Non-radial Data Envelopment Analysis Research Articles

Evaluation of undergraduate academic programs through data envelopment analysis and time-to-degree estimates at Spanish public universities

This article proposes nonparametric and parametric methods for conducting meaningful evaluations of academic programs. In particular, it proposes the use of data envelopment analysis (DEA) along with regression analysis for the evaluation of Spanish public universities' undergraduate curricula. Unlike the radial DEA models, which assume an equiproportional expansion of all outputs to achieve efficiency, the current study also relies on the use of non-radial DEA models for assessing the efficiency of Spanish public universities. A non-radial DEA measure (the so-called Russell measure) allows for unlike proportional augmentations in each positive output. In this study, non-radial DEA measures allow us to identify different levels of inefficiency for each output considered (social sciences and non-social sciences degrees). Specifically, the results show that the inefficiency in the production of scientific and technical degrees is greater than that in the production of humanities and social sciences degrees, although there are differences among institutions. Bachelor's degree production time is also estimated, and a duration analysis explains the time to degree. A worrying result is the "excessive" time it takes for a university to produce four-year undergraduate degree programs. The mean graduation time is 5.7 years. The Cox proportional hazards regression shows that a shorter time to graduation is associated with higher teaching quality as well as a higher faculty-to-student ratio. Parametric survival analysis using a lognormal distribution with gamma frailty also verifies these latter results.

Effects of Vertical Unbundling on the Operational and Environmental Efficiency of Chinese Thermal Power Firms

Since the 1980s, many electricity markets around the world have undergone restructuring aimed at improving efficiency. In 2002, the Chinese government also introduced a vertical unbundling reform to dismantle vertical monopolies and boost efficiency in the power sector. This study estimates the unified, operational, and environmental efficiency to comprehensively assess the impact of China’s vertical unbundling on the production performance of thermal power firms. A non-radial data envelopment analysis (DEA) approach is employed to measure these efficiencies. Subsequently, a difference-in-difference model is used to assess the reform’s effects. Empirical results show that the vertical unbundling improved the unified efficiency of firms. Further analysis indicates that the reform improved the operational efficiency of firms by increasing their capacity utilization and coal quality. Additionally, we find that the reform led to an increased utilization of source control technologies but resulted in a reduction in the use of end-of-pipe treatments. Due to the opposing effects on different pollution treatment stages, the reform did not have a significant impact on environmental efficiency.

A hybrid returns to scale-DEA model for sustainable efficiency evaluation of urban transportation systems

The urban transportation network has an undeniable role in addressing the economic, social and environmental issues caused by the traffic. Transportation managers seek to use the existing facilities and capacities in an optimal way to increase customer satisfaction. Therefore, it is necessary to develop an approach to evaluate the performance of the urban transportation system to provide service to citizens effectively. This study develops an approach based on the extended version of the data envelopment analysis (DEA) model to measure the nonradial efficiency and super-efficiency of metro-stations considering the sustainability concept. The developed non-radial DEA model considers the hybrid returns to scale the form of technology by combining constant and variable returns to scale assumptions to improve its applicability to identify efficient and inefficient stations. This DEA model also incorporates the non-discretionary inputs and different types of outputs (i.e. undesirable, negative and non-negative) to improve discrimination power and the ability to interpret the results. The findings help decision-makers identify super-efficient stations as a benchmark for future planning and finding the best location to construct metro-stations. Furthermore, this research enables managers to optimally use the resources to increase the transferred passengers, reduce customer dissatisfaction and optimise the annual profit.

A bootstrapping dynamic two-stage SBM model: An application to assess industrial water use and health risk systems

Despite the great number of applications of bootstrapping data envelopment analysis (DEA) with a one-stage structure, there are limited attempts for approximating the distribution of the DEA estimator considering the two-stage structure across multiple periods. This research develops the dynamic two-stage non-radial DEA model based on smoothed bootstrap and subsampling bootstrap. Then, we run the proposed models on assessing the efficiency of China's industrial water use and health risk (IWUHR) systems and compare them with the bootstrapping results on standard radial network DEA. The results are as follows. (1) The proposed non-radial DEA model based on smoothed bootstrap can adjust original over-estimated and under-estimated values. (2) China's IWUHR system has good performance, and its HR stage performs better than the IWU stage for 30 provinces from 2011 to 2019. The poor performance of the IWU stage in Jiangxi and Gansu needs to be noticed. The provincial differences of the detailed bias-corrected efficiencies expand in the later period. (3) The rankings of IWU efficiency in the three regions are in agreement with that of HR efficiency: eastern, western, and central regions in this order. Particular attention should be paid to the downward trend of the bias-corrected IWUHR efficiency in the central region.

Carbon emission efficiency evaluation of wastewater treatment plants: evidence from China.

A scientific evaluation of the carbon emission efficiency is crucial for ensuring the sustainable development of wastewater treatment plants (WWTPs). In this paper, we applied a non-radial data envelopment analysis (DEA) model to calculate the carbon emission efficiency of 225 WWTPs located in China.The results showed that the average carbon emission efficiency of China's WWTPs was 0.59, indicating that the efficiencies of most samples still require improvement. The carbon emission efficiency of WWTPs from 2015 to 2017 decreased because of the decrease in technology efficiency. Among the influencing factors, different treating scales had positive impact on carbon emission efficiency improvement. WWTPs with anaerobic oxic process and the first-class A standard were likely to have higher carbon emission efficiency in the 225 WWTPs. By incorporating direct and indirect carbon emissions into WWTP efficiency evaluation, this study helped decision-makers and related water authorities to better understand the contribution of WWTPs to the aquatic and atmospheric environments.

China’s Green Total Factor Energy Efficiency Assessment Based on Coordinated Reduction in Pollution and Carbon Emission: From the 11th to the 13th Five-Year Plan

As the world’s largest energy consumer, China is facing multiple pressures to conserve energy, mitigate pollution and reduce carbon emissions. An objective and scientific assessment of green total factor energy efficiency (GTFEE) is an important prerequisite for achieving energy conservation, emission reduction and low carbon development. In this research, a non-radial data envelopment analysis (DEA) is conducted to assess the GTFEE in China, while the issue of climate and air pollution from energy use is considered in the assessment system. We use different indicators to refer to air pollutants, which provides a reference for related research in indicator selection. The results show that different indicators have different inefficiency values, but changes in the indicators have a minimal effect on the inefficiency values of the other constant indicators. We also assess the GTFEE for the last three five-year plan periods to inform future policy development. The results show that China’s average annual GTFEE exhibits a unique trend in each five-year plan period, with an overall “v” shaped trend. The annual average GTFEE of different regions also varies. The other changes in the rankings of the provinces are mainly concentrated in the 11th to 12th Five-Year Plan period.

Environmental efficiency assessment of Dublin Port using two-stage non-radial DEA model

The operation of a port is of critical economic importance because of its role in international trade. However, increased operations increase the port's environmental impact and contribute to climate change and global warming. Dublin Port is one of the busiest ports in Ireland, and throughput is expected to increase as the economy continues to grow. Achieving a balance between growing economic activity and reducing environmental impacts is critical to a sustainable future. The efficiency of the port considering operation, economic, and environmental factors can be evaluated using the well-known nonparametric model of Data Envelopment Analysis (DEA). In this study, a novel two-stage non-radial DEA model is used to evaluate the environmental efficiency of Dublin Port considering landward and seaward operations. The proposed two-stage model DEA isolates the efficiency of the two port sides and calculates the overall efficiency. The model minimizes Dublin Port's annual CO2 emissions by reducing a combination of variables that can be realistically controlled by engineering and policy measures. The analysis was extended to account for possible variations in a number of key environmental output factors to compensate for the lack of real-world data availability. The study found that the number of terminals and capital expenditures have significant impacts on the port's environmental efficiency. Small adjustments or reductions in key indicators can improve Dublin Port's efficiency. This methodology can be applied to other ports in growing economies that use similar indicators to assess their environmental efficiency.

Managerial practices and school efficiency: a data envelopment analysis across OECD and MENA countries using TIMSS 2019 data

School-level inefficiencies and mismanagement can have serious repercussions for human resource development and labor market outcomes. This paper investigates the extent and consequences of existing technical inefficiency of schools with respect to their resource- and people-management aspects at a cross-country level across Organisation for Economic Cooperation and Development (OECD) and the Middle East and North Africa (MENA) regions. It employs a non-radial Data Envelopment Analysis (DEA) alongside a second stage Tobit regression model using datasets in the latest Trends in International Mathematics and Science Study (TIMSS) 2019. The analysis covers 5164 schools across 26 countries. In the first stage of DEA analysis, it is evident that technical inefficiencies exist similarly across schools of both OECD and MENA nations, irrespective of the method used for efficiency calculation. While availability of educational resources is a necessary condition for improving learning outcomes, it is surely not sufficient. In the second stage of the Tobit regression, the model confirms that improved utilization of the existing resources through better educational management systems can yield higher cognitive achievement at the school-level. The empirical findings also reveal that discipline maintained within the student body at school is one of the most important and significant factors associated with higher school level input- and output-efficiency across both MENA and OECD regions. Moreover, different aspects of people management, particularly target setting, student as well as teacher motivation, and parental involvement in school management are found to be positively associated with school-level technical efficiency across the two regions, albeit in varying degrees. Overall, educational management policies should shift focus from solely providing higher quantity of resources to improving the technical efficiency of schools through enhanced school-level management, by encouraging disciplinary action, as well as by supporting stakeholder incentives that foster motivation and participation.

Using nonradial metafrontier data envelopment analysis to evaluate the metatechnology and metafactor ratios for the Taiwanese hotel industry

AbstractThe main purpose of this study was to develop nonradial metafrontier data envelopment analysis (DEA). Previous studies commonly used assumptions involving the redial measure and convexity of metafrontier to evaluate efficiency and the technology gap. The two assumptions have limitations, and a model that modifies the two assumptions simultaneously has not been proposed. In this study, a nonradial metafrontier DEA is proposed that incorporates the assumptions of nonconvexity as well as the nonradial measure. The study also developed the MFR index for measuring the technology gap based on individual factors.

Operational performance model for banks: a dynamic data envelopment approach

PurposeOperational performance is critical for the banking sector for both managers and other stakeholders as it strongly affects the overall performance of the banking system. Traditional performance measures such as ratio analysis encountered certain shortcomings. At this juncture, data envelopment analysis (DEA) approaches are increasingly applied in bank efficiency studies. However, basic DEA models ignored the interactions between consecutive terms and focused primarily on measuring performance independently for each study period. All this is required to develop an operational performance model that can enable the long-term decision model.Design/methodology/approachAn attempt has been made to develop a dynamic DEA within a non-radial category to measure interconnection activities considering non-performing loans as an undesirable link. This study uses the Indian banking dataset from 2015 to 2019. The study's research design directs three directions: ‘comparison of the dynamic DEA with the traditional static DEA model, areas of inefficiencies that are investigated for each factor using the factor efficiency index and the robustness results highlighting the performance difference between bank categories.'FindingsComparing with static DEA results, the study confirms that the dynamic model best measures long-term operational performance due to the linkage between consecutive terms. The efficiency analysis concludes that the input factor that requires the most improvement is ‘fixed assets' and ‘deposits'. The output factor that needs the most progress is ‘non-interest income'. The robustness of the developed model is proven by ownership categories present within the Indian banking system. At a significance level of 10%, the result of both the separate and dynamic model for privately owned banks is significantly better than that of publicly owned banks.Originality/valueThis paper proposes an operational efficiency model for Indian banks in line with undesirable output. The mean factor efficiency analysis related to non-radial DEA modelling enhances managerial flexibilities in determining improvement initiatives.

A Modified-Range Directional Measure for Assessing the Sustainability of Suppliers by DEA/UTASTAR

Sustainability in the supply chain means considering environmental, social, and economic practices. Conventional data envelopment analysis (DEA) models deal with desirable, discretionary, and nonnegative data. However, there might be undesirable outputs, nondiscretionary factors, and negative data. On the other hand, some criteria can be considered as outputs and inputs. These factors are named as the dual-role criteria. The objective of this paper is to develop a non-radial DEA model for dealing with negative data in the presence of undesirable, non-discretionary, and dual-role factors in weight restrictions context. The ordinal regression method, UTASTAR, is performed to define priorities in terms of criteria. The capabilities of the proposed method are compared with other methods. A case study is presented in which the best sustainable suppliers of SAPCO are selected. To check the importance of dual-role variables, two extra cases are considered.

Robust non-radial data envelopment analysis models under data uncertainty

Russell measure (RM) and enhanced Russell measure (ERM) are popular non-radial measures for efficiency assessment of decision-making units (DMUs) in data envelopment analysis (DEA). Input and output data of both original RM and ERM are assumed to be deterministic. However, this assumption may not be valid in some situations because of data uncertainty arising from measurement errors, data staleness, and multiple repeated measurements. Interval DEA (IDEA) has been proposed to measure the interval efficiencies from the optimistic and pessimistic viewpoints while the robustness of the assessment is questionable. This paper draws on a class of robust optimisation models to surmount uncertainty with a high degree of robustness in the RM and ERM models. The contribution of this paper is fivefold; (1) we develop new robust non-radial DEA models to measure the robust efficiency of DMUs under data uncertainty, which are adjustable based upon conservatism levels, (2) we use Monte-Carlo simulation in an attempt to identify an appropriate range for the budget of uncertainty in terms of the highest conformity of ranking results, (3) we introduce the concept of the price of robustness to scrutinise the effectiveness and robustness of the proposed models, (4) we compare the developed robust models in this paper with other existing approaches, both radial and non-radial models, and (5) we explore an application to assess the efficiency of the Master of Business Administration (MBA) programmes where data uncertainties influence the quality and reliability of results.

Comparison of Different Methods for Estimation of Returns to Scale in Nonradial Data Envelopment Analysis Models

Comparison of Different Methods for Estimation of Returns to Scale in Nonradial Data Envelopment Analysis Models

Operational Performance Modelling of Indian Banks: A Data Envelopment Analysis Approach

The rising level of non-performing loans (NPLs) posits risk on the operational working of the banking sector. The study focuses on developing an operational performance model for banks by considering the NPLs. The study uses non-parametric methodology to develop a non-oriented and non-radial Data Envelopment Analysis (DEA) model with NPLs as undesirable output. The dataset of 39 Indian commercial banks for period of 5 years is selected for the study. The findings of the study prove that considering risk into performance modelling leads to an unbiased efficiency indicator. Also, the non-parametric test confirms the relationship between operational efficiency and ownership. The study uses non-orientation modelling results to reveal inefficient sources within under-performing banks. As per the findings, slack variable analysis reveals two problem areas: ‘fixed assets’ and ‘NPLs’. A higher focus on improving utilization of fixed assets as well as controlling the level of rising NPL (risk) is highly significant for benchmarking performance. Overall, the study supports the business decision to control excess inputs and outputs for banks to achieve the efficient frontier. Significant managerial implications are linked to findings of the study focusing towards performance improvement of banks.

Operational Performance of Banks: Frontier-Based Data Envelopment Analysis with Undesirable Output

The current banking scenario marks the rising level of non-performing loans (NPLs), which negatively impacts the operational performance of banks. There is a need for an operating performance model that integrates the NPLs into the measurement of operational efficiency to provide the overall performance profile for Indian banks. The study applies a non-radial data envelopment analysis model developed by Tone (2001) and incorporates NPLs as undesirable output. The study draws empirical insights on operational performance for Indian banks for a recent five-year period. With the adoption of the non-orientation approach of the developed model, the significant contribution of the study is related to mean factor efficiency analysis, which allows the identification of inefficient sources causing operational inefficiency within banks. The robustness test result throws light on efficiency differences between two ownership categories of banks. Implications of empirical findings suggest that the regulators devise measures to control the level of NPLs to help improve the operational efficiency of the Indian banking system.

Measuring environmental efficiency and technology inequality of China's power sector: methodological comparisons among data envelopment analysis, free disposable hull, and super free disposable hull models.

China's power sector has received great research attention because of its large energy consumption and CO2 emissions. This study assesses the environmental efficiency and technology inequality of China's power sector from 2008 to 2017. Methodologically, this study proposes a non-radial FDH (free disposable hull) model and a super non-radial FDH model. The non-radial FDH model relaxes the convex assumption and captures all inefficiencies of inputs, desirable output and undesirable output. The super non-radial model is capable of discriminating efficient power sectors and always has feasible solutions. We also compare their performance with the non-radialdata envelopment analysis (DEA) model. The main conclusions are summarized as follows: First, the environmental efficiency of China's power sector has experienced steady growth; the power sectors in the east region outperform those in other regions. Second, the proposed FDH models are more applicable and reliable than the non-radial DEA model in efficiency measurement of China's power sector, due to the indivisibility of labor. Third, there has been growing technology inequality and the main driving factor determining technology inequality is the inter-region efficiency Gini coefficient. To improve environmental efficiency and eliminate technology inequality, the government should mainly solve the issue of excessive labor input and establish a free technology market for technology trading.

Measuring operational and quality-adjusted efficiency of Chilean water companies

The path to a sustainable management of the urban water cycle requires the assessment of both operational and quality-adjusted efficiency in a unified manner. This can be done by the use of non-radial Data Envelopment Analysis models. This study used Range Adjusted Measure models to evaluate the operational, quality-adjusted, and operational & quality-adjusted efficiency (O&QAE) scores of the Chilean water industry including water leakage and unplanned interruptions as undesirable outputs. It was found that on average water utilities presented large O&QAE scores over time. The mean O&QAE score was 0.964 which means that water utilities could further reduce costs and undesirable outputs by 3.6% on average, while trying to expand the scale of operation. This finding suggests that excellent quality-adjusted efficiency at an efficient expenditure could be feasible. It was also evidenced that customer density, mixed water resources, and ownership influenced the O&QAE of Chilean water companies.

Integrating group frontier and metafrontier directional distance functions to evaluate the efficiency of production units

Over the last decade, many studies have adopted the data envelopment analysis (DEA)-type metafrontier framework to relax the constraint of technological heterogeneity. In conventional metafrontier analyzes, either radial or nonradial DEA models are used in two separate DEA efficiency analyzes. The results of a metafrontier analysis may be biased if this approach is adopted in a nonradial DEA model. One possible problem is that the value of the technology gap ratio is greater than one because the technology gaps to be estimated for decision-making units (DMUs) under different technology groups relative to the metatechnology have no connection between group frontiers and metafrontiers. Furthermore, the direction of projection of each DMU onto the metafrontier is totally independent of the direction to the efficiency frontier of the respective group frontier. Our proposed integration of group technology and metatechnology constraints into a union model offers a possible solution to the existing metafrontier problem and provides more robust metafrontier results regardless of whether radial or nonradial DEA models are used. Four numerical examples and one empirical application are demonstrated, and the results appear to support the use of our proposed model.

Eco-efficiency and Eco-productivity assessments of the states in the United States: A two-stage Non-parametric analysis

This study implements radial and non-radial Data Envelopment Analysis (DEA) models to assess eco-efficiency and eco-productivity of the 50 states in the United States in 2018. The models are based on three inputs (capital stock, employment, and energy consumption), a single desirable output (real gross domestic product) and a single undesirable output variable (CO2 emissions). The radial DEA models reveal that at least 32 states are operated efficiently. Five states perform at the most optimal scale size, whereas 17 states have considerable potential to boost their productive efficiencies by enlarging available resources, and 28 states are overinvested in their input variables given their current output levels. The non-radial DEA models show that, overall, the states’ capital efficiency is very high, whereas energy and emission efficiencies are very low. The states’ eco-productivity is relatively higher than the eco-efficiency levels. In the second stage of the analysis, non-parametric statistical tests and Tobit regressions are conducted for further investigation. According to the non-parametric statistical test, high capital stock, labor force, and energy usage do not affect the states’ productive efficiency. However, states with low carbon dioxide emissions have significantly higher eco-efficiency and eco-productivity levels. The Tobit regression results illustrate that nuclear power and renewable energy consumption significantly affect the states’ relative efficiencies.

What explains the technical efficiency of banks in Tunisia? Evidence from a two-stage data envelopment analysis

In this study we examine the potential determinants of technical efficiency for the Tunisian commercial banking sector over the period of 1995–2017. First, we estimate banking technical efficiency with a radial and non-radial bootstrap data envelopment analysis. For the radial technique, we use an input-oriented approach and for non-radial we use the Range Adjusted Measure (RAM). Second, we use a double bootstrapping regression technique to estimate the influence of a set of eventual determinants on technical efficiency. Finally, based on all possible regressions, we gauge the overall effect of each determinant. Our results reveal that the input-oriented and RAM approach gave somewhat similar results. We found that the return on equity, the expense to income ratio, the loan to deposit ratio, and the growth rate are insignificant to Tunisian banking technical efficiency. In particular, banking technical efficiency increases with capitalization and inflation, whereas, it decreases with size, number of bank branches, management to staff ratio, and loan to asset ratio. In addition, we identified evidence supporting the moderate success of the last decade of reforms and a noticeable one for the post-revolution reforms in helping improve banking technical efficiency. The post-revolution reforms, largely revolving around reinforcing the rules of good governance and banking supervision, coupled with the restructuring of public banks, were found to be insufficient to raise overall banking technical efficiency despite improvement in the technical efficiency of private banks.