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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


Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





1 IntroductionMotivationMethodsAnalysis

2 ResultsProminence: Gross ExportsPolarity: Gross ExportsProminence: Normalized ExportsPolarity: Normalized Exports


Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





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.


Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





Trade Patterns





Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





Trade Patterns





Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





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.


Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





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)


Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





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.


Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





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.


Ben Mazzotta

Outline

Introduction

Motivation

Methods

Analysis

Results





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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