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In the nonparametric data envelopment analysis literature, scale elasticity is evaluated in two alternative ways: using either the technical efficiency model or the cost efficiency model. This evaluation becomes problematic in several situations, for example (a) when input proportions change in the long run, (b) when inputs are heterogeneous, and (c) when firms face ex-ante price uncertainty in making their production decisions. To address these situations, a scale elasticity evaluation was performed using a value-based cost efficiency model. However, this alternative value-based scale elasticity evaluation is sensitive to the uncertainty and variability underlying input and output data. Therefore, in this study, we introduce a stochastic cost-efficiency model based on chance-constrained programming to develop a value-based measure of the scale elasticity of firms facing data uncertainty. An illustrative empirical application to the Indian banking industry comprising 71 banks for eight years (1998–2005) was made to compare inferences about their efficiency and scale properties. The key findings are as follows: First, both the deterministic model and our proposed stochastic model yield distinctly different results concerning the efficiency and scale elasticity scores at various tolerance levels of chance constraints. However, both models yield the same results at a tolerance level of 0.5, implying that the deterministic model is a special case of the stochastic model in that it reveals the same efficiency and returns to scale characterizations of banks. Second, the stochastic model generates higher efficiency scores for inefficient banks than its deterministic counterpart. Third, public banks exhibit higher efficiency than private and foreign banks. Finally, public and old private banks mostly exhibit either decreasing or constant returns to scale, whereas foreign and new private banks experience either increasing or decreasing returns to scale. Although the application of our proposed stochastic model is illustrative, it can be potentially applied to all firms in the information and distribution-intensive industry with high fixed costs, which have ample potential for reaping scale and scope benefits.

Efficiency Analysis for Multicomponent Production Processes Conventional models concerned with efficiency analysis of organizations typically consider a single production process, or technology, in which all inputs are used in the production of all outputs. This approach does not account well for the situations in which the organizations are involved in several component production processes whose inputs and outputs may be shared by different processes. The main difficulty in modeling such technologies is the fact that we often do not know the exact allocation of the shared inputs and outputs to individual processes. In “Variable and Constant Returns-to-Scale Production Technologies with Component Processes,” V. V. Podinovski shows how this problem can be overcome by the consideration of the worst-case scenario for the allocation of the shared inputs and outputs to different components of the technology. This approach leads to the development of multicomponent variants of two well-established nonparametric models. An application involving universities in England demonstrates the usefulness and improved discriminating power of the new models compared with their conventional analogues.

Efficiency measurement has been a major indicator of the performance of a firm. In the study the level of efficiency of the steel firms operating in India was ascertained by adopting an input oriented data envelopment analysis (DEA). Twenty steel producing firms for the period 2018–19 was considered for the study. The study was conducted by using the input variables such as raw material cost, power and fuel cost, gross fixed assets, current assets, and the output variables considered to measure the efficiency was sales turnover. The result of the study indicates the efficient and less efficient firms with respective to their technical efficiency scores. It also indicates the bench marking firms. Out of twenty firms, eleven firms were on the efficient frontier and rest nine were below the frontier. For the relatively less efficient firm the slack and target values were calculated and further analysis was done to indicate the area of improvement for the inputs, which will result in improving the efficiency of the firms.

In Data Envelopment Analysis (DEA) the situation of inputs vs. outputs is positioned as cause and effect. Effects include desirable (ordinary) outputs and undesirable outputs, e.g. pollutants. This situation is well studied and many applications have been published. In this paper, we introduce a new type of inputs, called Good (Desirable) Inputs. As explained in Introduction, we find several examples of such inputs, e.g. Electric car, Women in office and Test takers of vaccine. We handle this by means of SBM (Slacks-based Measure). Usually, efficiency values of DEA models are in the range (0, 1], while in this model a negative efficiency value may be assigned to inefficient DMUs (decision making units). This is caused by shortages of Good Input values. As an example, we refer to “Women’s Rights Movements” in a country where women’s right is not fully guaranteed. Suppose local governments where men and women are serving as officers. They are inputs to office, while Women are Desirable input and Men are Ordinary input. As outputs, we assume Service as Ordinary output and Claim as Undesirable output. Several extensions of this model are introduced. (a) Variable returns to scale, (b) Weight restrictions, (c) Super-efficiency issue and (d) SBM_Max model., https://www.grips.ac.jp/list/jp/facultyinfo/tone_kaoru/

The property insurance industry grows fast in China and it is necessary to further investigate the profitability of the Chinese property insurance industry. This study investigates the evolution and determinants of the profitability of 53 Chinese property insurers during the year 2013–2017. Profitability is measured by profit ratio efficiency by data envelopment analysis (DEA) methodology and a profit ratio change index is applied to compare the performance of these insurers over different periods. Tobit regression models are used to investigate several influencing factors of profitability. The empirical results show the importance of proper arrangement of costs and revenues for an insurer and help to better understand the effect of firm size, age, and product specification on profitability. Some policy implications and suggestions are also proposed.

The objective of this study is to measure the energy efficiency and energy security by using Data Envelopment Analysis (DEA) and an econometric estimation such as ordinary least square method (OLS) to measure the relationship between energy efficiency, energy security and economic development with macro-economic indicators such as energy consumption, economic growth, and environmental degradation factors by using the data from 1976 to 2016 while the energy efficiency has been measured during the period of 2010 to 2018. Results show that Brazil and Russia are countries with less energy for these consecutive years. This work contributes to the existing literature on eco-friendly and sustainable policy design in BRICS countries based on multiple indicators. The analysis also indicates that the quality of a country’s laws and regulations are essential for expanding research on renewable energy because the right policy tools serve as the basis for the transition. It is also found that Brazil, Russia, and South Africa have the best score in terms of energy and economic development while China and India are among the lowest performing countries in clean energy. Energy efficiency results show that china has the highest score of 1 while India and South Africa energy score is about 0.623 and 0.64 respectively. This serves as a panacea to study the country’s energy insecurity and bridge the gap in the literature. As the renewable energy industry is considered a high-risk area, it is necessary to develop essential aversion tools for financial policy risks to attract private capital.

The aim of this paper is to measure the relative efficiency of commercial banks in two developing countries, the Republic of North Macedonia and the Republic of Croatia under the operating (incomebased) approach by using the leading non-parametric methodology data envelopment analysis (DEA). We follow Banker et al. (2010) in the selection of the approach, variables (two inputs: interest expense and other operating expense, and two outputs: interest revenue and other operating revenue) and the model (output-oriented BCC DEA model) as in their first stage. The observed period is five years (2015-2019) and we use a balanced panel data for both samples (total of 65 Macedonian and 100 Croatian bank-year observations). Outliers are identified and excluded by using the Banker and Gifford (1988) super-efficiency procedure, and the BCC output-oriented model is rerun for both samples (total 55 Macedonian and 95 Croatian bank-year observations). We provide relative efficiency scores for each bank in both sectors, as well as an average score for the banking sectors. In addition, we analyse few banks for both sectors that have decreased or increased the efficiency, or show variable results over time. Besides, we explain how inefficient banks can improve the efficiency in future by setting targets for improvement. Our study provides valuable information for banking management and regulatory bodies.

Data envelopment analysis (DEA) is a useful tool for measuring the relative efficiencies of participating nations in the Olympic Games. DEA models with restricted multipliers have been used to refine efficiency evaluations by imposing additional information. Existing DEA models for evaluating Olympic medals do not focus on multiplier restrictions regarding input. To fill this research gap, this study incorporates a data fitting technique of medal prediction using ordinary least squares regression in input multiplier restrictions of the conventional DEA model. We show that the efficiency of the proposed model can be decomposed into the achievement ratio of substantial medal total and the unit value index of medals. Such decompositions can be used to analyze the effectiveness of host nations and athlete development initiatives. For an illustrative empirical application, we examine the target that the Brazilian Olympic Committee (BOC) set for the 2016 Summer Olympic Games (Rio 2016). Our results explain the extremely high feasibility of Brazil’s target of being in the top 10 medals table in Rio 2016.

Ghasemi, Ignatius, and Rezaee (2019) (Improving discriminating power in data envelopment models based on deviation variables framework. European Journal of Operational Research 278, 442– 447) propose a procedure for ranking efficient units in data envelopment analysis (DEA) based on the deviation variables framework. They claim that their procedure improves the discriminating power of DEA and can be an alternative to the super-efficiency model that is well-known to have the infeasibility problem and the cross-efficiency approach which suffers from the presence of multiple optimal solutions. However, we demonstrate, in this short note, that their procedure is developed based upon inappropriate use of deviation variables which leads to the development of a ranking approach that does not meet their expectations and as a result, an unreasonable ranking of decision making units (DMUs). We also show that the use of deviation variables, if interpreted and used correctly, can lead to developing a cross-inefficiency matrix and approach.

The research aims to apply the data envelope analysis method to measure the efficiency of Iraqi government banks for the period (2010-2019) and to implement the objectives of the research, the method of data envelope analysis was chosen on (7) government banks for the period (2010-2019), this research means measuring the efficiency of the Iraqi banking system The research reached a main conclusion that (6) banks did not achieve efficiency, namely (Al-Rafidain Bank, Al-Rasheed Bank, Agricultural Bank, Industrial Bank, Real Estate Bank, Trade Bank of Iraq) and one bank that achieved efficiency during the period 2010-2019, which is (Al-Nahrain Bank Islamic(. Therefore, the research recommends Iraqi government banks to work to raise their efficiency by modernizing banks to strengthen their financial positions, and to provide new banking services to face intense competition in light of the liberalization of banking services trade, and the research recommends the necessity of financing provided by government banks to achieve economic development.

In general, each country continuously focuses on improving the performance of the education system. Our study provides suggestions regarding the improvement by taking the data in the Indian context using data envelopment analysis (DEA). To the best of our knowledge, our paper is the first study which is using the National Institutional Ranking Framework (NIRF) data. In this study, we consider 61 educational institutions of India to rank them according to their performances. The ranking is done on the basis of the efficiency scores of the institutions. To rank the efficient institutions super-efficiency DEA model is applied. Finally, some suggestions and concluding remarks are given for inefficient institutions to make them efficient.

The traditional markets in Salatiga City are managed by the Unit Pelaksana Teknis Dinas (UPTD) Dinas Perdagangan. UPTD Dinas Perdagangan policy in Salatiga City is free rental fees for 3 years for newly built traditional markets. The objective of this study was to analyze the comparison of the level of efficiency of market management at the UPTD Dinas Perdagangan. The data used in this research are primary and secondary data obtained from the UPTD and Dinas Perdagangan Salatiga City. The method used in this study was a quantitative research method with the tools used is the Data Envelopment Analysis model of the Charnes, Cooper, dan Rhodes (CCR) dan Banker, Charnes, dan Cooper (BCC). The variable that is regulated in the research is the efficiency value of the UPTD Class A and B Dinas Perdagangan. The independent variables in the study included 2 variables, namely the input and output variables. Based on the analysis, the results of this study reveal that the two UPTDs have a balanced efficiency in their management and both UPTD Class A and Class B still make the development of traditional markets become in harmony with each other. In addition, market revitalization managed by UPTD for Class A and ClassB has been developing. The research suggestion is to maintain the results that have been achieved by the two UPTD Dinas Perdagangan. Both UPTDs have had innovations in managing traditional markets that have included the appropriate target market environment.

Energy quota trading (EQT) is a market-driven method to reduce distortions in energy allocation. In 2016, an EQT policy was proposed in China. However, the economic and environmental effects of this new policy remain unclear. To study these problems, this paper conducts an empirical analysis using data envelopment analysis (DEA) and index decomposition analysis (IDA). Following a simulation of the optimal energy input and maximum output in three sectors of the 30 Chinese provinces that have adopted China's EQT policy, the main conclusions are as follows: (1) The energy consumption of the industrial sectors is excessive, yet the primary and service sectors need further energy input, while there is vast economic potential in China's western provinces and the service sectors. (2) The optimal energy intensity effect generated by the trading policy is the largest impetus of carbon intensity reduction in China, but the obstacles to carbon intensity reduction are different in each region. (3) EQT has the potential to drive a significant decline in carbon intensity in China, resulting in a simulated average annual decline of approximately 4.09% from 2005 to 2016.

This paper applies the additive relational network DEA model to examine the multi-period efficiency and productivity of Regional Banks I and II in Japan between 2002 and 2017. The examined timeframe covers two turbulent periods; the Global Financial Crisis and the economic recession in the aftermath of the Great East Japan Earthquake. We extend the additive relational network DEA model into a multi-period structure in order to evaluate the overall efficiency for the time period in question, as well as the annual period efficiencies. We show that the overall efficiency can be expressed as a weighted average of the period efficiencies. In addition, we examine the implications of different returns to scale assumptions. The newly proposed model is able to calculate common-weight Malmquist Productivity Indices. The results reveal a dispersion of inefficiency levels for Japanese Regional Banks in general, and a difference between Regional Banks I and II in particular. Furthermore, the Global Financial Crisis caused a significant negative effect on the productivity growth and the technical progress of Japanese Regional Banks, while the Great East Japan Earthquake had a negative effect on the technical efficiency change.

This paper develops a novel methodology to estimate in a dynamic setting the causal impact of a policy on efficiency. Classical efficiency techniques evaluate multidimensional performance but ignore the endogeneity issues in policy evaluations. We develop an indicator which accounts for the dynamic performance of the observations and for the possible correlation between the treatment status and the efficiency score. Besides, we propose a decomposition of the indicator to disentangle the effect of the policy on the performance of the observations from the effect of the policy on the environmental harshness that the observations have to face. This innovative design allows us to introduce the notion of causality in efficiency studies and to shed light on the mechanisms underlying the inefficiency at the unit and policy level. In the application, the present study assesses the impact on the efficiency of a funding program that aims to foster educational equality.

The paper aims at the evaluation of efficiency in sports. Many articles are dealing with the application of data envelopment analysis (DEA) models in this area. They are mainly oriented on efficiency evaluation of teams and not the individual players. On the contrary, the main aim of this paper is to combine both approaches and investigate the relation between individual efficiency of the players and the efficiency of the teams. The first step is the evaluation of individual efficiencies, and the second one is its aggregation into the teams' performance within a competition (League). The idea is to evaluate the efficiency of individual players in certain positions and explore how the individual efficiencies contribute to the efficiency of the teams. Individual efficiency is measured using traditional radial and slacks-based measure DEA models. Team efficiency is derived in several ways—traditional DEA models with the variables describing the true achievements of the teams, parallel DEA models that consider all positions and players, and actual results of the teams in the League, which is the true performance of the team. The study is based on the Canadian-American National Hockey League (NHL) statistics in 2019/2020. The results of the analysis are compared and discussed. They show that the true performance of the team is not always directly dependent on individual performances of the members of the team.

This paper first gauges the level of firm efficiency using the Stochastic Nonparametric Envelopment of Data (StoNED) approach. Our firm efficiency score closely reflects a firm’s actual operating conditions when using the statistical foundations of both Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis. Secondly, we estimate the nonlinear effects of intellectual capital on StoNED-based firm efficiency using the Generalized Additive Model (GAM). This model lets us depict the possible nonlinear relationship between explanatory variables and the explained variables in an additive manner. Our analysis of 1898 firm-year observations for U.S.-listed firms from 1999 to 2019 indicates that (i) our sample firms generally have about 65% of room left for improvement that could transform resources into wealth, and (ii) of the three major components of intellectual capital, human capital exhibits a concave-up curve, while structural capital and relational capital both demonstrate an upward trend, with each having an inflection in the middle of that curve. The GAM results remain qualitatively similar even after we re-estimate firm efficiency using the network slacks-based measure DEA model, and (iii) we discuss these comparisons and the respective implications of the three components.

Most data envelopment analysis (DEA) studies on scale elasticity (SE) and returns to scale (RTS) of efficient units arise from the traditional definitions of them in economics, which is based on measuring radial changes in outputs caused by the simultaneous change in all inputs. In actual multiple inputs/outputs activities, the goals of expanding inputs are not only to obtain increases in outputs, but also to expect the proportions of such increases consistent with the management preference of decision-makers. However, the management preference is usually not radial changes in outputs. With the latter goal into consideration, this paper proposes the directional SE and RTS in a general formula for multi-output activities, and offers a DEA-based model for the formula of directional SE at any point on the DEA frontier, which is straightforward and requires no simplifying assumptions. Finally, the empirical part employs the data of 16 basic research institutions in Chinese Academy of Sciences (CAS) to illustrate the superiority of the proposed theories and methods.

A better understanding of regional differences in green development (GD) is crucial to achieving the sustainability goals in China's high-quality development stage. Data envelopment analysis–based productivity indexes, which link multiple inputs and multiple outputs, have been widely used to evaluate GD. However, most of these models do not take into account the imprecision of regional economic data due to over- or under-reporting by local governments. Here, to alleviate the GD calculation errors due to point estimation as far as possible, we propose a novel global Malmquist–Luenberger productivity index with the interval slacks-based measure to evaluate the provincial green total factor productivity (GTFP) in China from 2000 to 2018. The results showed that the overall GTFP in China during the study period exhibits an increasingly upward trend, accompanied by a declining and converging growth rate in recent years. Moreover, we found an emerging shift in the patterns of inter-regional GTFP from “East–Center–West” to “South–North”, as the differences in the GTFP between the eastern, central and western regions shrink while the gap between the southern and the northern regions expands. A further impact mechanism analysis based on dynamic spatial Durbin panel models revealed that the green development mode in the central, western and northern regions is more exogenously government-dependent, whereas that in the eastern and southern regions is more endogenously market-driven. These findings provide a new reference for optimizing the relationship between the market and government and coordinating the regional green development of China.

Purpose As returns to scale (RTS) describes the long run connection of the changes of outputs relative to increases in the inputs, the purpose of this study is to answer the following questions: If the proportionate changes exist in the inputs, what is the rate of changes in outputs with respect to the inputs’ variations in the two-stage networks over the long term? How can the authors investigate quantitative RTS in the two-stage networks? In other words, the purpose of this research is to introduce a different approach to estimate the performance, RTS and scale economies (SE) in network structures. Design/methodology/approach This paper proposes a novel non-radial approach based on data envelopment analysis to analyze the performance and to investigate RTS and SE in two-stage processes. Findings The findings show that the range adjusted measure (RAM)/RTS approach can identify reference sets for overall systems and each stage. In addition, the models presented in this paper can classify decision-making units and determine the increasing/decreasing trends of RTS. Originality/value The majority of previous RTS studies have been examined in black-box structures and have been discussed in a radial framework. Therefore, in this study, RTS and SE in the two-stage networks are dealt with using an extended RAM approach. Actually, the efficiency and RTS for each stage and the overall model are calculated using the proposed technique.

The aims of this paper is to analyze the effect of access to credit on the technical efficiency of farms in Cameroon’s rural area. Using a sample of 545 farm households, we first estimate a Data Envelopment Analysis (DEA) model with constant returns to scale; then a censored TOBIT model enabling us to identify factors of efficiency, especially the effect of access to credit on efficiency. Two main results emerge from our analysis. First, we find that on average, the level of technical efficiency of farms is 56.78%; showing therefore the possibility of substantial efficiency gains. Second, farm size, association membership, and fertilizer expenditure negatively affect technical efficiency, while access to credit, age and education increase it. Based on these results, we believe that it’s interesting for farm householders to organize themselves in associations to benefit from available credits and financial facilities and to share their experiences in the agricultural field in order to improve their efficiency.

PurposeThe purpose of this paper is to assess the efficiency determinants of mergers and acquisitions (M&A) in the context of Latin American airlines based on business-related variables commonly found in the literature. The idea is to identify preferable potential airline matches in light of fleet mix, ownership structure and geographical proximity.Design/methodology/approachIn order to achieve the objective, all possible combinations of M&A pairs are considered in the analysis, which is developed in a two-stage approach. First, the M&A Data Envelopment Analysis model efficiency and returns-to-scale estimates are computed. Then, robust regression and multinomial logistic regression are respectively used to discriminate these estimates in terms of such business-related variables.FindingsThe results reveal that these different contextual variables significantly impact virtual efficiency and returns-to-scale levels. Private ownership, passenger focus and a better match between aircraft size and demand for flights appear to be key drivers for merged airline efficiency.Research limitations/implicationsThe study makes theoretical contributions, though limited to analyzing Latin American airlines only. The use of bootstrapped robust/multinominal logistic regression, compared to the methods adopted by previous literature studies, generates more accurate and robust results related to the efficiency drivers due to its special feature and ability to allow the discrimination of increasing, decreasing, and constant returns to scale in light of a given set of contextual variables.Practical implicationsThis study examines the pure effect of the merging activity on efficiency gains. Not only private ownership but also a hybrid public–private ownership has a positive influence on virtual efficiency, suggesting an important governmental role in promoting M&A in the airline industry.Originality/valueThe authors present an original take on the issue of airline mergers by exploring what are the major drivers possibly involved in efficiency gains of potentially merged (virtual) airlines. The authors identify preferable potential airline matches where efficiency gains would be positive in light of business-related variables such as fleet mix, ownership structure and geographical proximity. The analysis also includes an assessment of the impact of contextual variables such as cargo type, ownership structure and geographical proximity in relation to the strategic fit of mergers considering the resulting efficiency and returns-to-scale scores of virtually merged airlines. To the authors’ knowledge, no previous research has addressed these issues in Latin American airlines. Further research directions for this industry are also discussed.

This study aims to estimate the technical efficiency of food and beverage industry in East Java in 2011 to 2013 using micro data at the company level. Stochastic Frontier Analysis (SFA) and Data Envelopment Analysis (DEA) are used to estimate technical efficiency. The output variable was the value of production, while input variables were capital, labor, raw material, and energy. The Likelihood Ratio test dictates that the Translog production function is more appropriate for use in this study. The estimation results show that the efficiency of food and beverage companies in East Java by using SFA has decreased significantly by 3.02%, whereas with the DEA method, the average technical efficiency has increased by 0.583% compared to the beginning of the year in 2011. In addition, there is difference in the efficiency value between SFA and DEA. The technical efficiency value of SFA calculation is greater than that of DEA. The dissimilarity is caused by the difference of specification in both methods related to the interaction between uncaptured variables in the DEA method. The results of this policy have implications on the government's obligation to pay attention to the food and beverage industry in order to suppress the company’ various operating costs, such as maintenance for old machines, which has an impact on on technical efficiency or improve the ability of labor in terms of machinery utilization. Therefore, in the following year, the performance of the food and beverage industry as the largest sub-sector in manufacturing is able to show the progress.

Measures of capacity utilization and capacity output are important metrics for evaluating firm performance. This is true whether capacity is considered from a cost accounting framework, an engineering standpoint, or an economics orientation. Ultimately, the crux of the capacity question is about resources not being used to their fullest extent. In an economics context, understanding where firms are producing on their average cost curve provides information about whether capacity utilization is greater than, less than or equal to unity. In real life, most firms are multi-output, multi-input in nature which makes estimation of capacity utilization and capacity output challenging if a cost based measure is desired. This is further complicated in a short run analysis incorporating multiple outputs and multiple fixed inputs where the relevant concept is the minimum of ray average cost. This paper develops appropriate mathematical programming models for measuring capacity utilization in the short-run for multiple-outputs and multiple fixed inputs. We offer a simple transformation that linearizes the non-linear DEA problem to estimate average, or ray average cost, and to determine capacity utilization and optimal output. The models are empirically tested on data from a number of U.S. electricity producers for the single output case, and a sample of dental practices for the multi-output case. Results show that for both industries, most firms were operating at less than full capacity, and needed to expand output to minimize their average costs.

Purpose – The purpose of this study was to analyze the effect of Bank Age, Loan to Assets Ratio (LAR), Net Interest Margin (NIM), and non-interest margin (Non NIM) on bank efficiency, especially in ASEAN-5 countries (Indonesia, Singapore, Thailand, Malaysia and Philippines). In this research, bank size was used as a control variable. Design/methodology/approach – The sample of the research was taken form general banking entities listed in each country over the period of 2014-2018. Purposive sampling method was used to select 58 banks as the sample population. We use two-stage methodology in this research by using Data Envelopment Analysis (DEA) to calculate bank efficiency and Multiple Regression Analysis (MRA). Findings- The result showed that capitalization and bank age had a negative significant effect on bank efficiency, while the variable of Loan to Asset Ratio (LAR), Net Interest Margin (NIM) and No-NIM had a positive significant effect on bank efficiency. Originality/value – The measurement of bank efficiency was conducted by utilizing DEA in this study to examine the bank efficiency in ASEAN 5.

This study measures the technical efficiency score of the manufacturing sector of the palm oil products-based subsector in Malaysia and investigates the major determinants that influence efficiency. Based on the Industrial Manufacturing Survey, this study explores the data from 2000 to 2015 over sixteen years, with a total of eleven subsectors of palm oil products-based. The first stage of the study is carried out with a data envelopment analysis (DEA) to calculate the technical efficiency score, which is considered a dependent variable. The second stage of the study uses a panel regression model to examine the determinants of efficiency comprise the variable capital-labour ratio, research and development, information communication technology, training, and skilled labour. The study findings show that most of the palm oil products-based subsectors are not operating efficiently. Skilled labour, technical and supervisory, and professional is one of the main determinants contributing to the efficiency level. The variable capital-labour ratio though significant, but harms the efficiency level. The moderating effects show that skilled technical and supervisory workers relatively affect the food industry’s efficiency larger than the non-food industry. Therefore, the industry still has room to improve efficiency by utilising input efficiently. Moreover, the efforts involve organisational management, equipped appropriate technology and related factors that will improve efficiency, increase productivity and competitiveness of palm oil products-based industries.

PurposeThe Indian power sector is dominated by coal. Environmental awareness and advances in techno-economic front have led to a slow but steady shift towards greener alternatives. The distributions of both fossil fuel resources and renewable energy potential are not uniform across the states. Paper attempts to answer how the states are performing in the sector and how the renewable energy and conventional resources are affecting the dynamics.Design/methodology/approachThe authors employ a two-stage data envelopment analysis (DEA) to rank the performance of Indian states in the power sector. Multi-stage analysis opens up the DEA black-box through disaggregating power sector in two logical sub-sectors. The performance is evaluated from the point-of-view of policy formulating and implementing agencies. Further, an econometric analysis using seemingly unrelated regression equations (SURE) is conducted to estimate the determinants of total and industrial per-capita electricity consumption.FindingsEfficiency scores obtained from the first phase of analysis happens to be a significant explanatory variable for power consumption. The growth in electricity consumption, which is necessary for economic wellbeing, is positively affected by both renewable and non-renewable sources; but conventional sources have a larger impact on per-capita consumption. Yet, the share of renewables in the energy mix has positive elasticity. Hence, the findings are encouraging, because development in storage technologies, falling costs and policy interventions are poised to give further impetus to renewable sources.Originality/valueThe study is one of the very few where entire spectrum of the Indian power sector is evaluated from efficiency perspective. Further, the second phase analysis gives additional relevant insights on the sector.

This paper provides evidence regarding the impact of soft information on foreign bank efficiency in the ASEAN-5 market in the years following the Asian crisis. The analysis presented here develops previous literature by disaggregating soft information into five difference types and examining whether institutional quality (proxied for by host-country economic risk stability index) mitigates the impact. Using a three-stage estimation process we find that soft information does indeed affect foreign banks’ efficiencies in an identifiable manner, but host-country institutional quality acts so as to significantly lessen the effect.

In banking system the evaluation of productivity and performance is the key factor among the fundamental concepts in management. For identify the potential performance of a bank efficiency is the parameter to evaluate effective banking system. To measure the efficiency of a bank selection of appropriate input-output variables is one of the most vital issues. The suitable identification of input-output variables helps to create and identify model in order to evaluate the efficiency and analysis. The Data Envelopment Analysis (DEA) is a mathematical approach used to measure the efficiency of identified Decision Making Units (DMUs). The DEA is a methodology for evaluating the relative efficiency of peer decision making units of identified input/output variables for the financial year 2018-19. In this study the basic DEA CCR, BCC models used for measure the efficiency of DMUs. In addition to these models for minimize the input excess and output shortfall Slack Based Measure (SBM) efficiency used. The SBM is a scalar measure which directly deals with slacks of input, output variables which help in obtain improved efficiency score compare with previous model. The result from the analysis is

The article is devoted to the implementation of nonparameterized method of data envelopment analysis (DEA) to the world’s economy as an array of countries with different levels of technological activity. The main purposes of this research are as follows: systematical presentation of methodological foundations of international technology frontier concept, presentation of the main controversial problems of this concept, presentation of the possible future development tendencies. Both the research itself and presented research materials are based on geometrical approach – plane geometry with the elements of projective geometry. This has allowed us to obtain important theoretical results. Firstly, the possibility of “subsequent” usage of DEA method to any data that remain after the exclusion of points with the highest relative efficiency has been illustrated. In relation to the subject at hand this has made it possible to offer the concept of world’s technological frontier with different subtypes. Countries within specific frontier form sequential groups from the most efficient to the least efficient ones. Secondly, the foundations of a new international technological frontier model (ITF model) of virtual countries have been presented. This model functions are the following: after determining the countries with the highest efficiency level, the frontier line modifies itself in a way that allows all country-leaders to move from an angular position to an intermediate position on the modified ITF section. These new sections named “national leaders’ sections” are interpreted as specific production functions that reflect conditions of ITF “neighboring” countries. Virtual countries instead of the real ones become angular points in the model. Based on the model of virtual countries, the known paradoxes, such as the paradox of equal efficiency of lateral frontier sections (which is immanent to any model based on the classical DEA method) and the paradox of technological regression arising from the intersection of ITF lines of different periods, are solved. Thirdly, based on the simultaneous analysis of countries’ states on two sections – the “capital and labor intensity” section and the section of manufacturing function parameters – the method of ITF composition as an envelopment of national manufacturing functions has been created. Comparative analysis of ITF models created with the help of this method and the ITF models based on traditional method of national technological state envelopments has been made.

In this study, we develop multivariate returns to scale (MRTS) and illustrate the advantages of MRTS over the existing standard returns to scale (RTS) such as: constant RTS (CRS), variable RTS (VRS...

Financial development of a country is a broad concept and can be measured in different ways. The article presents the indicators of financial development applicable for the development of the Financial Development Index (FDI) of a particular country. Financial development index could be used to rank and compare the level of financial development among countries or to test the relationship between financial development and economic growth. The paper points out the advantages and limitations of several methods including standardized method, Principle Component Analysis (PCA) method, Data Envelopment Analysis (DEA) method. The author also suggests to use Data Envelopment Analysis as an updated and advanced method to construct Financial Development Index. It is expected that this method will strengthen the quantitative approach in the research of financial development and economic growth.

Highlights • We develop nested dynamic network DEA models to measure a portfolio's efficiency. • We measure the multi-period efficiency of a portfolio and its comprised assets. • Target portfolio is benchmarked against all other possible portfolios. • A CVaR optimization problem is included. • A two-stage method is proposed to help investors construct their desired portfolios., Portfolio performance evaluation is a major data envelopment analysis (DEA) application in the finance field. Most proposed DEA approaches focus on single-period portfolio performance assessment based on aggregated historical data. However, such an evaluation setting may result in the loss of valuable information in past individual time periods, and violate real-world portfolio managers’ and investors’ decision making, which generally involves multiple time periods. Furthermore, to our knowledge, all proposed DEA approaches treat the financial assets comprising a portfolio as a “black box”: thus there is no information about their individual performance. Moreover, ideal portfolio evaluation models should enable the target portfolio to compare with all possible portfolios, i.e., enabling full diversification of portfolios across all financial assets. Hence, this research aims at developing nested dynamic network DEA models, an additive model being nested within a slacks-based measure (SBM) DEA model, that explicitly utilizes the information in each individual time period to fully and simultaneously measure the multi-period efficiency of a portfolio and its comprised financial assets. The proposed nested dynamic network DEA models, referred to as NDN DEA models, are linear programs with conditional value-at-risk (CVaR) constraints, and infinitely many decision making units (DMUs). In conducting the empirical study, this research applies the NDN DEA models to a real-world case study, in which Markov chain Monte Carlo Bayesian algorithms are used to obtain future performance forecasts in today's highly volatile investment environments.

This paper analyzes the efficiency of Badan Aml Zakat Nasional (BAZNAS) and Dompet Dhuafa from 2002 to 2018. Based on selected input and output, the intermediary approach assumes that BAZNAS and Dompet Dhuafa act as a link between muzakki (giver) and beneficiaries. Furthermore, BAZNAS and Dompet Dhuafa were selected as decision-making units (DMU) from 2002 to 2018, and their efficiency was measured using Data Envelopment Analysis (DEA) method under output-orientation with Constant Return to Sclae (CRS) and Variable Return to Scale (VRS) assumptions. The results showed both BAZNAS and Dompet Dhuafa raise the optimum efficiency in the years before 2007. Meanwhile, their inefficiency was mostly due to lack of input such as higher personalia (amil/volunteers) expenses. Therefore, these findings suggests that both technical and scale efficiency should be improved by adjusting the input. This is to achieve the most efficient and productive level of performance in order to fulfill the institutions' objectives as an intermediary between muzakki and the beneficiaries. This paper is among the pioneers that analyzed the efficiency of zakat institutions from their initial establishment to present. Also, existing papers examined data spanning 5 years or less. Hence, long duration of data analysis provides a comprehensive evaluation of fluctuations in the zakat institutions efficiency and their supporting or inhibiting factors.

Professional teams are commercial and recreational organizations, and team managers always set their goals to be playing well and benefitting more in a highly competitive environment. In order to measure the ability of the professional teams to make reasonable use of resources and create various outputs, this study employs the Data Envelopment Analysis (DEA) model to measure the efficiencies of 30 Major League Baseball (MLB) teams. The results showed that the inefficiencies were due to pure technical inefficiencies rather than scale effects, and the scale efficiency on average is more higher than the other efficiencies, applying the managers in the Major League Baseball Teams have higher ability of controlling the scale change. Keywords: Major League Baseball, Data Envelopment Analysis, Technical efficiency, Pure technical efficiency, Scale efficiency.

PurposeThis study used data envelopment analysis (DEA) models to measure financial efficiency of twelve commercial banks listed in the Egyptian stock exchange (CBLSE), along with evaluating changes to the financial efficiency during the period 2017–2019.Design/methodology/approachThe study used BCC-I, cross-efficiency, super-efficiency models, and Malmquist productivity index (MPI) to assess financial efficiency of the examined banks. The available data from both inputs and outputs were analyzed using R. studio V.I.3. 1056 software.FindingsOut of twelve banks examined, only four banks were efficient under BCC-I model over different years of the study period; however, only one bank (CIB) appeared to be the most efficient compared to other peers in the study sample. Moreover, MPI results revealed decreased financial efficiency during the study period, due to the decreased technological innovation, except for HDB. Tobit regression results confirmed that total assets and total equity are significant factors impacted financial efficiency of CBLSE.Practical implicationsThis study sheds light on the importance of evaluating financial efficiency of CBLSE to all stakeholders, to pinpoint weaknesses in banks' performance, and for evaluating financial policies and investment decisions.Originality/valueSeveral studies sought to implement different models of DEA to assess banking performance in different regions of the world, but very few studies examined financial efficiency of banks. To the best of authors’ knowledge, this study is one of those few that addressed financial efficiency of banks in Egypt.

The traditional data envelopment analysis (DEA) model usually ignores the influence of external environmental factors and random interference. This can easily lead to deviations in efficiency estimates. In order to solve this problem, a three-stage DEA model was used to better reflect the carbon emission efficiency of Chinese construction industry (CEECI) (2006–2017) from the perspective of non-management factors. The internal influencing factors of CEECI are analyzed by the Tobit model, which provides a more accurate basis for formulating policies. It is found that the CEECI is significantly affected by the GDP, the level of industrialization, the degree of opening-up, technological innovation and energy structure. After excluding environmental factors and random interference, the average CEECI increased by 16%. The resulting calculations were noteworthy in three aspects. First, there are significant regional differences in the CEECI. Both the multi-polarization phenomenon of CEECI and regional differences also reduced gradually over time. Second, the CEECI can be decomposed into pure carbon emission efficiency (PCEE) and scale efficiency (SE), which is mainly caused by SE. Excluding external environmental factors and random interference will have a specific impact on the CEECI. All the 30 provinces are divided into four categories to analyze the reasons and solutions of the differences in the CEECI in provinces. Third, many factors had inhibitory effects on the CEECI, PCEE and SE; these included energy structure optimization, labor force number, total power of construct ion equipment and construction intensity in the construction industry. Nevertheless, the development level of the construction industry did have a significant positive effect.

The efficiency of banks has a critical role in development of sound financial systems of countries. Data Envelopment Analysis (DEA) has witnessed an increase in popularity for modeling the performance efficiency of banks. Such efficiency depends on the appropriate selection of input and output variables. In literature, no agreement exists on the selection of relevant variables. The disagreement has been an on-going debate among academic experts, and no diagnostic tools exist to identify variable misspecifications. A cognitive analytics management framework is proposed using three processes to address misspecifications. The cognitive process conducts an extensive review to identify the most common set of variables. The analytics process integrates a random forest method; a simulation method with a DEA measurement feedback; and Shannon Entropy to select the best DEA model and its relevant variables. Finally, a management process discusses the managerial insights to manage performance and impacts. A sample of data is collected on 303 top-world banks for the periods 2013 to 2015 from 49 countries. The experimental simulation results identified the best DEA model along with its associated variables, and addressed the misclassification of the total deposits. The paper concludes with the limitations and future research directions. - The National Research Center of Lebanon. - The University Research Board of the American University of Beirut