A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system

Fukuyama, Hirofumi; Matoušek, Roman; Tzeremes, Nickolaos

doi:10.1016/j.omega.2020.102198

Cited by 51 publications

(25 citation statements)

References 103 publications

(139 reference statements)

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“…These include that of Sturm and Williams (2004) for Australian banks; Fukuyama et al. (2020) for Turkish banks; Hasan and Marton (2003) for Hungarian banks; and Fujii et al. (2014), and Tzeremes (2015) for Indian banks.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Because banks that produce more risky loans could be labeled as more efficient when compared to banks with low NPLs, the quality and risk of loans must be properly controlled to capture their effect on efficiency estimates (Mester, 1996). However, despite there being a large number of network DEA papers in banking literature, only a handful of these tackle the issue of NPLs (Fukuyama et al., 2020). In a second stage, we regress the efficiency scores on bank ownership types and several other contextual variables by applying a robust regression approach.…”

Section: Introductionmentioning

confidence: 99%

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Does ownership structure affect firm performance? Evidence of Indian bank efficiency before and after the Global Financial Crisis

Skully

Wijesiri

Walker

et al. 2021

Int Trans Operational Res

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This paper investigates the relationship between bank ownership and efficiency before and after the 2008–2009 Global Financial Crisis. Using a sample of 58 Indian commercial banks from 2005 to 2017, we examine the interconnection between these factors in a dynamic data envelopment analysis (DEA) framework. We use an innovative modeling strategy based on a two‐stage dynamic DEA framework. The first stage employs the Dynamic Slack‐Based Measure model to measure bank efficiency, explicitly considering the effects of desirable and undesirable carry‐overs between consecutive periods. In a second stage, we perform a regression of the efficiency scores on bank ownership types and several other contextual factors, while also accounting for the presence of endogenous relationships between the variables involved. Our results show that foreign banks outperform their domestic competitors, with bank size and profitability being the main drivers. We also find that while foreign and state‐owned banks were more efficient than their rivals during the Global Financial Crisis, private banks recovered quickly, reaching the efficiency standards of state‐owned banks by 2017. The latter faced a prolonged decrease in efficiency, gradually losing their initial advantage over their private domestic counterparts. Our results are robust to alternative regression specifications, especially those aimed at addressing potential endogeneity issues. The applied methodology and the findings of our research should be of interest to scholars, bank managers, and policymakers. The latter, in particular, should be concerned about the medium‐term effects of reforms that unevenly affect banks with different ownership structures, especially in terms of bank resilience to aggregate shocks.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Does ownership structure affect firm performance? Evidence of Indian bank efficiency before and after the Global Financial Crisis

Skully

Wijesiri

Walker

et al. 2021

Int Trans Operational Res

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show abstract

“…To demonstrate the practical aspect of the proposed indexes, we consider the annual data for 39 Indian commercial, including both public and private banks from 2008 to 2019. Fukuyama and Weber [28,29],…”

Section: Empirical Studymentioning

confidence: 99%

Malmquist-Luenberger productivity indexes for dynamic network DEA with undesirable outputs and negative data

Bansal

Mehra

2022

RAIRO-Oper. Res.

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The data envelopment analysis (DEA) technique is well known for computing the Malmquist-Luenberger productivity index (MLPI) in measuring productivity change in the decision-making units (DMUs) over two consecutive periods. In this research, we detect infeasibility of the directional distance function (DDF) based DEA model of MLPI under the variable returns to scale technology when data takes on negative values. We address this problem by developing a novel DDF-based DEA model that computes an improved MLPI. We extend the DDF approach to the dynamic network structure and introduce the dynamic MLPI for analyzing the performance of DMUs over time. We also develop the dynamic sequential MLPI to detect shifts in the efficient frontiers due to random shocks or technological advancements over time. The dynamic network structure in the two indexes comprises multiple divisions in DMUs connected vertically by intermediate productivity links and horizontally over time by carryovers. The proposed models are feasible and bounded with undesirable features and negative and non-negative data values. Real data of 39 Indian commercial public and private banks from 2008 to 2019 used to illustrate the two indexes.

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“…Forghani and Najafi [6], applied the sensitivity analysis to DMUs for two-stage DEA. Furthermore, in recent years, numerous reports and articles have been published in esteemed global journals verifying that two-stage DEA is operational; see [7,8,9,10,11,12,13,14].…”

Section: Introductionmentioning

confidence: 99%

“…The Model (14) is a nonlinear mathematical programming model and its global optimum cannot be found easily. Moreover, Model (14) is dependent on the -cut and should be solved for differentcut levels with a predetermined step-size. The values of Equations (8-13) has been replaced in Model (14):…”

mentioning

confidence: 99%

A Novel Two-Stage DEA Model in Fuzzy Environment: Application to Industrial Workshops Performance Measurement

Soltani¹,

Edalatpanah²,

Sobhani³

et al. 2020

IJCIS

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One of the paramount mathematical methods to compute the general performance of organizations is data envelopment analysis (DEA). Nevertheless, in some cases, the decision-making units (DMUs) have middle values. Furthermore, the conventional DEA models have been originally formulated solely for crisp data and cannot handle the problems with uncertain information. To tackle the above issues, this paper presents a two-stage DEA model with fuzzy data. The recommended technique is based on the fuzzy arithmetic and has a simple construction. Furthermore, to illustrate the new model, we investigate the efficiency of some industrial workshops in Iran. The results show the effectiveness and robustness of the new model.

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A Nerlovian cost inefficiency two-stage DEA model for modeling banks’ production process: Evidence from the Turkish banking system

Cited by 51 publications

References 103 publications

Does ownership structure affect firm performance? Evidence of Indian bank efficiency before and after the Global Financial Crisis

Does ownership structure affect firm performance? Evidence of Indian bank efficiency before and after the Global Financial Crisis

Malmquist-Luenberger productivity indexes for dynamic network DEA with undesirable outputs and negative data

A Novel Two-Stage DEA Model in Fuzzy Environment: Application to Industrial Workshops Performance Measurement

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