Complex Network-Based Global Value Chain Accounting System: From the Perspective of Econophysics
Autor Lizhi Xingen Limba Engleză Paperback – 10 feb 2023
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Specificații
ISBN-13: 9789811692666
ISBN-10: 9811692661
Pagini: 358
Ilustrații: XV, 358 p. 160 illus., 153 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.53 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
ISBN-10: 9811692661
Pagini: 358
Ilustrații: XV, 358 p. 160 illus., 153 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.53 kg
Ediția:1st ed. 2022
Editura: Springer Nature Singapore
Colecția Springer
Locul publicării:Singapore, Singapore
Cuprins
Preface1
Part I Background3
Chapter 1 Fundamental Issues in This Book4
1.1 Concept of Econophysics4
1.2 Purpose of Book4
1.3 Literature Review51.3.1 Industrial Complex Network6
1.3.2 Global Value Chain7
1.3.3 Input-Output Network8
1.4 Data Structure10
1.4.1 Advantage of IO Table10
1.4.2 Available ICIO Database10
1.4.3 Hierarchy of Economies14
1.4.4 Classification of Industrial Sectors24
1.5 ICIO Network Model24
1.6 Summary30
Part II Topological Structure32
Chapter 2 Recognize the Trade Roles of Industrial Sectors33
2.1 Introduction33
2.2 Definition34
2.2.1 Trade Types on the GVC34
2.2.2 Decomposition of Trade Roles36
2.3 Measurement382.3.1 Statistical Inference on TBPs38
2.3.2 Measurment of Dependency40
2.4 Empirical Analysis: Economies’ Two Sorts of Dependence on Foreign Trade41
2.4.1 Statistics on All Economies42
2.4.2 Significance of Dual Circulation43
2.4.3 Economic Meanings of Network-Based Dependency45
2.5 Summary47
Chapter 3 Probe the Industrial Linkages Reasonably and Effectively48
3.1 Introduction48
3.2 Methodology49
3.2.1 Path Issue in Similarity-Weight Network49
3.2.2 Revised Floyd-Warshall Algorithm50
3.2.3 Theoretical Basis of SRPL in GIVCN Model52
3.2.4 Computation of SRPL in Consideration of Self-Loops55
3.3 Empirical Analysis: Fragments of GVC57
3.3.1 Single-Tuple Motif573.3.2 Double-Tuple Motif58
3.3.3 Triple-Tuple Motif60
3.4 Summary65
Chapter 4 Find the Vital Industrial Sectors and IO Relations66
4.1 Introduction66
4.2 Measurement67
4.2.1 Average/Maximum Strongest Relevance Degree67
4.2.2 Betweenness Centrality of Node68
4.2.3 Betweenness Centrality of Edge70
4.2.4 Closeness Centrality of Node71
4.3 Connectedness/Compactness of NVC734.4 Pivotability of Industrial Sectors77
4.4.1 Overall Statistics77
4.4.2 Cross-National Analysis78
4.4.3 Robustness Analysis81
4.5 Pivotability of IO Relations82
4.5.1 Heterogeneity of Pivotability82
4.5.2 Domestic Pivotability86
4.5.3 International Pivotability87
4.5.4 Global Pivotability89
4.6 Coordinates of Industrial Sectors91
4.6.1 Overall Statistics914.6.2 Time-Series Analysis95
4.6.3 Cross-Country Analysis99
4.6.4 Cross-Sector Analysis104
4.7 Comparison with Similar Studies108
4.8 Summary108
Part III Markov Process111
Chapter 5 Measure the Global Impact of Industrial Sectors112
5.1 Introduction112
5.2 Methodology112
5.2.1 Features of Value Stream in Economic System112
5.2.2 Industrial Impact on the GVC113
5.2.3 Structural Holes Theory in Dynamic Network114
5.3 Measurement115
5.3.1 Random Walk Centrality116
5.3.2 Global Industrial Impact Coefficient117
5.4 Empirical Analysis: Macroeconomic Trend Forecast117
5.4.1 Comparative Analysis with Classic IO Theory117
5.4.2 Robustness Analysis120
5.4.3 Statistics on Major Economies125
5.4.4 Geographical Distribution of GIIC129
5.4.5 Correlation Analysis with GDP131
5.5 Summary132Chapter 6 Measure the Impact of Final Demands on the Global Production System134
6.1 Introduction134
6.2 Measurement134
6.2.1 Counting First Passage Betweenness134
6.2.2 Global Demand Dependence Index136
6.3 Empirical Analysis: Macroeconomic Trend Forecast136
6.3.1 Robustness Analysis136
6.3.2 The Econometric Analysis of Import/Export and GDDI138
6.3.3 Statistics on the Global Level142
6.3.4 Statistics on the Sectoral Level143
6.4 Discussion146
6.4.1 Economic Difference between Static Metrics and Dynamic Metrics146
6.4.2 Intrinsic Relationship of Dynamic Metrics147
6.4.3 Correlation Analysis between Static Metrics and Dynamic Metrics148
6.5 Summary150
Chapter 7 Industrially Economic Impacts of Trump Administration's Trade Policy toward China1527.1 Bibliometrics on Sino-US Trade War152
7.2 Decomposition of GIIC158
7.3 Decomposition of GDDI160
7.4 Regression Analysis163
7.5 Simulation on the Year of 2020165
7.6 Conclusion167
Part IV Competition and Collaboration170
Chapter 8 Quantify the Competitive Strength and Weakness of Economies171
8.1 Introduction171
8.2 Methodology172
8.2.1 Bipartite Graph172
8.2.2 Resource Allocation Process173
8.3 Modeling176
8.3.1 Database Selection176
8.3.2 Modeling Framework176
8.3.3 GIVCNBG Model178
8.3.4 GIRCN Model180
8.4 Measurement181
8.4.1 Sector-Level Indices181
8.4.2 Country-Level Indices183
8.4.3 Correlation with GDP185
8.5 Empirical Analysis: Competitive Strength of TPP-Related Nations187
8.5.1 Time-Series Analysis on TPP-Relat ed Nations188
8.5.2 Simulation on International Trade Policy192
8.6 Summary195
Chapter 9 Quantify the Collaborative Opportunity and Threat of Economies201
9.1 Introduction201
9.2 Methodology202
9.3 Modeling204
9.3.1 Database Selection204
9.3.2 GPCCN Model207
9.4 Measurement211
9.4.1 Sector-Level Indices211
9.4.2 Country-Level Indices212
9.4.3 Correlation Analysis between Competitive Strengths and Collaborative Opportunities215
9.5 Empirical Analysis: Collaborative Opportunity of BRI-Related Nations216
9.5.1 Simulation on Asian Nations217
9.5.2 Simulation on European Nations222
9.5.3 Simulation on African Nations226
9.5.4 Results and Discussions230
9.6 Summary231
Part V Evolutionary Mechanism233
Chapter 10 Extract the Backbone of Global Value Chain234
10.1 Introduction23410.2 Formal Problem Setting235
10.2.1 Global Threshold236
10.2.2 Disparity Filter236
10.3 Proposed Algorithms237
10.3.1 Searching Paths238
10.3.2 Filtering Edges238
10.3.3 Mixed Strategy240
10.4 Results and Discussions241
10.4.1 Preservation of Structural Information242
10.4.2 Preservation of Functional Information244
10.5 Summary246
Chapter 11 Identify the Worldwide Industrial Transfer Pattern247
11.1 Introduction247
11.1.1 Neoclassical School248
11.1.2 Behavioral School248
11.1.3 Institutional School248
11.2 Methodology249
11.2.1 Econometric Model in Industrial Economics249
11.2.2 Link Prediction in Complex Networks250
11.3 Framework251
11.3.1 GISRN Model251
11.3.2 Training and Evaluation Metrics252
11.3.3 Link Prediction Indices253
11.3.4 Accuracy of Prediction Algorithms256
11.4 Empirical Analysis I: Evolutionary Characteristics of Globalization257
11.4.1 Density of GVC Backbone257
11.4.2 Centralization of GVC Backbone259
11.4.3 Global Efficiency of GVC Backbone260
11.5 Empirical Analysis II: Evolutionary Mechanism of Globalization261
11.5.1 Overall Statistics263
11.5.2 Industrial Convergence264
11.5.3 Mega-Merger Tendency266
11.5.4 Industrial Agglomeration26711.5.5 Niche Advantage267
11.6 Summary268
Chapter 12 Depict the Nested Structure of Production System270
12.1 Introduction271
12.2 Modeling272
12.3 Measurement274
12.3.1 Sorting Methods274
12.3.2 Nestedness Quantification275
12.4 Statistical Analysis278
12.4.1 Divergence Analysis278
12.4.2 Trend Analysis280
12.4.3 Robustness Analysis282
12.4.4 Evolutionary Mechanism284
12.5 Econometric Analysis285
12.5.1 Correlation between Variables286
12.5.2 Regression Model290
12.6 Empirical Analysis II: Brexit’s Impact on European Nations297
12.6.1 Brexit297
12.6.2 Simulation Setting298
12.6.3 Results and Discussions298
12.7 Empirical Analysis II: RCEP’s Economic Significance to Relevant Nations301
12.7.1 RCEP30112.7.2 Simulation Setting302
12.7.3 Results and Discussions302
12.8 Summary307
Part VI Causal Inference309
Chapter 13 Connect the Structural Features and Economic Status310
13.1 Introduction310
13.2 Literature Review311
13.3 Econometric Model312
13.4 Hypotheses313
13.4.1 Hypothesis Formulation313
13.4.2 Hypothesis Testing314
13.5 Results and Discussions316
13.5.1 The Effects of Structural Capital316
13.5.2 The Effects of Relational Capital318
13.5.3 The Effects of Cognitive Capital319
13.6 Summary320
Postscript321Appendix323
References337
Notă biografică
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.
Textul de pe ultima copertă
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.
Caracteristici
Provides a comprehensive review of the rapidly expanding field of global value chain accounting system
Presents in-depth discussions on input–output analysis
Covers a vast array of special topics and applications illustrating the wide use econophysics
Presents in-depth discussions on input–output analysis
Covers a vast array of special topics and applications illustrating the wide use econophysics