Abstract
There are two typical subprocesses in bank production—deposit generation and loan generation. Aiming to open the black box of input-output production of banks and provide comprehensive and accurate assessment on the efficiency of each stage, this paper proposes a two-stage network model with bad outputs and supper efficiency (US-NSBM). Empirical comparisons show that the US-NSBM may be promising and practical for taking the nonperforming loans into account and being able to rank all samples. Applying it to measure the efficiency of Chinese commercial banks from 2008 to 2012, this paper explores the characteristics of overall and divisional efficiency, as well as the determinants of them. Some interesting results are discovered. The polarization of efficiency occurs in the bank level and deposit generation, yet does not in the loan generation. Five hypotheses work as expected in the bank level, but not all of them are supported in the stage level. Our results extend and complement some earlier empirical publications in the bank level.
Highlights
When the black box of traditional DEA model is opened, the super efficiency and undesirable outputs should be considered in the network model to provide more accurate and comprehensive measurement of bank efficiency
This paper extends the network slacks-based measure model (NSBM) model proposed by Tone and Tsutsui [20] to a new two-stage network model named as US-NSBM by means of combing it with super efficiency and undesirable outputs
Based on the data of Chinese commercial banks from 2008 to 2012, we make empirical comparisons between black box models and network models to show that the proposed US-NSBM model may be promising and practical
Summary
Since it was first developed by Charnes et al [1], DEA (data envelopment analysis) has been widely used to measure the performance of DMUs (decision making unites) that convert multi-inputs into multi-outputs, such as bank performance [2, 3], company performance [4, 5], hospital web security [6], production planning [7], energy consumption productivity [8], bankruptcy assessment [9], electricity distribution [10], R&D performance [11], agricultural economics [12], airport performance [13], and other applications [14]. In traditional DEA models, DMU is treated as a “black box,” in which the inputs enter and outputs exit, neglecting the intervening steps [15]. The first motivation of this paper is to fill up this approach gap, which is explained in detail as follows
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