Abstract
In recent years, Chinese economic development has slowed down and competition in the financial industry has become increasingly fierce. The purpose of this paper is to study the efficiency characteristics of China’s banking industry in the new environment and provide suggestions for banks to improve efficiency. This paper uses a data envelopment analysis (DEA) SBM-undesirable model and window analysis to measure the technical efficiency of 13 nationwide commercial banks in China during the period from 2008 to 2017. Furthermore, the convergence characteristics of bank technical efficiency are examined. The empirical results show that state-owned banks were more efficient than joint stock banks before 2012. After 2012, state-owned banks were less efficient than joint stock banks. Finally, this paper explores the influential factors of technical efficiency. Noninterest income ratio, net interest margin, growth rate of total investment in fixed assets, and consumer price index have a significant positive impact on bank efficiency. The cost-to-income ratio has a significant negative impact on bank efficiency. Further research using the threshold model shows that noninterest income ratio has a threshold effect on bank efficiency.
Highlights
In terms of dynamic evaluation of bank efficiency, Charnes et al [14] combined the data envelopment analysis (DEA) model with window analysis to evaluate DMU efficiency trends. is method treats the same DMU in different periods as different DMUs
Is article extends the previous research as follows: (1) is paper uses the Slack-Based Measure (SBM)-undesirable model to combine with window analysis. e DEA window analysis method is suitable for horizontal and vertical analysis of efficiency, and SBM-undesirable model can consider the influence of nonperforming loans of Chinese banking efficiency. is method has not been used in bank efficiency analysis
The technical efficiency convergence test indicates that the overall sample bank does not show σ convergence trend and absolute β convergence, but there is conditional β convergence. e research results further show that the noninterest income ratio (NIIR), net interest margin (NIM), cost-to-income ratio (CIR), Total Investment in Fixed Assets (TIFA), and consumer price index (CPI) have significant effects on the efficiency of the Chinese banking industry
Summary
Scholars generally use the production method, the intermediary method, or a combination of the two methods to select indicators. According to the research of Li and Gao [24], they selected 55 papers with high citation rate on the efficiency of China’s banking industry, among which 31 adopted net fixed assets as the input. In the DEA model, there is generally a significant positive correlation between input and output indicators. It is reasonable that there is a weak positive correlation between undesirable output indicator (nonperforming loans) and the above indicators
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