement some earlier empirical publications in the bank level.

Suggested Citation

  • Jianhuan Huang & Juanjuan Chen & Zhujia Yin, 2014. "A Network DEA Model with Super Efficiency and Undesirable Outputs: An Application to Bank Efficiency in China," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-14, April.
    • Handle: RePEc:hin:jnlmpe:793192
    DOI: 10.1155/2014/793192
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    Cited by:

    1. Ningyi Liu & Yongyu Wang, 2022. "Urban Agglomeration Ecological Welfare Performance and Spatial Convergence Research in the Yellow River Basin," Land, MDPI, vol. 11(11), pages 1-18, November.
    2. Mocholi-Arce, Manuel & Sala-Garrido, Ramon & Molinos-Senante, Maria & Maziotis, Alexandros, 2022. "Performance assessment of the Chilean water sector: A network data envelopment analysis approach," Utilities Policy, Elsevier, vol. 75(C).
    3. Ningyi Liu & Yongyu Wang & Sisi Liu, 2024. "Spatial Evolution and Driving Factors of Ecological Well-Being Performance in the Yellow River Basin," Sustainability, MDPI, vol. 16(14), pages 1-23, July.
    4. Qu, Jingjing & Wang, Baohui & Liu, Xiaohong, 2022. "A modified super-efficiency network data envelopment analysis: Assessing regional sustainability performance in China," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    5. Danxue Fan & Meiyue Li, 2024. "Digital Economy Development and Green Innovation Efficiency from a Two-Stage Innovation Value Chain Perspective," Sustainability, MDPI, vol. 16(11), pages 1-20, May.
    6. Riju Chaudhary & H. D. Arora, 2022. "Efficiency evaluation of public and nationalized Indian banks using data envelopment analysis," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 469-478, February.
    7. Chen, Xiang & Wang, Yujia & Wu, Xin, 2022. "Exploring the source of the financial performance in Chinese banks: A risk-adjusted decomposition approach," International Review of Financial Analysis, Elsevier, vol. 80(C).
    8. Joohwan Kim & Hwayoung Kim, 2021. "Evaluation of the Efficiency of Maritime Transport Using a Network Slacks-Based Measure (SBM) Approach: A Case Study on the Korean Coastal Ferry Market," Sustainability, MDPI, vol. 13(11), pages 1-17, May.

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