This book extends the efficiency literature to the case of intertemporal models. First, the authors introduce static network models which serve as building blocks for the intertemporal budgeting models and the dynamic models. Next, the authors devote two chapters to productivity measurements, which are considered as comparative static models. Intertemporal budgeting models and dynamic models are taken up subsequently. Each chapter, except the first, contains empirical applications.

This book presents a mathematical programming approach to the analysis of production frontiers and efficiency measurement. The authors construct a variety of production frontiers, and by measuring distances to them are able to develop a model of efficient producer behaviour and a taxonomy of possible types of departure from efficiency in various environments. Linear programming is used as an analytical and computational technique in order to accomplish this. The approach developed is then applied to modelling producer behaviour. By focusing on the empirical relevance of production frontiers and distances to them, and applying linear programming techniques to artificial data to illustrate the type of information they can generate, this book provides a unique study in applied production analysis. It will be of interest to scholars and students of economics and operations research, and analysts in business and government.

This book brings together academic work on contemporary issues in financial institutions and markets. The general theme is designed to allow for a wide range of topics, covering the diverse nature of academic enquiry in banking and finance. The contributions thus address a broad spectrum of contemporary issues including bank diversification and securitization activities; bank regulatory reforms and competition; the performance of mutual funds and alternative asset classes; role of liquidity in price discovery for credit derivatives; and the existence of the compass rose pattern within option contracts market. This book was originally published as a special issue of The European Journal of Finance.

In a relatively short period of time, data envelopment analysis (DEA) has grown into a powerful analytical tool for measuring and evaluating performance. DEA is computational at its core and this book is one of several Springer aim to publish on the subject. This work deals with the micro aspects of handling and modeling data issues in DEA problems. It is a handbook treatment dealing with specific data problems, including imprecise data and undesirable outputs.

This handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U.S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball. Chapter 6 examines efficiency and productivity of U.S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education. Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U.S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.

This second edition systematically presents the underlying theory, model development, and applications of network Data Envelopment Analysis (DEA). It discusses the models used to measure the efficiency of systems in specific network structures and introduces readers to the latest applications. It demonstrates how the “network DEA” approach helps identify and manage the specific components that cause inefficiencies in the overall system. The existing models for measuring the efficiency of systems in specific network structures are also discussed, and the relationships between system efficiency and component efficiency are explored. Moreover, the book provides an advanced exposition on performance evaluation of systems with network structures. It explores the networked nature of most production and operation systems, and explains why network analyses are necessary. Accordingly, the book will inspire new research and applications based on the state of the art. In this new edition, the latest research advances and discoveries are discussed. Two new chapters on Linkage Efficiency and External and Internal Evaluations have been added. This book is mainly aimed at researchers and graduate students who are interested in performance evaluation, DEA, and multi-criteria decision analysis. Practitioners who want to measure the performance of production, operation, or any type of decision-making units will also find it useful.