International Specialization Dynamics E-Book

Didier Lebert

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch
• Veröffentlichungsjahr: 2017

Seitenzahl: 189

Beschreibung

This book deals with the dynamics of international specializations during the present period of trade globalization. It discusses international trade as a network linking countries, and uses structural techniques to analyze the evolving structure of this network. It offers a new approach to address the economic emergence of countries. Using these structural methods, the book also explains knowledge exchange. Indeed, the structure transformation of the international trade is partly due to an exchange of competencies between regions. Many concrete examples are proposed.

Leseprobe

Table of Contents

Cover

Title

Copyright

Introduction

1 Overview of the Globalization of Trade in Industrial Goods: 1980–2004

1.1. Introduction

1.2. Data

1.3. Structural indicators resulting from social networks analysis

1.4. Main results

1.5. Conclusion

2 The Dynamics of International Industrial Specializations

2.1. Introduction

2.2. Influence matrix and country centrality indicators

2.3. The dynamics of revealed comparative advantages

2.4. Conclusion

3 Dominance Relationships in an Influence Graph

3.1. Introduction

3.2. Spanning trees with a single impulse node (STSIN)

3.3. Arc-impelled dominances

3.4. The value of a dominance impelled by an arc between nodes that belong to two different substructures

3.5. Conclusion

4 Economic Dominance Theory and Structural Indicators on Egocentric Networks

4.1. Introduction

4.2. Egocentric networks: sectorization, inclusion, insertion and integration

4.3. Application to African countries’ international trade

4.4. Conclusion

5 Economic Dominance Theory and Intra- and Inter-Regional Flow of Technological Knowledge

5.1. Introduction

5.2. Measuring the dynamic resilience of regions

5.3. Measuring the significance and forms of the technological autonomy of regions

5.4. Conclusion

6 Technological Landscapes Analysis: Europe, 2010–2012

6.1. Introduction

6.2. Four technological landscapes

6.3. Some findings

6.4. Conclusion

Conclusion

Appendix

A.1. Categories of industrial goods (industrial codes and titles)

A.2. Country codes (in bold: Africa: in italics: EU15)

Bibliography

Index

End User License Agreement

List of Tables

1 Overview of the Globalization of Trade in Industrial Goods: 1980–2004

Table 1.1. Structural indicators used in the works of the LEM

Table 1.2. Measures of clustering for directed structures (according to [FAG 07])

Table 1.3. Top rankings for RWBC centrality indicator – aggregate flows

Table 1.4. Top rankings for RWBC centrality indicator – product flows

Table 1.5. Top rankings for α-centrality indicator – aggregate flows

Table 1.6. Top rankings for α-centrality indicator – product flows

3 Dominance Relationships in an Influence Graph

Table 3.1. Impulse nodes selected per year

Table 3.2. Source of maximum impulse for France / Morocco dominance arc in EU15 / Africa relations – 1980–2004

4 Economic Dominance Theory and Structural Indicators on Egocentric Networks

Table 4.1. Measures on egocentric networks

Table 4.2. Descriptive statistics – Structural indicators – Means and standard deviations – Aggregate products – World versus Africa

Table 4.3. Descriptive statistics – Pearson correlation – Aggregate Products – Africa

6 Technological Landscapes Analysis: Europe, 2010–2012

Table 6.1. Example of construction of a technology flow matrix for α or β region (Landscape 3)

Table 6.2. Indicators for the study of landscapes

Table 6.3. Betweenness centrality of regions

Table 6.4. Betweenness centrality of technologies

Table 6.5. Betweenness centrality of companies in “regions x regions” landscape

Table 6.6. Betweenness centrality of companies in “technologies x technologies” landscape

Table 6.7. Companies contributions to betweenness centrality of three regions

Table 6.8. Companies’ contributions to betweenness centrality of three technologies

Table 6.9. Overview of Île-de-France region

List of Illustrations

Introduction

Figure I.1. Structure of international trade in industrial goods in 1980 (CEPII data, TradeProd database)

1 Overview of the Globalization of Trade in Industrial Goods: 1980–2004

Figure 1.1. MDS of international trade in industrial goods – 1980

Figure 1.2. MDS of international trade in industrial goods – 2004

Figure 1.3. Density of the network of international trade in industrial goods – 1980–2004

Figure 1.4. Distribution of nodes degrees (kernel density)

Figure 1.5. Distribution of total strengths on weighted structures – Comparison 1980–2004

Figure 1.6. Correlations between degrees and strengths

Figure 1.7. Correlations between in and out degrees and strengths in directed structures

Figure 1.8. Spearman’s rank correlation for RWBC indicator – 1980–2004

Figure 1.9. Spearman’s rank correlation on alpha-centrality indicator – 1980–2004

Figure 1.10. Correlations on average values of assortments – 1980–2004

Figure 1.11. Average clustering coefficients

Figure 1.12. Correlations between clustering indicators and total degrees and strengths

Figure 1.13. Disaggregation of average values of clustering indicators

Figure 1.14. Disaggregation of average values of cluster indicators

2 The Dynamics of International Industrial Specializations

Figure 2.1. Flow of intra-European industrial trade – 1993

Figure 2.2. Flow of intra-European industrial trade – 2004

Figure 2.3. Flow data structure

Figure 2.4. Ranking of global centrality (Salancik)

Figure 2.5. Ranking of closeness centrality (Friedkin)

Figure 2.6. Ranking of betweenness centrality (diagonal cofactors)

Figure 2.7. Contribution of influences to the global centrality of countries – 2004

Figure 2.8. MDS contributions to global centrality (relative values) – 1993

Figure 2.9. MDS contributions to global centrality (relative values) – 2004

Figure 2.10. Dynamics of international specialization in 10 European countries – 1993–2004

Figure 2.11. The dynamics of revealed comparative advantages – 1993–2004

3 Dominance Relationships in an Influence Graph

Figure 3.1. Example of an influence graph (dominant demand)

Figure 3.2(a). Influence graph hierarchies and HPG in Figure 3.1

Figure 3.2(b). Influence graph hierarchy in Figure 3.1

Figure 3.3. STSIN at maximum value – 1980. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 3.4. STSIN at maximum value – 1985. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 3.5. STSIN at maximum value – 1990. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 3.6. STSIN at maximum value – 1995. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 3.7. STSIN at maximum value – 2000. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 3.8. STSIN at maximum value – 2004. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 3.9. Maximum, minimum and average value of STSINs

Figure 3.10. Hierarchies associated with impulse arc i → j

Figure 3.11. Major dominance impelled by arc – World – 1980

Figure 3.12. Major dominance impelled by arc – World – 2004

Figure 3.13. Major dominance impelled by arc – Africa – 1980

Figure 3.14. Major dominance impelled by arc – Africa – 2004

Figure 3.15. Dominance transmission of a transmitting node SG

1

to the entire SG

2

by an intermediary node

Figure 3.16. Maximum and average impulse value of France / Africa dominance arcs from EU15 countries – 1980–2004

Figure 3.17. Dominance arcs of a transmitting node in SG

1

to a receiving node in SG

2

Figure 3.18. Average and maximum impulse value of France / Morocco dominance arc in EU15 / Africa relations – 1980–2004

4 Economic Dominance Theory and Structural Indicators on Egocentric Networks

Figure 4.1. Illustration of partition theorem [LAN 13]

Figure 4.2. Example of egocentric structures from a simplified influence graph

Figure 4.3. Scale of values for indicators on egocentric networks

Figure 4.4. Situation of African economies in 1980. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 4.5. Situation of African economies in 2004. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

5 Economic Dominance Theory and Intra- and Inter-Regional Flow of Technological Knowledge

Figure 5.1. Dynamics of international specialization in Germany between 1993 and 2004

Figure 5.2. Rank correlation among cohesion centralities between 1975–1979 and 1995–1999

Figure 5.3. Contribution of technologies between 1975–1979 and 1995–1999 for California – standardized scores

Figure 5.4. Structural indicators for inter-regional exchanges of technological knowledge

Figure 5.5. Autarky value in inter-regional exchange of technological knowledge

Figure 5.6. “Interdependence on dependence” ratio in inter-regional exchanges of technological knowledge

Figure 5.7. Graph of internal technological flows in California over the period 1995–1999

Figure 5.8. Descriptive statistics of internal technological flow graphs in California

Figure 5.9. Standardized “interdependence on dependence” ratio of internal technological flow graphs in California

6 Technological Landscapes Analysis: Europe, 2010–2012

Figure 6.1. The four technological landscapes

Figure 6.2. From the technology flow matrix to the influence matrix

Figure 6.3. Disaggregation of matrices and graphs by company

Figure 6.4. Procedure for intra-regional flows analysis

Figure 6.5. Swatch company in the regional flows graph. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 6.6. Valéo company in the technology flows graph. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Figure 6.7. Technology flows internal to Île-de-France region. For a color version of this figure, see www.iste.co.uk/lebert/specialization.zip

Guide

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Smart Innovation Set

coordinated byDimitri Uzunidis

Volume 9

International Specialization Dynamics

Didier Lebert

Hafida El Younsi

First published 2017 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

ISTE Ltd

27-37 St George’s Road

London SW19 4EU

UK

www.iste.co.uk

John Wiley & Sons, Inc.

111 River Street

Hoboken, NJ 07030

USA

www.wiley.com

© ISTE Ltd 2017

The rights of Didier Lebert and Hafida El Younsi to be identified as the authors of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

Library of Congress Control Number: 2016959658

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

ISBN 978-1-84821-987-8

Introduction

This book is an overview of six years of joint research conducted within the Department of Applied Economics of ENSTA ParisTech. It introduces new tools for the analysis of economic exchange structures.

The first theme covered is that of the globalization of trade in industrial goods. In 2010, we published an article in the European Journal of Economic and Social Systems which introduced an original method to historically identify this phenomenon from international trade data for the period 1980–2004 [LEB 10]. These same tools were also used to quantify the scale of the phenomenon, identify contributing countries, in terms of industrial goods, whose exchange structure changed with time. In this method, trade in goods is represented in the form of graphs in which the nodes / poles correspond to countries and the links between these nodes to (physical or financial) flows that interconnect these countries. These flows can be divided according to the goods that are traded. Information on the direction of flow (who exports and who imports) and on their intensity (what amounts) is integrated in the graph. In the end, the structure of international trade, on a given date, looks like the graph in Figure I.1.

Figure I.1.Structure of international trade in industrial goods in 1980 (CEPII data, TradeProd database)

The graph is drawn such that the most “important” economies of the structure are positioned in its center. The term centrality here refers to a measure of the relative significance of a node. This significance is understood as a sum of influences that it exerts on the overall structure: capacity of transmitting to its partners the disruptions / economic shocks affecting it, or ability to control flows transiting through the structure.

In this book, we will present toolkits to interpret these graphs, both generally and also in a more restricted manner:

– by “subgraphs”, that is focusing the analysis on a restricted sample of countries (European or African for example);

– by “partial graphs”, that is for a particular product group (for example, low-medium- or high tech products).

These two “restrictions” can be combined, and we may occasionally analyze “partial subgraphs”.

The book’s added value is above all methodological. We present tools that help to navigate between the different scales of analysis while maintaining a solid basis for comparison. When we move from a complete graph to a subgraph, the links within the subgraph are relativized by inner and outer links. In other words, when studying trade relations between France and Germany in a “Europe” subgraph, the relationships that these countries have with their other economic partners play a role even if these partners are outside the European continent. The same applies for the partial graphs: relationships between these same two countries in high-tech products take into account those they develop for products with fewer new technologies. These tools are essentially derived from the economic dominance theory (EDT).

According to Lanther [LAN 74], economic dominance theory [LAN 74] initially applies to inter-industry trade flows as reported within the framework of the National Accounts. The indicators of centrality of nodes in exchange structures that Lantner presents, production multipliers and elasticities, reflect those traditionally handled within the framework of input–output analysis (which studies the interdependencies between productive sectors of an economy). The originality of the tool that the author develops in this context, the “influence graphs theory”, is to articulate the mathematical graphs theory on the one hand and the fundamental elements of the input-output analysis on the other. Indeed, “the analysis of the effects of dominance in an exchange structure has been up to now subject to detrimental fragmentation”, between matrix calculation, allowing for the understanding of global influences but not the process of disruptions, and the qualitative approach from unweighted graphs neglecting “unbalanced intensities of connections”. The objective of the influence graphs theory is to “bridge the gap” between these two approaches “by revealing the conditions of general dependence and interdependence, related to the process of quantitative distribution of the influence” of poles in a given exchange structure [LAN 74].

This objective led the author to provide entirely new topological interpretations to inter-industry exchange structures. Roland Lantner showed that structural analysis is an intuitive way of calculating the determinant of matrices representative of directed and weighted exchange structures. This determinant will be subsequently considered as an indicator of the hierarchical distribution of influence through this structure. This led Lantner to formulate the following three theorems, which are informally1 presented here before attempting a synthetic interpretation:

–

Loops and circuits theorem

: [LAN 74] shows that the value of the determinant associated with an exchange structure is a function of the value of “Hamiltonian partial graphs” (HPG) of the graph representing this structure. A Hamiltonian partial graph is a partial graph (i.e. initial graph without arcs interconnecting the poles) with the nodes having in and out “degrees” (number of connections) strictly equal to 1. The value of an HPG is in absolute value, the product of intensity coefficients that comprise it (see

Chapter 3

).

–

Amortization theorem

: The value of the determinant is an increasing function of the general distribution of influence within an exchange structure. The looping effect generated by a non-Hamiltonian circuit (a “partial circularity”), disrupts the distribution of influence and reduces this value. The value of the determinant is therefore a decreasing function of partial circularities.

–

Partition theorem

: This theorem defines the relationships between different sub-structures (“parts”) of a given structure [LAN 00].

The determinant of the exchange structure is less than or equal to the product of the determinants of the parts

. The difference measures “interdependence” between the parts. The general idea is to know the part of the general circulation of influence inside the structure (synthesized by the determinant) that is to be used in the circulation between the parts (which is, in our own word “external” to the parts) and that which is “internalized” in the parts. The aim is to try to determine if there exists a circulation base within the structure to, where appropriate, identify a hierarchy between the parts. In an extreme case where each pole constitutes a part, the difference between the product of the determinants of these parts (the product of the diagonal terms of the exchange structure) and the determinant of the structure measure the “general interdependence” of the structure. In the other extreme case where all the nodes are included in a single part, the difference (whose value is 0) indicates that all the interdependent relationships within the structure are internalized by the part.

We think the important point to note in these theorems, which we will refer to from time to time in the main development of the work, while re-explaining and illustrating them, is the following: the determinant of the matrix representing the graph allows for the separation of the results of influence / dependency, that is asymmetrical/ hierarchical relations between the poles, from the results of interdependence, that is symmetrical / circular relationships between these same poles. Exchange relationships are divided between these two structural phenomena: they reveal more or less dependence, and more or less interdependence. All things being equal, interdependence increases when the value of the determinant decreases. It is around this general result that the EDT toolkit will be constituted.

As [FRE 04] points out, the tools of mathematical graphs theory have been at the heart of the development of sociometric techniques (social network analysis) since the late 1940s, and more precisely since the pioneering intuitions of [BAV 48, LUC 49, SMI 50, LEA 51], to the more formal works of Frank Harary and of his colleagues from the University of Michigan [HAR 53, HAR 65]. What is required, according to Freeman, in what he called “l’école de la Sorbonne” (the school of Sorbonne), with [FLA 63] and [BER 58], is to establish “the earliest general synthesis showing explicitly that a wide range of problems could all be understood as special cases of a general structural model” [FRE 04]. The topological analyses of [PON 68, PON 72] and [LAN 72a, LAN 72b, LAN 74], continuing the reflections of François Perroux on the phenomena of power in economics (1973/1994 for a summary), are the most concrete manifestation of the breakthrough of this research tradition in the field of political economy.

The bridges between input–output analysis research traditions and social network analysis (SNA) have existed for a long time. The pioneering structural measures of global influence of [KAT 53, HUB 65, BON 72, COL 73, BUR 82] are thus, at least in part, derived from the application of matrix calculation concepts and techniques in SNA. These concepts and techniques are commonly used in input–output analysis. More recently, [SAL 86, BON 87, FRI 91, BON 01] offered general frameworks for use of these tools in SNA. In his study on “informational power in an organization,” [GAL 06] takes the opposite path: using matrix techniques for input–output analysis, he introduced qualitative measures of centrality in the tradition of [FRE 79, TIC 79, BRA 92] in order to “reconcile the micro and macro dimensions of organizational behavior within which the phenomena of power falls”. He extends this approach by mobilizing [SAL 86, p. 152] quantitative measures of centrality and economic dominance theory.

According to [SAL 86], the advantage of the matrix approach of input–output analysis in the measure of centrality / influence of a pole in an exchange structure is that it ascertains the following three assumptions:

– a pole is central in terms of contribution to the resources of this structure to the extent that the other poles use the resources of the pole to provide their own resources in return;

– a pole is central in the structure to the extent that it contributes to the resources of other central poles of this same structure (relative significance of dependent parts).

These first two assumptions ignore the possibility that a pole can be significant outside the service provided to others. This brings us to the third assumption:

– a pole can be central regardless of its contributions to the structure, when it has a high “intrinsic value” compared to the other parts.

The method developed by Salancik is empirically applied to the classification of academic journals in the field of organizational sciences from the flow of cross citations. One of the essential