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The threshold effects of income diversification on bank stability: an efficiency perspective based on a dynamic network slacks-based measure model

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Abstract

This study examines whether nonlinear effects of income diversification on bank stability occurs in the European banking system. Based on the framework developed by Tone and Tsutsui (Omega 42(1):124–131, 2014), we propose a novel three-stage (deposit producing, intermediation, and value maximization) dynamic network slacks-based measure model to assess bank stability in a sample of 114 European commercial banks during the post financial crisis period (2010–2019). We use the panel smooth transition regression model (PSTR), as introduced by González et al. (Panel smooth transition regression models, CREATES research paper 2017-36. Department of Economics and Business Economics, Aarhus University, 2017), to investigate the potential regime-switching behavior of the relationship between income diversification and stability. Our findings show that higher levels of income diversification, into and within nontraditional banking generating activities, worsen bank stability. It results that, in the case of European banks, income diversification is sub-optimal as no benefits are found from “over diversification”. Important policy implications arise from our findings pertaining to the optimality of income diversification and stability, which could be in conflict with banks’ traditional lines of business aiming at promoting lending activities.

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Notes

  1. Slacks in DEA are the excess of inputs and the shortfalls of outputs, that reflect a decision-making unit (DMU) inefficiency, if they are different from zero. Stated differently, they are the additional improvement (increase in outputs and/or decrease in inputs) needed for a unit to become efficient.

  2. A brief review of the literature on the DEA approach in the banking sector as well as on the use of dynamic network models is presented in Sect. 2.2.

  3. The SBM model, as opposed to the radial measure used in the CCR (Charnes, Cooper and Rhodes, 1978) and BCC (Banker, Charnes and Cooper, 1984) models, measures efficiency based on the input excesses and output shortfalls. Traditional DEA models are commonly either radial or non-radial. The two radial models consist of the Charnes-Cooper-Rhodes (CCR) and Banker-Charnes-Cooper (BCC) models, while the four non-radial models are the additive, the multiplicative, the range-adjusted measure (RAM), and the slacks-based measure (SBM) models.

  4. Traditionally, both radial and non-radial DEA models consider the DMU production system as a “black box” when efficiency is to be measured. However, recent research has opened this “black box” to better understand the production structure of banks and to observe the connections between different stages of the production system.

  5. Evaluating bank efficiency in China, Zha et al. (2016) use dynamic two-stage slacks-based measure approach and treat non-performing loans as undesirable output of the profitability stage as well as undesirable input to the profitability stage in the subsequent time period. they explain their choice by the fact that non-performing loans directly affect bank returns and harms their operational efficiency.

  6. Remark 1. A debate regarding the two-stage DEA studies is still ongoing. Simar and Wilson (2007) raise the issue regarding the limitations of the two-stage censored Tobit regression and state that Tobit and OLS regressions provide biased estimators because of the violation of the independence between non-discretionary variables and the error terms. Consequently, Simar and Wilson (2007, 2011) recommend using the two-step bias-corrected semi-parametric estimator, i.e., the Simar and Wilson’s (2007) bootstrap estimator, in order to maintain consistent inferences on the estimated parameters in the second stage of the regression. However, Banker et al. (2019) assert that the consistency of the Simar and Wilson’s procedure depends crucially on restrictive assumptions with regard to the data generating process they assumed, which is subsequently outperformed by the Banker and Natarjan’s (2008) procedure. Additionally, according to a set of Monte Carlo simulations, Banker et al. (2019) demonstrate that, in some cases, conventional OLS and Tobit regression models provide more consistent estimators in two-stage DEA studies than the complex Simar and Wilson model. What is more, our efficiency score derives from a dynamic network slacks-based efficiency DEA model, which is different from the standard radial DEA model that has been considered in the development of the Simar and Wilson’s (2007) procedure. Also, we apply a nonlinear econometric model with different statistical characteristics than those of OLS and Tobit estimators. As a result, the bootstrap estimator of Simar and Wilson cannot be directly applicable to our study.

  7. See Table 2 for a summary regarding definition and measure of these variables.

  8. For more details on these two tests, please refer to González et al. (2017).

  9. HAC stands for Heteroskedasticity and Autocorrelation Consistency.

  10. To achieve their goals, bank managers could skip certain management practices in the loan selection and monitoring process, allowing the bank to appear more efficient due to reduced operating costs. Thus, according to the “skimming” hypothesis, and increase in efficiency explains the increase in NPL.

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Authors and Affiliations

  1. IPAG Business School, Paris, France

    Béchir Ben Lahouel

  2. Ecole Supérieure des Sciences Économiques et Commerciales de Tunis, Université de Tunis, Tunis, Tunisia

    Lotfi Taleb

  3. Department of Banking and Investment, Faculty of Economics, Technical University of Košice, Košice, Slovakia

    Kristína Kočišová

  4. EDC Paris Business School, Paris, France

    Younes Ben Zaied

Authors
  1. Béchir Ben Lahouel
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  2. Lotfi Taleb
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  3. Kristína Kočišová
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  4. Younes Ben Zaied
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by BBL, LT, KK, and YBZ. The current version of the manuscript was written and approved by all authors.

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Correspondence to Béchir Ben Lahouel.

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Appendix

Appendix

See Tables 7, 8 and 9.

Table 7 Distribution and representativeness of European commercial banks in the final sample
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Table 8 Efficiency scores
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Table 9 Results of the panel-data unit root tests
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Ben Lahouel, B., Taleb, L., Kočišová, K. et al. The threshold effects of income diversification on bank stability: an efficiency perspective based on a dynamic network slacks-based measure model. Ann Oper Res 330, 267–304 (2023). https://doi.org/10.1007/s10479-021-04503-4

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