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E-raamat: Measurement of Productivity and Efficiency: Theory and Practice

(Rice University, Houston), (University of Queensland)
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  • Ilmumisaeg: 28-Mar-2019
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108654166
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  • Formaat: PDF+DRM
  • Ilmumisaeg: 28-Mar-2019
  • Kirjastus: Cambridge University Press
  • Keel: eng
  • ISBN-13: 9781108654166

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Methods and perspectives to model and measure productivity and efficiency have made a number of important advances in the last decade. Using the standard and innovative formulations of the theory and practice of efficiency and productivity measurement, Robin C. Sickles and Valentin Zelenyuk provide a comprehensive approach to productivity and efficiency analysis, covering its theoretical underpinnings and its empirical implementation, paying particular attention to the implications of neoclassical economic theory. A distinct feature of the book is that it presents a wide array of theoretical and empirical methods utilized by researchers and practitioners who study productivity issues. An accompanying website includes methods, programming codes that can be used with widely available software like MATLAB® and R, and test data for many of the productivity and efficiency estimators discussed in the book. It will be valuable to upper-level undergraduates, graduate students, and professionals.

This book provides a comprehensive approach to productivity and efficiency analysis for upper-level undergraduates, graduate students, and professionals. An accompanying website includes programming codes that can be used with widely available software like MATLAB® and R, and test data for many of the estimators in the book.

Arvustused

'Sickles and Zelenyuk have written the most comprehensive book on production theory and the measurement of productivity in the history of these subjects. Researchers, students and teachers who are interested in production theory and the measurement of productivity will treasure this book.' W. Erwin Diewert, University of British Columbia and University of New South Wales 'Productivity and efficiency measurement has been a growth area for fifty years. Sickles and Zelenyuk bring together all the theory and practice, including the rich history, the full range of methods, and all the schools of thought. This is an amazingly comprehensive survey of the field.' William Greene, New York University 'Robin Sickles and Valentin Zelenyuk have written an outstanding book on a very important topic the measurement of productivity and efficiency. The book provides a complete and thorough introduction to the economic theory of production and its many applications to national accounting and econometric modeling. An especially valuable feature of the book is the detailed treatment of productivity and growth in the world economy.' Dale Jorgenson, Samuel W. Morris University Professor, Harvard University 'Rarely does a book comprehensively survey and creatively extend both the theoretical underpinnings and the practical implementation of a field of research. This is such a book, and it belongs on the desk of all scholars interested in producer performance measurement.' C. A. Knox Lovell, University of Queensland 'Sickles and Zelenyuk, outstanding researchers from two generations, impart their knowledge and wisdom into this monumental book on theory and practice of efficiency and productivity measurement. This is an invaluable resource for graduate students, researchers and practitioners alike. Strongly recommended.' D. S. Prasada Rao, University of Queensland 'This book is a comprehensive treatment of the economic and statistical issues involved in the estimation of the efficiency of production. It covers both stochastic frontier models and envelopment-type methods such as DEA and FDH. It is the best and most comprehensive currently available treatment of this topic.' Peter Schmidt, Michigan State University 'This book is an important contribution to both economic theory of productivity and efficiency and its measurement. It is a seminal work that highlights the significance of 'theory with measurement' and 'measurement with theory'.' H. Pyo, Seoul National University 'Robin Sickles and Valentin Zelenyuk have brought together a large set of research results both in the theoretical analysis and in the empirical aspects of productivity and efficiency. This results in a comprehensive textbook with deep insights on the theory of production and large discussions on the econometrics implementations. The book will, for sure, be a useful resource for researchers in the field.' Léopold Simar, Université Catholique de Louvain 'The book thoroughly covers theory (index numbers, axiomatic production theory, DEA, FDH, stochastic models, inference) and practice of measurement of efficiency and productivity, including issues and sources of data with links, as well as links to software and models. A rigorous and truly indispensable guide - and all this and more in a mere 630 pages.' Rolf Fare and Shawna Grosskopf, Oregon State University

Muu info

Provides a comprehensive approach to productivity and efficiency analysis using economic and econometric theory.
List of Figures xvii
List of Tables xix
Preface xxi
Acknowledgments xxiv
Introduction 1(8)
1 Production Theory: Primal Approach 9(30)
1.1 Set Characterization of Technology
9(4)
1.2 Axioms for Technology Characterization
13(6)
1.3 Functional Characterization of Technology: The Primal Approach
19(7)
1.4 Modeling Returns to Scale in Production
26(5)
1.5 Measuring Returns to Scale in Production: The Scale Elasticity Approach
31(3)
1.6 Directional Distance Function
34(2)
1.7 Concluding Remarks on the Literature
36(1)
1.8 Exercises
37(2)
2 Production Theory: Dual Approach 39(20)
2.1 Cost Minimizing Behavior and Cost Function
39(3)
2.2 The Duality Nature of Cost Function
42(3)
2.3 Some Examples of Using the Cost Function
45(2)
2.4 Sufficient Conditions for Cost and Input Demand Functions
47(2)
2.5 Benefits Coming from the Duality Theory for the Cost Function: A Summary
49(1)
2.6 Revenue Maximization Behavior and the Revenue Function
49(5)
2.7 Profit-Maximizing Behavior
54(3)
2.8 Exercises
57(1)
2.9 Appendix
57(2)
3 Efficiency Measurement 59(37)
3.1 Various Measures of Technical Efficiency
59(6)
3.2 Relationships Among Efficiency Measures
65(9)
3.2.1 Shephard vs. Directional Distance Functions
66(2)
3.2.2 Farrell vs. Russell Measures
68(2)
3.2.3 Directional Distance Function vs. Additive Measure
70(2)
3.2.4 Hyperbolic vs. Others
72(2)
3.3 Properties of Technical Efficiency Measures
74(6)
3.4 Cost and Revenue Efficiency
80(2)
3.5 Profit Efficiency
82(2)
3.6 Slack-Based Measures of Efficiency
84(5)
3.7 Unifying Different Approaches
89(1)
3.8 Remarks on the Literature
90(1)
3.9 Exercises
91(1)
3.10 Appendix
92(4)
4 Productivity Indexes: Part 1 96(47)
4.1 Productivity vs. Efficiency
96(3)
4.2 Growth Accounting Approach
99(3)
4.3 Economic Price Indexes
102(4)
4.4 Economic Quantity Indexes
106(4)
4.5 Economic Productivity Indexes
110(4)
4.6 Decomposition of Productivity Indexes
114(3)
4.7 Directional Productivity Indexes
117(2)
4.8 Directional Productivity Change Indicators
119(1)
4.9 Relationships among Productivity Indexes
120(8)
4.10 Indexes vs. Growth Accounting
128(1)
4.11 Multilateral Comparisons, Transitivity, and Circularity
129(12)
4.11.1 General Remarks on Transitivity
129(1)
4.11.2 Transitivity and Productivity Indexes
130(7)
4.11.3 Dealing with Non-Transitivity
137(3)
4.11.4 What to Do in Practice?
140(1)
4.12 Concluding Remarks
141(1)
4.13 Exercises
141(2)
5 Aggregation 143(23)
5.1 The Aggregation Problem
143(2)
5.2 Aggregation in Output-Oriented Framework
145(7)
5.2.1 Individual Revenue and Farrell-Type Efficiency
145(1)
5.2.2 Group Farrell-Type Efficiency
146(4)
5.2.3 Aggregation over Groups
150(2)
5.3 Price-Independent Weights
152(1)
5.4 Group-Scale Elasticity Measures
153(5)
5.5 Aggregation of Productivity Indexes
158(6)
5.5.1 Individual Malmquist Productivity Indexes
158(1)
5.5.2 Group Productivity Measures
159(1)
5.5.3 Aggregation of the MPI
160(2)
5.5.4 Geometric vs. Harmonic Averaging of MPI
162(1)
5.5.5 Decomposition into Aggregate Changes
163(1)
5.6 Concluding Remarks
164(1)
5.7 Exercises
165(1)
6 Functional Forms: Primal and Dual Functions 166(41)
6.1 Functional Forms for Primal Production Analysis
167(18)
6.1.1 The Elasticity of Substitution: A Review of the Allen, Hicks, Morishima, and Uzawa Characterizations of Substitution Possibilities
168(3)
6.1.2 Linear, Leontief, Cobb-Douglas, CES, and CRESH Production Functions
171(4)
6.1.3 Flexible-Functional Forms and Second-Order Series Approximations of the Production Function
175(7)
6.1.4 Choice of Functional Form Based on Solutions to Functional Equations
182(3)
6.2 Functional Forms for Distance Function Analysis
185(2)
6.3 Functional Forms for Cost Analysis
187(8)
6.3.1 Generalized Leontief
189(1)
6.3.2 Generalized Cobb-Douglas
190(1)
6.3.3 Translog
190(2)
6.3.4 CES-Translog and CES-Generalized Leontief
192(1)
6.3.5 The Symmetric Generalized McFadden
193(2)
6.4 Technical Change, Production Dynamics, and Quasi-Fixed Factors
195(4)
6.5 Functional Forms for Revenue Analysis
199(2)
6.6 Functional Forms for Profit Analysis
201(2)
6.7 Nonparametric Econometric Approaches to Model the Distance, Cost, Revenue, and Profit Functions
203(1)
6.8 Concluding Remarks
204(1)
6.9 Exercises
205(2)
7 Productivity Indexes: Part 2 207(36)
7.1 Decomposition of the Value Change Index
207(1)
7.2 The Statistical Approach to Price Indexes
208(2)
7.3 Quantity Indexes: The Direct Approach
210(1)
7.4 Quantity Indexes: The Indirect Approach
211(2)
7.5 Productivity Indexes: Statistical Approach
213(1)
7.6 Properties of Index Numbers
214(7)
7.7 Some Key Results in the Statistical Approach to Index Numbers
221(4)
7.8 Relationship between Economic and Statistical Approaches to Index Numbers
225(13)
7.8.1 Flexible Functional Forms
225(2)
7.8.2 Relationships for the Price Indexes
227(2)
7.8.3 Relationships for the Quantity Indexes
229(5)
7.8.4 Relationships for the Productivity Indexes
234(4)
7.9 Concluding Remarks on the Literature
238(1)
7.10 Exercises
239(1)
7.11 Appendix
240(3)
8 Envelopment-Type Estimators 243(43)
8.1 Introduction to Activity Analysis Modeling
243(8)
8.2 Non-CRS Activity Analysis Models
251(5)
8.3 Measuring Scale
256(5)
8.4 Estimation of Cost, Revenue, and Profit Functions and Related Efficiency Measures
261(6)
8.5 Estimation of Slack-Based Efficiency
267(2)
8.6 Technologies with Weak Disposability
269(4)
8.7 Modeling Non-Convex Technologies
273(4)
8.8 Intertemporal Context
277(1)
8.9 Relationship between CCR and Farrell
278(5)
8.10 Concluding Remarks
283(2)
8.11 Exercises
285(1)
9 Statistical Analysis for DEA and FDH: Part 1 286(30)
9.1 Statistical Properties of DEA and FDH
286(6)
9.1.1 Assumptions on the Data Generating Process
287(2)
9.1.2 Convergence Rates of DEA and FDH
289(1)
9.1.3 The Dimensionality Problem
290(2)
9.2 Introduction to Bootstrap
292(15)
9.2.1 Bootstrap and the Plug-In Principle
292(2)
9.2.2 Bootstrap and the Analogy Principle
294(2)
9.2.3 Practical Implementation of Bootstrap
296(1)
9.2.4 Bootstrap for Standard Errors of an Estimator
297(2)
9.2.5 Bootstrapping for Bias and Mean Squared Error
299(2)
9.2.6 Bootstrap Estimation of Confidence Intervals
301(2)
9.2.7 Consistency of Bootstrap
303(4)
9.3 Bootstrap for DEA and FDH
307(7)
9.3.1 Bootstrap for Individual Efficiency Estimates
307(7)
9.4 Concluding Remarks
314(1)
9.5 Exercises
315(1)
10 Statistical Analysis for DEA and FDH: Part 2 316(36)
10.1 Inference on Aggregate or Group Efficiency
316(5)
10.2 Estimation and Comparison of Densities of Efficiency Scores
321(13)
10.2.1 Density Estimation
321(4)
10.2.2 Statistical Tests about Distributions of Efficiency
325(9)
10.3 Regression of Efficiency on Covariates
334(14)
10.3.1 Algorithm 1 SW2007
335(1)
10.3.2 Algorithm 2 SW2007
336(2)
10.3.3 Inference in SW2007 Framework
338(2)
10.3.4 Extension to Panel Data Context
340(2)
10.3.5 Caveats of the Two-Stage DEA
342(6)
10.4 Central Limit Theorems for DEA and FDH
348(2)
10.4.1 Bias vs. Variance
348(2)
10.5 Concluding Remarks
350(1)
10.6 Exercises
350(2)
11 Cross-Sectional Stochastic Frontiers: An Introduction 352(42)
11.1 The Stochastic Frontier Paradigm
355(2)
11.2 Corrected OLS
357(2)
11.3 Parametric Statistical Approaches to Determine the Boundary of the Level Sets: The "Full Frontier"
359(6)
11.3.1 Aigner-Chu Methodology
360(2)
11.3.2 Afriat-Richmond Methodology
362(3)
11.4 Parametric Statistical Approaches to Determine the Stochastic Boundary of the Level Sets: The "Stochastic Frontier"
365(22)
11.4.1 Olson, Schmidt, and Waldman (1980) Methodology
371(1)
11.4.2 Estimation of Individual Inefficiencies
372(2)
11.4.3 Hypothesis Tests and Confidence Intervals
374(4)
11.4.4 The Zero Inefficiency Model
378(2)
11.4.5 The Stochastic Frontier Model as a Special Case of the Bounded Inefficiency Model
380(7)
11.5 Concluding Remarks
387(1)
11.6 Exercises
388(1)
11.7 Appendix
389(5)
11.7.1 Derivation of E(8i)
389(1)
11.7.2 Derivation of the Moments of a Half-Normal Random Variable
389(2)
11.7.3 Derivation of the Distribution of the Stochastic Frontier Normal-Half-Normal Composed Error
391(3)
12 Panel Data and Parametric and Semiparametric Stochastic Frontier Models: First-Generation Approaches 394(25)
12.1 Productivity Growth and its Measurement
394(1)
12.1.1 Residual-Based Productivity Measurement
394(1)
12.2 International and US Economic Growth and Development
395(3)
12.2.1 The Neoclassical Production Function and Economic Growth
396(1)
12.2.2 Modifications of the Neoclassical Production Function and Economic Growth Model: Endogenous Growth
396(2)
12.3 The Panel Stochastic Frontier Model: Measurement of Technical and Efficiency Change
398(2)
12.4 Index Number Decompositions of Economic Growth-Innovation and Efficiency Change
400(1)
12.4.1 Index Number Procedures
401(1)
12.5 Regression-Based Decompositions of Economic Growth-Innovation and Efficiency Change
401(2)
12.6 Environmental Factors in Production and Interpretation of Productive Efficiency
403(1)
12.7 The Stochastic Panel Frontier
404(13)
12.7.1 Cornwell, Schmidt, and Sickles (1990) Model
407(4)
12.7.2 Alternative Specifications of Time-Varying Inefficiency: The Kumbhakar (1990) and Battese and Coelli (1992) Models
411(1)
12.7.3 The Lee and Schmidt (1993) Model
412(3)
12.7.4 Panel Stochastic Frontier Technical Efficiency Confidence Intervals
415(1)
12.7.5 Fixed versus Random Effects: A Prelude to More General Panel Treatments
416(1)
12.8 Concluding Remarks
417(1)
12.9 Exercises
417(2)
13 Panel Data and Parametric and Semiparametric Stochastic Frontier Models: Second-Generation Approaches 419(31)
13.1 The Park, Sickles, and Simar (1998, 2003, 2007) Models
419(4)
13.1.1 Implementation
420(3)
13.2 The Latent Class Models
423(3)
13.2.1 Implementation
425(1)
13.3 The Aim, Lee, and Schmidt (2007) Model
426(2)
13.3.1 Implementation
426(2)
13.4 Bounded Inefficiency Model
428(1)
13.5 The Kneip, Sickles, and Song (2012) Model
428(4)
13.5.1 Implementation
430(2)
13.6 The Ahn, Lee, and Schmidt (2013) Model
432(3)
13.6.1 Implementation
433(2)
13.7 The Liu, Sickles, and Tsionas (2017) Model
435(2)
13.7.1 Implementation
436(1)
13.8 The True Fixed Effects Model
437(3)
13.8.1 Implementation
438(2)
13.9 True Random Effects Models
440(2)
13.9.1 The Tsionas and Kumbhakar Extension of the Colombi, Kumbhakar, Martini, and Vittadini (2014) Four Error Component Model
440(2)
13.9.2 Extensions on the Four Error Component Model
442(1)
13.10 Spatial Panel Frontiers
442(6)
13.10.1 The Han and Sickles (2019) Model
445(3)
13.11 Concluding Remarks
448(1)
13.12 Exercises
448(2)
14 Endogeneity iit Structural and Non-Structural Models of Productivity 450(19)
14.1 The Endogeneity Problem
450(1)
14.2 Simultaneity
451(1)
14.3 Selection Bias
452(1)
14.4 Traditional Solutions to the Endogeneity Problem Caused by Input Choices and Selectivity
453(1)
14.5 Structural Estimation
454(4)
14.6 Endogeneity in Nonstructural Models of Productivity: The Stochastic Frontier Model
458(5)
14.7 Endogeneity and True Fixed Effects Models
463(1)
14.8 Endogeneity in Environmental Production and in Directional Distance Functions
464(1)
14.9 Endogeneity, Copulas, and Stochastic Metafrontiers
465(1)
14.10 Other Types of Orthogonality Conditions to Deal with Endogeneity
466(1)
14.11 Concluding Remarks
467(1)
14.12 Exercises
467(2)
15 Dynamic Models of Productivity and Efficiency 469(14)
15.1 Nonparametric Panel Data Models of Productivity Dynamics
469(7)
15.1.1 Revisiting the Dynamic Output Distance Function and the Intertemporal Malmquist Productivity Index: Cointegration and Convergence of Efficiency Scores in Productivity Panels
470(6)
15.2 Parametric Panel Data Models of Productivity Dynamics
476(4)
15.2.1 The Ahn, Good, and Sickles (2000) Dynamic Stochastic Frontier
477(3)
15.3 Extensions of the Ahn, Good, and Sickles (2000) Model
480(1)
15.4 Concluding Remarks
481(1)
15.5 Exercises
482(1)
16 Semiparametric Estimation, Shape Restrictions, and Model Averaging 483(26)
16.1 Semiparametric Estimation of Production Frontiers
484(9)
16.1.1 Kernel-Based Estimators
484(3)
16.1.2 Local Likelihood Approach
487(2)
16.1.3 Local Profile Likelihood Approach
489(1)
16.1.4 Local Least-Squares Approache
490(3)
16.2 Semiparametric Estimation of an Average Production Function with Monotonicity and Concavity
493(6)
16.2.1 The Use of Transformations to Impose Constraints
494(1)
16.2.2 Statistical Modeling
495(2)
16.2.3 Empirical Example using the Coelli Data
497(1)
16.2.4 Nonparametric SFA Methods with Monotonicity and Shape Constraints
497(2)
16.3 Model Averaging
499(7)
16.3.1 Insights from Economics and Statistics
499(2)
16.3.2 Insights from Time-Series Forecasting
501(1)
16.3.3 Frequentist Model Averaging
501(1)
16.3.4 The Hansen (2007) and Hansen and Racine (2012) Model Averaging Estimators
502(4)
16.3.5 Other Model Averaging Approaches to Develop Consensus Productivity Estimates
506(1)
16.4 Concluding Remarks
506(1)
16.5 Exercises
507(2)
17 Data Measurement Issues, the KLEMS Project, Other Data Sets for Productivity Analysis, and Productivity and Efficiency Software 509(30)
17.1 Data Measurement Issues
509(3)
17.2 Special Issue of the International Productivity Monitor from the Madrid Fourth World KLEMS Conference: Non-Frontier Perspectives on Productivity Measurement 511
17.2.1 Productivity and Economic Growth in the World Economy: An Introduction
512(1)
17.2.2 Recent Trends in Europe's Output and Productivity Growth Performance at the Sector Level, 2002-2015
513(1)
17.2.3 The Role of Capital Accumulation in the Evolution of Total Factor Productivity in Spain
514(2)
17.2.4 Sources of Productivity and Economic Growth in Latin America and the Caribbean, 1990-2013
516(1)
17.2.5 Argentina Was Not the Productivity and Economic Growth Champion of Latin America
517(1)
17.2.6 How Does the Productivity and Economic Growth Performance of China and India Compare in the Post-Reform Era, 1981-2011?
518(2)
17.2.7 Can Intangible Investments Ease Declining Rates of Return on Capital in Japan?
520(2)
17.2.8 Net Investment and Stocks of Human Capital in the United States, 1975-2013
522(1)
17.2.9 ICT Services and Their Prices: What Do They Tell Us About Productivity and Technology?
523(2)
17.2.10 Productivity Measurement in Global Value Chains
525(2)
17.2.11 These Studies Speak of Efficiency but Measure it with Non-Frontier Methods
527(1)
17.3 Datacets for Illustrations
527(1)
17.4 Publicly Available Data Sets Useful for Productivity Analysis
528(4)
17.4.1 Amadeus
528(1)
17.4.2 Bureau of Economic Analysis
528(1)
17.4.3 Bureau of Labor Statistics
528(1)
17.4.4 Business Dynamics Statistics
528(1)
17.4.5 Center for Economic Studies
529(1)
17.4.6 CompNet
529(1)
17.4.7 DIW Berlin
529(1)
17.4.8 Longitudinal Business Database
529(1)
17.4.9 National Bureau of Economic Research
529(1)
17.4.10 OECD
530(1)
17.4.11 OECD STAN
530(1)
17.4.12 Penn World Table
530(1)
17.4.13 Statistics Canada
530(1)
17.4.14 UK Fame
530(1)
17.4.15 UK Office of National Statistics
531(1)
17.4.16 UNIDO
531(1)
17.4.17 USDA-ERS
531(1)
17.4.18 World Bank
531(1)
17.4.19 World Input-Output Database
531(1)
17.4.20 World KLEMS Database
532(1)
17.5 Productivity and Efficiency Software
532(1)
17.6 Global Options
533(2)
17.6.1 Model Setup
533(1)
17.6.2 Weighted Averages of Efficiencies
533(1)
17.6.3 Truncation
534(1)
17.6.4 Figures and Tables
534(1)
17.7 Models
535(3)
17.7.1 Schmidt and Sickles (1984) Models
535(1)
17.7.2 Hausman and Taylor (1981) Model
535(1)
17.7.3 Park, Sickles, and Simar (1998, 2003, 2007) Models
535(1)
17.7.4 Cornwell, Schmidt, and Sickles (1990) Model
535(1)
17.7.5 Kneip, Sickles, and Song (2012) Model
536(1)
17.7.6 Battese and Coelli (1992) Model
536(1)
17.7.7 Almanidis, Qian, and Sickles (2014) Model
536(1)
17.7.8 Jeon and Sickles (2004) Model
536(1)
17.7.9 Simar and Zelenyuk (2006) Model
536(1)
17.7.10 Simar and Zelenyuk (2007) Model
537(1)
17.8 Concluding Remarks
538(1)
Afterword 539(2)
Bibliography 541(47)
Subject Index 588(6)
Author Index 594
Robin C. Sickles is the Reginald Henry Hargrove Professor of Economics and Professor of Statistics at Rice University, Houston. He served as Editor-in-Chief of the Journal of Productivity Analysis as well as an Associate Editor for a number of other economics and econometrics journals. He is currently an Associate Editor of the Journal of Econometrics. Valentin Zelenyuk is the Australian Research Council Future Fellow at the School of Economics at the University of Queensland, where he served as Research Director and Director of the Centre for Efficiency and Productivity Analysis. He is an Associate Editor of the Journal of Productivity Analysis and the Data Envelopment Analysis Journal.