Data Envelopment Analysis Results Research Articles

Financial Performance Evaluation of Small Finance Banks: A CAMEL Analysis

Small finance banks (SFBs) are a special category of banks incepted by the Reserve Bank of India in 2015 to cater to the financial needs of the underserved population of the country with the aim of financial inclusion. They serve the priority sector and the unorganised sector of the country. This article analyses and evaluates the performance of these banks on the basis of various financial parameters such as capital adequacy, asset management, management quality, earning quality and liquidity derived from the CAMEL framework. Following a robust evaluation methodology, namely data envelopment analysis (DEA), a sample of all the SFBs operating in India to date is taken. As per the composite rating of CAMEL, results show that the majority of SFBs exhibit satisfactory performance. Utkarsh SFB Limited is ranked at the first position and is followed by Fincare SFB Limited on the second position. Jana SFB Limited stands at the last position amongst all the banks, preceded by ESAF SFB Limited. The results of DEA show average inefficiency among SFBs. The calculated efficiency scores of SFBs highlight that Capital SFB, Fincare SFB, Jana SFB, Shivalik SFB and Utkarsh SFB are the most efficient banks, with a technical efficiency score of 1. ESAF SFB has the lowest efficiency score. Scale inefficiency is the major reason for inefficiency. The study provides valuable insights to the authorities, managers and investors about the performance of these banks and gives them a future vision of the sustenance of these banks.

Evaluation of tourism performance of OECD countries with integrated intuitionistic fuzzy approaches

ABSTRACT Evaluating a country’s tourism performance using different indicators and analysis methods is an important issue in strategic decision-making. In this regard, the main purpose of the study is to examine the tourism performances of OECD countries through macro tourism variables. In the research, data from 25 OECD countries for the period 2011–2020 were analysed in two periods. The intuitionistic fuzzy Entropy (IFEntropy) method was used to weigh the importance levels of the evaluation criteria. In evaluating the tourism performances, the extended intuitionistic fuzzy TOPSIS (EIFTOPSIS) and intuitionistic fuzzy CRADIS (IFCRADIS) methods were preferred. In addition, to look at the research from a different perspective, the tourism productivity of the sample countries was measured by data envelopment analysis. According to the results of the intuitionistic methods used, the 3 countries with the best tourism performance were determined as Spain, France, and Italy, respectively, and the 3 countries with the lowest performance scores were determined as Lithuania, Latvia, and Slovakia, respectively. It is understood from the results of data envelopment analysis that countries with high tourism performance cannot show the same success in the efficient use of macro tourism resources. The results obtained in the study are expected to guide decision-makers and contribute to the literature.

The Role of Worship Values in Enhancing the Efficiency of Islamic Commercial Banks: A Data Envelopment Analysis (DEA) Approach

This study investigates the efficiency of Islamic banking in Indonesia from 2018 to 2021 through the application of Data Envelopment Analysis (DEA), incorporating Islamic principles. The analysis utilizes secondary financial data from four Islamic banks, sourced from the Financial Services Authority (OJK) and other researchers. The input variables considered include third-party funds allocated for profit-sharing and labor costs, while the output variables consist of revenue derived from the management of public funds and other operational income. The DEA results indicate fluctuating efficiency levels among the Islamic banks, with Bank Syariah Y consistently achieving 100% efficiency, signifying its status as the most efficient institution. The study identifies that revenue from public fund management negatively impacts the growth of Islamic banks, whereas labor costs and other operational income positively influence their development. These findings highlight areas requiring improvement and the factors driving efficiency in the development of Islamic banking in Indonesia.

Efficiency and sustainability: Evaluating and optimizing energy use and environmental impact in cucumber production

This study assesses the energy-economic-environmental aspects of cucumber production in the face of environmental issues and resource scarcity, which threaten global food security and demand sustainable agriculture solutions. Data were collected by questionnaire and analyzed for inputs from planting to harvesting. The results indicated that diesel fuel (37.41%) and nitrate fertilizer (37.22%) constitute the primary inputs. The energy ratio, energy efficiency and net energy gain were 0.43, 0.54 kgMJ−1 and -30,153.31 MJha−1, respectively. Input optimization by Data Envelopment Analysis (DEA) reduced the carbon footprint from 178.433 to 168.900 kg CO2 per ton, the abiotic depletion from 1228.291 to 964.305 MJ and the acidification from 0.838 to 0.749 kg SO2 eq. Also, the results of DEA showed that 52.5% and 35% of the units were fully efficient under Variable Return to Scale (VRS) and Constant Return to Scale (CRS) models, respectively, while the others were inefficient to varying degrees. The average of technical, pure technical and scale efficiencies were 92.88%, 97.64%, and 95.07%, respectively. The results of economic evaluation showed that production cost is 6058.84 $ha−1 with a benefit-cost ratio of 1.71. Based on the primary results, it is suggested to use precision agriculture techniques and the exploration of alternative energy sources, such as biofuel for machinery. Additionally, using slow-release fertilizers or organic substitutes can decrease nitrogen fertilizer dependency while maintaining crop yields and improve the sustainability in this production.

Performance optimization of fourth-generation biorefinery departments: A novel mixed macroergonomics-sustainability framework

Today, crises caused by environmental pollution such as climate change have emerged as fundamental challenges with far-reaching and sometimes irreparable effects on the environment. Fourth-generation biorefineries (FGBs) have been recognized as a primary solution to these crises. There is a need to establish a comprehensive framework that can evaluate and enhance the performance of various departments in FGBs. This study introduces an integrated framework for evaluating and enhancing the performance of FGB departments, focusing on macroergonomics and sustainability indicators. First, the relative importance of each indicator was calculated using the best-worst method. The necessary data were collected using a validated questionnaire, and factor analysis was employed to assess its validity. The efficiency score of each decision-making unit was then computed using data envelopment analysis (DEA). Principal component analysis (PCA) was performed to validate the DEA results. In the next step, the positive or negative effects of each indicator on the performance of the refinery departments were investigated using sensitivity analysis and statistical tests such as Kolmogorov-Smirnov, Levene, paired t-test, and Wilcoxon. The results of sensitivity analysis indicated that the FGB had the highest performance in reducing injury rates but the lowest in reducing lost workdays caused by injuries or illnesses. In addition, appropriate strategies were developed using Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis to improve the performance of the refinery's departments. To the best of our knowledge, this research is the first attempt to assess the qualitative and quantitative performance of FGB departments on the basis of sustainability and macroergonomics indicators.

Analysis of cassava farming efficiency as a supporter of post-COVID-19 rural economic changes in wonogiri regency

Cassava farming, as an important commodity in agriculture in Wonogiri Regency, is necessary for structural changes in the rural economy of Wonogiri Regency in postCOVID-19 conditions. This study aims to determine the level of efficiency and analyse the elasticity of cassava farming. The Data Envelopment Analysis is used in analysing the efficiency of cassava farming, while multiple linear regression analysis is used in analysing production elasticity. The results of Data Envelopment Analysis with an inputoriented model in the study show that farmers have a level of technical efficiency of 64.6%, allocative efficiency of 47.1%, and economic efficiency of 30%. Farmers who are not efficient can increase their efficiency value by using cassava farming inputs. The elasticity of the production of seedlings, manure, SP36 fertilizer, Phonska fertilizer, urea fertilizer, pesticides, and labour is positive. The scale of business results show that farming is in a condition of increasing returns to scale. The addition of production factors has a positive effect on cassava production. However, if production factors are not considered, it will result in additional inputs, decreasing yields. Optimal use of inputs can increase the production scale of cassava farming and increase efficiency. This study can be helpful for the farmers to increase the efficiency level through the efficient use of inputs. They can be helpful for the government in the formulation of rural development plans with farming development and implementing policies to increase the efficiency

Tourism Efficiency: Bootstrap-Data Envelopment and Tobit Panel Data Analysis

Tourism is an important sector for countries, not only for cultural but also for economic activities. The tourism sector, which operates effectively, contributes to the development of the country's economy. Therefore, the study aims at calculating tourism efficiency and identifying factors that influence it. The study discussed 18 European countries that are among the world's major destination centers. Firstly, tourism efficiency scores were calculated with the data covering the period 2002-2019. Inputs are the number of tourists and tourism expenditures, and output is tourism revenues. Tourism sector efficiency was calculated with the standard Data Envelopment Analysis (DEA) model. Because of the possible statistical limitations of the DEA method, analysis was repeated with the Bootstrap-DEA method. The resulting efficiency scores were used as dependent variables in the Tobit model. The variables including per capita income, digitalization, energy consumption, financial development, political stability, and life expectancy at birth were handled as new trends of tourism efficiency in the Tobit panel data analysis. Boostrap DEA results, which yield more accurate results, gave efficiency results for a smaller number of countries than the efficiency analysis performed with standard DEA. Tobit panel data analysis results showed that income per capita, digitalization, political stability, and life expectancy at birth enhanced tourism efficiency. In the study, unlike the literature, tourism efficiency was not considered at the level of companies, but at the level of countries. In addition to the standard DEA analysis, the Bootstrap DEA method was used, which yielded superior results. Additionally, not only the efficiency values were calculated in the study, but also the factors affecting the tourism efficiency were determined.

Efficiency of cassava farming with an input-output analysis approach in the Wonogiri Regency, Indonesia

Wonogiri Regency was the area with the highest cassava production in Central Java in 2021, but the level of productivity is lower than the national productivity. The level of productivity is related to the inefficiency of inputs. The purpose of this study is to analyze the efficiency of cassava farming through the relationship between the inputs and outputs of cassava and socioeconomic factor which affects the efficiency. The basic method of the research is descriptive and quantitative, with primary data from 105 cassava farmers. Input-output analysis of the efficiency level use approach of Data Envelopment Analysis (DEA) and analysis of socioeconomic factors affecting the efficiency level with Tobit’s regression. The input-output analysis with DEA results showed that 32% of cassava farmers in Wonogiri were already technically efficient, with an average value of 0.855. Increasing productivity through efficiency can be done by reducing the use of slack inputs, especially in the seed, organic fertilizer, and urea fertilizer. The Tobit regression results showed that farmers’ experience, education, and participation in the farmers’ group positively affected cassava’s efficiency in the Wonogiri Regency. The study’s implication is to increase farmer groups’ role in providing information on the efficient use of inputs needed to increase cassava productivity.

Spatial developement and disparities among algerian cities econometric study by using spatial econometrics tools

The study of mechanisms of spatial convergence, and their impact on achieving spatial development requires to study the spatial economic structure to find out the strengths and weaknesses in the construction of local economies which have become more of a necessity to reduce disparities and spatial differences within the same geographic structure, in order to guide economic and social public policies for targeting development possibilities and resources more effectively, we use this spatial econometrics analysis, in order to identify the disparities among Algerian cities (35 city) and determine factors that affect spatial development. The results of data envelopment analysis and spatial econometric methods indicate the presence of heterogeneity and disparities among Algerian cities. also, this analysis shows that the application of spatial and regional development mechanisms is still limited in creating links and interactions between regions, which are a component of the national economy.

Dealing the problem of heteroscedasticity in Data Envelopment Analysis

There are various performance or efficiency measurements of similar type of organizations known as Decision Making Units (DMU). One of the effective tools for assessing the effectiveness of DMUs is the non-parametric Data Envelopment Analysis (DEA) approach. However, the presence of heteroscedasticity in the inputs and outputs affect the results of DEA. By computing the efficiency estimates in this work, the author tried to eliminate the heteroscedasticity. Additionally, for the sake of an empirical inquiry, the author took into account information on non-life insurance businesses in India. The study's goal is to determine how heteroscedasticity affects DEA efficiency estimations.

Efficiency Evaluation of Local Government Expenditures Towards The Human Development Index

The purpose of this study was to analyze the efficiency of government expenditure based Human Development Index (HDI) in Local Government at the Indonesian’s Province using Data Envelopment Analysis (DEA) and Tobit Regression. DEA method used to measure the efficiency of a government entity by using multi-input and multi-output. DEA result also can be used to identify which input or output must be evaluated so that Decision Making Unit (DMU) can increase its efficiency. While tobit model was conducted to investigate the connection of the efficiency result to some variables such as population, size, and differences between Java or non-Java provinces The Result from this research showed that Yogyakarta, Jakarta, Central Java, and East Kalimantan were the most efficient provinces in 2015-2018, while East Java was the most inefficient. This research also indicates that education sector was the main source inefficiency both in input and output variables. Tobit analysis showed that all independent variables used in this study had a significant effect on efficiency. Population and size variables had a negative significant effect on the efficiency, otherwise dummy variables of Java and non-Java has positive significant effect to the efficiency achievement. Keywords: Public Expenditure Efficiency; HDI; DEA; Tobit JEl Classification : D24, H72, H75

Analysis of Technical Efficiency of Rice Farmers under Farmer Producer Organisation (FPO) in Guntur District of Andhra Pradesh

The study investigated the Technical efficiency (TE) of rice farmers under Farmer Producer Organisation (FPO) in Guntur District of Andhra Pradesh using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) from a sample size of 40 farmers. The results from DEA and SFA revealed that the mean TE of rice farmers was 98.7 per cent and 86 per cent respectively. The mean TE from DEA is indicating that they can reduce their input by 1.3 per cent to produce the same level of output and SFA revealed that the coefficients of seed (0.7543), urea (0.877), MOP (0.979) and machine hours (0.877) were positive and highly significant, implying that these inputs are productive enough whereas DAP (0.877), PPC (0.765) and human labour (0.988) were positive but non-significant implying that they are comparatively less productive. From the results of DEA and SFA, the mean efficiency of rice farmers through DEA (0.98) was more than SFA (0.86). The results from Garrett ranking revealed that price versatility, high input prices, lack of proper labour supply, lack of adequate capital and storage facilities etc., are the major constraints faced by rice farmers under FPO in Guntur district and they need to be strengthened.

On a Choquet Integral DEA Cross-Efficiency Evaluation Method Involving the Satisfaction of Decision-Making Units and Weight Balance of Indicators

The secondary goal approach is an effective way to address the problem of the non-uniqueness of the optimal weights The secondary goal approach is an effective way to address the problem of the non-uniqueness of the optimal weights for decision-making units (DMUs) in the cross-efficiency evaluation process of data envelopment analysis (DEA). However, on the one hand, existing secondary-goal models seldom consider the willingness of DMUs to accept and be satisfied with the cross-efficiency evaluation results of DEA. On the other hand, the problem of zero weights and excessive differences of indicators has not been given enough attention. Furthermore, the traditional DEA approach ignores interactions between indicators. To overcome these problems, in the context of considering the interaction of input (or output) indicators, this study introduces the idea of the minimum dissimilarity of weights and constructs an improved method for evaluating the cross-efficiency of the secondary goal based on the DMUs’ satisfaction. In this method, the 2-additive Choquet integral is used as a more feasible attempt to reflect the pairwise interaction between input (or output) indicators to improve the differentiation of DEA evaluation results. Additionally, the satisfaction targets of DMUs are adjusted according to the three ethical principles of fairness (fraternity), utilitarianism and equity in social choice theory to obtain a choice of optimal weights that is consistent with the values of individual DMUs. This paper validates the performance of the improved DEA method through comparative analysis of arithmetic cases. Then the method is applied to the study of the input–output efficiency assessment of the water–energy–food nexus in China.

Measuring Spanish airport performance: A bootstrap data envelopment analysis of efficiency

This work studies the efficiency of Spanish airports for the year 2018. In the first stage, efficiency is calculated by applying the Data Envelopment Analysis ( DEA) method. As inputs, variables have been selected representing the airports' physical aspects, such as the number of boarding gates or the runway area. As outputs, the number of passengers, tons of cargo and the number of operations are included. By means of bootstrapping, it is empirically demonstrated that the airports operated under variable returns to scale. In the second stage, a truncated regression is carried out to determine which variables influence the efficiency of the airports. Previously a bootstrapping with 2000 iterations is applied to correct the possible bias of the DEA results. Airport apron and opening hours are relevant factors to explain the efficiency of airports as well as being a tourist airport. • Of the 37 Spanish airports analysed, 14 are efficient. • It is empirically demonstrated, by means of bootstrapping, that the airports operated under variable returns to scale. • Opening hours, runway length and tourist airport positively influence the physical efficiency of airports. • Apron size negatively influences airport efficiency.

An integrated framework to assess and improve the financial soundness of private banks

Banks have faced significant challenges due to national economic problems in recent years. There is now a greater need to assess the existing banking system and implement corrective action and strengthening measures. In this paper, financial data of 11 Iranian private banks are examined for 15 different periods and their performance is evaluated using the CAMELS indicator. Expert opinions were used to determine the weights of indicators and ratios, and their final weights were determined using the Best-Worst Method (BWM). Data Envelopment Analysis (DEA) was then used to calculate the efficiency score units (DMUs) and their ranking. Principal component analysis (PCA) was used to validate the DEA results. Several sensitive analyses were conducted on all private banks in Iran to verify their rankings. All 11 Iranian private banks were evaluated through a comprehensive sensitivity analysis, and they were evaluated independently based on the suggested indicators based on their respective performance. The intuitive results of sensitivity analysis were accurate through statistic test. Also, appropriate strategies for improving the performance of the banking system are presented using Strengths-Weaknesses-Opportunities-Threats (SWOT) matrix. Using the results, designers can build more intelligent system CAMELS perspective.

Economic efficiency analysis of fish farming in Bharatpur District, Rajasthan: A corporate social responsibility (CSR) initiative

The innovative corporate social responsibility (CSR) initiative of Lupin Human Welfare and Research Foundation, Bharatpur, Rajasthan, India, through fish farming in Kaman Block of Bharatpur has transformed the lives of the local population. Fish farming started with 240 families in 1980, is now benefiting 2000 people and has brought economic and social transformation in this region. Cost-benefit analysis indicated that fish farmers culturing Indian major carps (IMC) for 18 months from spawn to table size registered a benefit-cost ratio (BCR) of 2.15. The fish farmers who cultured IMC for 12 months (from fingerlings to table size ) achieved a BCR of 1.77. The results of data envelopment analysis (DEA) showed that the technical efficiency was around 0.95, indicating highly efficient farms. Polluted water, disruption in power supply and poaching were found to be major constraints faced by the farmers. Keywords: Constraint analysis, Costs and returns analysis, Data envelopment analysis, Indian major carps

Use of Data Envelopment Analysis (DEA) to assess management alternatives in the presence of multiple objectives

Marine ecosystem-based management often aims to satisfy a wide range of economic, social and environmental objectives. These are often measured in non-commensurable units, making identification of a preferred option difficult when outcomes and measures vary considerably. In this paper, we examine the potential for data envelopment analysis (DEA) as an alternative approach to traditional multi-criteria decision analysis (MCDA). We derive multi-output composite indicator scores for three case study examples. The management alternatives are assessed both with and without exogenously determined objective preference weights. The frontier-based analysis does not require weights to be assigned to each objective, instead generating flexible “benefit of the doubt” weights that allow for different objectives to be influencing each management option. Applying stakeholder objective importance weights allows the assessment of the objective alignment of the different stakeholder groups, providing additional information on management option performance. The DEA results were found to be consistent with those from an earlier study that used a more traditional MCDA approach. DEA has the additional advantage of not requiring measures to be commensurable and can also directly incorporate undesirable outcomes into the analysis.

Operational performance model for banks: a dynamic data envelopment approach

PurposeOperational performance is critical for the banking sector for both managers and other stakeholders as it strongly affects the overall performance of the banking system. Traditional performance measures such as ratio analysis encountered certain shortcomings. At this juncture, data envelopment analysis (DEA) approaches are increasingly applied in bank efficiency studies. However, basic DEA models ignored the interactions between consecutive terms and focused primarily on measuring performance independently for each study period. All this is required to develop an operational performance model that can enable the long-term decision model.Design/methodology/approachAn attempt has been made to develop a dynamic DEA within a non-radial category to measure interconnection activities considering non-performing loans as an undesirable link. This study uses the Indian banking dataset from 2015 to 2019. The study's research design directs three directions: ‘comparison of the dynamic DEA with the traditional static DEA model, areas of inefficiencies that are investigated for each factor using the factor efficiency index and the robustness results highlighting the performance difference between bank categories.'FindingsComparing with static DEA results, the study confirms that the dynamic model best measures long-term operational performance due to the linkage between consecutive terms. The efficiency analysis concludes that the input factor that requires the most improvement is ‘fixed assets' and ‘deposits'. The output factor that needs the most progress is ‘non-interest income'. The robustness of the developed model is proven by ownership categories present within the Indian banking system. At a significance level of 10%, the result of both the separate and dynamic model for privately owned banks is significantly better than that of publicly owned banks.Originality/valueThis paper proposes an operational efficiency model for Indian banks in line with undesirable output. The mean factor efficiency analysis related to non-radial DEA modelling enhances managerial flexibilities in determining improvement initiatives.

Factors Affecting the Technical Efficiency of Cassava Farming in Wonogiri Regency

Cassava is a potential commodity to develop because it is used for consumption and industrial raw materials. Wonogiri Regency has the highest harvest and production area in Central Java. Such a high production level is not comparable to a low level of productivity. The productivity of cassava in Wonogiri Regency is only 18.99 tons per hectare lower than the national productivity, which is 25.95 tons per hectare. Therefore, it is necessary to use inputs as an efficient resource to increase productivity. This study analyzed the technical efficiency and factors affecting the efficiency of cassava. Primary data from 75 cassava farmers in Wonogiri Regency. Data Envelopment Analysis (DEA) and regression Tobit were the data analysis methods. DEA results showed efficiency scores ranging from 0.107 to 1.28% of cassava farmers in Wonogiri were technically efficient in carrying variable return to scale, with an average score of 0.580. Cassava farmers in Wonogiri Regency should increase the efficiency value by reducing slack inputs, namely cassava seeds, phonska fertilizer, and urea fertilizer. Tobit regression results show that farmer education and group membership significantly affect the efficiency of cassava. Farmers with higher education have better skills to adopt new technologies and use inputs more efficiently. The farmer group has a positive influence because it can provide information and discussion to overcome the problem of cassava farming. Keywords: cassava farming, DEA, efficiency, productivity, tobit regression

An Evaluation of Chinese Rapeseed Production Efficiency Based on Three-Stage DEA and Malmquist Index

As a widely grown oil crop in China, rapeseed accounts for 38.6% of China’s total oil crop production. In order to ensure the sustainable and healthy development of the rapeseed industry, this essay uses the input-output panel data of 15 central rapeseed-producing provinces (cities) in China from 2011 to 2019. The reasons for the change of the index were further studied using three-stage data envelopment analysis (DEA) and the Malmquist method. The three-stage DEA results show that the scale efficiency of rapeseed production increases in other provinces, except in Qinghai Province and Gansu Province, and that scale efficiency can be improved through additional investment. Planting enterprises can improve their efficiency by improving the efficiency of scientific and technological progress. On the other hand, government expenditure has a positive impact on rapeseed production efficiency. The Malmquist index shows that the technical progress efficiency index has the most significant impact during this period and the rapeseed total factor productivity can be improved by improving technological progress efficiency. In addition, according to the measured results, corresponding suggestions are put forward.