An illustration of dynamic network DEA in commercial banking including robustness tests

The main motivation of this article is to illustrate dynamic network data envelopment analysis (DN-DEA) in commercial banking with emphasis on testing robustness. To this end, sixteen foreign banks in China are benchmarked against thirty-two domestic banks for the post-2007 period that follows major...

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Bibliographic Details
Published in:Omega (Oxford) Vol. 55; pp. 141 - 150
Main Author: Avkiran, Necmi Kemal
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.09.2015
Pergamon Press Inc
Subjects:
Banks
Banks as networks
Benchmarks
Commercial banks
Data envelopment analysis
Decision making units
Dynamic network DEA
Efficiency
Estimating techniques
Inter-temporal bank performance
Robustness
Studies
China
Robustness
Banks as networks
Dynamic network DEA
Inter-temporal bank performance
ISSN:0305-0483, 1873-5274
Online Access:Get full text
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Summary:The main motivation of this article is to illustrate dynamic network data envelopment analysis (DN-DEA) in commercial banking with emphasis on testing robustness. To this end, sixteen foreign banks in China are benchmarked against thirty-two domestic banks for the post-2007 period that follows major reforms. When network and dynamic dimensions are brought together, a more comprehensive analysis of the period 2008–2010 is enabled where divisional and between-period interactions are reflected in efficiency estimates. Weighted, variable returns-to-scale, non-oriented dynamic network slacks-based measure is used within the framework of the intermediation approach to bank behavior. A bank network (i.e., a decision-making unit, DMU) is conceptualized as comprised of two divisions or sub-DMUs, namely, interest-bearing operations and non-interest operations linked by number of referrals. Undesirable outputs from sub-DMUs 1 and 2 (non-performing loans, and proportion of fruitless referrals, respectively) are treated as carry-overs that impact the efficiency of the following periods. Under robustness testing, the illustrative application discusses discrimination by efficiency estimates, dimensionality of the performance model, stability of estimates through re-sampling (leave-one-out method), and sensitivity of results to divisional weights and returns-to-scale assumptions. The results based on Chinese commercial banks are illustrative in nature because of simulated data used on two of the variables. •Benchmarking sixteen foreign banks against thirty-two domestic banks after reforms in China.•Divisional and between-period interactions are reflected in efficiency estimates.•Interest-bearing operations and non-interest operations are linked by number of referrals.•Undesirable outputs are treated as carry-overs that impact efficiency of following periods.•Robustness examines dimensionality, stability and sensitivity to weights and returns-to-scale.
Bibliography:SourceType-Scholarly Journals-1
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ISSN:0305-0483
1873-5274
DOI:10.1016/j.omega.2014.07.002