Random effects logistic regression model for data envelopment analysis with correlated decision making units

Data Envelopment Analysis (DEA) is a data-oriented performance evaluation method that has a very satisfactory performance when there are multiple inputs and outputs presented in the form of set of peer Decision Making Units (DMU). In the discussion of DEA, basically measures the efficiency of each DMU in specific performance based on DEA Efficiency Scores. In DEA, when there is a value of Non-Zero Input and Output Slacks then this often means inefficiency. This scalar measures directly with the input surplus and the output of the short decision of the decision making unit (DMU). Universitas Malikussaleh as one of the State Universities in Indonesia, it is necessary to follow the regulations of the Ministry of Research, Technology and Higher Education of the Republic of Indonesia (KEMENRISTEKDIKTI), one of which is the measurement of efficiency based on the number of lecturers research, the efficient use of resources included in the number of lecturers and employees, as well as students who can be accommodated based on efficiency measurements using DEA. This paper will measure the efficiency of each department in Malikussaleh University and if there is an inefficient department then it will be measured based on slack-based measures to advise aspects that need attention so that the department can be efficient.

This study aimed to measure the efficiency scores of 75 dairy buffalo farms in the province of Nueva Ecija, Central Luzon, Philippines, using an input-oriented, variable-return-to-scale Data Envelopment Analysis (DEA) model. The farmer-informants or decision-making units (DMUs) were categorized as smallholders, family modules, and semi-commercial in operations. Personal interviews using structured questionnaires were done to gather various information on the socio-economic and management practices of the DMUs. Output in the form of volume and value of milk produced and inputs such as quantities and costs of biologics, feeds, forage, and labor were also collected and evaluated among individual DMUs. The efficiency scores were computed using PIM-DEA software, which identified fully efficient DMUs lying on the frontier line (scores of 1.0) and those enveloped by it (inefficient DMUs with scores of less than 1.0). The overall mean Technical Efficiency (TE), Allocative Efficiency (AE), and Economic Efficiency (EE) scores among the DMUs were 0.80, 0.81, and 0.65, respectively. Most of the inefficient DMUs were in the smallholder category. In sum, smallholder DMUs classified under low and moderate TE clusters should reduce their inputs by 53.31% and 40.01%, respectively, to become fully efficient. Likewise, higher lambda values among efficient peer DMUs indicate the best practice frontiers that the inefficient peer DMUs can benchmark with. Extension and advisory services can help promote the best management practices of the frontiers to improve the TE, AE, and EE of the inefficient DMUs.

Data envelopment analysis (DEA) is a non-parametric technique for measuring the efficiency of decision making units (DMU) with exact input and output terms. So far, a number of DEA models with interval data have been developed. The radial DEA model such as CCR or BCC model with interval data are well known as basic DEA models with interval data. However, these radial DEA models have some defects. In this study, we develop a new approach based upon the slacks-based measurement (SBM) of super-efficiency model for dealing with interval data in DEA. The SBM super-efficiency DEA model can not only discriminate the performance of efficient DMUs from inefficient ones, but can distinguish between the efficient DMUs. In addition, this model can overcome the drawback that the super-efficiency model can be infeasible under variable returns to scale (VRS) assumption. An illustrative example is provided to verify the idea of this paper and show its potential application and validity.

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.

Performance evaluation for demand responsive transport services: A two-stage bootstrap-DEA and ordinary least square approach

PurposeThe present study seeks to investigate the effects of Mergers & Acquisitions (M&As) on the efficiency level of the merging units in the Indian banking sector. It intends to address the following research questions: Does the efficiency level of banks change after the merger event? What are the changes in efficiency scores of banks when the assumption of constant returns to scale is rejected? Do input prices contribute towards efficiency scores of merging banks? What do these variations in efficiency measures signify?Design/methodology/approachThe study employs an input-oriented Data Envelopment Analysis (DEA) model with window approach to investigate the technical, pure technical, scale, allocative, and cost efficiencies of the Indian Scheduled Commercial Banks (SCBs) that merged during the period 2000–2018.FindingsAlthough an overall conclusion of efficiency increase/decrease cannot be claimed, the results indicate a higher potential of merger-induced synergy in cases with low pre-merger efficiency levels.Originality/valueThe study provides a comprehensive and exhaustive efficiency analysis of M&As. An ordinal transformation of DEA efficiency scores enabled the derivation of several additional findings with respect to M&As in the Indian banking scenario. While the DEA efficiency scores provide an intensive reflection of the impact of past M&As in the Indian banking industry, the technique of ordinal transformation can be employed just as effectively in future studies, irrespective of the choice of industry or country.

Emerging application of robust data envelopment analysis for optimization of graft copolymerization of poly(2-hydroxyethyl methacrylate) to Tamarindus indica seed polysaccharide

In most models of data envelopment analysis (DEA), the best performers have the efficiency score of unity. Usually there are plural decision making units (DMUs) which have this ‘efficient status’ and DEA cannot provide more information about these efficient DMUs. Discrimination between these efficient DMUs is an interesting research subject. In effect, most conventional methods to rank efficient DMUs are based on the concept of common weights analysis (CWA). In this paper, after reviewing the existing common weights models, we propose a new methodology to rank the common weights models for the performance indices of only DEA efficient DMUs based on a voting model. Also, an approach for combining the results obtained from the common weights models is presented. Then, we give an example to illustrate our approach, and finally the new method is employed to rank efficient units in a real world problem.

Survey on rank preservation and rank reversal in data envelopment analysis

It is important for policymakers and managers of higher education institutions knowing how well their universities are operating. This article aimed to show that data envelopment analysis (DEA) can be an excellent benchmarking instrument in higher education. First, by using several inputs and outputs at the institutional level, DEA can identify technically efficient institutions that may work as a benchmark in the sector becoming a reliable tool for ranking universities. Second, a bootstrapped–truncated regression allows us to understand the factors affecting technical efficiency of the institutions under evaluation. The case of Spanish public universities is taken as an example to verify the usefulness of the proposed methods. Our empirical strategy was based on a two-stage procedure to evaluate their internal efficiency in the provision of teaching and research. In the first stage, we estimated a technical efficiency score for each university. The average efficiency among Spanish universities was about 92%. In the second stage, we regressed the efficiency scores against a set of covariates to investigate their association with the level of university (in)efficiency. We found that universities with a higher percentage of grantees tend to be less inefficient, and a higher percentage of academics with tenure enhances the productive efficiency of the Spanish higher education sector. Finally, we computed Spearman’s rank correlations between DEA efficiency scores and the classification of Spanish institutions in university rankings such as the SCImago and Shanghai rankings. The results revealed that the ranking positions given by DEA scores to Spanish universities matched their positions in recognized rankings.

Data Envelopment Analysis (DEA) is employed as a tool to evaluate the efficiency score of Tehran Stock Exchange. For predicting financial distress, it is designed and tested a model base on efficiency score. Its accuracy was verified by employing another model designed by financial ratios and variables based on Multivariate Discriminant Analysis (MDA). Ultimately, to investigate the effectiveness of firm’s efficiency score on financial distress prediction, its score combined with financial ratios is entered in an MDA model. The results show that all the three proposed models, in this paper, have better ability of predicting financial distress in Tehran Stock Exchange for two years prior to its occurrence. It implies that, DEA efficiency score is an effective predictor variable. Keywords: Efficiency score, Data envelopment analysis, Financial distress, Tehran stock exchange.

This study proposes a new efficiency measurement technique CDS as combination of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) and compares the CDS efficiency score with the DEA and SFA efficiency scores. The financial companies listed in Malaysian Stock Exchange for the period 2007-2016 are used to estimate the different types of efficiency score. Besides, linear regression analysis and ULFR (unreplicated linear functional relationship) analysis are used to analyze the performance of this CDS technique with the DEA and SFA techniques. The result suggests that the most efficient model is CDS which has a significant positive correlation with profit risk. Among the CDS, DEA and SFA techniques, the recommended technique (CDS) shows higher coefficient of determination values for both ULFR (0.9994) and linear regression (0.292) analysis. Also, based on the results of CDS, this study postulates that the most efficient firm is ACSM (Aeon Credit Service (M) Bhd) and the least efficient firm is MAY (Malayan Banking Bhd).

In Data envelopment analysis (DEA), the best performers are called DEA efficient and the efficiency score of a DEA efficient unit is denoted by unity. Ranking DEA efficient units is an interesting subject. This paper proposed a super-efficiency DEA model based on slack-based measure to evaluate engineering camouflage projects. A slack-based measure of DEA model was introduced, and then it was extended to super-efficiency issue. The method differs from the traditional radial measure, it takes account of the slacks of decision making units (DMU). The efficient DMUs can also be ranked completely. A numerical example illustrates the availability in the evaluating process of engineering camouflage projects.

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

BackgroundThe objective of this study was to assess public hospital efficiency, including quality outputs, inefficiency determinants, and changes to efficiency over time, in an Italian region. To achieve this aim, the study used secondary data from the Veneto region for the years 2018 and 2019.MethodsA nonparametric approach—that is, multistage data envelopment analysis (DEA)—was applied to a sample of 43 hospitals. We identified three categories of input: capital investments (Beds), labor (FTE), operating expenses. We selected five efficiency outputs (outpatient visits, inpatients, outpatient visit revenue, inpatient revenue, bed occupancy rate) and two quality outputs (mortality rate and inappropriate admission rate). Efficiency scores were estimated and decomposed into two components. Slack analysis was then conducted. Further, DEA efficiency scores were regressed on internal and external variables using a Tobit model. Finally, the Malmquist Productivity Index was applied.ResultsOn average, the hospitals in the Veneto region operated at more than 95% efficiency. Technical and scale inefficiencies often occurred jointly, with 77% of inefficient hospitals needing a downsizing strategy to gain efficiency. The inputs identified as needing significant reductions were full-time employee (FTE) administrative staff and technicians. The size of the hospital in relation to the size of the population served and the length of patient stay were important factors for the efficiency score. The major cause of decreased efficiency over time was technical change (0.908) rather than efficiency change (0.974).ConclusionsThe study reveals improvements that should be made from both the policy and managerial perspectives. Hospital size is an important feature of inefficiency. On average, the results show that it is advisable for hospitals to reorganize nonmedical staff to enhance efficiency. Further, increasing technology investment could enable higher efficiency levels.