Potential structural efficiency of Chinese commercial banks
DOI: https://doi.org/10.3846/jbem.2026.25750Abstract
Given that structural efficiency serves as a significant instrument, this paper applies a novel approach to measure structural efficiency levels within Chinese banks from the perspective of potential improvement. To further investigate the patterns of structural efficiency, the overall structural efficiency is disaggregated into a series of variable-specific structural efficiencies. It reveals that fixed assets and non-interest incomes constitute the primary sources of structural inefficiency during the study period. Furthermore, the structural efficiencies of small-medium commercial banks surpass those of large state-owned commercial banks, although the efficiency gap between the two types of banks has narrowed. Based on the variable-specific structural efficiencies, this paper further explores the structural efficiency patterns within the Chinese banking sector.
Keywords:
structural efficiency, multi-directional efficiency analysis, potential improvement, overall efficiency, variable-specific efficiency, Chinese bankingHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Antunes, J., Hadi-Vencheh, A., Jamshidi, A., Tan, Y., & Wanke, P. (2024). Cost efficiency of Chinese banks: Evidence from DEA and MLP-SSRP analysis. Expert Systems with Applications, 237, Article 121432. https://doi.org/10.1016/j.eswa.2023.121432
Asmild, M., Hougaard, J. L., Kronborg, D., & Kvist, H. K. (2003). Measuring inefficiency via potential improvements. Journal of Productivity Analysis, 19, 59–76. https://doi.org/10.1023/A:1021822103696
Asmild, M., & Matthews, K. (2012). Multi-directional efficiency analysis of efficiency patterns in Chinese banks 1997–2008. European Journal of Operational Research, 219(2), 434–441. https://doi.org/10.1016/j.ejor.2012.01.001
Asmild, M., Baležentis, T., & Hougaard, J. L. (2016). Multi-directional productivity change: MEA-Malmquist. Journal of Productivity Analysis, 46, 109–119. https://doi.org/10.1007/s11123-016-0486-y
Begen, M. A., Ødegaard, F., & Sadeghi, J. (2024). On aggregation of technical and revenue efficiency measures. Journal of Productivity Analysis, 62, 335–350. https://doi.org/10.1007/s11123-023-00710-2
Bogetoft, P., & Hougaard, J. L. (1999). Efficiency evaluations based on potential (non-proportional) improvements. Journal of Productivity Analysis, 12, 233–247. https://doi.org/10.1023/A:1007848222681
Boussemart, J.-P., Leleu, H., Shen, Z., Vardanyan, M., & Zhu, N. (2019). Decomposing banking performance into economic and credit risk efficiencies. European Journal of Operational Research, 277(2), 719–726. https://doi.org/10.1016/j.ejor.2019.03.006
Chen, Z., Poncet, S., & Xiong, R. (2020). Local financial development and constraints on domestic private firm exports: Evidence from city commercial banks in China. Journal of Comparative Economics, 48(1), 56–75. https://doi.org/10.1016/j.jce.2019.09.005
Duong, K. D., Le, H. V., Vu, D. N., & Le, A. N. N. (2023). Do bank funding diversity and bank lending affect net interest margins? Evidence from Asia markets before and during the COVID-19 pandemic. SAGE Open, 13(4). https://doi.org/10.1177/21582440231214044
Fang, L., & Li, H. (2013). A comment on “solving the puzzles of structural efficiency”. European Journal of Operational Research, 230(2), 444–446. https://doi.org/10.1016/j.ejor.2013.04.037
Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A (General), 120(3), 253–290. https://doi.org/10.2307/2343100
Førsund, F., & Hjalmarsson, L. (1979). Generalised Farrell measures of efficiency: An application to milk processing in Swedish dairy plants. The Economic Journal, 89(354), 294–315. https://doi.org/10.2307/2231603
Fu, Y., & Liu, L. (2023). On the accessibility of financial services and income inequality: An international perspective. Technological and Economic Development of Economy, 29(3), 814–845. https://doi.org/10.3846/tede.2023.18722
Fukuyama, H., & Tan, Y. (2022). Implementing strategic disposability for performance evaluation: Innovation, stability, profitability and corporate social responsibility in Chinese banking. European Journal of Operational Research, 296(2), 652–668. https://doi.org/10.1016/j.ejor.2021.04.022
He, Z., & Wei, W. (2023). China’s financial system and economy: A review. Annual Review of Economics, 15, 451–483. https://doi.org/10.1146/annurev-economics-072622-095926
Karagiannis, G. (2015). On structural and average technical efficiency. Journal of Productivity Analysis, 43, 259–267. https://doi.org/10.1007/s11123-015-0439-x
Lee, C.-C., Li, X., Yu, C.-H., & Zhao, J. (2021). Does fintech innovation improve bank efficiency? Evidence from China’s banking industry. International Review of Economics and Finance, 74, 468–483. https://doi.org/10.1016/j.iref.2021.03.009
Leleu, H., & Briec, W. (2009). A DEA estimation of a lower bound for firms’ allocative efficiency without information on price data. International Journal of Production Economics, 121(1), 203–211. https://doi.org/10.1016/j.ijpe.2009.05.002
Li, S. K., & Cheng, Y. S. (2007). Solving the puzzles of structural efficiency. European Journal of Operational Research, 180(2), 713–722. https://doi.org/10.1016/j.ejor.2006.05.010
Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
Modigliani, F., & Miller, M. H. (1958). The cost of capital, corporation finance and the theory of investment. The American Economic Review, 48(3), 261–297.
Nasrabadi, N., Dehnokhalaji, A., Korhonen, P., Lokman, B., & Wallenius, J. (2022). Robustness of efficiency scores in data envelopment analysis with interval scale data. European Journal of Operational Research, 297(3), 1151–1161. https://doi.org/10.1016/j.ejor.2021.06.049
Oanh, T. T. K., Van Nguyen, D., Le, H. V., & Duong, K. D. (2023). How capital structure and bank liquidity affect bank performance: Evidence from the Bayesian approach. Cogent Economics & Finance, 11(2), Article 2260243. https://doi.org/10.1080/23322039.2023.2260243
Shapley, L. S. (1953). A value for n-person games. In H. Kuhn & A. Tucker (Eds.), Contributions to the theory of games II (pp. 307–317). Princeton University Press.
Song, Z., & Xiong, W. (2018). Risks in China’s financial system. Annual Review of Finance Economics, 10, 261–286. https://doi.org/10.1146/annurev-financial-110716-032402
Sun, X. F., & Liu, L. Y. (2023). Determinants of branching decisions of state-controlled commercial banks: Evidence from China. International Journal of Bank Marketing, 41(6), 1389–1413. https://doi.org/10.1108/IJBM-03-2022-0093
Wei, X., Chen, Y., Zhou, M., & Zhou, Y. (2016). SOE preference and credit misallocation: A model and some evidence from China. Economic Letters, 138, 38–41. https://doi.org/10.1016/j.econlet.2015.11.023
Wu, G. L. (2018). Capital misallocation in China: Financial frictions or policy distortions. Journal of Development Economics, 130, 203–223. https://doi.org/10.1016/j.jdeveco.2017.10.014
Xie, Q., Xu, Q., Chen, L., Jin, X., Li, S., & Li, Y. (2022). Efficiency evaluation of China’s listed commercial banks based on a multi-period leader-follower model. Omega, 110, Article 102615. https://doi.org/10.1016/j.omega.2022.102615
Xu, G., & Zhou, Z. (2020). Assessing the efficiency of financial supply chain for Chinese commercial banks: A two-stage AR-DEA model. Industrial Management & Data Systems, 121(4), 894–920. https://doi.org/10.1108/IMDS-01-2020-0022
Xu, S., Zhu, N., Copestake, J., Cai, G., & Peng, X. (2024). Is there a trade-off between MFls’ financial efficiency and outreach in macroeconomic and institutional context? International Review of Economics & Finance, 94, Article 103403. https://doi.org/10.1016/j.iref.2024.103403
Yang, J. (2023). Disentangling the sources of bank inefficiency: A two-stage network multi-directional efficiency analysis approach. Annals of Operations Research, 326, 369–410. https://doi.org/10.1007/s10479-023-05335-0
Ylvinger, S. (2000). Industry performance and structural efficiency measures: Solution to problems in firm models. European Journal of Operational Research, 121(1), 164–174. https://doi.org/10.1016/S0377-2217(99)00014-4
Zelenyuk, V., & Panchenko, V. (2024). Generalized theory for measuring efficiency of individuals and groups. Annals of Operations Research, 332, 781–806. https://doi.org/10.1007/s10479-023-05599-6
Zhou, X., Xu, Z., Chai, J., Yao, L., Wang, S., & Lev, B. (2019). Efficiency evaluation for banking systems under uncertainty: A multi-period three-stage DEA model. Omega, 85, 68–82. https://doi.org/10.1016/j.omega.2018.05.012
Zhu, N., Wang, B., & Wu, Y.-R. (2015). Productivity, efficiency, and non-performing loans in the Chinese banking industry. The Social Science Journal, 52(4), 468–480. https://doi.org/10.1016/j.soscij.2014.10.003
Zhu, N., Hougaard, J. L., & Ghiyasi, M. (2019a). Ranking production units by their impact on structural efficiency. Journal of the Operational Research Society, 70(5), 783–792. https://doi.org/10.1080/01605682.2018.1457486
Zhu, N., Wu, Y., Wang, B., & Yu, Z. (2019b). Risk preference and efficiency in Chinese banking. China Economic Review, 53, 324–341. https://doi.org/10.1016/j.chieco.2018.11.001
Zhu, N., Hougaard, J. L., Yu, Z., & Wang, B. (2020). Ranking Chinese commercial banks based on their expected impact on structural efficiency. Omega, 94, Article 102049. https://doi.org/10.1016/j.omega.2019.03.007
Zhu, N., Farrier, G., Vardanyan, M., & Shen, Z. (2025). Measuring potential profit gains using a multi-stage production technology: Evidence from the Chinese banking industry. Applied Economics, 57(29), 4242–4262. https://doi.org/10.1080/00036846.2024.2354514
View article in other formats
CrossMark check
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License
This work is licensed under a Creative Commons Attribution 4.0 International License.