Unveiling endogeneity between competition and efficiency in Chinese banks: a two-stage network DEA and regression analysis
Yong Tan (),
Jorge Antunes and
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Yong Tan: University of Huddersfield
Peter Wanke: Federal University of Rio de Janeiro, Rua Paschoal Lemme, 355
Jorge Antunes: Federal University of Rio de Janeiro, Rua Paschoal Lemme, 355
Ali Emrouznejad: Aston University Birmingham
Annals of Operations Research, 2021, vol. 306, issue 1, No 7, 171 pages
Abstract Although there is a growing number of research articles investigating the performance in the banking industry, research on Chinese banking efficiency is rather focused on discussing rankings to the detriment of unveiling its productive structure in light of banking competition. This issue is of utmost importance considering the relevant transformations in the Chinese economy over the last decades. This is a development of a two-stage network production process (production and intermediation approaches in banking, respectively) to evaluate the efficiency level of Chinese commercial banks. In the second stage regression analysis, an integrated Multi-Layer Perceptron/Hidden Markov model is used for the first time to unveil endogeneity among banking competition, contextual variables, and efficiency levels of the production and intermediation approaches in banking. The competitive condition in the Chinese banking industry is measured by Panar–Rosse H-statistic and Lerner index under the Ordinary Least Square regression. Findings reveal that productive efficiency appears to be positively impacted by competition and market power. Second, credit risk analysis in older local banks, which focus the province level, would possibly be the fact that jeopardizes the productive efficiency levels of the entire banking industry in China. Thirdly, it is found that a perfect banking competition structure at the province level and a reduced market power of local banks are drivers of a sound banking system. Finally, our findings suggest that concentration of credit in a few banks leads to an increase in bank productivity.
Keywords: Network data envelopment analysis; Chinese banking; Competition; GMSS DEA; MLP; Hidden Markov models (search for similar items in EconPapers)
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