Comparison of country risk, sustainability and economic safety indices/Salies rizikos, tvarumo ir ekonominio saugumo rodikliu palyginimas.
Stankeviciene, Jelena ; Sviderske, Tatjana ; Miecinskiene, Algita 等
Introduction
Every year it becomes more and more difficult to analyse and
predict changes in the financial, economic, and political sectors of
business. The importance of country risk analysis is now more
understandable and potential for it is growing by establishing a growing
number of country risk rating agencies, which combine a wide range of
qualitative and quantitative information regarding alternative measures
of economic, financial and political risk with associated composite risk
ratings. However, the accuracy of any rating agency with regard to any
or all of these measures is open to questioning. In the study, Hoti
(2005a) provides a qualitative comparison of country risk rating systems
used by seven leading rating agencies, as well as a novel analysis of
four risk ratings using univariate and multivariate volatility models
for nine East European countries. These ratings are compiled by the
International Country Risk Guide, which is the only risk rating agency
to provide consistent monthly data on a large number of countries since
1984. The empirical results enable a comparative assessment of the
conditional means and volatilities associated with county risk returns,
defined as the rate of change in country risk ratings, across the
aforementioned nine East European countries.
Over the past two decades, interest has grown in developing
indicators to measure sustainability. Sustainability is presently seen
as a delicate balance between the economic, environmental and social
health of a community, nation and of course the earth. At present,
measures of sustainability tend to be an amalgam of economic,
environmental and social indicators. Economic indicators have been used
to measure the state of the economy for much of this century. Social
indicators are largely a post-war phenomenon and environmental
indicators are more recent still. Interest in developing these
indicators largely began when their respective became stressed, aiming
to monitor performance and indicate any required ameliorating action.
Whereas economists have no difficulty deriving objective and
quantitative indicators, sociologists had and still have great
difficulty in deriving indicators, because of intangible quality of life
issues. Environmental scientists have less difficulty when limiting
themselves to abundance of single species rather than biodiversity and
ecological integrity.
Sustainability, however, is more than just the interconnectedness
of the economy, society and the environment. Although, these are
important, they are largely the external manifestations of
sustainability. The internal, fundamental and existential dimensions are
neglected. Sustainability, therefore, may be something more grand and
noble: dynamics, a state of collective grace, a facet of Gaia or even of
the Spirit. Rather than asking how we can measure sustainability, it may
be more appropriate to ask how we measure up to sustainability.
1. Definitions of country risk
For some researchers, country risk refers to the "probability
of occurrence of political events that will change the prospects for
profitability of a given investment" (Haendel et al. 1975). One of
approaches adopts a practical stance and analyses risk as a negative
outcome. With this meaning, risk will exist if it implies a possible
loss or at least, a potential reduction of the expected return, as
stated by Meldrum (2000).
The concept of risk has different meanings and could be understood
either as a performance variance or just as the likelihood of a negative
outcome that reduces the initially expected return. The concept of
downside risk was already mentioned in Markowitz (1959); though, it is
mainly because of computational difficulties in handling this type of
model as well as the assumption of normally distributed returns that the
variance was favoured as a measure of risk. The paper of Nawrocki (1999)
reviews the literature and presents the advantages of using a downside
risk approach in view of a total risk stance.
Roy (1952) and Bawa and Lindenberg (1977) had already integrated
the notion of downside risk into portfolio theory, but Estrada (2000)
and Reuer and Leiblein (2000) have emphasised the usefulness of the
downside risk approach for studying emerging markets and international
joint ventures. Quer, Claver and Rienda (2007) have introduced an
integrated approach by comparing the impact of country risk and cultural
distance on entry mode choice. Busse and Hefeker (2006) have also
analysed the risk and its influence of foreign direct investments.
Literature analysis of the last 40 years revealed changes in
country risk analysis emergent from an increasing number of companies
conducting their business abroad. This situation results in specific
risks, no matter the source of risk or the nature of the industry.
Without a doubt, specific features of each investment or transaction
type must be taken into account. Country risk analysis (CRA) tries to
define the potential for these risks in order to decrease the expected
return of a cross-border investment. Such definition re-j oins the very
early articles of Gabriel (1966) or Stobaugh (1969) where the
investigation was made on difference in investment climate at home and
abroad--in a foreign country. It highlights the specific risks
associated with doing business abroad, outside the national borders of
the company's country of origin. Sometimes, the economic level of
country's development is not so important, as even economically
developed countries can face a degree of country risk. As Finnerty
(2001) noted "many project finance professionals would argue that
natural resource projects in the United States are exposed to political
risk because of the proclivity within the United States to change the
environmental laws and apply the new laws retroactively".
A comprehensive formulation of country risk theory is yet in
progress. Until now, research literature has usually been indicating the
implicit assumption that for a given country, imbalances in the
economic, social and political fields are likely to increase the risk of
investing. Because of the multiplicity of risk sources, the complexity
of their interactions and the variety of social sciences involved, an
underlying theory of country risk is still missing. Such a
conceptualisation would greatly help in identifying variables at stake.
It would make it possible to test the respective relevance of various
approaches on offer. So far, most of the research has merely consisted
of a classification and description of various potential sources of
risk, and the assessment methods have turned these elements into
numerical variables without any scientific justification. Fitzpatrick
(1983) writes on the subject "the literature is found to define
political event risk rather than political risk". Citron and
Nickelsburg (1987) have proposed a country risk model for foreign
borrowing as well as estimated the one that incorporates a political
instability variable. The proposed model predicts high probabilities of
default for most of the actual default dates for six countries looking
from the historical perspective. This is suggestive of how to understand
the phenomenon of foreign debt default. There are many studies related
to country risk, its financial integration in a country, the impact on
economic and other aspects of country's welfare (Cathy, Goldberg
2009; Kesternich, Schnitzer 2010; Benitez et al. 2007; Bordo et al.
2009; D'Argensio, Laurin 2009).
2. Evaluation of country risk
The country risk of one country could be expressed by a single
index, which shows the degree of the overall risk to invest in or loan
to this country. Two types of indices that represent the degree of
country risk--discrete and continuous --exist. Discrete type includes
several risk levels, which are predefined and every country is in one
level. The number of risk levels may vary from 1 to 20. The single index
representing the degree of country risk is a set of different factors
about the country. The main interested factors are political and
economic-financial ones, and the total number of factors used may vary
from less than ten to more than twenty.
Ratha et al. (2011) suggest predicting sovereign ratings for
developing countries that do not have risk ratings from agencies (such
as Fitch, Moody's, and Standard and Poor's). It is important
to determine the volume and cost of capital flows to developing
countries through international bond, loan and equity markets. Sovereign
rating also acts as a ceiling for the foreign currency rating of
sub-sovereign borrowers and can be important for their access to
international debt and equity capital. Shadow ratings for several
developing countries that have never been rated could be generated and
result in a finding that unrated countries do not always remain at the
bottom of the rating spectrum. Several of them will be in a similar
range to that of the emerging market economies with capital market
access.
Chen, Gang and Jianping (2008) proposed a new approach for country
risk evaluation, which is based on the multicriteria decision aid method
MH DIS (MultiGroup Hierarchical Discrimination). They took a sample
consisting of 40 main oil-producing countries and used it to estimate
the performance of the method in classifying the countries into two
groups. A comparison with multiple discriminant analysis, logit analysis
and probit analysis were also performed. The results indicated the
superiority of the MH DIS method as opposed to these traditional
discrimination techniques already applied in country risk assessment.
Similarly, Cathy and Goldberg (2009) introduced their point of view on
country risk and financial integration by presenting a case study.
Marshall et al. (2009) have estimated and determined the country risk of
an emerging market as well as dynamic conditional correlation by using
GARCH model, which could be one of alternatives for country risk
evaluation.
In her paper, Schroeder (2008) also surveys the history and current
status of country risk assessment. The goal is to understand why it is
that country risk assessors have such poor track record in anticipating
the onset of financial crises. The development of the field reflects
changes in the composition of international capital flows. These changes
have confounded a definition of country risk, especially if a definition
is centred on a particular event. It is then argued that the field has
reached an impasse, and this impasse is related to the methods of
abstraction and the current crisis of vision within the science of
economics. This crisis of vision, as it pertains to theories of
financial crises, has led to increased reliance on quantitative methods
in the field of country risk. Thus, it is very important to find the
object of country risk assessment, which is not to monitor for a
particular event or symptom of financial crisis but rather to monitor
for a particular state of the economy. Besten (2007) has introduced an
analysis on similar risk assessment approaches for European countries.
3. Euromoney Country Risk Index
Euromoney Country Risk (Euromoney ... 2013) evaluates the
investment risk of 186 countries across 15 criteria (or factors) to
determine the risks of default on a bond, losing direct investment or to
global business relations, by polling more than 400 international
economists and other risk experts. The qualitative scores are averaged
and combined with three basic quantitative values to give an overall ECR
score on a 100-point scale, where 100 is the safest and 0--the riskiest.
Evaluation includes such risk as: default on a bond, losing direct
investment, risk posed to global business relations, etc., by taking a
qualitative model, which seeks an expert opinion on risk variables
within a country (70% weighting) and combining it with three basic
quantitative values (30% weighting).
Factors included in the ranking of countries by risk:
--Political risk;
--Economic performance/projections;
--Structural assessment;
--Debt indicators;
--Credit ratings;
--Access to bank finance;
--Access to capital markets.
Euromoney assigns a weighting to six categories (Euromoney ...
2013). The three qualitative expert opinions are political risk (30%
weighting), economic performance (30%) and structural assessment (10%).
The three quantitative values are debt indicators (10%), credit ratings
(10%) and access to bank finance/capital markets (10%).
The qualitative average. The qualitative average is produced by
combining evaluations of political, economic and structural assessments
from experts around the world. When applying political, economic and
structural assessments to a 100 point scale for the qualitative average
only (rather than the full Euromoney Country Risk score), the following
weighting is used: political 43%, economic 43% and structural 14%
(Euromoney ... 2013).
Qualitative assessments. Economic risk: participants rate each
country of which they have knowledge from 0-10 across 6 sub-factors to
equal a score out of 100. The categories of economic risk scored are as
follow: bank stability/risk; GNP outlook; unemployment rate; government
finances; and monetary policy/currency stability. Political risk:
participants rate each country of which they have knowledge from 0-10
across 5 sub-factors to equal a score out of 100. The categories of
political risk scored are as follow: corruption; government
non-payments/nonrepatriation; government stability; information access/
transparency; institutional risk; regulatory and policy environment.
Structural risk: participants rate each country of which they have
knowledge from 0-10 across 4 sub-factors to equal a score out of 100.
The categories of structural risk scored are as follow: demographics;
hard infrastructure; labour market/industrial relations; and soft
infrastructure. Individual experts must apply a value to each sub-factor
before their score is accepted into the system. Individual experts can
also modify sub-factor weights to modify their effect on the overall
score of 100. The weight of an individual sub factor can be lowered to a
minimum of 10% and to a maximum of 30%. This allows the system to
capture a second attribute alongside of the evaluation of that category,
which is the estimated effect of the category. For instance, a user may
judge that the single most important issue facing a given country is
maintaining the stability of its currency, thus deciding to increase the
weighting of the monetary policy/currency stability category from 20% to
30%. Within each sub factor, ECR also asks experts for further
information on the reasons behind each individual score, and these fall
under the category of related factors. These are more like poll points,
and do not directly affect the score. Instead, they inform a change made
to a sub-factor score and weight. For example, within the economic risk
category of bank stability lie four further related factors: regulatory
risk, trading exposures, asset quality and undercapitalisation.
Individual experts are able to add more related factors and ignore the
ones that are not applicable (Euromoney ... 2013).
The quantitative score factors. Access to bank finance/ capital
markets: participants rate each country's accessibility to
international markets on a scale of 0-10 (0 = no access at all and 10 =
full access). These scores are averaged and then weighted to 10%. Debt
indicators: calculated using the following ratios from the World
Bank's Global Development Finance figures: total debt stocks to GNP
(A), debt service to exports (B); current account balance to GNP (C).
Developing countries that do not report complete debt data score a zero.
Credit ratings: nominal values are assigned to sovereign ratings from
Moody's, Standard & Poor's and Fitch IBCA. The ratings are
converted into a score using a set scoring chart. This score is then
averaged and the score is weighted to 10%. The higher the average value,
the better (Euromoney ... 2013).
4. Indicators and indices of sustainability
For the past two decades, there have been many local, regional,
state/provincial, national and international efforts to find useful
sustainability indicators. The key feature of some of these suggested
indicators is that they are defined through public participation.
Therefore, these indicators are meaningful to a respective community.
However, indicators based on asymmetric information and the
heterogeneous interests of stakeholders often make them incomparable,
and therefore, less usable in other environments. International
Institute for Sustainable Development (IISD) hosts and manages the
compendium of sustainable development indicator initiatives around the
world. Currently, the site provides information on 669 initiatives (IISD
2006).
The UN Commission on Sustainable Development (UNCSD) from its
working list of 134 indicators derived a core set of 58 indicators for
all countries to use. The CSD is currently updating this set of
indicators. A universal set of indicators can be defined but local
sustainability concerns should be addressed in assessing the
sustainability of an economic activity (Meadows 1998). Recent
initiatives include the development of aggregate indices, headline
indicators, goal-oriented-indicators, and green accounting systems.
Early composite indices include Measure of Economic Welfare (MEW), Index
of Social Progress (ISP), Physical Quality of Life Index (PQLI), and
Economic Aspects of Welfare (EAW) and challenges the concept of
distinguishing economic welfare from non-economic welfare (Dewan 2006).
Indices developed in the 1990s to measure the aggregate performance
of the economy or the sustainability include Human Development Index
(HDI) by the UNDP (1990), Sustainable Progress Index (SPI), Ecological
Footprint, Material Input Per Service Unit (MIPS), Index for Sustainable
Economic Welfare (ISEW), Genuine Progress Indicator (GPI), Genuine
Savings Indicator (GSI), Barometer of Sustainability, and Environmental
Pressure Indicators (EPI) (Dewan 2006).
The Consultative Group on Sustainable Development Indicators
(CGSDI) at IISD as part of their effort to create "an
internationally accepted sustainable development index" produced
the Dashboard of Sustainability, a performance evaluation tool, in 2001.
More recently developed indices include Total Material Requirement,
Eco-efficiency Indices, the Compass of Sustainability, Environmental
Sustainability Index (ESI) and Environmental Performance Index (EPI).
Most of these indices are not used by policy-makers due to measurement,
weighting, and indicator selection problems. However, some of them are
popular among different stakeholders (Dewan 2006).
Two distinct methodologies can be found in all of these. Mainstream
economists use monetary aggregation method, whereas scientists and
researchers in other disciplines prefer to use physical indicators.
Economic approaches include greening the GDP, resource accounting based
on their functions, sustainable growth modelling, and defining weak, and
strong sustainability conditions. For example, recently developed ISEW
and GPI are corrections of the National Income (NI) accounts for
environmental and some other non-market activities to reflect Hicksian
income (Dewan 2006).
Some of the indicators that are unaccounted for, or not accounted
for as costs, in the GDP, but are included in either ISEW or GPI as
'defensive expenditures' are private expenditures on health
and education; costs of commuting, urbanization and auto accidents;
costs of different types of pollution, depletion of non-renewable
resources and long term environmental damage; the value of volunteer
work; and the costs of crime, family breakdown, underemployment, etc.
(Dewan 2006).
5. The European Economic Sustainability Index (EESI)
In light of the unprecedented turmoil in the eurozone and the
uncertainty over what the future holds, it is important to not only
understand the current pressures on public finances but also the medium-
to long-term factors which will affect the economic stability and
sustainability of EU countries in the future. The long-term
competitiveness of European economies, their governance and their
ability to carry out structural reforms to cope with long-term
challenges will all influence whether countries have a sustainable
economy in the long-run. This will also determine the success or failure
of the euro. To assess the economic sustainability of Europe's
economies, the EPC has developed an index to assess simultaneously the
short-, medium- and long-term economic sustainability of EU countries
relative to each other. This index is constructed using six domains:
deficits, national debt, growth, competitiveness, governance/corruption,
and cost of ageing.
To examine economic sustainability in more detail, the European
Policy Centre developed the European Economic Sustainability Index
(EESI) in 2010. This Policy Brief updates the EESI with the most recent
data. Not only does it take into account deficits (average 2011-2012)
and debt levels (2011), but also considers growth forecasts (average
2011-2012). Furthermore, the EESI is oriented towards the long term: it
incorporates the Global Competitiveness Index (2011), the Corruption
Perceptions Index (2011) and the Labour Market Adjusted Dependency Ratio
(2011). These indicate how an economy is likely to perform in the
future. All these different factors are combined in the EESI to produce
a relative ranking for all EU-27 countries.
Of course, no index can fully capture how a country's economy
is likely to perform. There are always issues linked to each component
of such an index: what are the appropriate indicators? Any analysis that
fails to take into account indicators of long-term performance is both
incomplete and misleading. The trajectory of the crisis will also depend
on these long-term factors. A poor performance in the index doesn't
mean there is no chance of economic sustainability in the long term.
Rather, the index suggests that those countries at the bottom of the
ranking need to focus more on implementing the kind of reform that
boosts efficiency and growth. It also suggests that these countries will
need to do more to invest in future growth, and some of this investment
will need to come from their stronger European partners.
One of the key questions surrounding any index is its sensitivity
to any changes in the weight of its various domains. If more emphasis is
put on short-term indicators (deficits and growth) and less on long-term
indicators (Corruption Perceptions Index and Global Competitiveness
Index), it tends to improve the position of the CEE-MS: for example,
Latvia's and Bulgaria's rankings would improve significantly.
At the same time, Ireland, France and the UK would all fall
significantly in the rankings.
These indicators have been chosen to reflect a balance between
short-, medium- and long-term pressures on economic sustainability. They
have to be available in all EU Member States and ideally -updated on a
regular basis. They also have to enable a clear ranking i.e. there has
to be a clearly identifiable performance scale which enables a ranking
from high performance to low performance.
6. Theoretical approach to economic security
A successful state is a state that exports more than imports.
Historically, the main reason for export promotion was the only way for
a state to accumulate substantial amounts of gold, which was the symbol
of power. Having power meant being stable and secure. No enemy would
attack a rich state as riches meant power. Gold guaranteed peace and
stability. Mercantilist view on economic stability and security emerged
from the point of view of a state. The powerful rich state was a
warrantor for stability and welfare. This method of trade is known as
zero sum game (only one can gain) (Udovic 2011).
Reassuming this we can point out that for mercantilists, the
crucial security was state security and they did not acknowledge other
types of security or other possible insecurities (such as environmental,
political, personal ect.). They also realised that the political
instability emerged from economic instability, because the primary goal
of a state was trade and economic welfare. If the latter was not
achieved then people were unsatisfied. Discontentment (that arose from
economic instability) provoked riots, wars and revolutions. Svetlicic
and Rojec (2002) explain, "security depends equally on reality and
perception and it is today understood and guaranteed as "economic
and political stability, social cohesion, democracy and employment.
Security is a state of mind and that it strongly depends on others and
not only on oneself."
Simple explanation (obviously, subject to many possible objections)
is that "economic security is a never-ending (and not a standstill)
process, firstly determined by macroeconomic environment, which is
strictly connected with and effects the mezo level (firms and
enterprises); and determines the micro level (individual needs) economic
security. This last, through perception that (personal) economic
security exists, and is fixed and stable, directly and indirectly exerts
influence on the macroeconomic environment, which becomes, for the sake
of confidence, even more stable, secure and consecutively reproduces the
economic security feelings through "hard macroeconomic
indexes" (inflation rate, employment ...) back to the micro
economic level. The circle of reproduction is infinite" (Udovic
2011).
Damijan (1996) established its own criteria called Aggregate value
of state (AVS), which is composed of three variables: (1) percentage of
the state area in the entire world area, (2) percentage of the
population in the entire world population and (3) percentage of the
national GDP in the global GDP. The result is not the sum, but the
weighted sum with weights 0.108; 0.205 and 0.976 (Udovic 2011).
7. Analysis of indices
The main task is to find out the relationship between country risk,
economic sustainability, and economic security (Fig. 1).
In order to prove the relationship, each ratio from the box was
analysed. The ratios taken are Euromoney country risk index for
evaluation of country risk, European economic sustainability index for
evaluation of economic sustainability, and aggregate value of state
index for evaluation of economic security. All ratios of European Union
member states for 2011 were analysed.
The results of aggregated valuation of three indices and ranking by
each index are presented in Table 2.
We consider n elements to be compared, [C.sub.1] ... [C.sub.n] and
denoting the relative "weight" (or priority or significance)
of [C.sub.i] with respect to [C.sub.j] by [a.sub.ij] and forming a
square matrix A = ([a.sub.ij]) of order n with the constraints that
[a.sub.ij] = 1/[a.sub.ji], for i [not equal to] j, and [a.sub.ii] = 1,
all i. Such a matrix is said to be a reciprocal matrix.
[FIGURE 1 OMITTED]
The weights are consistent if they are transitive, that is
[a.sub.ik] = [a.sub.ij][a.sub.jk] for all i, j, and k. Such a matrix
might exist if the [a.sub.ij] are calculated from exactly measured data.
Then, find a vector [omega] of order n such that A[omega] =
[lambda][omega]. For such a matrix, co is said to be an eigenvector (of
order n) and [lambda] is an eigenvalue. For a consistent matrix,
[lambda] = n.
As the field of interest is the Baltic States, we have summarised
the data (Table 3).
These indices should be compared with each other, for the reason a
Table 4 with three attributes is presented as a matrix.
The eigenvector of the relative importance or value of each index
is (0.089; 0.642; 0.270). Thus, sustainability index is the most
valuable, while the country risk index and economic security index are
behind.
The next stage is to calculate [[lambda].sub.max] to lead to the
Consistency Index and the Consistency Ratio. First, multiply on the
right the matrix of judgements by the eigenvector, obtaining a new
vector. The calculation for the first row in the matrix is:
6'0.089+1'0.642+3'0.270 = 1.983 and the remaining two
rows give 0.661 and 0.330. This vector is of three elements (1.983;
0.661; 0.330); the product Ad according to the AHP theory is A[omega] =
[[lambda].sub.max][omega]), so now it is possible to get three estimates
of [[lambda].sub.max] by simply dividing each component of (1.983;
0.661; 0.330) by the corresponding eigenvector element. This gives
1.983/0.089 = 22.33 together with 1.03 and 1.23. The mean of these
values is 8.20 and that is our estimate for [[lambda].sub.max]. If any
of the estimates for [[lambda].sub.max] turns out to be less than n, or
8 in this case, there has been an error in the calculation, which is a
useful sanity check.
The Consistency Index for a matrix is calculated from
([[lambda].sub.max] - n)/(n - 1) and, since n = 3 for this matrix, the
CI is 2.6. The final step is to calculate the Consistency Ratio for this
set of judgments using the CI for the corresponding value from large
samples of matrices of purely random judgments using the table below,
derived from Saaty's book (2010), in which the upper row is the
order of the random matrix, and the lower is the corresponding index of
consistency for random judgments.
For this case, it gives 2.6/1.41 = 1.84. Saaty (2010) argues that
CR > 0.1 indicates that the judgments are at the limit of consistency
though had to be accepted sometimes. It means that calculated results
are rather relevant for making of conclusions.
8. Concluding remarks
1. The aim of this study was to develop a system, which based on
existing research, mainly on indices and multicriteria evaluation
methodology, could be used for complex valuation of country risk,
sustainability and economic safety. It was demonstrated that the
proposed aggregation system of three indicators--Euromoney country risk
index, European economic sustainability index and Aggregate value of
state index of 27 EU countries--offers the possibility to compare and
benchmarking of each country according to the complex valuation of main
risk drivers.
2. The proposed complex valuation system of country risk,
sustainability and economic safety could be used to evaluate and
standardise country risk, sustainability, and economic safety as a ratio
system, reference point and multiplicative form appropriately suitable
for cases, where there are several alternatives (EU countries or the
Baltic States), and several objectives.
3. Later studies could explore new methods for country risk
assessment and sustainability evaluation (for example, MOORA and
MULTMOORA) and compare results to those received by using the method.
Additionally, a new investigation on the interrelationship between
country risk, sustainability and economic safety could be introduced.
Caption: Fig. 1. Interdependence between ratios (Source: compiled
by the authors)
http://dx.doi.org/10.3846/btp.2014.01
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Jelena Stankeviciene (1), Tatjana Sviderske (2), Algita
Miecinskiene (3)
Vilnius Gediminas Technical University, Sauletekio al. 11, LT-10223
Vilnius, Lithuania E-mails: 1jelena.stankeviciene@vgtu.lt (corresponding
author); 2tatjana.sviderske@vgtu.lt; (3) algita.miecinskiene@vgtu.lt
Received 11 November 2013; accepted 09 January 2014
Iteikta 2013-11-11; priimta 2014-01-09
Jelena STANKEVICIENE is an Associate Professor at the Department of
Finance Engineering at Vilnius Gediminas Technical University
(Lithuania). Her main research topics include assets and liability
management, regulation of financial institution, financial management
for value creation, value engineering.
Tatjana SVIDERSKE is a PhD student at Vilnius Gediminas Technical
University in Lithuania. Her research areas are country risk assessment
and management.
Algita MIECINSKIENE is an Associate Professor at the Department of
Finance Engineering at Vilnius Gediminas Technical University
(Lithuania). Her main research topics pricing, foreign direct
investment, greenfield investments, mergers and acquisitions.
Table 1. Six indicators, which are included in EESI
Indicator domain Description
GDP growth (a) Annual change in GDP
(average of two years)
Debt levels (b) Total government debt
measured as a percentage
of GDP--part of the
so-called Maastricht or
Convergence Criteria of
Economic and Monetary
Union
Deficit/surplus (c) Government's net
borrowing requirement,
i.e. the difference
between revenues and
expenditure--part of the
so-called Maastricht or
Convergence Criteria of
Economic and Monetary
Union
Global Competitive A composite indicator,
Index (World capturing microeconomic
Economic Forum) (d) and macro-economic
foundations of
competitiveness, defined
"as the set of
institutions, policies,
and factors that
determine the level of
productivity of a
country. The level of
productivity, in turn,
sets the sustainable
level of prosperity that
can be earned by an
economy (e)
Corruption Perception "Measures the perceived
Index (f) level of public-sector
(Transparency corruption in 180
International) countries and territories
around the world. The CPI
is a "survey of surveys",
based on 13 different
expert and business
surveys" (g)
Future cost of ageing Long-term expenditure
projections covering
pensions, health care,
long-term care, education
and unemployment
transfers for all Member
States (h)
Indicator domain Reason for inclusion
GDP growth (a) Short-term indicator of
economic performance and
of ability to repay debt
Debt levels (b) Medium- to long-term
indicator of public
finance performance
Deficit/surplus (c) Short-term indicator of
public finance
performance
Global Competitive Long-term index of
Index (World competitiveness and
Economic Forum) (d) future growth potential
Corruption Perception Underlying index of
Index (f) governance/rule of law
(Transparency and proxy for public
International) sector efficiency
Future cost of ageing Very long-term indicator
of public finance
pressure and proxy for
structural reform
Source: compiled by the authors.
Table 2. EU countries ranking based on
three criteria: Euromoney Country Risk
Index, European Economic Sustainability
Index and Aggregate Value of State Index
for 2011
No. EU country Euromoney
Country Risk
Index
Overall Rank
score
1 Austria 84.36 7
2 Belgium 76.78 10
3 Bulgaria 53.77 24
4 Cyprus 75.56 11
5 Czech Republic 74.52 13
6 Denmark 89.07 2
7 Estonia 57.50 22
8 Finland 87.31 4
9 France 81.42 8
10 Germany 85.73 6
11 Greece 49.72 26
12 Hungary 58.75 21
13 Ireland 63.38 19
14 Italy 70.60 17
15 Latvia 52.38 25
16 Lithuania 57.18 23
17 Luxembourg 90.86 1
18 Malta 74.49 14
19 Poland 71.15 16
20 Portugal 60.73 20
21 Romania 49.59 27
22 Slovakia 73.82 15
23 Slovenia 74.92 12
24 Spain 66.53 18
25 Sweden 88.72 3
26 The Netherlands 86.97 5
27 United Kingdom 80.21 9
No. EU country European
Economic
Sustainability
Index
Overall Rank
score
1 Austria 0.26 7
2 Belgium 0.05 9
3 Bulgaria -0.17 18
4 Cyprus -0.01 11
5 Czech Republic -0.10 13
6 Denmark 0.51 2
7 Estonia 0.36 5
8 Finland 0.51 2
9 France 0.00 10
10 Germany 0.32 6
11 Greece -0.88 26
12 Hungary -0.21 19
13 Ireland -0.15 16
14 Italy -0.47 25
15 Latvia -0.14 14
16 Lithuania -0.04 12
17 Luxembourg 0.37 4
18 Malta -0.24 21
19 Poland -0.14 15
20 Portugal -0.23 20
21 Romania -0.26 22
22 Slovakia -0.31 24
23 Slovenia -0.15 17
24 Spain -0.27 23
25 Sweden 0.76 1
26 The Netherlands 0.46 3
27 United Kingdom 0.16 8
No. EU country Aggregate
value of State
Index
Overall Rank
score
1 Austria 0.5766 10
2 Belgium 0.4109 9
3 Bulgaria 0.3158 25
4 Cyprus 0.2813 24
5 Czech Republic 0.2070 16
6 Denmark 0.1064 13
7 Estonia 0.0868 23
8 Finland 0.0851 11
9 France 0.0693 2
10 Germany 0.0617 1
11 Greece 0.0534 12
12 Hungary 0.0457 17
13 Ireland 0.0446 15
14 Italy 0.0381 4
15 Latvia 0.0303 21
16 Lithuania 0.0250 19
17 Luxembourg 0.0250 22
18 Malta 0.0123 27
19 Poland 0.0084 8
20 Portugal 0.0078 14
21 Romania 0.0070 26
22 Slovakia 0.0060 18
23 Slovenia 0.0048 20
24 Spain 0.0033 5
25 Sweden 0.0020 7
26 The Netherlands 0.0018 6
27 United Kingdom 0.0011 3
Source: compiled by the authors based on
http://www.euromoneycountryrisk.com;
http://www.epc.eu and Damijan's criteria
(1996).
Table 3. Baltic States indices
EU country Country Sustainability Economic
risk index index security index
Estonia 57.50 0.36 0.0868
Latvia 52.38 -0.14 0.0303
Lithuania 57.18 -0.04 0.0250
Source: compiled by the authors.
Table 4. Matrix with weights for each country
Indices Estonia Latvia Lithuania
Country risk index 6 1 3
Sustainability index 2 1/3 1
Economic security index 1 1/6 1/2
Total
Indices Root of Eigenvector
product
of values
Country risk index 0.363 0.089
Sustainability index 2.621 0.642
Economic security index 1.101 0.270
Total 3.931 1.000
Source: compiled by the authors.
Table 5. Indices of consistency for random judgments
1 2 3 4 5 6 7 8 9 10
0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49
