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
Introduction
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 brief introduction we discuss motivations for the study of productivity, when optimizing behavior is not always practiced by the firm or productive unit under observation, that mostly come from important literatures that we will not cover in depth in our book. We discuss several sets of motivating factors and introduce them under the generic headings of management practices, behavioral economics, and X-efficiency. We purposely introduce the arguments in these literatures without reference to the more recent work in DEA and stochastic frontiers that make up the methodological coverage in the remaining chapters of the book. We do so in order to highlight the broad consensus that has existed since at least the early twentieth century for the persistent and transitory existence of suboptimal behaviors coming from these relatively distinct but very related literatures in management, psychology, sociology, and from classical economics.
Management Practices and Inefficiency in Production
Inefficiency in production, and the approaches to address it that we detail in the chapters to follow, can be linked to the sometimes overlooked but quite important literature on management practices, variations in which are what we almost always interpret as changes in the level of a firm's operating efficiency. Empirical studies have shown large differences in productivity across both firms and countries (Lieberman et al., 1990; Foster et al., 2008; Hsieh and Klenow, 2009; Hall and Jones, 1999), and one of the clear determinants of such differences can be attributed to management practices, as pointed out by Glaister (2014). In a study of microdata from 45 developing countries, Nallari and Bayraktar (2010) found that research and development, capacity utilization, and adoption of foreign technology were clearly related to productivity differences among the micro-units, and these are all determined by the decisions of management, which for all intents and purposes is an unobserved latent variable. In the classic work on variations in management practices, Bloom et al. (2012) find an R2 = 0.81 in their regressions of gross domestic product (GDP) per capita on management practices across 17 countries. Bloom et al. (2013) found similar variations in productivity explained by management practices within India.
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- Information
- Measurement of Productivity and EfficiencyTheory and Practice, pp. 1 - 8Publisher: Cambridge University PressPrint publication year: 2019