Unveiling endogeneity between competition and efficiency in Chinese banks: a two-stage network DEA and regression analysis

Although there is a growing number of research articles investigating the performance in the banking industry, research on Chinese banking efficiency is rather focused on discussing rankings to the detriment of unveiling its productive structure in light of banking competition. This issue is of utmost importance considering the relevant transformations in the Chinese economy over the last decades. This is a development of a two-stage network production process (production and intermediation approaches in banking, respectively) to evaluate the efficiency level of Chinese commercial banks. In the second stage regression analysis, an integrated Multi-Layer Perceptron/Hidden Markov model is used for the first time to unveil endogeneity among banking competition, contextual variables, and efficiency levels of the production and intermediation approaches in banking. The competitive condition in the Chinese banking industry is measured by Panar–Rosse H-statistic and Lerner index under the Ordinary Least Square regression. Findings reveal that productive efficiency appears to be positively impacted by competition and market power. Second, credit risk analysis in older local banks, which focus the province level, would possibly be the fact that jeopardizes the productive efficiency levels of the entire banking industry in China. Thirdly, it is found that a perfect banking competition structure at the province level and a reduced market power of local banks are drivers of a sound banking system. Finally, our findings suggest that concentration of credit in a few banks leads to an increase in bank productivity.

     

How to measure the impact of environmental factors in a nonparametric production model

The measurement of technical efficiency allows managers and policy makers to enhance existing differentials and potential improvements across a sample of analyzed units. The next step involves relating the obtained efficiency estimates to some external or environmental factors which may influence the production process, affect the performances and explain the efficiency differentials. Recently introduced conditional efficiency measures (,  and ), including conditional FDH, conditional DEA, conditional order-m and conditional order-α, have rapidly developed into a useful tool to explore the impact of exogenous factors on the performance of Decision Making Units in a nonparametric framework. This paper contributes in a twofold fashion. It first extends previous studies by showing that a careful analysis of both full and partial conditional measures allows the disentangling of the impact of environmental factors on the production process in its two components: impact on the attainable set and/or impact on the distribution of the efficiency scores. The authors investigate these interrelationships, both from an individual and a global perspective. Second, this paper examines the impact of environmental factors on the production process in a new two-stage type approach but using conditional measures to avoid the flaws of the traditional two-stage analysis. This novel approach also provides a measure of inefficiency whitened from the main effect of the environmental factors allowing a ranking of units according to their managerial efficiency, even when facing heterogeneous environmental conditions. The paper includes an illustration on simulated samples and a real data set from the banking industry.     

Quantile regression for robust bank efficiency score estimation

We discuss quantile regression techniques as a robust and easy to implement alternative for estimating Farell technical efficiency scores. The quantile regression approach estimates the production process for benchmark banks located at top conditional quantiles. Monte Carlo simulations reveal that even when generating data according to the assumptions of the stochastic frontier model (SFA), efficiency estimates obtained from quantile regressions resemble SFA-efficiency estimates. We apply the SFA and the quantile regression approach to German bank data for three banking groups, commercial banks, savings banks and cooperative banks to estimate efficiency scores based on a simple value added function and a multiple-input–multiple-output cost function. The results reveal that the efficient (benchmark) banks have production and cost elasticities which differ considerably from elasticities obtained from conditional mean functions and stochastic frontier functions.     

A Comparison of Data Envelopment Analysis and Artificial Neural Networks as Tools for Assessing the Efficiency of Decision Making Units

This paper is concerned with the comparison of two popular non-parametric methodologies—data envelopment analysis and artificial neural networks—as tools for assessing performance. Data envelopment analysis has been established since 1978 as a superior alternative to traditional parametric methodologies, such as regression analysis, for assessing performance. Neural networks have recently been proposed as a method for assessing performance. In this paper, we use a simulated production technology of two inputs and one output for testing the success of the two methods for assessing efficiency. The two methods are also compared on their practical use as performance measurement tools on a set of bank branches, having multiple input and output criteria. The results demonstrate that, despite their differences, both methods offer a useful range of information regarding the assessment of performance.     

Consistency of returns-to-scale characterizations of production frontiers with respect to model specification

Returns-to-scale (RTS) characterizations and the underlying notion of scale elasticity are important characteristics of production frontiers, in both parametric and nonparametric methodologies of efficiency and productivity analysis. In practical applications of these methodologies, the model of technology is often experimented with and modified before it is finalized, which involves, for example, a change of the data set, incorporation, exclusion or aggregation of inputs and outputs, or experimentation with the production assumptions, or axioms, on which the model is based. While it is well-known how such modifications of technology affect the efficiency scores, their effect on the RTS characterization of the production frontier has not been sufficiently explored in the literature. In this paper we obtain several general results that clarify this issue.     

How to generate regularly behaved production data? A Monte Carlo experimentation on DEA scale efficiency measurement

Monte Carlo experimentation is a well-known approach used to test the performance of alternative methodologies under different hypotheses. In the frontier analysis framework, whatever the parametric or non-parametric methods tested, experiments to date have been developed assuming single output multi-input production functions. The data generated have mostly assumed a Cobb–Douglas technology. Among other drawbacks, this simple framework does not allow the evaluation of DEA performance on scale efficiency measurement. The aim of this paper is twofold. On the one hand, we show how reliable two-output two-input production data can be generated using a parametric output distance function approach. A variable returns to scale translog technology satisfying regularity conditions is used for this purpose. On the other hand, we evaluate the accuracy of DEA technical and scale efficiency measurement when sample size and output ratios vary. Our Monte Carlo experiment shows that the correlation between true and estimated scale efficiency is dramatically low when DEA analysis is performed with small samples and wide output ratio variations.     

Sensitivity analysis of efficiency and Malmquist productivity indices: An application to Spanish savings banks

Hypothesis testing and statistical precision in the context of non-parametric efficiency and productivity measurement have been investigated since the early 1990s. Recent contributions focus on this matter through the use of resampling methods—i.e., bootstrapping techniques. However, empirical evidence is still practically non-existent. This gap is more noticeable in the case of banking efficiency studies, where the literature is immense. In this work, we explore productivity growth and productive efficiency for Spanish savings banks over the (initial) post-deregulation period 1992–1998 using Data Envelopment Analysis (DEA) and bootstrapping techniques. Results show that productivity growth has occurred, mainly due to improvement in production possibilities, and that mean efficiency has remained fairly constant over time. The bootstrap analysis yields further evidence, as for many firms productivity growth, or decline, is not statistically significant. With regard to efficiency measurement, the bootstrap reveals that the disparities in the original efficiency scores of some firms are lessened to a great extent.     

Evaluation of China's regional hospital efficiency: DEA approach with undesirable output

This paper investigates regional hospital efficiency in China. As hospital operations naturally increase along with an undesirable output of patient mortality, which may induce medical disputes and ligations, this study adopts data envelopment analysis to evaluate and compare the efficiency scores obtained with and without considering the undesirable output. On the basis of province-level panel data over 2002–2008 and considering the risk-adjusted undesirable output, empirical estimates indicate that hospital efficiency is moderate, but increases gradually from 0.6881 to 0.8159. Importantly, without considering the undesirable output, the average efficiency score is overestimated and the efficiency ranking across provinces changes considerably. An efficiency gap is found between coastal and non-coastal regions, but this gap’s drop is mainly contributed by the fast efficiency improvement of the western regions. However, the central regions continue to achieve a significantly lower efficiency score than the eastern and western regions. Moreover, the initiation of the New Rural Cooperative Medical System has overall enhanced hospital efficiency in China, especially for the non-coastal regions.     

Reconciling ratio analysis and DEA as performance assessment tools

The assessment of operational performance remains a fundamental challenge both in practice and in theory. Data envelopment analysis (DEA) is one method developed in production economic theory and applied by researchers to study groups of enterprises. In practice, individual enterprises almost universally rely on simple output–input ratios. Each approach has its strengths and weaknesses, but the theoretical connection between the two has not been fully articulated. This paper uses the framework of DEA to establish a mathematical relationship between DEA efficiency scores and corresponding ratio analysis. The relationship can be expressed as a product of seven components: technical efficiency, technical change, scale efficiency, input slack factor, input substitution factor, output slack factor and output substitution factor.     

Measuring the impacts of production risk on technical efficiency: A state-contingent conditional order-m approach

This article studies the influence of risk on farms’ technical efficiency levels. The analysis extends the order-m efficiency scores approach proposed by Daraio and Simar (2005) to the state-contingent framework. The empirical application focuses on cross section data of Catalan specialized crop farms from the year 2011. Results suggest that accounting for production risks increases the technical performance. A 10% increase in output risk will result in a 2.5% increase in average firm technical performance.     

Modelling banking sector stability with multicriteria approaches

Banking crises can be damaging for the economy, and as the recent experience has shown, nowadays they can spread rapidly across the globe with contagious effects. Therefore, the assessment of the stability of a county’s banking sector is important for regulators, depositors, investors and the general public. In the present study, we propose the development of classification models that assign the banking sectors of various countries in three classes, labelled “low stability”, “medium stability”, and “high stability”. The models are developed using three multicriteria decision aid techniques, which are well-suited to ordinal classification problems. We use a sample of 114 banking sectors (i.e., countries), and a set of criteria that includes indicators of the macroeconomic, institutional and regulatory environment, as well as basic characteristics of the banking and financial sector. The models are developed and tested using a tenfold cross-validation approach and they are benchmarked against models developed with discriminant analysis and logistic regression.     

Comparative efficiency analysis of Portuguese bank branches

The advent of Internet banking and phone banking is changing the role of bank branches from a predominantly transaction-based one to a sales-oriented role. This paper reports on an assessment of the branches of a Portuguese bank in terms of their performance in their new roles in three different areas: Their efficiency in fostering the use of new transaction channels, their efficiency in increasing sales and their customer base, and their efficiency in generating profits. Service quality is also a major issue in service organisations like bank branches, and therefore we analyse the way this dimension of performance has been accounted for in the literature and take it into account in our empirical application. We have used data envelopment analysis (DEA) for the different performance assessments, but we depart from traditional DEA models in some cases. Performance comparisons on each dimension allowed us to identify benchmark bank branches and also problematic bank branches. In addition, we found positive links between operational and profit efficiency and also between transactional and operational efficiency. Service quality is positively related with operational and profit efficiency.     

Computing nonperforming loan prices in banking efficiency analysis

Within the framework of banking efficiency analysis, we propose a methodology for computing unobservable shadow prices for nonperforming loans (NPL). Our approach is to include NPL as an undesirable output variable in a distance function stochastic frontier analysis. We conduct a panel study of US and European banks during the most recent financial crisis by adopting a semi-nonparametric Fourier specification, which ensures convergence to the true values of both the estimated function and the related efficiency. Computing NPL prices has several advantages, such as identifying approaching crises, quantifying the responsibilities of governments and banks for credit risk and determining appropriate regulatory interventions.

     

Incorporating performance measures with target levels in data envelopment analysis

Data envelopment analysis (DEA) is a technique for evaluating relative efficiencies of peer decision making units (DMUs) which have multiple performance measures. These performance measures have to be classified as either inputs or outputs in DEA. DEA assumes that higher output levels and/or lower input levels indicate better performance. This study is motivated by the fact that there are performance measures (or factors) that cannot be classified as an input or output, because they have target levels with which all DMUs strive to achieve in order to attain the best practice, and any deviations from the target levels are not desirable and may indicate inefficiency. We show how such performance measures with target levels can be incorporated in DEA. We formulate a new production possibility set by extending the standard DEA production possibility set under variable returns-to-scale assumption based on a set of axiomatic properties postulated to suit the case of targeted factors. We develop three efficiency measures by extending the standard radial, slacks-based, and Nerlove–Luenberger measures. We illustrate the proposed model and efficiency measures by applying them to the efficiency evaluation of 36 US universities.     

Discriminating efficient units using MAJ FDH

Free Disposal Hull (FDH) is one of the tools in the theoretical and empirical work on the measurement of productive efficiency. Excluding linear combinations of extremal observations to construct this reference technology entails that many of the observations belonging to an evaluated dataset are labeled efficient by this method. Few researchers have sought to improve the discrimination power of FDH. Van Puyenbroeck [H. Tulkens, On FDH efficiency analysis: some methodological issues and applications to retail, banking, courts and urban transit, Journal of Productivity Analysis 4 (1993) 183-210] modified standard FDH method by using Andersen and Petersen [N. Adler, L. Friedman, Z. Siunuany-Stern, Review of ranking methods in the data envelopment analysis context, European Journal of Operational Research, 140 (2002) 249-265], referred to A&P FDH. Jahanshahloo et al. [J. Doyle, R. Green, Efficiency and cross-efficiency in DEA: derivation, meanings and uses, Journal of Operational Research Society 45 (5) (1994) 567-578] used 0-1 linear programming (LP), referred to 0-1 LP FDH to find FDH-efficient units. The purpose of this paper is two-folds: to propose MAJ FDH, similar to in spirit as the ranking method in data envelopment analysis by Mehrabian et al. [S. Mehrabian, M.R. Alirezaee, G.R. Jahanshahloo, A complete efficiency ranking of decision making units in data envelopment analysis, Communicational Optimization and Applications 14 (1999) 261-266] that may thus be used to discriminate between FDH-efficient units and to examine the tie-breaking ability of A&P FDH, 0-1 LP FDH, and MAJ FDH by using three numerical examples. Results of the comparisons show: (i) as the number of DMU, input and output is small where all of input and output levels are positive, the A&P FDH can provide a full ranking; (ii) as the number of DMU, input and output is small where some of input and output levels are equal to zero, none of three extended FDH methods can provide a full ranking; and (iii) as the number of DMU, input and output are increased where all of input and output levels are positive, it seems that ranking by MAJ FDH is more precise than other FDH methods.     

Network DEA efficiency in input–output models: With an application to OECD countries

We undertake network efficiency analysis within an input–output model that allows us to assess potential technical efficiency gains by comparing technologies corresponding to different economies. Input–output tables represent a network where different sectoral nodes use primary inputs (endowments) to produce intermediate input and outputs (according to sectoral technologies), and satisfy final demand (preferences). Within the input–output framework it is possible to optimize primary inputs allocation, intermediate production and final demand production by way of non-parametric data envelopment analysis (DEA) techniques. DEA allows us to model the different subtechnologies corresponding to alternative production processes, to assess efficient resource allocation among them, and to determine potential output gains if inefficiencies were dealt with. The proposed model optimizes the underlying multi-stage technologies that the input–output system comprises identifying the best practice economies. The model is applied to a set of OECD countries.     

Performance evaluation: An integrated method using data envelopment analysis and fuzzy preference relations

In a multi-attribute decision-making (MADM) context, the decision maker needs to provide his preferences over a set of decision alternatives and constructs a preference relation and then use the derived priority vector of the preference to rank various alternatives. This paper proposes an integrated approach to rate decision alternatives using data envelopment analysis and preference relations. This proposed approach includes three stages. First, pairwise efficiency scores are computed using two DEA models: the CCR model and the proposed cross-evaluation DEA model. Second, the pairwise efficiency scores are then utilized to construct the fuzzy preference relation and the consistent fuzzy preference relation. Third, by use of the row wise summation technique, we yield a priority vector, which is used for ranking decision-making units (DMUs). For the case of a single output and a single input, the preference relation can be directly obtained from the original sample data. The proposed approach is validated by two numerical examples.     

Prior-Ratio-Analysis procedure to improve data envelopment analysis for performance measurement

This paper proposes the use of a Prior-Ratio-Analysis procedure, analysing output/input ratio indicators, allowing the improvement in efficiency measurement by means of data envelopment analysis (DEA) methodology. This prior analysis is based on the existence of a relationship of individual ratio in the firms to DEA efficiency scores. Use of the proposed procedure allows (i) detection of efficient units whose efficiency could be overestimated and (ii) identification of certain inputs/outputs enhancing particular behaviours. Accordingly, the DEA efficiency analysis could be improved with a major understanding about the factors determining the unit efficiency, and with a measure as a true indicator for discriminating between units, and for ranking them.     

Technical efficiency and economies of scale in state owned enterprises: The Hellenic telecommunications organisation

In this paper we propose methodologies for assessing the relative efficiency of state owned enterprises that operate under monopolistic conditions. Irrespective of the ownership structure (private or public) monopolistic operation is often given unclear/conflicting operating objectives by their principals or regulators and thus efficiency should be assessed using alternative objectives such as cost, production and revenue. On the methodological side the paper employs parametric functional forms using ordinary least squares and goal programming methods of estimation. A secondary estimation stage was also pursued with the input/output variables of the models being corrected for technical inefficiency. The methodology was applied to examine the technical efficiency and economies of scale of the Hellenic telecommunications organisation over the period 1971–1993.