Data Envelopment Analysis Window Research Articles

Measuring Efficiency in Korean Banking Industry by Window Data Envelopment Analysis

Measuring Efficiency in Korean Banking Industry by Window Data Envelopment Analysis

A comparative analysis of China’s regional energy and emission performance: Which is the better way to deal with undesirable outputs?

Measuring and improving the energy performance with considering emission constraints is an important issue for China’s energy conservation, pollutant emissions reduction and environment protection. This study utilizes several data envelopment analysis (DEA) based models to evaluate the total-factor energy and emission performance of China’s 30 regions within a joint production framework of considering desirable and undesirable outputs as well as separated energy and non-energy inputs. DEA window analysis is applied in this study to deal with cross-sectional and time-varying data, so as to measure the performance during the period of 2000–2009. Two treatments for undesirable outputs are combined with DEA models and the associated indicators for simplex energy performance and unified energy and emission performance measurement are proposed and compared. The evaluation results indicate that the treatment of undesirable outputs transformation is more appropriate for China’s regional energy and emission performance evaluation because it has stronger discriminating power and can provide more reasonable evaluation results that characterize China’s regions. The empirical result shows that east China has the highest and the most balanced energy and emission performance. The energy and emission performance of China remained stable during 2000–2003, decreased slightly during 2004–2006, and has continuously increased since 2007.

A Comparative Analysis of Window Width Selection Technique in Data Envelopment Analysis

The aim of this paper is to develop a methodology for the selection of window width in Data Envelopment Analysis (DEA). Analytical hierarchal process (AHP) is employed to achieve that purpose. Using panel data, the selected width is experimented with factual data, and results show robustness to efficiency assessment. This paper is the first to use AHP approach in the selection of a window width in DEA window analysis.

Evaluation on agricultural production efficiency of Guangdong Province by using DEA window analysis

Agricultural production efficiency is of the basis of comprehensive productive capacity of agriculture.Guangdong Prov-ince is now in promotion stage of modern agricultural development,which emphasizes agricultural production efficiency research.Using DEA(data envelopment analysis) theory,agricultural input-output efficiencies in 21 city-regions in Guangdong Province were calculated for 1994～2007.The relative efficiency and dynamic behavior of agricultural production were compared and evaluated laterally(i.e.,the same region but at different time windows) and longitudinally(i.e.,different regions at the same time window).The results depicted some gaps in average agricultural productivity and dynamic stability among regions.Agricultural input-output per-formance of the Pearl River Delta was relatively optimal.Although the performance level in the "east-west double wings" region was less than that in the Pearl River Delta region,developmental trend in agricultural productivity in the "east-west double wings" was more stable.Regions of lower agricultural productivity and significant fluctuations were concentrated in the mountain areas of north-ern Guangdong.While the lowest agricultural productivity efficiency was in Qingyuan City,the highest variation in agricultural pro-ductivity was in Shaoguan City.The findings laid not only the basis for in-depth insight into provincial agricultural input-output effi-ciency and development trends,but also provided information basis for related departments to establish management policies that can enhance agricultural productivity.

China’s regional energy and environmental efficiency: A DEA window analysis based dynamic evaluation

Data envelopment analysis (DEA) has recently become a popular approach in measuring the energy and environmental performance at the macro-economy level. A common limitation of several previous studies is that they ignored the undesirable outputs and did not consider the separation of inputs into energy resources and non-energy resources under the DEA framework. Thus, within a joint production framework of considering both desirable and undesirable outputs, as well as energy and non-energy inputs, this study analyzes China’s regional total-factor energy and environmental efficiency. This paper utilizes improved DEA models to measure the energy and environmental efficiency of 29 administrative regions of China during the period of 2000–2008. In addition, the DEA window analysis technique is applied to measure the efficiency in cross-sectional and time-varying data. The empirical results show that the east area of China has the highest energy and environmental efficiency, while the efficiency of the west area is worst. All three areas of China have similar trends in the variation of efficiency and in general the energy and environmental efficiency of China slightly increased from 2000 to 2008. The regions of the east area have a more balanced development than the regions of the central area and west area according to energy and environmental efficiency.

Dynamic efficiency assessment of the Chinese hotel industry

The paper introduces for the first time a totally dynamic two-stage approach to analyzing the hotel industry's technical efficiency at the sub-national level. The first stage uses data envelopment window analysis (DEWA) to assess regional hotel sectors' technical efficiency over time. Unlike previous studies, the second stage uses a dynamic Tobit model to investigate the impact of macro contextual factors on the hotel sector efficiency. The study chooses the Chinese hotel industry during the period 2001–2006 as its application setting. The findings of the investigation indicate that the Chinese hotel industry is approaching an efficient operation in general, recovering from a major dip in 2003 resulting from the Severe Acute Respiratory Syndrome (SARS) outbreak. In addition, the study introduces a novel two-dimensional efficiency-based matrix to assess the competitive advantage of different regions of the Chinese hotel sector. The paper presents strategic market implications for hoteliers, government decision-makers, and destination management organizations. The proposed methods are applicable for situations in which an exogenous event of a destabilizing impact (e.g., SARS) does occur.

An investigation into the relationship between size and efficiency of the Italian hospitality sector: A window DEA approach

This paper analyses the efficiency of hotels across all of the 20 regions in Italy using a data envelopment analysis (DEA). The empirical results indicate that Sardinia can be considered as a region “falling further behind”, whereas some regions in the North and Centre of Italy can be regarded as “moving ahead”. Using the island of Sardinia as a case study, approximately 150 firms are analysed in detail over the time span 2002–2005. Via a window DEA, both technical and scale efficiencies are computed. An efficiency comparison amongst hotels categorised by size and municipality is run. Finally, policy implications are drawn from the empirical findings that advise how to improve hotels that attained low efficiency scores.

An extended DEA windows analysis: Middle East and East African seaports

PurposeThe aim of this paper is to evaluate the efficiency of 22 cargo seaports situated in the regions of East Africa and Middle East.Design/methodology/approachThe data envelopment analysis (DEA) with window analysis model evaluates the efficiency score in terms of normal efficiency and super efficiency. The analysis is based on the panel data for the period of 2000‐2005.FindingsThe number of efficient decision making units (DMUs) under super efficiency is more than the number under normal efficiency.Originality/valueUsing panel data, this paper is the first study to use super efficiency with window analysis that compares the efficiency estimated with the normal efficiency and with super efficiency.

Economic efficiency and growth in the EU enlargement

This paper deals with the effects of EMU enlargement (European Economic and Monetary Union) by evaluating the economic efficiency of growth policies of the 25 member countries. By using Data Envelopment Window Analysis the paper measures the policies adopted initiating economic growth of the 25 EU members for the time period of 1995–2005. Different factors reflecting countries’ investment policies have been used in order to measure chronically countries’ economic efficiency. The results reveal that the old 15 EU members have faced problems reforming their economic policies in order to cope with the EU enlargement which in turn had an impact on their economic efficiencies.

Bank efficiency and share prices in China: empirical evidence from a three-stage banking model

This paper examines the relationship between the efficiency of China banks and its share price performance. Our analysis consists of three parts. First, we calculate the annual share price returns of the banks for each year between 1997 and 2006. Then we employ the data envelopment analysis (DEA) window analysis method to estimate the efficiency of each bank. Finally, we regress the annual share price returns on the change in efficiency, while controlling for other bank specific traits. The empirical findings suggest that large Chinese banks have exhibited higher technical and pure technical efficiency levels compared to their small and medium sized bank counterparts, while the medium sized banks have exhibited higher scale efficiency. The relationship between Chinese banks' efficiency and share price performance suggest that bank efficiency estimates derived from the DEA window analysis method contributes significant information towards share price returns beyond that provided by financial information.

Using DEA window analysis to measure efficiencies of Taiwan's integrated telecommunication firms

Taiwan's telecommunication firms are faced with an important challenge arising from a situation in which the growth rate of telephone subscribers has gradually decreased owing to limited market size and the policy of continuous deregulation propagated by the government. To deal with the challenge, several acquisitions among telecommunication firms have taken place and have caused the three currently leading firms to capture a market share of more than 80%. Considering how acquisitions and related strategies might affect the firms, this paper uses data envelopment analysis (DEA) under constant and variable returns-to-scale to measure firms’ efficiencies over the period 2001–2005. Because there are only three firms, DEA window analysis is employed to increase the number of decision-making units so that discriminating power can be increased. According to the results, the first finding shows that the acquisitions are justified by exhibiting higher scale efficiency (but lower pure technical efficiency) in the short run. The second finding indicates that adjusting the strategies such as enlarging market share to improving financial portfolios helps firms to achieve better scale size. The third finding justifies government efforts to privatize state-owned enterprises and liberalize the market to strengthen competitiveness.

Productive Efficiency of World Container Ports

This study evaluates the productive efficiency of 140 global hub and national gateway container ports in almost all countries of the world for the years 1991 and 2004. It adopts diverse models of data envelopment analysis (DEA), including two specific cases of DEA window analysis: intertemporal and contemporaneous. The study finds that world ports have experienced inter- and intraregional efficiency gaps, which have not lessened over time. In general, the larger-scale ports are more efficient because they drive technological development and adopt better managerial practices. However, many large ports operate under decreasing returns-to-scale at each port level. This implies a potential for interport competition as a way to increase port efficiency at an aggregated regional level. In addition, port size is not the sole factor in determining port efficiency. Newly developed ports in Asia and the Middle East and small Central American ports operate efficiently. They have created strong partnerships with emerging, globally franchised terminal operators. With small hinterlands, Oceanian ports have tried to improve efficiency by reforming institutional structures and managerial practices. However, they have not fully overcome the disadvantages of the lack of economies of scale. For them, interport competition may not be an effective means to improve efficiency.

A DEA window analysis on the product family mix selection for a semiconductor fabricator

In a competitive market, semiconductor fabricator must face an environment with multi-product types, multi-priority orders and demand changes in time. Since semiconductor fabrication has a very complicated production process, the above-stated characteristics make the production planning even more difficult. This paper applies data envelopment analysis (DEA) to find a set of product family mix that is efficient for the company to produce. To ensure long-term effectiveness in productivity and in profit gaining, window analysis is adopted to seek the most recommended set of product family mixes for manufacturing by measuring the performance changes over time. With this method, the performance of a mix in one period is compared not only with the performance of other mixes but also with its own performance in other periods. The proposed mechanism can provide guidance to the fabricator regarding strategies for aggregate planning so as to improve manufacturing efficiency.

Trends in the efficiency of Singapore's commercial banking groups

PurposeThe purpose of this paper is to examine the long‐term trend in the efficiency of the Singapore banking sector.Design/methodology/approachThe paper utilises the data envelopment analysis (DEA) window analysis methodology, which allows us to distinguish between three different types of efficiency namely, technical, pure technical and scale efficiencies. Given that the sample population is small, the DEA window analysis allows for a greater degree of freedom to the sample. The paper further examines whether the Singapore banking groups were drawn from the same environment during two sub‐periods by performing a series of parametric and non‐parametric tests. Finally, the paper attempts to investigate the consistency of the estimated DEA efficiency scores by examining its relationship with the traditional measures of banks’ performance.FindingsDuring the period of study, the results suggest that the Singapore banking groups have exhibited mean overall or technical efficiency of 88.4 per cent. It was found that the Singapore banking groups’ overall efficiency was on a declining trend during the earlier part of the studies, before increasing dramatically during the later period.Research limitations/implicationsThe paper could be extended to consider the production approach along with the intermediation approach, which has been applied in this paper. Investigation of changes in productivity over time as a result of technical change or technological progress or regress by employing the Malmquist Total Factor Productivity Index, could be another extension to the paper.Originality/valueThis paper provides new empirical evidence on the long‐term trends in the efficiency of the Singapore banking groups.

Combining DEA Window Analysis with the Malmquist Index Approach in a Study of the Canadian Banking Industry

The banking industry in Canada is essentially an oligopoly with five large participants controlling about 90% of the market. To evaluate the industry's performance over time, we need to deal with the problem of a small number of DMU's compared to the number of relevant inputs and outputs. To overcome this problem we use data envelopment analysis (DEA) window analysis, whereby efficiency scores for the 20 year period 1981–2000 are obtained. To measure productivity changes over time, Malmquist indices can be calculated from DEA scores. Using DEA window analysis scores, however, raise the question of how to define the “same period frontier” in a DEA window analysis. We show that for both the adjacent and the base period Malmquist index and for all suggested definitions of same period frontier, the standard decomposition into frontier shift and catching up effects gives inappropriate results when Malmquist indices are based on DEA window analysis scores.

An Application of DEA Windows Analysis to Container Port Production Efficiency

There have been various analyses of the efficiency of container port (or terminal) production using Data Envelopment Analysis (DEA) based on cross-sectional data. When time is not considered, the efficiency results derived using this approach can be biased. In order to overcome this problem, this paper applies DEA windows analysis, utilising panel data, to a sample of the world’s major container ports in order to deduce their relative efficiency. The results suggest that estimates of container port efficiency fluctuate over time. The paper concludes that existing programming methods for estimating efficiency are inadequate in capturing the long-term increased efficiency and competitiveness that accrue from significant investments.

DEA Window Analysis for Measuring Port Efficiencies in Serbia

The aim of the paper is to apply Data Envelopment Analysis (DEA) method in measuring and analyzing the efficiencies of ports on the river Danube. DEA window analysis is used to determine the efficiency of ports and to observe the possibility of changes in the port efficiency over time. A study is conducted to evaluate the efficiencies of ports on the territory of Serbia in order to identify the sources of inefficiencies and formulate proposals for improving the services of those ports and their operations through a four-year window analysis with port efficiency trends and average efficiencies. The progress is made in the measurement of port efficiency in relation to port productive activities - total area of warehouses, quay length, number of cranes and port throughput, for the Serbian river ports. Keywords: river ports, total area of warehouses, quay length, number of cranes, port throughput, port efficiency, DEA window analysis