The International-Trade Network: Statistical Properties and Modeling

Transcription

1 The International-Trade Network: Statistical Properties and Modeling Giorgio Fagiolo 1 giorgio.fagiolo@sssup.it 1 LEM, Sant Anna School of Advanced Studies, Pisa (Italy) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 1 / 31

2 Introduction Complex-Network Approaches in Economics What is a network? A fast-growing literature... Many economic systems A and graph-theoretic their evolutionrepresentation over time can be described and studied of relationships using complex-network (links) between tools (Schweitzer et al., 2009, units (nodes) Science) of a system in a A better understanding of given howpoint heterogeneous in time (or time economic agents interact in non-trivial ways and give interval) rise to unexpected aggregate phenomena Empirical vs. theoretical Nodes: investigations entities, units, agents, possibly heterogeneous... but mostly in micro and finance Links: existence of relation Applications: networks of between consumers, nodes banks, financial institutions, companies, traders, stocks and financial products, etc. Giorgio Fagiolo, Course on Economic Networks. lunedì 6 febbraio 2012 Giorgio Fagiolo (LEM) The ITN: Empirics and Models 2 / 31

3 Introduction Complex-Network Approaches in Economics What is a network? A fast-growing literature... Many economic systems A and graph-theoretic their evolutionrepresentation over time can be described and studied of relationships using complex-network (links) between tools (Schweitzer et al., 2009, units (nodes) Science) of a system in a A better understanding of given howpoint heterogeneous in time (or time economic agents interact in non-trivial ways and give interval) rise to unexpected aggregate phenomena Empirical vs. theoretical Nodes: investigations entities, units, agents, possibly heterogeneous... but mostly in micro and finance Links: existence of relation Applications: networks of between consumers, nodes banks, financial institutions, companies, traders, stocks and financial products, etc. Giorgio Fagiolo, Course on Economic Networks. lunedì 6 febbraio 2012 What about meso/macro economics? International trade network (ITN) Product-space network (Hausmann, Hidalgo et al; Tacchella, Pietronero et al.) International financial network (Haldane; Fagiolo et al; Reyes & Minoiu) Other macro-related networks: FDI, migrations and mobility, etc. Giorgio Fagiolo (LEM) The ITN: Empirics and Models 2 / 31

4 Introduction This Talk: An Overview of ITN-Related Research 1 Why characterizing trade flows using a network representation may be relevant for trade economists? Giorgio Fagiolo (LEM) The ITN: Empirics and Models 3 / 31

5 Introduction This Talk: An Overview of ITN-Related Research 1 Why characterizing trade flows using a network representation may be relevant for trade economists? 2 Can the knowledge of the ITN topological properties shed new light on issues like growth, globalization and trade integration? Giorgio Fagiolo (LEM) The ITN: Empirics and Models 3 / 31

6 Introduction This Talk: An Overview of ITN-Related Research 1 Why characterizing trade flows using a network representation may be relevant for trade economists? 2 Can the knowledge of the ITN topological properties shed new light on issues like growth, globalization and trade integration? 3 Can we separate ITN topological properties that are the sheer outcome of randomness from those that are instead statistically significant? Giorgio Fagiolo (LEM) The ITN: Empirics and Models 3 / 31

7 Introduction This Talk: An Overview of ITN-Related Research 1 Why characterizing trade flows using a network representation may be relevant for trade economists? 2 Can the knowledge of the ITN topological properties shed new light on issues like growth, globalization and trade integration? 3 Can we separate ITN topological properties that are the sheer outcome of randomness from those that are instead statistically significant? 4 Are standard int l trade models (i.e. gravity) able to replicate the observed ITN structure? Giorgio Fagiolo (LEM) The ITN: Empirics and Models 3 / 31

8 Introduction This Talk: An Overview of ITN-Related Research 1 Why characterizing trade flows using a network representation may be relevant for trade economists? 2 Can the knowledge of the ITN topological properties shed new light on issues like growth, globalization and trade integration? 3 Can we separate ITN topological properties that are the sheer outcome of randomness from those that are instead statistically significant? 4 Are standard int l trade models (i.e. gravity) able to replicate the observed ITN structure? 5 Can we explain the properties of the ITN in terms of standard economic forces such as country specialization and comparative advantage? Giorgio Fagiolo (LEM) The ITN: Empirics and Models 3 / 31

9 This is joint work with... Introduction Javier& Reyes& Giuseppe& Mangioni& Ma9eo& Barigozzi& Stefano& Schiavo& Ma9eo& Chinazzi& Giorgio&& Fagiolo& Tiziano&& Squar?ni& Diego& Garlaschelli& Marco& Duenas& Rossana& Mastrandrea& Giorgio Fagiolo (LEM) The ITN: Empirics and Models 4 / 31

10 Introduction The International-Trade Network (ITN) What is it? Network where nodes are world countries and links represen What is it? flows Network where nodes are world countries and links represent bilateral Different The trade flows empirical World-Trade representations: Web binary/weighted, (WTW) undirec Timeaggregate/commodity-specific evolution of the ITN (data from 1950 to 2010) Different Time empirical evolution representations: of the ITN binary/weighted, (data from 1950 undirected/directed, to 2010) aggregate/commodity-specific Aggregate vs commodity-specific multi-network USA USA USA Trade relation Total bilateral flow (exports plus imports) btw USA and USA LUX2 Export/import 2 relations USA 6 LUX Total Export from USA to LUX LUX LUX Total Export from LUX to USA Total bilateral flow (exports plus imports) btw USA and Total bilateral flow (exports plus imports) btw USA and USA 3 Colors: Giorgio Fagiolo (LEM) The ITN: Empirics and Models 5 / LUX 4 5 LUX Total from U LU Total from U LU

11 Why Networks of International Trade? Trade Networks... An old Idea Source: De Benedictis & Tajoli (2008) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 6 / 31

12 Why Networks of International Trade? Trade Networks... An old Idea Source: De Benedictis & Tajoli (2008) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 7 / 31

13 Why Networks of International Trade? From Qualitative to Quantitative Approaches The ITN in 2000: Link weight=total trade; Node size=gdp; Node shape=continent. Only strongest 1% of link weights are shown. See Fagiolo, Giorgio Fagiolo (LEM) The ITN: Empirics and Models 8 / 31

14 Why Networks of International Trade? From Qualitative to Quantitative Approaches Political-Science Literature Applying SNA tools to extract core-periphery structure of ITN (world dependency theories) Snyder and Kick (1979), Nemeth and Smith (1985), Breiger (1981), Smith and White (1992), Kim and Shin (2002), etc. Giorgio Fagiolo (LEM) The ITN: Empirics and Models 9 / 31

15 Why Networks of International Trade? From Qualitative to Quantitative Approaches Political-Science Literature Applying SNA tools to extract core-periphery structure of ITN (world dependency theories) Snyder and Kick (1979), Nemeth and Smith (1985), Breiger (1981), Smith and White (1992), Kim and Shin (2002), etc. Complex-Network Approach Characterizing the time evolution of topological properties of the ITN as a binary and weighted network Correlation among topological measures and node attributes (pcgdp), community structure; rich-club emergence; distributional stability/persistence over time; etc. Li et al. (2003); Serrano and Boguna (2003); Garlaschelli and Loffredo (2004, 2005); Garlaschelli et al. (2007); Serrano et al. (2007); Bhattacharya et al. (2007, 2008); Fagiolo et al. (2008, 2009); Reyes et al. (2008); Fagiolo et al. (2010); Fagiolo (2010); Barigozzi, Fagiolo and Garlaschelli (2010); Barigozzi, Fagiolo and Mangioni (2010); De Benedictis and Tajoli (2011) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 9 / 31

16 Why Networks of International Trade? Correlation structure among topological properties is stationary over tim and identifies a characteristic trade structure ITN Correlation Structure (Fagiolo et al, 2009, PRE) Fagiolo et al (2008, PHYSA; 2009, PRE) Correlation Structure Stationary over Time (Globalization?) More-intensively connected countries are more central and tend to form highlyconnected trade triangles Countries with many trade partners do not necessarily trade more intensively Weighted WTW is only weakly disassortative: More-intensively connected countries tend to trade with relatively less connected countries Countries holding more partners tend to trade with countries with very few partners (strong disassortativity) and do not typically form trade triangles Correlation Coefficients Binary WTW profoundly different from weighted WTW!! See Fagiolo et al, 2008, Physica A Giorgio Fagiolo, The World-Trade Web ND/NS=Node Degree/Strength; ANND/ANNS; Average Nearest-Neighbor Degree/Strength; BCC/WCC=Binary/Weighted Clustering Coefficient; RWBC=Random-Walk Betw Centrality Giorgio Fagiolo (LEM) The ITN: Empirics and Models 10 / 31

17 Why Networks of International Trade? Why Should Trade Economists Care About Networks? Generating Fresh Stylized Facts A network approach employs a holistic perspective, where trade is not viewed as a bilateral phenomenon anymore, where only direct links are important Countries can be characterized in terms of their global embeddedness in the ITN (unlike in standard approaches) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 11 / 31

18 Why Networks of International Trade? Why Should Trade Economists Care About Networks? Generating Fresh Stylized Facts A network approach employs a holistic perspective, where trade is not viewed as a bilateral phenomenon anymore, where only direct links are important Countries can be characterized in terms of their global embeddedness in the ITN (unlike in standard approaches) Are Indirect-Trade Links Important? Abeysinghe and Forbes (2005): impact of shocks on a given country is explained by indirect trade links Dees and Saint-Guilhem (2011): countries that do not trade very much with the U.S. are largely influenced by its dominance over other trade partners linked with the U.S. Ward and Ahlquist (2011): bilateral trade is not independent of the production, consumption, and trading decisions made by firms and consumers in third countries Giorgio Fagiolo (LEM) The ITN: Empirics and Models 11 / 31

19 Why Networks of International Trade? Why Should Trade Economists Care About Networks? Can ITN Structure Explain Macro Dynamics? Kali et al. (2007) and Kali and Reyes (2010): country position in the trade network has substantial implications for economic growth and a good potential for predicting episodes of financial contagion Giorgio Fagiolo (LEM) The ITN: Empirics and Models 12 / 31

20 Why Networks of International Trade? Why Should Trade Economists Care About Networks? Can ITN Structure Explain Macro Dynamics? Kali et al. (2007) and Kali and Reyes (2010): country position in the trade network has substantial implications for economic growth and a good potential for predicting episodes of financial contagion Country Centrality and Economic Development Reyes, Schiavo, Fagiolo (2010, JITED): country centrality in the ITN may help to account for the evolution of international economic integration better than what standard statistics, like openness to trade, do Example: LATAM vs East-Asian Countries Giorgio Fagiolo (LEM) The ITN: Empirics and Models 12 / 31

21 Why Networks of International Trade? Why Should Trade Economists Care About Networks? Can ITN Structure Explain Macro Dynamics? Kali et al. (2007) and Kali and Reyes (2010): country position in the trade network has substantial implications for economic growth and a good potential for predicting episodes of financial contagion Country Centrality and Economic Development Reyes, Schiavo, Fagiolo (2010, JITED): country centrality in the ITN may help to account for the evolution of international economic integration better than what standard statistics, like openness to trade, do Example: LATAM vs East-Asian Countries Main Idea ITN topology describes the architecture of real interaction channels among world countries, where indirect as well as direct linkages are explicitly taken into consideration Studying the ITN can give us insights about macro issues such as economic globalization, internationalization, spreading of international crises, transmission of economic shocks Giorgio Fagiolo (LEM) The ITN: Empirics and Models 12 / 31

22 Why Networks of International Trade? How Can We Explain ITN Statistical Properties? Two levels Null models of the ITN Economic models of the ITN Giorgio Fagiolo (LEM) The ITN: Empirics and Models 13 / 31

23 Why Networks of International Trade? How Can We Explain ITN Statistical Properties? Two levels Null models of the ITN Economic models of the ITN Null models of the ITN Can observed properties be replicated by a null random network model that only preserves some local (1 st -order) statistics? What is (if any) the minimal amount of information about the ITN needed to reproduce all its properties using an otherwise random model? Can one discriminate between statistically relevant and irrelevant properties? Giorgio Fagiolo (LEM) The ITN: Empirics and Models 13 / 31

24 Why Networks of International Trade? How Can We Explain ITN Statistical Properties? Two levels Null models of the ITN Economic models of the ITN Null models of the ITN Can observed properties be replicated by a null random network model that only preserves some local (1 st -order) statistics? What is (if any) the minimal amount of information about the ITN needed to reproduce all its properties using an otherwise random model? Can one discriminate between statistically relevant and irrelevant properties? Economic models of the ITN Standard Int l Trade Models: Gravity Model (GM) Economics-Inspired Stochastic Models of Network Formation Giorgio Fagiolo (LEM) The ITN: Empirics and Models 13 / 31

25 Null Models Null Models of the ITN Main Idea Given observed network, define a set of local properties of the network (constraints) that must be preserved (density, degree or strength sequence, etc.) Characterize the ensemble of all networks that preserve on average these constraints but are otherwise purely random Obtain expected value and standard deviation of higher-order network statistics (assortativity, clustering, centrality, etc.) over the ensemble Compare observed vs. expected values Giorgio Fagiolo (LEM) The ITN: Empirics and Models 14 / 31

26 Null Models Null Models of the ITN Main Idea Given observed network, define a set of local properties of the network (constraints) that must be preserved (density, degree or strength sequence, etc.) Characterize the ensemble of all networks that preserve on average these constraints but are otherwise purely random Obtain expected value and standard deviation of higher-order network statistics (assortativity, clustering, centrality, etc.) over the ensemble Compare observed vs. expected values Application to the ITN We study null models where we keep fixed either (in/out) degree or strength sequences and we check higher order statistical network properties (disassortativity, clustering) By product: Are standard (local) international-trade statistics sufficient for explaining higher-order network properties? Squartini, Garlaschelli, Fagiolo (2011a, 2011b; PRE) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 14 / 31

27 Null Models of the ITN A New Randomization Method Features (Squartini & Garlaschelli, 2010) Fit to observed network the probability P(G) of a random graph satisfying a list of local constraints (inferred from observed network) Fully analytical method: no random variant must be generated Works for directed/undirected, binary/weighted, sparse/dense networks Expected properties computed in same time as empirical ones Giorgio Fagiolo (LEM) The ITN: Empirics and Models 15 / 31

28 Null Models of the ITN A New Randomization Method Features (Squartini & Garlaschelli, 2010) Fit to observed network the probability P(G) of a random graph satisfying a list of local constraints (inferred from observed network) Fully analytical method: no random variant must be generated Works for directed/undirected, binary/weighted, sparse/dense networks Expected properties computed in same time as empirical ones A 3-Step Method Find the graph probability distribution P(G; θ ) that maximizes graph entropy subject to constraints Use observed data to estimate via ML free parameters θ in the graph probability distribution obtained above Use ML estimates of free parameters θ to compute expected values and standard deviations of higher-order network statistics X(G) E(X θ ) = G P(G θ )X(G) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 15 / 31

29 m 100 Null Models of the ITN The Binary ITN: 50Disassortativity r k tottot, k tot, r k tottot, k tot year Orange: Observed. Green: Expected. c year s r k tottot, k tottot Contraint: Degree sequence Null model always predicts strong disassortativity ITN is strongly disassortative only after 1965 Null model well predicts disassortativity (when it is a robust network feature) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 16 / 31

30 Null Models of the ITN The Weighted ITN: Disassortativity Orange: Observed. Green: Expected year m s year r s tottot, s tot, r s tottot, s tot c r s tottot, s tottot Contraint: Strength sequence Null model always predicts extreme weighted disassortativity Weighted (weak) disassortativity patterns (arising consistently from 1950 to 2000) cannot be replicated Giorgio Fagiolo (LEM) The ITN: Empirics and Models 17 / 31

31 Null Models of the ITN Null Models: Implications General Results Binary ITN: Degrees are sufficient to reproduce all higher-order statistics Weighted ITN: Strengths are not sufficient to reproduce higher-order statistics Giorgio Fagiolo (LEM) The ITN: Empirics and Models 18 / 31

32 Null Models of the ITN Null Models: Implications General Results Binary ITN: Degrees are sufficient to reproduce all higher-order statistics Weighted ITN: Strengths are not sufficient to reproduce higher-order statistics Implications for network analysis Binary ITN: disassortativity and clustering patterns do not convey any interesting information Weighted ITN: higher-order statistics convey fresh information, which is not already contained in strength sequences Giorgio Fagiolo (LEM) The ITN: Empirics and Models 18 / 31

33 Null Models of the ITN Null Models: Implications General Results Binary ITN: Degrees are sufficient to reproduce all higher-order statistics Weighted ITN: Strengths are not sufficient to reproduce higher-order statistics Implications for network analysis Binary ITN: disassortativity and clustering patterns do not convey any interesting information Weighted ITN: higher-order statistics convey fresh information, which is not already contained in strength sequences Implications for international-trade empirics A weighted-network analysis brings value added wrt standard (local) int l-trade statistics Degree sequences are maximally informative: trade models should focus on explaining new-link formation and degrees (in addition to trade flows) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 18 / 31

34 Economic Models Economic Models and the ITN Can economic models explain/reproduce ITN architecture? Giorgio Fagiolo (LEM) The ITN: Empirics and Models 19 / 31

35 Economic Models Economic Models and the ITN Can economic models explain/reproduce ITN architecture? Two examples: 1 Standard Int l Trade Models: Gravity Model (GM) 2 Stochastic Models of Network Formation Giorgio Fagiolo (LEM) The ITN: Empirics and Models 19 / 31

36 The Gravity Model Economic Models The Microfounded GM The GM explains international-trade bilateral flows as the equilibrium prediction of micro-founded models of trade A Newton s formula for trade Export a b Sizea Size b dist(a, b) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 20 / 31

37 The Gravity Model Economic Models The Microfounded GM The GM explains international-trade bilateral flows as the equilibrium prediction of micro-founded models of trade A Newton s formula for trade Export a b Sizea Size b dist(a, b) The Empirical GM Adding explanatory factors to the basic GM equation Country-specific: population, area, land-locking effects, etc. Bilateral: geographical contiguity, common language and religion, colony relation, bilateral trade agreements, etc. Giorgio Fagiolo (LEM) The ITN: Empirics and Models 20 / 31

38 Economic Models GM Specification (Duenas & Fagiolo, 2012) Exports from i to j at t GDP Geographical Distance Country Vars (Area, Population) exp w ij (t) = 0 Y i (t) 1 Y j (t) 2 d 3 ij HX h D ijh (t)+ h=1 " Y K # C ik (t) 1k C jk (t) 2k k=1! LX ( 1l Z il + 2l Z jl ) ij (t) =exp{x ij } ij, l=1 Bilateral-relationship variables (contiguity, common language, past and current colonial ties, common religion, common currency, regional trade agreements) Country-specific dummies (land-locking effects, continent membership, etc.) Errors Giorgio Fagiolo (LEM) The ITN: Empirics and Models 21 / 31

39 Economic Models What We Do... Fitting the GM to the data Two setups: 1 Binary structure given: estimate flows only (OLS on log-linearized model) 2 Binary structure estimated together with flows (PPML, ZIP) Giorgio Fagiolo (LEM) The ITN: Empirics and Models 22 / 31

40 Economic Models What We Do... Fitting the GM to the data Two setups: 1 Binary structure given: estimate flows only (OLS on log-linearized model) 2 Binary structure estimated together with flows (PPML, ZIP) We employ GM predictions to build a weighted predicted ITN, whose topological properties are compared to observed ones Giorgio Fagiolo (LEM) The ITN: Empirics and Models 22 / 31

41 Economic Models What We Do... Fitting the GM to the data Two setups: 1 Binary structure given: estimate flows only (OLS on log-linearized model) 2 Binary structure estimated together with flows (PPML, ZIP) We employ GM predictions to build a weighted predicted ITN, whose topological properties are compared to observed ones Results: A Sneak-in Preview The GM successfully replicates the weighted-network structure of the ITN, only if one fixes its binary architecture The GM performs very badly when asked to predict the presence of a link; or the level of the trade flow whenever the binary structure must be simultaneously estimated Giorgio Fagiolo (LEM) The ITN: Empirics and Models 22 / 31

42 Economic Models Weighted Correlation Structure Weighted Disassortativity: Correlation between ANNS and NS 0.5 Corr(NS tot,anns tot ) Observed OLS Corr(NS tot,anns tot ) Observed PPML Corr(NS tot,anns tot ) Observed ZIP Year Year Year OLS can correctly replicate observed disassortativity PPML/ZIP always predict extreme disassortativity (as in null-model exercises, see Fagiolo, Squartini, Garlaschelli, 2011) Why: The GM is not able to correctly predict the binary structure! Giorgio Fagiolo (LEM) The ITN: Empirics and Models 23 / 31

43 Economic Models Stochastic Models of Network Formation Main Idea Employ network-formation models ideas to replicate structure of ITN. See: Riccaboni and Schiavo (2010, NJP), Caldarelli et al. (2012, arxiv) Here: Building a model where link formation is driven by economic rationales coming from international-trade theories Example: Comparative advantage and country specialization Giorgio Fagiolo (LEM) The ITN: Empirics and Models 24 / 31

44 Economic Models Stochastic Models of Network Formation Main Idea Employ network-formation models ideas to replicate structure of ITN. See: Riccaboni and Schiavo (2010, NJP), Caldarelli et al. (2012, arxiv) Here: Building a model where link formation is driven by economic rationales coming from international-trade theories Example: Comparative advantage and country specialization A Sketch of the Model (Duenas and Fagiolo, fc) N countries operating in K different industries/or markets (traits) Countries are located on a ring (geographical distance) The performance of country i in industry κ is π iκ Trade of a certain good between any pair of countries increases the more these countries are different in their performance levels Country i is more likely to export product κ to j if π iκ π jκ > 0 Overall likelihood for i to export any product to j depends on λ ij = κ [π iκ π jκ ] 1 {πiκ π jκ >0} Giorgio Fagiolo (LEM) The ITN: Empirics and Models 24 / 31

45 Link Formation Economic Models Edges are drawn independently with probability p ij, the probability of having a particular graph A = {a ij } is (Park & Newman, 2004): Γ(A) = Γ 0 a ij A ( pij 1 p ij ) aij = Γ 0 a ij A Λ a ij ij, (1) with S i S j Λ ij = βλ ij dij α, with S i = k π i,k (2) Then, Γ(A) = Γ 0 β L a ij A d α a ij ij a ij A λ a ij ij i S k out i i j S k in j j, (3) where L is the number of edges; ki out and ki in are in- and out-degrees; α controls for geographical distance; β controls for density. Giorgio Fagiolo (LEM) The ITN: Empirics and Models 25 / 31

46 Economic Models Distribution of Perfomance P(π) Two Extreme Scenarios 1 Homogeneous Performances: Countries have similar performances in all traits, with comparable overall sizes S i 2 Heterogeneous Performances: Countries have very dissimilar capabilities and overall sizes S i Giorgio Fagiolo (LEM) The ITN: Empirics and Models 26 / 31

47 Economic Models Distribution of Perfomance P(π) Two Extreme Scenarios 1 Homogeneous Performances: Countries have similar performances in all traits, with comparable overall sizes S i 2 Heterogeneous Performances: Countries have very dissimilar capabilities and overall sizes S i Main Idea Comparing a world where countries do not specialize with a more realistic picture were more competitive countries are more likely to export Giorgio Fagiolo (LEM) The ITN: Empirics and Models 26 / 31

48 Economic Models Distribution of Perfomance P(π) Two Extreme Scenarios 1 Homogeneous Performances: Countries have similar performances in all traits, with comparable overall sizes S i 2 Heterogeneous Performances: Countries have very dissimilar capabilities and overall sizes S i Main Idea Comparing a world where countries do not specialize with a more realistic picture were more competitive countries are more likely to export Implementation 1 Homogeneous Performances: Draw π from a Uniform distribution 2 Heterogeneous Performances: Draw π from a Pareto distribution Giorgio Fagiolo (LEM) The ITN: Empirics and Models 26 / 31

49 Economic Models Reproducing Node-Degree Distribution Homogeneous Scenario Heterogeneous Scenario The model is able to reproduce degree distributions in both scenarios Giorgio Fagiolo (LEM) The ITN: Empirics and Models 27 / 31

50 Economic Models Reproducing Correlation Structure Homogeneous Scenario Heterogeneous Scenario X-axis: network density ( 0.45 for the ITN) The heterogeneous scenario captures the magnitude of correlations for empirically-observed network-density values. The homogeneous scenario cannot. Giorgio Fagiolo (LEM) The ITN: Empirics and Models 28 / 31

51 Economic Models Null vs. Economic Models: Take-Home Messages The ITN vs. Null Models Degrees are responsible for higher-order binary structure Most of higher-order evidence about correlation is meaningless if one knows degree sequences Explaining binary structure of first-trades (and thus degrees) is fundamental Giorgio Fagiolo (LEM) The ITN: Empirics and Models 29 / 31

52 Economic Models Null vs. Economic Models: Take-Home Messages The ITN vs. Null Models Degrees are responsible for higher-order binary structure Most of higher-order evidence about correlation is meaningless if one knows degree sequences Explaining binary structure of first-trades (and thus degrees) is fundamental The ITN vs. the GM The GM turns out to be a good model for estimating trade flows, but cannot predict the presence of a link (and thus degree sequences) However, conditional on the information that a link exists, the GM can well predict weighted-network properties Giorgio Fagiolo (LEM) The ITN: Empirics and Models 29 / 31

53 Economic Models Null vs. Economic Models: Take-Home Messages The ITN vs. Null Models Degrees are responsible for higher-order binary structure Most of higher-order evidence about correlation is meaningless if one knows degree sequences Explaining binary structure of first-trades (and thus degrees) is fundamental The ITN vs. the GM The GM turns out to be a good model for estimating trade flows, but cannot predict the presence of a link (and thus degree sequences) However, conditional on the information that a link exists, the GM can well predict weighted-network properties The ITN vs. Stochastic Models of Network Formation Important role of specialization in explaining degree distribution and correlation structure Work in progress: calibration with real world data, scenario and sensitivity analysis, etc. Giorgio Fagiolo (LEM) The ITN: Empirics and Models 29 / 31

54 Papers Economic Models Topological Properties of the ITN Barigozzi, M., Fagiolo, G. and Garlaschelli, D. (2010), "The Multi-Network of International Trade: A Commodity-Specific Analysis", Physical Review E, 81, Fagiolo, G., Reyes, J. and Schiavo, S. (2009), "The World-Trade Web: Topological Properties, Dynamics, and Evolution", Physical Review E, 79, (19 pages) Null Models Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), Randomizing World Trade. Part I: A Binary Network Analysis, Physical Review E, 84, Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), Randomizing World Trade. Part II: A Weighted Network Analysis, Physical Review E, 84, Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), Null Models of Economic Networks: The Case of the World Trade Web, J of Econ Int & Coord, forthcoming Gravity Models Duenas, M. and Fagiolo, G. (2011), Modeling the International-Trade Network: A Gravity Approach, arxiv: [q-fin.gn]. Also in: LEM Working Paper, 2011/25. Fagiolo, G. (2010), The International-Trade Network: Gravity Equations and Topological Properties, J of Econ Int & Coord, 5:1-25. Giorgio Fagiolo (LEM) The ITN: Empirics and Models 30 / 31

55 Economic Models Thanks Giorgio Fagiolo Laboratory of Economics and Management (LEM) Institute of Economics Sant Anna School of Advanced Studies, Pisa, Italy Giorgio Fagiolo (LEM) The ITN: Empirics and Models 31 / 31