Output-oriented Data Envelopment Analysis Research Articles

Efficiency assessment of two-stage systems with fixed-sum outputs: A noncooperative DEA model with uncertain stage priority

Exogenous constraints such as fixed-sum output can affect the efficiency assessment of traditional two-stage data envelopment analysis (DEA) models. To address this issue, several studies have evaluated the DEA efficiencies for two-stage systems with fixed-sum output. They have proposed centralized models based on cooperative game theory and models under global mindset. However, in many cases, the first and second stages play a noncooperative game, and information on stage priority is often unobservable. In this study, we develop a noncooperative two-stage fixed-sum output DEA model with uncertain stage priority for calculating the overall system and stage efficiencies. Instead of stage priority as an endogenous variable, with aid of acceptaiblty analysis, we analyze the dominance relationship among decision-making units (DMUs) and provide a robust ranking by considering all possible stage priorities. We also conduct an empirical analysis with data from commercial banks to demonstrate the effectiveness of the developed model. After 10,000 groups of stage priority iterations, we report a robust ranking of commercial banks in which Hefei branch ranks first with 100 % probability

Level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia: an application of the data envelopment analysis

BackgroundBesides the scarcity of resources, inefficient utilization of available health service resources has been the bottleneck to deliver quality health services in Ethiopia. However, Information regarding the efficiency of health service providers is limited in the country. Health service managers and policy makers must be well informed about the efficiency of health service providers and ways of using limited resources efficiently to make evidence-based decisions. This study aimed to assess the level of technical efficiency and associated factors among health centers in East Gojjam Zone, Northwest Ethiopia.MethodsA facility-based cross-sectional study was conducted among 27 randomly selected health centers in East Gojjam zone, Northwest Ethiopia, from October 30, 2022, to April 30, 2023. Using an interviewer-administered questionnaire and document review checklist, health centers’ data was collected and entered to Epi-Data version 4.6. The data was exported to Microsoft office excel and Stata version 14 for analysis. A two-stage output-oriented data envelopment analysis with a variable return to scale assumption was employed to determine the level of technical efficiencies. Finally, the tobit regression model was applied to identify the associated factors at 5% level of significance.ResultsIn this study, 59.3% of the health centers were technically efficient. The mean technical efficiency score of the health centers was 0.899 ± 0.156. Inefficient health centers could provide more 22, 433 outpatient visits, 1,351 family planning visits, 155 referral services, 206 skilled deliveries and 385 fully vaccinations of children if they were technically efficient as their peer health centers for the same year. From the tobit regression, the catchment population and number of administrative staffs were statistically significant determinants of the technical efficiency of health centers.ConclusionsThe mean technical efficiency of the health centers in East Gojjam zone, Northwest Ethiopia was high. However, nearly half of the health centers were technically inefficient, which indicates the exitance of a space for further improvements in the productivity of these health centers. Employing excess number administrative staffs (above the optimal level) should be discouraged and selecting appropriate sites where the health centers to be constructed (to have large catchment population coverage) could improve the productivity of health centers.

Sustainable approach towards alternatives for the use of iron ore tailings in the construction sector using Data Envelopment Analysis methodology.

Iron ore tailings (IOTs) need to be properly managed to mitigate the environmental, social, and economic impacts of mining activities. To cope with this issue, we use data envelopment analysis (DEA) to evaluate alternatives for using IOT in the construction sector. The classical and weight restriction output-oriented DEA models were used in this analysis. The results show that the ranking of alternatives depends on the aspect being evaluated. Concrete block is the most environmentally friendly alternative when analysing both models. For both social and economic aspects, ceramics produced better results in the classical model, whereas Portland cement showed better outcomes in the weight restriction model. In this sense, the results suggest great potential for the use of IOT in the construction sector, enabling the reduction of risks and social and environmental impacts of tailings dams.

The Efficiency of Alternative and Conventional Energy Exchange-Traded Funds: Are Clean Energy Exchange-Traded Funds a Safer Asset?

This paper examines the efficiency of alternative energy equity Exchange-Traded Funds (ETFs) and conventional energy equity ETFs from 2018 to 2020, utilizing a combination of an output-oriented Slack-Based Data Envelopment Analysis (DEA) model and cluster analysis. In the context of an output-oriented DEA model, efficiency is defined as the ability of an ETF to maximize its outputs (annualized average return; environmental, social responsibility, and corporate governance; and net asset value) given a fixed level of inputs (expense ratio and beta). The findings indicate that alternative energy ETFs have the potential for long-term outperformance compared to conventional energy ETFs in terms of efficiency. However, during financial crises, the performance differences between the two types of ETFs diminish, with no significant outperformance observed in either category. The expense ratio and net asset value are identified as key factors influencing the efficiency of both ETF types. Additionally, social and governance metrics have a notably stronger positive impact on conventional energy ETFs relative to alternative energy ETFs, highlighting the increasing significance of these factors in financial asset performance.

Evaluating health systems' efficiency towards universal health coverage: A data envelopment analysis.

To estimate the technical efficiency of health systems toward achieving universal health coverage (UHC) in 191 countries. We applied an output-oriented data envelopment analysis approach to estimate the technical efficiency of the health systems, including the UHC index (a summary measure that captures both service coverage and financial protection) as the output variable and per capita health expenditure, doctors, nurses, and hospital bed density as input variables. We used a Tobit simple-censored regression with bootstrap analysis to observe the socioeconomic and environmental factors associated with efficiency estimates. The global UHC index improved from the 2019 estimates, ranged from 48.4 (Somalia) to 94.8 (Canada), with a mean of 76.9 (std. dev.: ±12.0). Approximately 78.5% (150 of 191) of the studied countries were inefficient (ϕ < 1.0) with respect to using health system resources toward achieving UHC. By improving health system efficiency, low-income, lower-middle-income, upper-middle-income, and high-income countries can improve their UHC indices by 4.6%, 5.5%, 6.8%, and 4.1%, respectively, by using their current resource levels. The percentage of health expenditure spent on primary health care (PHC), governance quality, and the passage of UHC legislation significantly influenced efficiency estimates. Our findings suggests health systems inefficiency toward achieving UHC persists across countries, regardless of their income classifications and WHO regions, as well as indicating that using current level of resources, most countries could boost their progress toward UHC by improving their health system efficiency by increasing investments in PHC, improving health system governance, and where applicable, enacting/implementing UHC legislation.

On concept of undesirable outputs in data envelopment analysis: ratio independent undesirable outputs

On concept of undesirable outputs in data envelopment analysis: ratio independent undesirable outputs

An Exploration of Prediction Performance Based on Projection Pursuit Regression in Conjunction with Data Envelopment Analysis: A Comparison with Artificial Neural Networks and Support Vector Regression

Data envelopment analysis (DEA) is a leading approach in performance analysis and discovering newer benchmarks, and the traditional DEA models cannot forecast the future efficiency of decision-making units (DMUs). Machine learning, such as the artificial neural networks (ANNs), support vector machine/regression (SVM/SVR), projection pursuit regression (PPR), etc., have been viewed as beneficial for managers in predicting system behaviors. PPR is especially suitable for small and non-normal distribution samples, the usual cases in DEA analysis. This paper integrates DEA and PPR to cover the shortcomings we faced while using DEA and DEA-BPNN, DEA-SVR, etc. This study explores the advantages of combining these complementary methods into an integrated performance measurement and prediction model. Firstly, the DEA approach is used to evaluate and rank the efficiency of DMUs. Secondly, we establish two DEA-PPR combined models to describe the DEA efficiency scores (also called the production function) and the DEA-efficient frontier function. The first combined model’s input variables are input–output indicators in the DEA model, and the output variable is the DEA efficiency. In the second model, its input variables are input or output indicators in the DEA model, and the output variable is the optimal input indicator for input-oriented DEA or the output indicator for output-oriented DEA. We conducted positive research on two examples with actual data and virtual small, medium-sized, and large samples. Compared with the DEA-BPNN and DEA-SVR models, the results show that the DEA-PPR combined model has more vital global optimization ability, better convergence, higher accuracy, and a simple topology. The DEA-PPR model can obtain robust results for both small and large cases. The DEA-BPNN and DEA-SVR models cannot obtain robust results for small and medium-sized samples due to overfitting. For large samples, the DEA-PPR model outperforms DEA-BPNN, DEA-SVR, etc. The DEA-PPR combined model possesses better suitability, applicability, and reliability than the DEA-BPNN model, the DEA-SVR model, etc.

How to promote the sustainable development of strategic emerging industries: Evidence from Guangzhou

The strategic emerging industry (SEI) can effectively cope with high energy consumption, high pollution and low efficiency in traditional production mode, which attracts high attention. However, few studies quantitatively analyze the input-output relationships and the sustainable development level of SEIs. Moreover, existing method frameworks fail to consider both the current situation and development potential of SEIs. To deal with the above research gaps, a novel static-dynamic intuitionistic fuzzy evaluation framework integrating output-oriented data envelopment analysis model, improved Malmquist model, triangular intuitionistic fuzzy number, clustering method and Tobit regression model is first proposed. 120 SEIs from Guangzhou are selected for empirical study. Results reveal that all SEIs have almost no expected movement in operational cost but obvious redundancies in employee expenditure with a standard deviation of 1.771. It's urgent to optimize the employment, assessment and salary systems for staff. Moreover, SEI sustainable development scores vary from 0.473 to 1. It's necessary to strengthen cultivation of excellent SEIs and pay attention to supervision of the weak. Research findings can not only guide local government and industry associations to make scientific evaluation, but also help SEIs carry out self-inspections, which can further promote sustainable development of the entire industry.

Measuring economic resilience of manufacturing organization leveraging integrated data envelopment analysis (DEA)-machine learning approach

ABSTRACT This study aims to assess the economic resilience of manufacturing firms through a combination of output-oriented data envelopment analysis (DEA) and machine learning techniques. The research draws on economic resilience factors identified in the literature and focuses on three categories: economic-related factors (financial flexibility, microeconomic market, macroeconomic stability), production-related factors (restoration of production, backup inventories, resource pooling/sharing), and management-related factors (diversification of activities, good governance (management), relocation). Using DEA, a mathematical approach, the study computes and analyzes the contributions of various components to economic resilience. The results of DEA normalization indicate that the highest weighted criteria are financial flexibility, good governance (management), and resource pooling (sharing). To gain a deeper understanding of the data structure, the K-means algorithm is employed for clustering and analysis. K-means clustering is a popular exploratory data analysis technique that aims to group samples into clusters of equal variances by minimizing inertia or the sum of squares within each cluster. The combination of these techniques with sensitivity analysis provides a novel analytical approach for policy formulation and decision-making. The findings have implications for practitioners and domain experts, offering valuable insights into enhancing economic resilience in the manufacturing sector.

Cost and Revenue Efficiency Models for Heterogeneous Inputs and Outputs in Data Envelopment Analysis

Cost and Revenue Efficiency Models for Heterogeneous Inputs and Outputs in Data Envelopment Analysis

Efficiency of Turkish Health Management Departments through Data Envelopment Analysis

The purpose of this study was to determine which of the input and output variables made a statistically significant difference in the efficiency status of undergraduate departments offering Health Management education in Turkey, to identify areas where inefficient departments could improve, and to determine which of the input and output variables made a statistically significant difference in the efficiency status of inefficient departments. The output-oriented data envelopment analysis model was employed in the efficiency analysis. As a decision-making unit, there were 43 Health Management departments. Mann-Whitney U test was used to analyse whether or not there was a statistically significant difference between the efficiencies of the departments based on the input and output variables. In Turkey, 15 of 43 state universities providing formal undergraduate education in the field of Health Management are fully efficient. Also, there was a significant difference in terms of the “number of completed projects” and the “number of papers in journals screened within the scope of Web of Science Core Collection (WOSCC)” variables. No study examining the efficiency of Health Management departments has been found in the literature. Department administrators are encouraged to increase their publications and look into ways to design more initiatives.

Planning for potential increases in disbursements and risk of managed funds conditional on desired short-term performance levels

ABSTRACT As increasing disbursements (such as costs and fees) and risk (such as portfolio risk and leverage risk) may affect managed fund performance, analysts go beyond risk-adjusted return measures for performance appraisal. A methodology that assesses performance in a multidimensional framework is data envelopment analysis (DEA). A variant of DEA is inverse DEA. In this paper, inverse DEA is applied to determine output (investment income and benefit payments) targets for a given fund to perform at a desired efficiency level when increase in disbursements and risk at known levels is envisaged. An output-oriented inverse DEA model assuming variable returns to scale is formulated with theoretical underpinning. The proposed modelling framework ensures that, when an input augmented fund with estimated output targets is included in the observed fund set, the frontier of best performance established with the observed fund set does not change. The inverse DEA model is applied to a sample of Australian superannuation funds to demonstrate how a given fund may obtain pathways to improve its performance under different input-augmentation scenarios. As different pathways suggest feasible output targets, fund managers may find them valuable in forward planning.

Assessing the efficiency of health systems in achieving the universal health coverage goal: evidence from Sub-Saharan Africa

ObjectiveUniversal health coverage (UHC) is a major pathway to save many people from catastrophic and impoverishing healthcare spending and address the inequality in health and healthcare. The objective of this paper is to assess the efficiency with which health systems in sub-Saharan Africa (SSA) are utilizing healthcare resources to progress towards achieving the UHC goal by 2030.MethodsThe study followed the guidelines proposed by the World Health Organization (WHO) and World Bank joint UHC monitoring framework and the computational operationalization approach proposed by Wagstaff et al. (2015) to estimate the UHC index for each of the 30 selected SSA countries. The bootstrapping output-oriented data envelopment analysis (DEA) was used to estimate the bias-corrected technical efficiency scores and examine the environmental factors that influence health system efficiency.ResultsThe estimated UHC levels ranged from a minimum of 52% to a maximum of 81% (SD=8.6%) with a median coverage of 66%. The average bias-corrected efficiency score was 0.81 left(95% CI:0.77-0.85right). The study found that education, governance quality, public health spending, external health funding, and prepayment arrangements that pool funds for health had a positive significant effect on health system efficiency in improving UHC, while out-of-pocket payment had a negative impact.ConclusionThe results show that health systems in SSA can potentially enhance UHC levels by at least 19% with existing healthcare resources if best practices are adopted. Policymakers should aim at improving education, good governance, and healthcare financing architecture to reduce out-of-pocket payments and over-reliance on donor funding for healthcare to achieve UHC.

The Impact of COVID-19 on Bank Efficiency in the Western Balkans: A DEA Approach

Abstract This paper aims to assess and compare the relative efficiency of commercial banks in six Western Balkan developing countries (North Macedonia, Serbia, Montenegro, Bosnia and Herzegovina (B&amp;H), Kosovo1, and Albania), using the leading nonparametric methodology Data Envelopment Analysis (DEA) for the period 2016-2020, and investigating the impact of the COVID-19 pandemics on the performance of banking systems in these countries. The output-oriented DEA model has been implemented with interest expenses and non-interest expenses as inputs and interest revenue and non-interest revenue as outputs. Our findings show that the average efficiency of the six Western Balkan banking systems differs, and Kosovo’s banking system has noted the highest average efficiency in the whole observed period (2016–2020). In contrast, the banking system in Bosnia and Herzegovina noted the lowest average efficiency. The COVID-19 pandemic decreased the relative efficiency of the banking sectors in all six of the Western Balkan countries observed, except Kosovo. However, additional research that includes all pandemic years is recommended to assess the impact of the COVID-19 pandemic on the banking systems in the Western Balkans. This study provides invaluable insights for academic members, banking management and regulatory bodies, governments, and the interested public. It is the first empirical study that includes a glimpse of the first impact of the COVID-19 pandemic on banking systems in the developing countries of the Western Balkans.

Assessment of Efficiency and Effectiveness of Bus Transport Organizations Using DEA Incorporating Emissions and Accidents

Environmental pollution due to vehicular emissions and accidents reflect upon the sustainability, and social responsibility of transport organizations. However, it is also necessary to attain higher levels of performance by ensuring higher transit ridership measured in terms of passengers carried per day. The present work is focused on the analysis of performance efficiency and service effectiveness of 25 selected State Road Transport Undertakings (SRTUs) in India for the year 2004-05, 2009-10 and 2014-15. Here, it was proposed to use a hybrid output-oriented Data Envelopment Analysis (DEA) approach developed by Seiford and Zhu in 2002 to handle undesirable outputs such as annual Carbon-di-oxide (CO2) emitted per passenger-km, and total accidents per year in addition to overall productivity. The results of the analysis provided details on targets that could be achieved for the available input resources allocated. Transport organizations can adopt similar approaches in performance evaluation and benchmarking considering sustainability, and social responsibility along with efficiency.

A novel algorithm for evaluating the configurations of omni-channel distribution network considering transparency and consensus formation

In this paper, an algorithm has been developed to evaluate the performance of omni channel distribution network configurations based on data envelopment analysis (DEA) models and consensus formation. At first, bi-objective input- and output-oriented simultaneous DEA models are developed to evaluate the configurations with respect to two common criteria, i.e., the service and cost, and the transparency criterion which is defined based on the blockchain technology. Then, a consensus is formed on the evaluation results, based on a risk-based optimisation model. The proposed models are solved by fuzzy goal programming. In addition, the application of the proposed models is demonstrated through a case study in the context of the Indian retail industry. The computational results extracted from the optimisation models developed in our paper showed that the transparency criterion is very effective in the configurations evaluations and scores.

Assessment of the status and trends of photovoltaic distributed generation in Brazil: An in-depth approach based on big data processing

The deployment of photovoltaic (PV) distributed generation (DG) has been increasing substantially in Brazil. In this context, the present paper exploits a robust dataset provided by the Brazilian Electricity Regulatory Agency (ANEEL) to evaluate the status and trends concerning PV DG. As of Nov/2022, such a dataset consists of more than 1,4 million lines (one for each system) and thirty columns (various information concerning the systems). For an in-depth assessment, three items are addressed: (i) the application of machine learning algorithms to estimate the installed power of individual systems based on other information available in the dataset, (ii) the application of forecasting models to predict the installed power of PV DG over time in Brazil and its regions, and (iii) the application of the data envelopment analysis (DEA) method to rank Brazilian states in terms of efficiency in PV DG deployment. While items (i)-(iii) present distinct specific goals, they use the same dataset and provide essential insights concerning PV DG in Brazil. In item (i), elastic net (EN), decision tree (DT), random forest (RF), extra tree (ET), AdaBoost (AB), and gradient boosting (GB) are applied to select the most accurate algorithm. In item (ii), autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), Holt-Winters exponential smoothing (HWES), Bass diffusion model (BDM), and multilayer perceptron artificial neural network (MLP-ANN) are applied. In item (iii), output-oriented DEA CCR and BCC are applied. Concerning item (i), results demonstrate that estimating the installed power of individual systems is not very simple. Nonetheless, the machine learning algorithms imply a significant accuracy increase compared to taking the dataset’s average as the estimation, especially the DT and RF algorithms with a coefficient of determination of 29%. Regarding item (ii), results demonstrate that the installed capacity in Brazil and its regions is expected to increase approximately linearly in the horizon of months but with weekly seasonal characteristics. Moreover, the most accurate model depends on the region, i.e., the ANN is superior for the Northeast (MAPE of 3.25%) and South (MAPE of 1.28%) regions, the HWES method is superior for Brazil (MAPE of 0.51%) and the Southeast region (MAPE of 2.26%), and the BDM is superior for the Mid-west (MAPE of 1.95%) and North (MAPE of 3.37%) regions. Concerning item (iii), results demonstrate that DEA CCR is more representative of the Brazilian reality since DEA BCC assigns several states as efficient, even if they showcase a very low installed capacity, particularly small states. DEA CCR indicates that Minas Gerais, Rio Grande do Sul, and Mato Grosso are the most efficient states in PV DG deployment relative to their inputs, closely followed by São Paulo with an efficiency of 95%. Furthermore, states located in the North and Northeast region require more attention from ANEEL since their efficiencies are generally low. A literature review demonstrated that this paper is the first of its kind in exploiting so thoroughly and robustly the dataset provided by ANEEL. Therefore, the paper is expected to assist ANEEL in the task of properly regulating the sector and also assist researchers and PV aggregator companies through in-depth insights. Moreover, the conducted procedures can also be applied to other countries with available datasets, thus ensuring high applicability for the research.

Perbandingan Tingkat Efisiensi OPZ Sebelum dan Saat Pandemi di Indonesia

This study aims to compare the level of efficiency of OPZ before and during the pandemic. This research is a quantitative research with the output-oriented Data Envelopment Analysis (DEA) method. The DEA approach used is the production approach. The input variables used include the total cost of amil salaries, total socialization costs and total operational costs. While the output variables include the amount of funds collected and the amount of funds channeled. The type of data used is secondary data originating from financial reports in 2019 and 2020. The sampling technique that the author uses is Purposive Sampling, which takes 9 national-level OPZs consisting of BAZNAS, LMI, Dompet Dhuafa, Rumah Zakat, Orphanage and Zakat. , Indonesian Islamic Da'wah Council, Arrahman Indonesia Orphanage House, Darul Qur'an Nusantara and LAZISNU. The results show that on average, the OPZ efficiency value in 2019 was 0.74 and in 2020 it was 0.70. This trend indicates that there is a decrease in efficiency in the OPZ. it can be concluded that there is no significant difference in the results of OPZ efficiency before the pandemic and during the pandemic. There are 4 OPZs that experience continuous efficiency in 2019 and 2020. Meanwhile, the other five OPZs have yet to experience efficiency. The results of this study can be used as a reference and consideration for policy making in OPZ and relevant institutions/work

Efficiency in Brazilian Federal Universities: a study with Data Envelopment Analysis (DEA)

This paper aimed to analyze the level of technical efficiency of Brazilian federal universities, from 2012 to 2018, using the Data Envelopment Analysis (DEA) method, with an output-oriented DEA BCC model. Four management indicators instituted by TCU were used as inputs and two quality indicators, as outputs. For that, a sample of 56 federal universities was used. Considering the standard efficiency scores, the results indicated high levels of technical efficiency during the years under analysis. The average technical efficiency of federal universities was 93.7% in 2012, with 17 efficient universities, and 94.5% in 2018, with 18 efficient universities. The years 2015 and 2016 stood out, with 28 and 27 efficient universities respectively, whose national averages were 96.1%. However, due to the benevolence of the BCC model with very small or very large units, some universities may have been considered efficient by default. From 2012 to 2018, the Malmquist Index indicated an increase in the productivity of universities by 2.2%, which occurred in a greater proportion due to the catch-up effect (1.8%) and in a lower proportion by the frontier shift effect (0.4%). In addition, the increase of 1.8% in relative efficiency occurred in a greater proportion due to the increase in pure technical efficiency (1.1%) and in a lower proportion due to the increase in scale efficiency (0.7%). Yet, despite the increase in productivity, there is still room to achieve better quality results and to improvements in the management of resources, in order to reduce waste.

Productivity Change in European Banks in the Post-Crisis Period

The paper analyses the productivity change of a balanced panel of 1915 European banks during the 2013–2018 post-crisis period. To study productivity changes, the paper applies the non-parametric output-oriented Data Envelopment Analysis (DEA) approach and the Malmquist productivity index (MPI). The total productivity change estimated by the MPI is further decomposed into technical efficiency change and technological change. The overall MPI estimates show a modest increase in the productivity of banks in half of the EU countries. Further decomposition of the MPI indicates that the productivity growth was mainly a result of technological improvement, which was particularly high among the new EU member states, whereas there was a significant drop in technical efficiency. The productivity growth was higher among banks in the non-euro area and among savings banks. The practical implications drawn from the paper are that European banks should further develop their business models to rationalize the costs and increase their operational efficiency and stimulate the adoption of fintech solutions and technological development so as to enhance their productivity.