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Export Dendograms provides a visualization of trade clusters in the OECD between 1960 and 2007, using four different export measures. This unique visualization provides rich intuition about prominence and polarity in international export markets.
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Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Clusters in OECD Trade Since 1960
Ben Mazzotta
Fletcher School, Tufts University
November 24, 2009
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
1 IntroductionMotivationMethodsAnalysis
2 ResultsProminence: Gross ExportsPolarity: Gross ExportsProminence: Normalized ExportsPolarity: Normalized Exports
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Trade Patterns
Aggregate statistics typically ignore relationships betweenspecific countries. Dyads are the fundamental unit ofanalysis.
Good data exist for the last 50 years. I have analyzedflows reported by OECD; but a larger dataset is availablefrom IMF’s Direction of Trade Statistics.
Hierarchic clustering is a simple, powerful way tocharacterize a large number of dyadic relationships.
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Trade Patterns
Aggregate statistics typically ignore relationships betweenspecific countries. Dyads are the fundamental unit ofanalysis.
Good data exist for the last 50 years. I have analyzedflows reported by OECD; but a larger dataset is availablefrom IMF’s Direction of Trade Statistics.
Hierarchic clustering is a simple, powerful way tocharacterize a large number of dyadic relationships.
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Trade Patterns
Aggregate statistics typically ignore relationships betweenspecific countries. Dyads are the fundamental unit ofanalysis.
Good data exist for the last 50 years. I have analyzedflows reported by OECD; but a larger dataset is availablefrom IMF’s Direction of Trade Statistics.
Hierarchic clustering is a simple, powerful way tocharacterize a large number of dyadic relationships.
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Distance
Distance Distance, d , is the reciprocal of a trade flow, T .
d = 1/T (1)
Dyads Partners report imports and exports.
Ti ,j 6= Tj ,i (2)
Logarithms For convenience, I’m going to plot d = log(1/T ).
Clustering Clusters require a single distance measure foreach dyad i,j. Therefore I’m going to distinguishbetween two types of distance measures.
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Distance measures
Definition
Bilateral distance
db ≡ max
(1
Ti ,j,
1
Tj ,i
)(3)
Definition
Unilateral distance
du ≡ min
(1
Ti ,j,
1
Tj ,i
)(4)
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Clustering
Clustering The algorithm chooses an arbitrarily smalldistance such that all countries are isolated, andgradually increases the threshold until thedistance between countries is smaller than thethreshold. When two countries are closer thanthe threshold, they join a cluster.
Hierarchic Once two countries have joined in a cluster, theymust remain joined at all higher thresholds.
Complete clusters The condition for addition to a cluster isthat all pairwise members of the new cluster mustsatisfy the distance condition.
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Prominence
Prominence arises from bilateral clusterings. Groups ofcountries that share a high degree of mutual trade areprominent in one another’s trade policy. Prominent clustersmay be measured either with gross trade flows or normalizedexports. These clusters reflect the largest mutualconcentrations of trade among groups of countries, suggestingthat countries will tend to orchestrate their foreign policy tomutual benefit.
Clusters inOECD TradeSince 1960
Ben Mazzotta
Outline
Introduction
Motivation
Methods
Analysis
Results
Prominence:Gross Exports
Polarity: GrossExports
Prominence:NormalizedExports
Polarity:NormalizedExports
Polarity
Polarity arises from unilateral clusterings. Polar clusters reflectgroups where all dyads exhibit at least one highly concentratedexport market, indicating the potential for leader and followerdynamics among these countries. Polar clusters may be:
unipolar when one member of the cluster is the principalexport market for all others in the cluster,
multipolar or bipartite, when two or more members of thecluster constitute a large share of partner countryexports, or
transitive where one or more countries exhibit highconcentration of exports with different partnerson the supply side and the demand side.
There are no obvious thresholds for defining membership inthese categories. With large cluster size, considerablehybridization may occur.
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