Analysing banks’ intermediation and operating efficiencies using the two-stage network DEA model

Abstract

Purpose The purpose of this paper is to present a holistic approach for measuring overall bank efficiency and its decomposition in intermediation and operating efficiencies. Design/methodology/approach Recently developed two-stage network data envelopment analysis model by Liang et al. (2008) has been used for obtaining intermediation and operational efficiencies along with overall bank efficiency. The bootstrapped truncated regression algorithm as proposed by Simar and Wilson (2007) has been employed to explore the influential determinants of intermediation and operating efficiencies. Findings The empirical results reveal that the operating inefficiency is the dominant source of overall bank inefficiency in Indian banking sector. Another interesting finding is that public sector banks are more efficient than private banks in the intermediation stage of production process, while private banks are more efficient in the operating stage of production process. Finally, the results of bootstrapped truncated regression show that variations in intermediation efficiency are explained by bank size, liquidity position, directed lending and intermediation cost, while inter-bank differences in operating efficiency are influenced by profitability and income diversification. Practical implications The most significant practical implication that has been derived from the research findings is that at the industry level, overall efficiency enhancement needs improvement both in terms of resource-utilization and income-generating abilities of the banks. However, the relatively easy way to achieve higher bank efficiency is to improve the efficiency of banks in generating incomes from interest and fee-based sources. Originality/value This paper is the first to provide a comprehensive assessment of performance of Indian banks by examining the efficiency of individual banks considering both the intermediation and operating approaches simultaneously.

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“…We also would like to point out that other DEA methodologies have been used to assess the efficiency of banks; for example, Network DEA (e.g., Matthews 2013; Grigoroudis et al 2013;Akther et al 2013;Fukuyama and Matousek 2017;Gulati and Kumar 2017), Network DEA with undesirable variables (e.g., An et al 2015;Liu et al 2015), Dynamic DEA (e.g., Avkiran and Goto 2011;Fukuyama andWeber 2015, 2017), Dynamic Network DEA (e.g., Avkiran 2015;Chao et al 2015;Fukuyama andWeber 2015, 2017;Zha et al 2016;Wu et al 2016;Fukuyama and Weber 2017b), Fuzzy DEA (e.g., Wang et al 2014;Wanke et al 2016;Hatami-Marbini et al 2017), DEA with Bootstrapping (e.g., Ferrier and Hirschberg 1997), Fuzzy DEA with Bootstrapping (e.g., Wanke et al 2016), and Stochastic DEA (e.g., Kao and Liu 2009). For a recent survey, we refer the reader to Kaffash and Marra (2017).…”

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