Financial sustainability and poverty outreach within a network of village banks in Cameroon: A multi-DEA approach

Microfinance institutions face a double bottom-line. They perform financial tasks by giving microcredits to their customers and support projects aiming at reducing poverty. In doing so, they have to be financially self-sufficient and to target poor people excluded from the traditional financial systems. However, a trade-off may exist between financial sustainability and poverty outreach for these institutions. By using a multi-DEA approach, this paper shows that even if a trade-off exists for 15% of the MC² (Mutuelles Communautaires de Croissance) in Cameroon, there is no trade-off for 46% of them. In order to increase, without trade-off, financial and social performance of inefficient MC², a benchmarking approach combing DEA and performance indicators has been developed. DEA is used for identifying best-practices and setting benchmarking goals. Performance indicators are used for characterizing areas needing improvements and following the evolution of MC² toward their goals, i.e., for implementing benchmarking. Complementarity of both approaches provides a tool box for improving financial and social efficiency and reducing the trade-off between financial sustainability and poverty outreach of microfinance institutions.     

Network DEA approach to airports performance assessment considering undesirable outputs

In this paper, a directional distance approach is proposed to deal with network DEA problems in which the processes may generate not only desirable final outputs but also undesirable outputs. The proposed approach is applied to the problem of modelling and benchmarking airport operations. The corresponding network DEA model considers two process (Aircraft Movement and Aircraft Loading) with two final outputs (Annual Passenger Movement and Annual Cargo handled), one intermediate product (Aircraft Traffic Movements) and two undesirable outputs (Number of Delayed Flights and Accumulated Flight Delays). The proposed approach has been applied to Spanish airports data for year 2008 comparing the computed directional distance efficiency scores with those obtained using a conventional, single-process directional distance function approach. From this comparison, it can be concluded that the proposed network DEA approach has more discriminatory power than its single-process counterpart, uncovering more inefficiencies and providing more valid results.     

Efficiency decomposition for general multi-stage systems in data envelopment analysis

Conventional data envelopment analysis (DEA) models only consider the inputs supplied to the system and the outputs produced from the system in measuring efficiency, ignoring the operations of the internal processes. The results thus obtained sometimes are misleading. This paper discusses the efficiency measurement and decomposition of general multi-stage systems, where each stage consumes exogenous inputs and intermediate products (produced from the preceding stage) to produce exogenous outputs and intermediate products (for the succeeding stage to use). A relational model is developed to measure the system and stage efficiencies at the same time. By transforming the system into a series of parallel structures, the system efficiency is decomposed into the product of a modification of the stage efficiencies. Efficiency decomposition enables decision makers to identify the stages that cause the inefficiency of the system, and to effectively improve the performance of the system. An example of an electricity service system is used to explain the idea of efficiency decomposition.     

Context-based competition strategy and performance analysis with fixed-sum outputs: an application to banking sector

In the prior literature on performance measurement of firms with fixed-sum outputs, an equilibrium-efficient frontier is constructed. This paper shows that a single equilibrium-efficient frontier needs a significant trade-off between efficient and inefficient firms, and this may be impossible in practical applications. We develop a data envelopment analysis (DEA) model to construct multiple equilibrium-efficient frontiers in the presence of fixed-sum outputs. The approach uses context-dependent DEA that refers to a DEA approach where a set of firms are assessed against a particular assessment context. Numerical examples are used to illustrate the applicability of the approach.     

DEA model with shared resources and efficiency decomposition

Data envelopment analysis (DEA) has proved to be an excellent approach for measuring performance of decision making units (DMUs) that use multiple inputs to generate multiple outputs. In many real world scenarios, DMUs have a two-stage network process with shared input resources used in both stages of operations. For example, in hospital operations, some of the input resources such as equipment, personnel, and information technology are used in the first stage to generate medical record to track treatments, tests, drug dosages, and costs. The same set of resources used by first stage activities are used to generate the second-stage patient services. Patient services also use the services generated by the first stage operations of housekeeping, medical records, and laundry. These DMUs have not only inputs and outputs, but also intermediate measures that exist in-between the two-stage operations. The distinguishing characteristic is that some of the inputs to the first stage are shared by both the first and second stage, but some of the shared inputs cannot be conveniently split up and allocated to the operations of the two stages. Recognizing this distinction is critical for these types of DEA applications because measuring the efficiency of the production for first-stage outputs can be misleading and can understate the efficiency if DEA fails to consider that some of the inputs generate other second-stage outputs. The current paper develops a set of DEA models for measuring the performance of two-stage network processes with non splittable shared inputs. An additive efficiency decomposition for the two-stage network process is presented. The models are developed under the assumption of variable returns to scale (VRS), but can be readily applied under the assumption of constant returns to scale (CRS). An application is provided.     

A joint measurement of efficiency and effectiveness for non-storable commodities: Integrated data envelopment analysis approaches

Efficiency and effectiveness for non-storable commodities represent two distinct dimensions and a joint measurement of both is necessary to fully capture the overall performance. This paper proposes two novel integrated data envelopment analysis (IDEA) approaches, the integrated Charnes, Cooper and Rhodes (ICCR) and integrated Banker, Charnes and Cooper (IBCC) models, to jointly analyze the overall performance of non-storable commodities under constant and variable returns to scale technologies. The core logic of the proposed models is simultaneously determining the virtual multipliers associated with inputs, outputs, and consumption by additive specifications for technical efficiency and service effectiveness terms with equal weights. We show that both ICCR and IBCC models possess the essential properties of rationality, uniqueness, and benchmarking power. A case analysis also demonstrates that the proposed novel IDEA approaches have higher benchmarking power than the conventional separate DEA approaches. More generalized specifications of IDEA models with unequal weights are also elaborated.     

Benchmarking countries’ environmental performance

Environmental performance assessments are often conducted using environmental indicators. Although these indicators provide a starting point for performance assessments, they do not provide guidelines that countries should follow to improve performance. This paper develops an enhanced Data Envelopment Analysis (DEA) model that provides a single summary measure of countries’ environmental performance, based on the aggregation of the indicators that underlie the estimation of the Environmental Performance Index (EPI). The DEA model used is based on a novel specification of weight restrictions. The main contribution of the methodology used in this paper is to enable benchmarking in such a way that it becomes possible to identify the strengths and weaknesses of each country, as well as the peers with similar features to the country under assessment. These peers provide examples of good environmental practices that countries with worse performance should follow to improve performance.     

基于CAR-DEA方法的环境效率评价研究

现有环境效率评价的DEA方法没有考虑多维偏好约束问题,即不同决策单元对不同期望产出和不期望产出的偏好不同. 以地区为例,不同地区对GDP、废水和废气赋予的权重偏好各不相同. 在这种情况下,由于各决策单元的偏好约束不同,形成多维偏好约束集,在传统DEA模型中容易出现无可行解现象. 针对这一问题,基于CAR-DEA方法,结合保证域理论,提出一种解决多维偏好约束集问题的环境效率评价模型. 采用中国工业系统的环境效率评价实例对提出的方法进行了分析和说明.     

General and multiplicative non-parametric corporate performance models with interval ratio data

The increasing intensity of global competition has led organizations to utilize various types of performance measurement tools for improving the quality of their products and services. Data envelopment analysis (DEA) is a methodology for evaluating and measuring the relative efficiencies of a set of decision making units (DMUs) that use multiple inputs to produce multiple outputs. All the data in the conventional DEA with input and/or output ratios assumes the form of crisp numbers. However, the observed values of data in real-world problems are sometimes expressed as interval ratios. In this paper, we propose two new models: general and multiplicative non-parametric ratio models for DEA problems with interval data. The contributions of this paper are fourfold: (1) we consider input and output data expressed as interval ratios in DEA; (2) we address the gap in DEA literature for problems not suitable or difficult to model with crisp values; (3) we propose two new DEA models for evaluating the relative efficiencies of DMUs with interval ratios, and (4) we present a case study involving 20 banks with three interval ratios to demonstrate the applicability and efficacy of the proposed models where the traditional indicators are mostly financial ratios.     

Weight-restricted DEA in action: from expert opinions to mathematical models

A common problem in real-world DEA applications is that all inputs and outputs may not be equally relevant to the organizations analysed and their stakeholders. In many cases, one is also faced with a data set where the decision-making units do not clearly outnumber the quantity of inputs and outputs. This study reports an application where DEA embellished with weight restrictions is used to analyse the efficiency of public organizations to overcome the above-mentioned problems. Whereas there are numerous documented applications of weight-restricted DEA in the literature, the process of defining the actual weight restrictions is seldom described. However, that part — defining the actual weights restrictions based on price, preference or value information — is the most difficult step involved in using the weight-restricted DEA. Comparing various weight restriction schemes with real data suggests that the ability to consider and include preference information in DEA adds important insights into the analysis.     

Supply chain DEA: production possibility set and performance evaluation model

Performance evaluation is of great importance for effective supply chain management. The foundation of efficiency evaluation is to faithfully identify the corresponding production possibility set. Although a lot of researches have been done on supply chain DEA models, the exact definition for supply chain production possibility set is still in absence. This paper defines two types of supply chain production possibility sets, which are proved to be equivalent to each other. Based upon the production possibility set, a supply chain CRS DEA model is advanced to appraise the overall technical efficiency of supply chains. The major advantage of the model lies on the fact that it can help to find out the most efficient production abilities in supply chains, by replacing or improving inefficient subsystems (supply chain members). The proposed model also directly identifies the benchmarking units for inefficient supply chains to improve their performance. A real case validates the reasonableness and acceptability of this approach.     

基于两阶段DEA方法的中国商业银行效率评价

本文考虑商业银行吸收存款和发放贷款的两阶段特点,引入“顾客满意度”这一产出指标,应用两阶段DEA模型计算了我国15家商业银行在2008年和2011年的服务质量效率和盈利效率,结果显示我国商业银行的盈利效率显著高于服务质量效率。进而综合考虑服务和盈利指标,分析了各银行的综合效率,并通过计算各银行Malmquist指数研究我国商业银行2008年到2011年间的效率变化。结果表明:各商业银行从2008年到2011年的效率整体呈上升趋势,各商业银行的技术效率以及整个银行业的效率都有一定的提高。最后,应用Tobit模型分析了影响银行效率的因素,结果显示银行的贷存比、资产利润率和不良贷款率对效率影响显著。     

A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA–DA (Discriminant Analysis)

This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.     

Modelling undesirable outputs with zero sum gains data envelopment analysis models

Data envelopment analysis (DEA) literature has proposed alternative models for performance assessment in the presence of undesirable outputs, such as pollutant emissions, where increased outputs imply reduced performance. However, the case where global equilibrium of outputs should be imposed has not yet been considered. We propose that the zero sum gains DEA (ZSG-DEA) models look especially suitable for treating equilibrium models, where the sum of the quantities produced by all decision-making units can be set as the upper admissible bound. This paper uses ZSG-DEA models to evaluate the carbon dioxide emission case study, which can be considered part of the Kyoto Protocol statement.     

A new approach in modelling undesirable output in DEA model

With an increasing attention on the environment, one of the major research thrusts in Data Envelopment Analysis (DEA) based performance evaluation is the undesirable output in the conventional DEA model. There is considerable research published on the undesirable aspects of production outputs. However, the economic implications and the suitability of the DEA models for incorporating the undesirable outputs are less carefully investigated and discussed. In this paper, a comparative study is conducted of typical eco-DEA models to illustrate this issue. We propose a ratio model to evaluate the undesirable as well as the desirable outputs simultaneously. We apply the specially developed model to investigate the impact of production pollutants while conducting the efficiency evaluation in the textile industry of China. The results reveal that the production output-oriented efficiency evaluation can be significantly altered once the environmental aspects are factored into the model.     

Context-dependent performance standards in DEA

Data envelopment analysis (DEA) is a mathematical approach to measuring the relative efficiency of peer decision making units (DMUs). It is particularly useful where no a priori information on the tradeoffs or relations among various performance measures is available. However, it is very desirable if “evaluation standards,” when they can be established, be incorporated into DEA performance evaluation. This is especially important when service operations are under investigation, because service standards are generally difficult to establish. The approaches that have been developed to incorporate evaluation standards into DEA, as reported in the literature, have tended to be rather indirect, focusing primarily on the multipliers in DEA models. This paper introduces a new way of building performance standards directly into the DEA structure when context-dependent activity matrixes exist for different classes of DMUs. For example, two sets of branches, whose transaction times are known to be different from each other, usually have two different activity matrixes. We develop a procedure so that a set of standard DMUs can be generated and incorporated directly into the DEA analysis. The proposed approach is applied to a sample of 100 branches of a major Canadian bank where different sets of time standards exist for three distinct groups of branches.     

Productivity change and innovation in Norwegian electricity distribution companies

Regulators of electricity distribution networks have typically applied Data Envelopment Analysis (DEA) to cross-section data for benchmarking purposes. However, the use of panel data to analyse the impact of regulatory policies on productivity change over time is less frequent. The main purpose of this paper is to construct a Malmquist productivity index to examine the recent productivity change experienced by Norwegian distribution companies between 2004 and 2007. The Malmquist index is decomposed in order to explore the sources of productivity change, and to identify the innovator companies that pushed the frontier forward each year. The input and output variables considered are those used by the Norwegian regulator. In order to reflect appropriately the exogenous conditions where the companies operate, the efficiency model used in this paper incorporates geography variables as outputs of the DEA model. Unlike the model used by the regulator, we included virtual weight restrictions in the DEA formulation to correct the biases in the DEA results that may be associated to a judicious choice of weights by some of the companies.     

A DEA two-stage decision processes with shared resources

Data envelopment analysis (DEA) is an approach for measuring the relative efficiency of peer decision making units that have multiple inputs and outputs. In most practical applications of DEA presented in the literature, the presented models assume that outputs are produced perfectly (see Charnes et al. Eur J Oper Res 2:429–444, 1978). However, in many real situations, some outputs are imperfect and they need to be repaired. This paper develops a DEA approach for measuring the efficiency of decision processes which can be divided into two interdependent stages, arranged in series. The novelty of the proposed approach is the existence of perfect and imperfect outputs in a two-stage decision process. This application of two-stage process involves shared resources and the paper gives a best split of these shared resources between two stages. The case of Iranian car representatives is presented.     

Analysis of hedge fund strategies using slack-based DEA models

Hedge funds have made a significant impact on the performance of world financial markets in recent times. Our objective in this paper is to develop a robust framework for the evaluation of hedge funds by incorporating a maximum number of performance measures through public data sources. We analyse the hedge fund strategies (styles) using a variety of classical risk-return measures with the help of slack-based Data Envelopment Analysis (DEA) models to determine a unique performance indicator. The main thrust is to investigate the risk return profile of 4730 hedge funds classified under 18 different strategies using multiple inputs and outputs. The originality of the work lies in applying Slack-Based DEA to decipher the risk-return profile of these strategies using advanced risk-return measures such as Value at Risk, drawdown, lower and higher partial moments and skewness. We find that the correlation between the ranking of hedge fund strategies based on Sharpe ratio and the DEA models is very low; at the same time, there is a significant correlation between rankings obtained by the application of DEA using different sets of input/output measures. We have also compared the DEA rankings with other traditional financial ratios such as modified Sharpe ratio, Sortino ratio and Calmar ratio. The paper also studies the impact of events such as the Asian financial crisis on the performance of hedge funds. The study around the event shows that only a relatively small number of strategies performed better during times of turmoil.     

Modeling undesirable factors in data envelopment analysis

Data envelopment analysis is a mathematical programming technique for identifying efficient frontiers for peer decision making units with multiple inputs and multiple outputs. These performance factors (inputs and outputs) are classified into two groups: desirable and undesirable. Obviously, undesirable factors in production process should be reduced to improve the performance. In the current paper, we present a data envelopment analysis (DEA) model in which can be used to improve the relative performance via increasing undesirable inputs and decreasing undesirable outputs.