Complex Network-Based Global Value Chain Accounting System: From the Perspective of Econophysics

ISBN-13: 9789811692666 / Angielski / Miękka / 2023 / 358 str.

Lizhi Xing

Opis

This book aims to theoretically and empirically enrich the GVC accounting framework with statistical physics and complex network theory from the perspective of econophysics, thus adding up to the existing theories. Besides, it also aims at capturing the essences of network models such as topological complexity, hierarchy, transmissibility, interaction, and causality and reflecting the objective interrelations among economies or between economies and economic systems on the GVC, so as to reveal the inherent evolution of the cross-regional and even global economic systems.

Spis treści

Preface 1

Part I  Background 3

Chapter 1  Fundamental Issues in This Book 4

1.1 Concept of Econophysics 4

1.2 Purpose of Book 4

1.3 Literature Review 5

1.3.1 Industrial Complex Network 6

1.3.2 Global Value Chain 7

1.3.3 Input-Output Network 8

1.4 Data Structure 10

1.4.1 Advantage of IO Table 10

1.4.2 Available ICIO Database 10

1.4.3 Hierarchy of Economies 14

1.4.4 Classification of Industrial Sectors 24

1.5 ICIO Network Model 24

1.6 Summary 30

Part II  Topological Structure 32

Chapter 2  Recognize the Trade Roles of Industrial Sectors 33

2.1 Introduction 33

2.2 Definition 34

2.2.1 Trade Types on the GVC 34

2.2.2 Decomposition of Trade Roles 36

2.3 Measurement 38

2.3.1 Statistical Inference on TBPs 38

2.3.2 Measurment of Dependency 40

2.4 Empirical Analysis: Economies’ Two Sorts of Dependence on Foreign Trade 41

2.4.1 Statistics on All Economies 42

2.4.2 Significance of Dual Circulation 43

2.4.3 Economic Meanings of Network-Based Dependency 45

2.5 Summary 47

Chapter 3  Probe the Industrial Linkages Reasonably and Effectively 48

3.1 Introduction 48

3.2 Methodology 49

3.2.1 Path Issue in Similarity-Weight Network 49

3.2.2 Revised Floyd-Warshall Algorithm 50

3.2.3 Theoretical Basis of SRPL in GIVCN Model 52

3.2.4 Computation of SRPL in Consideration of Self-Loops 55

3.3 Empirical Analysis: Fragments of GVC 57

3.3.1 Single-Tuple Motif 57

3.3.2 Double-Tuple Motif 58

3.3.3 Triple-Tuple Motif 60

3.4 Summary 65

Chapter 4  Find the Vital Industrial Sectors and IO Relations 66

4.1 Introduction 66

4.2 Measurement 67

4.2.1 Average/Maximum Strongest Relevance Degree 67

4.2.2 Betweenness Centrality of Node 68

4.2.3 Betweenness Centrality of Edge 70

4.2.4 Closeness Centrality of Node 71

4.3 Connectedness/Compactness of NVC 73

4.4 Pivotability of Industrial Sectors 77

4.4.1 Overall Statistics 77

4.4.2 Cross-National Analysis 78

4.4.3 Robustness Analysis 81

4.5 Pivotability of IO Relations 82

4.5.1 Heterogeneity of Pivotability 82

4.5.2 Domestic Pivotability 86

4.5.3 International Pivotability 87

4.5.4 Global Pivotability 89

4.6 Coordinates of Industrial Sectors 91

4.6.1 Overall Statistics 91

4.6.2 Time-Series Analysis 95

4.6.3 Cross-Country Analysis 99

4.6.4 Cross-Sector Analysis 104

4.7 Comparison with Similar Studies 108

4.8 Summary 108

Part III  Markov Process 111

Chapter 5  Measure the Global Impact of Industrial Sectors 112

5.1 Introduction 112

5.2 Methodology 112

5.2.1 Features of Value Stream in Economic System 112

5.2.2 Industrial Impact on the GVC 113

5.2.3 Structural Holes Theory in Dynamic Network 114

5.3 Measurement 115

5.3.1 Random Walk Centrality 116

5.3.2 Global Industrial Impact Coefficient 117

5.4 Empirical Analysis: Macroeconomic Trend Forecast 117

5.4.1 Comparative Analysis with Classic IO Theory 117

5.4.2 Robustness Analysis 120

5.4.3 Statistics on Major Economies 125

5.4.4 Geographical Distribution of GIIC 129

5.4.5 Correlation Analysis with GDP 131

5.5 Summary 132

Chapter 6  Measure the Impact of Final Demands on the Global Production System 134

6.1 Introduction 134

6.2 Measurement 134

6.2.1 Counting First Passage Betweenness 134

6.2.2 Global Demand Dependence Index 136

6.3 Empirical Analysis: Macroeconomic Trend Forecast 136

6.3.1 Robustness Analysis 136

6.3.2 The Econometric Analysis of Import/Export and GDDI 138

6.3.3 Statistics on the Global Level 142

6.3.4 Statistics on the Sectoral Level 143

6.4 Discussion 146

6.4.1 Economic Difference between Static Metrics and Dynamic Metrics 146

6.4.2 Intrinsic Relationship of Dynamic Metrics 147

6.4.3 Correlation Analysis between Static Metrics and Dynamic Metrics 148

6.5 Summary 150

Chapter 7  Industrially Economic Impacts of Trump Administration's Trade Policy toward China 152

7.1 Bibliometrics on Sino-US Trade War 152

7.2 Decomposition of GIIC 158

7.3 Decomposition of GDDI 160

7.4 Regression Analysis 163

7.5 Simulation on the Year of 2020 165

7.6 Conclusion 167

Part IV  Competition and Collaboration 170

Chapter 8  Quantify the Competitive Strength and Weakness of Economies 171

8.1 Introduction 171

8.2 Methodology 172

8.2.1 Bipartite Graph 172

8.2.2 Resource Allocation Process 173

8.3 Modeling 176

8.3.1 Database Selection 176

8.3.2 Modeling Framework 176

8.3.3 GIVCNBG Model 178

8.3.4 GIRCN Model 180

8.4 Measurement 181

8.4.1 Sector-Level Indices 181

8.4.2 Country-Level Indices 183

8.4.3 Correlation with GDP 185

8.5 Empirical Analysis: Competitive Strength of TPP-Related Nations 187

8.5.1 Time-Series Analysis on TPP-Relat  ed Nations 188

8.5.2 Simulation on International Trade Policy 192

8.6 Summary 195

Chapter 9  Quantify the Collaborative Opportunity and Threat of Economies 201

9.1 Introduction 201

9.2 Methodology 202

9.3 Modeling 204

9.3.1 Database Selection 204

9.3.2 GPCCN Model 207

9.4 Measurement 211

9.4.1 Sector-Level Indices 211

9.4.2 Country-Level Indices 212

9.4.3 Correlation Analysis between Competitive Strengths and Collaborative Opportunities 215

9.5 Empirical Analysis: Collaborative Opportunity of BRI-Related Nations 216

9.5.1 Simulation on Asian Nations 217

9.5.2 Simulation on European Nations 222

9.5.3 Simulation on African Nations 226

9.5.4 Results and Discussions 230

9.6 Summary 231

Part V  Evolutionary Mechanism 233

Chapter 10  Extract the Backbone of Global Value Chain 234

10.1 Introduction 234

10.2 Formal Problem Setting 235

10.2.1 Global Threshold 236

10.2.2 Disparity Filter 236

10.3 Proposed Algorithms 237

10.3.1 Searching Paths 238

10.3.2 Filtering Edges 238

10.3.3 Mixed Strategy 240

10.4 Results and Discussions 241

10.4.1 Preservation of Structural Information 242

10.4.2 Preservation of Functional Information 244

10.5 Summary 246

Chapter 11  Identify the Worldwide Industrial Transfer Pattern 247

11.1 Introduction 247

11.1.1 Neoclassical School 248

11.1.2 Behavioral School 248

11.1.3 Institutional School 248

11.2 Methodology 249

11.2.1 Econometric Model in Industrial Economics 249

11.2.2 Link Prediction in Complex Networks 250

11.3 Framework 251

11.3.1 GISRN Model 251

11.3.2 Training and Evaluation Metrics 252

11.3.3 Link Prediction Indices 253

11.3.4 Accuracy of Prediction Algorithms 256

11.4 Empirical Analysis I: Evolutionary Characteristics of Globalization 257

11.4.1 Density of GVC Backbone 257

11.4.2 Centralization of GVC Backbone 259

11.4.3 Global Efficiency of GVC Backbone 260

11.5 Empirical Analysis II: Evolutionary Mechanism of Globalization 261

11.5.1 Overall Statistics 263

11.5.2 Industrial Convergence 264

11.5.3 Mega-Merger Tendency 266

11.5.4 Industrial Agglomeration 267

11.5.5 Niche Advantage 267

11.6 Summary 268

Chapter 12  Depict the Nested Structure of Production System 270

12.1 Introduction 271

12.2 Modeling 272

12.3 Measurement 274

12.3.1 Sorting Methods 274

12.3.2 Nestedness Quantification 275

12.4 Statistical Analysis 278

12.4.1 Divergence Analysis 278

12.4.2 Trend Analysis 280

12.4.3 Robustness Analysis 282

12.4.4 Evolutionary Mechanism 284

12.5 Econometric Analysis 285

12.5.1 Correlation between Variables 286

12.5.2 Regression Model 290

12.6 Empirical Analysis II: Brexit’s Impact on European Nations 297

12.6.1 Brexit 297

12.6.2 Simulation Setting 298

12.6.3 Results and Discussions 298

12.7 Empirical Analysis II: RCEP’s Economic Significance to Relevant Nations 301

12.7.1 RCEP 301

12.7.2 Simulation Setting 302

12.7.3 Results and Discussions 302

12.8 Summary 307

Part VI  Causal Inference 309

Chapter 13  Connect the Structural Features and Economic Status 310

13.1 Introduction 310

13.2 Literature Review 311

13.3 Econometric Model 312

13.4 Hypotheses 313

13.4.1 Hypothesis Formulation 313

13.4.2 Hypothesis Testing 314

13.5 Results and Discussions 316

13.5.1 The Effects of Structural Capital 316

13.5.2 The Effects of Relational Capital 318

13.5.3 The Effects of Cognitive Capital 319

13.6 Summary 320

Postscript 321

Appendix 323

References 337

O autorze

Lizhi Xing, Ph. D. in management, is Associate Researcher in College of Economics and Management, Beijing University of Technology. His work adopts Inter-Country Input–Output (ICIO), also called Multi-Region Input–Output (MRIO), data to build weighted and directed complex network models, which are the basis of measurement, simulation, and prediction on the international trade.