Book contents
- Frontmatter
- Dedication
- Contents
- List of Figures
- List of Tables
- Preface
- Acknowledgments
- Introduction
- 1 Production Theory: Primal Approach
- 2 Production Theory: Dual Approach
- 3 Efficiency Measurement
- 4 Productivity Indexes: Part 1
- 5 Aggregation
- 6 Functional Forms: Primal and Dual Functions
- 7 Productivity Indexes: Part 2
- 8 Envelopment-Type Estimators
- 9 Statistical Analysis for DEA and FDH: Part 1
- 10 Statistical Analysis for DEA and FDH: Part 2
- 11 Cross-Sectional Stochastic Frontiers: An Introduction
- 12 Panel Data and Parametric and Semiparametric Stochastic Frontier Models: First-Generation Approaches
- 13 Panel Data and Parametric and Semiparametric Stochastic Frontier Models: Second-Generation Approaches
- 14 Endogeneity in Structural and Non-Structural Models of Productivity
- 15 Dynamic Models of Productivity and Efficiency
- 16 Semiparametric Estimation, Shape Restrictions, and Model Averaging
- 17 Data Measurement Issues, the KLEMS Project, Other Data Sets for Productivity Analysis, and Productivity and Efficiency Software
- Afterword
- Bibliography
- Subject Index
- Author Index
3 - Efficiency Measurement
Published online by Cambridge University Press: 15 March 2019
- Frontmatter
- Dedication
- Contents
- List of Figures
- List of Tables
- Preface
- Acknowledgments
- Introduction
- 1 Production Theory: Primal Approach
- 2 Production Theory: Dual Approach
- 3 Efficiency Measurement
- 4 Productivity Indexes: Part 1
- 5 Aggregation
- 6 Functional Forms: Primal and Dual Functions
- 7 Productivity Indexes: Part 2
- 8 Envelopment-Type Estimators
- 9 Statistical Analysis for DEA and FDH: Part 1
- 10 Statistical Analysis for DEA and FDH: Part 2
- 11 Cross-Sectional Stochastic Frontiers: An Introduction
- 12 Panel Data and Parametric and Semiparametric Stochastic Frontier Models: First-Generation Approaches
- 13 Panel Data and Parametric and Semiparametric Stochastic Frontier Models: Second-Generation Approaches
- 14 Endogeneity in Structural and Non-Structural Models of Productivity
- 15 Dynamic Models of Productivity and Efficiency
- 16 Semiparametric Estimation, Shape Restrictions, and Model Averaging
- 17 Data Measurement Issues, the KLEMS Project, Other Data Sets for Productivity Analysis, and Productivity and Efficiency Software
- Afterword
- Bibliography
- Subject Index
- Author Index
Summary
For ages that passed (and probably ages yet to come), economists have had an enormous interest in the ability to measure and analyze the performance of various economic systems and their individual decision-making units (often abbreviated as DMUs), such as an employee or a group thereof, a firm, a shop, a public agency, a bank, a hospital, an industry of these units, an entire country or a region of countries, or the entire world.
Only recently has such an interest culminated in a relatively young and fast-growing area of economic and econometric thought – the efficiency and productivity analysis – which has become one of the sub-fields of modern economics, as indicated, for example, by its inclusion in the RePEc rankings in economics.
Theoretical research in this area has provided practitioners with various tools for answering such important questions as, for example, which types of management or policy measures or ownership structures or types of regulations of various firms (industries, countries, etc) are associated with greater efficiency and productivity in practice.
Before considering practical estimation issues, however, it is imperative to understand the major underpinnings of the theoryof efficiency measurement. The goal of this chapter is to outline the essence of this theory, which, as the reader will recognize, is heavily based on the neoclassical production theory and briefly outlined in the previous chapters. In particular, we will consider a few of the most commonly used measures of efficiency, their relationship among each other, and some of their major properties.
VARIOUS MEASURES OF TECHNICAL EFFICIENCY
In the previous chapter, we tried to tame the functional characterization of technology via the distance functions and noticed that it is a convenient measure of efficiency. In this section, we elaborate on this measure as well as considering a few other alternatives offered in the literature.
The definition of the frontier is very important here. In general, the frontier of a set characterization of technology (Tor P(x)or L(y)) can be thought of as the intersection of the set itself and the closure of the complement of this set. This general definition, however, is not very functional. One very convenient definition of the “frontier of the output sets” or the “output isoquants” that has been frequently used in practice for measuring output efficiency was already given in Chapter 1, i.e.
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- Measurement of Productivity and EfficiencyTheory and Practice, pp. 59 - 95Publisher: Cambridge University PressPrint publication year: 2019