Bank efficiency estimation in China: DEA-RENNA approach

Jorge Antunes, Abdollah Hadi-Vencheh, Ali Jamshidi, Aaron Tan, Peter Wanke

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Number of pages26
JournalAnnals of Operations Research
Early online date24 May 2021
DOIs
Publication statusE-pub ahead of print - 24 May 2021

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