Data Envelopment Analysis

Data Envelopment Analysis (DEA) has grown has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a host of different entities engaged in a wide variety of activities in many complex, multi-layered contexts worldwide.

DATA ENVELOPMENT ANALYSIS: A Comprehensive Text with Models, Applications, References, And DEA-Solver Software, 2nd Edition is designed to provide a systematic introduction to DEA and its uses as a multifaceted tool for evaluating problems in a variety of contexts. Each chapter accompanies its developments with simple numerical examples and discussions of actual applications. Emphasis is placed on the use as well as an understanding of DEA and the topics in this book have been selected and treated accordingly. The first nine chapters cover the basic principles of DEA and the final seven chapters are more advanced treatment of DEA. These final chapters were completely revised into new chapters, reflecting recent developments that greatly extend the power and scope of DEA and lead to new directions for research and DEA uses. These chapters are as follows:

• Chapter 10 provides a new treatment of "super efficiency models."
• Chapter 11 deals with efficiency changes over time, covering more "window analysis" than in the first edition and extending to the "Malmquist Index."
• Chapter 12 turns to scale elasticity and congestion which are combined and extended for joint use in new applications.
• Chapter 13 illustrates both facets of outputs with the use of fossil fuels to generate electric power (good output) also being accompanied by fuel emissions (bad outputs).
• Chapter 14 deals with "economies of scope" which address whether it is more efficient to produce multiple products in the same plant or by producing them in separate plants or companies.
• Chapter 15 outlines "n person cooperative games" and demonstrates how the rich array of concepts from game theory may be combined with DEA to determine rewards in a large variety of real-world situations.
• Chapter 16 treats and extends "stochastic frontier analysis" (SFA) by introducing a three-stage process that combines DEA with SFA.

These chapters systematically present the new developments in DEA and used together with the first ten chapters of the basic principles will provide students and researchers with a solid understanding of the methodology, its uses and its potential.