Data envelopment analysis with uncertain data: An application for Iranian electricity distribution companies

The main aim of the study was to evaluate the efficiency of Islamic Azad University branches in East Azerbaijan using stochastic frontier analysis (SFA) and to compare the findings with the results of data envelopment analysis (DEA). In this respect, SFA and DEA techniques were applied to measure production efficiency of 28 branches of East Azerbaijan Islamic Azad University considering the following variables as either output or input: 3 variables as output (including the number of graduated students and students accepted for the further pursuit of their studies, as well as the number of articles, books, and research projects) and 6 variables as input (including the number of university faculty members, educational fields, current students, staff working in different sections of the university, and the total university campus in square meters as well as university costs). Estimation of DEA and obtaining of the units' efficiency indicated that the average efficiency of the universities under investigation were 0.66 and 0.80 in SFA and DEA techniques, respectively. In addition, efficiency distributions in SFA and DEA methods were tremendously different from each other so that in SFA method nearly 40% while in DEA method more than 60% of unit efficiency was between 0.8-1 intervals. The minimum level of estimated efficiency (0.14) was observed for Hadishahr using SFA method, and in DEA approach, the minimum level of efficiency belonged to Khoda Afarin branch (0.065).

The government of Bangladesh has been trying to encourage potato consumption to reduce pressure on rice consumption and earn foreign currency along with ensuring zero hunger that helps to achieve the Sustainable Development Goal. It is necessary to use farmers' resources and current technology more efficiently to meet the demand. Therefore, the authors aimed to evaluate the farm-level efficiency of potato farming in Bangladesh. The Cobb-Douglas Stochastic Frontier Analysis (SFA) and the input-oriented Data Envelopment Analysis (DEA) methods are used to compute farm-level technical, allocative, and economic efficiencies and inefficiency of potato farming. The primary data were collected through interviews of 300 potato farmers from Munshigonj, Rangpur, Dinajpur, and Joypurhat districts of Bangladesh. The findings revealed that the efficiency score of the SFA model is higher than the DEA model, which implies that the SFA frontier fits better than the DEA frontier. In the case of DEA, variable returns to scale (VRS) technical efficiency (TE) enveloped data more closely than constant returns to scale (CRS) TE. Results of efficiency suggest significant economic, technical, and allocative inefficiencies in potato farming and there is a scope to increase potato production levels through efficiency improvement. Inefficiency analysis shows that infrastructure and socio-economic factors jointly influence potato production variability. The authors suggest for using the SFA to find efficiencies in the agriculture sector. To achieve efficiency in potato production, the government needs to pay attention for improving the allocative and economic efficiencies along with emphasizing to choose the appropriate technology and efficient use of resources for the scale of operation.

This paper examines the strategic fit of mergers and acquisitions (M&A) in Mozambican banks, using a two‐stage data envelopment analysis (DEA) approach to compute the impact of contextual variables on efficiency scores and returns to scale of the resulting virtual merged banks. In an ‘M&A DEA’ model, different pairs of bidder and target companies are considered. In the first stage an M&A DEA model is used. Simplex regression is adopted in the second stage. The results reveal that ownership (public or foreign) impacts virtual efficiency levels. However, the findings also show that care must be exercised with M&A because the resultant banking organization could be oversized when foreign ownership is predominant, especially when public ownership is low. Given the relative sizes of the markets in terms of productive resources, M&A involving Mozambican banks can easily lead to decreasing returns to scale. Thus, a greater emphasis should be given to merging purely public banks with foreign ones and vice versa.

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

A robust super-efficiency data envelopment analysis model for ranking of provincial gas companies in Iran

The article presents the results of assessing the development efficiency of the tourism and recreation complex of Russia’s regions for the period from 2017 to 2021. This period includes both years of stability, characterized by predominance of positive trends in tourism development, and crisis and post-crisis years associated with the effects of the COVID-19 pandemic. Taking into account the complexity of the structure of the tourism and recreation system and the need to improve territorial planning and management, an urgent task is to introduce a comprehensive, aggregated efficiency indicator to be used for assessment of regional tourism. The efficiency coefficient (efficiency score) calculated by means of the DEA (Data Envelope Analysis) method is proposed as such an indicator. The study is novel in that it develops a methodology for assessing the efficiency of regional tourism based on DEA. The essence of this method is as follows: basing on the theory of duality, having data of input and output parameters, it is possible to calculate the value of the efficiency indicator for a set of N-objects (in our study – subjects, i.e., constituent entities of the Russian Federation). For the calculations, we used a model focused on the ‘input’ and taking into account the variable scale effect. In models of this type, each inefficient object is compared with efficient objects that have the structure (ratios) of indicator values closest to the structure of this inefficient object. This allows one to determine how much to change the parameters of the tourism and recreation complex in order to achieve efficiency equal to 1 (which is the maximum score). The model used seven ‘input’ indicators and six ‘output’ indicators. The analysis of the results was carried out in the context of the tourist macro-territories designated in the strategic documents for the development of tourism in the Russian Federation. The data obtained indicate a decrease in the efficiency of tourism development in the Russian Federation, with the exception of a number of regions and two tourist macro-territories. The results of the study can be used to adjust regional tourism development programs.

In urban sustainability researches, benchmarking methods have become the most needed ways to measure urban energy efficiency. Benchmarking the efficiency of urban energy with parametric and non-parametric methods are important cases within the energy field. Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) are ideal approaches to measure performance of various industries with multiple indicators. Stochastic method considers the noise in data and evaluates the critical success parameters of energy efficiency by separating noise from efficiency scores. This study evaluates urban energy efficiency by deterministic and stochastic ways with deploying DEA and SFA methodologies. The aim of the study is to show the effects and results of deterministic and stochastic approaches in urban energy efficiency measurement and to evaluate how Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) can be used to derive measures of efficiency and productivity change over time in complex multi‐output multi‐input contexts in the production and consumption of energy services. Using data gathered from Turkish Statistical Institute (TURKSTAT) and Energy Market Regulatory Authority (EMRA) Development Reports. In the study, 30 cities, which are accepted as metropolitans of Turkey by government, are selected as Decision Making Units (DMUs) of both methods. As a result, different efficiency estimates are presented and evaluated within the scope of statistical noise, multiple inputs and outputs by DEA and SFA methods. Keywords: Urban Energy Efficiency; Urban Sustainability; Stochastic Frontier Analysis; Data Envelopment Analysis

Traditional data envelopment analysis (DEA) and diversification DEA are two common data-driven evaluation approaches, which have been widely used in the estimation of portfolio efficiency. The above two DEA approaches usually use the risk and expected return indicators to build the input-output process of portfolios. However, this input-output process derived from the risk and expected return is inconsistent with the actual investment process, since the real input should be the initial wealth, and the output should be the terminal wealth. To address this problem, we propose a novel input-output process based on the initial and terminal wealth of portfolios. We transform the terminal wealth into the rate of return and construct a stochastic attainable set by using portfolio returns. We provide three deterministic estimation approaches to deal with the stochastic attainable set, and then obtain three deterministic attainable sets. We further propose three stochastic DEA models to estimate the portfolio efficiency by using the above three deterministic attainable sets. Finally, we provide an empirical analysis to assess the portfolio efficiency of 50 open-ended funds in China. The results show that there are some differences in the portfolio efficiency and its ranking between the proposed DEA models and the existing DEA models, which further verify the rationality of the proposed DEA models.

Purpose The purpose of this paper is to provide an outline of the major contributions in the literature on the determination of the least distance in data envelopment analysis (DEA). The focus herein is primarily on methodological developments. Specifically, attention is mainly paid to modeling aspects, computational features, the satisfaction of properties and duality. Finally, some promising avenues of future research on this topic are stated. Design/methodology/approach DEA is a methodology based on mathematical programming for the assessment of relative efficiency of a set of decision-making units (DMUs) that use several inputs to produce several outputs. DEA is classified in the literature as a non-parametric method because it does not assume a particular functional form for the underlying production function and presents, in this sense, some outstanding properties: the efficiency of firms may be evaluated independently on the market prices of the inputs used and outputs produced; it may be easily used with multiple inputs and outputs; a single score of efficiency for each assessed organization is obtained; this technique ranks organizations based on relative efficiency; and finally, it yields benchmarking information. DEA models provide both benchmarking information and efficiency scores for each of the evaluated units when it is applied to a dataset of observations and variables (inputs and outputs). Without a doubt, this benchmarking information gives DEA a distinct advantage over other efficiency methodologies, such as stochastic frontier analysis (SFA). Technical inefficiency is typically measured in DEA as the distance between the observed unit and a “benchmarking” target on the estimated piece-wise linear efficient frontier. The choice of this target is critical for assessing the potential performance of each DMU in the sample, as well as for providing information on how to increase its performance. However, traditional DEA models yield targets that are determined by the “furthest” efficient projection to the evaluated DMU. The projected point on the efficient frontier obtained as such may not be a representative projection for the judged unit, and consequently, some authors in the literature have suggested determining closest targets instead. The general argument behind this idea is that closer targets suggest directions of enhancement for the inputs and outputs of the inefficient units that may lead them to the efficiency with less effort. Indeed, authors like Aparicio et al. (2007) have shown, in an application on airlines, that it is possible to find substantial differences between the targets provided by applying the criterion used by the traditional DEA models, and those obtained when the criterion of closeness is utilized for determining projection points on the efficient frontier. The determination of closest targets is connected to the calculation of the least distance from the evaluated unit to the efficient frontier of the reference technology. In fact, the former is usually computed through solving mathematical programming models associated with minimizing some type of distance (e.g. Euclidean). In this particular respect, the main contribution in the literature is the paper by Briec (1998) on Hölder distance functions, where formally technical inefficiency to the “weakly” efficient frontier is defined through mathematical distances. Findings All the interesting features of the determination of closest targets from a benchmarking point of view have generated, in recent times, the increasing interest of researchers in the calculation of the least distance to evaluate technical inefficiency (Aparicio et al., 2014a). So, in this paper, we present a general classification of published contributions, mainly from a methodological perspective, and additionally, we indicate avenues for further research on this topic. The approaches that we cite in this paper differ in the way that the idea of similarity is made operative. Similarity is, in this sense, implemented as the closeness between the values of the inputs and/or outputs of the assessed units and those of the obtained projections on the frontier of the reference production possibility set. Similarity may be measured through multiple distances and efficiency measures. In turn, the aim is to globally minimize DEA model slacks to determine the closest efficient targets. However, as we will show later in the text, minimizing a mathematical distance in DEA is not an easy task, as it is equivalent to minimizing the distance to the complement of a polyhedral set, which is not a convex set. This complexity will justify the existence of different alternatives for solving these types of models. Originality/value As we are aware, this is the first survey in this topic.

Entrepreneurship orientation (EO) approach provides universities with a plan and roadmap for getting over rising uncertainty and complexity. Inspiring universities to embrace transformation and innovation, risk taking and proactive policy for planning and executing development strategies for success in the dynamic competitive environment arise from entrepreneurship orientation spirit. To reach this status, the key factors of success in entrepreneurship should be taken into consideration and assessed in universities. Structural and entrepreneurial policies as key non-financial factors are two concepts that have received considerable attention over the past years. Measuring efficiency of universities by the mentioned factors and using mathematical programming like Data Envelopment Analysis (DEA) method is the aim of this study. Hence, this paper applied DEA method for measuring efficiency of 16 faculties and institutes in Ferdowsi University of Mashhad (FUM) in Iran. Descriptive-survey methodology was used and data were collected through questionnaire survey. The faculties and institutes were ranked based on structural policies and EO through DEA. Finally, the DEA method recommended the development roadmaps template for the 11 faculties and institutes which were inefficient.

BackgroundThe present study has been undertaken with the aim to evaluate performance and ranking of various universities of medical sciences that are responsible for providing public health services and primary health care in Iran.MethodsFour models; Weighted Factor Analysis (WFA), Equal Weighting (EW), Stochastic Frontier Analysis (SFA), and Data Envelopment Analysis (DEA) have been applied for evaluating the performance of universities of medical sciences. This study was commenced based on the statistical reports of the Ministry of Health and Medical Education (MOHME), census data from the Statistical Center of Iran, indicators of Vital Statistics, results of Multiple Indicator of Demographic and Health Survey 2010, and results of the National Survey of Risk Factors of non-communicable diseases.ResultsThe average performance scores in WFA, EW, SFA, and DEA methods for the universities were 0.611, 0.663, 0.736 and 0.838, respectively. In all 4 models, the performance scores of universities were different (range from 0.56–1, 0.53–1, 0.73–1 and 0.83–1 in WFA, EW, SFA and DEA models, respectively). Gilan and Rafsanjan universities with the average ranking score of 4.75 and 41 had the highest and lowest rank among universities, respectively. The universities of Gilan, Ardabil and Bojnourd in all four models had the highest performance among the top 15 universities, while the universities of Rafsanjan, Ahvaz, Kerman and Jiroft showed poor performance in all models.ConclusionsThe average performance scores have varied based on different measurement methods, so judging the performance of universities based solely on the results of a model can be misleading. In all models, the performance of universities has been different, which indicates the need for planning to balance the performance improvement of universities based on learning from the experiences of well-performing universities.

Financial distress is a company’s inability to meet their financial obligation, which finally leads to going bankruptcy. Financial distress is then used as an early warning signal before going bankrupt. Therefore, financial distress should be predicted as preventive actions. This study main objective is to compare the traditional predictions tools, Altman’s Z-score model, with the new propose Data Envelopment Analysis (DEA) approach method. Focusing on Indonesia steel and iron industry, this study examines using 7 steels and iron companies which listed in IDX from the period of 2013- 2018. Starting from constructing the model of DEA in predicting distress, the accuracy test of both models is compared. The results reveal that DEA’s approach prediction has a higher accuracy rate compared to the Altman’s model. DEA with a total of 39 correct predictions out of 42 samples generate an accuracy rate of 92.86%. This rate is higher than the Altman’s model with the accuracy rate of 85.71% which resulting from a total of 36 correct predictions out of 42 samples. The method, especially DEA to predict financial distress for Steel and Iron Companies in Indonesia is the significant contribution to science. Keywords: Altman (Z-Score), DEA, Financial Distress

This study set out to measure the performance of organic farming in India. The study utilized data of 21 Indian states for the period 2016–2017, and the states are considered as decision making units for performance assessment. In the first step, we compared the “pure organic production” or “production technology set A” with the “production including the conversion period” of organic farming or “production technology set B” by incorporating the Data Envelopment Analysis (DEA) method of performance evaluation. In the second step, we estimated the performance of “pure organic production” and applied the bootstrap DEA method to correct the biasness in the traditional DEA approach. The input‐oriented DEA model reveals that on average technical efficiency of organic farming production is 84.7%, and the output‐oriented DEA model concludes 20% technical efficiency which means organic farming production can be increased by 80% with the same amount of inputs utilization. Evidence is presented in the conclusion which shows that organic farming practice is highly beneficial for land degraded states of India. The findings can contribute to implement policies for organic farming production in Indian states.

Performance evaluation of complex electricity generation systems: A dynamic network-based data envelopment analysis approach

This study proposes a three-staged approach to data envelopment analysis (DEA) modeling for hospital efficiency. The approach aims to overcome the constraint on the number of inputs/outputs relative to the number of DMUs. Initially, the principal components of all inputs and outputs are determined using principal component analysis (PCA). Next, these principal components enter a factor analysis (FA) process to construct a two-level hierarchy of inputs/outputs and to establish a weighting scheme based on explained variances of components. Finally, a two-level DEA (TLDEA) method is applied to the resultant framework to determine the relative efficiency of hospitals using data from the healthcare context of Iran as an emerging economy. The outcomes of applying the proposed PCA-FA-TLDEA approach are argued to offer a substantial increase in the discriminatory power of classical DEA methods and could incorporate a relatively large set of inputs/outputs already existing in the hospital efficiency literature. As demonstrated in the evaluated hospitals, the PCA-FA-TLDEA methodology improved the discrimination from 0% in the original DEA to 45%. The paper proposes a novel three-stage DEA model by using PCA to extract the principal components from the inputs and outputs; therefore, reducing the number of inputs and outputs and their inter-correlations. Secondly, a hierarchy of inputs and outputs by applying FA to the principal components is constructed. Finally, the TLDEA method to the hierarchy of inputs and outputs is applied to evaluate the performance of public hospitals.