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IS THE WORLD BECOMING SMALLER?

GLOBALIZATION AND CONVERGENCE ACROSS COUNTRIES

Heather Berry

George Washington University

Mauro F. Guillén

University of Pennsylvania

Arun Hendi

University of Pennsylvania*

May 2011 Version

* Authors listed alphabetically. Corresponding author: guillen@wharton.upenn.edu.

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IS THE WORLD BECOMING SMALLER?

GLOBALIZATION AND CONVERGENCE ACROSS COUNTRIES

Abstract

We seek to establish whether distances between pairs of countries have decreased or not over the

last half century. We consider various dimensions of distance, including demographic, economic,

financial, knowledge, political, global connectedness, and cultural. Using a new methodology

that enables the calculation of the minimum volume ellipsoid encompassing all countries during

the 1960-2009 period, we find no evidence that the world has become smaller. We also

investigate if distances between countries within specific subcomponents of the global system

have become smaller. Taking into account core-semiperiphery-periphery clusters or trade blocs

does not produce evidence of shrinkage within each subcomponent either. Finally, we predict

that subcomponents are becoming more distant from one another. We find that core-

semiperiphery-periphery subcomponents and trade blocs are becoming more distant from one

another.

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―Certainly,‖ returned Ralph. ―I agree with Mr. Fogg. The world has

grown smaller, since a man can now go round it ten times more

quickly than a hundred years ago.‖

—Gauthier Ralph and Phileas Fogg in Jules Verne’s Around the

World in Eighty Days (1873:19).

Introduction

One of the most common ways of defining globalization involves the metaphor of a

shrinking world. The globalizing processes caused by sweeping economic, financial, political

and technological forces have led a number of social scientists to conceptualize globalization in

terms of its effects on the actual and perceived magnitude of changes in space (for reviews, see

Guillén 2001a; Hargittai and Centeno 2001; Sklair 1991). Thus, sociologist Anthony Giddens

(1990:64, 1991:21) proposed to regard globalization as a decoupling or ―distanciation‖ between

space and time, and Roland Robertson (1992:8) argued that globalization ―refers both to the

compression of the world and the intensification of consciousness of the world as a whole.‖

Other social scientists have also dwelled upon the shrinking metaphor. For instance, geographer

David Harvey (1989) and political scientist James Mittelman (1996) observed that globalization

entails a ―compression‖ of space and time, social anthropologist Thomas Eriksen (2007:16)

proposed that ―a minimal definition of globalization could delimit it simply as all the

contemporary processes that make distance irrelevant,‖ and management scholar C. Gopinath

(2008:30) argued that ―digital technology has seemingly shrunk the world… Distances are not

significant anymore.‖

In this paper we examine the extent to which there is institutional convergence in the

world using a spatial approach in terms of distance between pairs of nation-states. Social

scientists have emphasized that, in spite of popular images, globalization is far from being a

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uniform, irreversible, and inexorable trend. Rather, they propose that it is a fragmented,

incomplete, discontinuous, contingent, and in many ways contradictory and puzzling process

(Gilpin 2000:294; Guidry et al. 1999; Held et al. 1999:431), thus casting doubt on the argument

that the world is shrinking or converging. Giddens (1990:64, 175) noted that globalization ―is a

process of uneven development that fragments as it coordinates. […] The outcome is not

necessarily, or even usually, a generalized set of changes acting in a uniform direction, but

consists in mutually opposed tendencies.‖ In another book, Giddens (1991:21–22) asserted that

―globalization has to be understood as a dialectical phenomenon, in which events at one pole of a

distanciated relation often produce divergent or even contrary occurrences at another‖ (see also

Held et al 1999:431, 441). In a similar vein, anthropologist Jonathan Friedman (1994:210–211)

asserted that globalization is the product of cultural fragmentation as much as it is the result of

modernist homogeneity. Other scholars also saw globalization as a process that preserves local

variations. Thus, Zelizer (1999) argued that ―the economy […] differentiates and proliferates

culturally in much the same way as other spheres of social life do, without losing national and

even international connectedness,‖ while Robertson (1995:34–35) saw globalization as the

―linking of localities.‖

Social scientists have also empirically examined different aspects of convergence and the

shrinking of the world as a result of globalization. Empirical research focused on the impact of

globalization on various economic outcomes, including variations in the patterns of organization

of national capitalist economies (Boyer 1996; Soskice 1998, Streeck 1991; Garrett 1998),

convergence in income and output levels (Barro 1997; Bond et al. 2001), the resilience of

national systems of innovation, trade, and investment (Doremus et al. 1998; Storper and Salais

1997), and the interaction among business firms and states (Orrù et al. 1997; Stopford and

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Strange 1991; Guillén 2001b), among others. These studies show contradictory results ranging

from non-convergence (Soskice, 1998 and Garrett, 1998) to convergence (Barro, 1997; Bond et

al., 2001), with some convergence studies finding institutional and/or policy convergence that

exhibits distinctive national and regional patterns (Polillo and Guillén 2005; Henizs et al. 2005).

Drawing on sociological and political theories of globalization, we formulate predictions

about the effect of globalization on dyadic distances between nation-states. We consider a broad

range of socio-economic and institutional dimensions of nation-states, including demographic,

economic, financial, knowledge, political, global connectedness, and cultural variables to

examine convergence between countries. We use a new methodology for measuring the volume

that encompasses the distances between nodes (nation-states) and its evolution over time, which

enables us to assess empirically whether the world is becoming greater or smaller, taking into

account subcomponents such as regions, blocs, and core-periphery structures. We generally find

little overall convergence, though there is increasing similarity within some clusters in the global

system.

Globalization, Institutions, and Convergence

We define institutional variation and convergence in the wake of globalization in spatial

terms. From this perspective, increasing convergence in the world would be the result of reduced

distance between nodes. Following the world-society approach, we adopt the nation-state as the

locus of institution-building and therefore the fundamental node in the global system (Meyer et

al. 1997). During the 20th

century the expansion of rationalized state activities acquired a

momentum of its own, fueled by the ―exigencies of global social organization whose logic and

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purposes are built into almost all states.‖ From this perspective, nation-states are seen as

exhibiting convergent structural similarity, although there is a ―decoupling between purposes and

structure, intentions and results.‖ World-society researchers argued that conformity came both

from the world-culture of rationalized modernity and from domestic groups that made claims on

the state following the consensus over the formal acceptance of ―matters such as citizen and

human rights, the natural world and its scientific investigation, socioeconomic development, and

education‖ (Meyer et al. 1997:145, 148, 152–154, 161).

The contemporary intellectual origins of the convergence thesis are to be found in

modernization theory. Rostow (1960) proposed that nation-states evolve from ―undeveloped‖ to

―developed‖ via five stages as long as the right value incentives are in place: traditional society,

preconditions for take-off, take-off, maturity, and high mass-consumption. Each stage was seen

as a prerequisite for the next because new political, economic, and social institutions were

supposed to make possible ever more economically advanced and differentiated activities over

time (see also Kerr et al. 1960). Political scientists (e.g. Apter 1965) refined the argument when

asserting that the primary engine of change was a piecemeal shift from traditional to modern

values, i.e., a transformation of authority structures, a perspective also embraced by many

sociologists (see the review by Smelser 1976:144-163). Modernization theorists thought of

economic, political and social development as contributing to a ―shrinking‖ of the world, a

convergence of economies and societies, a trend towards homogeneity, or at least towards a

restricted set of alternatives (Kerr et al. 1960; see also Albrow 1997:49; Robertson 1992:91;

Waters 1995:13-19). World trade, migration and flows of capital should all work to take

resources and consumption goods from where they are cheap to where they are dear. As they

travel with increasing speed and volume (given falling transportation and communication costs),

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these commodity and production factor flows should erode the differences between productivity

and living standards across continents and national economies (Dowrick and DeLong, 2003).

Even further, Bell (1973) argued for a technologically-driven convergence of postindustrial

societies.

While the concepts of globalization and convergence have been widely used in the social

sciences, there is a relative dearth of empirical studies (Drezner, 2001; Guillén 2001a). Studies

on structural convergence are often limited to the advanced industrial states of the OECD, which

neglects the impact of developing and emerging economies on convergence patterns. .

Traditional approaches within the economics literature have focused on analyzing the

contribution of physical capital to output in order to examine relative levels of development

across countries. Economists have empirically tested the hypothesis that per capita output and

incomes across economies will converge over time, and that income levels of poor countries will

tend to converge towards the income levels of rich countries. In a famous study using a panel of

80 countries with three observations per country for the periods 1965-1975, 1975-1985 and

1985-1990, Barro (1997) found that the speed of convergence is about 2.5% per year.

Subsequent empirical research has addressed endogeneity concerns from regression models by

using panel data methods. For instance, using a system GMM estimator, Bond et al (2001)

reported a similar convergence rate of about 2 percent. Overall, these models revealed a tendency

for the economic system to reach a situation of steady-state income convergence. However, they

also showed that while the poor do eventually catch up with the rich, the speed at which this

happens is rather low: it takes 35 years to close just half of the gap between the rich and the poor

countries (Quah, 1996).

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In this paper we broaden the concept of cross-national convergence beyond income

disparities to include a full set of institutional characteristics. The convergence thesis argues that

since the end of World War II globalizing processes have eroded national boundaries, making

state structures, policymaking, political institutions, the economy, social and cultural practices,

and norms and traditions more similar across countries (for reviews of the theoretical and

empirical literature, see Guillén 2001a; Held et al. 1999; Campbell 2004; Evans 1997; Berger

and Dore eds. 1996; Inglehart and Baker 2000; Strange 1996). In spatial terms, increasing

similarities imply shorter distances between countries, and therefore a ―smaller‖ world. Thus, we

formulate the general prediction that globalization has reduced the size of the world as it

generates convergence:

Hypothesis 1: Distance between nation-states is decreasing over time, thus

reducing the size of the world.

Convergence within Global Subcomponents

Very much in response to modernization and convergence theories, social scientists have

challenged the premise that postwar socioeconomic change was homogeneous in its effects.

During the 1950s and 1960s dependency scholars noted that developing countries were

dependent on more advanced ones, often former colonizers, for capital, technology and access to

markets, with important implications not only for the economy but also for the political system.

Dependency theorists observed that the terms of trade between advanced (core) countries and

developing (peripheral) countries tended to evolve against the latter, who would become more

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impoverished as they engaged in international trade (Prebisch 1950; Frank 1967; Furtado 1970;

Bruton 1998). Thus, the tendency of capitalist development was to create exploitative

relationships between developed and underdeveloped countries, i.e. a duality of effects as a result

of global economic forces, which affected different subcomponents of the global system in

different ways. They also predicted that development efforts resulted in the gradual

displacement of the small-scale local bourgeoisie not connected to foreign and state capital, with

the ―triple alliance‖ of state-owned enterprises, subsidiaries of foreign multinationals, and local

business groups gaining in importance (Evans 1979; Frank 1967; Cardoso and Faletto 1973).

Economic theories of growth that incorporate technological differences across regions

also predict divergence in income levels and growth rates across regions (Grossman and

Helpman, 1991). Even if trade across countries may facilitate convergence, growth rates and

productivity gaps within industries across countries might persist when factor endowments are

different because of the mismatch between labor skills available in more developing countries

versus more sophisticated technologies imported from more developed countries, for example.

This suggests that fundamental differences in initial conditions across countries can impact both

the diffusion of technology across countries and resulting convergence.

Building on the dependency perspective, Wallerstein (1974) proposed another influential

theory of development that emphasized systemic patterns of dependence in the global political

economy and the emergence of subcomponents in the global system. He saw underdevelopment

as the result of a country’s integration into the modern ―world-system‖ created by the capitalist

development of Western Europe since the sixteenth century. In this view, global capitalist forces

have not only generated oppression and duality between the ―core,‖ on the one hand, and the

undeveloped ―periphery‖ and developing ―semi-periphery,‖ on the other, but also a momentum

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of their own as the capitalist world-system inexorably experiences a series of recurrent crises that

result from its inherent contradictions (see also Ragin and Chirot 1984:292-294). Unlike

dependency theorists, however, states (and not social classes) are central to world-system

analysis because they manage the social problems generated by the expansion of world

capitalism and thus contribute to the stabilization of the world-system (Waters 1995:22-26).

Recent empirical research has found that the world-system is indeed formed of core, semi-

peripheral, and peripheral subcomponents in terms of the role that countries play in global

economic, financial, political, diplomatic, and military affairs (Van Rossem 1996; Smith and

White 1992; Chase-Dunn et al. 2000). Building on this insight, world-society scholars were

among the first to point out that ―the world as a whole shows increasing structural similarities of

form among societies without, however, showing increasing equalities of outcomes among

societies‖ (Meyer & Hannan 1979:3, 13–15). This cross-national institutional diversity has been

the dominant theme in much recent theorizing about nation-states and their effects on the society,

the economy, and the culture (Campbell 2004; Orrù, Biggart, and Hamilton 1997; Dobbin 1994;

Guillén 2006).

A third theory emphasizing the emergence of subcomponents in the global system has to

do with trade blocs. The first modern trade bloc was the German Zollverein of 1834, which

created a customs union among the various German-speaking principalities. Beginning in the

1980s, the world witnessed the emergence of continental-size trade blocs such as the European

Union (EU), the North American Free Trade Agreement (NAFTA), and the Mercosur, among

others. A simple functional analysis highlights that trade blocs tend to be formed by countries

geographically adjacent or close, with similar trade policies or regimes, and sharing a desire to

organize regionally. While they are an attempt to enhance trade, in practice they destroy, divert,

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and create trade in complex ways (De Melo and Panagariya 1992; Mansfield and Milner 1999).

Nevertheless, based on a variety of empirical models, a consensus has emerged in the political

economy literature that regional trade agreements are trade-creating for member states (Thursby

and Thursby 1987; Frankel and Rose 2000; Rose 2000), but could lead to trade diversion or

destruction relative to non-members For example, Bayoumi and Eichengreen (1997) found that

the formation of the European Economic Community reduced the annual growth of trade

between members and industrialized non-members by 1.7 percentage points, while Leonardi

(1995) analyzed per capita income convergence relative to the period 1970-1995 and found

convergence at both the regional and national level for European countries. Soloaga and Winters

(2001) showed that the European Union (EU), the European Free Trade Association (EFTA) and

the North American Free Trade Agreement (NAFTA) led to a decline in levels of trade with third

countries. Moreover, the formation of a trade bloc requires political commitments and extensive

institution building and policy coordination among member countries (Gilpin 1987; Mansfield

and Milner 1999; Fligstein and Sweet 2002).

In addition to generating convergence among member countries, trade blocs tend to

diverge from one another. Perhaps the most important reason for divergence across trade blocs

is that they tend to have very different characteristics. For example, some entail a coordination of

overall trade policy (e.g. a customs union such as the EU) while others are limited to the removal

of internal barriers (the NAFTA), and some blocs entail deeper agreements over matters such as

labor mobility, taxes, regulation, antitrust policies, and even monetary matters. There are other

reasons why trade agreements generate convergence within the bloc but divergence across blocs.

Trade blocs have often resulted in pressures to initiate political reforms (as in Southern and

Eastern Europe prior to accessing the EU), enhanced power in global trade negotiations, and an

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extension of influence over weaker nation-states, especially in the developing world (Mansfield

and Milner 1999).

Dependency, world-system, and political-economic trade-bloc theories emphasize the

emergence of a multi-layered world as a result of economic and sociopolitical development, one

in which countries within each subcomponent—developed and developing, core and periphery,

or trade blocs—become more similar to one another while each component becomes more

distinct than the others. In other words, convergence is expected within components while

divergence obtains across components. Thus, we predict that

Hypothesis 2: Distances within each subcomponent of the global system of

nation--states decrease over time, thus reducing the size of each subcomponent.

Hypothesis 3: Distance across subcomponents of the global system of nation

states is increasing over time, thus expanding the size of the world.

Data

We approach cross-national institutional variation and convergence taking into account

the dimensions that the world-society approach (Meyer et al. 1997) associates with social change

on a global scale, including demographic, economic, financial, knowledge, political, global

connectedness, and cultural dimensions (Berry, Guillén and Zhou, 2010). These dimensions can

be operationalized following Whitley’s (1992) sociological analysis of national business

systems, Henisz and Williamson’s (1999) and La Porta et al.’s (1998) economic frameworks of

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national governance institutions, Nelson and Rosenberg’s (1993) concept of national innovation

institutions, and Inglehart and Baker’s (2000) theory of cross-national cultural change. National

business systems are ―particular arrangements of hierarchy-market relations becoming

institutionalized and relatively successful in particular contexts‖ (Whitley 1992:10). Countries

differ to varying degrees in terms of the characteristics of their business systems, specifically

their economic, financial, and administrative practices. Whitley (1992:231) argued that such

differences originate in demographic, geographic, cultural, and political institutions, which make

some countries more different, or distant, than others from a given focal country.

National governance systems refer to the ―set of incentives, safeguards, and dispute-

resolution processes used to order the activities of various corporate stakeholders‖ such as

owners (i.e. shareholders), managers, workers, creditors, suppliers and customers (Kester,

1996:109). They originate in administrative, legal and political institutions that historically make

certain stakeholders more powerful in certain countries than others (Glendon et al. 1994; Henisz

& Williamson 1999; Henisz 2000; La Porta et al. 1998). While this theoretical tradition

emphasizes a smaller set of institutional dimensions than the theory of business systems, the

underlying logic is also one of institutional variation that produces longer distances between

countries.

National innovation systems refer to configurations of institutions that foster the

development of technology and innovation (Nelson and Rosenberg 1993). A central tenet

documented by this literature is that countries differ in their ability to produce knowledge and in

the extent to which they can leverage that knowledge by being connected to other countries

(Porter 1990; Furman et al. 2002). Finally, we also took into consideration the model of cultural

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change proposed by Inglehart and Baker (2000), in which cross-national cultural values are

considered to be the result of the intersection of forces tending towards modernization (including

economic and political influences) and those tending to preserve traditions.

As described in Table 1, we included several variables for each of our seven measures of

cross-national institutional variation. We consulted several sources to create the seven measures

of cross-national institutional variation. For the economic, financial, demographic and global

connectedness measures, our data came from the World Bank’s World Development Indicators

database. For the political measure, we used data from Henisz’ POLCONV database, Freedom

House, and the World Trade Organization. The knowledge measures came from the U.S. Patent

and Trademark Office and the Institute for Scientific Information. Finally, the cultural measures

are all based on the World Values Surveys. Each data source, year availability, and country

coverage are summarized in Table 2.1

Methods

We measured patterns in global convergence through a multidimensional modeling

approach. We relied on a panel of data where we observe characteristics of nation-states across

many years. For a given distance dimension (e.g. political, economic, cultural), we observed a set

of variables2 each year for each country being considered in the analysis. We can thus think of

countries as points lying in a -dimensional characteristic space, where the points move over

time. Convergence can be conceptualized as the process of these points moving closer together—

1 Data used in our analysis are available from http://www.lauder.wharton.upenn.edu/ciber/research/faculty.asp

2 is in general different for each distance dimension

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that is, countries becoming less distant over time. Figure 1 depicts a three-dimensional example

at one point in time.

We use changes in global volume to measure the process of country points compressing

or expanding over time. To quantify the extent of the compacting of distance across nation-

states, we analyze the minimum-volume ellipsoid (MVE) measure. For each year, we first

calculate the -dimensional ellipsoid of minimum volume that contains all of the country points.

The volume of this ellipsoid is the MVE measure. Let be the set of -dimensional

country points in year (as in Figure 1a). Formally, the minimum volume ellipsoid in year is

the -dimensional ellipsoid { ( ̂) ̂

( ̂) }, where ̂ is a matrix

and ̂ is a vector that jointly solve

( ( ))

( )

( ) ( )

We compute a different ( ̂ ̂) pair for each year and then calculate the associated ellipsoid

volume for each year. Because we have data for several distance dimensions, we calculate a

separate volume for each distance dimension in each year. We use the cluster package

(Maechler, Rousseeuw, Struyf, and Hubert 2005) in the R statistical software (R Development

Core Team 2010) to compute the minimum volume ellipsoid of a set of points.

As an example of our approach, consider Figure 1a, which depicts one year of our

political distance data for all country points in a 3-dimensional political space. The

minimum volume ellipsoid enclosing the country points is shown in Figure 1b. This is, by

definition, the smallest ellipsoid that contains all the points, and it thus is a good estimate of the

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political ―size‖ of the world in 1972. If we repeat the process for 1973 and later years and find

progressively smaller volumes, we consider this to be evidence of convergence along the

political dimension. In our first hypothesis, we consider the global system as a whole. For our

second and third hypotheses, we consider subcomponents of nation-states and compare our

minimum volume ellipsoid measures both within and across these subcomponent groupings.

Figure 2 provides an example of how these subcomponent patterns might look when examining

the patterns suggested by the second and third hypotheses. Frame (a) in figure 3 shows country-

level data for a particular year. When attempting to measure global volumes, we would enclose

the points in the minimum volume ellipsoid shown in frame (b). If we suspect distinct group

patterns, we would define the subcomponents and enclose each set of subcomponent points in its

own minimum volume ellipsoid as in frame (c). Frame (d) in Figure 3 shows the group ellipsoids

along with the world ellipsoid. If subcomponents have their own distinct patterns over time, then

it is easy to imagine a scenario where within-subcomponent convergence coexists with

worldwide divergence.

There are a few useful features of the MVE measure. One of the most appealing

properties of the MVE measure is that it is invariant to affine transformations of .3 This

implies that the MVE measure is not affected by time-invariant issues of scale or correlation in

the data. This is particularly useful for our purposes since the variables are in general measured

in different units and are correlated with each other. Second, the ellipsoid volumes are indices, so

that multiplying any time series of volumes by a fixed constant does not affect our inference. A

3 If we were to premultiply each ⋃ by the same fixed nonzero matrix and add a -vector to each

product, the resultant volumes would not be affected except perhaps by a constant multiple. Since the

MVE measure is an index, the constant multiple is irrelevant and would not affect our inference.

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third appealing feature of this method is that the assumption of an ellipsoidal pattern closely

approximates the shape of the actual data. Finally, the MVE method has a nice intuitive

interpretation that lends itself to our hypotheses and for consideration of how small the world has

become over time.

This methodology is different from the approach used in the economics literature on

convergence. For example, Barro (1997) estimated a regression of economic growth rates on

national incomes to examine whether national incomes will converge to the same steady-state

level. Our definition of convergence is broader—we analyze convergence across multiple

distance dimensions each containing multiple variables, and thus use a measure of joint

convergence.

For comparison and robustness, we calculate volume indices using two other methods: a

mean-based and a median-based measure of the size of the world. The mean-based volume

algorithm finds the mean Mahalanobis distance between each country point and the centroid of

the set of points. It then calculates the volume of a -sphere with radius equal to the

aforementioned mean. The median-based calculation proceeds similarly, but replaces the mean

of distances to the centroid with the median. While the mean-based calculation is relatively

simple and provides a good description of the process being modeled, it is susceptible to the

problem of extreme observations. The median-based measure, though more robust to outlying

observations, only describes the volume occupied by half of the countries. Both the mean- and

median-based measures assume spherical volumes, and thus will not approximate the shape of

as closely as the MVE volume. However, these two measures act as robustness checks for the

MVE volume and also highlight different aspects of the data.

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Results

The first hypothesis predicted smaller distances over time and hence a smaller world.

Figure 3 shows the volume ellipsoid calculations for each of the seven distance dimensions over

time. There are different time periods reflected in each of these graphs due to data availability

across dimensions. In general, the graphs in Figure 3 show that world volumes increased over

time for the economic, cultural, demographic, financial, and global connectedness dimensions.

The knowledge and political volumes appear to exhibit more cycles over time than the other

variables, but also reveal increasing trends. These graphs suggest that global volume is not

monotonically decreasing as a function of time for any of the seven dimensions of distance.

To formally test hypothesis 1, we implement a variant of Kendall’s tau test. Kendall’s tau

is a nonparametric statistic that measures the rank correlation between two sets of data. By

estimating the rank correlation between the volumes and the years in which they were measured,

we can obtain a quantity that summarizes the extent to which the volumes exhibit time trends.

We specify a tau test where the null hypothesis is zero correlation (no trend) and the alternative

hypothesis is negative correlation (decreasing trend). These tests, reported in Table 5, fail to

reject the null hypothesis at the 5% significance level for any of the seven distance dimensions.

Thus, we do not find evidence in favor of the first hypothesis.4

We implemented several robustness checks. We found that the MVE volume measure is

positively correlated with both the mean-based and median-based volume measures. Since the

median-based volume measure is less sensitive to outliers, the positive correlation implies that

4 A regression of volumes on time show similar patterns of rejection. Kendall’s tau, however, is less restrictive due

to its ordinal nature.

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inference based on the MVE measure is not heavily influenced by outlying countries.

Furthermore, the MVE volume measure is robust to issues of scale and time-invariant correlation

in the country-level data. To evaluate the claim that distances between countries may have

become smaller in recent years as opposed to during the entire 1960-2009 period, we calculated

the tau trend test for the 1985-2009 period. With the exception of the knowledge distance

dimension, we do not find evidence of smaller distances and volumes at the 5% significance

level (results available upon request).

In the second hypotheses, we argued that smaller distances and volumes may be expected

within subcomponents of the global system of nation-states. We considered two different

groupings of nation-states to examine subcomponents of the global system, including: core-

semiperiphery-periphery groups, and trade blocs. We start by discussing the core-periphery

grouping results.

We used the core, semi-periphery and periphery country groupings from Van Rossem

(1996) that are reported in Table 3 to calculate the minimum ellipsoid volumes for the core-

periphery subcomponents of the global system. These volumes are plotted in figures 4a, 4b and

4c. Visually, there are appear to be no trends of smaller distances or volumes. Table 6 shows the

results of the Kendall’s tau trend tests, which shows little to no evidence of convergence within

the core, semiperiphery, and periphery subcomponents.5

We also calculated all volume measures and tests for three free trade areas (the

Association of Southest Asian Nations, ASEAN, the Central European Free Trade Agreement,

5 For all subcomponents, we calculate the tau trend tests restricting the years to 1985-2009. The rejection patterns,

reported in tables 9 through 12, are very similar to the full set of volumes (1960-2009), indicating a lack of evidence

for recent convergence.

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CEFTA, and the South Asian Association for Regional Cooperation, SAARC). We report the

results in Figures 5a, 5b and 5c, respectively. We took into account when a country became part

of the trade bloc (see Table 4). In general, the graphs in Figures 5a, 5b and 5c reveal a similar

lack of convergence. We report the Kendall’s tau estimates in Table 6. Only two of them were

significant: demographic in the core and knowledge in the semiperiphery, indicating that there is

a decline in volume over time.

Next we examined the patterns within trade blocs (Figure 5). We show results for

ASEAN, CEFTA, SAARC, Andean Pact, Mercosur, SACU, Caricom, EU27, the Eurozone-11,

and NAFTA. As reported in Table 7, only Mercosur, SACU, Mercosur and NAFTA show some

evidence of decreasing volumes over time, but only along one or at most two dimensions

(NAFTA). In sum, we find little support for Hypothesis 2 that distances and volumes within

subcomponents of the global system are dropping over time.

To examine Hypothesis 3 on distances and volumes between subcomponents, we also

calculated Kendall’s tau tests (see Tables 8 and 9). We find that core-semiperiphery-periphery

subcomponents and trade blocs are becoming more distant from one another, indicating that

globalizing processes have exerted different effects within and between subcomponents.

Discussion and Conclusion

***This section needs to be written after all empirical results are verified.

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30

FIGURE 1

COUNTRIES IN 3-DIMENSIONAL POLITICAL CHARACTERISTIC SPACE, 1972

(A) (B)

D

ddad

31

FIGURE 2

EXAMPLE OF SUBCOMPONENT VOLUME CALCULATION

(a) (b)

(c) (d)

32

32

FIGURE 3

GLOBAL VOLUMES, 1960-2009

NOTE.—All volume time series are calculated using MVE method and are scaled to equal 1 in 2000. The

penultimate graph, labeled ―Global‖, refers to the volume measure for the global connectedness distance

dimension.

Year

Vo

lum

e

0.51.01.52.02.53.03.5

0.60.8

1.0

1.2

1.4

0.20.40.60.81.01.21.4

1.0

1.5

2.0

2.5

0.20.40.60.81.01.21.4

0.5

1.0

1.5

2.0

3040506070

1960 1970 1980 1990 2000

Econom

icD

em

ogra

phic

Cultu

ral

Know

ledge

Fin

ancia

lG

lobal

Politic

al

33

33

FIGURE 4a: Core-Periphery Results

CORE COUNTRY VOLUMES, 1960-2009

NOTE.—All volume time series are calculated using MVE method and are scaled to equal 1 in 2000. The

penultimate graph, labeled ―Global‖, refers to the volume measure for the global connectedness distance

dimension.

Year

Vo

lum

e

0.5

1.0

1.5

2.0

1234567

0.50.60.70.80.91.01.1

0.5

1.0

1.5

2.0

0.2

0.4

0.6

0.8

1.0

1.2

1

2

3

4

1960 1970 1980 1990 2000 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

34

34

FIGURE 4b: Core-Periphery Results

SEMIPERIPHERY COUNTRY VOLUMES, 1960-2009

NOTE.—All volume time series are calculated using MVE method and are scaled to equal 1 in 2000. The

penultimate graph, labeled ―Global‖, refers to the volume measure for the global connectedness distance

dimension.

Year

Vo

lum

e

0.5

1.0

1.5

2.0

2.5

0.5

1.0

1.5

2.0

0.8

1.01.2

1.4

1.6

0.20.40.60.81.01.21.4

0.2

0.4

0.6

0.8

1.0

0.4

0.6

0.8

1.0

1.2

1.4

1960 1970 1980 1990 2000 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

35

35

FIGURE 4c: Core-Periphery Results

PERIPHERY COUNTRY VOLUMES, 1960-2009

NOTE.—All volume time series are calculated using MVE method and are scaled to equal 1 in 2000. The

penultimate graph, labeled ―Global‖, refers to the volume measure for the global connectedness distance

dimension.

Year

Vo

lum

e

0.51.01.52.02.53.0

0.60.70.80.91.01.11.2

123456

0.51.01.52.02.53.0

0.2

0.40.6

0.8

1.01.2

0.60.81.01.21.41.6

1960 1970 1980 1990 2000 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

36

36

FIGURE 5a: TRADE BLOC RESULTS

Free Trade Agreement - ASEAN

Year

Vo

lum

e

0

1

2

3

4

0.2

0.4

0.60.8

1.0

5

10

15

0.5

1.0

1.5

2.0

2468

101214

0.20.40.60.81.0

1970 1980 1990 2000 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

37

37

FIGURE 5E: TRADE BLOC RESULTS

Free Trade Agreement CEFTA

Year

Vo

lum

e

2

4

6

8

102030405060

0.5

1.0

1.5

2.0

0.5

1.0

1.5

2.0

2.5

10

20

30

40

50

5

10

15

1995 2000 2005 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

38

38

FIGURE 5c – TRADE BLOC RESULTS

Free Trade Agreement - SAARC

Year

Vo

lum

e

0.5

1.0

1.5

0.5

1.0

1.5

0.51.01.52.02.53.03.5

2

4

6

8

20

40

60

80

0.60.81.01.21.41.61.8

1985 1990 1995 2000 2005 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

39

39

FIGURE 5d: TRADE BLOC RESULTS

Customs Union ANDEAN Community

Year

Vo

lum

e

0.20.40.60.81.01.21.4

0.2

0.4

0.6

0.8

1.0

1.2

0.5

1.0

1.5

0.20.40.60.81.01.21.4

0.2

0.4

0.6

0.8

1.0

1.2

1970 1980 1990 2000 2010

Econom

icK

now

ledge

Fin

ancia

lG

lobal

Politic

al

40

40

FIGURE 5e: TRADE BLOC RESULTS

Customs Union - MERCOSUR

Year

Vo

lum

e

0.2

0.4

0.6

0.8

1.0

1.2

0.2

0.4

0.6

0.8

1.0

1.2

0.20.40.60.81.01.21.4

0.2

0.4

0.6

0.8

1.0

1.2

1.0

1.5

2.0

2.5

3.0

1995 2000 2005 2010

Econom

icK

now

ledge

Fin

ancia

lG

lobal

Politic

al

41

41

FIGURE 5f – TRADE BLOC RESULTS

Customs Union - SACU

Year

Vo

lum

e

0.5

1.0

1.5

2.0

2.5

0.5

1.0

1.5

2.0

2.5

0.20.40.60.81.01.21.4

0.4

0.6

0.8

1.0

1.2

0.5

1.0

1.5

0.5

1.0

1.5

2.0

1970 1980 1990 2000 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

42

42

FIGURE 5g: TRADE BLOC RESULTS

Common Market - CARICOM

Year

Vo

lum

e

0.20.40.60.81.01.2

0.8

1.0

1.2

1.4

0.2

0.4

0.6

0.8

1.0

0.20.40.60.81.0

0.5

1.0

1.5

0e+001e+092e+093e+094e+095e+096e+097e+09

1975 1980 1985 1990 1995 2000 2005 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

43

43

FIGURE 5h: TRADE BLOC RESULTS

Common Market - EU - 27

Year

Vo

lum

e

1

2

3

4

2

4

6

8

0.40.50.60.70.80.91.0

1

2

3

4

1.0

1.5

2.0

2.5

0.5

1.0

1.5

1960 1970 1980 1990 2000 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

44

44

FIGURE 5G: TRADE BLOC RESULTS

Monetary Union – Eurozone 11

Year

Vo

lum

e

0.50.60.70.80.91.01.1

1.0

1.1

1.2

1.3

1.4

0.750.800.850.900.951.001.051.10

0.4

0.6

0.8

1.0

0.60.70.80.91.01.11.21.3

1.0

1.5

2.0

2.5

3.0

2000 2002 2004 2006 2008 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

45

45

FIGURE 6: NAFTA RESULTS USING MINIMUM VOLUME HYPERSPHERE VOLUMES

Year

Vo

lum

e

0.6

0.7

0.8

0.9

1.0

1.0

1.5

2.0

2.5

0.7

0.8

0.9

1.0

1

2

3

4

5

0.5

1.0

1.5

2.0

1

2

3

4

1995 2000 2005 2010

Econom

icD

em

ogra

phic

Know

ledge

Fin

ancia

lG

lobal

Politic

al

46

46

Table 1: Component Variables Used in Cross-National Institutional Measures Dimension: Component Variables: 1. Economic Income GDP per capita, 2000 USD Exports Exports of goods and services (% GDP) Imports Imports of goods and services (% GDP) 2. Financial Private Credit Domestic credit to private sector (% GDP)

Stock Market Cap Market capitalization of listed companies (% GDP) Listed Companies Number of listed companies (per one million population)

3. Political Policy Making Uncertainty Political stability measured by considering independent

institutional actors with veto power Democratic character Democracy Score Size of the state Government consumption (% GDP) 4. Cultural Power distance WVS question on obedience and respect for authority Uncertainty avoidance WVS questions on trusting people and

job security Individualism WVS questions on independence and the

role of government in providing for its citizens Masculinity WVS questions on the importance of family and work 5. Demographic Life expectancy Life expectancy at birth, total (years) Birth rate Birth rate, crude (per 1,000 people)

Population under 14 Population ages 0-14 (% of total) Population under 65 Population ages 65 and above (% of total) 6. Knowledge Patents Number of patents per one million population Scientific Articles Number of scientific articles per one million population 7. Global Connectedness International Tourism Expend International tourism, expenditures (% GDP) International Tourism Receipts International tourism, receipts (% GDP) Internet use Internet users per 1,000 people

47

47

Table 2: Data Sources, Year Availability, and Country Coverage Dimension: Source Years Available # of Countries (in 2004) 1. Economic Income WDI 1960-2005 179 Exports WDI 1960-2005 165 Imports WDI 1960-2005 165 2. Financial Private credit WDI 1960-2005 122

Stock market Cap WDI 1988-2005 122 Listed companies WDI 1988-2005 122

3. Political Policymaking uncertainty POLCONV 1960-2005 155 Democracy score Freedom House 1960-2003 151 Size of the state WDI 1960-2005 155 4. Cultural Power distance WVS 1980-2004 68 Uncertainty avoidance WVS 1980-2004 66 Individualism WVS 1980-2004 69 Masculinity WVS 1980-2004 69 5. Demographic Life expectancy WDI 1960-2004 202 Birth rate WDI 1960-2004 202 Population under 14 WDI 1960-2005 203

Population under 65 WDI 1960-2005 203 6. Knowledge Patents USPTO 1977-2005 166 Scientific articles WDI & ISI 1960-2003 110 7. Global Connectedness International tourism Expend WDI 1995-2004 119 International tourism Receipts WDI 1995-2004 115

Internet users WDI 1995-2004 209

48

48

TABLE 3

CORE-SEMIPERIPHERY-PERIPHERY COUNTRY LIST

Core United States, France, Germany, United

Kingdom, Italy, Japan, Russia, Belgium, The

Netherlands, Canada, Brazil, Spain, China,

Saudi Arabia, Australia

Semiperiphery Sweden, Switzerland, India, Egypt, Austria,

Nigeria, Czech Republic, Iran, Argentina,

Romania, Algeria, Pakistan, Mexico, Iraq,

Poland, Turkey, Portugal, Libya, Greece,

Indonesia, Thailand

Periphery Venezuela, Kuwait, Denmark, Kenya,

Ethiopia, Morocco, Philippines, Norway,

Korea, Senegal, Syria, Peru, Bulgaria, Finland,

Cuba, Hungary, Malaysia, Tunisia, Colombia,

Tanzania, United Arab Emirates, Ivory Coast,

Sudan, Ecuador, Jordan, Bangladesh, Ghana,

Zimbabwe, Israel, Sri Lanka, Chile, Uruguay,

Zambia, Costa Rica, Ireland, New Zealand,

Mozambique, Cameroon, Panama, Guinea,

Bolivia, Nicaragua, Angola, Guatemala,

Somalia, Myanmar, Gabon, Oman, Dominican

Republic, Haiti, Congo, El Salvador,

Honduras, Paraguay, Qatar, Singapore,

Jamaica, Yemen, Liberia, Laos, Uganda,

Madagascar, Cyprus, Nepal, Malta, Mali,

Trinidad and Tobago, Sierra Leone, Central

African Republic, Iceland, Bahrain,

Mauritania, Togo, Papua New Guinea, Niger,

Rwanda, Burkina Faso, Burundi, Benin,

Guyana, Guinea-Bissau, Mauritius, Suriname,

Botswana, Djibouti, Seychelles, Fiji, Malawi,

The Gambia, Lesotho, Equatorial Guinea,

Antigua & Barbuda, Belize, Cape Verde,

Taiwan, Barbados, Chad, Swaziland, Bahamas,

Sao Tome & Principe, Hong Kong, Grenada,

Tonga, Comoros, Maldives, Samoa,

Netherlands Antilles, Greenland, Brunei,

Bermuda, St. Kitts and Nevis, St. Lucia, St.

Vincent and the Grenadines, Namibia,

Solomon Islands, Faeroe Islands, Gibraltar,

Guam, New Caledonia, American Samoa,

French Polynesia

49

49

TABLE 4

TRADE BLOC MEMBER STATES

Trade Bloc Nations and their Years of Participation*

ASEAN Brunei Darussalam (1984), Indonesia (1967), Cambodia (1994), Laos (1997),

Myanmar (1997), Malaysia (1967), Philippines (1967), Singapore (1967),

Thailand (1967), Vietnam (1995)

CEFTA Albania (2007), Bulgaria (1999-2007), Bosnia and Herzegovina (2007), Czech

Republic (1992-2004), Croatia (2007), Hungary (1992-2004), Kosovo (2007),

Moldova (2007), Macedonia, FYR (2006), Montenegro (2007), Poland (1992-

2004), Romania (1997-2007), Serbia (2007), Slovak Republic (1992-2004),

Slovenia (1996-2004)

SAARC Afghanistan (2005), Bangladesh (1985), Bhutan (1985), India (1985), Sri Lanka

(1985), Maldives (1985), Nepal (1985), Pakistan (1985)

NAFTA Canada (1992), Mexico (1992), United States (1992)

Andean

Community

Bolivia (1969), Colombia (1969), Ecuador (1969), Peru (1969)

Mercosur Argentina (1991), Brazil (1991), Paraguay (1991), Uruguay (1991)

SACU Botswana (1969), Lesotho (1969), Namibia (1969), South Africa (1969),

Swaziland (1969)

CARICOM Antigua and Barbuda (1973), Barbados (1973), Belize (1973), Dominica (1973),

Grenada (1973), Guyana (1973), Jamaica (1973), St. Kitts and Nevis (1973), St.

Lucia (1973), St. Vincent and the Grenadines (1973), Trinidad and Tobago (1973)

EU-27 Austria (1995), Belgium (1952), Bulgaria (2007), Cyprus (2004), Czech Republic

(2004), Germany (1952), Denmark (1973), Spain (1986), Estonia (2004), Finland

(1995), France (1952), United Kingdom (1973), Greece (1981), Hungary (2004),

Ireland (1973), Italy (1952), Lithuania (2004), Luxembourg (1952), Latvia (2004),

Malta (2004), Netherlands (1952), Poland (2004), Portugal (1986), Romania

(2007), Slovak Republic (2004), Slovenia (2004), Sweden (1995)

Eurozone-

11

Austria (1999), Belgium (1999), Finland (1999), France (1999), Germany (1999),

Ireland (1999), Italy (1999), Luxembourg (1999), Netherlands (1999), Portugal

(1999), Spain (1999)

50

50

*Years of participation are given in parentheses. A single year indicates that the country entered

the bloc and is still a member.

51

51

TABLE 5

TREND TEST FOR WORLD VOLUMES, 1960-2009

*p<0.05,

**p<0.01,

***p<0.001

NOTE.— Test statistics are Kendall’s tau estimates of rank correlation between world volumes

and time. Significance implies that the volume of the world is decreasing over time.

TABLE 6

TREND TEST FOR WITHIN CORE, SEMIPERIPHERY, AND PERIPHERY VOLUMES, 1960-2009

Core Semiperiphery Periphery

Economic 0.36 0.61 0.72

Demographic -0.40***

0.44 0.60

Knowledge 0.75 -0.30* -0.19

Financial 0.62 0.48 0.67

Global 0.71 0.27 0.76

Political -0.12 0.45 0.38 *p<0.05,

**p<0.01,

***p<0.001

NOTE.— Test statistics are Kendall’s tau estimates of rank correlation between subcomponent

volumes and time. Significance implies that the volume of the subcomponent is decreasing over

time.

Distance Dimension Correlation

Economic 0.76

Demographic 0.62

Cultural 0.67

Knowledge -0.14

Financial 0.60

Global 0.78

Political 0.29

52

52

TABLE 7

TREND TEST FOR WITHIN TRADE BLOC VOLUMES

ASEAN CEFTA SAARC

Andean

Communit

y Mercosur SACU

CARICO

M EU Eurozone NAFTA

Economic 0.70 0.52 0.55 0.28 0.14 0.14 0.76 0.77 -0.33 0.57

Demographic 0.62 0.53 0.63 - - -0.32**

0.43 0.34 0.24 -0.34***

Knowledge 0.61 -0.07 0.34 0.16 0.22 -0.13 0.38 0.73 0.39 0.42

Financial 0.19 0.24 0.51 0.24 0.31 0.27 -0.47 0.45 -0.20 0.70

Global 0.65 0.85 0.93 0.71 0.59 0.43 0.87 0.80 -0.64**

0.08

Political 0.66 0.05 0.33 0.16 -0.72***

0.40 0.18 0.38 -0.44 -0.26*

*p<0.05,

**p<0.01,

***p<0.001

NOTE.— Test statistics are Kendall’s tau estimates of rank correlation between trade bloc volumes and time. Significance implies that the volume of a

subcomponent is decreasing over time.

53

53

TABLE 8

TREND TEST FOR BETWEEN WORLD-SYSTEM SUBCOMPONENT DYADIC DISTANCES, 1960-2009

Core

Core

- -

- -

- - Semiperiphery

S

emip

erip

he

ry

0.31***

0.23 - -

0.58***

0.52**

- -

0.88***

0.32**

- - Periphery P

erip

h

ery

0.66***

0.41**

0.69***

0.26* - -

0.51***

0.54**

-0.07 0.54**

- -

-0.28 -0.13 -0.24 0.02 - - *p<0.05,

**p<0.01,

***p<0.001

NOTE.— Test statistics are Kendall’s tau estimates of rank correlation between dyadic distances and time. The column and row names indicate the

world-system subcomponents between which dyadic distances are calculated. Within each cell the test statistics for different distance dimensions are

arranged into columns in the following order: column 1 is economic, demographic, knowledge, and column 2 is financial, global connectedness, and

political. Significance implies that the distance between subcomponents is increasing over time.

54

54

TABLE 9

TREND TEST FOR BETWEEN TRADE BLOC DYADIC DISTANCES

ASEAN

AS

EA

N

- -

- -

- - CEFTA

CE

FT

A

0.75***

-0.44

- -

0.06 -0.16 - -

0.60***

-0.38 - - SAARC

SA

AR

C

0.50**

-0.76 0.25 0.38* - -

-0.50 0.19 0.51**

0.54**

- -

0.90***

0.03 0.13 -0.15 - - Andean Comm.

Andea

n C

om

0.58***

-0.71 0.71***

0.16 0.26* -0.09 - -

0.07 -0.03 0.53**

0.63***

0.21 -0.16 - -

0.88***

-0.03 0.13 -0.25 -0.84 -0.30 - - Mercosur

Mer

co

sur

0.58***

-0.60 0.62***

0.43**

0.23 -0.39 0.19 0.03 - -

-0.23 -0.76 0.90***

0.47**

0.10 -0.05 0.41* -0.49 - -

0.83***

0.18 0.03 -0.18 0.78***

0.28 0.75***

-0.28 - - SACU

SA

CU

0.48**

-0.70 -0.81 0.47**

-0.64 -0.07 -0.10 -0.06 -0.32 0.35* - -

0.51**

0.85***

0.75***

0.45**

0.40**

-0.32 0.92***

0.43* 0.87

*** -0.34 - -

0.78***

-0.38* 0.07 -0.22 -0.05 -0.42 0.02 0.04 0.74

*** -0.37 - - CARICOM

CA

RI

CO

M 0.61

*** -0.57 -0.26 0.26 -0.48 0.67

*** 0.02 0.81

*** -0.54 0.61

*** -0.31 0.12 - -

-0.46 -0.30 0.65***

-0.32 -0.58 0.14 -0.35 -0.10 0.71***

-0.34 0.24* -0.03 - -

0.80**

-0.30 -0.40 0.22 0.05 0.10 -0.02 -0.39 0.33 -0.49 0.45* 0.26

* - -

EU

0.33* 0.07 0.49

** 0.49

** 0.57

*** 0.45

** 0.80

*** 0.44

** 0.33

* 0.71

*** 0.56

*** 0.24 0.59

*** 0.46

**

0.40* -0.01 -0.28 0.85

*** 0.59

*** 0.78

*** 0.84

*** 0.63

*** 0.63

*** 0.45

** 0.88

*** 0.89

*** 0.62

*** -0.34

0.10 -0.23 0.68***

0.57***

0.70***

-0.35 0.69***

-0.47 0.57***

-0.65 0.79***

-0.48 0.60**

-0.28

E u r o z o n e 0.64**

0.64**

0.83***

-0.20 0.87***

0.29 0.73***

0.38 0.24 0.51* 0.87

*** 0.28 0.82

*** 0.47

*

55

55

0.56**

0.73***

0.64**

0.87***

-0.07 1.00***

0.78***

0.60**

0.62* 0.38 0.91

*** 1.00

*** 0.82

*** 0.64

**

-0.22 0.11 0.83***

0.44* 0.78

*** 0.39 0.78

*** 0.83

*** 0.78

*** 0.56

* 0.83

*** -0.89 1.00

*** 0.67

**

NA

FT

A

0.75***

-0.18 0.70***

0.22 0.97***

0.60***

0.83***

0.77***

0.90***

0.71***

0.65***

0.70***

0.02 0.69***

0.29* 0.56

** -0.07 0.80

*** -0.37 0.96

*** 0.23 0.60

*** -0.10 0.32 0.99

*** 1.00

*** 0.07 -0.21

0.17 0.59**

0.50**

-0.54 0.45**

0.18 0.45**

-0.18 0.43**

-0.69 0.40* -0.23 0.80

** 0.26

EU

EU

- -

- -

- - Eurozone

Euro

zone 0.91

*** 0.24 - -

0.24 0.51* - -

0.06 0.33 - - NAFTA

NA

FT

A

0.75***

0.41**

0.33 -0.24 - -

0.31* 0.16 0.29 -0.07 - -

0.32* 0.38

* -0.78

** -0.47 - -

*p<0.05,

**p<0.01,

***p<0.001

NOTE.— Test statistics are Kendall’s tau estimates of rank correlation between dyadic distances and time. The column and row names indicate the

trade blocs between which dyadic distances are calculated. Within each cell the test statistics for different distance dimensions are arranged into

columns in the following order: column 1 is economic, demographic, knowledge, and column 2 is financial, global connectedness, and political.

Significance implies that the distance between subcomponents is increasing over time.

56

56