Slack-based Data Envelopment Analysis Research Articles

Environmental and social performance relationships to firm efficiency: Evidence from the semiconductor industry

Escalating global demand for high-performance computing and smart devices has driven rapid expansion in the semiconductor industry. This expansion raises environmental and societal sustainability concerns among stakeholders. Although there is a growing commitment to environmental and social responsibilities by semiconductor companies, the impact of these responsibilities and initiatives on corporate performance remains unclear—especially with respect to general firm operational efficiency. Thus, this study employs a sample of 129 listed Chinese semiconductor companies and a slack-based data envelopment analysis (SBM-DEA) methodology to examine the relationship between corporate environmental and social performance and firm efficiency. We also investigate how supply chain position—an important industrial characteristic—moderates these relationships. The findings reveal that: (1) China's semiconductor industry efficiency is relatively low, particularly in the chip design sub-sector; (2) there is a positive relationship between corporate environmental performance and firm efficiency; and (3) supply chain position moderates the relationship between both corporate environmental and social performance and firm efficiency. These findings offer important implications for managers and researchers aiming to foster greater semiconductor industry sustainable development.

All Lives Matter: A Model for Resource Allocation to Fire Departments in Portugal

Optimizing Resource Allocation in Fire Departments (RAFD) is crucial for enhancing Fire Protection Services (FPS) and ultimately saving lives. Efficient RAFD ensures that fire departments have the necessary resources to respond effectively to emergencies. This paper presents a method for optimizing RAFD based on performance assessment results, examining its impact on Fire Department (FD) efficiency in Portugal. Evaluating data from 353 FDs, two RAFD optimization methods were assessed: one adhering to Portuguese regulations and constraints, such as budget allocation limitations, and another without such constraints. Integrating a slack-based data envelopment analysis model and mixed-integer linear programming, the study found that incorporating FD efficiency scores in RAFD improved overall efficiency at national, district, and FD levels. While adherence to Portuguese regulations led to balanced resource allocation and a 4% performance improvement at the national level, relaxing constraints yielded an 8% improvement, albeit with potential performance deterioration in some FDs. The detailed budget and efficiency metric analysis provided in this paper offers actionable insights for fire protection services enhancement. This underscores the importance of diverse optimization strategies to enhance FD efficiency, with implications for decision-makers at the Portuguese National Authority for Emergency and Civil Protection and similar organizations globally.

Korean Paradox of Public Support for the Research and Development Investment in the Sustainable Performance of the Regional Economy

The Swedish Paradox is a well-known phenomenon related to high research and development (R&D) investment with supposedly low aggregate economic performance owing to economic saturation. The Korean economy has not yet become an advanced economy; however, its R&D performance is negligible. Recently, also the R&D share of the GNP has become much higher, and its contribution to the economic growth rate is rapidly decreasing, implying a negative relationship between R&D activities and economic performance. This study uses slacks-based data envelopment analysis to investigate investment performance at the local government level in Korea. Our findings reveal that the average score for R&D investment performance in Korea is 64%, indicating huge potential for an efficiency enhancement of 36%. Notably, among the 16 local governments examined, Seoul and its surrounding metropolitan areas showed the lowest R&D efficiency, while Gangwon and Gwangju exhibited superior performance. Since these two regions have promoted specific missions, such as the medical hub in Gangwon and the optical fiber strategic platform in Gwangju, precise and accurate differentiation appears necessary to avoid a lack of governance. To determine the workable mechanism of R&D support policies, we further divided R&D productivity into three categories by incorporating the Malmquist Index (MI). The paper productivity of R&D shows an increasing trend over the experimental period from 2016 to 2021. However, overall, the MI shows slightly deteriorating productivity with 0.978, owing to the aggravating effect of patents and commercialization of R&D. The success in the paper comes from the harmonized partnership between the strong push factor of the government and voluntary pull factor of the R&D support receiving universities. Thus, we suggest that the Korean government should not depend on the superficial effectiveness of R&D in the term but on public–private partnerships with stronger performance-oriented responsibility.

The Efficiency of Alternative and Conventional Energy Exchange-Traded Funds: Are Clean Energy Exchange-Traded Funds a Safer Asset?

This paper examines the efficiency of alternative energy equity Exchange-Traded Funds (ETFs) and conventional energy equity ETFs from 2018 to 2020, utilizing a combination of an output-oriented Slack-Based Data Envelopment Analysis (DEA) model and cluster analysis. In the context of an output-oriented DEA model, efficiency is defined as the ability of an ETF to maximize its outputs (annualized average return; environmental, social responsibility, and corporate governance; and net asset value) given a fixed level of inputs (expense ratio and beta). The findings indicate that alternative energy ETFs have the potential for long-term outperformance compared to conventional energy ETFs in terms of efficiency. However, during financial crises, the performance differences between the two types of ETFs diminish, with no significant outperformance observed in either category. The expense ratio and net asset value are identified as key factors influencing the efficiency of both ETF types. Additionally, social and governance metrics have a notably stronger positive impact on conventional energy ETFs relative to alternative energy ETFs, highlighting the increasing significance of these factors in financial asset performance.

Information and Communication Technologies and Labor Productivity: A Dynamic Slacks-Based Data Envelopment Analysis

Information and Communication Technologies and Labor Productivity: A Dynamic Slacks-Based Data Envelopment Analysis

Performance evaluation of Chinese commercial banks by an improved slacks-based DEA model

The national economy heavily relies on the banking sector, and researchers have been focused on discovering the most effective method of enhancing bank's overall efficiency. Data Envelopment Analysis (DEA) is a commonly utilized tool for measuring banking efficiency. We proposed an improved slacks-based measure model (SBM) with undesirable outputs using a by-production framework. In such case, the bank's operating process is considered as a parallel system. Inputs are categorized into interest costs and operating costs, which are then evaluated in two separate parallel stages. In the first stage, outputs are categorized into interest income and non-interest income, which are shared and considered as final outputs. In the second stage, non-performing loans are deemed as undesirable outputs. This proposed approach splits bank efficiency into two parallel stages, enabling us to identify the source of overall inefficiency, and effectively capture the efficiency differences between the two types of costs. To demonstrate the feasibility of the proposed approach, we have successfully applied it to 36 commercial banks in China from 2016 to 2021. The empirical results of the research prove that the changes of the banks' overall efficiencies were mainly due to the changes of stage 2. And this approach provides a rich source of information to assist in decision making.

Evaluating Building Construction Safety Performance in Different Regions in China

This article employs a three-stage slack-based data envelopment analysis (SBM-DEA) model to evaluate the construction safety performance (CSP) of 30 provinces and cities in China, focusing on enhancing the sustainable development of construction safety in the industry, in line with the concept of sustainable development. The research findings indicate that the supervision environment of each province and city exerts a more substantial influence on the sustainable development of construction safety compared with the level of socio-economic development. Significant changes have been observed in the regional distribution of construction safety management levels within the construction industry by eliminating the impact of economic development, the supervision environment, and random errors. The original pattern of “East > West > Central > Northeast” has shifted to “East > Central > Northeast > West.” Moreover, it has been discovered that high-efficiency values of safety performance in certain provinces and cities are partially attributed to external environmental (EE) pressure. In contrast, low-efficiency values cannot be solely attributed to their lack of willingness to implement safety management. Finally, the article proposes strategies, including government policy-led approaches, technology prioritization, and management prioritization, to enhance the sustainable development of construction safety in the construction industry based on the internal safety performance of each province.

Investigating the energy efficiency determinants in EU countries by using multi-criteria decision analysis and the Tobit regression model

ABSTRACT This study investigates the effects of several contextual variables, including renewable energy intensity, capital stock per labor, natural resource rent, the share of imported energy in total energy consumption, the ratio of carbon emissions to GDP, population, and energy production on energy efficiency in EU countries. While Tobit regression is used to examine the effects of contextual variables on energy efficiency, Data Envelopment Analysis (DEA), Slack-Based Data Envelopment Analysis (SBM-DEA), and Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) are used to calculate energy efficiency values of countries. Four different Tobit regression models are estimated as a function of the censored value for the energy efficiency series. The results show that renewable energy intensity, the ratio of carbon emissions to GDP, and population size have negative effects on energy efficiency. In contrast, regardless of the efficiency level, the share of imported energy in total energy consumption, total energy production, capital stock per labor, and technological progress have positive effects on energy efficiency.

Crop Cultivation Efficiency and GHG Emission: SBM-DEA Model with Undesirable Output Approach

Crop production relies on the use of natural resources and is a source of greenhouse gas (GHG) emissions. The present study uses survey data from 250 Polish farms to investigate the eco-efficiency of three main crops: winter wheat, winter triticale, and winter oilseed rape. First, the slack-based Data Envelopment Analysis (SBM-DEA) model with undesirable output (GHG emissions) was applied. In the next step, the Generalized Additive Model for Location, Scale and Shape (GAMLSS) was used to explain the efficiency scores. The calculated GHG emissions per hectare of crop were 1.9 tCO2e, 3.2 tCO2e, and 4.3 tCO2e for winter triticale, wheat, and oilseed rape, respectively. Fully efficient farms used significantly less fertilizer (13.6–29.3%) and fuel (16.6–25.3%) while achieving higher yields (14.4–23.2%) and lower GHG emissions per hectare (10.8–17.7%). In practice, this means that efficient farms had a 20–32% lower carbon footprint per kilogram of yield than inefficient farms, depending on the crop. It was also shown that increasing the size of the cultivated area contributed to improving efficiency scores, while no conclusive evidence was found for an influence of economic size or farm type on their performance. Weather conditions had a significant impact on the efficiency score. In general, higher temperatures and precipitation in spring had a positive effect on efficiency, while an opposite relationship was observed in summer.

Are electric vehicles more efficient? A slacks-based data envelopment analysis for European road passenger transportation

The deployment of electric vehicles (EVs) is rapidly progressing worldwide, and energy efficiency is an important indicator for both the economy and the environment. It is necessary to verify whether EVs are more energy efficient than internal combustion engine (ICE) vehicles. However, studies have yet to evaluate the energy efficiency of EVs using the data envelopment analysis (DEA) approach. To fill this research gap, this paper evaluated the energy efficiency of EVs for road passenger transportation using a slacks-based measure (SBM) approach in six European countries from 2012 to 2018. EVs have been separated from the entire transport sector and compared to ICE vehicles. In the case of EVs that use electricity as fuel, the primary energy supply must be used as energy input to reflect conversion loss during power generation. The primary energy supply was calculated by applying the power generation efficiency of each country for use as an energy input to reflect conversion loss. EVs were more energy efficient than ICE vehicles in four out of six countries but less energy efficient in two countries. Based on the measured slack results, the potential energy savings were quantified, and the main factors in reducing energy efficiency were identified.

Development vs efficiency of Polish farms - trade-off or synergy effects?

he article aims to determine the nature of the relationship between farm development and its technical efficiency understood from the perspective of data envelopment analysis (DEA). The time scope of the analysis refers to the period 2004-2019. The empirical part of the article is based on the individual unpublished data for Polish farms conducting agricultural accounting according to Farm Accountancy Data Network (FADN). We employed a super-efficiency slack-based DEA model with variable returns to scale. This model enables us to compare and rank efficient farms as well as investigate the sources of farm (in)efficiency. We did not identify the substitution (trade-off) effect between farms` sustainability and efficiency. For level of technical efficiency and these differences were statistically significant. The main policy recommendation that can be derived from these results is that agricultural policy should support both efficiency improvements and progress toward higher sustainability.

Assessing the environmental efficiency of OECD countries through the lens of ecological footprint indices

Environmental efficiency plays a crucial role in achieving sustainable economic development. This study aims to enhance the current understanding of dynamic environmental efficiency by using Data Envelopment Analysis (DEA) in conjunction with the ecological footprint index. This study evaluates 27 OECD countries' environmental performance from 2000 to 2017, employing net capital stock, labor force, and energy consumption as inputs, ecological footprint as undesirable output, and GDP as desirable output. We utilize 16 window Slack-Based Measurement DEA (SBM-DEA) models, each representing consecutive years within the observation period. Additionally, we adopt the Global Malmquist-Luenberger Index (GMLI) techniques to facilitate a simultaneous evaluation of the efficiency levels for each country. Our findings reveal that the United Kingdom and Lithuania were the most and least ecologically efficient countries among the 27 OECD countries, respectively. Over the 18-year observation period, all countries showed both progress and setbacks in environmental efficiency, with a modest overall improvement. Poland, Denmark, Slovakia, and Lithuania were the most improved countries in environmental performance, while Canada and Japan showed the most significant regressions in environmental efficiency. We highlight the need for policymakers to prioritize sustainable economic growth and consider ecological footprints when making economic decisions to enhance environmental efficiency in OECD countries. Our findings have can guide policymakers in designing effective policies and strategies to enhance environmental efficiency and promote sustainable economic development.

A Model for Fire Departments’ Performance Assessment in Portugal

Fire departments’ performance assessment (FDPA) is an important task for fire protection service (FPS) authorities to evaluate fire departments’ (FDs) efficiencies, identify the efficient FDs, and identify areas for improvement of the inefficient units. Therefore, many countries all around the world conducted FDPA research and published its results. Although Portugal is a country with many old cities and buildings, to the best of our knowledge, no FDPA study has been conducted. Hence, the objective of this paper is to conduct a FDPA on Portuguese (PT) FDs using the general FDPA framework and slack-based data envelopment analysis. The results of analyzing the 376 PT-FDs’ data in 2020 showed that only 22 out of 376 FDs were efficient; in addition, in most districts in Portugal, less than 10% of FDs were efficient, and a high percentage of FDs had less than 50% efficiency. Further details and findings are discussed, and some recommendations are provided throughout the paper. The paper’s findings could help the FPS decision maker have a better view of the FDs’ performances and take corrective actions to improve efficiencies. It can also help the FDPA evaluator to have a better understanding of how a FDPA can be conducted and discussed.

Selecting slacks-based data envelopment analysis models

Data envelopment analysis (DEA) is a well-known data-driven mathematical modeling approach that aims at evaluating the relative efficiency of a set of comparable decision making units (DMUs) with multiple inputs and multiple outputs. The number of inputs and outputs (performance factors) plays a vital role for successful applications of DEA. There is a statistical and empirical rule in DEA that if the number of performance factors is high in comparison with the number of DMUs, then a large percentage of the units will be determined as efficient, which is questionable and unacceptable in the performance evaluation context. However, in some real-world applications, the number of performance factors is relatively larger than the number of DMUs. To cope with this issue, selecting models have been developed to select a subset of performance factors that lead to acceptable results.In this paper, we extend a pair of optimistic and pessimistic approaches, involving two alternative individual and summative selecting models, based on the slacks-based model. We mathematically validate the proposed models with some theorems and lemmas and illustrate the applicability of our models using 18 active auto part companies in the largest stock exchange in Iran.

Economic complexity and human development: comparing standard and slack-based data envelopment analysis models

Economic complexity and human development: comparing standard and slack-based data envelopment analysis models

Social Mobility as an Income Inequality Determinant

Abstract The paper focuses on the social mobility as the key driver of the income inequality. The aim of the paper is to contribute to the assessment of the social mobility and explore its links to the level of inequality and the age structure of the country. Employing composite social mobility indicator featuring benefit-of-the-doubt weighting principle, scores, ranks and benchmarks for 55 countries worldwide are determined by slack-based data envelopment analysis (DEA) reduced model. The proposed measure is compared to a regular Global Social Mobility Index (GSMI). Score and rank correlations reveal some significant differences although Denmark is robustly confirmed as a leader in social mobility worldwide. The suggested DEA-based approach proved more flexible as to determining benchmarks for underperforming countries. Second stage regression analysis reveals higher levels of income inequality for the countries of lower social mobility and with higher share of young people.

EVALUATING THE ENTREPRENEURIAL PERFORMANCE OF MICRO-ENTERPRISES IN THE BICOL REGION, PHILIPPINES

This article examines the entrepreneurial performance of micro-enterprises in the Bicol region, Philippines, using the 14 pillars of the Global Entrepreneurship Index. It also examines the effect of managerial efficiency on economic performance and the significant influence between the development index and economic performance. Micro enterprises contribute to economic development as they comprise the largest portion of the micro, small and medium enterprise sector and provide the biggest share of the country’s employment. However, micro-enterprises still face significant obstacles to growth. Using descriptive statistics, slack-based data envelopment analysis, bootstrap-bias corrected and accelerated regression, multivariate analysis, and hierarchical regression, the study found out that micro-enterprises usually practice opportunity perception, start-up skills, networking, cultural support, and high growth. Micro enterprises that showed strong development performance have managerial efficiency indicating no shortage in sales and their potential. Moreover, entrepreneurial activity stimulates attitude, aspiration, and monthly sales of micro-enterprises. It directly and highly affects sales because micro-enterprises always practice the pillars of technology absorption, opportunity start-up, competition, and human capital. This article illustrates a model suggesting that micro-enterprises should improve opportunity perception, start-up skills, risk acceptance, process innovation, high growth, and internationalization to improve sales. Furthermore, the study may provide tips for micro-enterprises in the Philippines and may be used as a basis for programs and policies to support their development.

Comprehensive efficiency evaluation of wastewater treatment plants in northeast Qinghai–Tibet Plateau using slack–based data envelopment analysis

Comprehensive efficiency analysis of wastewater treatment plants (WWPTs) in the alpine region with harsh environment and poor techniques as well as managing experience could provide targeted and effective improvement evidences for local wastewater treatment industry and help to improve the water quality of downstream reaches. In this paper, slack–based data envelopment analysis (SBM–DEA) was adopted to assess the operating efficiencies of WWPTs in northeast Qinghai–Tibet Plateau (QTP). Results showed that the average efficiency score for all WWPTs was 0.608, and 32.5% of WWPTs were efficient. Some WWPTs had large improvement potentials in operating costs and pollutant removal rates. Lowering expenditures and promoting facility construction for WWPTs to overcome the climate difficulties and improve management level was necessary according to their improvement potentials. In addition, the relative importance of the quantitative influential factors to efficiencies scores calculated by random forest regression (RFR) indicated that design capacity and temperature were important quantitative factors affecting the performance of WWPTs. Furthermore, geographical location and design capacity also had significant influence on the comprehensive efficiency of WWPTs verified by Kruskal–Wallis test. Our results highlight the importance of facilities upgrading, scientific management for WWPTs. And the relative improvement suggestions on overcoming the high and cold environment should also be considered for the efficient operations of WWTPs as well as the protection the aquatic environment.

How Efficiently Does the EU Support Research and Innovation in SMEs?

The European Regional Development Fund devoted around 66 billion Euros to the financial support of innovation and productivity in European enterprises over the 2014–2020 programming period. In this framework, we assessed the implementation of the Operational Programmes dedicated to fostering research and innovation, particularly in small and medium-sized enterprises. With this aim, we used a network slack-based data envelopment analysis model paired with cluster analysis that encompasses a multitude of performance framework indicators to assess 53 Operational Programmes from 19 countries. Our findings suggest that compared to transition and less developed regions, more developed regions present a higher room for improvement. Also, less developed regions present a better performance when they employ their funding against more developed regions, suggesting that further funding should be channelled for leveraging research and innovation in the former regions. Finally, Operational Programme managers should focus on solving the problems both inherent to the poor outcomes in terms of enhancing the number of researchers working in improved research infrastructures and promoting the technology transfer between research institutions and enterprises.

How Efficient Is the Cohesion Policy in Supporting Small and Mid-Sized Enterprises in the Transition to a Low-Carbon Economy?

Funds from the European Union that are devoted to fostering a low-carbon economy are aimed at assisting Member States and regions in implementing the required investments in energy efficiency, renewable energy, and smart distribution electricity grids, and for research and innovation in these areas. In this context, we assessed the implementation of these funds in small and medium-sized enterprises across different beneficiary countries and regions of the European Union. Therefore, this study uses a non-radial slack-based data envelopment analysis model coupled with cluster analysis that covers multiple aspects of evaluation, including two inputs and two outputs, to assess 102 programs from 22 countries. Overall, we were able to ascertain that there are 25 efficient operational programs that remain robustly efficient, whereas 51 remain robustly inefficient for data perturbations of 5 and 10%. Under the current output level, there was almost no input surplus. Therefore, to promote a low-carbon economy, operational program managers should concentrate on solving the problems behind the poor results achieved, both in terms of greenhouse gas emissions reduction and the pace of the programs’ implementation.