Credit risk shocks and banking efficiency: a study based on a bootstrap-DEA model with nonperforming loans as bad output

Purpose This paper investigates the impact of credit risk shocks on the evolution of banking efficiency in China. Design/methodology/approach This paper introduces credit risk as a bad output into a bootstrap data envelopment analysis (bootstrap-DEA) model. Findings During a credit risk shock, the efficiency levels of both state-owned commercial banks and joint-stock commercial banks are significantly higher than those of urban/rural commercial banks, and the efficiency differences between these banks further increase during a period of economic slowdown. This paper also finds that the efficiencies of joint-stock commercial banks are the most sensitive to credit risk shocks; these banks are the first to be affected and the first to completely adjust. However, urban/rural commercial banks adjust very slowly. Originality/value Most scholars still use the traditional DEA method to estimate China's banking efficiency. The bootstrap-DEA method is clearly able to obtain a more exact estimated efficiency score. In fact, in comparison with the bootstrap-DEA model, we found that the traditional DEA method overestimates China's banking efficiency, and this is an especially serious problem for those banks that have a high efficiency score.