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Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China

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  • Zhujia Yin
  • Yantuan Yu
  • Jianhuan Huang

Abstract

The performances of different types of banks may vary due to heterogeneous technology, which can be examined by metafrontier analysis. However, the metafrontier constructed in most existing literature is concave, resulting in a biased estimation of efficiency. Based on 93 Chinese commercial banks over the period of 2005–2016, we first evaluate the banking efficiency by using the proposed data envelopment analysis (DEA) model, NCMeta-US-NSBM, which simultaneously incorporates a non-concave metafrontier technique, undesirable outputs, and super efficiency into a network slacks-based measure (NSBM) model. Subsequently, the evolution of banking efficiency during the study period is investigated on the basis of the Dagum Gini index and kernel density estimation methods. The main empirical results show the following. 1) There exists significant disparity/heterogeneity in banking efficiency for overall efficiency, productivity efficiency, and profitability efficiency. 2) The results of the technology gap ratio (TGR) and the evaluation of stated-owned banks (SOB), joint-stock banks (JSB), and city commercial banks (CCB) in the productivity stage are higher than those in the profitability stage, indicating that most of the banks have a large space for improvement, especially for SOB and JSB in the profitability stage. 3) The major contribution of the overall difference of banking efficiency in China is the intensity of the transvariation. 4) Although the kernel density estimations for different efficiency scores have similar distributions in corresponding years, the multilevel differentiation phenomenon of banking efficiency may appear after 2008.

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  • Zhujia Yin & Yantuan Yu & Jianhuan Huang, 2018. "Evaluation and evolution of bank efficiency considering heterogeneity technology: An empirical study from China," PLOS ONE, Public Library of Science, vol. 13(10), pages 1-19, October.
  • Handle: RePEc:plo:pone00:0204559
    DOI: 10.1371/journal.pone.0204559
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    as
    1. Rolf Färe & Shawna Grosskopf & Gerald Whittaker, 2014. "Network DEA II," International Series in Operations Research & Management Science, in: Wade D. Cook & Joe Zhu (ed.), Data Envelopment Analysis, edition 127, chapter 0, pages 307-327, Springer.
    2. Alin Marius Andries & Bogdan Capraru, 2013. "Impact of Financial Liberalization on Banking Sectors Performance from Central and Eastern European Countries," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-9, March.
    3. Tone, Kaoru & Sahoo, Biresh K., 2003. "Scale, indivisibilities and production function in data envelopment analysis," International Journal of Production Economics, Elsevier, vol. 84(2), pages 165-192, May.
    4. 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.
    5. Matthews, Kent, 2013. "Risk management and managerial efficiency in Chinese banks: A network DEA framework," Omega, Elsevier, vol. 41(2), pages 207-215.
    6. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    7. Dai, Zhifeng & Wen, Fenghua, 2018. "Some improved sparse and stable portfolio optimization problems," Finance Research Letters, Elsevier, vol. 27(C), pages 46-52.
    8. Park, Kang H. & Weber, William L., 2006. "A note on efficiency and productivity growth in the Korean Banking Industry, 1992-2002," Journal of Banking & Finance, Elsevier, vol. 30(8), pages 2371-2386, August.
    9. Holod, Dmytro & Lewis, Herbert F., 2011. "Resolving the deposit dilemma: A new DEA bank efficiency model," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2801-2810, November.
    10. Chiu, Ching-Ren & Chiu, Yung-Ho & Chen, Yu-Chuan & Fang, Chen-Ling, 2016. "Exploring the source of metafrontier inefficiency for various bank types in the two-stage network system with undesirable output," Pacific-Basin Finance Journal, Elsevier, vol. 36(C), pages 1-13.
    11. Wanke, Peter & Barros, Carlos Pestana, 2016. "Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach," Economic Modelling, Elsevier, vol. 53(C), pages 8-22.
    12. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
    13. Wang, Qunwei & Hang, Ye & Sun, Licheng & Zhao, Zengyao, 2016. "Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 254-261.
    14. Lucas Fievet & Didier Sornette, 2018. "Calibrating emergent phenomena in stock markets with agent based models," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-17, March.
    15. Chuangxia Huang & Jie Cao & Fenghua Wen & Xiaoguang Yang, 2016. "Stability Analysis of SIR Model with Distributed Delay on Complex Networks," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-22, August.
    16. Lozano, Sebastián, 2016. "Slacks-based inefficiency approach for general networks with bad outputs: An application to the banking sector," Omega, Elsevier, vol. 60(C), pages 73-84.
    17. Jolly Puri & Shiv Prasad Yadav & Harish Garg, 2017. "A new multi-component DEA approach using common set of weights methodology and imprecise data: an application to public sector banks in India with undesirable and shared resources," Annals of Operations Research, Springer, vol. 259(1), pages 351-388, December.
    18. Yang, Wei & Shi, Jinfeng & Qiao, Han & Shao, Yanmin & Wang, Shouyang, 2017. "Regional technical efficiency of Chinese Iron and steel industry based on bootstrap network data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 57(C), pages 14-24.
    19. Tone, Kaoru & Tsutsui, Miki, 2009. "Network DEA: A slacks-based measure approach," European Journal of Operational Research, Elsevier, vol. 197(1), pages 243-252, August.
    20. Yung-Ho Chiu & Yu-Chuan Chen & Xue-Jie Bai, 2011. "Efficiency and risk in Taiwan banking: SBM super-DEA estimation," Applied Economics, Taylor & Francis Journals, vol. 43(5), pages 587-602.
    21. Fenghua Wen & Jihong Xiao & Chuangxia Huang & Xiaohua Xia, 2018. "Interaction between oil and US dollar exchange rate: nonlinear causality, time-varying influence and structural breaks in volatility," Applied Economics, Taylor & Francis Journals, vol. 50(3), pages 319-334, January.
    22. Chen, Chien-Ming, 2013. "Super efficiencies or super inefficiencies? Insights from a joint computation model for slacks-based measures in DEA," European Journal of Operational Research, Elsevier, vol. 226(2), pages 258-267.
    23. Fukuyama, Hirofumi & Matousek, Roman, 2017. "Modelling bank performance: A network DEA approach," European Journal of Operational Research, Elsevier, vol. 259(2), pages 721-732.
    24. Huang, Tai-Hsin & Lin, Chung-I & Chen, Kuan-Chen, 2017. "Evaluating efficiencies of Chinese commercial banks in the context of stochastic multistage technologies," Pacific-Basin Finance Journal, Elsevier, vol. 41(C), pages 93-110.
    25. C-W Huang & C-T Ting & C-H Lin & C-T Lin, 2013. "Measuring non-convex metafrontier efficiency in international tourist hotels," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 64(2), pages 250-259, February.
    26. Robert Webb, 2003. "Levels of efficiency in UK retail banks: a DEA window analysis," International Journal of the Economics of Business, Taylor & Francis Journals, vol. 10(3), pages 305-322.
    27. Md Zobaer Hasan & Anton Abdulbasah Kamil & Adli Mustafa & Md Azizul Baten, 2012. "A Cobb Douglas Stochastic Frontier Model on Measuring Domestic Bank Efficiency in Malaysia," PLOS ONE, Public Library of Science, vol. 7(8), pages 1-5, August.
    28. Zhu, Joe, 2000. "Multi-factor performance measure model with an application to Fortune 500 companies," European Journal of Operational Research, Elsevier, vol. 123(1), pages 105-124, May.
    29. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    30. Min Zhou & Xiaoqun Liu & Bin Pan & Xin Yang & Fenghua Wen & Xiaohua Xia, 2017. "Effect of Tourism Building Investments on Tourist Revenues in China: A Spatial Panel Econometric Analysis," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(9), pages 1973-1987, September.
    31. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    32. Fukuyama, Hirofumi & Weber, William L., 2010. "A slacks-based inefficiency measure for a two-stage system with bad outputs," Omega, Elsevier, vol. 38(5), pages 398-409, October.
    33. Md Zobaer Hasan & Anton Abdulbasah Kamil & Adli Mustafa & Md Azizul Baten, 2012. "Stochastic Frontier Model Approach for Measuring Stock Market Efficiency with Different Distributions," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    34. Jolly Puri & Shiv Prasad Yadav, 2017. "Improved DEA models in the presence of undesirable outputs and imprecise data: an application to banking industry in India," 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. 8(2), pages 1608-1629, November.
    35. Torben Tiedemann & Tammo Francksen & Uwe Latacz-Lohmann, 2011. "Assessing the performance of German Bundesliga football players: a non-parametric metafrontier approach," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(4), pages 571-587, December.
    36. Liang Liang & Feng Yang & Wade Cook & Joe Zhu, 2006. "DEA models for supply chain efficiency evaluation," Annals of Operations Research, Springer, vol. 145(1), pages 35-49, July.
    37. Thanh Pham Thien Nguyen & Son Hong Nghiem & Eduardo Roca & Parmendra Sharma, 2016. "Bank reforms and efficiency in Vietnamese banks: evidence based on SFA and DEA," Applied Economics, Taylor & Francis Journals, vol. 48(30), pages 2822-2835, June.
    38. Zha, Yong & Liang, Nannan & Wu, Maoguo & Bian, Yiwen, 2016. "Efficiency evaluation of banks in China: A dynamic two-stage slacks-based measure approach," Omega, Elsevier, vol. 60(C), pages 60-72.
    39. Necmi Avkiran & Lin Cai, 2014. "Identifying distress among banks prior to a major crisis using non-oriented super-SBM," Annals of Operations Research, Springer, vol. 217(1), pages 31-53, June.
    40. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    41. 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.
    42. Eskelinen, Juha & Kuosmanen, Timo, 2013. "Intertemporal efficiency analysis of sales teams of a bank: Stochastic semi-nonparametric approach," Journal of Banking & Finance, Elsevier, vol. 37(12), pages 5163-5175.
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