Skip to main content
Log in

Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output

  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Bank industry plays a critical role in the economic development of China. In this paper, we develop a new two-stage data envelopment analysis approach for measuring the slacks-based efficiency of Chinese commercial banks during years 2008–2012, where the banking operation process of each bank is divided into a deposit-generation stage (division) and a deposit-utilization stage (division). In the approach, the increase of desirable outputs and the decrease of undesirable outputs are simultaneously considered in order to identify the inefficiency of a bank. Three efficiency statuses are first defined for such a system to investigate its input-output performance and divisional performances, and a full efficiency status is then defined based on these statuses. The empirical results show that the improvement of the banks’ performances during this period was mainly contributed by the improvement of deposit-utilization stage. Besides, the results also show that our approach can provide a benchmark for the intermediate measures of the two stages of an inefficient bank.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  • Akther, S., Fukuyama, H., & Weber, W. L. (2013). Estimating two-stage network slacks-based inefficiency: An application to Bangladesh banking. Omega-International Journal of Management Science, 41, 88–96.

    Article  Google Scholar 

  • Ariff, M., & Can, L. (2008). Cost and profit efficiency of Chinese banks: A non-parametric analysis. China Economic Review, 19, 260–273.

    Article  Google Scholar 

  • Asmild, M., & Matthews, K. (2012). Multi-directional efficiency analysis of efficiency patterns in Chinese banks1997–2008. European Journal of Operational Research, 219, 434–441.

    Article  Google Scholar 

  • Avkiran, N. K. (2009). Opening the black box of efficiency analysis: An illustration with UAE banks. Omega, 37, 930–941.

    Article  Google Scholar 

  • Avkiran, N. K., & McCrystal, A. (2012). Sensitivity analysis of network DEA: NSBM versus NRAM. Applied Mathematics and Computation, 218, 11226–11239.

    Article  Google Scholar 

  • Aysan, A. F., Karakaya, M. M., & Uyanik, M. (2011). Panel stochastic frontier analysis of profitability and efficiency of Turkish banking sector in the post crisis era. Journal of Business Economics and Management, 12, 629–654.

    Article  Google Scholar 

  • Bergendahl, G. (1998). DEA and benchmarks-an application to Nordic banks. Annals of Operations Research, 82, 233–249.

    Article  Google Scholar 

  • Bian, Y., Liang, N., & Xu, H. (2015). Efficiency evaluation of Chinese regional industrial systems with undesirable factors using a two-stage slacks-based measure approach. Journal of Cleaner Production, 87, 348–356.

    Article  Google Scholar 

  • Chen, X., Skully, M., & Brown, K. (2005). Banking efficiency in China: Application of DEA to pre-and post-deregulation eras: 1993–2000. China Economic Review, 16, 229–245.

    Article  Google Scholar 

  • Cheng, M., Zhao, H., & Zhang, J. (2014). What precludes the development of noninterest activities in Chinese commercial banks from the perspective of the price of interest activities? Applied Economics, 46, 2453–2461.

    Article  Google Scholar 

  • Chung, Y. H., Fare, R., & Grosskopf, S. (1997). Productivity and undesirable outputs: A directional distance function approach. Journal of Environmental Management, 51, 229–240.

    Article  Google Scholar 

  • Cook, W. D., & Seiford, L. M. (2009). Data envelopment analysis (DEA)-Thirty years on. European Journal of Operational Research, 192, 1–17.

    Article  Google Scholar 

  • Dyckhoff, H., & Allen, K. (2001). Measuring ecological efficiency with data envelopment analysis (DEA). European Journal of Operational Research, 132, 312–325.

    Article  Google Scholar 

  • Eskelinen, J., Halme, M., & Kallio, M. (2014). Bank branch sales evaluation using extended value efficiency analysis. European Journal of Operational Research, 232, 654–663.

    Article  Google Scholar 

  • Färe, R., & Grosskopf, S. (1996). Productivity and intermediate products: A frontier approach. Economics Letters, 50, 65–70.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., Lovell, C. A. K., & Pasurka, C. (1989). Multilateral productivity comparisons when some outputs are undesirable: A nonparametric approach. The Review of Economics and Statistics, 71, 90–98.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., Lovell, C. A. K., & Yaiswarng, S. (1993). Deviation of shadow prices for undesirable outputs: A distance function approach. The Review of Economics and Statistics, 75, 374–380.

    Article  Google Scholar 

  • Färe, R., Grosskopf, S., Noh, D. W., & Weber, W. (2005). Characteristics of a polluting technology: Theory and practice. Journal of Econometrics, 126, 469–492.

    Article  Google Scholar 

  • Fukuyama, H., & Mirdehghan, S. M. (2012). Identifying the efficiency status in network DEA. European Journal of Operational Research, 220, 85–92.

    Article  Google Scholar 

  • Fukuyama, H., & Weber, W. L. (2009). A directional slacks-based measure of technical inefficiency. Socio-Economic Planning Sciences, 43, 274–287.

    Article  Google Scholar 

  • Fukuyama, H., & Weber, W. L. (2010). A slacks-based inefficiency measure for a two-stage system with bad outputs. Omega, 38, 398–409.

    Article  Google Scholar 

  • Fung, M. K., & Leung, M. K. (2011). X-efficiency and convergence of productivity among the national commercial banks in China. In Proceedings of the 19th China economic association (UK) annual conference. UK: University of Cambridge.

  • Hailu, A. (2003). Non-parametric productivity analysis with undesirable outputs: Reply. American Journal of Agricultural Economics, 85, 1075–1077.

    Article  Google Scholar 

  • Hailu, A., & Veeman, T. (2001). Non-parametric productivity analysis with undesirable outputs: An application to Canadian pulp and paper industry. American Journal of Agricultural Economics, 83, 605–616.

    Article  Google Scholar 

  • Leleu, H. (2013). Shadow pricing of undesirable outputs in nonparametric analysis. European Journal of Operational Research, 231, 474–480.

    Article  Google Scholar 

  • Lensink, R., & Meesters, A. (2007). Institutions and Bank performance: A stochastic frontier analysis. SSRN: http://ssrn.com/abstract=965825 or doi:10.2139/ssrn.965825.

  • Liu, W. B., Meng, W., Li, X. X., & Zhang, D. Q. (2010). DEA models with undesirable inputs and outputs. Annals of Operations Research, 173, 177–194.

    Article  Google Scholar 

  • Liu, W., & Sharp, J. (1999). DEA models via goal programming. In G. Westermann (Ed.), Data envelopment analysis in the service sector (pp. 79–101). Wiesbaden: Deutscher Universititätsverlag.

    Chapter  Google Scholar 

  • Maghbouli, M., Amirteimoori, A., & Kordrostami, S. (2014). Two-stage network structures with undesirable outputs: A DEA based approach. Measurement, 48, 109–118.

    Article  Google Scholar 

  • Mahlberg, B., & Sahoo, B. K. (2011). Radial and non-radial decompositions of Luenberger productivity indicator with an illustrative application. International Journal of Production Economics, 131, 721–726.

    Article  Google Scholar 

  • Mahlberg, B., Luptacik, M., & Sahoo, B. K. (2011). Examining the drivers of total factor productivity change with an illustrative example of 14 EU countries. Ecological Economics, 72, 60–69.

    Article  Google Scholar 

  • Mandal, S. K. (2010). Do undesirable output and environmental regulation matter in energy efficiency analysis? Evidence from Indian cement industry. Energy Policy, 38, 6076–6083.

    Article  Google Scholar 

  • Oral, M., & Yolalan, R. (1990). An empirical study on measuring operating efficiency and profitability of bank branches. European Journal of Operational Research, 46, 282–294.

    Article  Google Scholar 

  • Paradi, J. C., Rouatt, S., & Zhu, H. Y. (2011). Two-stage evaluation of bank branch efficiency using data envelopment analysis. Omega, 39, 99–109.

    Article  Google Scholar 

  • Reinhard, S., Lovell, C. A. K., & Thijssen, G. J. (2000). Environmental efficiency with multiple environmentally detrimental variables: Estimated with SFA and DEA. European Journal of Operational Research, 121, 287–303.

    Article  Google Scholar 

  • Ruggiero, J. (2007). A comparison of DEA and the stochastic frontier model using panel data. International Transactions in Operational Research, 14, 259–66.

    Article  Google Scholar 

  • Sherman, H. D., & Gold, F. (1985). Bank branch operating efficiency: Evaluation with data envelopment analysis. Journal of Banking and Finance, 9, 297–316.

    Article  Google Scholar 

  • Scheel, H. (2001). Undesirable outputs in efficiency evaluations. European Journal of Operational Research, 132, 400–410.

    Article  Google Scholar 

  • Seiford, L. M., & Zhu, J. (2002). Modeling undesirable factors in efficiency evaluation. European Journal of Operational Research, 142, 16–20.

    Article  Google Scholar 

  • Seiford, L. M., & Zhu, J. (1999). Profitability and market ability of the top 55 US commercial banks. Management Science, 45, 1270–1288.

    Article  Google Scholar 

  • Song, M., Wang, S., & Liu, W. (2014). A two-stage DEA approach for environmental efficiency measurement. Environmental Monitoring and Assessment, 186, 3041–3051.

    Article  Google Scholar 

  • Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197, 243–252.

    Article  Google Scholar 

  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130, 498–509.

    Article  Google Scholar 

  • Tone, K. (2004). Dealing with undesirable outputs in DEA: A slacks-based measure (SBM) approach. Toronto: Presentation at NAPW III.

    Google Scholar 

  • Wang, K., Huang, W., Wu, J., & Liu, Y. N. (2014). Efficiency measures of the Chinese commercial banking system using an additive two-stage DEA. Omega, 44, 5–20.

    Article  Google Scholar 

  • Wanke, P., & Barros, C. (2014). Two-stage DEA: An application to major Brazilian banks. Expert Systems with Applications, 41, 2337–2344.

    Article  Google Scholar 

  • Yang, C. C. (2014). An enhanced DEA model for decomposition of technical efficiency in banking. Annals of Operations Research, 214, 167–185.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Poh, K. L. (2006). Slacks-based efficiency measures for modeling environmental performance. Ecological Economics, 60, 111–118.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Poh, K. L. (2008). Measuring environmental performance under different environmental DEA technologies. Energy Economics, 30, 1–14.

    Article  Google Scholar 

  • Zhou, P., Poh, K. L., & Ang, B. W. (2007). A non-radial DEA approach to measuring environmental performance. European Journal of Operational Research, 178, 1–9.

    Article  Google Scholar 

  • Zhou, P., Ang, B. W., & Wang, H. (2012). Energy and \({\rm CO}_{2}\) emission performance in electricity generation: A non-radial directional distance function approach. European Journal of Operational Research, 221, 625–635.

    Article  Google Scholar 

Download references

Acknowledgments

The research is supported by National Natural Science Funds of China (No. 71501189, 71222106, 71371194, 71571173 and 71110107024), Research Fund for the Doctoral Program of Higher Education of China (No. 20133402110028), Foundation for the Author of National Excellent Doctoral Dissertation of P. R. China (No. 201279), Research Fund for Innovation-driven Plan of Central South University (2015CX010) and The Fundamental Research Funds for the Central Universities (No. WK2040160008).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jie Wu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, Q., Chen, H., Wu, J. et al. Measuring slacks-based efficiency for commercial banks in China by using a two-stage DEA model with undesirable output. Ann Oper Res 235, 13–35 (2015). https://doi.org/10.1007/s10479-015-1987-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-015-1987-1

Keywords

Navigation