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The different sectors of the livestock industry play an important role in meeting the increasing demand for animal products in both Hungary and Croatia. The general objective of this study was to investigate and compare the efficiency of the livestock sectors in Croatia and Hungary using the Data Envelopment Analysis method. In this paper, the technical efficiency of the dairy, beef, broiler, sheep and goat, and pig sectors from 2014 to 2017 is investigated based on the Farm Accountancy Data Network database. This paper compared farms of different size classes based on the Standard Production Value for more detailed insights. Among the five main livestock sectors, Hungary performs better in terms of farm technical efficiency in the dairy and beef sectors, while Croatia has higher efficiency scores in the sheep sector. The performances in the pig and broiler sectors are almost the same in both countries. Moreover, in the Hungarian poultry, pig, sheep and goat sectors and the Croatian dairy, beef and pig sectors, the technical efficiency of small-sized farms is better than that of medium-sized farms.

One of the attractive issues in Data Envelopment Analysis (DEA) literature is to find the anchor points of the production possibility set (PPS). Each extreme efficient unit which is located on the intersection of the strong and weak efficient frontiers of the PPS, is called an anchor point. In the other word, a decision making unit (DMU) is an anchor point, if there is at least one supporting hyperplane at the unit under consideration, in the situation that some components of its gradient vector are equal to zero, and so some input or output factors do not play any role in the performance of that unit. This study presents a new method to identify the anchor points of the PPS under the variable returns to scale (VRS) assumption and in the presence of the uncertain data. The proposed method is based on the robust optimization technique and finding the weak and strong defining supporting hyperplanes passing through the unit under evaluation. The potentially of the proposed method is illustrated by a data set, includes 20 banks in Iran. [ABSTRACT FROM AUTHOR]

One of the main important issues in Data Envelopment Analysis (DEA) is to recognize the set of anchor points which is the subset of the extreme efficient points of the production possibility set (PPS). An anchor point is an extreme efficient point which is located on the intersection of the strong efficient frontier and the weak efficient frontier. In the other word, each anchor point delineate the strong efficient frontier from the weak efficient frontier. So, if a decision making unit (DMU) is an anchor point, then there is at least one supporting hyperplane whose the gradient vector has some of zero components, and so some input\output factor does not play any role in the performance of the unit under evaluation. The concept of anchor point was used in DEA for the generation of unobserved DMUs in order to extend the DEA efficient frontier and so, this concept plays a critical role in the DEA theory and its applications. Given the importance of the anchor pints in the DEA literature, this study focuses on finding the anchor points and presents a new method to search the anchor points of the PPS under the variable returns to scale (VRS) assumption. For this purpose, we use the definition of the anchor points and present an approach to find the anchor points of the PPS. The proposed method is based on finding the weak and strong defining supporting hyperplanes passing through the unit under evaluation. The main advantage of the proposed method is that it exactly uses the definition of the anchor points to provide the approach and it is very simple to use and the anchor points can be easily identified by solving two simple models. In addition, the proposed approach is such that in addition to determining the anchor points, it also finds two important defining supporting hyperplanes on the PPS, which can be used in many problems in DEA. The potentially of the proposed method is illustrated by some numerical examples, reported in the literature to compare the proposed method with the existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central European Agriculture is the property of Journal of Central European Agriculture and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract In this study, variable returns to scale (VRS) data envelopment analysis was integrated into the Taguchi approach to optimize ternary additives for expansive soil enhancement. The ternary additives selected were sawdust ash (SDA), quarry dust (QD) and ordinary Portland cement (OPC). The additives were set as the input variables while multiple responses obtained from the experiments performed with the Taguchi orthogonal array were set as the output variables. Each row in the orthogonal array were defined as a decision making unit (DMU) in the optimization process and output-oriented VRS model was used to obtain the efficiency score for each DMU. Next, benevolent formulation was utilized to obtain the multipliers for the inputs and outputs which were subsequently used to determine the cross efficiency scores for each DMU. The cross-efficiency scores were used to construct the cross-efficiency matrix. Thereafter, the mean cross-efficiency score (MCES) was determined for each DMU. Parameter level that maximizes the MCES was chosen as the optimal level for that parameter. Optimum combination of additives was found at A6 B2 C3. Lastly, confirmatory experiments performed by blending the soil with the optimum combination of additives showed the effectiveness of this method in the enhancement of expansive soil properties.

Efficiency is the main issue in any data envelopment analysis. Realizing output by a minimum of input or reaching a maximum of output by a given input is the credo, scale effects often are only a sort of accessory. In modern economics scale effects play a prominent role, however. What is the right size of a decision making unit (DMU) and how to proceed there. Returns to scale inform a DMU about its hitherto disregard of scale effects and show the way towards its ideal activity size. Combining efficiency aspects and scaling effects leads to a new DEA-best practice criterion of DMUs and gives them a profound orientation of their current position. This combination turns out to be the relation of weighted outputs to weighted inputs – in optimal prices under variable returns to scale (VRS). It is the VRS-productivity. For DMUs with increasing returns to scale the recommended growth path is in accordance with economic rationales, for decreasing returns to scale the recommended shrinking path uncovers severe flaws in VRS-models and needs adjustment. All theoretical considerations are illustrated by little numerical examples. A real world application of 37 Brazilian banks demonstrates the benefits of the new concept. [ABSTRACT FROM AUTHOR]

The variable returns to scale data envelopment analysis (VRS-DEA) benchmarking models are developed by assuming convexity on technology. However, this assumption may be not consistent with the classical microeconomic theory where marginal product initially increases but diminishing returns eventually set in. Accordingly, the selected benchmarks may aggregate best practices, which are less attainable in performance improvement. This paper thus proposes a VRS-DEA benchmarking model with quasiconcave production function, consistent with classical microeconomic theory. A novel approach for determining the returns to scale of Decision-Making Units (DMUs) is developed. The hyperplane of the quasiconcave production function is characterized. Benchmarks are selected by minimizing the weighted distance between the evaluated DMU and the hyperplane. An empirical application of China's tourism sector is used to validate the approach. The empirical results show that Mergers and Acquisitions are beneficial for smaller hotels as the production of most regions in China's tourism sector is subject to increasing returns to scale. [ABSTRACT FROM AUTHOR]

In this study, variable returns to scale (VRS) data envelopment analysis was integrated into the Taguchi approach to optimize ternary additives for expansive soil enhancement. The ternary additives selected were sawdust ash (SDA), quarry dust (QD) and ordinary Portland cement (OPC). The additives were set as the input variables while multiple responses obtained from the experiments performed with the Taguchi orthogonal array were set as the output variables. Each row in the orthogonal array were defined as a decision making unit (DMU) in the optimization process and output-oriented VRS model was used to obtain the efficiency score for each DMU. Next, benevolent formulation was utilized to obtain the multipliers for the inputs and outputs which were subsequently used to determine the cross efficiency scores for each DMU. The cross-efficiency scores were used to construct the cross-efficiency matrix. Thereafter, the mean cross-efficiency score (MCES) was determined for each DMU. Parameter level that maximizes the MCES was chosen as the optimal level for that parameter. Optimum combination of additives was found at A6 B2 C3. Lastly, confirmatory experiments performed by blending the soil with the optimum combination of additives showed the effectiveness of this method in the enhancement of expansive soil properties. [ABSTRACT FROM AUTHOR]

Data envelopment analysis is a mathematical programming methodology commonly used to evaluate the relative efficiencies of each of a set of decision-making units in terms of a selected set of input and output variables. In many situations, however, it can happen that certain variables can play roles of either inputs or outputs. This dual-role or classification concept was examined in earlier research for settings where the production possibility set was defined in terms of a constant returns to scale (CRS) technology. The current article extends the classification models to those cases where a variable returns to scale (VRS) technology is present. We show that if one simply adds a free-in-sign variable to the CRS model, the resulting VRS model can be unbounded. To alleviate this circumstance, we develop an altered version of the conventional VRS model which is shown to be feasible and bounded. We provide numerical examples to demonstrate the applicability and strength of the proposed methodology. [ABSTRACT FROM AUTHOR]

In this paper, we used the DEA method, to apply to the agriculture sector in Burkina Faso. We applied the method with forty-one (41) agricultural farmers with data taking into account cultivated area, manual (human) labour, the liquidity fund of the working household, the number of crops (products) and the expected farm incomes. The results highlight the seven (7) technical efficiencies. Taking into account the variable scale yields, fourteen (14) efficient farms are obtained. These results show how non-efficient farms in the DEA sense are indexed to efficient farms that can serve as benchmarks or benchmarks in terms of performance improvement. This study shows that efficiency does not depend on the category of operator but on overall performance (efficiency and productivity). [ABSTRACT FROM AUTHOR]

The cross-efficiency ranking method is a well-known method in DEA which is frequently used under the constant returns to scale assumption; while various applications exist based on the variable returns to scale (VRS). This is due to the presence of negative input-oriented VRS cross-efficiencies. In this paper, each cross-efficiency is replaced by an equivalent distance measure as inefficiency measure. Then, the cross-inefficiency method is developed under the VRS assumption. [ABSTRACT FROM AUTHOR]

The Malmquist–Luenberger productivity index would cause infeasible problem when measuring mixed period change of total factor productivity. The former research focuses on the infeasible issue under the hypothesis of constant return to scale (CRS). There are many meaningful solutions which could avoid this infeasible problem of Malmquist–Luenberger productivity index. However, these solutions couldn't avoid infeasible problem under condition of variable returns to scale (VRS). The new solution is proposed in this paper which could solve this problem under VRS based on super-efficiency issue. The empirical results of environmental total factor productivity (ETFP) change of thirty regions in China indicate that China isn't efficient from 1997 to 2014. Among eight economic regions northwest and southern coastal decline, the ETFP decrease 15%, 10% respectively. From the perspective of provinces in China, Hainan, Qinghai and Ningxia have the lowest environmental total factor productivity and their environmental technical efficiency is also the lowest. These non-efficient provinces all have lower gross domestic product and they should improve technique change efficiency through adopting advanced technology in the future. The fixed effects regression model illustrates that energy intensity, research and development, foreign direct investment are factors of ETFP in China. Both research and development and foreign direct investment could improve total factor productivity and this indicates that Pollutant Heaven Hypothesis doesn't exist in China. [ABSTRACT FROM AUTHOR]

The Great Plains is the most important wheat producing region in the United States. Dwindling returns and changes in government farm programs have reduced wheat acreage, raising concerns over its future viability. Small farms and marginal areas are particularly vulnerable, including the western Great Plains (WGP). To assess the technical and economic viability of wheat farms, the efficiency of 141 wheat farms in the WGP was estimated. Results found substantial inefficiency among all producer types. The largest source of inefficiency was input use among smaller farms. The smaller farms were the most scale efficient, reducing concerns over their future viability. [ABSTRACT FROM AUTHOR]

An important branch of data envelopment analysis (DEA) is context-dependent DEA, which evaluates efficiency by combining the attractiveness and progress for a particular decision-making unit (DMU). Traditionally, context-dependent DEA models are based on the assumption of constant returns to scale. Two limitations are found when directly extending original radial context-dependent DEA (ORCD-DEA) models into variable returns to scale versions. One is that it may not be possible to determine the attractiveness of a DMU that logically must be attractive in that context. The other problem is that the progress measure cannot ensure an inefficient DMU projects to a Pareto-efficient frontier. A small numerical example is used to illustrate these two issues. In order to overcome these deficiencies, the concept of closest target is introduced to determine the attractiveness and progress for each DMU. The closest target method can further improve DMUs' performance with less wastes in inputs or underproduction in outputs. Finally, a practical application involving computer printers is presented. [ABSTRACT FROM AUTHOR]

It is well known that the super-efficiency (SE) data envelopment analysis (DEA) model can discriminate efficient decision-making units (DMUs), but it is infeasible under the variable returns to scale (VRS) condition. This paper introduces a new modified input-oriented VRS SE model and its corresponding algorithm. Compared with existing modified VRS SE models, the proposed approach has the following advantages: it is feasible in the presence of zero data; it can provide a robust SE score when a tiny perturbation occurs for some zero input of the evaluated DMU; it determines a unique and bounded SE score for all DMUs; it has a good ability of discriminating efficient DMUs. The proposed model can also be used under the constant returns to scale (CRS) condition to settle the infeasibility caused by zero data. To characterize SE further when infeasibility occurs, this paper provides the corresponding output-oriented VRS SE model. The proposed input-oriented and output-oriented VRS SE approaches give the same efficiency identification as the conventional input-oriented and output-oriented VRS SE models do, respectively. Numerical examples are used to demonstrate the practicality and superiority of our approach. [ABSTRACT FROM AUTHOR]

Efficiency analysis is very useful and important to measure the performance of the firms in com- petitive market of rapidly developing country like Bangladesh. The more efficient firms, and the decision making units (DMUs) are usually referred as benchmarking units for the development. In this study, efficiency scores are obtained using the non-parametric Data Envelopment Anal- ysis (DEA) technique for 1007 manufacturing firms in Bangladesh from the enterprise survey data. The DEA is used to calculate weights for inputs and outputs by assigning the maximum efficiency score for a DMU under evaluation. Total 29 firms are found efficient under variable returns to scale assumption. The significant determinants behind the inefficiency found in this analysis include mainly the firm size, manager's experience in respective sector, annual losses due to power outage, number of production workers. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Quality Assurance in Higher Education is the property of University of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Data envelopment analysis (DEA) is a non-parametric approach for performance analysis of decision making units (DMUs) which uses a set of inputs to produce a set of outputs without the need to consider internal operations of each unit. In recent years, there have been various studies dealt with two-stage production systems, i.e. systems which consume some inputs in their first stage to produce some intermediate outputs which are used as the inputs of the second stage in producing final outputs. One of these researches done by Kao and Hwang (2008) gives a decomposition of system efficiency score based on the efficiency of its sub-processes in the case of constant returns to scale (CRS) technology. This paper presents an extension of this approach for the technologies with variable returns to scale (VRS) and explains the results.

This paper investigates the infeasibility of super - efficiency Data Envelopment Analysis (DEA) model in which the observed unit under evaluation is excluded from the reference set. By applying Constant Return to Scale (CRS) and Variable Return to Scale (VRS) - DEA model we obtain the efficiency score of each unit. Further we applied Super Efficiency (SE) model to locate the position of the unit under evaluation when infeasibility occur. Here it is identified the endpoint position of the extreme efficient units. In our study we have been taken the district Central Cooperative Banks in India to investigate empirically and the results based on empirical investigations are highlighted. [ABSTRACT FROM AUTHOR]

The paper makes an attempt to measure the relative Technical Efficiency (TE) of twelve multi-brand retail firms in India, using Data Envelopment Analysis (DEA). An input oriented DEA under the assumption of Variable Returns to Scale (VRS) has been proposed for the study and analysis has been conducted for the period 2012-13. The study has relied on secondary source for data on the input and output variables. Based on the method and the choice variables, it has measured technical efficiencies and identified the efficient and relatively less efficient firms in the group.DEA revealed that five multi-brand retail firms, Out of the twelve, Are on the efficiency frontier and the remaining seven were found to be below it. For the relatively less efficient firms, the input slacks have been calculated which has important managerial implications for efficient resource utilization through better management practices. It was also found that among the twelve brands, Brand house Retails Ltd. and GIVO Ltd. are the most efficient multi-brand retail firms in the group. [ABSTRACT FROM AUTHOR]

The purpose of the study is firstly to use Data Envelopment Analysis (DEA) to aggregate the overall performance (technical efficiency) of firms to convert scarce resources into outputs that create wealth for shareholders, and secondly, to determine the degree to which this mentioned performance is reflected in a number of profitability and market value ratios. Annual financial statement data were used for 55 manufacturing companies listed on the JSE Limited over a five-year period in a cross-sectional analysis. The study found that return on equity has the most significant relationship with technical efficiency, followed by return on assets. The market value ratios price/earnings and dividend yield have no significant relationship with technical efficiency. The value of this study is that it is the first of its kind where technical efficiency, which aggregated operating, profitability and marketability efficiencies, is used to determine the relative importance of not only the readily available profitability ratios, but also market value ratios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Institute of Science & Technology of Dumlupinar University / Dumlupinar Üniversitesi Fen Bilimleri Enstitüsü Dergisi is the property of Dumlupinar University, Institute of Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper is about a model of Bertrand competition in a homogeneous-good market with free entry of identical firms and variable returns to scale. If the optimum number of active firms in the market is two or more, and the number of active firms is equal to that optimum number, then Bertrand equilibrium exists for that optimum number, and it does not exist if the number of active firms is less than the optimum. The model, however, does not rule out the existence of Bertrand equilibria with more active firms than the optimum number. Finally, when the optimum number of active firms in the market is one, Bertrand equilibrium does not exist. [ABSTRACT FROM AUTHOR]

In data envelopment analysis (DEA) an inefficient unit can be projected onto an efficient target that is far away, i.e. reaching the target may demand large reductions in inputs and increases in outputs. When the inputs and outputs modifications planned are large, it may be troublesome to carry them out all at once. In order to help an inefficient unit reach a distant target, a strategy of gradual improvements with successive, intermediate targets has been proposed. This paper extends such approach to the variable returns to scale (VRS) case. In the VRS scenario we distinguish between units that are technical efficient and those that are not. On the one hand, for those units that are not technical efficient the proposed approach determines successive intermediate targets leading to the technical efficiency frontier, i.e. the priority for those units is to attain technical efficiency. On the other hand, for those units that are technical efficient but not scale efficient the proposed approach computes a sequence of targets ending in the global efficiency frontier, i.e. when technical efficiency is guaranteed the goal is then to attain global efficiency. In both cases, the successive targets are obtained by iteratively solving specific DEA models that take into account given bounds on the rates of change in inputs and outputs that the unit can implement in each step. [ABSTRACT FROM AUTHOR]

The contributions of the paper are threefold: (i) compare with mathematical rigour the data envelopment analysis (DEA) model of Charnes, Cooper, and Rhodes and the Farrell model exhibiting constant returns to scale, (ii) reinterpret the contribution of Farrell and Fieldhouse that extended the analysis to variables returns to scale and establish the connection with the approach in Banker, Charnes, and Cooper, and (iii) provide graphical visualization of properties of the frontier function. Both papers by Farrell emphasized the importance of graphical visualization of non-parametric frontier functions, but, to our knowledge, this is seldom followed up in the literature. We use a graphical package (Effi Vision) with a numerical representation of the frontier functions, representing the contemporary development of visualization. By making suitable cuts through the DEA frontier in multidimensional space, various graphical representations of features of economic interest can be done. Development of ray average cost function and scale elasticity plots are novel illustrations. [ABSTRACT FROM AUTHOR]

The point of this paper is that the introduction of variable returns to scale in Lucas's 1988 model can produce paradoxical results: there can be negative relationships between the rate of growth of income and the rate of growth of human capital, between physical and human capital, and between the rate of growth of human capital and the rate of growth of wages. Moreover, the author argues the following "ontological paradox": the main equations of Lucas's 1988 model depend on the cardinal measurability of the external effects of human capital represented by a variable that is not cardinally measurable. [ABSTRACT FROM AUTHOR]

Learning from high performance projects is crucial for software process improvement. Therefore, we need to identify outstanding projects that may serve as role models. It is common to measure productivity as an indicator of performance. It is vital that productivity measurements deal correctly with variable returns to scale and multivariate data. Software projects generally exhibit variable returns to scale, and the output from ERP projects is multivariate. We propose to use Data Envelopment Analysis Variable Returns to Scale (DEA VRS) to measure the productivity of software projects. DEA VRS fulfills the two requirements stated above. The results from this empirical study of 30 ERP projects extracted from a benchmarking database in Accenture identified six projects as potential role models. These projects deserve to be studied and probably copied as part of a software process improvement initiative. The results also suggest that there is a 50 percent potential for productivity improvement, on average. Finally, the results support the assumption of variable returns to scale in ERP projects. We recommend DEA VRS be used as the default technique for appropriate productivity comparisons of individual software projects. Used together with methods for hypothesis testing, DEA VRS is also a useful technique for assessing the effect of alleged process improvements. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the origins of the protection argument based on increasing and decreasing returns to scale. The development of this theoretical framework is outlined in order to argue that F. Graham's demonstration consists of a synthesis of previous work. We then discuss the debate that took place in the 1930s, which ended with the rejection of Graham's argument as being a paradox or a theoretical curiosity. [ABSTRACT FROM AUTHOR]

• The reason of the limitation of the inverse DEA under VRS is identified by graphic method. • The change range of given outputs (inputs) in the inverse DEA under VRS is determined. • The optimal inputs increments (outputs diminutions) are obtained for the given condition. • The effect of efficiency change and technological change on the inverse DEA under VRS are discussed. As an important component of data envelopment analysis (DEA), the inverse DEA method often has no feasible solution under variable returns to scale (VRS). By analyzing the reason of this problem, the limitation of inputs and outputs in the inverse DEA method under VRS is identified. Then the outputs possible set and the inputs possible set are defined for different inverse DEA models; and some models are thus developed to determine the change range of outputs/inputs. Sequentially, by determining the optimal given outputs/inputs, the inverse DEA model under VRS is used to estimate the optimal inputs increment or outputs diminution for the optimal given efficiency, and the problem that the inverse DEA method has no feasible solution is avoided. In addition, the effects of efficiency change and technological change on the optimization of inputs and outputs are further discussed in this paper. Finally, two examples are provided to illustrate the validity and effectiveness of our methods. [ABSTRACT FROM AUTHOR]

This paper develops a simple general-equilibrium model of a closed economy. The economy under consideration produces two final goods, one private and one public, which are both produced with labor and an intermediate good under constant returns to scale. The intermediate good is produced by labor alone, and its production is subject to output-generated variable returns to scale. The public good can be interpreted as government spending on environmental quality, police protection, cultural activities, and publicly funded health care. The model is used to examine the impact of an exogenous change in labor supply on the size of the government, relative prices, and welfare. Within the context of the present study, an increase in labor supply can be attributed to either exogenous immigration or population growth. The model is also used to examine the relationship between the size of the country and the pattern of trade. [ABSTRACT FROM AUTHOR]

Cross-efficiency evaluation is an effective methodology for discriminating among a set of decision-making units (DMUs) through both self- and peer-evaluation methods. This evaluation technique is usually used for data envelopment analysis (DEA) models with constant returns to scale due to the fact that negative efficiencies never happen in this case. For cases of variable returns to scale (VRSs), the evaluation may generate negative cross-efficiencies. However, when the production technology is known to be VRS, a VRS model must be used. In this case, negative efficiencies may occur. Negative efficiencies are unreasonable and cause difficulties in calculating the final cross-efficiency. In this paper, we propose a cross-efficiency evaluation method, with the technology of VRS. The cross-efficiency intervals of DMUs were derived from the associated aggressive and benevolent formulations. More importantly, the proposed approach does not produce negative efficiencies. For comparison of DMUs with their cross-efficiency intervals, a numerical index is required. Since the concept of entropy is an effective tool to measure the uncertainty, this concept was employed to build an index for ranking DMUs with cross efficiency intervals. A real-case example was used to illustrate the approach proposed in this paper. [ABSTRACT FROM AUTHOR]