An Empirical Assessment of Country Risk Ratings and Associated Models

Michael John McAleer
Erasmus Universiteit Rotterdam, Netherlands; National Tsing Hua University, Taiwan
ABSTRACT Country risk has become a topic of major concern for the international financial community over the last two decades. The importance of country ratings is underscored by the existence of several major country risk rating agencies, namely the Economist Intelligence Unit, Euromoney, Institutional Investor, International Country Risk Guide, Moody's, Political Risk Services, and Standard and Poor's. These risk rating agencies employ different methods to determine country risk ratings, combining a range of qualitative and quantitative information regarding alternative measures of economic, financial and political risk into associated composite risk ratings. However, the accuracy of any risk rating agency with regard to any or all of these measures is open to question. For this reason, it is necessary to review the literature relating to empirical country risk models according to established statistical and econometric criteria used in estimation, evaluation and forecasting. Such an evaluation permits a critical assessment of the relevance and practicality of the country risk literature. The paper also provides an international comparison of risk ratings for twelve countries from six geographic regions. These ratings are compiled by the International Country Risk Guide, which is the only rating agency to provide detailed and consistent monthly data over an extended period for a large number of countries. The time series data permit a comparative assessment of the international country risk ratings, and highlight the importance of economic, financial and political risk ratings as components of a composite risk rating. Copyright Blackwell Publishers Ltd, 2004.

 SourceAvailable from: Michael John McAleer[Show abstract] [Hide abstract]
ABSTRACT: Country risk reflects the ability and willingness of a country to service its financial obligations. This paper analyses the country risk returns, or the rate of change in the risk ratings compiled by the International Country Risk Guide, which provides extended monthly data for numerous countries. A constant conditional correlation asymmetric VARMAGARCH model is estimated and tested for Australia, Canada, Japan and the USA. The empirical results enable an analysis of the conditional means and volatilities of risk returns, highlight the importance of the economic, financial and political components of a composite risk rating, and evaluate the spillover effects of risk returns.  SourceAvailable from: Ivan Montiel[Show abstract] [Hide abstract]
ABSTRACT: This conceptual article looks at corporate responsibility (CR) and country risk claiming that there is a relationship, and then positing the directionality of the relationship. An understanding of this relationship can help firms respond to a variety of pressures from organizations and this knowledge may help firms prevent negative media coverage with the need to “bolt” CR strategies on to existing corporate strategies. When firms have an understanding of how country risk affects them, they can plan entire clusters of CR initiatives to fulfill needs within the operating community. To understand the CR–country risk relationship, the authors build on Matten and Moon’s (2008) distinction between implicit and explicit CR. The first argument is that firms engage in no explicit CR (explicit CR that is voluntary and goes beyond legal requirement) when country risk is very high. As country risk lowers to high, firms engage in explicit CR, which creates little impact to the firm if CR must be withdrawn. The second argument is that as country risk shifts to moderate, firms commence to engage in high levels of explicit CR and low levels of implicit CR. The third argument concludes that when country risk shifts to low or very low, firms will engage in the least amount of explicit CR and the most amount of implicit CR. A set of three propositions develops these arguments.Business & Society 10/2014; 53(5):625651. · 1.94 Impact Factor
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Following the rapid growth in the international debt of less developed countries
in the 1970s and the increasing incidence of debt rescheduling in the early 1980s,
country risk, which reflects the ability and willingness of a country to service its
financial obligations, has become a topic of major concern for the international
financial community (Cosset and Roy, 1991). Political changes resulting from the
AN EMPIRICAL ASSESSMENT OF
COUNTRY RISK RATINGS AND
ASSOCIATED MODELS
Suhejla Hoti and Michael McAleer
University of Western Australia
Abstract. Country risk has become a topic of major concern for the international
financial community over the last two decades. The importance of country ratings
is underscored by the existence of several major country risk rating agencies,
namely the Economist Intelligence Unit, Euromoney, Institutional Investor, Inter
national Country Risk Guide, Moody’s, Political Risk Services, and Standard and
Poor’s. These risk rating agencies employ different methods to determine country
risk ratings, combining a range of qualitative and quantitative information regard
ing alternative measures of economic, financial and political risk into associated
composite risk ratings. However, the accuracy of any risk rating agency with regard
to any or all of these measures is open to question. For this reason, it is necessary
to review the literature relating to empirical country risk models according to
established statistical and econometric criteria used in estimation, evaluation and
forecasting. Such an evaluation permits a critical assessment of the relevance and
practicality of the country risk literature. The paper also provides an international
comparison of risk ratings for twelve countries from six geographic regions. These
ratings are compiled by the International Country Risk Guide, which is the only
rating agency to provide detailed and consistent monthly data over an extended
period for a large number of countries. The time series data permit a comparative
assessment of the international country risk ratings, and highlight the importance
of economic, financial and political risk ratings as components of a composite
risk rating.
Keywords. Country risk; Economic risk; Financial risk; Political risk; Composite
risk; Risk ratings; Risk returns; Volatilities; Component analysis; International
comparison.
1. Introduction
1.1. Country risk
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ators sometimes threaten to repudiate their ‘borrowings’ (Bourke, 1990).
Political risk is generally viewed as a nonbusiness risk introduced strictly
by political forces. Banks and other multinational corporations have identified
political risk as a factor that could seriously affect the profitability of their
international ventures (Shanmugam, 1990). Ghose (1988) argues that political
risk is analogous to sovereign risk and lies within the broader framework of
country risk. Political risk emerges from events such as wars, internal and external
fall of communism, and the implementation of marketoriented economic and
financial reforms, have resulted in an enormous amount of external capital
flowing into the emerging markets of Eastern Europe, Latin America, Asia, and
Africa (Ramcharran, 1999). These events have alerted international investors to
the fact that the globalisation of world trade and open capital markets are risky
elements that can cause financial crises with rapid contagion effects, which
threaten the stability of the international financial sector (Hayes, 1998). In light
of the tumultuous events flowing from September 11, 2001, the risks associated
with engaging in international relationships have increased substantially, and
become more difficult to analyse and predict for decision makers in the economic,
financial and political sectors.
Given these new developments, the need for a detailed assessment of country
risk and its impact on international business operations is crucial. Country risk
refers broadly to the likelihood that a sovereign state or borrower from a
particular country may be unable and/or unwilling to fulfil their obligations
towards one or more foreign lenders and/or investors (Krayenbuehl, 1985). A
primary function of country risk assessment is to anticipate the possibility of debt
repudiation, default or delays in payment by sovereign borrowers (Burton and
Inoue, 1985). Country risk assessment evaluates economic, financial, and political
factors, and their interactions in determining the risk associated with a particular
country. Perceptions of the determinants of country risk are important because
they affect both the supply and cost of international capital flows (Brewer and
Rivoli, 1990).
Country risk may be prompted by a number of countryspecific factors or
events. There are three major components of country risk, namely economic,
financial and political risk. The country risk literature holds that economic,
financial and political risks affect each other. As Overholt (1982) argues, inter
national business scenarios are generally politicaleconomic as businesses and
individuals are interested in the economic consequences of political decisions.
The lending risk exposure visa ` vis a sovereign government is known as sovereign
risk (Juttner, 1995). According to Ghose (1988), sovereign risk emerges when a
sovereign government repudiates its overseas obligations, and when a sovereign
government prevents its subject corporations and/or individuals from fulfilling
such obligations. In particular, sovereign risk carries the connotation that the
repudiation occurs in situations where the country is in a financial position to
meet its obligations. However, sovereign risk also emerges where countries are
experiencing genuine difficulties in meeting their obligations. In an attempt to
extract concessions from their lenders and to improve rescheduling terms, negoti
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Derivative assets, such as futures and options, are used to hedge against price
risk in commodity markets. In particular, country risk ratings are used to hedge
against issued bonds. Optimal hedging strategies and an evaluation of the risk
associated with risk ratings require knowledge of the volatility of the underlying
process. As volatility is generally unknown, it must be estimated. These estimated
and predicted volatilities are fundamental to risk management in financial models
that describe the riskreturn tradeoff. Although there does not yet seem to be an
conflicts, territorial disputes, revolutions leading to changes of government, and
terrorist attacks around the world. Social factors include civil unrests due to
ideological differences, unequal income distribution, and religious clashes.
Shanmugam (1990) introduces external reasons as a further political aspect of
country risk. For instance, if the borrowing nation is situated alongside a country
that is at war, the country risk level of the prospective borrower will be higher
than if its neighbour were at peace. Although the borrowing nation may not be
directly involved in the conflict, the chances of a spillover effects may exist.
Additionally, the inflow of refugees from the war would affect the economic
conditions in the borrowing nation. In practical terms, political risk relates to
the possibility that the sovereign government may impose foreign exchange and
capital controls, additional taxes, and asset freezes or expropriations. Delays in
the transfer of funds can have serious consequences for investment returns,
import payments and export receipts, all of which may lead to a removal of the
forward cover (Juttner, 1995).
Economic and financial risks are also major components of country risk. They
include factors such as sudden deterioration in the country’s terms of trade, rapid
increases in production costs and/or energy prices, unproductively invested
foreign funds, and unwise lending by foreign banks (Nagy, 1988). Changes in
the economic and financial management of the country are also important
factors. These risk factors interfere with the free flow of capital or arbitrarily
alter the expected riskreturn features for investment. Foreign direct investors are
also concerned about disruptions to production, damage to installations, and
threats to personnel (Juttner, 1995).
1.2. Country risk ratings
Since the Third World debt crisis in the early 1980s, commercial agencies such as
Moody’s, Standard and Poor’s, Euromoney, Institutional Investor, Economist
Intelligence Unit, International Country Risk Guide, and Political Risk Services,
have compiled sovereign indexes or ratings as measures of credit risk associated
with sovereign countries. Risk rating agencies provide qualitative and quantita
tive country risk ratings, combining information about alternative measures of
economic, financial and political risk ratings to obtain a composite risk rating.
This paper provides an international comparison of country risk ratings and
returns compiled by the International Country Risk Guide (ICRG), which is
the only risk rating agency to provide detailed and consistent monthly data
over an extended period for a large number of countries.
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at least 50 published empirical papers over the last three decades in refereed
journals. Although the first two papers were published by Frank and Cline
(1971) (Journal of International Economics) and Feder and Just (1977) (Journal
of Development Economics), there were 16 papers published in the 1980s, a
further 30 papers published in the 1990s, and with the 2 most recent papers
having been published in 2001. Thus, the literature is essentially two decades
old. There is no leading journal in the literature on country risk, with the Journal
established market for pricing risk ratings as a primary or derivative asset,
estimating and testing the volatility associated with risk ratings would seem to
be a first step in this direction. In the finance and financial econometrics litera
ture, conditional volatility has been used to evaluate risk, asymmetric shocks, and
leverage effects. The volatility present in risk ratings also reflects risk considera
tions in risk ratings. As risk ratings are effectively indexes, their rate of change (or
returns) merits attention in the same manner as financial returns (for further
details, see Hoti et al., 2002).
The plan of the paper is as follows. Section 2 provides a quantitative classi
fication of empirical country risk models, which forms the database, and also
classifies and describes the data. Various theoretical and empirical model
specifications used in the literature are reviewed analytically and empirically in
Section 3. Section 4 discusses the empirical findings of the published studies. A
comparison of ICRG country risk ratings, risk returns, and their associated
volatilities for twelve representative developing countries is given in Section 5.
Concluding remarks and some suggestions for future research are presented in
Section 6.
2. Classification of Country Risk Models and the Data
For purposes of evaluating the significance of empirical models of country risk, it
is necessary to analyse such models according to established statistical and
econometric criteria. The primary purpose of each of these empirical papers is
to evaluate the practicality and relevance of the economic, financial and political
theories pertaining to country risk. An examination of the empirical impact and
statistical significance of the results of the country risk models will be based on an
evaluation of the descriptive statistics relating to the models, as well as the
econometric procedures used in estimation, testing and forecasting.
This paper reviews 50 published empirical studies on country risk (the papers
are listed in the Appendix). A classification of the 50 empirical studies is given
according to the model specifications examined, the choice of dependent and
explanatory variables considered, the number of explanatory variables used,
econometric issues concerning the recognition, type and number of omitted
explanatory variables, the number and type of proxy variables used when vari
ables are omitted, the method of estimation, and the use of diagnostic tests of the
auxiliary assumptions of the models.
Scrutiny of the ECONLIT software package and the Social Science Citation
Index for the most widely cited articles in the Country Risk literature yields
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Table 1. Classification by Type of Data Used.
of Development Economics publishing 6 papers, the Journal of International
Business Studies publishing 4 papers, the Journal of Banking and Finance,
Economics Letters and Applied Economics each publishing 3 papers, Applied
Economics Letters and Global Finance Journal each publishing 2 papers, and 27
other journals each publishing one paper on the topic.
Country risk has been surveyed previously by Saini and Bates (1984) and Eaton
and Taylor (1986). Saini and Bates (1984) provide a survey of the quantitative
approaches to risk analysis by reviewing the problems in the statistical
approaches of published empirical papers. In particular, they examine the short
comings with regard to definitions of dependent variables, the quality and avail
ability of data, model specifications, appropriateness of statistical methods, and
the ability to forecast debt servicing difficulties adequately. Eaton and Taylor
(1986) review the theoretical aspects of numerous papers relating to LDC debt
and financial crises, with an emphasis on the policy implications to be drawn.
Although they do not analyse the empirical aspects of the various papers, they
examine the three main issues in empirical applications, namely the determinants
of rescheduling, how credit terms are fixed, and the factors determining the
amounts borrowed. While the primary purpose of Rockerbie (1993) is to explain
the interest spread on sovereign Eurodollar loans on the basis of various indica
tors of default risk in lesser developed countries and developed countries, he
provides a useful summary of risk indicators in the empirical papers examined.
Thus, the present paper may be seen as a continuation of these surveys using more
recently published contributions to the literature on country risk.
In Table 1, the 50 studies are classified according to the type of data used,
namely crosssection or pooled, which combines time series and crosssection
samples. Common sources of data are the International Monetary Fund, Bank
for International Settlements, various sources of the World Bank, Euromoney,
Institutional Investor, Moody’s, Standard and Poor’s, and various countryspecific
statistical bureaux. Almost threequarters of the studies are based on pooled data,
with the remaining onequarter based on crosssection data.
Table 2 classifies the 34 studies using pooled data according to the number of
countries, which varies from 5 to 95 countries, with mean 48 and median 47, with
the frequency of occurrence of each number generally being 1. The same 34
studies using pooled data are classified according to the number of annual and
semiannualobservationsinTables3and4,respectively.Fortheannualobservations,
the range of the 19 data sets is 5–24 years, with the mean, median and mode of the
number of observations being 12, 11 and 5, respectively, with the frequency of
Type of dataFrequency
Pooled
Crosssection
Total
34
16
50
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8, 10, 12, 19
9
11
13, 14, 15, 16, 17, 18, 22, 24
Total
occurrenceofeachnumbervaryingbetween1and5.Therangeofthe8datasetsusing
semiannualobservationsis8to38halfyears,withthemeanandmedianandmodeof
the number of observations being 18.5 and 17, respectively.
Tables 5 and 6 classify the studies using crosssection data according to the
number of countries and the number of time series observations, respectively. In
Table 5, 1 study did not report the number of countries used, while another study
used data on 892 municipalities. Of the remaining 16 studies, the range is 18 to
143 countries, with mean 55.3 and median 50.5. There are 29 data sets using time
series observations in Table 6, with range 1 to 23, mean 5.3, median 3, and mode
1. Indeed, the most commonly used number of time series observations is 1, with a
frequency of 10 in the 29 data sets, so that more than onethird of the cross
section data sets used are based on a single year.
3. Theoretical and Empirical Model Specifications
The general country risk model typically estimated, tested and evaluated is given as:
f Yt;Xt;ut;?
ð Þ ¼ 0
ð1Þ
in which f(.) is an unspecified functional form, Y is the designated (vector of)
endogenous variables, X is the (vector of) exogenous variables, u is the (vector of)
errors, ? is the vector of unknown parameters, and t¼1,...,n observations. As
Table 2. Classification of Pooled Data by Number of Countries.
Numbers of countries
Number of
studiesFrequency
5, 16, 17, 19, 24, 25, 26, 30, 32, 39, 41, 43, 48, 54, 55, 56, 60,
65, 68, 74, 75, 80, 85, 90, 95
27, 33, 40, 47, 59, 79
Total
25
6
1
2
37
Note: Three studies used two data sets.
Table 3. Classification of Pooled Data by Number of Annual Observations.
Numbers of observations Frequency
55
3
2
4
1
31
Note: One study used two annual data sets, two studies used one annual data set and one semiannual
data set, and another study used one annual data set, one semiannual data set, and one monthly data set.
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Numbers of countries
will be discussed below, equation (1) is typically given as a linear or loglinear
regression model, or as a logit, probit or discriminant model. The elements of Y
and X will also be discussed below. Defining the information set at the end of
period t?1 as It?1¼[Yt?1,Yt?2,Yt?3,...;Xt,Xt?1,Xt?2,Xt?3,...], the assumptions
of the classical model are typically given as follows:
(A1) E(ut)¼0 for all t;
(A2) constant unconditional and conditional variances of ut;
(A3) serial independence (namely, no covariation between utand usfor t6¼s);
(A4) X is weakly exogenous (that is, there is no covariation between Xtand us
for all t and s);
(A5) u is normally distributed;
(A6) parameters are constant;
(A7) Y and X are both stationary processes, or are cointegrated if both are
nonstationary.
Diagnostic tests play an important role in modern empirical econometrics, and
are used to check the adequacy of a model through testing the underlying
assumptions. The standard diagnostic checks which are used to test assumptions
(A1) through (A7) are various tests of functional form misspecification, hetero
scedasticity, serial correlation, exogeneity, third and higherorder moments of
the distribution for nonnormality, constancy of parameters and structural
change, unit root tests, and tests of cointegration. There is, in general, little or
no theoretical basis in the literature for selecting a particular model of country
risk. In empirical analysis, however, computational convenience and the ease of
Table 4. Classification of Pooled Data by Number of SemiAnnual Observations.
Numbers of observations Frequency
8, 17, 22
16, 38
Total
2
1
8
Note: One study used two semiannual data sets, two studies used one annual data set and one semi
annual data set, and another study used one annual data set, one semiannual data set, and one
monthly data set.
Table 5. Classification of Crosssection Data by Number of Countries.
Frequency
18, 20, 27, 29, 30, 35, 49, 52, 71, 88, 93, 143, 892, unstated
45, 70
Total
1
2
18
Note: One study used three data sets. The sample with 892 observations refers to municipalities rather
than countries.
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Others
interpretation of models are primary considerations for purposes of model
selection.
Of the 70 models used in the 50 studies, which are reported in Table 7, all but
six are univariate models. The most popular model in the literature is the logit
model, which is used 23 times, followed by the probit, discriminant, and Tobit
models, which are used 10, 7, and 3 times, respectively. Thus, more than half
of the models used in the literature are probabilitybased models. Given the
popularity of the linear and loglinear regression models in empirical economic
research, it is surprising to see that the linear regression model is used four times,
the loglinear regression model is used only twice, and both regression models
are used in the same study only twice. The artificial neural network model is
also used twice. Of the remainder, the multigroup hierarchical discrimination
model, twoway error components model, randomeffect error component equa
tions, naive model, combination model, Glogit model, nested trinomial logit,
Table 6. Classification of CrossSection Data by Number of Time Series Observations.
Numbers of observationsFrequency
1
2
3, 7, 8, 10, 11, 23
4, 20
5
Total
10
4
1
2
5
29
Note: More than one time series data set was used in some studies.
Table 7. Classification by Type of Model.
ModelFrequency
Only linear single equations
Only loglinear single equations
Both linear and loglinear single equations
Logit
Probit
Discriminant model
Tobit
System of equations
Artificial neural network model
4
2
2
23
10
7
3
6
2
11
Total70
Note: More than one model was used in some studies and two studies used no model. The ‘Others’
category includes one entry for each of multigroup hierarchical discrimination model, twoway error
components model, randomeffect error component equations, naı¨ve model, combination model,
GLogit model, nested trinomial logit, sequentialresponse logit, unorderedresponse logit, classification
and regression trees, and cluster analysis.
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probability of partial reneging when a borrower has decided to reschedule, trichotomous variable of
debt rescheduling, the probability of general, commercial, official, and band debt rescheduling (in
the current year or in the future), the probability of debt default, and discriminant score of whether
a country belongs to a rescheduling or nonrescheduling group.
3. Refers to Institutional Investor, Euromoney, Standard and Poor’s, Moody’s, and Economist
Intelligence Unit country or municipality credit risk ratings, and average agency country risk
ratings.
4. Includes one entry for each of limit on debt arrears, dummy for significant debt arrears, probability
of experiencing significant debt arrears, and probability of emerging debtservicing arrears.
sequentialresponse logit, unorderedresponse logit, classification and regression
trees, and cluster analysis, are used once each.
The dependent variable for purposes of analysing country risk is broadly
classified as the ability to repay debt. Of the different types of dependent variables
given in Table 8, with more than one dependent variable being used in some
studies, the most frequently used variable is debt rescheduling, which is used 36
times. This dependent variable is defined as the probability of general, commer
cial, and official debt rescheduling or debt default (in the current year or in the
future), and discriminant score of whether a country belongs to a rescheduling or
nonrescheduling group. The second most frequently used variable is agency
country risk ratings, which is used 18 times. In the empirical analyses, this
Table 8. Classification by Type of Dependent Variable Used1.
TypeFrequency
Debt rescheduling2
Agency country risk ratings3
Debt arrears4
(Average) value of debt rescheduling
Exchange rate movements
Fundamental valuation ratios
Demand for debt
Supply of debt
Propensity to obtain agency municipality credit risk ratings
Public debt to private creditors
Total reserves
(Relative) bond spreads
Weighted average loan spread
Spread over LIBOR
Yield spreads of international bonds
Payment interruption likelihood index
Sovereign loan default
Credit risk rating
Income classification
Stock returns
Secondary market price of foreign debt
Dummy for debt crisis
Total
36
18
4
3
3
3
3
3
2
2
2
2
1
1
1
1
1
1
1
1
1
1
91
Notes:
1. More than one dependent variable was used in some studies.
2. Includes variables defined as the probability of debt rescheduling (as proxy for debt default), the
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5, 9, 10
6
8
11, 14, 15, 18, 20, 23, 32
12
Total
dependent variable is defined as Institutional Investor, Euromoney, Standard and
Poor’s, Moody’s, and Economist Intelligence Unit country or municipality risk
ratings, and the average of agency country risk ratings. Ten types of dependent
variable are used more than once, with debt arrears (defined as the limit on
debt arrears), dummy for significant debt arrears, probability of experiencing
significant debt arrears, and probability of emerging debtservicing arrears
being used 4 times each, and average value of debt rescheduling, exchange rate
movements, fundamental valuation ratios, demand for debt, and supply of debt
being used 3 times each. Dependent variables, such as the propensity to obtain
agency municipality credit risk ratings, public debt to private creditors, total
reserves, and total or relative bond spread, are used twice each, with the remain
ing 10 types of dependent variable, which are used once each, including weighted
average loan spread, spread over LIBOR, yield spreads of international bonds,
payment interruption likelihood index, sovereign loan default, credit risk rating,
income classification, stock returns, secondary market price of foreign debt, and
dummy for debt crisis.
There are three types of explanatory variables used in the various empirical
studies, namely economic, financial and political. Treating country risk variables
as economic and/or financial, and regional differences as political, Tables 9 and
10 present the numbers of each type of variable and their frequency. In Table 9,
the number of economic and financial variables ranges from 2 to 32, with mean
11.5, median 8 and mode 6. Seven of the 19 sets of economic and financial
variables have a frequency of one, with a frequency of 2 occurring 3 times, a
frequency of 3 occurring 5 times, and frequencies of 4, 5, and 6 occurring once
each. In Table 10, the number of political variables ranges from 0 to 13, with
mean 1.86, median 0 and mode 0. The absence of any political variable occurs 30
times in the 50 studies.
Of the remaining 10 sets of political variables, 2 have a frequency of 4, one has
a frequency of 3, 2 have a frequency of 2, and five have a frequency of one.
Hundreds of different economic, financial and political explanatory variables
have been used in the 50 separate studies. The set of economic and financial
variables includes indicators for country risk ratings, debt service, domestic and
Table 9. Classification by Number of Economic and Financial Explanatory Variables.
NumberFrequency
2, 3, 7, 13, 16
4
3
4
2
7
5
1
6
50
Note: Country risk indicators are treated as economic and/or financial variables.
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2, 3, 4
8
international economic performance, domestic and international financial perform
ance, monetary reserves, and structural differences. Indicators for country
political risk ratings, domestic and international armed conflict, political events,
and regional differences are used in the set of political variables.
The unavailability of the required data means that proxy variables have fre
quently been used for the unobserved variables. Tables 11 and 12 are concerned
with the important issue of omitted explanatory variables in each of the 50
studies. It is well known that, in general, omission of relevant explanatory vari
ables from a linear regression model yields biased estimates of the coefficients of
the included variables, unless the omitted variables are uncorrelated with each of
the included explanatory variables. For nonlinear models, consistency replaces
unbiasedness as a desirable statistical characteristic of an estimation method. In
some studies, there is an indication of the various types of variables that are
recognised as being important. Nevertheless, some of these variables have been
omitted because they are simply unavailable. The classification in Table 11 is by
recognition of omitted explanatory variables, where the recognition is explicitly
stated in the study. Such an explicit recognition of omitted explanatory variables
is used primarily as a check of consistency against the number of proxy variables
used.
Of the 50 studies in Table 11, exactly threefifths did not explicitly recognise
that any variables had knowingly been omitted, with the remaining 20 studies
Table 10. Classification by Number of Political Explanatory Variables.
NumberFrequency
0
1, 2
3, 8, 10, 11, 13
4, 5
6
Total
30
4
1
2
3
50
Note: Regional differences are treated as political variables.
Table 11. Classification by Recognition of Omitted Explanatory Variables.
Number omitted Frequency
0
1
30
13
2
1
Total 50
Note: The classification is based on explicit recognition of omitted explanatory variables, and is used
primarily as a check of consistency against the number of proxy variables used in the corresponding
studies.
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correctly measured variable is less than in the case where the proxy variable is
excluded; (3) a reduction in measurement error is beneficial; and (4) it is prefer
able to include the proxy variable than to exclude it. When two or more proxy
variables are used to replace two or more variables, which are unavailable, it is
not necessarily the case that the four basic results stated above actually hold.
Thus, among other outcomes, the absolute bias in the estimated coefficients of
both the correctly measured and incorrectly measured variables may be higher if
recognising that 39 explanatory variables had been omitted. The number of
explanatory variables explicitly recognised as having been omitted varies from 1
to 8. Including and excluding the 30 zero entries for omitted explanatory variables
give mean numbers omitted of 0.78 and 1.95, respectively, medians of 0 and 1,
and modes of 0 and 1. Thirteen of the 20 studies, which explicitly recognised the
omission of explanatory variables, noted that a single variable had been omitted.
The classification in Table 12 is given according to the type of omitted explan
atory variable, which is interpreted as predominantly economic and financial or
political. More than twothirds of the omitted explanatory variables are pre
dominantly economic and financial in nature, and the remaining onethird is
predominantly political. Somewhat surprisingly, very few studies stated dynamics
as having been omitted from the analysis, even though most did not explicitly
incorporate dynamics into the empirical specifications.
As important economic, financial and political explanatory variables have been
recognised as having been omitted from twofifths of the 50 studies (see Table 11),
proxy variables have been used in most of these studies. Tables 13 and 14 are
concerned with the issues of the number and type of proxy variables used. The
problems associated with the use of ordinary least squares (OLS) to estimate the
parameters of linear models in the presence of one or more proxy variables are
generally well known in the econometrics literature, but extensions to nonlinear
models, which dominate the literature on country risk, are not yet available.
Nevertheless, as a guide for analysis, the basic results are outlined below. These
results are of special concern as onehalf of the studies explicitly recognises the
omission of at least one explanatory variable.
In the case where only one proxy variable is used to replace a variable which is
unavailable, the wellknown results are as follows: (1) the absolute bias in the
estimated coefficient of the proxy variable is less than the case where the proxy
variable is excluded; (2) the absolute bias in the estimated coefficient of the
Table 12. Classification by Type of Omitted Explanatory Variables.
Omitted variableFrequency
Economic and financial factors
Political factors
Total
28
11
39
Notes: The various omitted variables are classified according to whether they are predominantly
economic and financial or political in nature.
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two or more proxy variables are not used than when they are used, a reduction in
measurement error may not be beneficial, and it may not be preferable to include
two or more proxy variables than to exclude them. The reason for the different
outcomes is that the covariation in two or more measurement errors may exacer
bate the problem of measurement error rather than containing it.
Table 13 classifies the 20 studies by the use of proxy variables, which ranges
from 1 to 7. Including and excluding the 2 zero entries for the number of proxy
variables used give mean numbers omitted of 2.45 and 2.72, respectively, a
median of 2 in each case, and a mode of 1 in each case. By comparison with
Table 11, in which 13 of the 20 studies explicitly recognised the omission of a
single explanatory variable, Table 13 shows that only 7 studies used a single proxy
variable. Otherwise, the results in Tables 11 and 13 are reasonably similar.
The classification in Table 14 is given according to the type of proxy variable
used, which is interpreted as comprising predominantly economic and financial or
political factors. More than twothirds of the proxy variables are predominantly
economic and financial in nature, and the remaining onethird is predominantly
political, which is very similar to the results given in Table 12.
In Table 15 the classification is by method of estimation, in which more than one
estimation method is used in some studies. Five categories are listed, namely OLS,
maximum likelihood (ML), Heckman’s twostep procedure, discriminant methods,
and Others, which includes entries for, among others, propagation algorithm,
regressionbased techniques, approximation, minimax, Bayesian, optimal minimum
distance, stepwise, optimisation, binary splits, jackknife methods andOLS and WLS.
Even though logit, probit, and Tobit models in Table 7 are used 40 times in total, ML
Table 14. Classification by Type of Proxy Variables Used.
Proxy variables Frequency
Economic and financial factors
Political factors
Total
34
15
49
Note: Some studies used economic, financial and political proxy variables.
Table 13. Classification by Number of Proxy Variables Used.
Number Frequency
0, 3, 6
1
2
4, 5, 7
Total
2
7
4
1
20
Note: Two studies explicitly recognized the omission of explanatory variables but used no proxy
variables.
COUNTRY RISK RATINGS
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Type of diagnostics
is used for estimation purposes only 35 times. Moreover, while linear and loglinear
models are used only 7 times in total in Table 7, OLS is used 14 times in Table 15 (15
timesifbothOLSandWLSareincluded).Finally,whilediscriminantmodelsareused
7 times in Table 7, discriminant estimation is used only three times in Table 15.
Finally, the classification in Table 16 is by use of diagnostics to test one or more
auxiliary assumptions of the models. The role of diagnostic tests has become well
established in the econometrics literature in recent years, and plays an increasingly
prominent role in modern applied econometrics (see McAleer (1994) for further
details). Most diagnostic tests of the auxiliary assumptions are standard, and are
available in widely used econometric software packages. Unbelievably, 42 of the 50
studies did not report any diagnostic tests whatsoever. Of the eight which did report
any diagnostic tests at all, there were two entries for White’s standard errors for
heteroscedasticity, and one entry for each of WLS and heteroscedasticity, transfor
mation for nonnormality, White’s covariance matrix for heteroscedasticity, Chow
test, Hajivassiliou’s test for exogeneity, and serial correlation. This is of serious
concern, especially as the ML method is known to lack robustness to departures
from the stated assumptions, but is nevertheless used 35 times. Models such as the
logit and probit are also sensitive to departures from the underlying logistic and
normal densities, respectively, so that the underlying assumptions should be checked
rigorously. Asthe use of diagnosticshasbeen ignored inthe countryrisk literature, in
general, the empiricalresults should be interpreted with some caution and scepticism.
Table 15. Classification by Method of Estimation.
MethodFrequency
OLS
ML
Heckman’s twostep procedure
Discriminant methods
Others
Total
14
35
2
3
17
71
Note: More than one estimation method was used in some studies. The ‘Others’ category includes
entries for, among others, propagation algorithm, regressionbased technique, approximation, mini
max, Bayesian, optimal minimum distance, stepwise optimisation, binary splits, jackknife methods,
and OLS and WLS.
Table 16. Classification by Use of Diagnostics.
Frequencies
None
Others
Total
42
8
50
Note: The ‘Others’ category includes entries for WLS and heteroscedasticity, White’s standard errors
for heteroscedasticity, White’s covariance matrix for heteroscedasticity, Chow test, transformation for
nonnormality, Hajivassiliou test for exogeneity, and serial correlation.
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Variables
4. Empirical Findings for Country Risk Ratings
Of the 91 types of dependent variables used in the 50 studies, 27 studies examined
debt rescheduling on 36 occasions and 17 considered country risk ratings on 18
occasions (see Table 8 for definitions of these two types of variables). Table 17
reports four types of risk component variables used in the 17 country risk ratings
studies, namely economic, financial, political, and composite. Composite risk
variables are ratings or aggregates that comprise economic, financial and political
risk component variables, and were used in all 17 studies. Of these studies, only
two did not use economic variables and only one did not use financial variables.
Political variables have been used less frequently, namely in 10 studies. Table 18
presents the number of country risk components used, as well as their frequency.
All four country risk components have been used in 10 studies, 4 studies used
variables representing three risk components, 3 studies used variables represent
ing two risk components, and no study used variables representing only one risk
component.
In Table 19, the 17 are classified according to the risk rating agency they used,
namely Institutional Investor, Euromoney, Moody’s Standard and Poor’s, Inter
national Country Risk Guide, Economist Intelligence Unit, and Political Risk
Services. These agencies are leading commercial analysts of country risk. While
the rating system for the International Country Risk Guide will be analysed in the
next section, the rating systems for the other agencies are briefly discussed below.
Unless otherwise stated, the information regarding the agency rating systems has
been obtained from the website of Foreign Investment Advisory Service Program,
which is a joint service of two leading multilateral development institutions,
namely the International Finance Corporation and World Bank (http://www.fias.
net/investment_ climate.html).
Institutional Investor compiles semiannual country risk surveys, which are
based on responses provided by leading international banks. Bankers from 75
to 100 banks rate more than 135 countries on a scale of 0 to 100, with 100
representing the lowest risk. The individual ratings are weighted using the Institu
tional Investor formula, with greater weights assigned to responses based on the
extent of a bank’s worldwide exposure and the degree of sophistication of a
bank’s country risk model. The names of the participating banks are kept
strictly confidential (Howell, 2001). Institutional Investor country risk surveys
are published in the March and September issues of the monthly magazine. In the
Table 17. Risk Component Variables Used in Country Risk Ratings.
Frequency
Economic
Financial
Political
Composite
Number of studies
15
16
10
17
17
COUNTRY RISK RATINGS
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# Blackwell Publishing Ltd. 2004
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