The Sustainability Performance of Chinese Banks: A New Network Data Envelopment Analysis Approach and Panel Regression

Abstract

This paper develops a new network data envelopment analysis (DEA) model that simultaneously integrates the non-convex metafrontier and undesirable outputs and which is super efficient at performing dynamic network slacks-based measures. The model is employed to discuss the efficiency of 36 commercial banks in China during the years 2010–2014. The efficiency of these banks shows significant heterogeneity and the efficiency of most foreign banks has much room for improvement. Regarding both the non-convex metafrontier and the group frontier, state-owned banks perform the best, followed by joint-stock banks, with foreign banks performing the worst; the same is true for the technology gap ratios. The empirical results produced by the feasible generalized least squares estimation method indicate that liquidity and scale effects exert positive impacts on bank efficiency. An alternative estimation method confirmed that the conclusions were robust.