“Successful” Regionalism and the Role of Regional Hegemons
By
Jennifer Pédussel Wu1
Berlin School of Economics and Law
Badensche Straße 50-51, 10825 Berlin, Germany
[email protected]
This version: September 2009
Very Preliminary
Please do not quote without permission from the author
Abstract:
Nearly 300 Regional Trade Agreements (RTA) have been notified since the founding of
the WTO. Of these RTAs, Free Trade Agreements account for nearly 90% of the total.
Leading members of a region to trade more intensely among themselves is an observed
characteristic of a successful RTA. This paper examines the importance of regional
hegemons in the creation of successful RTAs. An empirical approach using cluster
analysis to determine the importance of hegemon-centered regional integration
arrangements is applied. The presence of a regional hegemon is found to be significant.
Keywords: Cluster Analysis, Network Analysis, Hegemon, RTA
JEL Codes: F15, C20
1
I thank Richard Manu Asamoah for research assistance; Ann Largey for comments.
1
Introduction
The dictionary defines a hegemon as “a leading or paramount power” or a “state
powerful enough to influence events throughout the world.” In the field of economics, a
hegemon is sometimes referred to, but never well defined. First introduced by
Kindleberger (1973), hegemonic stability theory relies on the assumption that free trade
is a public good. The public good provision requires a dominant country to ensure order
and prevent free-riding. The stability and resulting growth of the international economy
due to the leadership provided by the hegemon, leads to beneficial worldwide outcomes.
The underlying assumption of hegemonic stability theory is that there exists one hegemon
who ensures stability for world wide free trade.
Goodin et al (2005) model hegemony in a game-theoretic manner but concentrate
on the idea of a single hegemony. While a single hegemon is certainly possible, with
respect to international trade of goods and services, it is far more likely that a regional
hegemon is more important for a small country’s pattern of trade. When applying the
hegemonic stability theory to regional trade agreements, political scientists have been
interested in whether the dominance of one country in the RTA increases partners’
welfare.
The idea of hegemonic leadership in free trade is not supported by the standard
trade theory which suggests that a larger country would be more likely to gain from trade
restrictions than smaller countries. Thus, the idea that the large country would pursue a
first-best policy of free trade is difficult to justify.
Bhagwati, one of the few economists to explicitly describe a hegemon, has argued
that the hegemonic power in a Preferential Trade Agreement operates to pursue his own
selfish interest. He terms this distinguishing characteristic one of selfish hegemons. Thus,
traditional hegemons such as the United States pursue a sequential selection of
“vulnerable” members to be their partners in the trade agreement by means of both
bilateral and plurilateral approaches, e.g., FTA between USA and Chile or the Free Trade
Area of the Americas (FTAA). Moreover, Bhagwati believes that the existence of selfish
hegemons poses a threat to the world trading system as dominant countries use FTAs as a
2
strategy to advance their own trade policies resulting in aggressive unilateralism. These
strategies in turn hamper multilateralism through the promotion of regional trading blocs.
The importance of institution-specific characteristics is supported by Keohane
(2005). In order to test for the importance of institutional hegemons, Hafner-Burton et al.
(2002) empirically investigated whether institution-specific characteristics are more
important in ensuring the durability of Preferential Trade Agreements than the impact
made by influential institutions, i.e., the hegemons. In these studies, hegemony is defined
using a threshold level of the percentage of the country’s total world GDP in each year.
Using a duration model, they found that there was no statistical significance accorded to
the hegemony variable thus implying that stronger hegemons have little influence on the
long-term viability of institutions. They concluded that ‘systematic variations of
hegemons seem to have no influence on the durability of PTAs’. They did not, however,
examine whether the presence of the hegemon increases intra-regional trade among the
members nor did they examine what the potential would have been had the hegemon not
existed.
Several other researchers have investigated the idea that hegemon led regional
trade strategy will not result in increased trade for all members. The existence of “hubs”
and “spokes” or hegemons and non-hegemons has led the World Bank to argue that
hegemon led regionalism would result in disproportionate gains for the hegemons or hubs.
This would be due to nontrade issues, product exclusion, complex and differing rules of
origin as well as trade and investment diversion (World Bank, 2004). In the Global
Economic Prospects for 2005, it quantified how disadvantaged small sub-Saharan
African countries (non-hegemons or ‘spokes’) would be by 2015 when all of developing
counties sign bilateral trade agreements with the major hegemons or ‘hubs’. The Bank
estimates that the spokes will lose over $ 3 billion which also represents 0.7% of the
regions income.
Yarbrough and Yarbrough (1985) also argued that the best description to be given
to a hegemon is that of a principal or one who claims the residuals from free trade. Thus,
the major role of the hegemon is to relax the budget constraints of the geographically
defined trading system. This line of argumentation is directly related to the hegemonic
3
stability theory proposed by Kindleberger. Although residual claims from free trade
could be empirically calculated, this has thus far not been done.
There are few conclusions in the theoretical or empirical literature to link
hegemony and regional trade agreements. While discussed considerably in the political
science domain, economists have recently begun to be interested in the presence of
hegemons in international trade. Most of these works refer to a single hegemon, the
United States, and rarely interest themselves in evaluating the importance of hegemony in
regional trade agreements.
This paper will examine the importance of hegemonic presence for regional trade
agreement. A cluster analysis will be used to see if bilateral trade ratios are able to
identify groups of trade in the world today and whether these groups cross hemispheres.
The relationship to actual regional trade agreements of the clustered groups is then
evaluated. The next section will discuss regionalism followed by a discussion of cluster
analysis. The results are then examined before concluding.
Regionalism
The fall of the Berlin Wall precipitated a shift in the motivation of those countries
considering the possibility of regional integration. Alliances shifted dramatically from
the cold war based bi-polar regime to a new multi-polar regime. Furthermore, the
characteristics of Regional Integration Arrangements (RIA) have also shifted. Ferdandez
and Portes (1998) noted that Regional Trade Agreements (RTA) have deepened; widened
and increased in number. Baldwin (2008) contends that “regionalism is here to stay and
likely to become a stronger force over the coming decades.” The explosion of
international trade related to RIAs is attested to in Figure 1. Since the creation of the
WTO in 1995, over 240 additional arrangements covering trade in goods or services have
been notified.
4
Figure 1: Percent of trade in and between RTAs.
Percent of Trade in and between RTAs
45
40
Percent of Trade
35
30
25
Same RTA Trade
20
Both in Different RTA Trade
15
10
5
1996
1993
1990
1987
1984
1981
1978
1975
1972
1969
1966
1963
1960
0
Year
Source: Author’s calculations.
A search for explanations regarding the proliferation of RIAs has led to the
conclusion that more than the traditional gains from trade are provided by the
establishment of these types of organizations. For example, Schiff (2000) has examined
the proliferation of Free Trade Agreements (FTA) and Customs Unions (CU) and found
that the ratio of FTAs to CUs has increased in the 1990's, particularly in the case of
North-South agreements. Wu (2005) has shown a potential explanation to be that the
formation of regional integration agreements may aid in reducing risks and uncertainty
associated with foreign transactions, thereby leading to expanded trade and investment.
As the world shifted away from the Cold War alliance regime, the resulting uncertainty
encouraged the formation of regional integration agreements.
Recognizing the fact that one explanation may not apply to all recent
developments with respect to RIA proliferation, it is important to examine not only the
conditions under which such arrangements come into existence, but also the relative
success of different RIAs. Much of the empirical work has concentrated on the
difficulties of estimating the trade creation or diversion effects resulting from formation.
Empirical work concerning motivations of countries joining RIAs has shown that high-
5
income, democratic countries with high populations who are landlocked and are not
islands are more likely to belong to deeper levels of regional integration. It is expected
that developing countries do not have solid institutions designed to support deep levels of
RIA. This is reflected in the importance of democracy, a variable which is consistently
significant across time. In addition, Wu (2006) showed the importance of various forms
of uncertainty influencing the probability that a country will belong to a specific level of
regional integration.
Regional integration is also dependent on the choice of definition of a ‘region’.
Most generally define a region in terms of geographic proximity (Mansfield and Milner,
1997). Although, regionalism has also been defined by Winters (1999) as “any policy
designed to reduce trade barriers between a subset of countries, regardless of whether
those countries are actually contiguous or even close to each other.”2 Some have also
referred to regionalization or regionally-oriented policies.3 Regionalism can also refer
loosely to the growing number of international links of varying intensities between
nation-states. The latter is important here since we are interested not in purely
geographic regional linkages but in the possible links between nation-states not within a
specific geographical region.
Table 5 from Michaely (1996) shows the trade complementarity indices for
selected regional trade agreements. Michaely (1996) argued that with less than 30%
trade complementarity, the RTA was unsuccessful. Using this criterion, APEC would
potentially be considered a successful arrangement whereas the Sub-Saharan African
agreements would not. The EU, NAFTA and Mercosur all qualify as successful
agreements. In addition, each of these successful RTAs has a regional hegemon. One of
the motivating themes of this paper is to verify that these trade complementarity indices
are compatible with a cluster analysis where grouping leads to selection of a regional
hegemon.
2
3
Winters page 8. Muller, page 24 6
The SADC region has a distinctive leader in South Africa. This is in contrast to
both the Central and West African regions. South Africa is the largest economy in Africa
and therefore has an impact on the surrounding economies. We would therefore expect
to see South Africa at the center of a network of trade in the Southern African Region or
at least as the main trading partner of other countries in that region.
Brazil and Argentina form the core countries in terms of trade volumes in
Mercosur. We would therefore expect to see them at the center of a network of trade in
Latin America, i.e., as either joint hegemons or with one, perhaps Brazil, clearly being
the hegemon of regional trade.
It is not clear what the expectation is for Asia. There have been numerous studies
concerning the appropriateness of China or Japan as a regional hegemon. Examining
Table 1 below, we would expect to see Thailand, Singapore, Malaysia and Japan with
fairly good trade integration. Japan has had a leading political and developmental role,
yet the market is still not as dynamic as that of the US and the yen is still not the currency
of choice in the region. China, while a strong candidate, has not shown itself willing or
able to master adjustment shocks and their effects on neighbors. It is relatively clear
when looking at trade statistics that the hegemon continues to be a country outside of
Asia, the USA. Whether this is clearly shown in clustering the data remains to be seen.
7
Data
The integration of world trade is based on bilateral trade flows. This study
therefore uses hierarchical cluster analysis based on bilateral trade intensity. Data for
bilateral trade volumes come from the NBER-UN world trade data. Ratios of trade flows
for both exports and imports were calculated for the year 2000 as this year has very
complete data and is far enough post-WTO founding to be able to account for the number
of RTAs which were notified to the WTO post 1994. Data for countries with less than
1% trade were deleted from the dataset. This left a bilateral data set of 110 countries
consisting of nearly 6500 observations.
As a secondary dataset of bilateral trade flows, the IMF’s Direction of Trade
Statistics was used to examine the total trade between pairs of countries (i.e., exports plus
imports). Year 1999 was used for these calculations. Ideally, we would use averages
over a couple of different years.4
Included in several of the clustering analyses are several additional control
variables. Real GDP data comes from the World Bank. Richer countries trade more and
developed countries are more likely to participate in RTAs consisting of developed
4
Eventually, it would be interesting to include an economic diversification index. A more diversified
economy is likely to be a more stable economy and therefore more likely to participate in a RTA. It would
be satisfying to reexamine previous work (Wu 2006) by incorporating these indices into the cluster analysis.
8
countries. Data on democracy comes from Polity IV. Democracies also trade more and
are more likely to participate in an RTA due to a domino effect (á la Baldwin). Finally, it
might be important to control for geographic distances between countries. Although
countries may be very similar, if they are geographically distant, this may impinge on
their ability to form a trade agreement. Eventually, controls for institutional development
in both the government and the financial sector will also be added.
Cluster analysis
Cluster analysis is generally used for generating rather than testing a hypothesis.5
Countries found to be more homogeneous (clustered together) should be more likely to
form a regional trade agreement. The basic principle behind cluster analysis is that
objects that are similar to (highly correlated with) one another should be in the same
group, whereas objects that are dissimilar (uncorrelated) should be in different groups.
Thus, all cluster analyses begin with measures of similarity (dissimilarity) among objects
(distance matrices) or correlation matrices. Objects that are closer together based on
pairwise multivariate distances or pairwise correlations are assigned to the same cluster,
whereas those farther apart or having low pairwise correlations are assigned to different
clusters. Variables that have high pairwise correlations are assigned to the same cluster,
whereas those having low pairwise correlations are assigned to different clusters.
Thus, it is important to determine both a clustering method and a dissimilarity
measure choice.6 Clusters may be exclusive or overlapping. Hierarchical cluster analysis
is comprised of agglomerative methods and divisive methods that determine clusters of
observations within a data set. The divisive methods start with all of the observations in
one cluster and then proceeds to split (partition) them into smaller clusters. The
agglomerative methods begin with each observation being considered as a separate
cluster and then proceeds to combine them until all observations belong to one cluster.
Four of the better known algorithms for hierarchical clustering are average linkage,
5
Cluster analysis is also sometimes associated with classification or discrimination analysis. One may also want to perform data variable selection before clustering. I have chosen to forgo this step at
this time but will most likely return and do it later.
6
9
complete linkage, single linkage and Ward's linkage. This study confines itself to
average linkage clustering.7
Average linkage clustering uses the average similarity of observations between
two groups as the measure between the two groups. Mathematical properties such as
robustness are poor with hierarchical clustering techniques but average linkage clustering
works well in many situations and is generally considered in the literature to be a robust
method. A weighted average clustering method uses a weighting method which treats
those observations from smaller groups equally with larger groups. With a pair-group
average linkage, the distance between two objects is the average of all the distances
between all possible pairs formed by taking one item from each object. (This is similar to
centroid linkage.8) Objects are linked sequentially to form groups and at each stage of
the clustering procedure, the objects with the shortest distance between them are
combined and the distances between the resulting set of groups are recomputed.
Thus, hierarchial clustering begins with the calculation of distances or
correlations among all pairs of objects with groups being formed by agglomeration
(lumping of objects). The end result is a dendrogram (tree) which shows the distances
between pairs of objects. In addition, in order to validate the given solution, a cluster
stopping rule is applied. Stata provides two stopping rules, the Calinski & Harabasz or
the Duda & Hart indices. These are essentially indices of ‘distinctness’ of the clustering.
The stopping rules must be approached with some skepticism as they become less
informative when the number of elements in the groups becomes small – which is not the
case in this study.
In order to determine the existence of hegemon centered trading groupings, the
trade ratio data is clustered using average linkage clustering techniques in Stata. The
average linkage clustering results are reported below. Along with the trade ratios for
7
For the moment…. Centroid – the center of mass of an object having constant density….point in a system of masses each of
whose coordinates is a weighted mean of coordinates of the same dimension of points within the system,
the weights being determined by the density of the system. Centrold linkage – the distance between two
objects is the distance between the centroids of those objects. Complete, centroid and group-average
linkage clustering tend to create spherical clusters – exactly what we are interested in finding and the
centroid should be theoretically related to the regional hegemon.
8
10
exports and imports using the NBER-UN data, the IMF DOTS results using a single
linkage method including geographic distance, real gross domestic product and
population size are also reported. This is to ensure that other classifiers are included to
aid in weighting the similarities.
Results
Hierarchial clustering is best represented graphically through the use of
dendrogram. The lines of the dendrogram combine the different countries into groups.
The shorter the lines, the less dissimilar and the more homogenous are the emerging
clusters. Groupings of the same height indicate the number of clusters being proposed.
Further tests aid in determining the optimal number of clusters.
The dendrogram of single cluster analysis shown below in Figure 2 is clearly
dominated by bilateral trade with the United States. Lee (2004) found that there was a
clear identification of the NAFTA as one of the strongest bloc. That is not seen below.
Only Japan and South Korea are representing the Asian countries. In addition, the USBrazil and US-Argentina relationships seem to dominate that of Brazil-Argentina.
Figure 2
Dendrogram:single ratio cluster analysis
UKRAINE-UZBEKISTAN n=357
FRANCE-SPAIN n=4
UNITED KINGDOM-ITALY n=2
UNITED STATES-TURKEY n=2
FRANCE-ITALY n=1
UNITED KINGDOM-FRANCE n=1
UNITED STATES-ARGENTINA n=1
GERMANY-ITALY n=2
UNITED KINGDOM-GERMANY n=1
FRANCE-GERMANY n=1
UNITED STATES-AUSTRALIA n=1
UNITED STATES-NETHERLANDS n=1
UNITED STATES-KOREA,SOUTH(R) n=1
UNITED STATES-BRAZIL n=1
UNITED STATES-SPAIN n=2
UNITED STATES-ITALY n=1
UNITED STATES-UNITED KINGDOM n=1
UNITED STATES-FRANCE n=1
UNITED STATES-GERMANY n=1
UNITED STATES-JAPAN n=1
0
5e+024
1e+025 1.5e+025 2e+025
Euclidean similarity measure
11
2.5e+025
In order to further evaluate the existence of regional trade groupings, we reexamine the
clusters after removing the bilateral trade of which the US is a member. The results are
shown in Figure 3 below.
Dendrogram: single ratio no USA
UKRAINE-UZBEKISTAN n=290
RUSSIA-CHINA n=3
GERMANY-POLAND n=3
FRANCE-NORWAY n=1
SPAIN-TURKEY n=4
ITALY-GREECE n=4
CANADA-MEXICO n=11
CANADA-BRAZIL n=3
FRANCE-RUSSIA n=1
UNITED KINGDOM-NETHERLANDS n=1
CANADA-SPAIN n=2
BELGIUM-GERMANY n=1
UNITED KINGDOM-SPAIN n=1
FRANCE-SPAIN n=1
UNITED KINGDOM-ITALY n=1
FRANCE-ITALY n=1
UNITED KINGDOM-FRANCE n=1
GERMANY-ITALY n=1
UNITED KINGDOM-GERMANY n=1
FRANCE-GERMANY n=1
0
5.00e+23
1.00e+24
Euclidean Similarity measure
1.50e+24
Now, the importance of Canada-Mexico trade is evident. In addition, the core
countries of the European Union are unambiguously linked. The surprise relationships
here are that of Canada and Russia. The Ukraine-Uzbekistan trade relationship forms the
base of trade in Europe which includes that with Russia (more than with China). The
first cluster of countries seems to be related to the importance of their energy commodity
trade. The absence of the Asian, African and Latin American countries confirms our
suspicions concerning their lack of regional hegemon.
In order to further investigate these results, we separate the trade into imports and
exports. We then rerun the clustering analysis using trade ratios for both imports and
imports (NBER-UN data for year 2000). These are shown below.
12
Figure 4
Russian FedFinland
SwedenGermany
GermanySpain
GermanyItaly
GermanyNetherlands
New ZealandAustralia
TaiwanJapan
TFYR MacednaGreece
IrelandUK
CroatiaSlovenia
YugoslaviaTFYR Macedna
Korea Rep.Congo
GermanySyria
Russian FedUkraine
USAChina
SingaporeMalaysia
Untd Arab EmOman
Lao P.Dem.RThailand
BelarusRussian Fed
CanadaUSA
MexicoUSA
KazakhstanRussian Fed
Neth.Ant.AruVenezuela
Occ.Pal.TerrGermany
BermudaKazakhstan
China HK SARChina
Asia West NSSaudi Arabia
AustraliaPapua N.Guin
Russian FedRep Moldova
Russian FedBelarus
BrazilKorea D P Rp
GreeceCyprus
USACanada
USAMexico
YugoslaviaBosnia Herzg
0
L2 dissimilarity measure
.2
.4
.6
.8
1
Average Clustering of X-M ratios for year 2000
When examining the cluster tree, Europe seems to have similar trade relationships,
especially those of Germany, Spain, Sweden, the Netherlands, Greece, Ireland, and
Croatia-Slovenia. The presence of Germany in these paired trading relationships clearly
lends support to its role as a regional hegemon with respect to trade.
Furthermore, NAFTA clearly makes sense as a RTA since the USA-Mexico and
USA-Canada (and vice-versa) relationships hold and are similar. Since the USA is acting
as the anchor of these relationships, it should be considered at least the regional hegemon.
Beyond the clearly defined NAFTA and EU agreements, the other important RTAs are
not readily apparent.
The Russian Federation also has strong trade relationships with Finland, Belarus,
Kazakhstan, and the Ukraine. This is worthy of further exploration.
Clustering using exports and imports provides us with interesting Asian groupings.
New Zealand and Australia are similar to Taiwan and Japan. Another interesting
observation is the trade between Brazil and South Korea which seems to have similar
characteristics to that of Greece and Cyprus.
13
TurkeyFrance,Monac
PolandSlovenia
FinlandDem.Rp.Congo
UKSwitz.Liecht
PolandLithuania
GermanyItaly
SpainEgypt
USAPhilippines
FinlandEstonia
Russian FedGeorgia
GermanySyria
ItalyRomania
Korea Rep.Bahrain
SwedenEstonia
GermanyNetherlands
SloveniaBosnia Herzg
NorwayPanama
Russian FedMongolia
ThailandMyanmar
AustraliaPapua N.Guin
USACanada
AustraliaFiji
USANeth.Ant.Aru
Russian FedBelarus
USAEcuador
PortugalMozambique
ItalyYugoslavia
GermanyCzech Rep
ItalyCameroon
ItalyAzerbaijan
Korea Rep.Congo
YugoslaviaBosnia Herzg
BrazilKorea D P Rp
TurkeyGeorgia
GreeceCyprus
0
L2 dissimilarity measure
.2
.4
.6
.8
The African countries are not well represented in the top of the dendrogram. The
core Mercosur countries are also not present. In order to further examine the trade
relationships, the data is split into imports and exports ratios. These dendrograms are
shown below.
Figure5
Average Clustering of Xtrade ratios for year 2000
In the country pair name, the importing country is the first and the country from which it
is exporting is the second name.
14
TFYR MacednaSpain
Bosnia HerzgNetherlands
TaiwanSingapore
AlbaniaTurkey
France,MonacBelgium-Lux
GreenlandNorway
Switz.LiechtFrance,Monac
AzerbaijanTurkey
SamoaJapan
KiribatiJapan
JapanUSA
New ZealandUSA
FinlandGermany
CambodiaSingapore
Czech RepGermany
AlbaniaGreece
IrelandUK
US NESKorea Rep.
Trinidad TbgVenezuela
ZimbabweSouth Africa
Lao P.Dem.RThailand
China MC SARChina
MexicoUSA
ZambiaSouth Africa
BelarusRussian Fed
GuineaBissauPortugal
New CaledniaFrance,Monac
PanamaJapan
KazakhstanRussian Fed
Papua N.GuinAustralia
AustriaGermany
Oth.OceaniaKorea Rep.
UkraineRussian Fed
Neutral ZoneGreece
E Europe NESSaudi Arabia
0
L2 dissimilarity measure
.2
.4
.6
Average Clustering of Mtrade ratios for year 2000
We can see from the above two dendrograms that the export trade ratios give us
some interesting relationships. Historic trade relationships are well represented as are
some newer trade flows – perhaps due to energy trade. Of these, Finland / DR Congo
being very like Poland /Slovenia is one of the more difficult to explain. Overall, the trade
linkages which are most similar in terms of export trade ratios seem to be those which are
from large to smaller countries. There is very little representation of large to large
country trade.
With respect to the import trade ratios, this seems to be similar. Smaller countries
import from larger countries. Samoa-Japan seems to be a case in point. This relationship
is very like that of Azerbaijan-Turkey. Further control variables need to be added in
order to control for large and small countries in looking at the NBER-UN data.
There remains the question of how to define the hegemon. Is the hegemon to be
determined solely on the basis of trade integration? Further research includes the
addition of other political and institutional variables in order to distinguish the regional
hegemon in terms of both economic and other essential variables. Does it make sense to
use cluster results based on institutional differences? Potentially this approach may lose
15
information which would make it less justifiable as an analytical method. It may be
useful to validate the clustering analysis results with a more traditional logit using the
cluster regime dummies and then with country dummies.9 Effects differing significantly
between single country and cluster results would be a sign of serious information loss.
Summary statistics for the cluster analysis follow in the appendix.
Conclusion
This paper has examined the importance of hegemonic presence for regional trade
agreements through the use of a cluster analysis. Bilateral trade ratios were used to
identify groups of countries trading with each other and whether these groups cross
hemispheres. We also attempted to find a core country or a hegemon at the center of
those trade groupings.
The only evidence of trade clusters which resemble actual trade agreements today
is that of the European Union and NAFTA. While there is evidence of some crosshemisphere trade relationships, the groups are not clearly defined. In addition, some of
these cross relationships are clearly related to colonial ties. We conclude that there is an
absence of regional hegemons in actual regional trade agreements today which may
account for their inability to pursue fundamentally deeper trade integration. Further
research along these lines would shed light on the success of different regional trade
groups.
9
This method is applied by Leschke (2005). 16
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