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Pitching DEA Against SFA in the Context of Chinese Domestic Versus Foreign Banks

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Handbook of Operations Analytics Using Data Envelopment Analysis

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 239))

Abstract

The primary motivation is to show how the efficient frontier methods data envelopment analysis (DEA) and stochastic frontier analysis (SFA) can be used synergistically. As part of the illustration, we directly compare locally incorporated foreign banks with Chinese domestic banks. Both DEA and SFA reveal that foreign banks are less efficient. DEA shows the main source of inefficiency for foreign banks as managing interest income, whereas domestic banks are inefficient in managing non-interest income and interest expense. SFA reveals contextual variables such as interbank ratio, loan-to-deposit ratio and cost-to-income ratio are significant in explaining inefficiency. The correspondence of rankings based on DEA vs. SFA is positive and moderate in strength but efficiency estimates do not belong to the same distribution. Using DEA and SFA side-by-side can encourage more rigorous and in-depth bank efficiency studies where each method’s limitation can be overcome by the other.

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Notes

  1. 1.

    See ‘Regulations of the People’s Republic of China on Administration of Foreign-funded Banks’ (CBRC 2006). The same regulations also apply to the banking institutions established on Chinese mainland by financial institutions originating from the Hong Kong Special Administrative Region, the Macao Special Administrative Region, or Taiwan. For example, in our sample, Hang Seng bank (China) Ltd, and CITIC Ka Wah Bank (China) Ltd with home groups from the Hong Kong Special Administrative Region are treated as foreign banks rather than Chinese domestic banks (see Article 72).

  2. 2.

    The interbank ratio is the ratio of funds lent to other banks divided by funds borrowed from other banks. A ratio greater than 1 indicates that the bank is a net lender in the interbank market and is therefore more liquid.

  3. 3.

    http://www.cbrc.gov.cn/EngdocView.do?docID=B22DBFC5175C4AC0AC7926AD7AFEEE27.

  4. 4.

    SFA is even less sensitive to the presence of any outliers because it estimates the efficient frontier by fitting a regression line to the production possibilities set, rather than relying on extreme performers to define the frontier.

  5. 5.

    The higher number of efficient bank-years under DEA with the extended model reflects the impact of greater dimensionality when a second output is introduced; the impact of increased dimensionality is equally easily discernible when core model means and medians are compared against those from the extended model (see Table 5.5). Clearly, there is a loss of discrimination as dimensionality rises for a given sample size – better known as the curse of dimensionality.

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Acknowledgements

We wish to extend our sincere thanks to Chris O’Donnell and Valentin Zelenyuk for their guidance on SFA, Mette Asmild for general comments, Keay-shen See for his unwavering assistance in data collection (others include Jay Shen, Danielle Chang and Jimmy Leung), and Martin Cvelbar for his prompt service on all matters related to the use of library services. We also appreciate the pre-submission critical reading of the chapter by Tom Smith, Terry Walter, Barry Oliver and Ghon Rhee, as well as the suggestions made by the workshop participants at the Griffith University and the World Finance Conference.

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Authors and Affiliations

  1. UQ Business School, The University of Queensland, Brisbane, 4072, QLD, Australia

    Necmi Kemal Avkiran & Yushu (Elizabeth) Zhu

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  1. Necmi Kemal Avkiran
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  2. Yushu (Elizabeth) Zhu
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Correspondence to Necmi Kemal Avkiran .

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Editors and Affiliations

  1. Dept of Business Administration, Ming Chuan University, Taipei, Taiwan

    Shiuh-Nan Hwang

  2. College of Maritime Science & Mgmt, National Taiwan Ocean University, Keelung, Taiwan

    Hsuan-Shih Lee

  3. School of Business, Worcester Polytechnic Institute, Worcester, Massachusetts, USA

    Joe Zhu

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Avkiran, N.K., Zhu, Y.(. (2016). Pitching DEA Against SFA in the Context of Chinese Domestic Versus Foreign Banks. In: Hwang, SN., Lee, HS., Zhu, J. (eds) Handbook of Operations Analytics Using Data Envelopment Analysis. International Series in Operations Research & Management Science, vol 239. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7705-2_5

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