An application of data envelopment analysis for Korean banks with negative data

2017 ◽  
Vol 24 (4) ◽  
pp. 1052-1064 ◽  
Author(s):  
Yong Joo Lee ◽  
Seong-Jong Joo ◽  
Hong Gyun Park
Keyword(s):  
Data Envelopment Analysis ◽  
Data Sets ◽  
Data Envelopment ◽  
Negative Data ◽  
Ownership Type ◽  
Data Set ◽  
Translation Invariant ◽  
Content Type ◽  
Dea Models ◽  
Regional Banks

Purpose The purpose of this paper is to measure the comparative efficiency of 18 Korean commercial banks under the presence of negative observations and examine performance differences among them by grouping them according to their market conditions. Design/methodology/approach The authors employ two data envelopment analysis (DEA) models such as a Banker, Charnes, and Cooper (BCC) model and a modified slacks-based measure of efficiency (MSBM) model, which can handle negative data. The BCC model is proven to be translation invariant for inputs or outputs depending on output or input orientation. Meanwhile, the MSBM model is unit invariant in addition to translation invariant. The authors compare results from both models and choose one for interpreting results. Findings Most Korean banks recovered from the worst performance in 2011 and showed similar performance in recent years. Among three groups such as national banks, regional banks, and special banks, the most special banks demonstrated superb performance across models and years. Especially, the performance difference between the special banks and the regional banks was statistically significant. The authors concluded that the high performance of the special banks was due to their nationwide market access and ownership type. Practical implications This study demonstrates how to analyze and measure the efficiency of entities when variables contain negative observations using a data set for Korean banks. The authors have tried two major DEA models that are able to handle negative data and proposed a practical direction for future studies. Originality/value Although there are research papers for measuring the performance of banks in Korea, all of the papers in the topic have studied efficiency or productivity using positive data sets. However, variables such as net incomes and growth rates frequently include negative observations in bank data sets. This is the first paper to investigate the efficiency of bank operations in the presence of negative data in Korea.

scholarly journals Modifying the convexity condition in Data Envelopment Analysis (DEA)

2020 ◽  
Vol 33 (02) ◽  
pp. 454-467
Author(s):  
Roghyeh Malekii Vishkaeii ◽  
Behrouz Daneshian ◽  
Farhad Hosseinzadeh Lotfi
Keyword(s):  
Data Envelopment Analysis ◽  
Real Data ◽  
Data Envelopment ◽  
Convexity Condition ◽  
Discrimination Power ◽  
Data Set ◽  
Step Procedure ◽  
Proposed Model ◽  
Dea Models ◽  
Production Possibility Set

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.

RELAXING THE IMPACT OF EXTREME UNITS IN DATA ENVELOPMENT ANALYSIS

2012 ◽  
Vol 11 (05) ◽  
pp. 893-907 ◽  
Author(s):  
YIANNIS G. SMIRLIS ◽  
DIMITRIS K. DESPOTIS
Keyword(s):  
Data Envelopment Analysis ◽  
Original Data ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Programming Technique ◽  
Data Set ◽  
Usual Practice ◽  
Dea Models ◽  
Production Possibility Set ◽  
The Impact

Data envelopment analysis (DEA) is a nonparametric linear programming technique for measuring the relative efficiency of decision making units (DMUs) on the basis of multiple inputs and outputs. DEA assessments, however, are proved to be sensitive to extreme units that deviate substantially in their input/output patterns. In this paper we introduce an approach for handling extreme observations in DEA, i.e., observations that exhibit irregularly high values in some outputs and/or low values in some inputs. Unlike the usual practice of removing such observations, we retain them in the production possibility set reducing their impact on the other units. Our modeling approach is based on the concept of diminishing returns, assuming that the contribution of an output (input) to the efficiency score diminishes as the output increases beyond a pre-specified level, i.e., the level beyond which a value is characterized as extreme. According to our approach the original data set is transformed to an augmented data set, where standard DEA models can then be applied, remaining thus in the grounds of the standard DEA methodology. We illustrate our approach with a numerical example.

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Reza Kiani Mavi ◽  
Neda Kiani Mavi ◽  
Reza Farzipoor Saen ◽  
Mark Goh
Keyword(s):  
Sustainable Development ◽  
Data Envelopment Analysis ◽  
Oecd Countries ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Environmental Research ◽  
Data Envelopment ◽  
Discrimination Power ◽  
Content Type ◽  
Dea Models

PurposeDespite unanimity in the literature that eco-innovation (EI) leads to sustainable development, evidence remains limited on measuring EI efficiency with the Malmquist productivity index (MPI). In conventional data envelopment analysis (DEA) models, decision-making units (DMUs) are inclined to assign more favorable weights, even zero, to the inputs and outputs to maximize their own efficiency. This paper aims to overcome this shortcoming by developing a common set of weights (CSW). Design/methodology/approachUsing goal programming, this study develops a CSW model to evaluate the EI efficiency of the organization for economic co-operation and development (OECD) countries and track their changes with MPI during 2010–2018. FindingsAchieving a complete ranking of DMUs, findings show the higher discrimination power of the proposed CSW compared with the original DEA models. Furthermore, results reveal that Iceland, Latvia and Luxembourg are the only OECD countries that have incessantly improved their EI productivity (MPI > 1) from 2010 to 2018. On the other hand, Japan is the OECD country that has experienced the highest yearly EI efficiency during 2010–2018. This paper also found that Iceland has the highest MPI over 2010–2018. Practical implicationsMore investment in environmental research and development (R&D) projects instead of generic R&D enables OECD members to realize more opportunities for sustainable development through minimizing energy use and environmental pollution in any form of waste and greenhouse gas emissions. Originality/valueIn addition to developing a novel common weights model for DEA-MPI to measure and evaluate the EI of OECD countries, this paper develops a CSW model by including the undesirable outputs for EI analysis.

Evaluating Taiwanese Bank Efficiency Using the Two-Stage Range DEA Model

2016 ◽  
Vol 16 (04) ◽  
pp. 1043-1068 ◽  
Author(s):  
Wei-Hsin Kong ◽  
Tsu-Tan Fu ◽  
Ming-Miin Yu
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment Analysis Model ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Analysis Model ◽  
Negative Data ◽  
Two Stage ◽  
Data Set ◽  
Dea Model ◽  
Domestic Banks

This paper develops a range directional distance data envelopment analysis (DEA) model to simultaneously deal with the problems of negative data and undesirable outputs in the study of performance measurement with two-stage DEA. We report on the development of this model to handle both positive and negative data in a DEA framework and accommodate the problem of undesirable intermediate outputs in the first stage of operational processes. Unlike previous two-stage DEA models we allow for a nonuniform abatement factor imposing on stage 1’ production technology. Such a model is then applied to evaluate Taiwanese bank efficiencies both at the operational stage and profitability stage in banking activities based on a data set consisting of 35 domestic banks in Taiwan in the period 2007. The results indicate that, by the range directional two-stage data envelopment analysis model, the operational efficiency was smaller than the profitability efficiency. Many banks generated too many performing loans in which independent banks should reduce more performing loans than financial holding company subsidiary banks. Both the ratio of investments to loans and the ratio of nonperforming loans to performing loans did not have significant contributions to the efficiency. This paper is able to provide information for bank operators and researchers on the managerial and strategic implications of how negative data and undesirable outputs affect efficiency and how to measure efficiency appropriately.

Two-stage additive integer-valued data envelopment analysis models

2019 ◽  
Vol 14 (1) ◽  
pp. 199-213 ◽  
Author(s):  
Shahrooz Fathi Ajirlo ◽  
Alireza Amirteimoori ◽  
Sohrab Kordrostami
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Two Stage ◽  
Modified Model ◽  
Content Type ◽  
Multistage Processes ◽  
Multi Stage ◽  
Internal Structures ◽  
Dea Models ◽  
Analysis Models

Purpose The purpose of this paper is to propose a modified model in multi-stage processes when there are intermediate measures between the stages and in this sense, the new efficiency scores are more accurate. Conventional data envelopment analysis (DEA) models disregard the internal structures of peer decision-making units (DMUs) in evaluating their relative efficiency. Such an approach would cause managers to lose important DMU information. Therefore, in multistage processes, traditional DEA models encounter problems when intermediate measures are used for efficiency evaluation. Design/methodology/approach In this study, two-stage additive integer-valued DEA models were proposed. Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole. Findings Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole. Originality/value The advantage of the proposed models for multi-stage systems is that they can accurately determine the stages with the greatest weaknesses/strengths. By introducing an applied case in the Iranian power industry, the paper demonstrated the applications and advantages of the proposed models.

scholarly journals An Interval Efficiency Measurement in DEA When considering Undesirable Outputs

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Renbian Mo ◽  
Hongyun Huang ◽  
Liyang Yang
Keyword(s):  
Efficiency Score ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Mathematical Tool ◽  
Lower And Upper Bounds ◽  
Negative Data ◽  
Translation Invariant ◽  
Dea Models ◽  
Interval Efficiency ◽  
Decision Making Units

Data envelopment analysis (DEA) is a popular mathematical tool for analyzing the relative efficiency of homogenous decision-making units (DMUs). However, the existing DEA models cannot tackle the newly confronted applications with imprecise and negative data as well as undesirable outputs simultaneously. Thus, we introduce undesirable outputs into modified slack-based measure (MSBM) model and propose an interval-modified slack-based measure (IMSBM) model, which extends the application of interval DEA (IDEA) in fields that concern with less undesirable outputs. The novelties of the model are that it considers the undesirable outputs while dealing with imprecise and negative data, and it is slack-based. Furthermore, the model with undesirable outputs is proven translation-invariant and unit-invariant. Moreover, a numerical example is provided to illustrate the changes of the lower and upper bounds of the efficiency score after considering the undesirable outputs. The empirical results show that, without considering undesirable outputs, most of the lower bounds of the efficiency scores will be overestimated when the DMUs are weakly efficient and inefficient. The upper bound will also change after considering undesirable outputs when the DMU is inefficient. Finally, an improved degree of preference approach is introduced to rank the DMUs.

scholarly journals Assessment of transit transport corridor efficiency of landlocked African countries using data envelopment analysis

2018 ◽  
Vol 114 (1/2) ◽  
Author(s):  
Enagnon H. Fanou ◽  
Xuping Wang
Keyword(s):  
Data Envelopment Analysis ◽  
Transport Systems ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
African Countries ◽  
Comparative Performance ◽  
Data Set ◽  
Dea Models ◽  
And Performance ◽  
Using Data

We used a data envelopment analysis (DEA) to examine the efficiency and performance of transport systems of landlocked African countries (LLACs). We conducted a comparative performance efficiency analysis of transfer transport systems for LLACs’ corridors. Three different types of DEA models were proposed and used to measure the relative efficiencies of transit transport using a 6-year data set (2008–2013) of some selected LLACs. The results show that the average pure technical and scale efficiency scores are 90.89% and 37.13%, respectively. Two units (13.33%) are technically efficient (technical and scale efficiency) while four units (26.66%) are only purely technically efficient over the observed period. Swaziland was the most efficient corridor while the Central African Republic corridor was the least efficient throughout the monitored years. The results indicate the relevance of minimising trade costs to stimulate landlocked countries’ exports.

scholarly journals An integrated approach to grey relational analysis, analytic hierarchy process and data envelopment analysis

2016 ◽  
Vol 9 (1) ◽  
pp. 71-86 ◽  
Author(s):  
Mohammad Sadegh Pakkar
Keyword(s):  
Data Envelopment Analysis ◽  
Grey Relational Analysis ◽  
Data Envelopment ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Attribute Weights ◽  
Relational Analysis ◽  
Dea Models ◽  
Grey Relational ◽  
Hierarchy Process

Purpose This paper aims to propose an integration of the analytic hierarchy process (AHP) and data envelopment analysis (DEA) methods in a multiattribute grey relational analysis (GRA) methodology in which the attribute weights are completely unknown and the attribute values take the form of fuzzy numbers. Design/methodology/approach This research has been organized to proceed along the following steps: computing the grey relational coefficients for alternatives with respect to each attribute using a fuzzy GRA methodology. Grey relational coefficients provide the required (output) data for additive DEA models; computing the priority weights of attributes using the AHP method to impose weight bounds on attribute weights in additive DEA models; computing grey relational grades using a pair of additive DEA models to assess the performance of each alternative from the optimistic and pessimistic perspectives; and combining the optimistic and pessimistic grey relational grades using a compromise grade to assess the overall performance of each alternative. Findings The proposed approach provides a more reasonable and encompassing measure of performance, based on which the overall ranking position of alternatives is obtained. An illustrated example of a nuclear waste dump site selection is used to highlight the usefulness of the proposed approach. Originality/value This research is a step forward to overcome the current shortcomings in the weighting schemes of attributes in a fuzzy multiattribute GRA methodology.

How to use fuzzy screening system and data envelopment analysis for clustering sustainable suppliers? A case study in Iran

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mohammad Izadikhah ◽  
Reza Farzipoor Saen ◽  
Kourosh Ahmadi ◽  
Mohadeseh Shamsi
Keyword(s):  
Data Envelopment Analysis ◽  
Clustering Algorithm ◽  
Data Envelopment ◽  
Screening System ◽  
Content Type ◽  
Efficient Frontiers ◽  
Dea Models ◽  
Clustering Approach ◽  
First Time

PurposeThe aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.Design/methodology/approachFirst, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.FindingsThis paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.Originality/valueThe main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

A data envelopment analysis (DEA) model for building energy benchmarking

2019 ◽  
Vol 17 (4) ◽  
pp. 747-768 ◽  
Author(s):  
Baabak Ashuri ◽  
Jun Wang ◽  
Mohsen Shahandashti ◽  
Minsoo Baek
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Building Energy ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Data Set ◽  
Content Type ◽  
Dea Model ◽  
Energy Benchmarking

Purpose Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking. Design/methodology/approach Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency. Findings The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers. Originality/value The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.

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