The International-Trade Network - World Banksiteresources.worldbank.org/INTRANETTRADE/Resources/Internal... · The International-Trade Network (ITN) Why Networks of International

The International-Trade NetworkEmpirics and Modeling

Giorgio FagioloInstitute of Economics

Sant’Anna School of Advanced Studies, Pisa, Italy

@giorgiofagiolohttp://www.lem.sssup.it/fagiolo/Welcome.html

[email protected]

"Network and Connectivity Tools", May 21, 2013, The World Bank

http://www.lem.sssup.it/fagiolo/

http://www.lem.sssup.it/fagiolo/

mailto:[email protected]

mailto:[email protected]

Plan of the Talk

• The “International Trade Network” (ITN)

Plan of the Talk


• Why trade economist should care about networks?

Plan of the Talk



• Results: a bird’s eye view

Plan of the Talk




• Why understanding ITN topology may be relevant for economic analysis?

Plan of the Talk





• How can we explain observed ITN topology?

Plan of the Talk





• How can we explain observed ITN topology?

• Open issues

The International-Trade Network (ITN)Why Networks of International Trade?

The International-Trade Network (ITN)

What is it?Network where nodes are world countries and links representbilateral trade flowsTime evolution of the ITN (data from 1950 to 2010)Different empirical representations: binary/weighted, undirected/directed,aggregate/commodity-specific

Introduction


What is it?Network where nodes are world countries and links represent bilateral tradeflowsDifferent empirical representations: binary/weighted, undirected/directed,aggregate/commodity-specificTime evolution of the ITN (data from 1950 to 2010)

The World-Trade Web (WTW)

• Links defined as binary trade relationships: existence of non-zero trade flows

USA

LUXTrade relation

USA

LUXExport/import relations


• Links defined as binary trade relationships: existence of non-zero trade flows

USA

LUXTrade relation

USA

LUXExport/import relations


• Link weights defined by total bilateral flows (undirected) or directed import flows (always deflated)

USA

LUXTotal bilateral flow (exports plus imports) btw USA and

LUX



USA

LUXTotal Export from USA to

LUX

Total Export from LUX to

USA

USA


LUXThe World-Trade Web (WTW)


USA


LUX



USA


LUX


USA

USA


LUX



USA


LUX



USA


LUX


USA

USA


LUX

Giorgio Fagiolo (LEM) Modeling the ITN 3 / 23


• Aggregate vs commodity-specific multi-network

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Colors: Commodity-

specific networks

Multi-WTW: Union of colored slices

Giorgio Fagiolo (LEM) The ITN: Empirics and Models 5 / 31


Why Networks of International Trade?

Trade Networks. . . An old Idea

Source: De Benedictis & Tajoli (2008)


The International-Trade Network (ITN)Why Networks of International Trade?

Trade Networks. . . An old Idea

Source: De Benedictis & Tajoli (2008)


From Qualitative to Quantitative ApproachesWhy 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, 2010.


• Complex-network theory

• Visual tools not sufficient

• Characterizing the topology of the network and its evolution over time

• Modeling: replication vs explanation

Why Should Trade Economists Care About Networks?

• Int’l trade traditionally viewed as a bilateral phenomenon: gravity models (rem: multilateral resistance, remoteness?)



• A network perspective is based on the idea that indirect trade relationships may be 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. via indirect spillovers





• 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. via indirect spillovers

• Ward and Ahlquist (2013): bilateral trade is not independent of the production, consumption, and trading decisions made by firms and consumers in third countries

Why Should Trade Economists Care About Networks?Results (3): Weak vs strong links

Cascades can proceed directly by following successive direct trade channels (solid arrows) or indirectly following detours (dashed arrows)

An indirect cascade propagates through weak links, the weight of which is insufficient to transmit the cascade directly.

However, when the impact through weak channels combines with impacts through detours, the aggregate impact can be strong enough to transmit the cascade.

distributed network (GDN). In the GDN, each country can have asmany trading partners as is constrained only by the total tradevolume. Specifically, we constructed a GDN in the following way:i) Discretize and divide each trade link of GMN into unit links inone million US dollars. ii) Start from the network with all the unitin- and out-links unlinked. iii) Select one export unit and oneimport unit from the unlinked unit links. iv) If the export and theimport countries are different, connect the two by an arrow.Otherwise, discard the trial. v) Repeat iii)-iv) until all unit links areconnected. v) Merge unit links with same export and importcountries, restoring the weighted network structure. In thehypothetical globalized world represented by a GDN, theavalanche size distribution becomes even more polarized. In theGDN, only three countries2United States, Germany, and

China2have dominant avalanche sizes (Fig. 9C). Yet, the averageavalanche size of these three countries is 132, spanning as much as75% of the globe (Fig. 10B).

To quantify the degree of polarization in the avalanche impactfor different network structures, we calculated two quantities: thetypical size, and the likelihood of nonzero avalanches. The formeris given by the average of avalanche sizes over countries withnonzero avalanche sizes and describes the expected global impactthat the global economic system might suffer once the avalancheoccurs. The latter is given by the number of countries withnonzero avalanche sizes divided by the total number of countries,providing a measure of how likely an avalanche is to occur whencrises are initiated randomly. Both randomized networks have ahigher typical size, 2061.8 for the GSN (empirical P-value ,1023)and 132611.0 for the GDN (empirical P-value ,1023), compared

Figure 5. Full sequence of avalanche process starting from Hong Kong. Direct channels (solid arrows) and indirect channels (dashed arrows)are distinguished because they contribute to the avalanche process by different mechanisms. Countries are colored according to the sub-processthey belong to, and their size is given by the GDP (in million US dollars). The starting country, Hong Kong, is colored in gray.doi:10.1371/journal.pone.0018443.g005

Figure 6. Avalanche profiles. Bar plot showing the avalanche profileof countries with the ten largest avalanche sizes is displayed. The totalavalanche process is divided into four sub-processes and the coloredbar denotes their distribution. For most countries shown in the figure,the indirect avalanche (yellow) constitutes the largest fraction of thetotal avalanche process.doi:10.1371/journal.pone.0018443.g006

Figure 7. Avalanche durations. Relation between avalancheduration and avalanche size for 175 countries is displayed. Note thatsome countries have much longer or shorter durations compared totheir avalanche sizes, thereby deviating from the overall increasingtrend. The same color code for the continental associations as in Fig. 3are used.doi:10.1371/journal.pone.0018443.g007

Crisis Spreading in Global Macroeconomic Network

PLoS ONE | www.plosone.org 6 March 2011 | Volume 6 | Issue 3 | e18443

Lee et al, PlosOne, 2011: Indirect channels may amplify the impact of initial shocks occurring in a given node of the ITN


• ITN topology describes the architecture of (one class of) real interaction channels among countries, where direct as well as indirect relationships are taken into account



• Countries can be characterized by their local and global embeddedness (centrality) in the ITN, where indirect-trade relationships of any order can be taken into account




• Next:




• Next:

• How does the ITN topology looks like? How has it evolved?




• Next:


• Is ITN topology relevant to better understand issues like globalization, crisis spreading, country growth and development?




• Next:


• Is ITN topology relevant to better understand issues like globalization, crisis spreading, country growth and development?

• How can we replicate/explain ITN topology? What are its determinants?

What do we know about ITN topology and evolution?

1. Topology of aggregate ITN has been stable and quite persistent over time despite globalization (Fagiolo et al, 2009, PRE)



2. Binary vs weighted representations of ITN tell us different stories (Fagiolo et al., 2008, PhysA; 2009, PRE)




• Countries with many trade partners do not necessarily trade more intensively





• Countries holding more partners tend to trade with countries with very few partners (strong disassortativity) and do not typically form trade triangles






• More-intensively connected countries tend to trade with relatively less connected countries (weak disassortativity)






• More-intensively connected countries tend to trade with relatively less connected countries (weak disassortativity)

• More-intensively connected countries are more central and tend to form highly-connected trade triangles


3. Link weights are log-normally distributed (not a complex network) but become power law (a complex network) after having controlled for traditional factors explaining trade, like distance, size, etc. (Fagiolo, 2010, JEIC)



4. At the product-specific level, ITN topology is characterized by a strong cross-sector heterogeneity and the roles played by different commodities in the ITN have become more and more dissimilar over time due to increased trade specialization (Barigozzi et al, 2010, PRE)



4. At the product-specific level, ITN topology is characterized by a strong cross-sector heterogeneity and the roles played by different commodities in the ITN have become more and more dissimilar over time due to increased trade specialization (Barigozzi et al, 2010, PRE)

5. Data allow one to robustly identify trade communities (clusters of countries) that are relatively stable over time and are mostly explained by regional/geographical factors, and less by R/B/TAs (Barigozzi et al, 2011, PhysA)

Is ITN topology relevant for economic analysis?

• Kali et al., 2007, JITED: How do the number of trade partners and the dispersion of trade volumes among partners affect country growth?



• Exploring the trade-growth nexus by considering network-related indicators (in addition to standard controls) in growth regressions à la Mankiw et al (1992) and Barro (1991)




• Main finding: Structure of trade has an important effect on economic growth





• The number of trading partners is positively correlated with growth across all countries, and this effect is more pronounced for rich countries





• The number of trading partners is positively correlated with growth across all countries, and this effect is more pronounced for rich countries

• Trade volume dispersion is negatively correlated with growth for all countries, and the effect is concentrated in poor countries


Giorgio Fagiolo and Javier Reyes, The World-Trade Web Cambridge 2009

Some Puzzles

Introduction Preliminaries Results Conclusions

Reyes, Fagiolo and Schiavo, JITED, 2010

How can we explain observed ITN topology?

• Two possible classes of models



• Null (random) models




• Economic models




• Economic models

• Null (random) models: Can observed properties be the sheer outcome of randomness? What is the expected structure of the ITN when links and weights are placed at random, provided they satisfy some (minimal) economically-meaningful constraints?




• Economic models

• Null (random) models: Can observed properties be the sheer outcome of randomness? What is the expected structure of the ITN when links and weights are placed at random, provided they satisfy some (minimal) economically-meaningful constraints?

• Economic models: Can standard economic models replicate and explain observed ITN topology? Natural candidate: gravity model of trade.

Null Models of the ITN in a Nutshell

• Null (random) models (Fagiolo et al, 2012 PREa,b, JEIC): • Constraints: Fix some observed node property (number of partners,

total country trade)

• Compute expected values and standard deviations of relevant ITN topological properties when everything else in the ITN is assigned purely at random (provided constraints are satisfied)

• Compare observed vs expected properties

Null Models of the ITN in a Nutshell

• Null (random) models (Fagiolo et al, 2012 PREa,b, JEIC): • Constraints: Fix some observed node property (number of partners,

total country trade)

• Compute expected values and standard deviations of relevant ITN topological properties when everything else in the ITN is assigned purely at random (provided constraints are satisfied)

• Compare observed vs expected properties

Null Models of the ITN

The Binary ITN: Disassortativity

Orange: Observed. Green: Expected.

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Null Models of the ITN

The Weighted ITN: Disassortativity

Orange: Observed. Green: Expected.

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Binary ITN Weighted ITN

Gravity Model (GM) vs ITN Topology

• Fitting a GM to the ITN (Fagiolo et al, 2010, 2013, JEIC): • Fit trade data with a standard GM

• Use predictions of estimated GM to build predicted ITN

• Compare GM-predicted and observed properties

Gravity Model (GM) vs ITN Topology

• Fitting a GM to the ITN (Fagiolo et al, 2010, 2013, JEIC): • Fit trade data with a standard GM

• Use predictions of estimated GM to build predicted ITN

• Compare GM-predicted and observed properties

• Results• GM works well only if we keep binary structure as given, badly predicts

weighted ITN structure if one asks the GM to estimate both existence of a link and its weight

Economic Models

Weighted Correlation Structure

Weighted Disassortativity: Correlation between ANNS and NS

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PPML/ZIP always predict extreme disassortativity (as in null-model exercises, seeFagiolo, Squartini, Garlaschelli, 2011)

Why: The GM is not able to correctly predict the binary structure!


Weighted ITN: Disassortativity

Take-Home Messages

• A network perspective to trade can help in identifying novel properties of international-trade structure

Take-Home Messages


• Topological properties of ITN may shed light on issues like crisis spreading, growth and development patterns

Take-Home Messages



• Fitting ITN with null statistical models helps one to discriminate between relevant and unrelevant properties

Take-Home Messages




• Weighted ITN architecture cannot be entirely explained by sequences of trade partners or total country trade

Take-Home Messages




• Weighted ITN architecture cannot be entirely explained by sequences of trade partners or total country trade

• Gravity model is not able to predict well binary structure, which seems instead fundamental to understand weighted ITN architecture

Open Issues

• Predicting and explaining binary structure: need for better microfounded models of trade able to explain first-link emergence vs evolution of link weights (intensive vs extensive)

Open Issues


• Deeper understanding of the coevolution between network structure and macroeconomic dynamics (e.g. trade-growth link). Problem: endogeneity.

Open Issues


• Deeper understanding of the coevolution between network structure and macroeconomic dynamics (e.g. trade-growth link). Problem: endogeneity.

• We need a better comprehension of the macroeconomic multi network linking world countries (trade, migration, mobility, FDI, finance, etc.): models of shock diffusion in multi networks

Selected List of ReferencesEconomic Models

Papers

Topological Properties of the ITN

Barigozzi, M., Fagiolo, G. and Garlaschelli, D. (2010), "The Multi-Network ofInternational Trade: A Commodity-Specific Analysis", Physical Review E, 81, 046104Fagiolo, G., Reyes, J. and Schiavo, S. (2009), "The World-Trade Web: TopologicalProperties, Dynamics, and Evolution", Physical Review E, 79, 036115 (19 pages)

Null Models

Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), “Randomizing World Trade. PartI: A Binary Network Analysis”, Physical Review E, 84, 046117.Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), “Randomizing World Trade. PartII: A Weighted Network Analysis”, Physical Review E, 84, 046118.Squartini,T., Fagiolo, G. and Garlaschelli, D. (2011), “Null Models of EconomicNetworks: 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: AGravity Approach”, J of Econ Int & Coord, forthcomingFagiolo, G. (2010), “The International-Trade Network: Gravity Equations andTopological Properties”, J of Econ Int & Coord, 5:1-25.


Economic Models

Thanks

Giorgio FagioloLaboratory of Economics and Management (LEM)

Institute of EconomicsSant’Anna School of Advanced Studies, Pisa, Italy

[email protected]

http://www.lem.sssup.it/fagiolo/Welcome.html


Thanks!

Documents

The International-Trade Network - World Banksiteresources.worldbank.org/INTRANETTRADE/Resources/Internal... · The International-Trade Network (ITN) Why Networks of International