The World Trade Network

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1 Prelims Luca De Benedictis 1 Lucia Tajoli 2 1 Università di Macerata - Italy 2 Politecnico di Milano - Italy I Workshop PRIN PUE&PIEC Roma, January 29-30, 2009

2 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

3 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

4 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

5 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

6 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

7 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

8 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

9 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

10 Prelims The outline of the talk Introduction: World Trade is a Network Network Analysis from a Trade Perspective Network Analysis: a primer : Data Properties of the network Changes in the network structure along time Applications: The WTO and trade links Is trade regionalized? Intensive and extensive margins Conclusions

11 Introduction Part I Introduction

12 Introduction Trade is a network: past Figure: A natural way of representing international trade is through a network. The gure is from Folke Hilgerdt (1943), The Case for Multilateral Trade, American Economic Review

13 Introduction Trade is a network: present Figure: The most easy way of representing international trade is as a network. The gure is from Feenstra and Taylor (2008), International Economics, ch.1

14 Introduction Networks are not routes Figure: Ancient Trade Routes to Palestine. The distance between vertices is exogenous ( rst nature technology), while in networks it is determined endogenously.

15 Introduction Trade is a complex network Albania Tajikistan Solomon Islands Burkina Faso Djibouti Niger Vanuatu Seychelles Mongolia Sierra Leone Mauritania Guinea Slovenia Antigua and Barbuda El Salvador Nicaragua Hungary Comoros Swaziland Poland Czech Republic Ghana Senegal Lithuania Sudan Mali Oman Haiti Côte d Ivoire Peru Slovak Republic Ireland Samoa Finland Tunisia Paraguay Burundi Turkey Algeria Malta Ecuador Libya Gambia Greece Qatar Guinea-Bissau Cambodia South Africa Singapore Ukraine Venezuela Bahrain Guyana Uruguay Egypt Korea Argentina Hong Kong Philippines Gabon Central African Rep. New Zealand Congo India Thailand Saudi Arabia Kenya Dominican Republic Australia Luxembourg Bulgaria Indonesia Iran Cyprus Kuwait Israel China Ethiopia Brazil Canada Romania Japan Papua NewMacedonia Guinea Russia France United Kingdom Mexico Malaysia Estonia Vietnam Belgium Spain United States Fiji Morocco Iraq Angola Somalia Italy Chile Germany Pakistan Zambia Bermuda Sweden Netherlands Portugal Laos Armenia Honduras Colombia Maldives Switzerland Nigeria Bahamas Tanzania Equatorial Guinea Madagascar Austria Belize Panama Denmark Bangladesh St. Lucia Tonga Norway Guatemala Jordan Sri Lanka Chad Costa Rica Syria Mauritius Uganda Namibia Belarus Zimbabwe Barbados Lesotho Jamaica Rwanda Kazakhstan Cameroon Congo DR Trinidad and Tobago Bhutan Nepal Togo Benin Georgia Bolivia Iceland Yemen Azerbaijan Kyrgyz Republic Grenada Sªo TomØ and Príncipe Suriname Mozambique St. Vincent and Grens. RØunion Latvia Uzbekistan Croatia Moldova Cape Verde Dominica United Arab Emirates Liberia Botswana Malawi Turkmenistan Myanmar St. Kitts and Nevis Kiribati Figure: in Only trade ows > e 10 are made visible.

16 Introduction Aims and motivation (1) Economic integration is not a characteristic of a single country, but is the result of the relations that every single country has with the economic system. Relations are not properties of individual agents, but of the system of agents. This is precisely the focus of Network Analysis (NA): it focus more on the properties of the system rather than on individual characteristics. NA is very well-tted to analyze a system of (economic) relations between countries. NA is well-tted to analyze the characteristics of single countries in its relative position in the overall system of relations between countries. NA can contribute to meaningful testing of the evolution of the world trade system or its sub-components.

17 Introduction Aims and motivation (2) In this paper, we study international trade patterns focusing on the relationships between units (countries) and on the structure of the network. In network analysis, typical countries' characteristics (GDP, population, human capital,...) are important but are left in the background (exogenous properties); in this context countries' relevant characteristics depend on their relative position in the network (endogenous properties). We will see that there is a close correlation between countries' structural positions in the network and other economic variables [not today]

18 Introduction Aims and motivation (3) More specically, in this introductory paper we analyze the World Trade Network from 1950 to 2000 to assess its characteristics and its variations; we apply NA to the analysis of some trade issues recently debated in the literature to show how this approach allows to get new insights. With the results from NA we complement some of the empirical ndings of the gravity models, with a general perspective on the trading system. Interestingly, the results from the NA of the World Trade Network are related to some of the recent models of international trade.

19 Graphs and Networks Part II Graph Theory and Social Network Analysis

20 Graphs and Networks Graph G = (V, L) unit: vertex, node V link: line L directed (arc) A; undirected (edge) E; L = A E L is a binary set [0,1] dimensions: n = V, m = L example: n = 12, m = 23. Note: We will analyze networks that do not contain loops (c c) and parallel arcs (c g).

21 Graphs and Networks Network: Graph + Data (on V and L) N = (V, L, P, W) P is the vertex value function: (exogenous or endogenous) properties of vertices W is the line value function: (exogenous or endogenous) weights of lines Three kind of networks: if A = 0 N is an undirected network; if E = 0 N is a directed network; if W N is a weighted network.

22 Graphs and Networks Structural properties: Degrees and Neighborhood Indegree and outdegree: is the number, d, of arcs received and sent by a vertex. Neighborhood: the set of vertices that are connected to any given V i V denes its neighborhood V d i {j V : ij L} A network is said to be regular if every vertex has the same number of links, V d i = V d. A complete network, N c, is a regular network in which d = n 1.

23 Graphs and Networks Structural properties: Density and Centrality Density: is the number of lines in a network over the maximum possible number of lines In a directed network γ = m m max = m n(n 1) Two denitions of centrality: Closeness centrality and Betweenness centrality.

24 Graphs and Networks Structural properties: Centrality (1) The Closeness centrality of a vertex V i is the number of other vertices divided by the sum of all distances between V i and all others V j i. C c i = n 1 n 1 j i δ ij. (1) At the aggregate network level, if C c i is the centrality of the vertex that attains the maximum closeness centrality score, the degree of Closeness centralization of a network is n C c i=1 = Cc i C c i (n 2)(n 1)/(2n 3). (2) The closeness centralization is, therefore, the variation in the closeness centrality of vertices divided by the maximum variation in closeness centrality scores possible in a network of the same size.

25 Graphs and Networks Structural properties: Centrality (2) The Betweenness centrality of vertex V i is the proportion of all geodesic distances between pairs of other vertices that include this vertex: C b i = j k δ i jk δ jk (3) where δ jk is the total number of shortest paths joining any two vertices V k and V j, and δ i jk is the number of those paths that not only connect V k and V j, but also go through V i. The Betweenness centralization is the variation in the betweenness centrality of vertices divided by the maximum variation in betweenness centrality scores possible in a network of the same size. C b = n C b i C b i. (4) i=1

26 Graphs and Networks Some families of Networks Graphs: Empty Graph, Complete Graph, Star Graph, Circle Graph. Properties: Density=0 Closeness=0 Betweenness=0 Density=1 Closeness=0 Betweenness=0 Density=0.4 Closeness=1 Betweenness=1 Density=0.5 Closeness=0 Betweenness=0 The properties of vertices are not necessarily the ones of the entire network. In this complete network all vertices have closeness centrality equal to 1, and betweenness centrality equal to 0.

27 Literature Part III A (short) review of the literature

28 Literature Review of the literature Our paper is not the rst one on the application of NA to trade ows: Smith and White (Social Forces,1992) Serrano and Boguña (Phys. Rev., 2003) Garlaschelli and Loredo (Physica A, 2005) Fagiolo, Reyez and Schiavo (S.Anna WP, 2007) Kali and Reyes (J. Intern. Bus. Studies, 2007) But very little application of international trade models and economic interpretation of results so far This is one of the rst papers to address trade issues using NA

29 Literature Findings Recent applications of NA to trade ows found that the WTN is a complex network, with the following properties: Scale-free degree distribution (high heterogeneity) Small-world property (average path length between any pair grows logarithmically with system size) High clustering (neighbors are likely to be connected) Degree-degree correlation (hierarchical organization)

30 Literature Implications Some implications for international trade Countries have a very dierent number of trading partners Countries' positions in the system are very dierent, and some are more central than others The size of the system (in terms of number of countries) is only modestly important Trade is partially organized in `clubs'

31 Literature Issues arising from the evidence on NA and trade Can we dene economic integration in terms of system rather than countries? (A possible systemic measure of globalization?) Where does the countries' heterogeneity in terms of aggregate trade patterns come from? How does a country select its trading partners? Should we dene the comparative advantages of a country w.r.t. the rest of the world or a given group? Who is competing with whom? Do we have theoretical models that take into account these properties?

32 Part IV

33 The bilateral trade ows dataset Our dataset: From Subramanian and Wei (2007), The WTO promotes trade, strongly but unevenly, JIE, 72, Aggregate bilateral imports as reported by the importing country and measured in U.S. dollars, reported in the IMF Direction of Trade Statistics; in the rst part of the analysis we transformed the data in a dichotomous variable. Time span from 1950 to Observations for 60 to 157 countries. Geographical partition: macro continental regions (WB classication). Institutional partition: WTO membership

34 properties The evolution of the World Trade Network ( ) No. Countries No. Arcs Density In-Degree Closeness Centralization Out-Degree Closeness Centralization In-Degree St.Dev Out-Degree St.Dev Betweenness Centralization Total Betweenness Source: our elaboration on S-W data.

35 properties The evolution of the World Trade Network ( ) within a constant subset of countries No. Countries No. Arcs Density [*] [*] [*] [*] [*] In-Degree Closeness Centralization Out-Degree Closeness Centralization In-Degree St.Dev Out-Degree St.Dev Betweenness Centralization Total Betweenness Note: Here the network and its indices are computed including only the group of countries for which data are available over the entire time span [*] indicates that the density is signicantly dierent from the null hypothesis of γ=1 with p= Source: Our elaboration on S-W data.

36 distributions Outdegree and Indegree distributions in 1960, 1980 and 2000 (top). Changes (bottom).

37 in 1950 LucaFigure: De Benedictis The size and of Lucia eachtajoli vertex is given The World by its Trade betweenness Network index.

38 in 2000 Figure: The size of each vertex is given by its betweenness index.

39 Changes in the network structure From the network indices we observe that Density increases over time countries are more tightly interconnected Centralization decreases over time less polarized trading system (more `globalization' in the proper sense of the world) Average degree increases (slowly) over time more trading partners but still selection Dierences in distribution of export (outdegree) and import (indegree) changes in the two ows due to dierent causes. Structural (geodesic) distance often not related to geographical distance.

40 Stylized facts and IT theory This evidence is in line with some recent theoretical models that pin down important aspects of IT: Fixed costs in accessing foreign markets (Helpman, Melitz and Rubinstein, 2008) imply partners' selection and a limited number of partners Selection of partners is not random and it requires appropriate matching (Rauch and Trindade, 2003; Antràs and Helpman, 2006) Distance is important in dening trade patterns, but it is a relative concept that requires qualications (Harrigan, 2003; Evans and Harrigan, 2005) Economic integration can give rise to a core-periphery structure which is unstable over time (Krugman and Venables, 1995) Structural position in the network is correlated with economic variables relationship between trade patterns and GDP (Schott, 2006) Intensive and extensive margin (Hummels and Kleanow, 2005; Felbermayr and Kohler, 2007) NA can be used to provide evidence in support to international trade models and to improve them.

41 Applications: WTO and trade Part V Application (1): WTO and trade links

42 Applications: WTO and trade Analysis of the WTO sub-network Application of this analysis to a debated trade issue: the role of the WTO Is the WTO really promoting international trade? Answers by Rose (2004) and Subramanian and Wei (2007) Is the WTO system, rather than its members, more closely interconnected?

43 Applications: WTO and trade Properties of the WTO network Countries Arcs Share of total recorded arcs Density In-Degree Centralization Out-Degree Centralization In-Degree St.Dev Out-Degree St.Dev Figures and indices refer to the countries member of the WTO in each given year. Source: our elaboration on S-W data. We tested and conrmed the dierence between the WTO and the World Network densities

44 Applications: WTO and trade The WTO eect 1.0 World and WTO density Density world Density WTO

45 Applications: WTO and trade Comparison of the WTO network By comparing the total trade network with the WTO network we see that: The WTO network is much more dense WTO members are more closely interconnected. The WTO network is less centralized and with lower variance in degrees WTO system less hierarchical and more even that the entire system The heterogeneity in the WTO network is increasing quickly more dicult to negotiate (?)

46 Applications: regionalization Part VI Application (2): is trade regionalized?

47 Applications: regionalization Analysis of the continental sub-networks Is international trade regionalized? Is it more likely to trade with partners that are close by? We look at continental sub-networks to compare their characteristics with the whole world network

48 Applications: regionalization Properties of continental networks World Europe (EU) America Asia Africa Oceania No. Reporting Countries (9) (15) No. Arcs Regional share over tot. recorded arcs Density (1.00) (1.00) Source: our elaboration on S-W data.

49 Applications: regionalization Continental networks 1 Regional network densities World Europe America Asia Africa Oceania

50 Applications: regionalization Europe trade network in 2000

51 Applications: Extensive and Intensive Margins Part VII Application (3): Extensive and Intensive Margins

52 Applications: Extensive and Intensive Margins Extensive and Intensive Margins Extensive margin Intensive margin Intensive margin (positive) (negative) Countries (active) 113 (109 ) 113 (109 ) 113 (109 ) Arcs Share of total recorded arcs Density In-Degree average (St.Dev.) (15.98) (18.01) (16.30) Out-Degree average (St.Dev.) (5.52) (17.93) (13.35) In-Degree Closeness Centralization Out-Degree Closeness Centralization Betweenness Centralization Source: our elaboration on S-W data.

53 Part VIII Conclusions and future work

54 Summing up Through the use of NA we showed analytically that: Some structural changes occurred in the World Trade Network (not a neutral 'magnication eect') Increased heterogeneity between countries WTO plays a role in the structure of world trade Distance/proximity matter (structurally and geographically) and trade is to some extent regionalized The extensive and intensive margins networks are structurally dierent

55 Possible applications of NA to international trade issues Impact of emerging countries on the network structure Dierences in network structure for dierent goods and commodities [Rauch (1999) strikes again!] Networks of production Sectoral networks and the dynamics of comparative advantages (the product space)[haussmann, Hidalgo, Klinger and Barabasi (2007) ] Contagion and business cycle transmission Economic integration and growth Endogenous distance and position measures Relationship between trade and FDI networks

The World Trade Network

The World Trade Network Prelims Luca De Benedictis 1 Lucia Tajoli 2 1 Università di Macerata - Italy 2 Politecnico di Milano - Italy UC Berkeley - February 25th, 2010 Prelims The outline of the talk Introduction: World Trade