Extended Data Envelopment Analysis Research Articles

Bullying Among Pupils at School and a Country’s Educational System: An Efficiency Evaluation of Educational Performance in Europe by Means of an Extended Data Envelopment Analysis

Bullying Among Pupils at School and a Country’s Educational System: An Efficiency Evaluation of Educational Performance in Europe by Means of an Extended Data Envelopment Analysis

SUSTAINABLE DIGITAL TRANSFORMATION: THE NEXUS BETWEEN ICT AND GLOBAL GREEN ECONOMIC GROWTH

As a new engine to promote global economic and social development, information and communication technology (ICT) plays a key role in the field of modern economy. The aim of this paper is to investigate the nexus between ICT and green total factor productivity (GTFP) on a global scale. An extended data envelopment analysis model (DEA), named WINDOWS-US-SBM, was constructed for calculating the GTFP of 65 countries from 2007 to 2019. This paper empirically analyzes the spatial effect and the transmission mechanism of ICT development on GTFP in countries from different income groups. The results show a prominent imbalance between ICT development and GTFP in various countries. ICT can effectively improve GTFP and play a crucial role in lower middle-income countries. The development of ICT can improve GTFP through technological progress, energy intensity, and trade openness. This paper is helpful to provide policy guidance for the development of ICT and give a new perspective of global green development.

A branch and efficiency algorithm to design a sustainable two-echelon supply chain network considering traffic congestion and uncertainty.

This work aims to design a sustainable two-echelon supply chain not only based on the widely used cost perspective, but also based on the efficient use and preservation of limited resources. For this purpose, a branch and efficiency (B&E) algorithm is developed, which includes an optimization model and an evaluation model. The proposed tri-objective optimization model simultaneously minimizes the total cost of the supply chain, maximizes the sustainability score, and minimizes inequity among customers. The solutions obtained from the optimization model are then evaluated by extended data envelopment analysis (EDEA) models based on common criteria (i.e., cost and service) and traffic congestion criterion. To take into account real-world conditions, parameters related to labor and demand are assumed under uncertainty. Since the presented models consist of more than one objective function, fuzzy goal programming (FGP) method is utilized to tread the multi-objectiveness. The obtained results from tackling a case study problem demonstrate that considering sustainability issues can positively affect both the economic and social aspects of the problem. Furthermore, the developed B&E algorithm is able to reduce costs in each iteration; this is what supply chain managers are interested in. On the other hand, this algorithm can provide more services to applicants compared to one of the competing algorithms.

Measuring Health System Efficiency; A Protocol Study

Background: To improve healthcare services’ quality, countries should measure their health systems’ efficiency and performance by robust methods.
 Objectives: We aimed to develop a national study to measure the efficiency of the health system in Iran.
 Methods: The literature review identified several methods for measuring efficiency; the most common one was data envelopment analysis (DEA). We adopted DEA, but its findings were simplistic and inaccurate, so we began to modify the method by determining the weight of each indicator. We identified the efficiency measurement indicators, in line with international standards and uniformed units, and then readjusted our input/output indicators according to the study context through four expert panels. We collected data and classified the input/output indicators, followed by determining each indicator’s weight and standard limits. Then we rationalized our previous results by applying the revised model. The initial new results of the refined model were valid, accurate, and consistent with previous studies, as approved by experts. We defined proper modeling to achieve the stated objectives. After investigating various DEA models, we finally designed a new model that was consistent with the existing data and conditions, entitled EDEA (extended DEA), to analyze other subprojects.
 Conclusions: The conventional DEA methods may not be accurate enough to measure health systems’ efficiency. By modifying modeling process, we propose a modified DEA with a very low error rate. We suggest that others interested in measuring health system efficiency adopt our modified approach to increase accuracy and create more meaningful policy-oriented results.

Performance Evaluation of Omni-Channel Distribution Network Configurations considering Green and Transparent Criteria under Uncertainty

Satisfying customer demand is one of the growing concerns of supply chain managers. On the other hand, the development of internet communications has increased online demand. In addition, the COVID-19 pandemic has increased the demand for online shopping. One of the useful concepts that help to address this concern is the omni-channel strategy, which integrates online and traditional channels with the aim of improving customer service level. For this purpose, this paper proposes an algorithm for evaluating Omni-channel Distribution Network Configurations (OCDNCs). The algorithm applies an extended Data Envelopment Analysis (DEA) model to evaluate OCDNCs based on cost, service, transparency, and environmental criteria; and then, forms a consensus on the evaluation results generated according to different criteria by utilizing an uncertain optimization model. To the best of our knowledge, this is the first attempt in which such an algorithm has been employed to take into account the mentioned criteria in a model to evaluate OCDNCs. The application of the proposed models was investigated in a case study in relation to the Indian retail industry. The results show that the configuration with the most connections among its members was the most stable, robust, and efficient.

Does spending on social-welfare policies reduce poverty? An assessment of the European Union countries using impulse-response and efficiency methods

Purpose – An attempt to answer two questions: (i) does spending on social-welfare policies constitute a statistically-significant impulse for reducing poverty among various risk groups in the EU countries? And (ii) what is the level of efficiency of social spending when it comes to reducing various problems associated with poverty in the EU member states? Research method – Two research methods: Vector Error Correction Model (VECM) and extended Data Envelopment Analysis (DEA) are used. Results – It is established that social-welfare policies in most of the EU countries create a sufficient impulse to reduce poverty among elderly people and survivors, families with children and the unemployed. However, the impulse is often not sufficient in the case of people with problems in meeting housing needs, as well as the sick or disabled. What is more, the relative efficiency of social-welfare spending in some of the EU countries is low, which suggest that better outcomes may be achieved not only by increasing the spending, but also by improving the policies among current amount of funds. Surprisingly, the best-performing countries in reducing the poverty by social-welfare policies include, next to Denmark and Finland, also some Central and Eastern European countries: the Czech Republic, Poland, Slovakia and Slovenia. Originality/value/implications/recommendations – The research extends the knowledge on the efficiency and effectiveness of government activities for the purpose of limiting poverty.

Exploring the transition effects of foreign direct investment on the eco-efficiency of Chinese cities: Based on multi-source data and panel smooth transition regression models

Foreign direct investment (FDI) has not only been considered to be an important economic engine to promote economic growth in China, but also has played a pivotal role in the ecological environment. Empirically, it may exhibit two conflicting effects, namely, pollution haven effect and pollution halo effect. Hence, it is of great significance of re-examine the effects of FDI on the ecological environment. To this end, we emphasize and identify the transition effects of FDI in the different stages of economic development. In this first step, we employed an extended data envelopment analysis (DEA) method to evaluate the eco-efficiency scores of 284 Chinese cities from 2005 to 2016, based on multi-source data including remote sensing image data and statistical data. In the second step, a panel smooth transition regression (PSTR) model was applied to explore the transition effects of FDI on eco-efficiency. The main findings are as follows. (1) The average eco-efficiency scores presented a U-shaped curve during the sample period. Specifically, it firstly declined from 2005 to 2013, and then the decreasing trend was reversed after 2013. (2) The results of spatial heterogeneity test showed that recently the Low-Low spatial clustering was mainly concentrated on the Central Plain urban agglomeration and the northwestern region while the High-High spatial clustering could be found in three regions, namely the Yangtze River urban agglomeration, the Pearl River Delta urban agglomeration, and the Chengdu-Chongqing urban agglomeration. (3) The results of the PSTR models revealed that FDI exhibited different effects on eco-efficiency in the different stages of economic development. Specifically, in the lower stage of economic level, FDI has a negative effect on eco-efficiency. The main reason could be the pollution haven effect. The rapid economic growth causes the increase in environmental awareness. Hence, when the economic level passed a certain threshold (about 79, 000 RMB), FDI contributed to the improvements of eco-efficiency scores, indicating that it became to exhibit the positive pollution halo effect. To conclude, the transition effects of FDI on eco-efficiency have been confirmed. Besides, we also observed that innovation capacity and education level were the foremost contributors to the enhancement of the eco-efficiency of Chinese cities.

Assessing eco-performance of transport sector: Approach framework, static efficiency and dynamic evolution

As main undesirable outputs of transport sector, controlling traffic accidents and carbon dioxide (i.e., CO2) emissions are the key points for sustainable development in highway transport sector. This paper proposes an extended Data Envelopment Analysis (i.e., DEA), namely the unified Bounded-adjusted Measure (BAM), to measure the variable-specific technical inefficiency and the corresponding productivity change of highway transport sector during 2006–2015 in China. Note that, through the analysis of total factor decomposition, this paper seeks the key points for the safe operation and emissions reduction of provincial highway transportation sector in China. The results show that the quantity of traffic accidents, CO2 emissions from highway transport sector, along with investment constitute important source leading to the inefficiency of highway transport sector in China. The static inefficiency values associated with conventional provinces whose value added growth relies on transportation is high. Regarding Luenberger productivity indicator, the reduction of traffic accidents has had a significant positive effect on total factor productivity (TFP), and has been slightly better than the weak positive effect of CO2 emissions reduction since 2006. Thus, in the latter stage, Chinese highway transportation policy should address the regulation on CO2 emissions. From the perspective of decoupling theory, China's overall road transport output value is coordinated with the quantity of traffic accidents and CO2 emissions, but the internal differentiation appears.

Assessing the efficiency of Iran health system in making progress towards universal health coverage: a comparative panel data analysis

BackgroundBuilding upon decades of continuous reforms, Iran has been implementing various initiatives to reach universal health coverage (UHC). Improving efficiency is a crucial intermediate policy objective for UHC. Therefore, this article aimed to measure the efficiency and productivity changes of the Iranian health system in making progress towards UHC during 2010−2015 in comparison with 36 selected other upper-middle-income countries.MethodsWe used panel data to measure the variations in technical efficiency (TE) and total factor productivity (TFP) through an extended data envelopment analysis (EDEA) and Malmquist productivity index, respectively. General government health expenditure (GGHE) per capita (International dollar) was selected as the input variable. Service coverage of diphtheria, tetanus and pertussis; family planning; antiretroviral therapy; skilled attendants at birth; Tuberculosis treatment success rate; and GGHE as % of total health expenditure (THE) were considered as output variables. The data for each indicator were taken from the Global Health Observatory data repository and World Development Indicator database, for 6 years (2010−2015).ResultsThe TE scores of Iran’s health system were 0.75, 0.77, 0.74, 0.74, 0.97, and 0.84 in the period 2010–2015, respectively. TFP improved in 2011 (1.02), 2013 (1.01), and 2014 (1.30, generally). The overall efficiency and TFP increased in 2014. Changes made in CCHE per capita and GGHE/THE attributed to the increase of efficiency.ConclusionThere is a growing demand for efficiency improvements in the health systems to achieve UHC. While there are no defined set of indicators or precise methods to measure health system efficiency, EDEA helped us to draw the picture of health system efficiency in Iran. Our findings highlighted the essential need for targeted and sustained interventions, i.e., allocation of enough proportion of public funds to the health sector, to improve universal financial coverage against health costs aiming to enhance the future performance of Iran’s health system, ultimately. Such tailored interventions may also be useful for settings with similar context to speed up their movement towards improving efficiency, which in turn might lead to more resources to reach UHC.

Decision making on post-disaster rescue routing problems from the rescue efficiency perspective

The efficiency of emergency responses is at the heart of post-disaster rescue routing problems. Previous rescue models address the efficiency issue primarily by minimizing the total travel time, while not taking into account other significant factors in the rescue process, such as the number of affected people and the degree of building damage. To overcome these shortcomings, this paper aims to solve the rescue routing problem by maximizing the arc-based rescue efficiency, which is redefined as the ratio between the primary rescue input and output factors to represent the efficiency of the rescue operations. Therefore, a systematic methodology is proposed to decompose the original rescue routing problem into two decision making phases. First, an extended data envelopment analysis (DEA) model is constructed to evaluate the rescue efficiency along each arc. Specifically, a group decision constraint cone, which refers to the combined output of a group decision from experts, is constructed to distinguish the features and rescue focus of each disaster. Second, an efficiency-based routing model is developed to determine a feasible rescue tour for the entire transportation network, thus achieving the goal of maximizing the total rescue efficiency. An empirical example of a real earthquake disaster in Wenchuan, China, is provided to demonstrate the novelty and practical capabilities of the proposed approach in post-disaster emergency rescue operations. Finally, a comparison analysis is conducted with the traditional time-oriented routing method, and the results show that the method proposed in this study can improve the rescue performance by 21.2%.

Empirical Analysis of Railway Efficiency in Pakistan

This paper estimates change in Pakistan Railways (PR) efficiency over time. It is important to see the performance in a dynamic context. Based on the fundamental CCR-BCC (Charnes, Cooper and Rhodes, 1978; Banker, Charnes and Cooper, 1984) model, we use an extended Data Envelopment Analysis (DEA) model developed to deal with time series method for the period 1966-2017. The PR became financially inefficient from 1985 onwards. Fewer and redundant inputs were used for service delivery which caused product inefficiency. Rising expenditures increased cost of operations which became the source of allocative inefficiency. Both resulted in train closures and shrinking business. Our results show pure technical efficiency of production at 69 per cent, indicating the output waste of 31 per cent. The empirical findings suggest that the pure technical efficiency of production outweighs the pure technical efficiency of allocation. The main policy implication is that steady investment under an autonomous and professional management is required for a turn around. Keywords: Efficiency, Data Envelopment Analysis, Pakistan Railways, Transportation Policy

How to reduce energy intensity to achieve sustainable development of China's transport sector? A cross-regional comparison analysis

China's transport sector has been attracting great attention for its excessive energy consumption and ever-increasing pollution emissions. Thus, reducing energy intensity is one of the top priorities of China's ongoing transport upgrade. In this paper, by establishing a panel data regression model derived from the Cobb–Douglas cost function, we focus on investigating the impacts of energy price and transport productivity on transport energy intensity at the national and regional levels. The study uses the provincial panel data for 2005–2016 to perform regression analysis. The results show that: (1) energy price has a significantly negative effect on transport energy intensity in the whole China and the eastern region, whereas it has no significant impacts in the central and western China. (2) Improvements in transport productivity can effectively decrease transport energy intensity in the whole China and the three major regions. (3) Applying an extended data envelopment analysis (DEA) approach, we decompose transport productivity into four components (i.e., technical change, technology gap change, scale efficiency change, and pure efficiency change) and further differentiate their impacts in different regions. The results indicate that these four components have substantially different impacts in each region. These results provide some valuable insights for policymakers and enterprise entities aiming to adopt measures to reduce energy intensity and achieve sustainable development in China's transport sector.

Eco-efficiency of the Western Taiwan Straits Economic Zone: An evaluation based on a novel eco-efficiency model and empirical analysis of influencing factors

Obtaining the most economic benefits while using the least resources and causing the least environmental damage is an urgent and key problem in achieving sustainable development. The concept of eco-efficiency provides a comprehensive perspective for observing and solving this problem. This paper proposes an extended data envelopment analysis model, named the WIN-US-SBM, as a comprehensive eco-efficiency evaluation method that can accommodate undesirable outputs, all the identification problems of decision-making units (DMUs), possible slacks of variables, and the problems of an insufficient number of available DMUs and intertemporal comparability. China's Western Taiwan Straits Economic Zone (WTS Economic Zone) is an important regional economic growth center. By applying WIN-US-SBM and ArcGIS software, we investigated data on the temporal and spatial evolution characteristics of eco-efficiency in the WTS Economic Zone for the years 2009–2016. Later, the global Malmquist–Luenberger index was used to decompose the sources of growth in eco-efficiency. Finally, the main driving factors of eco-efficiency were explored through a Tobit regression analysis. The empirical results show that: (1) The overall eco-efficiency of the WTS Economic Zone had greatly improved, increasing by 11.46% annually, and the cumulative growth rate of eco-efficiency was 110.84% from 2009 to 2016. (2) The eastern area had the highest average eco-efficiency, followed by the western area and the middle area in that order. Also, the growth rate of the middle area ranked first (16.68%), whereas the eastern area had the slowest growth in eco-efficiency (7.22%). (3) On the whole, the growth of eco-efficiency was driven mainly by technological progress and was restricted by the decrease in management level, which implies that policy makers should focus on improving management and technical levels. (4) High levels of economic development, population density, market openness, and industrial structure had positive effects on the eco-efficiency of the WTS Economic Zone, whereas energy intensity and expenditure on science and technology had negative impacts.

A Novel Method for Evaluating Dredging Productivity Using a Data Envelopment Analysis-Based Technique

The increase in the frequency of extreme weather has caused the impact of natural disasters to become more extensive. Natural disasters reduce the effective storage capacity of reservoirs and affect their normal function. Reservoir dredging is a key issue in the management of water resources and is a complicated multiple-attribute decision-making (MADM) problem. The traditional assessment of dredging productivity has been performed using a labor productivity method to evaluate the related issues of dredging performance. However, the traditional labor productivity method only deals with the single-input/single-output evaluation factor for various forms of productivity. The traditional labor productivity method cannot address complicated MADM problems in the assessment of dredging productivity. To resolve the limitations of the traditional labor productivity method, this paper extended data envelopment analysis (DEA) and proposed a novel method for evaluating dredging productivity. The proposed method can handle various combinations of evaluation factors (single-input, multi-input, single-output, and multioutput). Three real cases of reservoir dredging are applied to verify the effectiveness of the proposed method. The simulation results show that the proposed method can be applied generally and correctly assesses the related issues of dredging performance.

Stochastic data envelopment analysis: A quantile regression approach to estimate the production frontier

Data Envelopment Analysis was developed as a deterministic model that assumed that deviations from the production frontier were one sided representing technical inefficiency. The model provides biased estimates of production and inefficiency if deviations from the frontier arise not only from inefficiency but also from statistical noise. Banker (1988, “Stochastic Data Envelopment Analysis,” Working Paper, Carnegie Mellon University) extended Data Envelopment Analysis with a stochastic model to allow not only inefficiency but also statistical noise. Banker's model can be considered a nonparametric quantile regression. Using the celebrated Afriat constraints, the model estimates a piecewise linear production function through the middle of the data. In this paper, we extend Banker's Stochastic DEA model by considering a semi-parametric model that identifies the most likely quantile based on assumptions of the composed error terms. We focus on the most common stochastic frontier model with an error structured constrained to a convolution of the normal and half-normal distributions. Using simulated data, we compare the model to the econometric stochastic frontier model under different distributional assumptions.

Do neighboring prefectures matter in promoting eco-efficiency? Empirical evidence from China

This study investigates the spatial externality of eco-efficiency using a novel panel dataset of 191 prefectural-level cities in China covering the period 2003–2013. We apply an extended data envelopment analysis (DEA) model, while simultaneously considering metafrontier, undesirable outputs, and super-efficiency slack-based measure (Meta-US-SBM) to estimate eco-efficiency. Then, we use a two-regime spatial Durbin model to examine the spatial externality of eco-efficiency. The empirical results indicate that: 1) there are significant spatiotemporal disparities in eco-efficiency and, on average, the eco-efficiency of the eastern region is relatively higher than that of the central and western regions; 2) the kernel density estimations of eco-efficiency of different regions reveal left-skewed distributions; 3) the estimates of the two-regime spatial Durbin model indicate the presence of emulation of higher eco-efficiency by the prefectural-level cities, while the sensitivity analysis indicates that the conclusions are robust for different spatial weighting matrix specifications. The policy implications presented are based on the empirical results.

Can More Environmental Information Disclosure Lead to Higher Eco-Efficiency? Evidence from China

The present paper investigates the impact of pollution information transparency index (PITI) on eco-efficiency using a novel panel dataset covering 109 key environmental protection prefecture-level cities in China over the period 2008–2015. We apply an extended data envelopment analysis (DEA) model, simultaneously incorporating metafrontier, undesirable outputs and super efficiency into slack-based measure (Meta-US-SBM) to estimate eco-efficiency. Then, the bootstrap Granger causality approach is utilized to test the unidirectional Granger causal relationship running from PITI to eco-efficiency. Results of DEA model show that there exist significant spatiotemporal disparities of eco-efficiency, on average, the eco-efficiency in eastern region is relative higher than those of central/western region. Estimates of ordinary least square (OLS) method, quantile regression, and spatial Durbin model document that the evidence of an inverted-U-shaped relation between PITI and eco-efficiency is supported, and the turning points vary from 0.3370 to 0.4540 with different model specifications. Finally, supplementary analysis of panel threshold model also supports the robust findings. Policy implications are presented based on the empirical results.

An extended data envelopment analysis for the decision-making

Based on the CCR model, we propose an extended data envelopment analysis to evaluate the efficiency of decision making units with historical input and output data. The contributions of the work are threefold. First, the input and output data of the evaluated decision making unit are variable over time, and time series method is used to analyze and predict the data. Second, there are many sample decision making units, which are divided into several ordered sample standards in terms of production strategy, and the constraint condition consists of one of the sample standards. Furthermore, the efficiency is illustrated by considering the efficiency relationship between the evaluated decision making unit and sample decision making units from constraint condition. Third, to reduce the computation complexity, we introduce an algorithm based on the binary search tree in the model to choose the sample standard that has similar behavior with the evaluated decision making unit. Finally, we provide two numerical examples to illustrate the proposed model.

Effective Clusters as Territorial Performance Engines in a Regional Development Strategy - A Triple-Layer DEA Assessment of the Aviation Valley in Poland

Regional development policy aims to cope with the challenge of spatial disparities. It is based on a smart combination of various critical capital assets in a region which functionally and spatially interact and which yield synergetic economic opportunities and promising challenges for innovation and progress. The present study regards sustainable territorial performance – as a manifestation of regional development – as the overarching principle for competitive advantages and economic growth in a system of regions, which is particularly induced by territorial capital, comprising human capital, infrastructural capital and social capital. In the long-standing tradition of regional development policy a wide variety of effective facilitators or drivers of accelerated spatial growth has been distinguished, for instance, industrial districts, growth poles, growth centers, industrial complexes, special economic zones, communication axes, and so forth. In the past decades, a new concept has been introduced, viz. economic-technological clusters. An avalanche of literature has been published on the conceptual, operational and policy foundation and relevance of this concept, especially in relation to previously developed regional growth concepts. In this paper, clusters will be regarded as the spatial foci of sustainable territorial performance strategies and synergetic actions by both public and private actors. The present paper aims to address the relevance of cluster concepts for an effective regional development policy, based on the above notion of territorial capital. It does so by introducing a new concept, viz. effective cluster, in which spatial-economic synergy, local/regional concentration of industry, and the supporting role of territorial capital are regarded as the main determinants of a highly performing cluster in a given territory. The effective cluster concept will be tested on the basis of a field study on the aviation and aerospace cluster ‘Dolina Lotnicza’ in the Podkarpackie region in South-East Poland. This is one of the most vibrant high-tech clusters in thecountry. A new approach based on a triple-layer architecture will be adopted here, viz.: a quantitative comparative analysis of the 16 Polish ‘voivodships’ (main administrative regions in the country, at a NUTS-2 level), a benchmark analysis of the 25 counties (‘powiats’) within the Podkarpackie voivodship (at a NUTS-4 level), and an effective industrial cluster analysis on the basis of the individual aviation firms located in the Podkarpackie region. In each step an extended Data Envelopment Analysis (DEA), characterised by a merger of a Slack-Based Measure (SMB) and a super-efficiency (SE) DEA, will be used in order to achieve an unambiguous ranking of the various regions or Decision Making Units (DMUs). The study will employ an extensive database on individual actors in the cluster, in combination with a broadly composed territorial-capital database for the areas under study. The paper will be concluded with some strategic policy lessons.

An extended 2-tuple linguistic DEA for solving MAGDM problems considering the influence relationships among attributes

The main purpose of this paper is to propose an extended data envelopment analysis (DEA) approach for solving multiple attribute group decision making (MAGDM) problems with unknown decision maker (DM) weights and attribute weights. The 2-tuple linguistic representation model is more suitable to deal with linguistic evaluations than the extension principle and the symbolic method. So we propose a 2-tuple linguistic DEA model and objective DM weights determination method in 2-tuple MAGDM problems. The DMs’ objective weights are dynamic with respect to each attribute over different alternatives, which are determined according to the grey relational degree of the decision information DM given with the ideal decision information obtained by solving a weighted least square optimization model. The optimized attribute weights based on the DEA model only reflect the beneficial side to each alternative and ignore the objective side like influence relationships among attributes. Therefore, a 2-tuple linguistic DEMATEL technique is presented and applied to adjust the attribute weights obtained by DEA, and then the comprehensive attribute weights are derived. Finally, the evaluation values of alternatives are obtained based on the comprehensive attribute weights according to the efficiency evaluation method of DEA. A case study is put forward to validate the effectiveness of the proposed approach.