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An insight into the specification of the input-output set for DEA-based bank efficiency measurement

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Abstract

Data envelopment analysis (DEA) has become one of the most widely used instruments for measuring bank efficiency. However, its application encounters many problems, which is evidenced by continuous evolvements in the DEA method so far. Our paper addresses the pitfalls of DEA in the context of measuring bank efficiency, with focus on the specification of performance factors. We aim at examining whether the input-output specification for banks in DEA applications is in consistence with the criteria upon which banks make decisions. Four bank behaviour models which are most popularly employed to determine input and output factors in DEA studies—the intermediation approach, production approach, user cost approach and value added approach—are comprehensively discussed and reviewed. The comparative reflection on the bank behaviour models and the standard DEA models shows that the input-output related pitfalls of a DEA application are associated with its implicitly fixed preference structure, flexible weight determination and limited explanatory power. Due to the pitfalls, the conventional DEA models may fail to capture bank behaviours. In such cases, DEA results can hardly reflect the performance in its true sense, i.e. how banks perform against the goals that they decide to pursue. The findings suggest focusing on (DEA-based) performance measurement from a goal-oriented perspective, i.e. from the point of view of multi criteria decision making.

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Notes

  1. The terms “input factor” and “output factor” specify types of objects from a stock-oriented view. From the view of a dynamic context, the term “input” classifies a factor which enters a production process, while the term ”output” classifies a factor which results from a production process. This distinction is relevant for the case of anti-isotonic data (see Sect. 4.1).

  2. According to Berger and Humphrey (1992), explicit revenues of deposits are calculated as extra charges on deposits when a bank pays a deposit interest rate at the market rate; implicit revenues are defined as a bank’s earnings from paying a deposit interest rate below the market rate.

  3. A review of major research thrusts in DEA together with the dual form of the models (2) and (3) can be found in Cook and Seiford (2009).

  4. Under the user cost approach, e.g., the determination of inputs and outputs alters when data for constructing user cost changes.

  5. See the discussion on user cost and value added in Sects. 2.2.3 and 2.2.4.

  6. It should be mentioned that it is also possible to use loans adjusted for bad loans or loan loss reserves as a variable in the DEA model in order to cover the risk factors. Such approach can even improve the discriminating power for the measurement results since the number of variables is reduced. However, the exclusion of risk factors from the variable set can cause difficulties in interpreting the results on whether and how the risks themselves affect the overall efficiency of banks.

  7. According to Lovell (1993), an efficiency measurement model needs to address three questions which are: (1) How many and which inputs and outputs should be included? (2) How should they be weighted in the aggregation process to obtain a single index? and (3) How should the potential production units be determined? As for the first question, DEA applications must rely on exogenous information to specify inputs and outputs. For measuring bank efficiency, bank behaviour models can provide an anchor to address this question. The standard DEA models answer the two last questions as follows: optimal weights are used in the aggregation process to obtain efficiency scores; the potential production units are derived directly from the data set, referring to the best practice ones.

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The authors would like to thank the two anonymous reviewers for their helpful and constructive comments.

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  1. Institut für Controlling und Unternehmensrechnung, Technische Universität Braunschweig, Fallersleber-Tor-Wall 23, 38100 , Braunschweig, Germany

    Heinz Ahn & Minh Hanh Le

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Ahn, H., Le, M.H. An insight into the specification of the input-output set for DEA-based bank efficiency measurement. Manag Rev Q 64, 3–37 (2014). https://doi.org/10.1007/s11301-013-0098-9

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