Classifying the EU Competitiveness Factors Using Multivariate Statistical Methods

Abstract

Although the EU is one of the most developed parts of the world with high living standards, there exist huge disparities having a negative impact on the balanced development across the EU and weaken thus its competitiveness in the global context The aim of the paper is to define factors of socioeconomic development of the EU by application of factor analysis based on Country/Regional competitiveness index. The results of the analysis are factors that determine socioeconomic environment of the EU. Based on factor analysis results, it is possible to classify EU territories through cluster analysis in distinct group.

Full text

Procedia Economics and Finance 23 ( 2015 ) 313 – 320
Available online at www.sciencedirect.com
2212-5671 © 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Selection and/ peer-review under responsibility of Academic World Research and Education Center
doi: 10.1016/S2212-5671(15)00508-0
ScienceDirect
2nd GLOBAL CONFERENCE on BUSINESS, ECONOMICS, MANAGEMENT and
TOURISM, 30-31 October 2014, Prague, Czech Republic
Classifying The EU Competitiveness Factors using Multivariate
Statistical Methods
Michaela Stanickova
a
*
a
Faculty of Economics, VŠB-Technical University of Ostrava, Sokolská třída 33, 701 21 Ostrava, Czech Republic
Abstract
Although the EU is one of the most developed parts of the world with high living standards, there exist huge disparities having a
negative impact on the balanced development across the EU and weaken thus its competitiveness in the global context The aim
of
the paper is to define factors of socioeconomic development of the EU by application of factor analysis based on
Co
untry/Regional competitiveness index. The results of the analysis are factors that determine socioeconomic environment of the
EU. Based on factor analysis results, it is
possible to classify EU territories through cluster analysis in distinct group.
© 2014 The Authors. Published by Elsevier B.V.
Selection and/ peer-review under responsibility of Academic World Research and Education Center.
Keywords: Cluster analysis, CCI, competitiveness, EU, factor analysis.
1. Introduction
The economy’s entry into globalization phase has radically
altered the nature of competition. Numerous new
actors from every market in the world are simultaneously in competition on every market. This new competition has
accentuated the interdependence of the different levels of globalization. Globalization has obliged all countries to
raise their standards of economic efficiency, whence the growing interest in and concern about competitiveness:
nations, regions and cities have no option but to strive to be competitive in order to survive in the new global market
place and th
e ‘new competition’ being forged by the new information or knowledge driven economy (Gardiner
Martin & Tyler, 2004). Policy-makers at all levels have been swept up in this competitiveness fever. This growing
in
terest may perhaps be partly attributable to their awareness of the fact that all countries are having to contend with
* Michaela Stanickova. Tel.: + 420-597-322-230.
E-mail address: michaela.stanickova@vsb.cz
© 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Selection and/ peer
-review under responsibility of Academic World Research and Education Center

314 Michaela Stanickova / Procedia Economics and Finance 23 ( 2015 ) 313 – 320
raised standards of economic efficiency as a result of the globalisation of goods and factor markets. The economy
may be competitive but if the society and the environment suff
er too much the country will face major difficulties,
and vice versa. Therefore governments in the long run cannot focus alone on the economic competitiveness of their
country; instead they need an integrated approach to govern the country. The complexity of competitiveness,
decomposed by (Esser Hillebrand, Messner & Meyer-Stamer, 1995), is used in this paper – every country has
co
mmon features which affect and drive the competitiveness of all the entities located there, even if the variability
of competitiveness level of the entities within the country may be very high.
In the European Union (EU), the process of achieving an in
creasing level of competitiveness is significantly
difficult by the heterogeneity of countries and regions in many areas. Although the EU is one of the most developed
parts of the world with high living standards, there exist significant disparities influencing a level of EU
competitiveness in global context. From this point of view, the aim of the paper is to define the main factors of
socioeconomic development determining competitiveness level of EU countries and to classify the EU Member
States to homogenous groups based on their competitive factor endowment.
2. Data and methodology
The empirical analysis starts from building
database of indicators that are part of Country Competitiveness Index
(CCI) approach – national level. Pillars of index are grouped according
to the different dimensions (input versus
output aspects) of national competitiveness they describe. The terms ‘inputs’ and ‘outputs’ are meant to classify
pillars into those which describe driving forces of competitiveness, also in terms of long-term potentiality, and those
w
hich are direct or indirect outcomes of a competitive society and economy (Annoni & Kozovska, 2010). CCI data
file consists of 66 CCI indicators – 38 inputs and 28 outputs. All CCI indicators are n
ot used in the paper, because
all indicators were not available for the whole reference period for each country – evaluated countries are EU27
(f
rom analysis is excluded Croatia because of data no availability for many of indicators and being non EU Member
or Candidate States for most of reference years). In this paper, only 61 indicators are used – 37 for inputs and 24 for
ou
tputs. Reference period (years 2004, 2007, 2008 and 2011) is determined by indicators availability at national
level. Years 2004 and 2007 characterize a growth period; years 2008 and 2011 characterize a crisis, resp. post-crisis
period.
Competitiveness measurement have a significant position in most of empirical studies, e.g. (M
elecký, 2013;
Staníčková & Melecký, 2014). The most common quantitative methods convenient for a high number of
multivariate measured variables can be identified as multivariate statistical methods. Multivariate analysis is an
ever-expanding set of techniques for data analysis that encompasses a wide range of possible research situation.
Facto
r analysis (FA) is a statistical procedure used to identify a small number of factors that can be used to represent
relationship among sets of interrelated variables. In this paper, FA
is applied as structure detection method (all
indicators are relevant to FA after correlation). Cluster analysis (CA) classifies objects that are very similar to others
in the cluster based on a set of selected characteristics-in the case of paper based on competitiveness factors-
indicators. The resulting cluster of objects should exhibit high internal (within-cluster) homogeneity and high
extern
al (between-cluster) heterogeneity. Because CCI is constr
ucted for ‘inputs’ – driving forces of
competitiveness and ‘outputs’ – direct or indirect outcomes of a com
petitive society and economy, policy and
activities; also empirical analysis by FA and CA is calculated separately for ‘inputs’ and ‘outputs’ aspects. For
empirical analysis, software IBM SPSS Statistics 22 was used.
3. Results of analysis
What is the background of national competitiveness? What are th
e crucial factors behind competitive differences
and gap among countries? Policy makers need a clear sense of its current competitive position and its functioning
and latent factors of competitiveness: the starting point. By understanding both its position and factors of
competitiveness, the policy makers can better understand the potential development options and limitations for
countries and plot a development trajectory towards a desired end state (Martin, 2003).

315
Michaela Stanickova / Procedia Economics and Finance 23 ( 2015 ) 313 – 320
3.1. Factors of competitiveness
Output factors represent direct or indirect outcomes of a competitive society and economy. In this paper, three
dom
inating factors for outputs explained 74,846 % of total variability in reference period (see Table 1), what can be
considered as very satisfactory result. For calculation of output factors by FA is used: Principal Component Analysis
as extraction method; Varimax with Kaiser Normalization as rotation method; Rotation was converged in 5
iterations. Table 1 shows 24 number indicators and their belonging to relevant output factors of competitiveness.
Table 1. Total variance explained – case of output factors.
Component
Rotation Sums of Squared Loadings
Total
% of Variance
Cumulative %
1
8,127
32,509
32,509
2
5,557
22,228
54,738
3
5,027
20,108
74,846
Rotated component matrix – output factors
Indicators
Component
1
2
3
Factor 1
Economic
performance
and
innovative
potential
(EPO) Patent applications to the EPO (1)
,871
(DI) Disposable income (2)
,821
,305
(HTI) High-tech patent applications to the EPO (1)
,803
(ICT) ICT patent applications to the EPO (1)
,802
(HRSTcore) Human resources in Science and Technology - core sectors (1)
,801
(GDP) Gross domestic product (2)
,778
(HRST) Human resources in Science and Technology (1)
,776
(PEoLMP) Public expenditure on Labour Market Policies (3)
,734
(LP) Labour productivity (3)
,726
(BioT) Biotechnology patent applications to the EPO (1)
,683
(FE) Female employment (3)
,578
,382
(GVA) Gross Value Added (GVA) in sophisticated sectors (4)
,519
Factor 2
Knowledge
based
economy
(ETKIedu) Employment in technology and knowledge - by education (1)
,982
(EiSS) Employment in sophisticated sectors (2)
,982
(ETKIocc) Employment in technology and knowledge - by occupation (1)
,982
(ETKIgen) Employment in technology and knowledge - by gender (1)
,982
(TPAp) Total patent applications (1)
,852
(CoE) Compensation of employees (3)
,843
Factor 3
Labour
market
(UR) Unemployment rate (1)
-,966
(MU) Male unemployment (1)
-,937
(LtUR) Long-term unemployment in % of active population (1)
-,898
(FU) Female unemployment (1)
-,890
(ME) Male employment (1)
,392
,760
(ER15to64) Employment rate (15 to 64 years) (1)
,578
,617
Factor 1 – Economic performance and innovative potential is composed
of indicators in groups: (1) innovation,
(2) Market size, (3) labour market efficiency and (4) business sophistication. Factor 2 – Knowledge based economy
is
composed of indicators in category: (1) innovation, (2) business sophistication and (3) market size. Factor 3 –
Labour market is composed of indicators: (1) labour market efficiency. Based on output factors on competitiveness
is clear, th
at the most economically advanced countries in the world offer excellent conditions for business, long-
term focus on supporting research and development. Substantial funding from both public budgets and business
bu
dgets, are oriented to promote new ideas and creative approach to economic activities. Domestic companies know
that the future belong to prepared companies offering something extra to their customers, i.e. the added value.
In the coming years, economic growth belong to countries experiencing "creative" companies. Profitability of
larg
e and small companies mainly depends on new ideas and thoughts. Promoting education and learning of
residents is very important for the future of countries. Innovative employees determine the success of companies.
The driving force are the ideas. The greatest asset of prosperous companies are not material things, but employees
who are able to create new values, to respond flexibly on changing market needs and to bring constantly new ideas.

316 Michaela Stanickova / Procedia Economics and Finance 23 ( 2015 ) 313 – 320
Table 2. Total variance explained – case of input factors.
Component
Initial Eigenvalues
Rotation Sums of Squared Loadings
Total
% of
Variance
Cumulative
%
Total
% of
Variance
Cumulative
%
1
11,491
31,057
31,057
10,259
27,728
27,728
…
…
…
…
…
…
…
6
1,694
4,579
68,659
2,240
6,054
68,659
Rotated component matrix – input factors
Indicators
Component
1
2
3
4
5
6
Factor 1
Economic growth and
development
(VA) Voice and Accountability (1)
,922
(RL) Rule of Law (1)
,917
(CC) Control of Corruption (1)
,915
(GE) Government Effectiveness (1)
,913
(GERD) Gross R&D Expenditure (2)
,873
(LPPE) Labour Productivity per Person Employed (2)
,863
(RQ) Regulatory Quality (1)
,851
(PS) Political Stability (1)
,765
(GFCF) Gross Fixed Capital Formation (2)
,742
-,347
(LIA) Level of Internet Access (3)
,735
-,431
(CDDR) Cancer Disease Death Rate (4)
-,696
-,315
,470
(IMR) Infant Mortality Rate (4)
-,695
,311
(RF) Road Fatalities (4)
-,672
,306
(LLPET) Lifelong Learning - Participation in Education
and Training (5)
,645
,373
(TPETLE) Total Public Exp.at Tertiary Education (5)
,553
,318
,521
(VFT) Volume of Freight Transport (6)
-,444
-,392
Factor 2
Level of infrastructure
(ISLB) Income, Saving, Net Lending/Net Borrowing (1)
,951
(AU) Accessibility to Universities (2)
,914
(ATP) Air Transport of Passengers (3)
,879
(MTLM) Motorway transport - Length of Motorways (3)
,862
(ATF) Air Transport of Freight (3)
,816
(RTLT) Railway transport - Length of Tracks (3)
,735
Factor 3
Health phenomena in
human life and
cultivation
(HP) Hospital Beds (1)
,852
(SDR) Suicide Death Rate (1)
,530
,392
(TPEPLE) Total Public Exp. at Primary of Education (2)
-,505
(PTR) Pupils to Teachers Ratio (3)
,399
,445
Factor 4
Inflation trends,
transport, healthy
lifestyle, performance of
educational institutions
and public administration
(HICP) Harmonised Index of Consumer Prices (1)
-,312
-,732
(VPT) Volume of Passenger Transport (2)
,665
(HLE) Healthy Life Expectancy (3)
,511
(ELET) Early Leavers from Education and Training (4)
,509
-,433
(FAS) Financial Aid to Students (4)
-,457
,334
(EA) E-government Availability (5)
,369
,423
Factor 5
Participation in education
(PEE) Participants in Early Education (1)
,350
-,663
(PHE) Participation in Higher Education (1)
-,326
,627
(MSTEG) Maths, Science and Technology Graduates (1)
,330
,614
Factor 6
Expenditure on education
and civilization diseases
(TPESLE) Total Public Exp. at Secondary Education (1)
,811
(HDDR) Heart Disease Death Rate (2)
-,308
-,466
Driven forces of competitiveness are divided into factors
that are crucial for EU economies. In this paper, six
dominating factors for inputs explained 68,659 % of total variability in reference period (see Table 2), what can be
considered as satisfactory result. For calculation of input factors by FA is used: Principal Component Analysis as
extraction method; Varimax with Kaiser Normalization as rotation method; Rotation was converged in 8 iterations.
Table 2 shows 37 number indicators and their belonging to relevant input factors of competitiveness.
EU competitiveness factors are divided into several areas
of national economy, which are nowadays key and
necessary for economy based on knowledge and innovation. Factor 1 – Economic growth and development is
co
mposed of indicators in groups: (1) institutional environment, (2) macroeconomic stability, (3) technological
readiness, (4) health, (5) education and (6) infrastructure. Factor 2 – Level of infrastructure is composed of
in
dicators in category: (1) macroeconomic stability, (2) training, (3) infrastructure. Factor 3 – Health phenomena in
h
uman life and cultivation is composed by category: (1) health, (2) education and (3) training. Factor 4 – Inflation

317
Michaela Stanickova / Procedia Economics and Finance 23 ( 2015 ) 313 – 320
trends, transport, healthy lifestyle, performance of educational institutions and public administration is composed by
groups: (1) macroeconomic stability, (2) infrastructure, (3) health, (4) education and (5) technological readiness.
Factor 5 – Participation in education is composed of indicato
rs in category: (1) education. Factor 6 – Expenditure on
education and civilization diseases is composed by groups: (1) education and (2) health.
3.2. Cluster profile of EU countries
Based on results of FA, it is possible to create cl
uster profile of EU Member States. CA is used for defining
country cluster profile based on the value of individual factors. For the final matrix to CA, it was used 6 factors of
inputs and 3 factors of outputs that represent the most frequently indicators of competitiveness. In this paper, the best
interpretation of data ensures five-cluster solution for inputs across the reference period. The best interpretation of data
e
nsures also five-cluster solution for outputs across the reference period. The number of inputs/outputs clusters has
been
set, based on previous analysis, thus at 5, as shows Figure 1 – Rescaled Distance Cluster Combine.
In the case of inputs factors, i.e. driven forces of competitiveness, Cluster I is created by less mature countries: old
EU Me
mber States such as Greece (EL), Portugal (PT), Italy (IT) and Spain (ES); and new EU Member States such as
Malta (MT), Latvia (LV), Lithuania (LT), Romania (RO), Slovenia (SI), Slovakia (SK) and Hungary (HU). These
countries are characterized with one the lowest level of indicators represent forces driven of competitiveness. The worst
results of all countries in the case of internal requirements for competitiveness shows Cluster 3 created by Bulgaria.
Cluster 2 rep
resent Estonia (EE), Netherlands (NT), Czech Republic (CZ), Belgium (BE) and Cyprus (CY), thus
countries with average level of driven indicators as aspects for competitiveness. Cluster 4 is created by countries
such as Germany (DE) and Finland (FI), thus the most economic powerful countries with good conditions and
facilities for competitiveness, resp. with best factor endowment. Cl
uster 5 represent also advanced old EU Member
States such as Denmark (DE), Sweden (SE), United Kingdom (UK), Austria (AT), France (FR), Luxembourg (LU)
and Ireland (IE) – thus countries with very similar levels of factor endowment as countries in Cluster 4. Then, to
Cluster 5 belo
ngs Poland (PL), whose economy facility is very favorable.
To very close intent, results of input-profile indicate results of output-profile. Affiliation of most countries within
a g
roup factor endowment determines its inclusion within the results of economic activities. In the case of outputs
factors of competitiveness, i.e. direct/indirect outcomes of economic activities, Cluster I is represented by IE, ES, PT
and BE from old EU countries, but BE is on boundary of belonging to Cluster 3; and CY, MT, CZ, SI, PL, EE, RO
from new EU countries. These countries are characterized with lower economic efficiency, especially as a result of
crisis. Cluster 2 is created by EL, HU, LV, LT, BG and SK. These countries have the worst economic prosperity and
level of performance. LU, FI, AT, IT belong to Cluster 3 – these are countries with satisfactory result in their economic
act
ivity, but IT is country on prosperity boundary and belonging to Cluster I. Cluster 4 represent countries such as NL,
UK, DK, FR and SE, which are distinguished by the high level of efficiency and performance trend. Last, Cluster 5 is
created by DE – by country reflecting stable and good economic results.
4. Conclusion
The main aim of this paper was to define the main factors of socioeconomic development that determine
co
mpetitiveness level of EU countries. Based on empirical analysis is possible to say, that in most of cases, the old
EU countries reflect best results in driven forces of competitiveness as assumption for better outcomes of economic
activities and functioning of society. The competitiveness of territory resides not only in the competitiveness of its
co
nstituent individual entities and their interactions, but also in the wider assets and social, economic, institutional
and public attributes of the country itself. The notion of competitiveness is as much about qualitative factors and
co
nditions (e.g. untraded networks of informal knowledge, trust, social capital, etc.) as it is about quantifiable
attributes and processes (e.g. inter-firm trading, patenting rates, labour supply, etc.). The causes of competitiveness
are u
sually attributed to the effects of an aggregate of factors rather than the impact of any individual factor. The
sources of competitiveness may also originate at a variety of geographical scales, from the local, through regional, to
national and even international (Martin, 2003). The emergence of new perspectives in creating competitive
advantages at national level clearly emphasizes the role of local factors and initiative in the general economic

318 Michaela Stanickova / Procedia Economics and Finance 23 ( 2015 ) 313 – 320
development of a country. This has major implications for the empirical analysis of regional competitiveness for
further research.
(a)

319
Michaela Stanickova / Procedia Economics and Finance 23 ( 2015 ) 313 – 320
(b)
Fig. 1. Dendogram using Ward linkage – Clusters of EU Member States (a) input factors; (b) output factors

320 Michaela Stanickova / Procedia Economics and Finance 23 ( 2015 ) 313 – 320
Acknowledgements
This paper was created under SGS project (SP2014/111) of Faculty of Economics, VŠB-Technical University of
Ostrava a
nd Operational Programme Education for Competitiveness – Project CZ.1.07/2.3.00/20.0296.
References
Annoni A & Kozovska K.(2010). EU Regional Competitiveness Index 2010. Luxembourg: Publication Office of the European Union; 2010.
Esser K., Hillebrand,W, Messner D & Meyer-Stamer J. (1995). Syst
emic Competitiveness. New Governance Patterns for Industrial Development.
London: Frank Cass; 1995.
Gardiner B, Martin R & Tyler P. (2004). Co
mpetitiveness, Productivity and Economic Growth across the European Regions. Retrieved from
http://www-sre.wu-wien.ac.at/ersa/ersaconfs/ersa04/PDF/333.pdf; 2004.
Martin R. (2003). A
Study on the Factors of Regional Competitiveness. Retrieved from
http://ec.europa.eu/regional_policy/sources/docgener/studies/pdf/3cr/competitiveness.pdf; 2003.
Melecký L.(2013). Comparing of EU15 and EU12 Countries Efficiency by Application of DEA Approach. In H. Vojáčk
ová (ed.), International
Conference Mathematical Methods in Economics 2013. Part II (pp. 618-623). Jihlava: The College of Polytechnics Jihlava; 2013.
Staníčko
vá M & Melecký L. (2014). Factors Determining Driving Forces of Competitiveness in EU Countries. In International Conference
Hradec Economic Days 2014 (pp. 338-348). Hradec Králové: University of Hradec Králové; 2014.