CCR Model Research Articles

Income Inequality and its Important Determinants in India

Income inequality is considerably high and still growing, which may cause a significant loss of India’s human development and economic performance in the post-pandemic period. Thus, using cointegrating models viz; FMOLS, DOLS, CCR, and ARDL models, we scrutinize short-run as well as long-run impact of natural disaster, economic development, technological innovation, and human capital on income inequality in India. Results show that the natural disasters and economic development worsen income inequality in both short- and long-run. Further, India’s human capital also aggravates income inequality in the short run. In contrast, India’s technological innovation and human capital in the long run improve income distribution significantly. Finally, the policy suggestions are mentioned in the conclusion section. Our results are consistent and robust with alternative modelling.

A Study of Environmental Degradation in Turkey and its Relationship to Oil Prices and Financial Strategies: Novel Findings in Context of Energy Transition

The current empirical literature ignores the possible influence of oil prices on environmental degradation through fiscal policy instruments. Contributing to the literature, this study explores the influence of oil price on the environmental degradation in Turkey through fiscal policy instruments, using a novel methodology of the bootstrap ARDL approach. The FMOLS, CCR, DOLS, and ARDL models are used to examine the long-run linkage among the tested variables. The findings from estimating models demonstrated that government expenditures positively affected environmental degradation in Turkey. In contrast, the taxation revenues negatively affected the environmental degradation. Furthermore, the empirical outcomes affirm that oil prices have a powerful effect on the levels of Turkey’s environmental pollution through taxation revenues, energy, and GDP factors. Therefore, the study suggests that the Turkish policymakers should design policies to avoid any undesirable impacts of the spillover effects of the oil price on the environment using fiscal policy channels. In this sense, the government in Turkey should design a framework that includes financial incentives such as low taxation rates on green energy investment. In addition, the policy markets in Turkey should start to use the carbon tax policy, which is one of the most efficient tools to reduce environmental pollution.

Assessing the Determinants of Efficiency: An Empirical Evidence from Developing Economy

Increasing banks' efficiency may help boost the country's economic activity. This study aims to focus on the Pakistani bank’s technical efficiency over the period 2014 to 2019. The study adopts the non-parametric estimation technique based on the output-oriented CCR model to measure banks' technical efficiency scores. The obtained results revealed that the six banks from 30 that remain technically efficient in the sample period from 2014-2019 were Bank Alfalah Ltd, CITI Bank NA Pakistan, Habib Metropolitan Bank Ltd, Industrial Development Bank, MCB Bank Ltd and Samba Bank Ltd. So, other banks should follow the efficient utilization of resources as these banks are utilizing and set as a benchmark of Technical Efficiency. From the results of GMM, it is concluded that the firm-specific determinants that have a significant negative effect on technical efficiency in the case of Pakistan are leverage. In contrast, profitability and solvency have a significant positive impact on the technical efficiency of banks. However, bank size and liquidity are found to be insignificant. Among the macroeconomic variables, GDP has a significant positive impact, whereas interest rate and regulatory quality have a significant negative effect on the technical efficiency of banks in Pakistan. However, political stability was found to be insignificant. The findings of this study have important policy implications for regulators and managers by focusing on the minimal utilization of input and by maximizing output through better management of resources like fixed assets, labor, operating expenses, deposits, and equity. To maximize the outcomes of the banks that are an investment, net profit, loans, other earning assets, and non-interest income, banks can maximize their technical efficiency. Secondly, the banking efficiency could also be enhanced through firm-specific and macroeconomic variables.

An Analysis on Technical Efficiency of Mango Farms

This study aimed to analyze the manage achievement and technical efficiency of mango farms. The Budget method for each crop, Data Envelopment Analysis (DEA), and bootstrap DEA are used for the analysis. In the case of efficiency is calculated through input-oriented CCR and BCC models. The data used for the analysis are results of an income survey conducted by visiting 19 mango farms. The main results are as follows. First, the total income is 30,885 thousand won, the management cost is 17,597 thousand won, and the income is 12,928 thousand won per unit (10a) of mango farms. Second, the average technical efficiency (TECRS) is 0.686. This means that 31.4% of the input factors are used inefficiently. Third, the average pure technology efficiency (PTEVRS) is 0.899, and the scale efficiency (SE) is 0.762. This means that 10.1% of the inefficiencies are caused by technology and 23.8% by scale. Forth, as a result of bootstrap DEA, the average bias of the CCR model is 0.185 and the BCC model is relatively low, 0.087. Fifth, most of the inefficient farms are producing in the area of increasing return to scale that can be expected to increase efficiency through scale expansion. Sixth, as a result of examining the scale and rate at which resources can be saved by benchmarking the reference group, the cost of establishing an orchard showed the highest saving rate with an average of 45.7%. On the other hand, the material cost, which has the lowest possible reduction ratio, is the largest, with an average of 15,229 thousand won.

Overhead Grants’ Usage and Educational Institutions in Nigeria: Data Envelopment Analysis Perspective

This study appraised the utilization of overhead grants’ efficiency among federal educational institutions in Nigeria between2011-2019. Data for the study were sourced from the Annual Audited Financial Statements of the public sector entities. The sampled size for the study comprised (25) federal educational institutions out of 69 federal educational institutions drawn across the country among four (4) geo-political zones. Data were analyzed using Data Envelopment Analysis. The results of the average efficiency scores from both CCR and BBC models showed that the entities were averagely efficient in overhead grants’ utilization. Overall results showed that federal educational institutions have high capacity to absorb sufficient overhead grants from the center. The study therefore concluded that there is need for an improved overhead grants releases to the federal educational institutions to achieve effective service deliveries of their core mandates. The study recommended a continuous assessment and periodic appraisal of the overhead grants’ utilization among the institutions by their supervising ministry to achieve full efficiency.

Super-SBM 모형을 이용한 수산식품 가공업체의 효율성 분석

This study is to analyze the efficiency of seafood processing companies using DEA Models. The average efficiency in the CCR model was 0.7732 and the average in the BCC model was 0.8862. The efficiency of scale was 0.8757, which was found to have no significant effect on the efficiency. The average efficiency of the SBM models show a bit lower average efficiency compared to the CCR and BCC models. This is because of the existence of slacks which were not considered in the CCR and BCC models. The average efficiency of the SBM-CCR and SBM-BCC models were 0.3746 and 0.6199, respectively. The average efficiency of the Super-SBM-CCR and Super-SBM-BCC models were 0.3861 and 0.6469, respectively. After Classifying 25 seafood processing companies into 3 groups according to the processing method - A(Processing and preserving of edible seaweeds), B(Manufacture of frozen aquatic animal products), C(Manufacture of smoked, seasoned and similar prepared aquatic animal products) - it was tested whether the average efficiency for each group had statistical significance. As a result of analysis, it was found that there was statistical significance that group B was more efficient than group C in four Models such as BCC, SBM-CCR, SBM-BCC, and Super-SBM-CCR.

DYNAMIC SIMULATION AND RANKING OF USING RESIDENTIAL-SCALE SOLAR WATER HEATER IN IRAN

A decrease in the utilization of fossil energies, mainly by replacing them with renewable energy sources (RESs), is regarded as a potential energy source in today’s applications. RESs are broadly utilized for heating purposes and particularly with applications in solar water heater (SWH). Despite the accessibility of SWH technologies and their affordable prices in Iran, there is no comprehensive study to explain the potential of Iranian regions to supply hot water for household applications. This one-year work, hence, attempts the first dynamical simulation of a solar heating system to provide sanitary hot water (SHW) as well as hot water demanded to heat 47 stations in Iran. Weather data were extracted from METEONORM and environmental-technical analyses performed by thermal solar (TSOL) software. Stations were ranked based on CCR and BCC models in data envelopment analysis (DEA) method using GAMS V 24.1. As with results, a total of 223.1 MWh solar heat is generated annually from all stations that prevent the emission of 64.5 t CO2 every year. According to CCR and BCC models, Bandar Abbas, Chabahar, Fasa, Iranshahr, Kermanshah, Khoramabad, Sarab, Shahr-e-kord, Yasuj, Zanjan, and Zahedan are the best in this regard. Also according to the economic analysis, the average price of home solar heating in Iran is 0.160 $/kWh.

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

Examples of the use of Data Envelopment Analysis (DEA) to assess the financial effectiveness of insurance companies

This paper surveys and critically evaluates the literature on the use of advanced methods to assess the financial effectiveness of insurance entities. The paper presents methodological assumptions for Data Envelopment Analysis (DEA) using the CCR (Charnes-Cooper-Rhodes) and BCC (Banker-Charnes-Cooper) models. The usefulness of these methods for assessing the financial efficiency of insurance companies was also shown on the example of entities conducting insurance activities in Nigeria, Malaysia, Singapore, Indonesia, Serbia, Poland and Slovakia.

A research level of efficiency treatment of Covid-19 using data envelopment analysis

Coronavirus Disease 2019 (Covid-19) is a collection of viruses that can infect the respiratory system. This virus only causes mild respiratory infections, such as the flu. The process of handling Corona infection patients is indeed much different from other disease patients. Covid-19 can cause various symptoms in its sufferer. These symptoms depend on the type of corona virus that attacks, and how serious the infection is. The design of this application uses DFD and ERD modeling which is built using the web-based PHP programming language and uses a MySQL database. The assessment of the efficiency level of COVID-19 was carried out in one of the provinces in Indonesia, namely in the province of Aceh, using samples from 12 existing hospitals. All the hospitals that we have taken data every day will continue to increase and decrease. This application is built using the Data Envelopment Analysis (DEA) method, where the search process is determined by the lindo software. The input criteria in this application are suspected patients, probable patients, confirmed patients, and patients being treated. While the output of this application is the patient recovers, and the patient dies. All the data we get is real data that we get directly from the hospital and the results are confirmed via the Internet. All calculation processes use predetermined formulas. The computational result obtained from the DEA CCR Model on the 8 DMUs is 1, it is the gain from dividing the output variable by the input variable, then the results obtained from 8 DMUs are efficient and 4 DMUs are inefficient.

Детермінанти ефективності масштабу функціонування лізингових компаній України

The article contains a regression model for assessing the average value of the efficiency of the scale of operations by leasing companies in Ukraine, which is built on the basis of DEA analysis. To determine the factors that cause a statistically significant impact on performance indicators obtained on the basis of DEA analysis, the authors use the appropriate regression models for panel data. To assess the efficiency of the scale of Ukrainian leasing companies for the analyzed period, the input-oriented DEA model with variable VRS and sustainable CRS scale effects was used. A statistical sample of the performance indicators of 21 leasing companies in Ukraine was formed, which was obtained from their financial and accounting statements for 2019-2020. Due to the significant sensitivity of SE scale efficiency to input and output variables, it was proposed to use an average scale efficiency value based on eight different DEA models. It is noted that if the CCR model determines the success of the management of a leasing company in the transformation of input resources into the output ones, then the efficiency of scale evaluates the correctness of choosing the scale of the leasing company. The analysis showed that the efficiency of the scale of the lessor’s activities in most cases depends on determinants such as administrative costs, fixed assets, and net profit of such companies. The obtained results indicate that with the increase in the size of the leasing company, the efficiency of the scale of their active operations decreases, since with an increase in the size of fixed assets and the volume of administrative costs, the average efficiency of the scale of their activities decreases. It is shown that the greater the net profit, the closer leasing companies work to the optimal scale of activity for them, which is determined by the volume of long-term receivables of the lessor and the size of his assets.

PERFORMANCE OF CZECH FOOTBALL CLUBS: MALMQUIST INDEX APPROACH

Sport has become an important part of our lives in the modern times and sporting sites contribute significantly to the image and texture of modern cities. Regarding the popularity of sport, and football in particular, it has become an important modern place where specific types of economic and social interaction take place. The aim of this article is to propose a method for evaluating the performance of football clubs based on DEA and Malmquist index. Professional Czech football clubs playing in the Czech football competition Fortuna:Liga were selected for empirical analysis. To analyze the relative efficiency of football clubs, BCC and CCR models were employed. The study was conducted on a sample of 20 clubs through 2 inputs and 1 output collected during the 2015/16 – 2019/20 seasons. For some clubs the values of the Malmquist index were calculated. With help of MI it was possible to quantify the total productivity change factor and to decompose it to technological change and technical efficiency change. The results show that Czech football clubs achieved a relatively high level of efficiency in the period monitored and that traditional clubs achieved the highest efficiency score. These results could help club managers improve the performance of their teams.

Efficiency Analysis of Agricultural Corporations with and without Technology Transfer : Focusing on Rise Cake Manufacturing Technology Developed by RDA

Economic outcomes of agricultural corporations in Korea have been deteriorated while agricultural technology have been increasingly commercialized. Thus, it is important to check the difference of technical efficiency of agricultural corporations with and without technology transfer. This study aimed to check the difference using output oriented CCR model and BCC model based on Data Envelopment Analysis (DEA) and suggested output levels attainable to reference corporation. For the study, we selected 14 agricultural corporations with and without technology transfer, respectively. Results showed that technical efficiency of agricultural corporation under CCR model was 0.906 with technology transfer and 0.791 without technology transfer. Pure technical efficiency under BCC model was 0.972 with technology transfer and 0.897 without technology transfer. In both models, the former was slightly higher than the latter and significant statistical difference was found. This study contributed to the expand of research area from farm sides to agricultural corporations concerning technology transfer. In addition, this study was differentiated by selecting input and output variables directly affecting management activities of agricultural manufacturing business.

Impact of Animal Welfare on the Efficiency of Pig Production in Korea

The purpose of this study is to analyse the effect of factors related to animal welfare on pig productivity in Korea. For the normal DEA and bootstrap DEA analysis, MSY was selected as the output variable and 6 items of animal welfare was selected as the input variables. As a result, the production efficiency of CCR model was 0.8613 for DEA and 0.8256 for bootstrap DEA, and the production efficiency of BCC model was 0.9140 for DEA and 0.8845 for bootstrap DEA. And the scale efficiency showed an overall average of 0.9425 for DEA (0.9354 for bootstrap DEA). In the OLS regression analysis, anmimal welfare point as an input factor had negative coefficient. In conclusion, animal welfare efforts have had little effect on MSY in hog farms in Korea yet.

ASSESSMENT OF THE PERFORMANCE OF LOGISTICS VILLAGES OPERATED BY THE TURKISH STATE RAILWAYS USING MCDM AND DEA METHODS

Increasing competition with globalization brought along the cost problem arising from logistics activities. In this context, logistics villages play an active role in reducing costs. Logistics villages provide significant benefits to users as areas where goods from different modes of transport are transferred, arranged and prepared for transportation, and all logistics-related activities are gathered in one region. Logistics villages have begun to be established by The Turkish State Railways (TSR-Türkiye Cumhuriyeti Devlet Demiryolları) using government resources after 2006 in Turkey. The aim of this study is to assess the logistics performances of the eight logistics villages in operation by analyzing their efficiencies using Multi Criteria Decision Making (MCDM) and Data Envelopment Analysis (DEA) methods. In the study, Entropy-based EDAS, MAUT and MOOSRA methods have been used. The efficiency scores of logistics villages were calculated using output-oriented DEA models. According to CCR and BCC models, İstanbul (Halkalı) and Uşak logistics villages were found to be efficient.

Healthy City Project: An application of data envelopment analysis.

PurposeThe importance of Korea’s Healthy City Project has recently increased due to the growth of the elderly population and chronic diseases. Consequently, local governments are expanding the project to manage health at the local level; however, because local government resources are limited, efficient business operation is required. Thus, the purpose of this study is to present a plan for effective project management by developing a strategy for a Healthy City Project that is suitable for the scale of local governments.MethodsFor efficiency analysis, data were collected from the homepages of 90 local governments that are implementing the Healthy City Project in Korea, and data envelopment analysis (DEA) was conducted using both the CCR model and the BCC model. The input variables for the DEA included budget, manpower, organization, planned projects, and research education. In addition, we selected program, network, and project performance as output variables.ResultsAnalysis results indicated that the CCR model identified 22 (out of 90) municipalities in which Healthy City Projects are implemented efficiently, while 68 are inefficient. The BCC model identified many more efficient regions than the CCR model; however, the difference was insignificant. Thus, the budget, manpower, planned projects, and network must be increased to improve efficiency.ConclusionFirst, to improve the efficiency of the Healthy City Project, a sufficient budget must be secured during its implementation. Second, long- and short-term plans are needed to expand the Healthy City Program. Third, networks must be created for local governments to benchmark Healthy City Projects.

Is it better to support tourism after pandemic regionally or globally? Case of European Union from the socio-economic perspective

ABSTRACT The study is devoted to examining the tourism efficiency of European Union countries in the years after the financial crisis and before the pandemic crisis to understand the processes that can be instrumental in post-pandemic crisis measures. The DEA CCR and BCC models and truncated regression analysis were implemented to reach the study’s goal. Double bootstrapped efficiency values were detailed to cluster analysis. Our findings confirmed that measures following the Coronavirus pandemic must be directed regionally with regards to socio-economic convergence. Some tourism-oriented countries, which were more affected by the previous crisis, tend to be less efficient. And more vulnerable. Also, the convergence of countries, which joined the European Union in the last decades, is insufficient, so the efficiencies tend to be lower. We have also shown that various socio-economic factories impact efficiency, and their improvement can also mean improving the state of tourist efficiency in European Union countries.

Performance Assessment on the Application of Artificial Intelligence to Sustainable Supply Chain Management in the Construction Material Industry

Along with global geopolitical complex, information network security issues and increased natural disasters, risk management should be well considered in the construction material industry to re-integrate and establish stiff and flexible supply chains in order to cope with emergencies in the future market. Taking the construction material industry in Taiwan as the research object, representative enterprises with artificial intelligence applied sustainable supply chain management are studied. With the Delphi method and data envelopment analysis, the public data of annual statistics reports of the enterprises are used for selecting the performance indicators of inputs and outputs. Empirical data analysis is also performed to provide reference for the improvement. The research results are summarized as follows. 1. Substituting various input/output index values into CCR and BCC models, the overall production efficiency and pure technical efficiency of enterprises are calculated; by dividing the two, the returns to scale of enterprises are acquired. 2. Critical factors in artificial intelligence applied sustainable supply chain management could be found out through sensitivity analysis. Using the rate of sensitivity change as the evaluation baseline, sensitive factors contain financial aspect, scale aspect, financial performance, and profit before tax. Finally, discussions are proposed according to the results, expecting to help domestic businesses in the construction material industry establish steady and flexible supply chains and present diversified procurement sources to reinforce the emergency defensive ability of the construction material industry.

Measuring paper industry's ecological performance in an imprecise and vague scenario: a fuzzy DEA-based analytical framework

PurposeProductivity improvement is key to sustainability performance improvements of organizations. In a real-world scenario, the nature of inputs and outputs is likely to be imprecise and vague, leading to complexity in comparing firms' efficiency measurements. Implementation of fuzzy-logic based measurement systems is a method for dealing with such cases. This paper presents a fuzzy weight objective function to solve Data Envelopment Analysis (DEA) CCR model for measuring paper mills' performance in India for 15 years.Design/methodology/approachAn integrated methodology is proposed to solve DEA models having fuzzy weights. The fuzzy DEA methodology is an extended version of the DEA approach that researchers have used for performance measurement purposes in imprecise and vague scenarios. The ecological performance of the paper industry is evaluated, considering some desirable and undesirable outputs. The effect of non-discretionary input on the performance of a paper mill is also analyzed.FindingsAnalysis suggests that the productivity of the paper industry is improving consistently throughout the period. The comparative evaluation of methods suggests that a diverse cluster of DMUs and integration of DEA with the fuzzy logic increases the diversity in the efficiency score while DEA-DE imitates the results of CCR DEA.Originality/valueProposed a fuzzy DEA-based analytical framework for measuring the paper industry's ecological performance in an imprecise and vague scenario. The model is tested on data from the paper industry in a developing country context and comparative performance analysis using DEA, fuzzy DEA and DE algorithm is done.

Data Envelopment Analysis and Analytics Software for Optimizing Building Energy Efficiency

This research was motivated by the need to identify the most effective Data Envelopment Analysis (DEA) model and associated data analytics software for measuring, comparing, and optimizing building energy efficiency. By analyzing literature sources, the authors identified several gaps in the existing DEA approaches that were resolved in this research. In particular, the authors introduced energy efficiency indices like energy consumption per square foot and per occupant as a part of DEA models’ outputs. They also utilized inverse and min-max normalized output variables to resolve the issue of undesirable outputs in the DEA models. The evaluation of these models was done by utilizing various data analytics software including Python, R, Matlab, and Excel. The authors identified that the CCR DEA model with inverse output variables provided the most reliable energy efficiency scores, and the Python’s PyDEA package produces the most consistent efficiency scores while running the CCR model.