Bank efficiency estimation in China: DEA-RENNA approach

The current study proposes a new DEA model to evaluate the efficiency of 39 Chinese commercial banks over the period 2010-2018. The paper also, in the second stage, investigates the inter-relationships between efficiency and some bank-specific variables (i.e. bank profitability, bank size, expenses management, traditional business and non-traditional business) under the Robust Endogenous Neural Network Analysis. The findings suggest that the sample of Chinese banks experiences a consistent increase in the level of bank efficiency up to 2015; the efficiency score is 0.915, after which the efficiency level declines and then experiences a slight volatility, while finally ending up with an efficiency score of 0.746 by the end of 2018. We also find that among different bank ownership types, the state-owned banks have the highest efficiency, the rural commercial banks are found to be least efficient and the foreign banks experience the strongest volatility over the examined period. The second-stage analysis shows that bank size exerts a positive influence on the development of non-traditional banking business and a proactive expense management, bank size and non-traditional businesses have a positive impact on efficiency levels, while bank profitability, traditional businesses and expenses management have negative influences on bank efficiency.

Automated summary

The study reveals that the efficiency level of Chinese commercial banks consistently increased until 2015, then declined and experienced slight volatility, finally reaching 0.746 by the end of 2018. State-owned banks were found to have the highest efficiency, rural commercial banks the lowest, and foreign banks the most volatile. Bank size has a positive impact on non-traditional banking business and expense management, while bank efficiency is negatively influenced by bank profitability, traditional businesses, and expense management.