Data Envelopment Analysis Technique Research Articles

Special economic zone efficiency factors given Russia's circumstances

Subject. This article discusses the importance of special economic zones in solving problems related to attracting investors, supporting regions of concern, and creating regional growth poles. Objectives. The article aims to consider the impact of economic and geographical location, characteristics of the region of base, the investment policy implementation quality, and the level of tax burden on the efficiency of Russian special economic zones. Methods. For the study, we used the Data Envelopment Analysis (DEA) technique, and a regression analysis. Results. The article presents the results of the analysis of the effectiveness of budget investments in the infrastructure of various special economic zones in Russia. Conclusions. The quality of the implementation of the regional investment policy has the most significant impact on the success of the development of special economic zones. Improving the methodology for assessing the effectiveness of special economic zones requires the collection and use of information on the results of scientific, technical and innovative activities of residents, taking into account the practices of the Ministry of Science and Technology of China.

MEASURING HOSPITAL EFFICIENCY DURING COVID-19 PANDEMIC IN INDONESIA

By addressing indicators of inefficiency at the initial of the COVID-19 pandemic, hospitals can immediately respond through resources. This research aims at assessing the efficiency of both government and commercial hospitals in Indonesia during the pandemic. Accounting data was collected from documentation of financial statements. The data was analyzed by using the data envelopment analysis technique and Tobit regression. The research showed that over three years of the pandemic, the hospital's efficiency rate decreased successively by 100%, 86%, and 76%. However, the optimization of hospital efficiency can be significantly improved by revenue factors, specifically the quantifiable quantity of income generated. This highlighted the need to increase revenue to ensure the provision of high-quality services to the general population. It is hoped that this research will shed light on the challenges faced by hospitals during these difficult times and provide insights for future improvements.

Analyzing the Performances and Efficiencies of European Electric Vehicles in the European Union Market Using Decision Analysis Methods

Concerns about the environment have grown worldwide as carbon emissions, one of the primary drivers of global warming, continue to rise. As the transportation industry is one of the most greenhouse-gas-emitting sectors in the European Union (EU), the need for environmentally friendly solutions has been increasing progressively. In this regard, electric vehicles (EVs) play an important part in mitigating some of the adverse effects of road transportation on human health, the environment, and climate. Therefore, the popularity of electric vehicles in the EU has recently increased significantly because of climate change mitigation efforts. In this paper, we aim to analyze and compare the performance of European EVs with multi-criteria decision-making (MCDM) methods, namely preference selection index (PSI) and axial-distance-based aggregated measurement (ADAM) techniques, which do not need the criteria weights for measurement and a novel three-dimensional evaluation technique. The results of both MCDM methods reveal that the EVs of the Tesla Inc. and Geely Group models are found to be the best-performing vehicles. We also investigate the efficiency of the European EV market by the super-efficiency data envelopment analysis (DEA) technique, which enables us to evaluate among efficient units. The efficiency scores indicate that five EVs from three different manufacturer groups obtain efficiency levels. To the best of our knowledge, European EV market evaluation by integrated and comparative analysis has not been encountered for the EU on a group basis. The suggested MCDM and DEA techniques can benefit stakeholders while assessing the European EV market.

Municipal waste management performance: a focus on Slovakia and its LAU-1 districts

In this paper, we analyse the changes made to the basic EU directive on waste and assess its impact on the waste legislation of EU members. We then examine the Slovak waste strategies/programs that have implemented the EU directive on waste, namely the Waste Prevention Program, the Waste Management Program, and the Envirostrategy 2030. Based on EU waste legislation, the Environmental Strategy 2030 sets the waste treatment aims for Slovakia until 2030. However, it is questionable whether Slovakia will achieve the set goals. Our research indicates that as of 2021, Slovakia’s rate of waste incineration with energy recovery and landfilling rate of municipal waste are below the EU average, while the recycling rate, both for materials and composting and digestion, is higher. In our quantitative analysis, we examine the progress of waste management performance in Slovakia from 2017 to 2021, focusing on the LAU-1 districts. We estimate composite efficiency indicators using the techniques of Data Envelopment Analysis and Malmquist Indices. In accordance with the hierarchy of waste treatment methods, the applied models consider desirable waste operations variables (recycling and incineration with energy recovery) and undesirable waste operation variables (landfilling). Our results reveal significant variations in efficiency across the LAU-1 districts. The average technical efficiency of the 72 districts has improved from 0.714 in 2017 to 0.852 in 2021, indicating that the performance of districts is generally improving and catching up with the best-performing districts. The total performance, as measured by the Malmquist index, has improved by 45.5 %. Districts with access to waste incineration facilities with energy recovery have exhibited higher efficiency scores, benefitting from this advantage.

Regional efficiency and total factor productivity change for small and medium sorghum producers in Zambia: A slacks-based measure and Malmquist index approach

Zambia's efforts to promote countrywide production of sorghum is backed by a number of apropos factors. It is not only an adaptive response to the climate change challenge, but also a dietary diversity improvement measure, to achieve food and nutrition security. Further, improved sorghum production will supply emerging agro-industries, especially specialty foods, beverages and stock feed manufacturing in the country. However, regional sorghum production efficiencies in Zambia are not well known. This paper attempts to evaluate regional sorghum production technical efficiencies (TE), as well as Total Factor Productivity (TFP) change from 2011 to 2022. To study these aspects, we apply non-parametric Data Envelopment Analysis (DEA) techniques such as the Slacks-Based Measure (SBM), and the Malmquist Productivity Index (MPI), followed by bootstrap OLS regression respectively. Cross-sectional estimates from the SBM model for the 2022 cropping season indicate that average TE for small and medium sorghum farmers in Zambia was 85 percent. Sorghum production could be expanded by 411.90 t by improving TE in five of ten provinces in Zambia. The MPI shows that TFP for sorghum production in Zambia declined by 21 percent, mainly due to deteriorating sorghum production technology. Populations, rurality, agro-ecological zone IIB are the factors associated with EC and TFP decline.

Panel Data Analysis of Subjective Well-Being in European Countries in the Years 2013–2022

Panel Data Analysis of Subjective Well-Being in European Countries in the Years 2013–2022

Optimization of the sustainable food supply chain with integrative data envelopment analysis approach

ABSTRACT In this study, the supply chain system of the food supply chain (FSC) is assessed. Initially, the operations of farmers are examined using the Data Envelopment Analysis (DEA) technique. Then, locations of relatively efficient farmers are used as potential collection sites for the distribution at the downstream operations. Next, the Multi-Objective Optimization Model is proposed to examine location and distribution channels under the sustainability paradigm. That is, the economic criterion is primarily considered by focusing on minimizing the total supply cost. The social criterion is next deliberated by evaluating the equality aspect of farmers for the fair proportion of vegetable supply. Then, the environmental criterion is incorporated by assessing the CO2 emissions of food transportation activity. Additionally, a trade-off analysis is analyzed to investigate the conflicting behavior of model using the Non-Preemptive Programming method. The sensitivity analysis is further examined by varying the time-requirement parameter to verify the model functionalities. Finally, the regional case study in Thailand based on the food supply data is applied to validate the model. Given that farmers are distant from diverse locations, the results of our study can provide a strategic choice for key decision-makers in the FSC network under sustainability consideration.

Corporate social responsibility and total factor productivity: the case of European mining industry

The aim of this research is to investigate the effect of corporate social responsibility (CSR) on total factor productivity (TFP) in the European mining industry, considering micro- and macroeconomic indicators of the relationship between CSR and TFP. Employing data from 40 European mining companies from content analysis, CSR Hub, and the World Bank between 2018 and 2021, this paper utilizes a combination of Data Envelopment Analysis (DEA) and panel regression techniques to test the research hypotheses. The findings suggest that the TFP of European mining firms is positively affected by CSR initiatives implemented by the companies. Also, the empirical results depict that the CSR-TFP relationship is mainly established on institutional criteria. The results also indicate that CSR-related factors, namely, transparency and reporting, training, health and safety, and resource management, are the impacting indicators. The study broadens the horizons of this line of research and can be beneficial to CEOs, managers, experts, policymakers, decision-makers, and economists in the field of mining who are willing to promote responsible and sustainable mining.

Políticas de fomento e indicadores de productividad de las pymes colombianas mediante análisis envolvente de datos e índices de Malmquist

Purpose: This study aimed to characterize the performance of Colombian Micro, Small, and Medium-sized Enterprises (MSMEs) in recent years, along with the legal framework and central public policies governing and promoting this business segment. In addition, it sought to design, validate, and implement a dynamic system of productivity indicators to help these companies improve their efficiency and competitiveness in the short, medium, and long terms. MSMEs represent approximately 99% of the business sector in Colombia, accounting for nearly 80% of national employment. Design/Methodology: In the initial descriptive–analytical phase, using secondary sources, an analysis was conducted on the performance and current situation of Colombian MSMEs, as well as the main legal provisions regulating and contributing to their development. The subsequent phase (of an applied nature) involved estimating and validating a dynamic system of productivity indicators using Data Envelopment Analysis (DEA) and Malmquist indexes for 2 samples of SMEs. Findings: After conducting the DEA, a significant decrease was observed in the productivity of the MSMEs, particularly in terms of technical efficiency in both the Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS) models. This means that, evaluated both under the CRS and VRS modes, these enterprises use more inputs than necessary for the amount of goods produced. In addition, the Malmquist indexes showed that, during the period under analysis, technical efficiency related to changes in innovation exhibited a positive behavior, attributed to the incorporation of technological changes for improving their productivity. Conclusions: Despite the development of public initiatives and institutional support in recent decades, Colombian SMEs still face adverse conditions that affect their competitiveness and limit their potential in the national economy. The proposed system of indicators, which is based on DEA techniques and Malmquist indexes, holds promise in helping these businesses to improve their productivity. Originality: This study makes a significant contribution to the fields of economics and business management in the region by providing a critical and updated evaluation of the efficiency of MSMEs in Colombia. Additionally, it examines their performance and current situation and summarizes the main legal provisions regulating and contributing to their development in the country.

Evaluation of the efficiency of urban solid waste management in Brazil by data envelopment analysis and possible variables of influence

AbstractThis article has the general objective of estimating the efficiency of urban solid waste management in 940 Brazilian municipalities through Data Envelopment Analysis (DEA) technique and has specific objectives: (i) to estimate efficiency scores; (ii) to compare the performance between different groups of municipalities; and (iii) to analyze the profile of efficient municipalities from the perspective of the guidelines of Law 12,305/2010 and socio-economic and environmental indicators. The technique used was DEA with output-oriented and variable scale to return modeling. The results showed higher efficiency scores in the municipalities with populations above 500,000 inhabitants. The score variation ranged from 0.5 (municipalities with populations <10,000 inhabitants) to 0.9 (municipalities with more than 500,000 inhabitants). Of the sample set, only 12.34% of the municipalities were considered efficient, and when analyzing the efficient group, it was found that adherence to legislation was not a major factor in achieving efficiency. Graphical abstract

A comparative analysis of efficiency in the Brazilian banking sector: A data envelopment analysis approach

This work aims to analyze the efficiency of Brazilian financial institutions until the COVID-19 pandemic period, from production and profitability perspectives. To accomplish this, the data envelopment analysis (DEA) techniques, specifically the CCR and BCC models, are applied to 213 Brazilian financial institutions in four methodological stages. The first step involved conducting a literature review of similar studies. The second step consisted of gathering financial information for each bank through the website of the Central Bank of Brazil. The third step involved selecting the variables to be used in the models. The fourth step was outlier detection using the jackstrap method. Subsequently, the mentioned efficiency models were applied, and the most efficient banks were identified based on each perspective. The results identified heterogeneous groups of efficient banks based on different market segments, with a focus on the efficiency of large banks and public banks when considering the production-oriented perspective. It is also observed that new digital banks are among the banks considered efficient. These findings are valuable for the scientific literature investigating the sustainability of financial institutions, as well as for decision-makers seeking to make more efficient investment allocations and for banking supervisory authorities in formulating risk regulatory policies.

Research on Informatization Mode of Higher Education Management and Student Cultivation Mechanism in the Internet Era

Abstract Education informatization, as a critical force to promote social informatization and education modernization, profoundly impacts the development of higher education. In this study, through the data envelopment analysis (DEA) technique, the resource input and output effects of higher education informatization are deeply explored, and a comprehensive evaluation system for the effectiveness of higher education informatization management is constructed. Using the coefficient of variation, McLean index and Worstegen index, this paper analyzes the development differences of higher education informatization in different regions and their causes. Thirty-two representative undergraduate colleges and universities are selected as the research objects, and the results show that 11 colleges and universities have achieved optimal efficiency in education management and talent cultivation, and are recognized as DEA effective decision-making units. This study provides valuable references and guidance for the government in allocating informatization resources, and for colleges and universities in improving their informatization management ability and talent cultivation effectiveness.

The relationship between firm size and efficiency: why does default on bank loans matter?

AbstractThis paper presents an investigation of the interconnection between firm size and efficiency under the financial constraints lens. Specifically, we used the Data Envelopment Analysis (DEA) technique to measure the efficiency of a sample of large, medium-sized and small private Italian firms, using the firms’ default risk as an undesirable output. Our findings indicate that larger companies perform better than medium-sized and smaller companies in terms of efficiency (across all business profiles), including default on bank loans. Based on indicators widely employed to characterize the bank-firm relationship, our study demonstrates the need to improve the efficiency of the Italian entrepreneurial system, consisting mainly of small companies, through their dimensional growth.

Efficiency effects of public hospital closures in the context of public hospital reform: a multistep efficiency analysis.

In the wake of hospital reforms introduced in 2011 in Turkey, public hospitals were grouped into associations with joint management and some shared operational and administrative functions, similar in some ways to hospital trusts in the English National Health Service. Reorganization of public hospitals effect hospital and market area characteristics and existence of hospitals. The objective of this study is to examine the effect of closure on competitive hospital performances. Using administrative data from Turkish Public Hospital Statistical Yearbooks for the years 2005 to 2007 and 2014 to 2017, we conducted a three-step efficiency analysis by incorporating data envelopment analysis (DEA) and propensity score matching techniques, followed by a difference-in-differences (DiD) regression. First, we used bootstrapped DEA to calculate the efficiency scores of hospitals that were located near hospitals that had been closed. Second, we used nearest neighbour propensity score matching to form control groups and ensure that any differences between these and the intervention groups could be attributed to being near a hospital that had closed rather than differences in hospital and market area characteristics. Lastly, we employed DiD regression analysis to explore whether being near a closed hospital had an impact on the efficiency of the surviving hospitals while considering the effect of the 2011 hospital reform policies. To shed light on a potential time lag between hospital closure and changes in efficiency, we used various periods for comparison. Our results suggest that the efficiency of public hospitals in Turkey increased in hospitals that were located near hospitals that closed in Turkey from 2011. Hospital closure improves the efficiency of competitive hospitals under hospital market reforms. Future studies may wish to examine the efficiency effects of government and private sector collaboration on competition in the hospital market.

Comperative Efficiency using Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA) in the Banking Industry

This study’s objective is to employ data envelopment analysis (DEA) and stochastic frontier analysis (SFA) to investigate the efficiency accomplishments of Indonesian commercial banking from 2018 to 2019. The first method of measuring efficiency employing a non-parametric data envelopment analysis (DEA) technique reveals that the average efficiency of 71 banks fell from 2018 (0.82) to 2019 (0.81). According to DEA findings, major banks outperform small banks on average. According to the approximated SFA Cobb-Douglas (CD) function, interest expenditure and labor expense have a positive and considerable influence on interest income. This occurs when deposit interest rates rise, banks gain interest revenue by raising lending rates, and banks increase non-interest income. According to the SFA of the Cobb-Douglas function, many banks are inefficient, particularly the first to 49th banks that arise from small banks. The Gamma value is near one (0.999), while the LR test yields a significant result of 36.14. The Cobb-Douglas SFA model is therefore applicable. The efficiency performance findings from the two models above reveal the same thing: large banks are more efficient than small banks.

Efficiency Evaluation of Business in IoT Supply Chains by Network DEA

<p>Recently Internet of things (IoT) and its applications are emerging as a momentous trend in industry. Numerous hardware and software providers have been entering the intense market competition in IoT related industries. Correspondingly, the attention on evaluation of IoT industry is growing. However, it is a main theme as how to consider the multiple dimensions and dependencies among the criteria in IoT supply chains simultaneously. By considering internal processes in DMUs as well as their interactions, this study designs the evaluation methods with network data envelopment analysis (NDEA) and multi-objective programming (MOP) techniques. This work intends to estimate the efficiency of IoT businesses from the perspectives of R&D, manufacturing, sales and finance, and the overall performance. The proposed models are implemented with empirical case studies in IoT supply chains in Taiwan. The results show the usefulness and validity of the proposed methods in evaluating IoT related business.</p> <p> </p>

Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan.

Profitability Analysis Of Selected IT Companies In India Using Data Envelopment Analysis (DEA): A Comprehensive Study

The IT sector in India plays a vital role in driving the country's economy, contributing significantly to its growth and employment landscape. As of 2023, it constitutes around 8% of India's total GDP, employing over 4.5 million professionals directly and supporting an estimated 10 million indirect jobs. With export revenue reaching approximately $194 billion, the IT sector stands as one of the largest contributors to India's foreign exchange reserves. This sector has experienced robust growth, with a projected annual growth rate of 7-9%. This growth can be attributed to its relentless focus on digitalization, innovation, and technology-driven solutions, enabling it to foster economic development, attract investments, and drive digital transformation across various industries. Conducting a profitability analysis of IT companies in India holds significant importance. It provides valuable insights into their financial performance and viability, assisting stakeholders in making informed decisions. Investors and policymakers can assess the efficiency and effectiveness of these companies in generating profits and maximizing returns on investments. Profitability analysis also aids in resource allocation, risk management, and policy formulation to create an enabling environment for IT sector growth. Additionally, profitability analysis allows companies to benchmark their performance, identify areas for improvement, and enhance competitiveness. By evaluating financial indicators, stakeholders can make informed decisions regarding expansion, diversification, and strategic partnerships. Ultimately, profitability analysis serves as a crucial tool for decision-making, resource allocation, and sustainable growth within the IT sector. Therefore, this study aims to analyze the profitability of six selected IT companies in India from 2008-09 to 2021-22 using the Data Envelopment Analysis (DEA) technique. By employing DEA, the study aims to provide valuable insights into the financial performance of these companies and contribute to the existing understanding of profitability dynamics within the Indian IT sector.

Road Safety Efficiency on Interurban Roads in Spain.

The slowdown in economic development caused by traffic accidents in Spain, together with the disparity in the risk of death or injury due to traffic accidents in its provinces, makes it necessary to evaluate their road safety performance. Therefore, the objectives of the present research are, on the one hand, to find out the level of road safety efficiency of Spanish provinces in the period 2014-2018. On the other hand, it is also aimed to determine to what extent the annual efficiency change is originated by pure changes in efficiency or by technological changes. To achieve both objectives, the nonparametric technique of Data Envelopment Analysis and the Malmquist Index have been used based on the consideration of the Safety Performance Indicators (SPI) as the inputs of the process. The research findings show changes in the level of road safety efficiency in each of the years of the study period. Thus, it is possible to identify a geographical delimitation of the causes that generate changes in efficiency in recent years. A contraction in efficiency and technological progress is identified in part of the Spanish provinces in the north, northeast, and south of Spain.

Measuring economic resilience of manufacturing organization leveraging integrated data envelopment analysis (DEA)-machine learning approach

ABSTRACT This study aims to assess the economic resilience of manufacturing firms through a combination of output-oriented data envelopment analysis (DEA) and machine learning techniques. The research draws on economic resilience factors identified in the literature and focuses on three categories: economic-related factors (financial flexibility, microeconomic market, macroeconomic stability), production-related factors (restoration of production, backup inventories, resource pooling/sharing), and management-related factors (diversification of activities, good governance (management), relocation). Using DEA, a mathematical approach, the study computes and analyzes the contributions of various components to economic resilience. The results of DEA normalization indicate that the highest weighted criteria are financial flexibility, good governance (management), and resource pooling (sharing). To gain a deeper understanding of the data structure, the K-means algorithm is employed for clustering and analysis. K-means clustering is a popular exploratory data analysis technique that aims to group samples into clusters of equal variances by minimizing inertia or the sum of squares within each cluster. The combination of these techniques with sensitivity analysis provides a novel analytical approach for policy formulation and decision-making. The findings have implications for practitioners and domain experts, offering valuable insights into enhancing economic resilience in the manufacturing sector.