List of Figures |
|
xvii | |
List of Tables |
|
xix | |
Preface |
|
xxi | |
Acknowledgments |
|
xxiv | |
Introduction |
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1 | (8) |
1 Production Theory: Primal Approach |
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9 | (30) |
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1.1 Set Characterization of Technology |
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9 | (4) |
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1.2 Axioms for Technology Characterization |
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13 | (6) |
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1.3 Functional Characterization of Technology: The Primal Approach |
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19 | (7) |
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1.4 Modeling Returns to Scale in Production |
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26 | (5) |
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1.5 Measuring Returns to Scale in Production: The Scale Elasticity Approach |
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31 | (3) |
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1.6 Directional Distance Function |
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34 | (2) |
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1.7 Concluding Remarks on the Literature |
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36 | (1) |
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37 | (2) |
2 Production Theory: Dual Approach |
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39 | (20) |
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2.1 Cost Minimizing Behavior and Cost Function |
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39 | (3) |
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2.2 The Duality Nature of Cost Function |
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42 | (3) |
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2.3 Some Examples of Using the Cost Function |
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45 | (2) |
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2.4 Sufficient Conditions for Cost and Input Demand Functions |
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47 | (2) |
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2.5 Benefits Coming from the Duality Theory for the Cost Function: A Summary |
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49 | (1) |
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2.6 Revenue Maximization Behavior and the Revenue Function |
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49 | (5) |
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2.7 Profit-Maximizing Behavior |
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54 | (3) |
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57 | (1) |
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57 | (2) |
3 Efficiency Measurement |
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59 | (37) |
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3.1 Various Measures of Technical Efficiency |
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59 | (6) |
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3.2 Relationships Among Efficiency Measures |
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65 | (9) |
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3.2.1 Shephard vs. Directional Distance Functions |
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66 | (2) |
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3.2.2 Farrell vs. Russell Measures |
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68 | (2) |
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3.2.3 Directional Distance Function vs. Additive Measure |
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70 | (2) |
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3.2.4 Hyperbolic vs. Others |
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72 | (2) |
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3.3 Properties of Technical Efficiency Measures |
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74 | (6) |
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3.4 Cost and Revenue Efficiency |
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80 | (2) |
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82 | (2) |
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3.6 Slack-Based Measures of Efficiency |
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84 | (5) |
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3.7 Unifying Different Approaches |
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89 | (1) |
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3.8 Remarks on the Literature |
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90 | (1) |
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91 | (1) |
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92 | (4) |
4 Productivity Indexes: Part 1 |
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96 | (47) |
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4.1 Productivity vs. Efficiency |
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96 | (3) |
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4.2 Growth Accounting Approach |
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99 | (3) |
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4.3 Economic Price Indexes |
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102 | (4) |
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4.4 Economic Quantity Indexes |
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106 | (4) |
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4.5 Economic Productivity Indexes |
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110 | (4) |
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4.6 Decomposition of Productivity Indexes |
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114 | (3) |
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4.7 Directional Productivity Indexes |
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117 | (2) |
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4.8 Directional Productivity Change Indicators |
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119 | (1) |
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4.9 Relationships among Productivity Indexes |
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120 | (8) |
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4.10 Indexes vs. Growth Accounting |
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128 | (1) |
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4.11 Multilateral Comparisons, Transitivity, and Circularity |
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129 | (12) |
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4.11.1 General Remarks on Transitivity |
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129 | (1) |
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4.11.2 Transitivity and Productivity Indexes |
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130 | (7) |
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4.11.3 Dealing with Non-Transitivity |
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137 | (3) |
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4.11.4 What to Do in Practice? |
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140 | (1) |
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141 | (1) |
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141 | (2) |
5 Aggregation |
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143 | (23) |
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5.1 The Aggregation Problem |
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143 | (2) |
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5.2 Aggregation in Output-Oriented Framework |
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145 | (7) |
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5.2.1 Individual Revenue and Farrell-Type Efficiency |
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145 | (1) |
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5.2.2 Group Farrell-Type Efficiency |
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146 | (4) |
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5.2.3 Aggregation over Groups |
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150 | (2) |
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5.3 Price-Independent Weights |
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152 | (1) |
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5.4 Group-Scale Elasticity Measures |
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153 | (5) |
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5.5 Aggregation of Productivity Indexes |
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158 | (6) |
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5.5.1 Individual Malmquist Productivity Indexes |
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158 | (1) |
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5.5.2 Group Productivity Measures |
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159 | (1) |
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5.5.3 Aggregation of the MPI |
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160 | (2) |
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5.5.4 Geometric vs. Harmonic Averaging of MPI |
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162 | (1) |
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5.5.5 Decomposition into Aggregate Changes |
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163 | (1) |
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164 | (1) |
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165 | (1) |
6 Functional Forms: Primal and Dual Functions |
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166 | (41) |
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6.1 Functional Forms for Primal Production Analysis |
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167 | (18) |
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6.1.1 The Elasticity of Substitution: A Review of the Allen, Hicks, Morishima, and Uzawa Characterizations of Substitution Possibilities |
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168 | (3) |
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6.1.2 Linear, Leontief, Cobb-Douglas, CES, and CRESH Production Functions |
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171 | (4) |
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6.1.3 Flexible-Functional Forms and Second-Order Series Approximations of the Production Function |
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175 | (7) |
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6.1.4 Choice of Functional Form Based on Solutions to Functional Equations |
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182 | (3) |
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6.2 Functional Forms for Distance Function Analysis |
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185 | (2) |
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6.3 Functional Forms for Cost Analysis |
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187 | (8) |
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6.3.1 Generalized Leontief |
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189 | (1) |
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6.3.2 Generalized Cobb-Douglas |
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190 | (1) |
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190 | (2) |
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6.3.4 CES-Translog and CES-Generalized Leontief |
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192 | (1) |
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6.3.5 The Symmetric Generalized McFadden |
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193 | (2) |
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6.4 Technical Change, Production Dynamics, and Quasi-Fixed Factors |
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195 | (4) |
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6.5 Functional Forms for Revenue Analysis |
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199 | (2) |
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6.6 Functional Forms for Profit Analysis |
|
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201 | (2) |
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6.7 Nonparametric Econometric Approaches to Model the Distance, Cost, Revenue, and Profit Functions |
|
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203 | (1) |
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204 | (1) |
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205 | (2) |
7 Productivity Indexes: Part 2 |
|
207 | (36) |
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7.1 Decomposition of the Value Change Index |
|
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207 | (1) |
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7.2 The Statistical Approach to Price Indexes |
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208 | (2) |
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7.3 Quantity Indexes: The Direct Approach |
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210 | (1) |
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7.4 Quantity Indexes: The Indirect Approach |
|
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211 | (2) |
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7.5 Productivity Indexes: Statistical Approach |
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213 | (1) |
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7.6 Properties of Index Numbers |
|
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214 | (7) |
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7.7 Some Key Results in the Statistical Approach to Index Numbers |
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221 | (4) |
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7.8 Relationship between Economic and Statistical Approaches to Index Numbers |
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225 | (13) |
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7.8.1 Flexible Functional Forms |
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225 | (2) |
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7.8.2 Relationships for the Price Indexes |
|
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227 | (2) |
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7.8.3 Relationships for the Quantity Indexes |
|
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229 | (5) |
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7.8.4 Relationships for the Productivity Indexes |
|
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234 | (4) |
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7.9 Concluding Remarks on the Literature |
|
|
238 | (1) |
|
|
239 | (1) |
|
|
240 | (3) |
8 Envelopment-Type Estimators |
|
243 | (43) |
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8.1 Introduction to Activity Analysis Modeling |
|
|
243 | (8) |
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8.2 Non-CRS Activity Analysis Models |
|
|
251 | (5) |
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256 | (5) |
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8.4 Estimation of Cost, Revenue, and Profit Functions and Related Efficiency Measures |
|
|
261 | (6) |
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8.5 Estimation of Slack-Based Efficiency |
|
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267 | (2) |
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8.6 Technologies with Weak Disposability |
|
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269 | (4) |
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8.7 Modeling Non-Convex Technologies |
|
|
273 | (4) |
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8.8 Intertemporal Context |
|
|
277 | (1) |
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8.9 Relationship between CCR and Farrell |
|
|
278 | (5) |
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283 | (2) |
|
|
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) |
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9.1.2 Convergence Rates of DEA and FDH |
|
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289 | (1) |
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9.1.3 The Dimensionality Problem |
|
|
290 | (2) |
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9.2 Introduction to Bootstrap |
|
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292 | (15) |
|
9.2.1 Bootstrap and the Plug-In Principle |
|
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292 | (2) |
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9.2.2 Bootstrap and the Analogy Principle |
|
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294 | (2) |
|
9.2.3 Practical Implementation of Bootstrap |
|
|
296 | (1) |
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9.2.4 Bootstrap for Standard Errors of an Estimator |
|
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297 | (2) |
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9.2.5 Bootstrapping for Bias and Mean Squared Error |
|
|
299 | (2) |
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9.2.6 Bootstrap Estimation of Confidence Intervals |
|
|
301 | (2) |
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9.2.7 Consistency of Bootstrap |
|
|
303 | (4) |
|
9.3 Bootstrap for DEA and FDH |
|
|
307 | (7) |
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9.3.1 Bootstrap for Individual Efficiency Estimates |
|
|
307 | (7) |
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|
314 | (1) |
|
|
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 |
|
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321 | (13) |
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10.2.1 Density Estimation |
|
|
321 | (4) |
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10.2.2 Statistical Tests about Distributions of Efficiency |
|
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325 | (9) |
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10.3 Regression of Efficiency on Covariates |
|
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334 | (14) |
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10.3.1 Algorithm 1 SW2007 |
|
|
335 | (1) |
|
10.3.2 Algorithm 2 SW2007 |
|
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336 | (2) |
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10.3.3 Inference in SW2007 Framework |
|
|
338 | (2) |
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10.3.4 Extension to Panel Data Context |
|
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340 | (2) |
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10.3.5 Caveats of the Two-Stage DEA |
|
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342 | (6) |
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10.4 Central Limit Theorems for DEA and FDH |
|
|
348 | (2) |
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|
348 | (2) |
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|
350 | (1) |
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|
350 | (2) |
11 Cross-Sectional Stochastic Frontiers: An Introduction |
|
352 | (42) |
|
11.1 The Stochastic Frontier Paradigm |
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355 | (2) |
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357 | (2) |
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11.3 Parametric Statistical Approaches to Determine the Boundary of the Level Sets: The "Full Frontier" |
|
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359 | (6) |
|
11.3.1 Aigner-Chu Methodology |
|
|
360 | (2) |
|
11.3.2 Afriat-Richmond Methodology |
|
|
362 | (3) |
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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 |
|
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371 | (1) |
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11.4.2 Estimation of Individual Inefficiencies |
|
|
372 | (2) |
|
11.4.3 Hypothesis Tests and Confidence Intervals |
|
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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 |
|
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380 | (7) |
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387 | (1) |
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388 | (1) |
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389 | (5) |
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11.7.1 Derivation of E(8i) |
|
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389 | (1) |
|
11.7.2 Derivation of the Moments of a Half-Normal Random Variable |
|
|
389 | (2) |
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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) |
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12.1 Productivity Growth and its Measurement |
|
|
394 | (1) |
|
12.1.1 Residual-Based Productivity Measurement |
|
|
394 | (1) |
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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) |
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12.3 The Panel Stochastic Frontier Model: Measurement of Technical and Efficiency Change |
|
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398 | (2) |
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12.4 Index Number Decompositions of Economic Growth-Innovation and Efficiency Change |
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|
400 | (1) |
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12.4.1 Index Number Procedures |
|
|
401 | (1) |
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12.5 Regression-Based Decompositions of Economic Growth-Innovation and Efficiency Change |
|
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401 | (2) |
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12.6 Environmental Factors in Production and Interpretation of Productive Efficiency |
|
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403 | (1) |
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12.7 The Stochastic Panel Frontier |
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404 | (13) |
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12.7.1 Cornwell, Schmidt, and Sickles (1990) Model |
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407 | (4) |
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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) |
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12.7.4 Panel Stochastic Frontier Technical Efficiency Confidence Intervals |
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415 | (1) |
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12.7.5 Fixed versus Random Effects: A Prelude to More General Panel Treatments |
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416 | (1) |
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|
417 | (1) |
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|
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 |
|
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419 | (4) |
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420 | (3) |
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13.2 The Latent Class Models |
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423 | (3) |
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425 | (1) |
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13.3 The Aim, Lee, and Schmidt (2007) Model |
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426 | (2) |
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426 | (2) |
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13.4 Bounded Inefficiency Model |
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428 | (1) |
|
13.5 The Kneip, Sickles, and Song (2012) Model |
|
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428 | (4) |
|
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430 | (2) |
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13.6 The Ahn, Lee, and Schmidt (2013) Model |
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432 | (3) |
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433 | (2) |
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13.7 The Liu, Sickles, and Tsionas (2017) Model |
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435 | (2) |
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436 | (1) |
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13.8 The True Fixed Effects Model |
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437 | (3) |
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438 | (2) |
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13.9 True Random Effects Models |
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440 | (2) |
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13.9.1 The Tsionas and Kumbhakar Extension of the Colombi, Kumbhakar, Martini, and Vittadini (2014) Four Error Component Model |
|
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440 | (2) |
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13.9.2 Extensions on the Four Error Component Model |
|
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442 | (1) |
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13.10 Spatial Panel Frontiers |
|
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442 | (6) |
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13.10.1 The Han and Sickles (2019) Model |
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445 | (3) |
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|
448 | (1) |
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|
448 | (2) |
14 Endogeneity iit Structural and Non-Structural Models of Productivity |
|
450 | (19) |
|
14.1 The Endogeneity Problem |
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450 | (1) |
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|
451 | (1) |
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|
452 | (1) |
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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 |
|
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458 | (5) |
|
14.7 Endogeneity and True Fixed Effects Models |
|
|
463 | (1) |
|
14.8 Endogeneity in Environmental Production and in Directional Distance Functions |
|
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464 | (1) |
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14.9 Endogeneity, Copulas, and Stochastic Metafrontiers |
|
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465 | (1) |
|
14.10 Other Types of Orthogonality Conditions to Deal with Endogeneity |
|
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466 | (1) |
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|
467 | (1) |
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|
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 |
|
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480 | (1) |
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|
481 | (1) |
|
|
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) |
|
|
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) |
|
|
506 | (1) |
|
|
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) |
|
|
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) |
|
|
529 | (1) |
|
|
529 | (1) |
|
17.4.8 Longitudinal Business Database |
|
|
529 | (1) |
|
17.4.9 National Bureau of Economic Research |
|
|
529 | (1) |
|
|
530 | (1) |
|
|
530 | (1) |
|
|
530 | (1) |
|
17.4.13 Statistics Canada |
|
|
530 | (1) |
|
|
530 | (1) |
|
17.4.15 UK Office of National Statistics |
|
|
531 | (1) |
|
|
531 | (1) |
|
|
531 | (1) |
|
|
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) |
|
|
533 | (2) |
|
|
533 | (1) |
|
17.6.2 Weighted Averages of Efficiencies |
|
|
533 | (1) |
|
|
534 | (1) |
|
17.6.4 Figures and Tables |
|
|
534 | (1) |
|
|
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) |
|
|
538 | (1) |
Afterword |
|
539 | (2) |
Bibliography |
|
541 | (47) |
Subject Index |
|
588 | (6) |
Author Index |
|
594 | |