1,078 research outputs found

    Efficiency and Returns to Scale Measurements with Shared Inputs in Multi-Activity Data Envelopment Analysis: An Application to Farmers' Organizations in Taiwan

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    This paper addresses the question how team production promotes efficiency of a firm when some inputs can be rewarded on the basis of outputs but some cannot because they are shared among outputs and non-separable. A multi-activity DEA model with variable returns to scale is proposed to provide information on the efficiency performance for organizations with inputs shared among several closely related activities. The model is applied to study the case of 279 farmers' associations in Taiwan. The result suggests that it is important to improve the efficiency of the non-profit oriented activities to improve their overall performances. Three out of four departments of TFAs can gain from economies of scale through expansion, while the remaining one gains through contraction. Thus, policies promoting structural adjustment and consolidations of TFAs would not be inconsistent with public interests.multi-activity DEA, shared inputs, efficiency measure, directional distance function, Productivity Analysis,

    The Dynamics of Productivity Changes in Agricultural Sector of Transition Countries

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    Relying on frontier production approach (e.g., Luenberger's shortage function), we investigated the performance of agricultural sector in transition countries and its changes over time, especially focusing on the dynamics of productivity changes. We found that; (i) CEE countries have improved their performance during the sample period whereas CIS have not; (ii) productivity changes in the last decade was attributable to the technical progress; (iii) overall performance was decelerated for the second 5-year sub-period (1997-2001) in both regions; (iv) agricultural reform has positive effects on the productivity and its components especially in CEE countries.transition countries, productivity, directional distance function, agricultural reform, Productivity Analysis,

    Eco-efficiency measurement and material balance principle:an application in power plants Malmquist Luenberger Index

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    Incorporating Material Balance Principle (MBP) in industrial and agricultural performance measurement systems with pollutant factors has been on the rise in recent years. Many conventional methods of performance measurement have proven incompatible with the material flow conditions. This study will address the issue of eco-efficiency measurement adjusted for pollution, taking into account materials flow conditions and the MBP requirements, in order to provide ‘real’ measures of performance that can serve as guides when making policies. We develop a new approach by integrating slacks-based measure to enhance the Malmquist Luenberger Index by a material balance condition that reflects the conservation of matter. This model is compared with a similar model, which incorporates MBP using the trade-off approach to measure productivity and eco-efficiency trends of power plants. Results reveal similar findings for both models substantiating robustness and applicability of the proposed model in this paper

    Data envelopment analysis: uncertainty, undesirable outputs and an application to world cement industry

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    Starting from the pioneering papers by Charnes, Cooper and Rhodes (CCR model) and Banker, Charnes and Cooper (BCC model), a large number of papers concerning Data Envelopment Analysis (DEA) with outputs uncertainty appeared in the literature. In particular, chance-constrained programming is the most used technique to include noise variations in data and to solve data envelopment analysis problems with uncertainty in data. Chance-constrained programming admits random data variations and permits constraint violations up to specified probability limits, allowing linear deterministic equivalent formulations in case a normal distribution of the data uncertainty is assumed. The standard DEA models rely on the assumption that inputs are minimized and outputs are maximized. However, both desirable and undesirable (e.g., pollutants or wastes) output factors may be present. The undesirable and desirable outputs should be treated differently when we evaluate the production performance: if inefficiency exists in the production, the undesirable pollutants should be reduced to improve efficiency. In order to include undesirable factors in DEA models, according to the literature, two different approaches can be used to model undesirable factors: one group of DEA models treats them as inputs, whereas a second group considers them as undesirable outputs. DEA models with undesirable factors are particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account, in order to provide an eco-efficiency measure. In this Ph.D thesis alternative DEA models, which consider both uncertain and undesirable outputs, are proposed and studied. In particular, in the first part of this thesis two different models with uncertain outputs and deterministic inputs are proposed with the aim to move away the classical chance-constrained method and to obtain a more accurate DMU ranking whatever situation occurs. Specifically speaking, the proposed models remove the hypothesis of normal data distribution and use a scenario generation approach to include data perturbations. For the sake of completeness, these models are compared with two further ones based on an expected value approach, where uncertainty is managed by means of the expected values of random factors both in the objective function and in the constraints. Deeply speaking, the main difference between the two proposed models and the expected value approaches lies in their mathematical formulation. In the new models, based on the scenario generation approach, the constraints concerning efficiency level are expressed for each scenario. On the other hand, in the expected value models the constraints are satisfied in expected value. As a consequence, the models proposed in the thesis result to be more selective in finding a ranking of efficiency, thus becoming useful strategic management tools aimed to determine a restrictive efficiency score ranking. In the second part of this study, we focus on environmental policy and eco-efficiency. Nowadays, one of the most intensively discussed concepts in the international political debate is, in fact, the concept of sustainability and the need for eco-efficient solutions that enable the production of goods and services with less energy and resources and with less waste and emissions (eco-efficiency). In particular, we consider the environmental impact of CO2 in cement and clinker production processes. Cement industry is, in fact, responsible for approximately 5% of the current worldwide CO2 emissions. DEA models can provide an appropriate methodological approach for developing eco-efficiency indicators. A cross-country comparison of the eco-efficiency level of the worldwide cement industry is presented by applying both a data envelopment analysis and a directional distance function approach. These tools result to be particularly suitable for models where several production inputs and desirable and undesirable outputs are taken into account. Strong and weak disposability assumptions are analyzed in order to evaluate the impact of environmental regulations interpreted as the cost of regulation. The few papers appeared in the literature of eco-efficiency in cement production analyze the emission performance trends only from an interstate point of view. In this thesis a worldwide study has been carried on, covering 90% of the world's cement production by means of 21 countries, European (EU) and non-European (non-EU) ones. The obtained results show that the efficiency level mainly depends on decisions to invest in alternative raw materials and alternative fuels, both in the case of regulated countries and in the case of voluntary emission-trading schemes. This study highlights, both at national and international levels, the possibility of reducing CO2 emissions and expanding cement production. The use of alternative raw materials, alternative fuels and the possibility of producing blended cements, which require less energy consumption and reduce pollutant emissions, seem to be appropriate means. Environmental regulations can provide incentives in terms of tax exemption benefits or more restrictive pollutant limits. Finally, we try to answer to the following questions: do undesirable factors modify the efficiency levels of cement industry? Is it reasonable to omit CO2 emissions in evaluating the performances of the cement sector in different countries? In order to answer to these questions, alternative formulations of standard data envelopment analysis model and directional distance function are compared both in presence and in absence of undesirable factors. This analysis shows that the presence of undesirable factors greatly affects efficiency levels. Efficiency levels are influenced by investments in best available technologies and by the utilization of alternative fuels and raw materials in cement and clinker production processes. The original results of this Ph.D. thesis have been collected in the following research papers: • Riccardi R. and R. Toninelli. Data Envelopment Analysis with outputs uncertainty. Journal of Information & Optimization Sciences, to appear. • Riccardi R., Oggioni G. and R. Toninelli. The cement industry: eco-efficiency country comparison using Data Envelopment Analysis. Journal of Statistics & Management Systems, accepted for publication. • Riccardi R., Oggioni G. and R. Toninelli. Eco-efficiency of the world cement industry: A Data Envelopment Analysis. Energy Policy, Vol. 39, Issue 5, p. 2842-2854, 2011, available online at: http://dx.doi.org/10.1016/j.enpol.2011.02.057 • Riccardi R., Oggioni G. and R. Toninelli. Evaluating the efficiency of the cement sector in presence of undesirable output: a world based Data Envelopment Analysis. Technical Report n. 344, Department of Statistics and Applied Mathematics, University of Pisa, 2011, submitted to Resource and Energy Economics. The research topic considered in this thesis shows many different lines for future developments. In particular, from a theoretical point of view, starting from the models proposed in Riccardi and Toninelli (2011), we are studying for a bi-objective like DEA formulation where both uncertainty desirable and undesirable factor are taken into account. As regards the applicative aspects, we are also studying and applying bootstrap techniques to manage uncertainty and generate empirical distributions of efficiency scores, in order to capture and analyze the sensitivity of samples with respect to changes in the estimated frontier

    Two-Phase Network Data Envelopment Analysis: An Example of Bank Performance Assessment

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    Data envelopment analysis (DEA) models assess decision-making units (DMUs), which directly convert multiple inputs into multiple outputs. Network DEA models have been studied extensively. However, the performance indices that link the two stages are assumed to be fixed or non-discretionary; their values are not adjustable. These models only assumed that the reductions on the inputs and additions on the outputs would improve the overall efficiency. But in the real world, the link is always adjustable. “Free links” means that the intermediate items are adjustable or discretionary, and each DMU can be increased or decreased from the observed one. The current chapter introduces a two-phase procedure with free links to assess system performance, Phase-I is a proposed slack-based measurement (SBM) model to partition the links into two sets: as-input and as-output. Phase-II is a modified SBM model to determine the slack of each input, as-input link, output and as-output link. This proposed model counts the slacks associated with the intermediate items in the efficiency scores and determines the entire system performance by the directional distance function. It is validated using network procedure and assesses the performance of supply chain management system

    Extending the Measurement of Composite Indicators Towards a Non-convex Approach: Corporate Social Responsibility for the Food and Beverage Manufacturing Industry

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    This paper computes composite indicators of corporate social responsibility (CSR) from an efficiency perspective for food and beverage manufacturing firms in various world regions over the period from 2011 to 2018. From a methodological perspective, we extend the measurement of composite indicators within data envelopment analysis, allowing for the non-convexities of the production set and for the appropriate comparison of indicators between groups of firms. From an empirical point of view, we contribute by comparing the efficiency in CSR practices of food and beverage companies across regions of Europe, the United States and Canada, and Asia Pacific. The study reveals differences in CSR efficiency between food and beverage firms in the regions considered, with USA and Canadian firms tending to perform best, followed by European firms, and Asian Pacific firms achieving the worst efficiency results. The study also shows that regional catching up in performance occurred over the analyzed period.Comment: 31 pages, 1 figure, 5 table

    Undesirable Outputs’ Presence in Centralized Resource Allocation Model

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    Data envelopment analysis (DEA) is a common nonparametric technique to measure the relative efficiency scores of the individual homogenous decision making units (DMUs). One aspect of the DEA literature has recently been introduced as a centralized resource allocation (CRA) which aims at optimizing the combined resource consumption by all DMUs in an organization rather than considering the consumption individually through DMUs. Conventional DEA models and CRA model have been basically formulated on desirable inputs and outputs. The objective of this paper is to present new CRA models to assess the overall efficiency of a system consisting of DMUs by using directional distance function when DMUs produce desirable and undesirable outputs. This paper initially reviewed a couple of DEA approaches for measuring the efficiency scores of DMUs when some outputs are undesirable. Then, based upon these theoretical foundations, we develop the CRA model when undesirable outputs are considered in the evaluation. Finally, we apply a short numerical illustration to show how our proposed model can be applied

    Valuing social sustainability in agriculture: An approach based on social outputs' shadow prices

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    Interest in sustainability has gained ground among practitioners, academics and policy-makers due to growing stakeholders' awareness of environmental and social concerns. This is particularly true for agriculture. However, relatively little research has been conducted on the quantification of social sustainability and the contribution of social issues to the agricultural production efficiency. This paper proposes a framework based on state-contingent outputs to compute shadow prices of social outputs. Our methodological approach is based on the directional distance function and illustrated using a farm-level dataset from a sample of Catalan arable crop farms in 2015. Our results indicate that in the sample of 180 farms included in the analysis, efficiency scores are relatively high for the three alternative states of the nature considered in our state-contingent analysis. In addition, our findings show that social outputs' shadow prices are positive, indicating that producing more social outputs is considered as great value to the farm. For the efficient farms, the social outputs' shadow prices are contingent upon on the state of nature, in a way that social outputs' shadow prices increase with the improvement in crop growth conditions. These results have implications in terms of EU farm payment redistribution.info:eu-repo/semantics/acceptedVersio
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