Most of the people all over the world claim that globalization is a result of dynamic interactions between economic, technological, social and political factors. The aim of this paper is to document some stylized facts on this phenomenon in order to takeglobalization, cluster analysis, k-means algorithm, international ranking

Castagna Alina

Furia Donatella

Scamuffa Domenico

English

Research Papers in Economics

276 
 
EU AND GLOBALIZATION: SOME STYLIZED FACTS 
 
Castagna Alina 
University of Chieti-Pescara Faculty of Management Sciences Viale Pindaro 42 – 65127 Pescara 
(Italy) alinacastagna@yahoo.it +39 085 4537974 
Furia Donatella 
University of Chieti-Pescara Faculty of Management Sciences Viale Pindaro 42 – 65127 Pescara 
(Italy) dfuria@unich.it +39 085 4537974 
Scamuffa Domenico 
University of Chieti-Pescara Faculty of Management Sciences Viale Pindaro 42 – 65127 Pescara 
(Italy) domenico.scamuffa@gmail.com +39 085 4537679 
 
Most of the people all over the world claim that globalization is a result of dynamic interactions between 
economic, technological, social and political factors. The aim of this paper is to document some stylized 
facts on this phenomenon in order to take a  further step forward in implementing a multidimensional 
framework as an overall assessment of the level of integration of European countries in the international 
community. In this connection we implement a k-means Clustering Algorithm to classify 73 countries into 
four groups (leaders, potential leaders, dynamic followers and marginalized) by considering a data set of 
16 selected variables as indicators of the main dimensions of a sustainable globalization in 2006.  
 
Keywords: globalization, cluster analysis, k-means algorithm, international ranking. 
 
JEL Classification: E60, F0,F15,F43,O1 
 
Introduction. 
There is  a large  consensus in the international community on the idea that studies on 
globalization require a careful analysis of numerous factors, not just economic integration, 
which are likely to affect development dynamics. 
This paper stems from the need to take a further step forward in the construction of a 
multidimensional framework combining different elementary dimensions of globalization 
in order to achieve an overall assessment of integration across countries. 
When the research goal is to define a composite index, which summarizes the extension of 
the phenomenon under investigation in each dimension, it may be appropriate to proceed 
in three different and complementary phases: descriptive phase, aggregation phase and 
inference phase (Chiappero Martinetti 2005). The first phase concerns the selection of 
those variables which are representative of the phenomenon; the aggregation phase is a 
crucial step that requires to deal with technical aspects of aggregation, for each unit of 
analysis,  across  the  dimensions  or  domains  of  globalization  in  order  to  make  the 
implications in the underlying theoretical concepts explicit. The inference phase refers to 
the possibility of inferring a logical conclusion starting from the results of the previous 
steps.  
In their earlier works the authors (Mattoscio N., Castagna A. and Furia D. 2007, Furia D. 
and  Castagna  A.  2008)  focused  on  the  second  phase,  giving  attention  to  the  need  to 
overcome  some  controversial  problems  linked  with  multidimensional  approach,  e.g. 
weight system definition. There is another focal point about indicators which has been 
underestimated by the authors: the choice of variables in the descriptive phase may have a 
value judgment which affects the measure. Trying to resolve this latter matter, this paper 
follows in the previous footsteps and focuses on the measurement of European countries 
integration  in  the  global  community.  This  goal  will  be  achieved  by  providing  an 
implementation of k-means Clustering Algorithm to classify 73 Countries into four groups 277 
 
(leaders, potential leaders, dynamic followers and marginalized) taking into account a 
data set of 16 selected and distinctive variables in 2006.  
The paper is structured as follows: the first section introduces the role of globalization in 
economic growth across countries and its measures proposed in the literature to provide 
appropriate  criteria  for  the  choice  of  variables.  The  second  section  presents  the  main 
results of the data analysis. Section three concludes. 
 
1. Globalization: its dimensions and measurement. 
In  spite  of  its  frequent  use,  globalization  is  an  unclear  term,  and  it  is  not  easily 
distinguishable  from  internalization.  Ravallion  M.  (2004)  argues  that  the  selection  of 
indicators  is  a  crucial  point  in  the  debate  between  globalization  supporters  and  its 
discontents because the effects that globalization generates on inequality may be affected 
by inherent value judgment of measurements and each opposing thesis may be sustained 
by data evidence. 
This paper will focus on this point, i.e. in the selection of indicators and their assessment, 
as a preliminary step in building a composite index.  
Over the last few years, there have been globalization measures concerning  economic 
indicators  analysis  which  make  a  distinction  between  prerequisites  and  outcomes,  i.e. 
reduction  of  transaction  barriers  and  results  of  integration  dynamics  (Brahmbhatt  M. 
1998). The economic dimension, which was a crucial element of integration in the past 
centuries, becomes a means of diffusion of ideals able to affect growth dynamics followed 
by people’s capabilities and improvement of their way of life. Economic integration has 
produced  benefits  to  those  countries  which  have  expanded  their  commercial  borders, 
especially where governments have played a central role in this process. The other face of 
the medal are the millions of people for whom globalization has been ran without control 
and whose living conditions are nowadays worsen off (Stiglitz J.E. 2002). The easy way 
by  which people get in  touch  with  other cultures,  and the  awareness of lifestyles  and 
living conditions different from their own, shift the focus of debate about inequality and 
poverty from a local point of view to a global level analysis and the related issues begin to 
gain the same weight of national ones (Milanovic B. 2002). Studies about globalization 
effects on per capita income inequality between countries show a convergence evolution 
over  the  past  two  centuries  attributable  to  complex  mechanisms  at  various  levels  of 
income hierarchy of citizens all over the world. When life expectancy at birth has been 
taken into account to examine lifelong income inequality in order to explain this kind of 
dynamics the result is a current divergence in the living conditions which has reached 
levels  like  two  centuries  ago  (Bouguignon  F.  and  Morrison  C.  2002).  Ben-David  D. 
(1993)  provides  evidence  that  income  convergence  among  specific  industrialized
146 
countries  may  be  related  to  movement  toward  free  trade.  Focusing  on 
countries’comparative  advantage  and  its  implication  for  trade,  Venables  A.  J.  (2003) 
yields predictions about the formation of custom unions leading to the conclusion that 
initial  income  levels  may  be  determinant  in  driving  convergence  between  members. 
Starting point conditions are crucial in the ongoing debate whether poor countries are or 
not  able to take up the opportunities provided by an  expanding  economy. In is  work, 
Ravillon M. (2001) argues that there are lots of factors, like location, social exclusion, 
exposure to insured risk, and not just endowments of physical and human capital, which 
need  more  attention.  Different  long  run  growth  paths,  therefore,  have  produced  a 
divergence  between  rich  and  poor  countries  because  idiosyncratic  characteristics 
                                                       
146 France, West Germany, Belgium, The Netherlands, Luxemburg and Italy during the transition period which lasted 
from 1959 until 1968 (Ben-David D. 1993, p. 654) 278 
 
regarding not just their distance from the technological frontier but also desegregation of 
social and institutional milieu may conduce to an implosion of the system which may lose 
growth  opportunities  (Pritchett  L.  1997).  Olson  M.J.  (1996)  has  come  to  similar 
conclusions  drawing  on  the  fact  that  a  subset  of  the  lower  income  countries,  those 
countries who have adopted relatively good economic policy along with solid institutions, 
are growing faster than higher income countries. Stiglitz J. E. (2002) sustains that the role 
of globalization in the development process is not clear and that a number of elements 
which  are  the  basis  of  the  democracy,  such  as  poor  people  interest,  environment 
preservation,  free  trade  and  human  rights,  has  to  be  taken  into  account  to  reach  its 
beneficial potentials. 
From a methodological point of view, there are few studies dealing with multidimensional 
frameworks  of  globalization  and  most  of  them  concern  criticisms  and  improvements 
(Andersen M. 2003, Lockwood B. 2004) of a pioneer work about this argument, the A.T. 
Kearney Foreign Policy Magazine Index (Kearney A.T. 2001a, 2001b). This index is an 
assessment  of  globalization  as  a  result  of  economic,  technological  and  political 
integration.  However,  there  are  lots  of  international  organizations  that  use  synthetic 
indexes  to  monitor  global  and  complex  phenomena  which  represent  some  different 
expressions of integration, like human development, global competitiveness, human rights 
and  environment  preservation.  In  the  present  work  the  following  five  elementary 
globalization  domains  underlining    its  sustainable  features  are  selected:  economic 
integration
147,  technological  potential
148,  social  awareness
149,  environment 
sustainability
150. The next section will present an original data application. 
 
2. Integration level of European countries and the rest of the world. 
This section presents some stylized facts concerning globalization as a main result of a 
cluster analysis based on k-means algorithm used to investigate the role of 16 variables
151 
in the description of integration level between European countries
152 and the rest of the 
world. The aim of k-means algorithm (Hartigan J.A. 1975, Hartigan J. A. and Wong, M. 
A. 1979) is to divide the selected 73 countries in 16 domains into four (k) clusters so that 
the within-cluster sum of squares is minimized
153. The implementation yielded as distinct 
as possible clusters, which are shown in Fig 1. Fig 2 is useful in going over the differences 
in means between groups and their accomplishments. 
   
                                                       
147 The variables, all expressed as percentages of GDP, are: Trade, FDI (net outflows plus inflows), Income payments 
and receipts. 
148 The selected variables are: Internet users (per 100 people), Secure Internet servers (per 1 million people), High-
technology exports as % of manufactured exports, Ict expenditure as % of GDP, R&D expenditure as % of GDP. 
149  The  indicators  are:  International  voice  traffic  out  and  in  min  pro  capita,  life  expectancy  at  birth  (years), 
international tourism (number of departures plus arrivals as % of population), workers' remittances and compensation 
of employees, received as % of GDP, international migration as % of population. 
150 Indicators has been chosen with reference to MDGs: Marine and Nationally protected areas as % of surface area, 
CO2 emissions (metric tons per capita), forest as % of total land area. 
151 Data source is WDI. 
152 Lack of lots of data for Luxemburg and Netherlands has caused their exclusion from the analysis. 
153 The general procedure is to search for a k-partition with locally optimal within-cluster sum of squares by moving 
the objects from a cluster to another, with the purpose to minimize the variance of elements within the cluster and to 
maximize the variance of elements outside the clusters.  279 
 
Fig.1: Countries grouped in clusters 
 
 
 
 
 
 
 
 
 
 
 
Most of the analyzed European countries demonstrate good levels of global integration, as 
well as North America and Pacific high income countries do, accordingly these countries 
can be classified as dynamic followers of leaders in the process of global integration . 
Ireland and Malta, along with Iceland are better performing than other members being far 
away from the rest of EU with reference to FDI and income, internet indicators and High-
tech exports, but they falls down for Ict expenditure (cfr. Fig.2). Moreover, this group is a 
good performer with reference to the domains considered as a whole and they may be 
named  potential  leaders.  Bulgaria,  Greece,  Hungary,  Italy,  Latvia,  Lithuania,  Poland, 
Portugal, Romania and Slovak Rep. reveal poor achievements related to those of middle 
and  low  income  countries.  In  this  descriptive  representation  these  countries  may  be 
referred to as a marginalized group.  
There  are  no  EU  members  in  the  first  cluster,  the  leaders,  which  scores  highly  on 
economic integration with the main difference on trade. This indicator is not useful in 
distinguishing the remaining groups as the cluster means are very close. Hong Kong and 
Singapore  present,  however,  good  achievements  with  reference  to  FDI  and  income, 
similar to cluster 3. Group 4 is ranking below the other groups due to lower achievements 
in  each  dimension  with  the  exception  of  expenditure  on  Ict,  forest  land,  marine  and 
national  protected  areas.  The  clusters  perform  differently  in  terms  of  technological 
dimension, which is a proxy of a country’s ability to be up to global competitiveness. 
Marked divergence between cluster 4 and the competitors is recorded with respect to R&D 
expenditure as percentage of GDP. 
Fig.2: means plot for clusters 
 
 
 
 
 
 
 
 
 
 
 280 
 
Living  condition  means  are  very  close  for  first  three  groups,  while  lower  and  middle 
income and development countries incidence in the last cluster puts its expectancy life at 
birth  mean  down  as  a  consequence  of  starvation,  illness,  lack  of  freshwater  and 
generalized poor standards of living. Social indicators may be considered good markers 
since they catch the variances among groups with the exception of workers’ remittances 
that show very close means. Cluster 2 presents the highest level of CO2 emissions and is a 
good performer on the environment dimension. The two groups of leaders devote similar 
attention  to  environment  preservation,  and  fall  behind  the  marginalized  group  on 
percentage of land covered by forest. The interest in the protection of threatened marine 
and national areas seems not to be useful in distinguishing the clusters. 
Economic integration is a crucial dimension of globalization. An exhaustive measure of 
this phenomenon has to include several other indicators. The analysis shows differences 
across countries in technological potential and the selected variables may be considered 
good  indicators,  as  they  are  a  fair  representation  of  different  achievements  in  global 
competitiveness.  With  reference  to  social  awareness,  workers’  remittances  could  be 
eliminated from the selected variables due to analogous performances registered across 
countries.  The  group  of  variables  representing  environment  sustainability  may  be 
expanded by adding other indicators, e.g. global ecological overshoot, depleting the very 
resources on which human life and biodiversity depend. 
 
3. Concluding remarks 
Policymakers  and  social  activists  are  in  agreement  with  scientists  in  claiming  that 
globalization is not a result of explicit political choices, with a specific reference to poor 
economies. Western countries give the impression of being performers in a play where the 
market may produce different effects with respect to raw materials distribution, human 
capital characteristics and institutional performances. The role of European countries in 
the  globalization process cannot be considered as a uniform block: most of them are good 
performers but some others need to improve their potential to meet the global challenge. 
From  a  methodological  point  of  view,  the  analysis  has  been  helpful  in  justifying  the 
selection of relevant variables through a critical review of some of the most important 
studies on globalization and its effects on growth. In addition, the cluster application has 
allowed  us  to  evaluate  the  suitability  of  data  to  understand  the  implications  of  the 
methodological  choices  during  the  aggregation  phase.  Next  steps  will  concern  the 
implementation of the second phase of a multidimensional framework, dealing with the 
aggregation of information across dimensions for each units of analysis. This is necessary 
to deepen the analysis of winners and losers from the process of global integration. 
 
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EU AND GLOBALIZATION: SOME STYLIZED FACTS

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EU AND GLOBALIZATION: SOME STYLIZED FACTS

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