Constant Return To Scale Model Research Articles

Analisis Efisiensi Bank Umum Persero Menggunakan Metode Data Envelopment Analysis (DEA)

The purpose of this study is to analyze the efficiency of state-owned commercial banks and to analyze their development. Where efficiency is a parameter in measuring performance for banks. The Data Envelopment Analysis approach was used in this study with a quantitative method. Where there are two models used, namely the VRS (Variable Return to Scale) model and the CRS (Constant Return to Scale) model. State-owned commercial banks are the sample in this study for the period 2019 to 2022. Capital, Third Party Funds (DPK), and operational costs are used as variable inputs. Meanwhile, credit is used as a variable output. Based on the results of the analysis, it shows that based on the VRS model, the efficiency of state-owned commercial banks was obtained in almost all years except in 2019. Furthermore, the results of the analysis using the CRS model, several banks obtained efficiency results and several banks obtained inefficiency results. Even based on the average efficiency that existed during the study period, all of them experienced inefficiency, it's just that the efficiency value was at 0.9. The difference in the results of the two models used is because the VRS model overrides the production scale factor, while the resulting CRS model value is Overall Technical Efficiency

Performance Evaluation of Mobile Network Operators Based on Input-Oriented Constant Return to Scale Model

This research evaluated the Performance of major Mobile Network Operators (MNOs) in Nigeria using Data Envelopment Analysis (DEA). The DEA method that is based on Input-oriented Constant Return to Scale (CRS) model is considered. The objectives of this research were to analyze existing methods used for the evaluation of MNOs' performance and explored DEA which is a non-parametric method that relies on a linear programming technique for optimization. The aim is to evaluate the technical efficiency (TE) of each network operator and consequently determine their ranking. The result obtained shows the distributions of MNOs that are technically efficient and those that are technically inefficient. Also, the ranking according to their relative efficiency was presented. The findings, which are based on the CRS model show that among the four Nigerian major MNOs examined (MTN, 9mobile, Glo, and Airtel), 9mobile ranked the highest efficient mobile operator followed by the MTN, then GLO while Airtel has the lowest rank.

The Extent, Dynamics, and Potential Predictors of Technical Efficiency and Capacity Utilisation in Small-Scale Fisheries in Oman

This paper investigates the extent, dynamics, and factors influencing technical efficiency (TE) and capacity utilisation (CU) in small-scale fisheries (SSF) using a two-stage data envelopment analysis (DEA) approach covering the period 2010–2012. A considerable extent of boat-level technical inefficiency, capacity underutilisation and scale inefficiency were evident. On average, TE and CU levels under the constant returns to scale (CRS) and variable returns to scale (VRS) models declined over time. The TE and CU scores of 2010 remained unaltered with the addition of ‘fishing time’ as an input to the model. The proportion of boats with unitary scale efficiency (SE) decreased from 26 % in 2010 to 12 % in 2012. The underutilisation rates of the inputs ‘crew’ and ‘fishing time’ ranged from 15.5 % to 31.6 % and 15.8 % to 28.6 %, respectively. Among the species category, the extent of excess capacity was 70 % to 156 % and 47 % to 119 % under the CRS and VRS models, respectively. The second-stage DEA results indicated that the explanatory variables ‘fishing location’, ‘catch per unit of effort’ (CPUE), ‘fuel costs’ and ‘crew share’ significantly influenced CU under the CRS model. In contrast, the significant influence of subsidies and other operating costs were noted under the VRS model. For the TE case, ‘age’, ‘education’, ‘subsidy’ and ‘CPUE’ were found to be significant under the CRS and VRS models. Other significant variables were found in the study under CRS and VRS models. Finally, the results from the descriptive and empirical analysis under the two-stage DEA model are discussed together with policy implications.

Measurement of Ministry/Institution Budget Performance Efficiency Using Data Envelopment Analysis (DEA)

Efficiency is one of the tools used by the Government to measure the success of an activity. Based on PMK Number 214/ PMK.02/2017 it is regulated regarding the measurement and Evaluation of Budget Performance for the implementation of Work Plans and Ministries/Institutions Budgets. To measure budget performance achievement based on aspects of implementation 4 (four) variables i.e. budget absorption, output achievements, efficiency, and consistency of budget absorption towards planning, are used. This study aims to measure the efficiency level of Ministries/Institutions budget performance using Data Envelopment Analysis (DEA). However, the variables used are not only based on measurement aspects of implementation according to PMK Number 214/PMK.02/2017 but also the number of satker (satuan Kerja). The approach used in the DEA model is Constant Return to Scale (CRS) model and Variable Return to Scale (VRS) model. This study uses the DEA model approach by comparing the results of the CRS model with VRS model. Based on DEA results, the number of Ministries/Institutions that have efficient scores is less than the number of Ministries/Institutions with inefficient scores. In addition, based on the value of slacks in each Ministries/Institutions, the variables that need to be improved are the coordination among satkers in when they are recording their performance achievements in the SMART application. Other variables that also require improvements are the ministry/agency’s willingness to revise their Disbursement Plan (RPD) after each budget revision and the speed at which they spend their budget once DIPA is stipulated.

Integration of Data Envelopment Analysis and Clustering Methods

Data Envelopment Analysis (DEA) has been applied creatively in various study domains to compare and evaluate different Decision Making Units (DMUs) based on multiple input–output attributes. In this paper, the performance of Jordanian public hospitals is assessed via a methodology combining DEA with data mining methods, specifically, clustering. Initially, inputs of inefficient hospitals were altered to check for waste in the allocated resources. Then, the number of inputs–outputs was manipulated to test if the number is strongly influencing the productivity of the DMUs. The number of DMUs used was 27 public hospitals and the applicable efficiency measurements used were constant return to scale (CRS) and variable return to scale (VRS) through the DEAP software. Experiments showed that the efficiency of a hospital might be more meaningfully assessed if it is compared with a group of hospitals that are similar in some factors. More specifically, results of applying the CRS model proved that 77% of the hospitals were efficient. Additionally, we found that the inefficiencies of some hospitals are linked to weak resource utilization. It is concluded that number of inputs–outputs inserted in the efficiency evaluation process impacts the resulted values.

Comparison of Performance Efficiency of Improved Network Coding Multicast Algorithms using Data Envelopment Analysis

In this paper, comparison of multicast algorithms was carried out in terms of performance Efficiency using Constant Return to Scale (CRS) and Variable Return to Scale (VRS) Model. The Improved Network Coding Algorithm (INCA) which was developed to minimize the consumption of Bandwidth during multicasting was used. The aim of the INCA is achieving cost effective multicast in order to encourage group communication over the internet. Data Envelopment Analysis was applied on the results obtained from the INCA in order to determine their performance efficiency using a number of input parameters. Simulation results obtained showed that the VRS Model performs better than the CRS Model. General Terms Multicasting, Modeling, Decision Making Units, Algorithms, Bandwidth Utilization, Performance Efficiency

DEA cross-efficiency evaluation under variable returns to scale

Cross-efficiency evaluation in data envelopment analysis (DEA) has been developed under the assumption of constant returns to scale (CRS), and no valid attempts have been made to apply the cross-efficiency concept to the variable returns to scale (VRS) condition. This is due to the fact that negative VRS cross-efficiency arises for some decision-making units (DMUs). Since there exist many instances that require the use of the VRS DEA model, it is imperative to develop cross-efficiency measures under VRS. We show that negative VRS cross-efficiency is related to free production of outputs. We offer a geometric interpretation of the relationship between the CRS and VRS DEA models. We show that each DMU, via solving the VRS model, seeks an optimal bundle of weights with which its CRS-efficiency score, measured under a translated Cartesian coordinate system, is maximized. We propose that VRS cross-efficiency evaluation should be done via a series of CRS models under translated Cartesian coordinate systems. The current study offers a valid cross-efficiency approach under the assumption of VRS—one of the most common assumptions in performance evaluation done by DEA.

Measuring Bank Efficiency: Evidence from the Tanzanian Banking Sector

The objective of this study is to measure the efficiency of commercial banks in Tanzania using a non-parametric approach, the input-oriented data envelopment analysis (DEA), both by constant return to scale (CRS) and variable return to scale (VRS). For this purpose, two outputs representing total loans and total interest income, and three inputs representing total deposits, the number of employees and total expenses are selected for seven-years (2006-2012) period in the analysis. The findings under CRS model identify four banks to be fully efficient in the year 2006, two banks in 2007 and one bank in 2008, 2009, 2010, 2011 and 2012, while VRS model results identify five banks to be fully efficient in the year 2006 and 2012, three banks in 2007 and two banks in 2008 and 2009, one bank 2010 and 2011. Four banks, Standard Charted bank, National Commercial Bank (NBC), Citibank and Barclays bank are found to be the most efficiency banks in Tanzania, which serve as the benchmark peers for inefficient banks in the sample, In addition Tobit regression has been used to determine the efficiency drivers.

Measuring the efficiencies of visiting nurse service agencies using data envelopment analysis

This study develops a measure of the efficiency of visiting nurse (VN) agencies in Japan, examining the issues related to the measurement of efficiency, and identifying the characteristics that influence efficiency. We have employed a data envelopment analysis to measure the efficiency of 108 VN agencies, using the numbers of 5 types of staff as the input variables and the numbers of 3 types of visits as the output variables. The median efficiency scores of the VN agencies were found to be 0.80 and 1.00 according to the constant returns to scale (CRS) and variable returns to scale (VRS) models, respectively, and the median scale efficiency score was 0.95. This study supports using both the CRS and VRS models to measure the scale efficiency of VN service agencies. We also found that relatively efficient VN agencies filled at least 30% of staff positions with experienced workers, and so concluded that this characteristic has a direct influence on the length of visits.

A Study on Eco-efficiency in power plants using DEA Analysis

This study aims to provide power generating plants with eco-efficiency information. To implement the purposes, of study, both DEA(Data, Envelopment Analysis) model and interview were incorporated in terms of methodologies. To analyze the managerial efficiency, total labor cost and number of employees were considered as input factors. CO2, NOx, and water also were considered as input factors to analyze eco-efficiency. Both annual total power product and annual total revenue were used as output factors. CRS(Constant Return to Scale) and VRS(Variable Return to) model were facilitated in this analysis. According to the findings, most of the power plants were evaluated as 'Efficient'' taking into consideration of average value, both 0.928 from CCR model and 0.969 from VRS model. 7 DMUs including DMU3 and DMU12 are efficient out of 35 DMUs relatively, other DMUs are inefficient. For results of inefficient output factors distribution, it was found that inefficiency for NOx was marked relatively higher than CO2. In order to improve the eco-efficiency in the power plants in the long term, the target amount of Co2 as well as NOx reduction needs to be properly proposed in consideration of particularity of power plants. In the long run, renewable energy, alternative fuels should be adapted to reduce the eco-inefficient.

Safety performance evaluation of Indian organizations using data envelopment analysis

PurposeThe main purpose of the present study is to develop appropriate construct to benchmark occupational health and safety performance in industrial setting so that deficiencies can be highlighted and possible strategies can be evolved to improve the performance.Design/methodology/approachData envelopment analysis (DEA), being a robust mathematical tool, has been employed to evaluate the safety performance of industries. DEA, basically, takes into account the input and output components of a decision‐making unit (DMU) to calculate technical efficiency (TE). TE is treated as an indicator for safety performance of DMUs and comparison has been made among them.FindingsA total of 30 Indian organizations under three industrial categories such as construction, refractory and steel are chosen for comparison purpose. It has been observed that safety performance of construction industries is consistently low as compared to other categories of industries. TE has been calculated using two types of models of DEA such as constant return to scale (CRS) and variable return to scale (VRS). A paired two‐samplet‐test indicates that TEs obtained using two models are significantly different. Mean efficiency of 30 samples is found as 0.898 using CRS model whereas same is calculated as 0.942 using VRS.Research limitations/implicationsThe limitations may be number of input and output components considered for each DMU. If different set of inputs and outputs may be considered, the results may be different. Another limitation may be the number of industrial sectors considered in the study.Practical implicationsThe method, being a generic one, can be adopted by the managers to assess present safety performance and find a suitable peer to which, it should follow to improve own TE followed by in what respect it has to improve. Industrial safety and occupational health concerns in industries is not only important from government regulation point of view but also essential for enhancing productivity and profitability to become competitive in the marketplace. The practical limitation may be collection data quantitatively from various DMUs because many a time the DMUs are unwilling to share data.Originality/valueThis work proposes use of simple mathematical tool like DEA for benchmarking based on safety performance in Indian industries. In Indian context, safety performance of industrial settings has hardly been assessed. The study provides a simple but comprehensive methodology for improving safety performance. The study also outlines comparative evaluation of safety practices in different industries.

Correlation of Early Neonatal Mortality and Technical Efficiency of Regional Maternal and Child Health Services in Oman

Health sector reforms are always linked to financial restraints. The relative low spending on healthcare by the majority of developing countries makes it even more crucial to plan the deployment of health services optimally. Many countries in the world have taken recourse to analyzing efficiency of the decision making units (DMU) in terms of inputs and outputs. A study from Ghana using this approach found 65% of the sampled health centres (HC) to be deficient in terms of services delivered in maternal and child health domains based on inputs such as number of staff, number of beds and the financial outlay. 1 The method used was Data Envelopment Analysis (DEA) which gave a relative measure of efficiency. DEA estimates how well a DMU is functioning compared to its peers. It is constructed non-parametrically using linear programming techniques and can analyze several outputs simultaneously. The technical efficiency (TE) of a DMU refers to the ability to minimize the use of inputs in reaching the given outputs or the ability to obtain the maximum output from a given set of inputs. A constant return to scale (CRS) model is applicable when all the DMUs are functioning at an optimal scale. On the other hand, a variable return to scale (VRS) model can be employed as an extension to CRS model so as not to be affected by the scale efficiency. 2 The model used in the paper from Ghana was input-oriented and instead of going for a constant return to scale (CRS) model, the study used a variable return to scale (VRS) model. The scale efficiency was derived by finding the ratio of CRS efficiency score to VRS efficiency score. Pure technical efficiency and scale efficiency was proposed as an aid to find the potential of increasing the total outputs for the existing capacity/size of the DMU. The study concluded that the centres (DMU/HC) were using more inputs than needed to produce the current outputs. Hence a cost minimization objective was put forth which aimed at input savings. In the year 2000, a DEA approach was applied to 20 hospitals in Oman. 3 Four outputs representing out-patient visits, in-patient

Production technologies based on combined proportionality assumptions

The assumption of full proportionality is incorporated in the constant returns-to-scale (CRS) technology and allows for proportional scaling of inputs and outputs of production units. The assumption of selective proportionality was recently incorporated in the hybrid returns-to-scale (HRS) technology in which only a subset of outputs is proportional to a subset of inputs. In this paper we develop a production technology that exhibits both the full and selective proportionality at the same time. Real examples of such technology are pointed out. Subject to certain conditions, the DEA models based on this technology provide better discrimination than the CRS and HRS models.

Technical, allocative and economic efficiencies in swine production in Hawaii: a comparison of parametric and nonparametric approaches

AbstractTechnical, allocative and economic efficiency measures arc derived for a sample of swine producers in Hawaii using the parametric stochastic efficiency decomposition technique and nonparametric data envelopment analysis (DEA). Efficiency measures obtained from the two frontiei approaches are compared. Firm‐specific factors affecting productive efficiencies are also analyzed. Finally, swine producers, potential for reducing cost through improved efficiency is also examined. Under the specification of variable returns to scale (VRS), the mean technical, allocative and economic efficiency indices are 75.9%, 75.8% and 57.1%, respectively, for the parametric approach and 75.9%, 80.3% and 60.3% for DEA; while for the constant returns to scale (CRS) they are 74.5%, 73.9% and 54.7%, respectively, for the parametric approach and 64.3%, 71.4% and 45.7% for DEA. Thus the results from both approaches reveal considerable inefficiencies in swine production in Hawaii. The removal of potential outliers increases the technical efficiencies in the parametric approach anil allocative efficiencies in DEA, but, overall, contrary to popular belief, the results obtained from DEA are found to be more robust than those from the parametric approach. The estimated mean technical and economic efficiencies obtained from the parametric technique are higher than those from DEA for CRS models but quite similar for VRS models, while allocative efficiencies are generally higher in DEA. However, the efficiency rankings of the sample producers based on the two approaches are highly correlated, with the highest correlation being achieved for the technical efficiency rankings under CRS. Based on mean comparison and rank correlation analyses, the return to scale assumption is found to be crucial in assessing the similarities or differences in efficiency measures obtained from the two approaches. Analysis of the role of various firm‐specific factors on productive efficiency shows that farm size has strong positive effects on efficiency levels. Similarly, farms producing market hogs arc‐more efficient than those producing feeder pigs. Based on these results, by operating at the efficient frontier the sample swine producers would be able to reduce their production costs by 38‐46% depending upon the method and returns to scale considered.

Technical, allocative and economic efficiencies in swine production in Hawaii: a comparison of parametric and nonparametric approaches

Abstract Technical, allocative and economic efficiency measures are derived for a sample of swine producers in Hawaii using the parametric stochastic efficiency decomposition technique and nonparametric data envelopment analysis (DEA). Efficiency measures obtained from the two frontier approaches are compared. Firm-specific factors affecting productive efficiencies are also analyzed. Finally, swine producers' potential for reducing cost through improved efficiency is also examined. Under the specification of variable returns to scale (VRS), the mean technical, allocative and economic efficiency indices are 75.9%, 75.8% and 57.1%, respectively, for the parametric approach and 75.9%, 80.3% and 60.3% for DEA; while for the constant returns to scale (CRS) they are 74.5%, 73.9% and 54.7%, respectively, for the parametric approach and 64.3%, 71.4% and 45.7% for DEA. Thus the results from both approaches reveal considerable inefficiencies in swine production in Hawaii. The removal of potential outliers increases the technical efficiencies in the parametric approach and allocative efficiencies in DEA, but, overall, contrary to popular belief, the results obtained from DEA are found to be more robust than those from the parametric approach. The estimated mean technical and economic efficiencies obtained from the parametric technique are higher than those from DEA for CRS models but quite similar for VRS models, while allocative efficiencies are generally higher in DEA. However, the efficiency rankings of the sample producers based on the two approaches are highly correlated, with the highest correlation being achieved for the technical efficiency rankings under CRS. Based on mean comparison and rank correlation analyses, the return to scale assumption is found to be crucial in assessing the similarities or differences in efficiency measures obtained from the two approaches. Analysis of the role of various firm-specific factors on productive efficiency shows that farm size has strong positive effects on efficiency levels. Similarly, farms producing market hogs are more efficient than those producing feeder pigs. Based on these results, by operating at the efficient frontier the sample swine producers would be able to reduce their production costs by 38–46% depending upon the method and returns to scale considered.