Market concentration, diversification and bank performance in China and India

Abstract

Purpose The purpose of this paper is to examine the operational efficiency and effects of market concentration and diversification on the efficiency of Chinese and Indian banks in the 1997-2011 period. Design/methodology/approach This study employs the two-stage bootstrap procedure of Simar and Wilson (2007) to obtain valid inferences on the efficiency scores and the efficiency determinants. Findings Using data set for each country separately, the authors found that the bias-corrected cost efficiency displays an upward trend in Chinese and Indian banks. This trend is consistent with profit efficiency among Chinese banks, but the trend is unclear in Indian banks. Market concentration is negatively related to cost and profit efficiencies of Chinese banks. However, market concentration is positively associated with cost efficiency, but unrelated to profit efficiency of Indian banks. In Chinese banks, diversification of revenue, earning assets and non-lending earning assets are associated with increasing profit efficiency, but their effects to cost efficiency are not clear. In Indian banks, diversification of earning assets increases profit efficiency while there are cost efficiency losses from diversification of revenue and earning assets. Practical implications Bank regulators and supervisors in China should consider establishing policies to reduce market concentration and encourage diversification of revenue, earning assets and non-lending earning assets, while increasing concentration and diversification of earning assets should be encouraged in Indian banks. Originality/value To the best of the authors’ knowledge, this is the first study employing the double bootstrap procedure proposed by Simar and Wilson (2007) which can address the problem of the two-stage data envelopment analysis or SFA estimator in the efficiency literature on Chinese and Indian banks that efficiency scores obtained in the first stage are inter-dependent, and hence violating the basic assumption in regression analysis in the second stage.