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
17 - Data Measurement Issues, the KLEMS Project, Other Data Sets for Productivity Analysis, and Productivity and Efficiency Software
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
In this chapter we briefly discuss some of the issues that arise when using standard index numbers as input quantity or price measures, as well as particular data sets that can be used in productivity research. In regard to the latter, we focus first on the World KLEMS project data and recent studies using it. These studies are based on modern approaches to productivity measurement using largely neoclassical approaches that assume perfectly competitive markets and frontier behaviors by firms, industries, and countries. We discuss in our summary of these papers how concepts we have put forth in our book speak to the topics and approaches used in these studies and how, in many ways, their frameworks and methods are closely aligned with modeling approaches and scenarios we have discussed in our earlier chapters. We then provide a short description of many other public use datasets and information on how to access them. Of course, it is important to be able to have accessible and easy to use software to analyze such data using methods we have discussed in this book. The software is detailed in the last section of this, our concluding chapter.
DATA MEASUREMENT ISSUES
The accurate modeling and measurement of the productivity growth determinants and their contributions in an aggregate economy, in its component industries, and in particular firms, has advanced considerably since the Jorgenson and Griliches (1967) seminal treatise on the measurement problems inherent in assessing productivity growth. However, the problems that Jorgenson and Griliches pointed out over 50 years ago are still with us, as noted in Chapter 4. Although major improvements in data collection and methodology have been incorporated in government and private-sector data collection protocols through the efforts of Jorgenson and Griliches and their many collaborators and colleagues, variations in the quality of data still affect the measurement and analysis of productivity growth. Such issues tend not to be discussed in applied work. Griliches (1994) summarized the potential measurement issues pertaining to productivity analysis, listing the following general problems and questions: 1. Coverage issues, definition of the borders of a sector, and the relevant concept of “output” for it. For example, is illegal activity included? Are pollution damages counted against the “output” of an industry?; 2. The difficulty in measuring “real” output over time as prices and the quality of output change; 3.
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- Measurement of Productivity and EfficiencyTheory and Practice, pp. 509 - 538Publisher: Cambridge University PressPrint publication year: 2019