Data Envelopment Analysis Efficiency Scores Research Articles

New clusterization of global seaport countries based on their DEA and FDEA network efficiency scores.

Global seaport network efficiency can be measured using the Liner Shipping Connectivity Index (LSCI) with Gross Domestic Product. This paper utilizes k-means and hierarchical strategies by leveraging the results obtained from Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) to cluster 133 countries based on their seaport network efficiency scores. Previous studies have explored hkmeans clustering for traffic, maritime transportation management, swarm optimization, vessel trajectory prediction, vessels behaviours, vehicular ad hoc network etc. However, there remains a notable absence of clustering research specifically addressing the efficiency of global seaport networks. This research proposed hkmeans as the best strategy for the seaport network efficiency clustering where our four newly founded clusters; low connectivity (LC), medium connectivity (MC), high connectivity (HC) and very high connectivity (VHC) are new applications in the field. Using the hkmeans algorithm, 24 countries have been clustered under LC, 47 countries under MC, 40 countries under HC and 22 countries under VHC. With and without a fuzzy dataset distribution, this demonstrates that the hkmeans clustering is consistent and practical to form grouping of general data types. The findings of this research can be useful for researchers, authorities, practitioners and investors in guiding their future analysis, decision and policy makings involving data grouping and prediction especially in the maritime economy and transportation industry.

Government spending efficiency in Latin America

We assess public spending efficiency of 20 Latin American countries over the period of 2000–2019, computing data envelopment analysis efficiency scores. For the Public Sector Performance composite indicator, we use the annual data of socio-economic indicators, and for the input measure we consider Total Public Spending as a percentage of GDP, by spending category. The results show that public spending during the period under study increased, but that overall governments were not efficient, as on average they could have used 27% less spending to achieve the same levels of performance. On the other hand, governments could have increased their performance by 18% whilst maintaining the same level of spending. The most-efficient countries were Chile, Guatemala, Panama, and Paraguay, with the least efficient being Bolivia, Venezuela, Nicaragua, Suriname, and Brazil.

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.

Assessment of Interorganizational Technology Transfer Efficiency

In this paper we propose a methodology for assessing the efficiency of technology transfer through merger and acquisition (M&A) and empirically estimate the effect of key factors impacting it. We implement data envelopment analysis (DEA) to calculate an efficiency score of the technology transfer process. The DEA efficiency score integrates a set of outputs (post-merger characteristics of an acquirer) and inputs (pre-merger technological parameters of a target); thus, it provides a multidimensional estimate of efficiency adjusted for the value of the acquired technology base.In the empirical part of this research, we collect data from 434 M&As to study a channel for transferring a technology base across organizational boundaries. Overall, empirical results suggest the adverse outcomes of accumulation of capability to value external technology: the higher the acquirer’s R&D intensity, the lower the efficiency of interorganizational technology transfer. The size of acquirer and relative size of the deal also affect the post-merger outcomes significantly and negatively. At the same time, the estimated effect of such technological characteristics of acquirer as capital expenditure intensity and number of patents is insignificant.

Data Envelopment Analysis for the Selection of Highway–Rail Grade Separation Projects

With the ambitious goal of zero deaths but growing traffic and congestion at highway–rail grade crossings (HRGCs), grade separation of these conflicting points has become an increasing strategy that improves railway safety but also increases network capacity and enhances livability attributes. Meanwhile, the engineering costs of grade separation are expensive and can vary considerably, which results in difficulties in the rational selection of such projects. This paper implemented the data envelopment analysis (DEA) for the benchmarking of high-risk HRGCs for grade separation considerations in Oklahoma. The top 1% of HRGCs were identified based on the New Hampshire Hazard Index (NHI) rankings and used as the decision-making units (DMUs) for DEA. The outputs of the DEA process included safety benefits, travel time savings, vehicle operating cost savings, and environmental benefits, and the input was the estimated cost of a grade separation project. The DEA efficiency scores of the DMUs were calculated, and nine HRGCs were identified on the frontiers with efficiency scores of 1.0. To further discriminate the DEA-efficient HRGCs, the enhanced DEA (E-DEA), which combines self-evaluation and cross-evaluation methods, was performed and the most efficient crossing was decreased to four with the E-DEA scores of 1.0. This study established a hierarchical framework combining the NHI, DEA, and E-DEA approaches, available data sets, and decision-making tools for the selection of the most efficient investment alternatives for highway–rail grade separation.

Analyzing the accuracy of variable returns to scale data envelopment analysis models

The data envelopment analysis (DEA) model is extensively used to estimate efficiency, but no study has determined the DEA model that delivers the most precise estimates. To address this issue, we advance the Monte Carlo simulation-based data generation process proposed by Kohl and Brunner (2020). The developed process generates an artificial dataset using the Translog production function (instead of the commonly used Cobb Douglas) to construct well-behaved scenarios under variable returns to scale (VRS). Using different VRS DEA models, we compute DEA efficiency scores with artificially generated decision-making units (DMUs). We employ five performance indicators followed by a benchmark value and ranking as well as statistical hypothesis tests to evaluate the quality of the efficiency estimates. The procedure allows us to determine which parameters negatively or positively influence the quality of the DEA estimates. It also enables us to identify which DEA model performs the most efficiently over a wide range of scenarios. In contrast to the widely applied BCC (Banker-Charnes-Cooper) model, we find that the Assurance Region (AR) and Slacks-Based Measurement (SBM) DEA models perform better. Thus, we endorse the use of AR and SBM models for DEA applications under the VRS regime.

Performance evaluation of investment with DEA models under pure jump processes

SummaryPortfolio optimization is a selection of the best combination of financial assets that maximizes the return and minimizes the risk. As the stock return distributions exhibit skewness, kurtosis, and heavy‐tailedness, for asset performance evaluation through the data envelopment analysis (DEA), we assume that the asset returns follow a pure jump Lévy process. For this purpose, two risk measures are provided as value‐at‐risk (VaR) and conditional VaR (CVaR). To deal with negative values, the range directional measure (RDM) model is utilized in the appropriate Lévy process, and inefficiency measurements are compared. Moreover, the asset price jumps have significant effects on the input and output of DEA models, risk measures, mean returns, and efficiency scores. In this regard, there are two optimization problems integrating DEA based on the variance gamma (VG) Lévy process, in which the input and output are stochastic. One of these two problems is a multi‐objective optimization model that evaluates more reliable efficiency scores than the other models. For this purpose, the VG parameters are estimated through the moments estimation method in order to analyze seven companies listed in the Tehran stock exchange (TSE). Finally, the Kolmogorov–Smirnov goodness‐of‐fit test is conducted. The results indicate that the VG model fits properly and is well suited to assets returns.

Attracting private capital for development: Are poorer countries less efficient?

According to common measures of private capital mobilization, such as the shares of foreign direct and portfolio investment in GDP, many low-income countries perform poorly in attracting private capital flows. However, these indicators do not account for differences in economic characteristics across countries, and thus cannot provide insights on countries’ performance relative to their potential levels of private capital flows as determined by their domestic economic structure and constraints. This paper addresses this gap by using nonparametric data envelopment analysis (DEA) to empirically calculate the efficient frontier for private capital mobilization as a function of countries’ domestic enabling environment, and then assess each country’s performance relative to that of other countries featuring similar domestic conditions. Our results reveal that many low-income countries, particularly in Africa and South Asia, which do not rank high according to achieved levels of private capital flows, are in fact performing on or very close to the efficient frontier. This indicates that these countries are good performers given the limited resources available to them. Tracking the performance of countries and regions over time, we furthermore find that sub-Saharan Africa experienced the strongest increases in DEA efficiency scores between 2007 and 2018, indicating that various countries in this region were catching up with the frontier.

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

Demand responsive transport (DRT) services have often been introduced to provide flexible public transport services, especially for areas with dispersed demand. However, objectively evaluating the performance of DRT services is challenging. This study uses a two-stage bootstrapped DEA and Ordinary Least Square (OLS) approach to measure performance using the Taiwanese DRT services as the empirical setting. In the first stage, bias-corrected Data Envelopment Analysis (DEA) efficiency scores are calculated by employing the smoothed homogenous bootstrapped procedure (Simar & Wilson, 2000). In the second stage, a OLS regression model is used to explore the impact from external factors with bias-corrected DEA efficiency scores as the dependent variable. Model results confirm that more than half of DRT services have increasing return to scale. The model results also show heavy rail services bring significant negative impact to the DRT service performance. This finding implies the main users of DRT services are local residents with fewer intercity travellers who typically would use heavy rail for access. The model results can lead to an improvement strategy for DRT services, for example, having a strategy to attract tourists to use DRT services to access tourist attraction sites would be one way to increase demand which would have the added advantage of improving sustainability. This study evaluates how this might improve public transport network accessibility and concludes with recommendations of a way forward to provide more sustainable DRT services.

ICT as a Key Determinant of Efficiency: A Bootstrap-Censored Quantile Regression (BCQR) Analysis for Vietnamese Banks

There is evidence that ICT developments can improve bank efficiency and performance. Previous studies often employ data envelopment analysis (DEA) to first examine bank performance and then use a second-stage regression to explain the influences of other environmental factors, including ICT, on such efficiency. Since DEA efficiency scores are bounded between the (0, 1] intervals, Tobit and truncated regressions are commonly used in this stage. However, none has accounted for the skewness characteristic of DEA efficiency. This paper applied a bootstrap-censored quantile regression (BCQR) approach to triply account for the issues of a small sample (via bootstrap), bounded intervals (via censored regression), and skewness (via quantile regression) in DEA analysis. We empirically examined the efficiency and performance of 27 Vietnamese commercial banks in the 2007–2019 period. The efficiency scores derived from our first stage revealed that they are skewed and thus, justify the use of the BCQR in the second stage. The BCQR results further confirmed that ICT developments could enhance bank efficiency, which supports the recent policy to restructure the Vietnamese banking sector toward innovation and digitalization. We also examined the impacts of other factors such as bank ownership, credit risk, and bank size on efficiency.

Bank stock performance during the COVID-19 crisis: does efficiency explain why Islamic banks fared relatively better?

This paper evaluates the stock performance of Islamic banks relative to their conventional counterparts during the initial phase of the COVID-19 crisis (from December 31, 2019, to March 31, 2020). Using 426 banks from 48 countries, we find that stock returns of Islamic banks were about 10–13% higher than those of conventional banks after controlling for a host of the bank- and country-level variables. This study explains the Islamic banks’ superior crisis stock performance by exploring the potential role of pre-crisis bank efficiency. In a univariate analysis, we document higher non-parametric Data Envelopment Analysis (DEA) efficiency levels for Islamic banks than conventional banks in the year preceding the COVID-19 crisis. Our multivariate regressions show that the risk-adjusted DEA efficiency scores can explain crisis stock returns for Islamic banks but not conventional banks. The evidence is robust to alternative measures of stock returns, efficiency models, and other empirical strategies. Finally, we present insight on the importance of key bank characteristics in determining the stock returns of conventional banks during the crisis period.

Homogeneity and Best Practice Analyses in Hospital Performance Management: An Analytical Framework

Performance modeling of hospitals using data envelopment analysis (DEA) has received steadily increasing attention in the literature. As part of the traditional DEA framework, hospitals are generally assumed to be functionally similar and therefore homogenous. Accordingly, any identified inefficiency is supposedly due to the inefficient use of inputs to produce outputs. However, the disparities in DEA efficiency scores may be a result of the inherent heterogeneity of hospitals. Additionally, traditional DEA models lack predictive capabilities despite having been frequently used as a benchmarking tool in the literature. To address these concerns, this study proposes a framework for analyzing hospital performance by combining two complementary modeling approaches. Specifically, we employ a self-organizing map artificial neural network (SOM-ANN) to conduct a cluster analysis and a multilayer perceptron ANN (MLP-ANN) to perform a heterogeneity analysis and a best practice analysis. The applicability of the integrated framework is empirically shown by an implementation to a large dataset containing more than 1,100 hospitals in Germany. The framework enables a decision-maker not only to predict the best performance but also to explore whether the differences in relative efficiency scores are ascribable to the heterogeneity of hospitals.

Inefficiency of public hospitals: a multistage data envelopment analysis in an Italian region

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.

Banking sector competition and intermediation efficiency in Kenya

AbstractThis paper seeks to evaluate efficiency and competition dynamics of the banking sector in Kenya for the period 2001–2017 using bank‐level data for 37 commercial banks. We apply non‐parametric data envelopment analysis to analyze measures of various aspects of efficiency in the banking sector. We also adopt Panzar–Rosse, H‐statistics to assess the level of competition in the banking sector. To examine the effect of efficiency on competition, panel estimators are applied. The study findings indicate that efficiency was on an upward trend and averaged 69%. The banking sector is characterized by monopolistic competition as shown by an H‐statistic of .59. Managerial ability measured by data envelopment analysis efficiency score was found to be an important factor in promoting competition for the Kenyan banking sector. The study shows that there is room for Kenyan banks to improve their efficiency by optimizing the scale of operations and continuing to adopt technology. This paper recommends policy makers to continue adopting policies that promote greater banking sector efficiency and competition.

Value-added intellectual capital and productive efficiencies: evidence from Taiwan listed electronics companies

PurposeThis study examines the effect of value-added (VA) intellectual capital on business performance from the perspective of productive efficiency, which is derived from its main contributors, namely, profitability and marketability efficiencies in two stages.Design/methodology/approachFirst, this study applies a dynamic network slacks-based measure in a data envelopment analysis (DEA) approach to estimate productive efficiency and its components of 766 Taiwan listed electronics companies over the period of 2010–2018. Second, this study performs regression analyses of the association between intellectual capital (IC), which is proxied by VA intellectual coefficient (VAICTM) and estimated DEA efficiency scores through various regression techniques.FindingsEmpirical evidence shows a significantly positive association between VAICTM and productive efficiency. This study finds the same result from the IC components after splitting VAICTM into (1) IC efficiency, which comprises human capital efficiency (HCE) and structural capital efficiency and (2) capital employed efficiency. Further examination reveals that HCE is the sole main contributor of the productive efficiency, and profitability and marketability efficiencies of a company.Practical implicationsThe findings of this study highlight the need to discuss the values of intellectual coefficient (IC) from the perspective of productive efficiency for better comprehensiveness.Originality/valueAlthough previous studies have shown that IC is a contributor of business performance, this study further zooms in VAIC and examines its effect on the efficiency of a company in transforming its inputs into outputs.

683Data envelopment analysis for predicting pre-diabetes for primary disease prevention

Abstract Background Identifying a population at risk of risks is imperative for primary disease prevention and allows the promotion of health maintenance in a healthy population. Previous studies on hypertension and dyslipidemia using data envelopment analysis (DEA) showed that populations at risk of risks could be identified using this method. In this study, we extended DEA to include pre-diabetes. Methods A retrospective cohort study was conducted using specific health check-up data from 2008 to 2013. DEA efficiency scores were calculated for healthy subjects with baseline glycated hemoglobin (HbA1c) <5.7%. Odds ratios (ORs) for pre-diabetes onset within 3 years were analyzed. Results Among 1,501 subjects, with 373 cases of disease onset (24.9%), the OR for the incidence of pre-diabetes (on the basis of a 0.1-point increase in the efficiency score) was 0.77 (90% confidence interval [CI] 0.68–0.86, p < 0.0002). After adjusting for age and sex, the OR was 0.66 (90% CI 0.58–0.75, p < 0.0001). Furthermore, for the subgroup with no conventional diabetes risk factors, the adjusted OR was 0.50 (90% CI 0.38–0.67, p = 0.0001). Conclusions We showed that the DEA efficiency score can help identify a pre-diabetes population at risk of risks. Further studies to validate these findings would be worthwhile for optimizing primary preventative measures. Key messages DEA was assessed for its ability to evaluate the risk of pre-diabetes. Results showed that an efficiency score could predict pre-diabetes onset and demonstrated the feasibility of applying this analysis in primary preventive healthcare.

Application of Hybrid Approach in Banking System: An Undesirable Operational Performance Modelling

Non-performing loans (NPLs) is a critical constituent that impacts the operational performance of banks. Rising level of risk leads to poor operational performance, especially when it is beyond the bank’s capabilities to control the increasing bad assets. This calls for real-time performance assessment coupled with futuristic decision making to support banking managers. This observation motivates the authors of this article to develop a two-stage performance prediction assessment model. Accordingly, a hybrid approach combining data envelopment analysis (DEA) and artificial neural network (ANN) is developed to measure and predict the operational efficiency scores of banks. DEA effectively explores the operational performance as well as improvable areas of inefficient banks. The training of ANN model is dependent on estimated operational DEA efficiency scores with the objective to estimate the efficiency scores. Domain for the validation of this study includes dataset derived from Indian banks. The validation result shows that trained ANN model has the prediction capacity with minimum error and maximum accuracy. Finally, the outcome of this study is significantly directed towards business managers who can rely on predictions based on empirical findings of this proposed hybrid modelling.

A genetic algorithm for solving bus terminal location problem using data envelopment analysis with multi-objective programming

Due to the urban expansion and population increasing, bus network design is an important problem in the public transportation. Functional aspect of bus networks such as the fuel consumption and depreciation of buses and also spatial aspects of bus networks such as station and terminal locations or access rate to the buses are not proper conditions in most cities. Therefore, having an efficient method to evaluate the performance of bus lines by considering both functional and spatial aspects is essential. In this paper, we propose a new model for the bus terminal location problem using data envelopment analysis with multi-objective programming approach. In this model, we want to find efficient allocation patterns for assigning stations terminals, and also we investigate the optimal locations for deploying terminals. Hence, we use a genetic algorithm for solving our model. By using the simultaneous combination of data envelopment analysis and bus terminal location problem, two types of efficiencies are optimized: Spatial efficiency as measured by finding allocation patterns with the most serving amount and the terminals’ efficiency in serving demands as measured by the data envelopment analysis efficiency score for selected allocation patterns. This approach is useful when terminals’ efficiency is one of the important criteria in choosing the optimal terminals location for decision-makers.

Inefficiency source tracking: evidence from data envelopment analysis and random forests

In the present era of complex environments, banks operate in a more dynamic environment, which in turn, affects their relative efficiency. Traditional Data envelopment analysis (DEA) models are widely used to measure efficiency. However, environmental/exogenous variables can significantly influence the DEA efficiency scores. Therefore, identifying the most important environmental variables is crucial in the evaluation of bank performances. This study introduces a three-stage DEA framework that employs a random forest as a powerful ensemble method for variable selection to search for the most influential environmental variables. The direction of influence of the selected environmental variables and their predictive power for predicting bank performances are investigated in the third stage, through a regression analysis. The proposed framework is tested with a sample of 110 banks in Middle East and North Africa countries, observed over a period of 3 years (2014 till 2016). Accordingly, a relevant set of environmental variables is identified and its effects on bank efficiency are studied. The findings indicate that the country where the bank operates has a significant effect on the bank’s efficiency. Results also show that the overall average efficiency score is stable (around 87%) for all banks. The study concludes with the limitations and suggested directions for further research.

TECHNICAL EFFICIENCY OF ARABLE FARMS IN SERBIA: DO SUBSIDIES MATTER?

This paper analyses the technical efficiency of arable farms in Serbia and its determinants using a two-stage double bootstrap Data Envelopment Analysis (DEA) approach on the Farm Accountancy Data Network (FADN) data from 2014 and 2015 with special emphasis on the impact of agricultural subsidies on the technical efficiency of arable farms in Serbia. Bias-corrected DEA efficiency scores were first calculated and then regressed on a set of explanatory variables using the double-truncated regression approach. The estimates suggest that the share of rented land, land to labour ratio and financial stress variables are the main determinants of arable farm efficiency in Serbia. For the subsidies we found that area payments and input subsidies have some impacts on the technical efficiency of arable farms. In contrast, investment and other subsidies were found to have an insignificant impact on farm technical efficiency. These results suggest that the future potential shift of the Serbian agricultural support towards the Common Agricultural Policy (CAP)-like area based payments is expected to have a minimal but likely positive impact on farms technical efficiency in Serbia.