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1 NETWORK INDICATORS: A NEW GENERATION OF MEASURES? EXPLORATORY REVIEW AND ILLUSTRATION BASED ON ESS DATA Elsa Fontainha ISEG Technical University of Lisbon.

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Presentation on theme: "1 NETWORK INDICATORS: A NEW GENERATION OF MEASURES? EXPLORATORY REVIEW AND ILLUSTRATION BASED ON ESS DATA Elsa Fontainha ISEG Technical University of Lisbon."— Presentation transcript:

1 1 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.ptelmano@iseg.utl.pt Edviges Coelho Statistics Portugal (INE) e-mail: edviges.coelho@ine.ptedviges.coelho@ine.pt Brussels 18-20 February 2009 NTTS 2009

2 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 3 Attribute and Relational Data Attribute Data Gross Domestic Product (GDP) Population growth Student/Teacher ratio... Relational Data Foreign Direct Investment Immigration Emigration Tourism flows...

4 4 How to study relational data? Matrix format Eurostat geo/partner input-ouput analysis From matrix format to network graph… A E D C ABCDE A-0100 B0-000 C00-10 D010-0 E0000- B

5 5 A E D C ABCDE A-0100 B0-000 C00-10 D010-0 E0000- Links are directed (represented by arrrows) E is an isolated node (ex: country) There are no reflexive links From adjacency matrix to network graph… B

6 6 Network Analysis Links 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 7 Network and Node Indicators (computed from flow matrices) Indicators Network Size Centrality Density Cohesion Reciprocity … Indicators Node/Ego In-degree Out-degree Power Isolated …

8 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 9 Methodology: main steps (for each indicator) 1.Selection, filtering and weighting original data (ESS) 2.Construction of the adjacency (association) matrix 3.Construction of network graphs from matrix( all links and only strongest links) 4.Computation of indicators (for nodes and network) from adjacency matrix

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

11 11 intra-EU Immigration 2006 267 ties, 17 nodes Blue arrows = reciprocal links Red arrows= Non reciprocal

12 12 intra-EU Immigration 2002 27 ties, 17 nodes (only strong links)

13 13 intra-EU Immigration 2006 36 ties, 17 nodes (only strong links)

14 14 Immigration (residence) Immigration (ctz) FD Invest. StudentsTeachers N countries ===== Network size ===== N of Ties Network Centr. (Outdegree) % Network Centr. (Indegree) % Outdegree Mean (StdDev) Indegree Mean (StdDev) Network Density Indicators – Networks Immigration, Foreign Investment, Teachers and Students (Year t - 2006)

15 15 Immigration (origin residence) Foreign D Investment 2006200220062002 Size in 16 (except CY, SI) 16 (except CY, LT, LV, PL, PT, SI, SK) NL(10) PT (7) SE (7) BE (11) SE (11) Size out16 (except FI, LT, LV, SI) 16 AT, DE, ES, NL, PL, SE, UK FR (14) NL (9) AT (13) FR (13) IT (13) NL (13) UK (13) In-degreeDE (1578) UK(1082) ES(577) DE (1153) UK (298) ES (280) BE (60) NL (47) FI (49) NL (46) Out - degreePL (701) SK(613) SK (551) PL (310) FR (48) ES (42) UK (47) SE (37) Indicators – Node (Countries) Immigration and Foreign Investment 2002-2006

16 16 Student mobility 2006/72004/5 Size in26 BE ES FR IT DE EE 26 BE DE ES FR IT PT FI SE UK LV LT AT PL FI25BE CZ DE EE ES FR IT LV LT AT PL FI Size out25 BE CZ DE EE ES FR IT LV LT AT PL FI 25 BE DE ES FR IT NL AT PT FI UK In-degreeES (26992) FR (20140) DE (16683) ES (25217) FR (20200) DE (16688) Out - degreeDE (2715) FR (21213) ES (20568) DE (2715) FR (21213) ES (20568) Indicators – Node (Countries) Student mobility (Socrates-Erasmus) 2004/5 – 2006/7

17 17 Conclusions and future research 1. 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. 2. 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. 3. 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. 4. 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 papers goals but could contribute to a greater understanding of EU networks.


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