Data Envelopment Analysis Inputs Research Articles

Technical efficiency of national HIV/AIDS spending in 78 countries between 2010 and 2018: A data envelopment analysis.

HIV/AIDS remains a leading global cause of disease burden, especially in low- and middle-income countries (LMICs). In 2020, more than 80% of all people living with HIV (PLHIV) lived in LMICs. While progress has been made in extending coverage of HIV/AIDS services, only 66% of all PLHIV were virally suppressed at the end of 2020. In addition to more resources, the efficiency of spending is key to accelerating progress towards global 2030 targets for HIV/AIDs, including viral load suppression. This study aims to estimate the efficiency of HIV/AIDS spending across 78 countries. We employed a data envelopment analysis (DEA) and a truncated regression to estimate the technical efficiency of 78 countries, mostly low- and middle-income, in delivering HIV/AIDS services from 2010 to 2018. Publicly available data informed the model. We considered national HIV/AIDS spending as the DEA input, and prevention of mother to child transmission (PMTCT) and antiretroviral treatment (ART) as outputs. The model was adjusted by independent variables to account for country characteristics and investigate associations with technical efficiency. On average, there has been substantial improvement in technical efficiency over time. Spending was converted into outputs almost twice as efficiently in 2018 (81.8%; 95% CI = 77.64, 85.99) compared with 2010 (47.5%; 95% CI = 43.4, 51.6). Average technical efficiency was 66.9% between 2010 and 2018, in other words 33.1% more outputs could have been produced relative to existing levels for the same amount of spending. There is also some variation between WHO/UNAIDS regions. European and Eastern and Southern Africa regions converted spending into outputs most efficiently between 2010 and 2018. Rule of Law, Gross National Income, Human Development Index, HIV prevalence and out-of-pocket expenditures were all significantly associated with efficiency scores. The technical efficiency of HIV investments has improved over time. However, there remains scope to substantially increase HIV/AIDS spending efficiency and improve progress towards 2030 global targets for HIV/AIDS. Given that many of the most efficient countries did not meet 2020 global HIV targets, our study supports the WHO call for additional investment in HIV/AIDS prevention and control to meet the 2030 HIV/AIDS and eradication of the AIDS epidemic.

Holistic aboveground ecological productivity efficiency modeling using data envelopment analysis in the southeastern U.S

Aboveground net primary productivity (ANPP) of an ecosystem is among the most important metrics of valued ecosystem services. Measuring the efficiency scores of ecological production (ESEP) based on ANPP using relevant variables is valuable for identifying inefficient sites. The efficiency scores computed by the Data Envelopment Analysis (DEA) may be influenced by the number of input variables incorporated into the models and two DEA settings—orientations and returns-to-scales (RTSs). Therefore, the objectives were threefold to: (1) identify soil-environmental variables relevant to ANPP, (2) assess the sensitivity of ESEP to the number of input variables and DEA settings, and (3) assess local management relations with ESEP. The ANPP rates were calculated for pine forests in the southeastern U.S. where 10 management types were used. This was followed by an all-relevant variable selection technique based on 696 variables that cover biotic, pedogenic, climatic, geological, and topographical factors. Five minimal-optimal variable selection techniques were further applied to create five parsimonious sets that contain a different number of variables used as DEA inputs. After setting ANPP as the output variable, two DEA orientations (input/output) and six RTS were applied to compute ESEP. The variable selection methods succeeded in objectively identifying the major factors relevant to ANPP variation. The site index showed the highest correlation with ANPP (r = 0.39), while various precipitation factors were negatively correlated (r = − 0.15~ − 0.29, p < 0.01). Parsimonious ESEP models observed a decrease in score variances as the number of input variables increased. Various RTS produced similar scores across orientations. Of the DEA settings, an output orientation with decreasing RTS produced the most progressive ESEP with large variation. Results also suggested that macro- and micronutrient fertilization is the best combination of management strategies to achieve high ESEP. This holistic benchmark approach can be applied to other ecological functions in diverse regions.

A Computational Procedure Using Robust Ranking Method to Formulate and Solve Fuzzy DEA Models

In some real-world applications, performance assessment often has to be considered under uncertainty with fuzzy input and output. Data envelopment analysis (DEA) is a new and effective approach to measuring the relative efficiency of decision-making units concerned with performance evaluation. Recently, DEA has studied with fuzzy input and output. In this paper, we present a computational procedure using a robust ranking method to formulate and solve fuzzy DEA models. Based on this approach, we convert the fuzzy DEA input and output from fuzzy form to crisp. Also, a case study has been presented as in [6] to assess the performance of eight manufacturing enterprises with fuzzy data.

Dealing with Desirable Inputs in Data Envelopment Analysis: A Slacks-based Measure Approach

In Data Envelopment Analysis (DEA) the situation of inputs vs. outputs is positioned as cause and effect. Effects include desirable (ordinary) outputs and undesirable outputs, e.g. pollutants. This situation is well studied and many applications have been published. In this paper, we introduce a new type of inputs, called Good (Desirable) Inputs. As explained in Introduction, we find several examples of such inputs, e.g. Electric car, Women in office and Test takers of vaccine. We handle this by means of SBM (Slacks-based Measure). Usually, efficiency values of DEA models are in the range <i>(0, 1]</i>, while in this model a negative efficiency value may be assigned to inefficient DMUs (decision making units). This is caused by shortages of Good Input values. As an example, we refer to “Women’s Rights Movements” in a country where women’s right is not fully guaranteed. Suppose local governments where men and women are serving as officers. They are inputs to office, while Women are Desirable input and Men are Ordinary input. As outputs, we assume Service as Ordinary output and Claim as Undesirable output. Several extensions of this model are introduced. (a) Variable returns to scale, (b) Weight restrictions, (c) Super-efficiency issue and (d) SBM_Max model.

Is size an input in the mutual fund performance evaluation with DEA?

It has been a common practice to evaluate the performance of mutual funds with data envelopment analysis (DEA). However, DEA itself is a “black box”, since there are no pre-determined inputs or outputs. This paper aims to add clarification to the “black box” nature of DEA by investigating whether fund size has to be included among DEA inputs in the Turkish mutual fund performance evaluation. Fund managers receive a proportion of fund size as compensation. Therefore, besides the traditional risk and expense inputs, economies or diseconomies of scale may also be effective in the fund’s performance. For these reasons, the evaluation of fund performance by using DEA may require fund size as an input. Yet, few international study adds size as an input to the DEA. The evidence is even scarcer for developing country fund markets. To the extent of our knowledge, size has not been utilized in the Turkish mutual fund performance evaluations. This paper aims to contribute to the literature by examining the linear and nonlinear relations between DEA scores and fund size for the Turkish mutual fund industry. For this aim, linear correlation, and Kendall and Spearman rank correlation coefficients are employed as well as a regression specification. The correlations and the regression results reveal a linear relationship between the efficiency scores and fund size. In general, this study presents stronger evidence for the fund size and fund efficiency relation than Basso and Funari (Eur J Finance 23:457–473, 2017) for the Turkish mutual fund market.

Eco-Efficiency Assessment of Intensive Rice Production in Japan: Joint Application of Life Cycle Assessment and Data Envelopment Analysis

To promote sustainable agriculture worldwide, it is important to understand what constitutes eco-efficiency for rice, a staple food in many countries. This study examined whether expanding the scale of rice farming contributed to the improved eco-efficiency of intensive rice production in Japan. Both life cycle assessment (LCA) and data envelopment analysis (DEA) were used to measure comprehensively the eco-efficiency of rice production. A DEA window analysis technique with two DEA inputs (global warming and eutrophication from the LCA results) and one DEA output (weight-based rice yield) was applied to the statistical data for 2005–2011 categorized by the size of rice farms. The results indicate that expanding the size of rice farms is an effective way of improving the eco-efficiency of intensive rice production in Japan. The important factors for improving eco-efficiency are the implementation of economies of scale, reduced outsourcing of farm work, and savings in chemical fertilizers and pesticides. Expansion of the size of rice farms through the recent abolition of the rice production adjustment program will also contribute to improving the eco-efficiency of Japanese rice production.

EVALUATION OF THE TECHNICAL EFFICIENCY AND CARBON FOOTPRINT REDUCTION POTENTIAL OF SPRING BARLEY CULTIVATION

The aim of this study is to evaluate the efficiency of spring barley cultivation to indicate the reasons for its inefficiency and assess the possibility of the carbon footprint reduction potential. Survey data from 113 farms cultivating spring barley in 2016 were used. DEA (Data Envelopment Analysis) input oriented models were applied to assess technical, pure technical and scale efficiency. The carbon footprint of crop cultivation and its reduction potential for inefficient farms were estimated. The Fractional Regression Model (FRM) was used to explain how farm specific variables (structural and environmental factors) influence the efficiency of spring barley cultivation. Results indicate that the improvement of spring barley cultivation technology, through the effective use of inputs, especially mineral fertilizers, could lead to a reduction in the carbon footprint of its cultivation by an average of 32%, which, in turn, leads to a reduction in greenhouse gas emissions by 744 kg CO2e per ha. The economic size of farms, farm area, soil quality and annual rainfall significantly affect the results of technical efficiency.

Ranking efficient DMUs using the Tchebycheff norm with fuzzy data in DEA

In many real applications, the data of production processes cannot be precisely measured. Hence the input and output of Decision Making Units (DMUs) in Data Envelopment Analysis (DEA) may be imprecise or fuzzy-numbered. In original DEA models, inputs and outputs are measured by exact values on a ratio scale, therefore conventional DEA can't easily measure the performance of DMUs and rank them. The researchers have introduced mane deferent model for ranking DMUs by fuzzy number. In this paper, we proposed a new method by using the Tchebycheff norm for ranking DMUs with fuzzy data. We explain our method by numerical example with the triangular fuzzy number.

Energy Efficiency of Intensive Rice Production in Japan: An Application of Data Envelopment Analysis

Intensive rice production has contributed to feeding the world’s growing world population, but it has also increased fossil energy consumption. This paper examines the effect of increasing the scale of rice farming on the energy efficiency of intensive rice production in Japan. A data envelopment analysis (DEA) approach is used to calculate energy efficiency scores and identify operational targets. A window analysis technique is applied to the 2005–2011 statistical data, with nine scales of rice farming, ranging from &lt;0.5 ha to ≥15 ha. Six energy inputs (fossil fuels, electricity, chemical fertilizers, pesticides, agricultural services, and agricultural machinery) and the weight-based rice yield are selected as the DEA inputs and the DEA output, respectively. The results show that the energy efficiency scores range from 0.732 for farms of 1 ha to &lt;2 ha, to 0.988 for farms ≥15 ha. Overall, increasing the scale of rice farming in Japan improves energy efficiency because of a great reduction in the energy consumed per unit area by agricultural machinery and agricultural services. These findings suggest that increasing the scale of farming is an effective way to enhance the energy efficiency of highly mechanized rice production in developed countries, such as Japan.

Considerations for using data envelopment analysis for the assessment of radiotherapy treatment plan quality

PurposeThe operations research method of data envelopment analysis (DEA) shows promise for assessing radiotherapy treatment plan quality. The purpose of this paper is to consider the technical requirements for using DEA for plan assessment.Design/methodology/approachIn total, 41 prostate treatment plans were retrospectively analysed using the DEA method. The authors investigate the impact of DEA weight restrictions with reference to the ability to differentiate plan performance at a level of clinical significance. Patient geometry influences plan quality and the authors compare differing approaches for managing patient geometry within the DEA method.FindingsThe input-oriented DEA method is the method of choice when performing plan analysis using the key undesirable plan metrics as the DEA inputs. When considering multiple inputs, it is necessary to constrain the DEA input weights in order to identify potential plan improvements at a level of clinical significance. All tested approaches for the consideration of patient geometry yielded consistent results.Research limitations/implicationsThis work is based on prostate plans and individual recommendations would therefore need to be validated for other treatment sites. Notwithstanding, the method that requires both optimised DEA weights according to clinical significance and appropriate accounting for patient geometric factors is universally applicable.Practical implicationsDEA can potentially be used during treatment plan development to guide the planning process or alternatively used retrospectively for treatment plan quality audit.Social implicationsDEA is independent of the planning system platform and therefore has the potential to be used for multi-institutional quality audit.Originality/valueTo the authors’ knowledge, this is the first published examination of the optimal approach in the use of DEA for radiotherapy treatment plan assessment.

Benchmarking sustainability of urban water infrastructure systems in China

The broad scope and definition of sustainability has perplexed assessment of water infrastructure systems, especially for the purpose of directing engineering practices when quantified criteria are desired. An input-oriented data envelopment analysis (DEA) was improved to benchmark the relative sustainability of the water infrastructure of 157 cities in China. The DEA calculates a single sustainability score using seven inputs and five outputs that represent the economic, resource and environmental dimensions of sustainability. Overall, 69 out of the 157 sampled systems obtained high sustainability scores. Eight specific efficiency indicators based on individual DEA input to output ratio were evaluated to identify the causes of performance differences. Compared to water supply systems, the performance of wastewater treatment plants has greater influence on the sustainability score of the overall system. For all systems, the sustainability scores are more sensitive to sludge production and electricity consumption than capital investment and removal efficiency of treatment processes. The DEA provides guidelines to cities for setting priorities in order to meet specific sustainability criteria. Statistical analysis indicates that the overall sustainability score primarily depends on the system scale, meteorological conditions such as air temperature and rainfall, and source water quality.

Impact of the efficiency of the tax administration on tax evasion

In this paper, we analyse the performance of the tax administration using data envelopment analysis (DEA) and regression analysis in 13 European countries. In the first phase, a DEA input- oriented model with the three input and two output parameters for the efficiency evaluation has been used. The influence of selected independent variables on the grey economy, which represents an approximation of tax evasion and efficiency of tax administration, was conducted by regression analysis in the second phase. The main goal is to investigate the influence of the relative efficiency and number of employees in tax administration as well as country employment rate on the grey economy level.

Life cycle assessment and data envelopment analysis approach for the selection of building components according to their environmental impact efficiency: a case study for external walls

Abstract Environmental criteria have to be taken into account when it comes to selecting a specific building component among a set of candidates with the same function. This article presents a methodological approach – based on both Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA) – for the selection of building components according to their environmental impact efficiency. A three-step LCA + DEA approach is proposed and tested through a case study for 175 external walls. The three steps of this approach involve data collection, life cycle impact assessment, and DEA of the sample of building components using environmental impacts as DEA inputs. Overall, from the availability of multiple data on the material and energy flows of each building component, the method provides decision makers with eco-efficiency scores and environmental benchmarks. A cautious definition of the set of candidates is critical, as relative efficiency scores are calculated. Data availability and functional homogeneity regarding the building components evaluated are the key requirements for the general use of the method. The three-step LCA + DEA approach proposed is proven to be a useful method to enhance decision making and environmental benchmarking in the building sector.

Data envelopment analysis: Prior to choosing a model

In this paper, we address several issues related to the use of data envelopment analysis (DEA). These issues include model orientation, input and output selection/definition, the use of mixed and raw data, and the number of inputs and outputs to use versus the number of decision making units (DMUs). We believe that within the DEA community, researchers, practitioners, and reviewers may have concerns and, in many cases, incorrect views about these issues. Some of the concerns stem from what is perceived as being the purpose of the DEA exercise. While the DEA frontier can rightly be viewed as a production frontier, it must be remembered that ultimately DEA is a method for performance evaluation and benchmarking against best-practice. DEA can be viewed as a tool for multiple-criteria evaluation problems where DMUs are alternatives and each DMU is represented by its performance in multiple criteria which are coined/classified as DEA inputs and outputs. The purpose of this paper is to offer some clarification and direction on these matters.

Using DEA cross-efficiency evaluation for suppliers ranking in the presence of non-discretionary inputs

Supply chain includes all activities associated with the flow and transformation of goods from the raw material stage through to the end user. Supplier selection is one of the most important parts of supply chain management (SCM). For selecting suppliers, data envelopment analysis (DEA), as a multiple criteria decision making tool, has been applied for several times. However, sometimes in supplier selection problem, there may exist some criteria that are beyond the control of a management. These criteria are called non-discretionary or exogenously fixed factors. Since in traditional treatment of non-discretionary inputs in DEA, free reign is given when deciding for each decision making unit (DMU) which outputs and inputs to emphasise, many different avenues are present by which a DMU can appear efficient. Therefore, it is common to have many DMUs that are relatively efficient. In addition, since each DMU has its own set of weights, all of its weight might be put on a single output and input. As a result, the objective of this paper is to propose a cross-efficiency model which is able to consider non-discretionary inputs. A numerical example demonstrates the application of the proposed model in supplier selection context.

Improved estimation of electricity demand function by using of artificial neural network, principal component analysis and data envelopment analysis

Due to various seasonal and monthly changes in electricity consumption and difficulties in modeling it with the conventional methods, a novel algorithm is proposed in this paper. This study presents an approach that uses Artificial Neural Network (ANN), Principal Component Analysis (PCA), Data Envelopment Analysis (DEA) and ANOVA methods to estimate and predict electricity demand for seasonal and monthly changes in electricity consumption. Pre-processing and post-processing techniques in the data mining field are used in the present study. We analyze the impact of the data pre-processing and post-processing on the ANN performance and a 680 ANN-MLP is constructed for this purpose. DEA is used to compare the constructed ANN models as well as ANN learning algorithm performance. The average, minimum, maximum and standard deviation of mean absolute percentage error (MAPE) of each constructed ANN are used as the DEA inputs. The DEA helps the user to use an appropriate ANN model as an acceptable forecasting tool. In the other words, various error calculation methods are used to find a robust ANN learning algorithm. Moreover, PCA is used as an input selection method, and a preferred time series model is chosen from the linear (ARIMA) and nonlinear models. After selecting the preferred ARIMA model, the Mcleod–Li test is applied to determine the nonlinearity condition. Once the nonlinearity condition is satisfied, the preferred nonlinear model is selected and compared with the preferred ARIMA model, and the best time series model is selected. Then, a new algorithm is developed for the time series estimation; in each case an ANN or conventional time series model is selected for the estimation and prediction. To show the applicability and superiority of the proposed ANN-PCA-DEA-ANOVA algorithm, the data regarding the Iranian electricity consumption from April 1992 to February 2004 are used. The results show that the proposed algorithm provides an accurate solution for the problem of estimating electricity consumption.

Congestion analysis in DEA inputs under weight restrictions

Data envelopment analysis (DEA), which is used to determine the efficiency of a decision-making unit (DMU), is able to recognize the amount of input congestion. Moreover, the relative importance of inputs and outputs can be incorporated into DEA models by weight restrictions. These restrictions or a priori weights are introduced by the decision maker and lead to changes in models and efficiency interpretation. In this paper, we present an approach to determine the value of congestion in inputs under the weight restrictions. Some discussions show how weight restrictions can affect the congestion amount.

A data envelopment analysis model with discretionary and non-discretionary factors in fuzzy environments

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evaluation process, input and output data in real-world problems are often imprecise or ambiguous. In addition, real-world problems may also include non-discretionary factors that are beyond the control of a DMU's management. Fuzzy logic and fuzzy sets are widely used to represent ambiguous, uncertain or imprecise data in DEA by formalising the inaccuracies inherent in human decision-making. In this paper, we show that considering bounded factors in DEA models results in a disregard to the concept of relative efficiency since the efficiency of the DMUs are calculated by comparing the DMUs with their lower and/or upper bounds. In addition, we present a fuzzy DEA model with discretionary and non-discretionary factors in both the input and output-oriented CCR models. A numerical example is used to demonstrate the applicability and the efficacy of the proposed models.

Enhancing the inclusion of non-discretionary inputs in DEA

The aim of this paper is to provide an alternative approach for estimating efficiency when a set of decision-making units uses non-discretionary inputs in the productive process. To test the influence of these variables, our proposal uses a multi-stage approach based on Tobit regressions. In order to avoid potential bias, a bootstrap procedure is used to estimate these regressions. This methodology allows enhancing other models previously proposed to introduce non-controllable inputs in data envelopment analysis (DEA) overcoming, thus, some of their main shortcomings. We illustrate our framework with an empirical application on Spanish high schools where non-controllable factors play a major role to explain educational achievements.

Three-stage DEA models for incorporating exogenous inputs

In this paper, we discuss three-stage models that control for exogenous, non-discretionary inputs in data envelopment analysis. In a recent article in this journal, Monte Carlo analysis was employed to compare and contrast alternative DEA models that measure efficiency in the presence of exogenous variables. The methodology for comparison was flawed, calling into question the results presented. We introduce new second-stage models and compare and contrast them with simulated data.