Showing papers on "Data envelopment analysis published in 2015"

A Practitioner's Guide to Stochastic Frontier Analysis Using Stata

Abstract: A Practitioner's Guide to Stochastic Frontier Analysis Using Stata provides practitioners in academia and industry with a step-by-step guide on how to conduct efficiency analysis using the stochastic frontier approach. The authors explain in detail how to estimate production, cost, and profit efficiency and introduce the basic theory of each model in an accessible way, using empirical examples that demonstrate the interpretation and application of models. This book also provides computer code, allowing users to apply the models in their own work, and incorporates the most recent stochastic frontier models developed in academic literature. Such recent developments include models of heteroscedasticity and exogenous determinants of inefficiency, scaling models, panel models with time-varying inefficiency, growth models, and panel models that separate firm effects and persistent and transient inefficiency. Immensely helpful to applied researchers, this book bridges the chasm between theory and practice, expanding the range of applications in which production frontier analysis may be implemented.

Total-factor carbon emission performance of the Chinese transportation industry: A bootstrapped non-radial Malmquist index analysis

Abstract: This paper proposes a non-radial Malmquist CO2 emission performance index (NMCPI) for measuring dynamic changes in total-factor CO2 emission performance over time. This index enables the consideration of non-radial slacks in the conventional Malmquist CO2 emission index (MCPI). The NMCPI is calculated based on a non-radial directional distance function derived by several data envelopment analysis (DEA) models. Furthermore, NMCPI could be decomposed into an efficiency change (EC) index and technological change (TC) index. A bootstrapping approach is conducted to introduce statistical inferences into the NMCPI and its decompositions. Based on the proposed indices, the dynamic CO2 emission performance change and its decompositions of the Chinese regional transportation industry from 2002 to 2010 are investigated. The empirical results demonstrate that the total-factor carbon emission performance of the transportation industry as a whole decreased by 32.8% over the period, and this reduction was primarily caused by technological decline.

The static and dynamic environmental efficiency of renewable energy: A Malmquist index analysis of OECD countries

Abstract: In recent years, renewable energy and environmental efficiency have been interesting issues for both academics and industry actors when discussing sustainable development. Against this background, the present study examines the environmental efficiency of renewable energy from the static and the dynamic perspective in 31 OECD countries, a study conducted to understand the impact of renewable energy across countries. As a non-parametric methodology, data envelopment analysis is used to measure environmental efficiency through multiple inputs and outputs. Labor, capital, and renewable energy supply are the three inputs, while GDP is the desirable output and carbon emissions are the undesirable output. To measure the dynamic environmental efficiency of renewable energy, the Malmquist productivity index is applied to estimate the average efficiency change from 2004 to 2011. The results demonstrate geographical differences in environmental efficiency across the OECD. Countries in OECD America have the highest average environmental efficiency and those in OECD Europe have the largest standard deviation. We also find that dynamic efficiency is influenced by the global financial crisis triggered in the United States. Further, we show that OECD countries implement both catch-up and frontier shift strategies to improve environmental efficiency.

Production Frontier Methodologies and Efficiency as a Performance Measure in Strategic Management Research

Abstract: The measurement of corporate performance is central to strategic management research. A common objective of this research is to identify top performers in an industry and their sources of competitive advantage. Despite this focus on best firms and practices, most researchers utilize statistical methods that identify average effects in a sample, and they assess a single performance dimension while ignoring other relevant dimensions. Emphasis on purely financial measures can overlook the fact that a firm's efficiency in transforming resources has been shown as a major source of competitive advantage. In this article we demonstrate how frontier methodologies, such as Data Envelopment Analysis and the Stochastic Frontier approach, can address these challenges. We provide an illustration based on longitudinal data from U.S. and Japanese automobile producers.

Statistical Approaches for Nonparametric Frontier Models: A Guided Tour

Abstract: A rich theory of production and analysis of productive eciency has developed since pioneering work by Koopmans (1951) and Debreu (1951). Farrell (1957) is the earliest published empirical study, and appeared in a statistical journal (JRSS), even though Farrell provided no statistical theory. The literature in econometrics, management sciences, operations research and mathematical statistics has since been enriched by hundreds of papers trying to develop or implement new tools for analyzing productivity and efficiency of firms. Both parametric and nonparametric approaches have been proposed. The mathematical challenge is to derive estimators of production, cost, revenue,or profit frontiers which represent, in the case of production frontiers, the optimal loci of combinations of inputs (like labor, energy, capital, etc.) and outputs (the products or services produced by the firms). Optimality is defined in terms of various economic considerations. Then the efficiency of a particular unit is measured by its distance to the estimated frontier. The statistical problem can be viewed as the problem of estimating the support of a multivariate random variable, subject to some shape constraints, in multiple dimensions. These techniques are applied in thousands of papers in the economic and business literature. This "Guided Tour" reviews the development of various nonparametric approaches since the early work of Farrell. Remaining challenges and open issues in this challenging arena are also described.

An illustration of dynamic network DEA in commercial banking including robustness tests

Abstract: The main motivation of this article is to illustrate dynamic network data envelopment analysis (DN-DEA) in commercial banking with emphasis on testing robustness. To this end, sixteen foreign banks in China are benchmarked against thirty-two domestic banks for the post-2007 period that follows major reforms. When network and dynamic dimensions are brought together, a more comprehensive analysis of the period 2008–2010 is enabled where divisional and between-period interactions are reflected in efficiency estimates. Weighted, variable returns-to-scale, non-oriented dynamic network slacks-based measure is used within the framework of the intermediation approach to bank behavior. A bank network (i.e., a decision-making unit, DMU) is conceptualized as comprised of two divisions or sub-DMUs, namely, interest-bearing operations and non-interest operations linked by number of referrals. Undesirable outputs from sub-DMUs 1 and 2 (non-performing loans, and proportion of fruitless referrals, respectively) are treated as carry-overs that impact the efficiency of the following periods. Under robustness testing, the illustrative application discusses discrimination by efficiency estimates, dimensionality of the performance model, stability of estimates through re-sampling (leave-one-out method), and sensitivity of results to divisional weights and returns-to-scale assumptions. The results based on Chinese commercial banks are illustrative in nature because of simulated data used on two of the variables.

Two-stage DEA models with undesirable input-intermediate-outputs $

Abstract: Data envelopment analysis (DEA) is a non-parametric approach for measuring the relative efficiencies of peer decision making units (DMUs). Many studies have examined DEA efficiencies of two-stage systems, where all the outputs from the first stage are the only inputs to the second stage. Although single-stage DEA models with undesirable input-outputs have been extensively studied, there still lacks of more systematical investigation on two-stage DEA with undesirable variables. For instance, depending on its operating model, even whether an intermediate variable is desirable or undesirable can be questionable for a particular two-stage system. Furthermore, most of the existing studies on two-stage systems focus on the case where only the final outputs are undesirable. In this work, we try to systematically examine two-stage DEA models with undesirable input-intermediate-outputs. Particularly, we utilize the free-disposal axioms to construct the production possibility sets (PPS) and the corresponding DEA models with undesirable variables. The proposed models are then used to illustrate some theoretical perspectives by using the data of China׳s listed banks.

An integrated approach for supplier portfolio selection

Abstract: Proposing a new integrated approach for supplier selection.Providing analytical results and managerial implications based on the suppliers group.Providing a supplier portfolio to be more dynamic in today's unpredictable environment. Supply chain environment is more dynamic and unpredictable than the past; therefore, it needs to be highly flexible in order to reconfigure in response to changes in their environment on the spur of the moment. This study presents a framework for supplier selection based on product-related and organization-related characteristics of the suppliers to be more competitive in the market and flexible to overcome probable changes in demands, supplies etc. Product-related and organization-related characteristics are those which are named in this study as lean and agile criteria respectively. Comprehensively digging up the literature, we extract the best criteria representing both leanness and agility of an organization. The aim of this paper is to select an appropriate supplier portfolio based on two aforementioned concepts. Supplier selection problem is solved using a combination of multi-criteria decision making (MCDM) methods. Due to the interaction between the criteria, analytical network process (ANP) is applied for determining the weight of each criterion for each alternative (supplier), and then data envelopment analysis (DEA) is used to rank them. The reason that DEA is used in this study is that when the number of suppliers increases, ANP approach tends to work inefficiently. Moreover, for determining the accurate interdependencies between the proposed criteria, fuzzy decision making trial and evaluation laboratory (DEMATEL) is applied. The framework is applied on a real case to demonstrate its applicability and feasibility.

A fuzzy hybrid project portfolio selection method using Data Envelopment Analysis, TOPSIS and Integer Programming

Abstract: Project portfolio selection is a complex and difficult task in fuzzy environments.A three-stage hybrid method is used to select an optimal combination of projects.Data Envelopment Analysis is used to screen the available projects.TOPSIS is used to rank the potentially promising projects.Linear Integer Programming is used to select the most suitable project portfolio. Project selection and resource allocation are critical issues in project-based organizations. These organizations are required to plan, evaluate, and control their projects in accordance with the organizational mission and objectives. In this study, we propose a three-stage hybrid method for selecting an optimal combination of projects. We obtain the maximum fitness between the final selection and the project initial rankings while considering various organizational objectives. The proposed model is comprised of three stages and each stage is composed of several steps and procedures. We use Data Envelopment Analysis (DEA) for the initial screening, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for ranking the projects, and linear Integer Programming (IP) for selecting the most suitable project portfolio in a fuzzy environment according to organizational objectives. Finally, a case study is used to demonstrate the applicability of the proposed method and exhibit the efficacy of the algorithms and procedures.

Human development and data envelopment analysis: A structured literature review ☆ ☆☆

Abstract: Given the importance the concept of productive efficiency has on analyzing the human development process, which is complex and multidimensional, this study conducts a literature review on the research works that have used the data envelopment analysis (DEA) to measure and analyze the development process. Therefore, we researched the databases of Scopus and Web of Science, and considered the following analysis dimensions: bibliometrics, scope, DEA models and extensions used, interfaces with other techniques, units analyzed and depth of analysis. In addition to a brief summary, the main gaps in each analysis dimension were assessed, which may serve to guide future researches.

Translation Invariance in Data Envelopment Analysis

Abstract: In this chapter we present an overview of the different approaches that have considered translation invariant Data Envelopment Analysis (DEA) models. Translation invariance is a relevant property for dealing with non-positive input and/or non-positive output values. We start by considering the classical approach and continue revising recent contributions. We also consider non-translation invariant DEA models that are able to deal with negative data at the expense of modifying the model itself. Finally, we propose to study translation invariance in a general framework through a recently introduced distance function: the linear loss distance function.

Social and financial efficiency of Islamic microfinance institutions:a Data Envelopment Analysis application

Abstract: Microfinance has been developed as alternative solution for global poverty alleviation effort in the last 30 years. Microfinance institution (MFI) has unique characteristic wherein they face double bottom line objectives of outreach to the poor and financial sustainability. This study proposes a two-stage analysis to measure Islamic Microfinance institutions (IMFIs) performance by comparing them to conventional MFIs. First, we develop a Data Envelopment Analysis (DEA) framework to measure MFIs' efficiency in its double bottom line objectives, i.e. in terms of social and financial efficiency. In the second stage non-parametric tests are used to compare the performance and identify factors that contribute to the efficiency of IMFIs and MFIs.