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European Network Indicators: Constructing Cohesion and Convergence Measures

This research explores the construction of indicators for cohesion and convergence across Europe using network analysis. It identifies the roles performed by various entities in European networks based on demographic, economic, financial, and communication flows. The proposed indicators complement the current measures and can be produced regularly.

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European Network Indicators: Constructing Cohesion and Convergence Measures

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  1. NTTS 2009 NETWORK INDICATORS: A NEW GENERATION OF MEASURES? EXPLORATORY REVIEW AND ILLUSTRATION BASED ON ESS DATA Elsa Fontainha ISEG Technical University of Lisbon e-mail: elmano@iseg.utl.pt Edviges Coelho Statistics Portugal (INE) e-mail: edviges.coelho@ine.pt Brussels 18-20 February 2009

  2. Aim of Research • To illustrate how to construct indicators of cohesion and convergence across Europe and to identify the roles (e.g. centrality, reciprocity) performed by several entities (countries, regions, institutions, individuals, enterprises, etc.) in European networks mapped by demographic, economic, financial and communication flows or links. • To attain that goal we adopted network analysis, derived from graph theory, to explore data from the European Statistical System (ESS) • The proposed indicators can be produced on a regular basis and complement the current indicators.

  3. Attribute Data Gross Domestic Product (GDP) Population growth Student/Teacher ratio ... Relational Data Foreign Direct Investment Immigration Emigration Tourism flows ... Attribute and Relational Data

  4. How to study relational data? • Matrix format • Eurostat • geo/partner • input-ouput analysis • From matrix format to network graph… A E B D C

  5. A E D C From matrix format to network graph… Links are directed (represented by arrrows) E is an isolated node (ex: country) There are no reflexive links From adjacencymatrix to network graph… B

  6. Network AnalysisLinks and Nodes Network analysis • Describes the structure of relations (represented by links, oriented or not) between agents (represented by nodes/egos) • e.g. Nodes  Countries Links  Immigration flows among countries • Applies quantitative techniques to compute measures which improve the knowledge of • the characteristics of the whole network (e.g. EU) • the position of nodes (e.g. countries) in the network structure.

  7. Indicators Network Size Centrality Density Cohesion Reciprocity … Indicators Node/Ego In-degree Out-degree Power Isolated … Network and Node Indicators (computed from flow matrices)

  8. Computing Network Indicators from ESS:some illustrations Data: • ESS • Immigration by country of previous residence (migr_immiprv) • Immigration by citizenship (migr_immictz) • EU direct investment flows, breakdown by partner country (bop_fdi_flows) • Socrates-Erasmus student and teacher mobility Problems encountered • Data • (e.g. missing information for some countries and/or years ( -> imputation ) • Methodology: limits imposed by methodology • (e.g. n x n matrix)

  9. Methodology: main steps(for each indicator) • Selection, filtering and weighting original data (ESS) • Construction of the adjacency (association) matrix • Construction of network graphs from matrix( all links and only strongest links) • Computation of indicators (for nodes and network) from adjacency matrix

  10. intra-EU Immigration 2002 251 ties, 17 nodes Blue arrows = reciprocal links Red arrows= Non reciprocal

  11. intra-EU Immigration 2006267 ties, 17 nodes Blue arrows = reciprocal links Red arrows= Non reciprocal

  12. intra-EU Immigration 200227 ties, 17 nodes (only strong links)

  13. intra-EU Immigration 200636 ties, 17 nodes (only strong links)

  14. Indicators – NetworksImmigration, Foreign Investment, Teachers and Students (Year t - 2006)

  15. Indicators – Node (Countries)Immigration and Foreign Investment2002-2006

  16. Indicators – Node (Countries)Student mobility (Socrates-Erasmus)2004/5 – 2006/7

  17. Conclusions and future research • It is possible to compute network indicators from the ESS data. They are useful for the understanding of the relations among countries and the network diffusion mechanisms. • There is, in general, an increase in the density of the network (EU countries) across time regardless of the phenomena under analysis (migrations, capital flows, etc.). Cohesion is increasing. EU recent enlargements are reflected in that increase. • The role of each country inside the network remains stable across time in some cases but changes in others. For example, with regard to immigration, Spain has increased its position as a destination country since 1998. • The results suggest that geography and language still matter for several EU networks of people, goods, services, capital and knowledge. Explanation for this is beyond this paper’s goals but could contribute to a greater understanding of EU networks.

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