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1 Building Bridges: Cities and Regions in a Transnational World RSA Annual Conference 2016, Graz, Austria
Intellectual Capital of the European Union Regions, on example
of the Visegrad Countries regions
Judyta Lubacha-Sember PhD Student, Cracow University of Economics, Faculty of Economics and International Relations
International PhD candidate, University of Bremen, Chair for Economics of Innovation and Structural Change 1 [email protected]
1. Introduction
From the very beginning of the European Community, the European regions have figured
very prominently in its policy. For the appropriate use of structural policy instruments, an adequate
diagnosis of regional needs and available resources is required. The measurement of regional
resources is an important instrument for regional policy, because it allows to present both the
strengths and weaknesses of the regions. The intellectual capital of the regions, for example, is an
intangible resource and is difficult to measure. However, the measurement of intellectual capital of
the European Union’s regions could give us important knowledge about regional assets which are
important for economic growth. The “Complexity of Regional Intellectual Capital Index” allows us to
present in one synthetic index many important factors in regional development.
The purpose of this conference paper is to present a proposal for the measurement of
intellectual capital of regions in the European Union. Firstly, a literature overview concerning
intellectual capital and its components is described. Secondly, a proposition of structure and
available indicators is presented, followed by a description of the relevant measurement
methodology. Finally, preliminary results and faced difficulties are discussed.
2. Intellectual capital: Literature overview
Intellectual capital is a category that originated within microeconomic research, the results of
which are discussed in the following models of business intellectual capital measurement: Skandia
Navigator [Edvinsson, 1997], IC-Index [Roos, Roos, 1997], Technology Broker [Brooking, 1996],
Intangible Asset Monitor [Sveiby, 1997], and MVA and EVA [Stewart, 1997]. The measurement of
intellectual capital of a business aims to include intangible assets in its financial statements. The
1 Text prepared based on preliminary results of research project “Intellectual Capital of the European Union Regions”. Research project is realized at the University of Bremen and financed by DAAD (German Academic Exchange Service) as a Research Grant for Doctoral Candidates and Young Academics and Scientists.
2 term “intellectual capital” first appeared in a 1958 newspaper article by M. Kronfeld and A. Rock
[1958, s. 90], and later in a private letter from John Kenneth Galbrait to Michał Kalecki on Kalecki’s
70th birthday [quoted in Hudson, 1993, p. 15].
First attempts to measure intellectual capital on the national level were made in 1996: in her
master’s thesis, M. Jerehov and C. Stenfelt in cooperation with L. Edvinnson created a model of
Swedish intellectual capital. Next came research of Israeli intellectual capital conducted by E. Pasher
with the help of Edvinnson and Stenfelt [Edvinsson i Stenfelt, 1999]. The first complete and
comprehensive model of national intellectual capital measurement was created by N. Bontis [2004],
who in 2001 conducted research on intellectual capital of Arab countries. He modified Edvinnson
and M. Malon’s model [1997] so as to accommodate the macroeconomic level.
The definition of intellectual capital introduced by Bontis [2004, p. 14] is the one most
frequently quoted by scientists in research in that field: “The intellectual capital of a nation includes
the hidden values of individuals, enterprises, institutions, communities and regions that are the
current and potential sources for wealth creation”. Research on intellectual capital on the national
and regional level include: D. Andriessen and C. Stam [2005], A. Pulic [2005], G. Schiuma, A. Lerro
and D. Carlucci [2008], Edvinsson and C. Y-Y. Lin [2011]. In Poland, there have been 4 attempts of
intellectual capital research: Polish Intellectual Capital [Raport 2008], the “Intellectual Capital of the
Lublin region – regional potential research” project [Wodecki, 2007], the Regional intellectual capital
model [Więziak-Białowolska, 2010], Intellectual capital in the development of Eastern Poland regions
[Wosiek, 2012].
Table 1. Overview of the previous researches on regional intellectual capital
Research Definition of regional intellectual capital Components of
intellectual capital
Italian regions
[Schiuma,
Lerro, Carlucci,
2008]
A Intellectual capital model based on the “Knoware Tree” idea,
where term “Knoware” designates all assets related to
knowledge and/or reflection of knowledge that result from
individual or group cognitive activities. It emphasises that
knowledge assets are a strategic resource characterised by the
nature of knowledge, which may manifest itself in different forms,
both tangible and intangible, unnamed or codified, different
content and aims related to its ability to fulfil specific requirements
and needs.
Wetware
Hardware
Software
Netware
Intellectual
capital of Polish
regions
[Więziak-
Białowolska,
2010]
In accordance with the definitions by Andriessen and Stam, and
Bontis, regional intellectual capital was defined as “directly
unobservable attributes of residents, businesses, institutions,
organisations, communities, and administrative units that are
actual and potential sources of improvement in future social
welfare and economic growth. All available assets (mainly
intangible, but also tangible) are components of the regional
intellectual capital, giving the region relative advantage over
other regions. Furthermore, used together and concurrently, they
may bring about concrete benefits in the future.”
Human capital
Social capital
Structural capital
Development capital
Intellectual
capital of the
Intellectual capital comprises a set of components in the form of
human capital and its “instrumentation”, necessary for translating
Human capital
Social capital
3
Eastern Poland
regions
[Wosiek, 2012].
knowledge and competencies into tangible economic results.
These are: social capital (supporting human capital from the
mental angle), structural capital (material support, as well as an
infrastructural, technological, and organisational pillar), and
relational capital (reflecting the ability to incorporate knowledge
in the collaboration and development network by means of
cooperation with external entities).
Structural capital
Relational capital
Intellectual
capital of
Polish regions
[Lubacha-
Sember, 2014]
Intellectual capital of regions can be understood as totality of
directly unobservable factors, which are disclosed on every one
of those levels: individual, enterprise, administration structures,
and regional society as whole. Intellectual capital consists
particularly of knowledge and experiences of local inhabitants,
social capital and structural capital, which allow further
development of human capital.
Human capital
Social capital
Structural capital
Development capital
Source: own elaboration based on: Schiuma, Lerro, Carlucci [2008], Więziak-Białowolska [2010], Wosiek [2012], Lubacha-Sember [2014].
Each of the presented definitions of intellectual capital emphasizes that the main component
of intellectual capital is human capital, and that the remaining forms of capital (social, structural,
relational, and development) play a supporting role for further development of human capital, and
help to translate knowledge and competencies into economic results. Three of the intellectual capital
components are common to all of the presented examples: human capital (Wetware2), social capital
(Software), and structural capital (Hardware). Human capital is understood generally as knowledge,
and competencies embodied in people living in a region. Social capital includes norms, habits and
values [Schiuma, Lerro, Carlucci, 2008], but also the cooperation of regional actors, and social
activity [Więziak-Białowolska, 2010; Wosiek, 2012; Lubacha-Sember, 2014]. Schiuma, Lerro,
Carlucci [2008] and Wosiek also distinguish relational capital (Netware), which includes both internal
and external relations (Netware), or only external relations (relational capital). Structural capital
represents tangible assets relevant for the development, acquisition, management and diffusion of
knowledge, like technical, informational or educational infrastructure. D. Więziak-Białowolska [2010]
and J. Lubacha-Sember [2014] also include the category of development capital, which reflect the
regional capacity for innovation (mostly by R&D activity indicators).
3. Components of the Regional Intellectual Capital Index
Based on previous research, the following components of the Regional Intellectual Capital
Index have been identified:
1. Human capital
2. Social capital
3. Structural capital
2 Names of given intellectual component in the Knoware Tree model
4 J. Mincer’s article [1958] was one of the pioneering works on human capital, and exposed
the influence of individuals economic actor’s training, years of training, and on-the-job experience
on personal income. It is commonly conjectured, that human capital theory was developed in the
1960s by T.W. Shultz [1960, 1961] and G.S. Becker [1962, 1964]. They emphasized that capital
could not only be physical or financial, but also human, and that therefore education and training
should be seen as an investment in capital, not as consumption. The main motivation for this
research was also a realisation that the growth of physical capital explains only small part of the
growth of income in the countries in question [Becker, 1964]. First works on human capital [Becker,
1962, 1964, 1967; Ben-Porath, 1967, 1970; Mincer, 1970, 1974, Chiswick, 1974] were concentrated
on models of returns from human capital investments and examined, theoretically as well as
empirically, the relation between human capital investments and individual earnings. Kiker [1966]
argued that the concept of human capital was not new in economics and showed its development in
the history of economic thought eg. A. Smith included in his category of fixed capital skills and useful
abilities of human beings, but without defining it as “human capital”.
Shultz [1960, p. 571] justified recognition of the category of human capital as follows: “Since
education becomes a part of the person receiving it, I shall refer to it as human capital. Since it
becomes an integral part of a person, it cannot be bought or sold or treated as property under our
institutions. Nevertheless, it is a form of capital if it renders a productive service of value to the
economy”. Shultz [1961] mentioned following areas of activities that could improve human capital:
health facilities and services;
on-the-job training;
formally organized education;
study program for adults;
migration of individuals and families to adjust to changing job opportunities.
Likewise, Becker [1962, 1964, 2002] discerned schooling, on-the-job training, medical care,
migration, searching for information, research and development activity, as possible ways to invest
in human capital. “All these investments improve skills, knowledge, or health, and thereby raise
money or psychic incomes” [Becker, 1964, p. 11.]. In a broader sense, “human capital refers to the
knowledge, information, ideas, skills, and health of individuals” [Becker, 2002, p. 3]. Health as a form
of human capital was suggested, beyond Becker [1964], in works of S.J. Mushkin [1962] and V.R.
Fuchs [1966]. M. Grossman [1972] developed a model of the demand for health. Health as human
capital is underrepresented in contemporary research, however, Becker [2007] argued that health
(measured by life expectancy) has a significant influence on countries income, and different forms
of human capital serve as complements. Narrowly, human capital is to be understood as “embodied
knowledge and skills” [Becker, Murphy, 1990, p. 15].
Social capital theory was developed in sociology in the 1980s. However, as A. Portes [1998]
underlined, term ‘social capital’ embodies ideas from the beginnings of sociology (eg. concepts
expounded by Durkheim and Marx). Term of ‘social capital’ may first have appeared in a work of L.J.
5 Hanifan [1916, p. 130] where it was referred to as “goodwill, fellowship, mutual sympathy, and social
intercourse among a group of individuals and families who make up a social unit.” The concept of
social capital was introduced into economics by G.C. Loury [1977] – he noted that social context
plays significant role in human capital development.
P. Bourdieu [1986, p. 246] conducted a first systematic analysis of social capital and defined
it as “aggregate of the actual or potential resources which are linked to possession of a durable
network of more or less institutionalized relationships of mutual acquaintance and recognition – or
in the other works, to membership in a group – which provides each of its members within the backing
of the collectivity-owned capital, a ‘credential’ which entitles them to credit, in the various sense of
the word. These relationships may exist only in the practical state, in material and/ or symbolic
exchanges which help to maintain them. They may also be socially instituted and guaranteed by the
application of a common name (the name of a family, a class, or a tribe or of a school, a party, etc.)
and by a whole set of instituting acts designed simultaneously to form and inform those who undergo
them; (…) Being based on indissolubly material and symbolic exchanges, the establishment and
maintenance of which presuppose reacknowledgment of proximity. (…) The profits which accrue
from membership in a group are the basis of the solidarity which makes them possible.” The profits
of membership may be material (all types of services accruing from useful relationships) or symbolic
(derived from association with a rare, prestigious group).
According to J.S. Coleman [1988, p. 98] “social capital is defined by its function. It is not a
single entity but a variety of different entities, with two elements in common: they all consist of some
aspect of social structures, and they facilitate certain actions of actors-whether persons or corporate
actors-within the structure. (…) Unlike other forms of capital, social capital inheres in the structure of
relations between actors and among actors.” Coleman [1988, 1990] distinguished the following forms
of social capital by defining its functions:
obligations and expectations;
information potential;
norms and effective sanctions;
authority relations;
appropriable social organization.
According to R.D. Putnam’s definition [1995, p. 67] “social capital refers to features of social
organization such as networks, norms, and social trust that facilitate coordination and cooperation
for mutual benefit. (…) In the first place, networks of civic engagement foster sturdy norms of
generalized reciprocity and encourage the emergence of social trust. Such networks facilitate
coordination and communication, amplify reputations, and thus allow dilemmas of collective action
to be I resolved. When economic and political negotiation is embedded in dense networks of social
interaction, incentives for opportunism are reduced. At the same time, networks of civic engagement
embody past success at collaboration, which can serve as a cultural template for future
collaboration.” Putnam [1993] distinguishes between three components of social capital:
6
social trust;
norms of reciprocity;
networks of civic engagement.
In literature, there is no separate theory of structural capital. This term was developed in
intellectual capital research on the organisational level and was adopted to national and regional
levels. J. Roos and G. Roos [1997a, p. 8] defined structural capital in comparison to human capital:
“It has come to view intellectual capital as both what is in the heads of employees (‘human capital’)
and what is left in the organisation when people go home in the evening (‘structural capital’).” In
Bontis’ [2004, p.21] and later research on the intellectual capital of nations and regions, structural
capital was defined as “the non-human storehouses of knowledge in a nation which are embedded
in its technological, information and communications systems as represented by its hardware,
software, databases, laboratories and organizational structures which sustain and externalize the
output of human capital.” Structural capital could include informational infrastructure, educational
infrastructure, or physical infrastructure like in “Knoware Tree” conception [Schiuma, Lerro, Carlucci,
2008]. According to Schiuma, Lerro, Carlucci [2008, p. 288] Hardware “includes all those assets
relevant for the development, acquisition, management and diffusion of knowledge, but tangible in
nature as well as all the components linked to structural features of the regions.” Więziak-
Białowolska [2010] includes in the notion of structural capital the related social and technical
infrastructure. Social infrastructure meets the social, educational and cultural needs of regional
society. Technical infrastructure is covered by communication and transport infrastructure.
4. Proposed structure of the Regional Intellectual Capital Index (RICI)
Presented in graph 1., the structure of the Regional Intellectual Capital Index is proposed and
is to be based on the conducted literature review. The Regional Intellectual Capital Index is
composed from three subcomponents: Human Capital, Social Capital, and Structural Capital.
Relations (networks) are included as a part of the social capital component. Development capital
(R&D indicators) will be included in the Human Capital subcomponent, depending on data
availability.
7 Graph 1. Proposed structure of Regional Intellectual Capital Index (RICI)
Source: own elaboration
Regional Intellectual Capital Index and its subcomponents are defined in graph 2.
Graph 2. Regional Intellectual Capital Index components definitions
Regional Intellectual Capital Index
The intellectual capital of regions may be understood as the totality of factors that are
not directly observable, which are disclosed on every one of those levels: individual,
enterprise, administration structures, and regional society as whole. Intellectual
capital consists particularly of the knowledge and experiences of local inhabitants,
social capital and structural capital, both of which allow for the further development
of human capital [Lubacha-Sember, 2014].
Human Capital Social Capital Structural Capital
“Human capital refers to the
knowledge, information,
ideas, skills, and health of
individuals”
[Becker, 2002, p. 3].
“Social capital refers to
features of social organization
such as networks, norms, and
social trust that facilitate
coordination and cooperation
for mutual benefit”
[Putnam, 1995, p. 67].
Structural capital3 is “the non-
human storehouses of
knowledge in a nation which are
embedded in its technological,
information and
communications systems as
represented by its hardware,
software, databases,
laboratories and organizational
structures”
[Bontis, 2004, p. 21].
Source: Lubacha-Sember, 2014, Bontis, 2004; Becker, 2002; Putnam, 1995;
3 named by Bontis as process capital
Regional Intellectual Capital Index
Structural Capital Social Capital Human Capital
Knowledge
Health
Trust
Values
Networks
Informational
infrastructure
8
4.1. Proposed indicators of the Regional Intellectual Capital Index (RICI) and data
availability
The proposed list of indicators (table 2.) was prepared while taking into account previous
research on the regional level pertaining to human capital [eg. Vogel, 2012; Izushi, Huggins, 2004;
Rodríguez-Pose, Vilalta-Bufí, 2004; Badinger, Tondl, 2002], social capital [eg. Forte, Peiró-
Palomino, Tortosa-Ausina., 2015; Fidrmuc and Gërxhani 2007; van Schaik 2002; Schneider,
Plümper, Baumann, 2000], and structural capital [eg. Schiuma, Lerro, Carlucci, 2008; Więziak-
Białowolska, 2010; Wosiek, 2012; Lubacha-Sember, 2014]. Additionally, the methodology of
measurement of human capital is discussed by eg. G. Folloni and G. Vittadini [2010], C. Dreger, G.
Erber and D. Glocker [2009]. Discussion about social capital measurement can be found in works of
eg. D. Narayan and M.F. Cassidy [2001], M. Paldam [2000].
Table 2. Proposed indicators of Regional Intellectual Capital Index (RICI) and data availability
RICI
subcomp
onent
Indicator
(abbreviation)
Basic data
name/ label
Data
source
[indicator
code]
Years Basic data description
Human
Capital
(HC)
Knowledge.1
(K.1)
% of
population
aged 25-64
with tertiary
education
(levels 5-8)
Eurostat
[edat_lfse_
04]
2008,
2010,
2012
The educational attainment
level of an individual is the
highest ISCED (International
Standard Classification of
Education) level successfully
completed, the successful
completion of an education
programme being validated by
a recognised qualification.
Knowledge.2
(K.2)
% of
population
taking part in
education and
training
Eurostat
[trng_lfse_0
4]
2008,
2010,
2012
Participation in education and
training is a measure of lifelong
learning. The participation rate
in education and training
covers participation in formal
and non-formal education and
training. The reference period
for the participation in
education and training is the
four weeks prior to the
interview.
Health.1
(H.1)
Life
expectancy at
age less than
1 year (in
years)
Eurostat
[demo_r_ml
ifexp]
2008,
2010,
2012
Life expectancy at given exact
age - the mean number of
years still to be lived by a
person who has reached a
certain exact age, if subjected
throughout the rest of his or her
life to the current mortality
conditions (age-specific
probabilities of dying).
9
Social
Capital
(SC)
Trust.1
(T.1)
Most people
can be trusted
or you can’t be
too careful
European
Social
Survey
[ppltrst]
2008,
2010,
2012
Generally speaking, would you
say that most people can be
trusted, or that you can't be too
careful in dealing with people?
0 means “you can't be too
careful” and 10 means that
“most people can be trusted”.
Trust.2
(T.2)
Trust in the
legal system
ESS [trstlgl] 2008,
2010,
2012
How much do you personally
trust each of the institutions?
0 means “you do not trust an
institution at all”, and 10 means
“you have complete trust”.
Networks.1
(N.1)
How often
socially meet
with friends,
relatives or
colleagues
ESS
[sclmeet]
2008,
2010,
2012
How often do you meet socially
with friends, relatives or work
colleagues?
1 means “never”, 7 means
“every day”.
Values.1
(V.1)
Important to
do what is told
and follow
rules
ESS
[ipfrule]
2008,
2010,
2012
How much each person is or is
not like you: She/he believes
that people should do what
they're told. She/he thinks
people should follow rules at all
times, even when no-one is
watching.
1 means “very much like me”, 6
means “not like me at all”.
Structural
capital
(StC)
Informational
infrastructure.1
% of
households
with internet
connection
Eurostat
[isoc_r_iacc
_h]
2008,
2010,
2012
The population of households
consists of entirely private
households having at least one
member in the age group 16 to
74 years.
% of
households
with personal
computer with
access to the
Internet
Central
Statistical
Office of
Poland1
2008,
2010,
2012
Private household - Group of
people living together in a
housing unit and jointly
maintaining themselves.
Persons living alone and
independently maintaining
themselves constitute a one-
person households. 1 Data from Eurostat for Poland were available only on NUTS1 level, and they were replaced by data from Central Statistical Office of Poland Source: own elaboration, based on Eurostat metadata website [2016a, 2016b, 2016c, 2016d]; ESS4-2008, ed. 4.3 - Multilevel Data Study Documentation; ESS5-2010, ed. 3.2 - Multilevel Data Study Documentation; ESS6-2012, ed.2.1 - Multilevel Data Study Documentation [downloaded automatically with data from ESS website]
4.2. Constructing procedure of the Regional Intellectual Capital Index (RICI)
The construction of a Regional Intellectual Capital Index was based on a 10-step procedure,
as proposed in the Handbook on Constructing Composite Indicators [OECD, 2008].
Data for indicators K1., K.2, H.1 and II.1 were collected for 35 NUTS-2 regions of Visegrad
countries (the Czech Republic, Hungary, Poland, Slovakia) and used for the construction of a
10 composite indicator as provided by the Eurostat database and the Central Statistical Office of
Poland. Data from the European Social Survey is individual level data, and before using the data in
the composite indicator construction, the following actions were taken:
1. For variable V.1 “Important to do what one is told and to follow the rules”, the scale of answers
was reversed, from 1 meaning “very much like me”, 6 meaning “not like me at all”, to 0
meaning “not like me at all” and 5 meaning “very much like me”. The aim of these operations
was to turn all the variables into stimulants.
2. For variables T.1 and T.2 the answers with the values: 77 (“Refusal”), 88 (“Don't know”), 99
(“No answer”); and for variables N.1 and V.1 the answers with the values 7 (“Refusal”), 8
(“Don't know”), 9 (“No answer”) have been removed, and these answers were counted as
invalid cases, and were excluded from calculations.
3. Data prepared in that way was weighted using post-stratification weight including design
weight (pspwght)4 by multiplying the value of the answers of each individual by a given post-
stratification weight for that individual.
4. The weighted individual data was aggregated to a regional level (NUTS-2) by the calculation
of the arithmetic average of values of all weighted individual answers from given regions.
Because of the limited availability of European Social Survey data, data for all chosen
indicators was collected for years 2008, 2010, 2012 so as to avoid missing data imputation for Social
Capital indicators.
All of the data was normalised in two ways:
1. according to the Min-Max5 normalisation formula [Nardo et all, 2005, p. 48]:
𝐼𝑞𝑟𝑡 =
𝑥𝑞𝑟𝑡 −𝑚𝑖𝑛𝑡∈𝑇𝑚𝑖𝑛𝑟(𝑥𝑞
𝑡 )
𝑚𝑎𝑥𝑡∈𝑇𝑚𝑎𝑥𝑟(𝑥𝑞𝑡 )−𝑚𝑖𝑛𝑡∈𝑇𝑚𝑖𝑛𝑟(𝑥𝑞
𝑡 ) (1)
Where:
𝑥𝑞𝑟𝑡 - value of 𝑞-th indicator in 𝑡-th year for 𝑟-th region
Minimum (𝑚𝑖𝑛) and maximum (𝑚𝑎𝑥) were calculated for each indicator both across all
regions and across the whole time of the analysis.
The normalized indicators 𝐼𝑞𝑟𝑡 have values between 0 and 1.
2. according to the “distance to a reference region” formula [Nardo et all, 2005, p. 48]:
4 Data from ESS should always be weighted, as we are informed: “In general, you must weight tables before quoting percentages from them. The Design weights (DWEIGHT) adjust for different selection probabilities, while the Post-stratification weights (PSPWGHT) adjust for sampling error and non-response bias as well as different selection probabilities. Either DWEIGHT or PSPWGHT must always be used. In addition, the Population size weights (PWEIGHT) should be applied if you are looking at aggregates or averages for two or more countries combined. See the guide Weighting European Social Survey Data for fuller details about which weights to use” [ESS website]. 5 The first choice of author was the Min-Max normalisation method, but in the later phase of the composite indicators construction difficulties connected with this normalisation method were faced, which is the reason for also applying the other normalisation method.
11
𝐼𝑞𝑟𝑡 =
𝑥𝑞𝑟𝑡
𝑥𝑞𝑟=𝑡 (2)
Where:
𝑥𝑞𝑟𝑡 – value of 𝑞-th indicator in 𝑡-th year for 𝑟-th region
the reference region () is a region with the highest value of a given indicator across the
whole time of the analysis.
The Cronbach coefficient alpha (c-alpha) was applied as one of the methods of multivariate
analysis. Results of c-alpha for normalised data are presented in table 3. C-alpha was calculated for
each of the subcomponents separately (Human Capita, Social Capita)6, and for all the indicators
together as well (table 3.). For the Min-Max normalised data c-alpha is higher, in all cases, than 0.7,
and it can be assumed “that the sub-indicators are measuring the same underlying construct” [Nardo
et al., 2005, p.27]. In the case of the second normalisation method, c-alpha is below 0.7 but some
researchers accept results of above 0.6 [Nardo et al., 2005, p.27]. C-alpha results also justify the
conducting of a two stage aggregation using Min-Max normalised data, because Human and Social
Capital can be considered as sub-indexes. In case of the second normalisation method, a two stage
aggregation cannot be applied.
Table 3. Cronbach coefficient alpha results
Normalisation method (Abbreviation) Min-Max (M) Distance to a reference
region (D)
Human Capital (K.1, K.2, H.1) 0.705278 0.371554
Social Capital (T.1, T.2, N.1, V.1) 0.740250 0.737509
All indicators (K.1, K.2, H.1, T.1, T.2, N.1, V.1, II.1) 0.733705 0.656126
Source: own calculation
The two main aggregation methods proposed by the OECD [2008, p. 31-33] – linear and
geometric - were applied in order to discuss the influence of aggregation methods for the ratings of
the regions under examination and so as to use aggregated indicators in further analysis (for a
methods description see table 4.). The geometric average is considered useful in trying to avoid the
compensability of the indicator’s performance, but its use may be impeded by the chosen
normalisation method7. „Poor performance in some indicators can be compensated by sufficiently
high values of other indicators” provided the method of additive aggregation is used [Nardo et al.,
2005, p.79]. However, regions “with low scores in some sub-indicators would prefer a linear rather
than a geometric aggregation (…). On the other hand, the marginal utility from an increase in low
absolute score would be much higher than in a high absolute score under geometric aggregation”
[Nardo et all, 2005, p. 80].
6 C-alpha for Structural Capital cannot be calculated because this component contains only from one variable. 7 In using Min-Max normalisation method results are in 0-1 scale, and appearance of 0 makes using geometric average virtually impossible.
12 Table 4. Aggregation methods applied in composite indicators construction
Abbreviation Method description Formula
IC.AM Arithmetic average of the
normalised Min-Max
data. With a two-stage
aggregation, when the
first arithmetic average is
counted for Human,
Social and Structural
Capitals (formulas 4, 5,
6), and in the second
stage the Regional
Intellectual Capital Index
is calculated as arithmetic
average of Human,
Social and Structural
Capitals (formula 3).
𝐼𝐶. 𝐴𝑀𝑟𝑡 =
𝐻𝐶𝑟𝑡+𝑆𝐶𝑟
𝑡+𝑆𝑡𝐶𝑟𝑡
3 (3)
𝐻𝐶𝑟𝑡 =
𝐾.1𝑟𝑡 +𝐾.2𝑟
𝑡 +𝐻.1𝑟𝑡
3 (4)
𝑆𝐶𝑟𝑡 =
𝑇.1𝑟𝑡 +𝑇.2𝑟
𝑡 +𝑁.1𝑟𝑡 +𝑉.1𝑟
𝑡
4 (5)
𝑆𝑡𝐶𝑟𝑡 =
𝐼𝐼.1𝑟𝑡
1 (6)
IC.GM The geometric average of
the normalised Min-Max
data. With two-stage
aggregation, when the
first geometric average is
counted for the Human,
Social and Structural
Capitals (formulas 7, 8,
9), and in the second
stage Regional
Intellectual Capital Index
is calculated as
geometric average of
Human, Social and
Structural Capitals
(formula 10). Where 0
values were replaced by
0.001 value (as low as
the lowest value of all
indicators).
𝐼𝐶. 𝐺𝑀𝑟𝑡 = √𝐻𝐶𝑟
𝑡 ∗ 𝑆𝐶𝑟𝑡 ∗ 𝑆𝐶𝑟
𝑡3 (7)
𝐻𝐶𝑟𝑡 = √𝐾. 1𝑟
𝑡 ∗ 𝐾. 2𝑟𝑡 ∗ 𝐻. 1𝑟
𝑡3 (8)
𝑆𝐶𝑟𝑡 = √𝑍. 1𝑟
𝑡 ∗ 𝑍. 2𝑟𝑡 ∗ 𝑁. 1𝑟
𝑡 ∗ 𝑉. 1𝑟𝑡4 (9)
𝑆𝑡𝐶𝑟𝑡 = √𝐼𝐼. 1𝑟
𝑡1 (10)
IC.AD The arithmetic average of
data normalised using the
“distance to a reference
region” method, as an
average of all indicators
(formula 11).
𝐼𝐶. 𝐴𝐷𝑟𝑡 =
𝐾.1𝑟𝑡 +𝐾.2𝑟
𝑡 +𝐻.1𝑟𝑡 +𝑇.1𝑟
𝑡 +𝑇.2𝑟𝑡 +𝑁.1𝑟
𝑡 +𝑉.1𝑟𝑡 +𝐼𝐼.1𝑟
𝑡
8 (11)
IC.GD Geometric average of
data normalised using the
“distance to a reference
region” method, as an
average of all
indicators(formula 12).
𝐼𝐶. 𝐺𝐷𝑟𝑡 =
√𝐾. 1𝑟𝑡 ∗ 𝐾. 2𝑟
𝑡 ∗ 𝐻. 1𝑟𝑡 ∗ 𝑍. 1𝑟
𝑡 ∗ 𝑍. 2𝑟𝑡 ∗ 𝑁. 1𝑟
𝑡 ∗ 𝑉. 1𝑟𝑡 ∗ 𝐼𝐼. 1𝑟
𝑡8 (12)
Source: own elaboration
A sensitivity analysis of the Regional Intellectual Capital Index was conducted. The
Spearman’s rank correlation coefficiency results are presented in table A1. and table A2. in the
13 Appendix. It can be seen that the changing of the normalisation or aggregation method does not
change the results statistically. Nevertheless, as will be elaborated in the next chapter, the changing
of the normalisation or aggregation method significantly influences the regions positions in a ranking.
5. The Regional Intellectual Capital Index of the Visegrad Countries’ regions – results and discussion
The ranking of the Visegrad Countries’ Regions according to the value of the Regional
Intellectual Capital Index in year 2008 is presented in table 5. (For tables with ranking of the Visegrad
Countries’ Regions according to the value of the Regional Intellectual Capital Index in the years 2010
and 2012 see tables A4. and A5. in the Appendix). Five of the best performing regions, according to
value of RICI in 2008 (independent on normalisation and aggregation methods), were the four capital
cities’ regions (CZ01, HU10, PL12, SK01) and one further region from Poland (PL21). In 2010 the
first six positions (independent on normalisation and aggregation methods) were occupied by two
Czech regions (CZ01, CZ02), one Hungarian region (HU10), two Polish regions (PL12, PL51) and
one Slovakian region (SK01). In 2012 first five positions in the ranking are different ( and depend on
the methods of normalisation and aggregation, and therefore, in the case of the IC.AM method five,
the best regions are: CZ01, CZ02, HU10, PL63, SK01, and in case of IC.GD method they are the
following: CZ01, CZ02, CZ05, PL12, PL63.
Table 5. Ranking of Visegrad Countries Regions according to value of Regional Intellectual Capital Index in year 2008
Region IC.AM IC.GM IC.AD IC.GD Rank diferrences
value rank value rank value rank Value rank IC.AM-
-IC.GM
IC.AM-
-IC.AD
IC.GM-
-IC.GD
IC.AD-
-IC.GD
CZ018 0.733 1 0.722 1 0.873 1 0.870 1 0 0 0 0
CZ02 0.472 6 0.429 6 0.719 7 0.685 7 0 -1 -1 0
CZ03 0.375 20 0.329 17 0.661 17 0.633 16 3 3 1 1
CZ04 0.418 12 0.181 29 0.721 6 0.642 14 -17 6 15 -8
CZ05 0.334 23 0.301 20 0.615 25 0.589 24 3 -2 -4 1
CZ06 0.457 7 0.403 7 0.709 10 0.683 8 0 -3 -1 2
CZ07 0.362 21 0.305 19 0.659 19 0.633 17 2 2 2 2
CZ08 0.390 16 0.342 15 0.669 16 0.626 19 1 0 -4 -3
HU10 0.524 3 0.498 3 0.742 3 0.713 4 0 0 -1 -1
HU21 0.221 32 0.130 33 0.532 35 0.491 33 -1 -3 0 2
HU22 0.225 31 0.156 31 0.534 34 0.480 35 0 -3 -4 -1
HU23 0.180 34 0.149 32 0.534 33 0.491 34 2 1 -2 -1
HU31 0.205 33 0.110 34 0.567 31 0.516 31 -1 2 3 0
HU32 0.174 35 0.159 30 0.546 32 0.512 32 5 3 -2 0
HU33 0.263 28 0.234 25 0.594 28 0.549 29 3 0 -4 -1
8 For explanation of region codes see table 3. in the Appendix
14
PL11 0.378 18 0.287 22 0.701 11 0.644 13 -4 7 9 -2
PL12 0.478 5 0.461 5 0.735 5 0.725 3 0 0 2 2
PL21 0.507 4 0.467 4 0.738 4 0.700 5 0 0 -1 -1
PL22 0.328 24 0.321 18 0.612 26 0.589 23 6 -2 -5 3
PL31 0.277 26 0.212 27 0.626 23 0.599 21 -1 3 6 2
PL32 0.265 27 0.221 26 0.571 30 0.533 30 1 -3 -4 0
PL33 0.232 30 0.047 35 0.597 27 0.563 26 -5 3 9 1
PL34 0.410 13 0.370 11 0.689 14 0.659 10 2 -1 1 4
PL41 0.309 25 0.285 23 0.619 24 0.589 22 2 1 1 2
PL42 0.353 22 0.346 14 0.651 21 0.634 15 8 1 -1 6
PL43 0.431 9 0.368 12 0.712 9 0.660 9 -3 0 3 0
PL51 0.435 8 0.395 8 0.718 8 0.687 6 0 0 2 2
PL52 0.402 14 0.363 13 0.689 13 0.659 11 1 1 2 2
PL61 0.251 29 0.237 24 0.578 29 0.552 27 5 0 -3 2
PL62 0.376 19 0.293 21 0.694 12 0.648 12 -2 7 9 0
PL63 0.398 15 0.390 9 0.651 20 0.628 18 6 -5 -9 2
SK01 0.564 2 0.546 2 0.756 2 0.750 2 0 0 0 0
SK02 0.429 10 0.341 16 0.659 18 0.585 25 -6 -8 -9 -7
SK03 0.426 11 0.377 10 0.675 15 0.622 20 1 -4 -10 -5
SK04 0.385 17 0.210 28 0.629 22 0.551 28 -11 -5 0 -6
Source: own calculations
Regions with the highest (higher than 5) differences in rank are underlined. Differences, in
most cases, are influenced by both the normalisation and aggregation methods. Looking to the
values of the normalised data of all indicators for these regions (see figure A3. in the Appendix), it
may be observed that in case of regions in which the arithmetic average bestows a higher value of
RICI and therefore a higher rank, the value of two or three indicators was very low in comparison to
the rest of the indicators. This is consistent with the limitations of the arithmetic average as a concept,
which were pointed out in the methodological introduction.
Differences in ranks and values caused by the chosen method of normalisation are also
noticeable. In the case of the “distance to a reference region method”, the dispersion of values is
lower than in case of the method of Min-Max normalisation. Using both methods, the highest value
is 1, but the lowest value of each normalised indicator is different in the “distance to a reference
region” method (from 0.13 to 0.91), and it is equal to 0 in Min-Max method. As a consequence, the
dispersion of the values of RICI is also lower in case of the “distance to a reference region” method
(the lowest value from all years of analysis is 0.48, the highest is 0.86) than in the Min-Max
normalisation method (the lowest value from all years of analysis is 0.04, the highest 0.75). The
comparison of basic measures of dispersion as standard deviation, variance and range (statistics)
confirms this observation (see table 6. and 7.). The values of all the measures are lower in the case
of distance to a reference region method.
Table 6. Value of measures of dispersion for all normalised indicators
15
Normalisation
method
Measure of
dispersion
Indicator
K.1 K.2 H.1 T.1 T.2 N.1 V.1 II.1
Min-Max variance 0.037 0.054 0.041 0.035 0.048 0.057 0.046 0.040
standard
deviation
0.193 0.232 0.203 0.188 0.219 0.239 0.214 0.199
range
(𝑚𝑎𝑥 − 𝑚𝑖𝑛)
1 1 1 1 1 1 1 1
Distance to a
reference
region
variance 0.025 0.040 0.000 0.010 0.014 0.013 0.015 0.015
standard
deviation
0.158 0.201 0.018 0.100 0.119 0.115 0.122 0.122
range
(𝑚𝑎𝑥 − 𝑚𝑖𝑛)
0.819 0.864 0.088 0.533 0.543 0.483 0.570 0.613
Source: own calculations
Table 7. Value of measures of dispersion for RICI for both aggregation methods
Normalisation method Measure of dispersion Aggregation method
Arithmetic average Geometric average
Min-Max variance 0,016 0,020
standard deviation 0,126 0,143
range (𝑚𝑎𝑥 − 𝑚𝑖𝑛) 0,578 0,681
Distance to a reference
region
variance 0,005 0,006
standard deviation 0,070 0,079
range (𝑚𝑎𝑥 − 𝑚𝑖𝑛) 0,341 0,389
Source: own calculations
The method of distance to a reference region was applied because of difficulties with using
the Min-Max normalised data to calculate the geometric average. Nevertheless, the changing of the
normalisation method influences the regions’ ranking significantly, something which had not been
expected by the author.
These results lead to an assumption that different normalisation and aggregation methods,
even though the results of all calculations are statistically highly correlated (compare table A1. and
A2. in the Appendix), can influence not only the regions’ rankings, but also further the use of the
composite indicator. In order to verify this assumption, the Pearson product-moment correlation
coefficient (Pearson's r) between RICI (measured in different ways) and data of GDP in euros per
inhabitant (GDP, Eurostat code: nama_10r_2gdp), patents applications to the EPO per million
inhabitants (PAT, Eurostat code: pat_ep_rtot) and persons employed in science and technology as
percentage of active population (HRST, Eurostat code: hrst_st_rcat) for the years 2008, 2010 and
2012 for 35 NUTS-2 Visegrad Countries regions were calculated.
Table 8. Pearson's r results between Regional Intellectual Capital Index measured in different ways (IC.AM, IC.GM, IC.AD, IC.GD) and GDP in euro per inhabitant (GDP), patent applications per million inhabitant (PAT), persons employed in science and technology as percentage of active population (HRST)
Indicator Regional Intellectual Capital Index
IC.AM 2008 IC.GM 2008 IC.AD 2008 IC.GD 2008
GDP 2008 0.757 0.739 0.675 0.720 PAT 2008 0.523 0.550 0.461 0.517
16
HRST 2008 0.810 0.825 0.740 0.796 Indicator Regional Intellectual Capital Index
IC.AM 2010 IC.GM 2010 IC.AD 2010 IC.GD 2010
GDP 2010 0.747 0.715 0.689 0.693 PAT 2010 0.582 0.532 0.531 0.515 HRST 2010 0.773 0.758 0.726 0.750 Indicator Regional Intellectual Capital Index
IC.AM 2012 IC.GM 2012 IC.AD 2012 IC.GD 2012
GDP 2012 0.726 0.579 0.636 0.631 PAT 2012 0.411 0.389 0.425 0.428 HRST 2012 0.742 0.639 0.686 0.689
Source: own calculation
The results presented in table 8. show that the chosen normalisation and aggregation
methods also influence the further analysis with a constructed composite indicator (the lowest
Pearson's r is displayed in bold font). The range (𝑚𝑎𝑥 − 𝑚𝑖𝑛) of the values of Pearson's r varies
from 0.039 to 0.147. On the one hand, it may be observed that in 2008 and 2010 the values of
Pearson's r for both Min-Max normalised indexes (IC.AM and IC.GM) are similar, but on the other
hand in 2012 the results for the “distance to the referenceregion” indexes (IC.AD and IC.GD) are
closer, and the correlation of IC.GM with the analysed indicator is the lowest. Therefore, it is not
clear whether a normalisation method or an aggregation method causes these differences in
correlation results (compare scatter plots on figure A2. in the Appendix).
6. Summary and further works
The main objective of the conducted research project9 is to build an index of intellectual
capital on the regional level and to measure it for the European Union’s regions. The limited data
availability for social capital indicators of the NUTS-2 European Union’s regions (based on the
European Social Survey database) allowed for me to present the preliminary results of the research
project for 35 NUTS-2 Visegrad Countries regions. However, data availability is not the only difficulty
in the measurement of Regional Intellectual Capital. As was demonstrated above, the normalisation
and the aggregation methods applied in the construction of composite indicator can influence the
regions ranking, something which may lead to different research conclusions.
The second objective of the research project is to analyse the relation between the Regional
Intellectual Capital Index and the regional economic and innovation performance. In the case of
correlation analysis between RICI measured in different ways and economic (GDP in euro per
inhabitant) and innovation (patent applications per million inhabitants, and persons employed in
science and technology as a percentage of the active population) indicators can be noticed that both
9 Text prepared based on preliminary results of research project “Intellectual Capital of the European Union Regions”. Research project is realized at the University of Bremen and financed by DAAD (German Academic Exchange Service) as a Research Grant for Doctoral Candidates and Young Academics and Scientists.
17 normalisation and aggregation methods cause varying values of Person’s r, but it is not clear which
influence is more significant.
Further work will concentrate on the estimation of econometric panel data models, so as to
analyse whether the Regional Intellectual Capital Index may be said to be a factor in regional growth
or regional innovation performance. But before that, methodological difficulties faced in Regional
Intellectual Capital Index measurement (as composite indicator) should be resolved.
18
Appendix Table A1. Sperman’s rho results for different normalisation methods
Indicators
with
normalisation
method
Sperman’s rho Indicators
with
normalisation
method
Sperman’s rho
K.1 (M) K.1 (M, 0
replaced
by 0.001)
K.1 (D) K.2 (M) K.2 (M, 0
replaced
by 0.001)
K.2 (D)
K.1 (M) 1.000000 1.000000 1.000000 K.2 (M) 1.000000 1.000000 1.000000
K.1 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000 K.2 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000
K.1 (D) 1.000000 1.000000 1.000000 K.2 (D) 1.000000 1.000000 1.000000
Indicators
with
normalisation
method
Sperman’s rho Indicators
with
normalisation
method
Sperman’s rho
H.1 (M) H.1 (M, 0
replaced
by 0.001)
H.1 (D) T.1 (M) T.1 (M, 0
replaced
by 0.001)
T.1 (D)
H.1 (M) 1.000000 1.000000 1.000000 T.1 (M) 1.000000 1.000000 1.000000
H.1 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000 T.1 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000
H.1 (D) 1.000000 1.000000 1.000000 T.1 (D) 1.000000 1.000000 1.000000
Indicators
with
normalisation
method
Sperman’s rho Indicators
with
normalisation
method
Sperman’s rho
T.2 (M) T.2 (M, 0
replaced
by 0.001)
T.2 (D) N.1 (M) N.1 (M, 0
replaced
by 0.001)
N.1 (D)
T.2 (M) 1.000000 1.000000 1.000000 N.1 (M) 1.000000 1.000000 1.000000
T.2 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000 N.1 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000
T.2 (D) 1.000000 1.000000 1.000000 N.1 (D) 1.000000 1.000000 1.000000
Indicators
with
normalisation
method
Sperman’s rho Indicators
with
normalisation
method
Sperman’s rho
V.1 (M) V.1 (M, 0
replaced
by 0.001)
V.1 (D) II.1 (M) II.1 (M, 0
replaced
by 0.001)
II.1 (D)
V.1 (M) 1.000000 1.000000 1.000000 II.1 (M) 1.000000 1.000000 1.000000
V.1 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000 II.1 (M, 0
replaced by
0.001)
1.000000 1.000000 1.000000
V.1 (D) 1.000000 1.000000 1.000000 II.1 (D) 1.000000 1.000000 1.000000
Source: own calculation
Table A2. Sperman’s rho results for different aggregation methods
Aggregation
method
Spearman’s rank correlation coefficiency.
Bolded correlation ranks are significant with p <05000
IC.AM IC.GM IC.AD IC.GD
IC.AM 1.000000 0.952851 0.887021 0.860792
IC.GM 0.952851 1.000000 0.883558 0.917862
IC.AD 0.887021 0.883558 1.000000 0.967012
IC.GD 0.860792 0.917862 0.967012 1.000000
Source: own calculation
19 Table A3. Regions NUTS-2 codes
Region code Region name
ČESKÁ REPUBLIKA Czech Republic
CZ01 Praha
CZ02 Strední Cechy
CZ03 Jihozápad
CZ04 Severozápad
CZ05 Severovýchod
CZ06 Jihovýchod
CZ07 Strední Morava
CZ08 Moravskoslezsko
MAGYARORSZÁG Hungary
HU10 Közép-Magyarország
HU21 Közép-Dunántúl
HU22 Nyugat-Dunántúl
HU23 Dél-Dunántúl
HU31 Észak-Magyarország
HU32 Észak-Alföld
HU33 Dél-Alföld
POLSKA Poland
PL11 Lódzkie
PL12 Mazowieckie
PL21 Malopolskie
PL22 Slaskie
PL31 Lubelskie
PL32 Podkarpackie
PL33 Swietokrzyskie
PL34 Podlaskie
PL41 Wielkopolskie
PL42 Zachodniopomorskie
PL43 Lubuskie
PL51 Dolnoslaskie
PL52 Opolskie
PL61 Kujawsko-Pomorskie
PL62 Warminsko-Mazurskie
PL63 Pomorskie
SLOVENSKO Slovakia
SK01 Bratislavský kraj
SK02 Západné Slovensko
SK03 Stredné Slovensko
SK04 Východné Slovensko
Source: based on REGULATION (EC) No 1059/2003 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 26 May 2003 on the establishment of a common classification of territorial units for statistics (NUTS)
20 Figure A1. Value of individual normalised indicators for regions with the highest (higher than 5) differences in rank in 2008
21
Source: own calculation
Table A4. Ranking of Visegrad Countries Regions according to value of Regional Intellectual Capital Index in year 2010
Region IC.AM IC.GM IC.AD IC.GD Rank diferrences
value rank value rank value rank value rank IC.AM- -IC.GM
IC.AM- -IC.AD
IC.GM- -IC.GD
IC.AD- -IC.GD
CZ01 0.738 1 0.724 1 0.847 1 0.842 1 0 0 0 0
CZ02 0.630 3 0.597 4 0.782 5 0.749 5 -1 -2 -1 0
CZ03 0.518 11 0.495 11 0.708 11 0.680 12 0 0 -1 -1
CZ04 0.499 15 0.435 19 0.712 9 0.666 17 -4 6 2 -8
CZ05 0.518 12 0.495 12 0.707 12 0.682 11 0 0 1 1
CZ06 0.535 8 0.518 8 0.698 17 0.679 14 0 -9 -6 3
CZ07 0.493 16 0.471 14 0.685 19 0.665 18 2 -3 -4 1
CZ08 0.408 29 0.401 25 0.651 28 0.628 25 4 1 0 3
HU10 0.624 5 0.556 6 0.765 6 0.716 6 -1 -1 0 0
HU21 0.362 32 0.297 32 0.596 33 0.549 33 0 -1 -1 0
HU22 0.404 31 0.328 31 0.631 31 0.570 31 0 0 0 0
HU23 0.247 35 0.127 35 0.547 35 0.504 35 0 0 0 0
HU31 0.336 33 0.254 33 0.625 32 0.565 32 0 1 1 0
HU32 0.259 34 0.223 34 0.566 34 0.521 34 0 0 0 0
HU33 0.429 27 0.338 30 0.681 21 0.610 29 -3 6 1 -8
PL11 0.416 28 0.396 26 0.683 20 0.654 20 2 8 6 0
PL12 0.626 4 0.620 3 0.794 4 0.786 3 1 0 0 1
PL21 0.546 7 0.526 7 0.712 8 0.689 9 0 -1 -2 -1
PL22 0.513 13 0.500 9 0.709 10 0.691 8 4 3 1 2
PL31 0.435 25 0.426 20 0.694 18 0.677 15 5 7 5 3
PL32 0.482 18 0.442 18 0.674 27 0.635 24 0 -9 -6 3
PL33 0.433 26 0.415 21 0.680 22 0.652 21 5 4 0 1
PL34 0.479 20 0.457 15 0.705 13 0.682 10 5 7 5 3
PL41 0.504 14 0.489 13 0.705 14 0.679 13 1 0 0 1
PL42 0.453 24 0.443 17 0.680 23 0.659 19 7 1 -2 4
PL43 0.530 9 0.500 10 0.729 7 0.692 7 -1 2 3 0
PL51 0.616 6 0.581 5 0.803 3 0.771 4 1 3 1 -1
PL52 0.458 22 0.380 29 0.676 25 0.645 22 -7 -3 7 3
PL61 0.408 30 0.389 27 0.642 30 0.614 28 3 0 -1 2
PL62 0.479 19 0.452 16 0.704 15 0.666 16 3 4 0 -1
PL63 0.456 23 0.408 23 0.649 29 0.636 23 0 -6 0 6
22
SK01 0.695 2 0.667 2 0.805 2 0.786 2 0 0 0 0
SK02 0.529 10 0.406 24 0.700 16 0.618 27 -14 -6 -3 -11
SK03 0.479 21 0.413 22 0.677 24 0.621 26 -1 -3 -4 -2
SK04 0.490 17 0.384 28 0.675 26 0.603 30 -11 -9 -2 -4
Source: own calculations
Table A5. Ranking of Visegrad Countries Regions according to value of Regional Intellectual Capital Index in year 2012
Region IC.AM IC.GM IC.AD IC.GD Rank diferrences
value rank value rank value rank value rank IC.AM- -IC.GM
IC.AM- -IC.AD
IC.GM- -IC.GD
IC.AD- -IC.GD
CZ01 0.752 1 0.728 1 0.845 1 0.838 1 0 0 0 0
CZ02 0.718 2 0.700 2 0.836 2 0.820 2 0 0 0 0
CZ03 0.596 8 0.571 6 0.755 7 0.738 6 2 1 0 1
CZ04 0.463 25 0.429 22 0.675 22 0.649 21 3 3 1 1
CZ05 0.650 6 0.618 4 0.799 3 0.774 3 2 3 1 0
CZ06 0.595 9 0.558 8 0.731 12 0.719 10 1 -3 -2 2
CZ07 0.533 16 0.508 12 0.707 14 0.690 13 4 2 -1 1
CZ08 0.575 10 0.554 9 0.753 9 0.736 7 1 1 2 2
HU10 0.668 4 0.571 7 0.773 6 0.719 11 -3 -2 -4 -5
HU21 0.511 21 0.421 23 0.679 21 0.620 22 -2 0 1 -1
HU22 0.492 23 0.410 24 0.671 23 0.613 23 -1 0 1 0
HU23 0.402 32 0.342 31 0.629 31 0.586 32 1 1 -1 -1
HU31 0.341 35 0.169 35 0.602 35 0.535 35 0 0 0 0
HU32 0.417 29 0.357 30 0.651 26 0.599 27 -1 3 3 -1
HU33 0.396 33 0.312 32 0.622 33 0.570 33 1 0 -1 0
PL11 0.481 24 0.435 20 0.704 15 0.659 17 4 9 3 -2
PL12 0.629 7 0.613 5 0.773 5 0.762 5 2 2 0 0
PL21 0.522 19 0.471 18 0.679 20 0.654 19 1 -1 -1 1
PL22 0.512 20 0.472 17 0.689 18 0.662 15 3 2 2 3
PL31 0.565 11 0.545 10 0.754 8 0.730 8 1 3 2 0
PL32 0.461 26 0.364 29 0.640 28 0.595 28 -3 -2 1 0
PL33 0.406 31 0.231 33 0.634 29 0.591 30 -2 2 3 -1
PL34 0.495 22 0.462 19 0.686 19 0.657 18 3 3 1 1
PL41 0.541 15 0.493 14 0.698 17 0.660 16 1 -2 -2 1
PL42 0.430 27 0.376 27 0.628 32 0.592 29 0 -5 -2 3
PL43 0.376 34 0.219 34 0.603 34 0.559 34 0 0 0 0
PL51 0.557 13 0.536 11 0.739 11 0.711 12 2 2 -1 -1
PL52 0.561 12 0.486 15 0.721 13 0.674 14 -3 -1 1 -1
PL61 0.411 30 0.385 26 0.633 30 0.604 25 4 0 1 5
PL62 0.426 28 0.385 25 0.647 27 0.602 26 3 1 -1 1
PL63 0.657 5 0.643 3 0.784 4 0.768 4 2 1 -1 0
SK01 0.679 3 0.507 13 0.751 10 0.720 9 -10 -7 4 1
SK02 0.528 17 0.431 21 0.664 24 0.605 24 -4 -7 -3 0
SK03 0.549 14 0.479 16 0.704 16 0.650 20 -2 -2 -4 -4
SK04 0.524 18 0.373 28 0.662 25 0.588 31 -10 -7 -3 -6
Source: own calculation
23 Figure A2. Scatter plots of GDP per capita and Regional Intellectual Capital Index for year 2008, 2010,
and 2012
24
Source: own preparation
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