View
224
Download
0
Category
Preview:
Citation preview
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 1/50
Determinants of Sovereign Credit Ratings
Using Ordered Logistic Model
Anne Michelle N. Andres
Ruben Carlo O. Asuncion
Myrlani G. Velvez
A Research Method II Paper, Department of Economics - De La Salle University-Manila
August 2005
© 2005 by Anne Michelle N. Andres, R. Carlo O. Asuncion, and Myrlani G. Velvez. All rights
reserved. Sections of the text may be quoted without permission provided that full credit is given
to the source. Comments are welcome at ruben.asuncion@dlsu.edu.ph.
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 2/50
Abstract
This paper focuses on the determinants of sovereign
credit ratings using ordered logistic model. Using Standard
& Poor’s credit ratings and a sample of 61 countries, thestudy has identified three main factors that affect sovereign
credit ratings. These are: a) perception on the level or
degree of corruption as seen by business people and
country analysts; b) gross national income (GNI) per
capita; and c) annual rate of percentage change or the year-
on-year change in the consumer price index (CPI) or
simply the inflation rate. The paper’s bottom line is thatgood governance is the key for countries to have high
sovereign debt ratings.
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 3/50
Table of Contents
Page
I. Introduction 1
II. Objectives of the Study 3
III. Scope and Limitations 3
IV. Significance of the Study 5
V. Review of Related Literature 6
VI. Data Sources and Research Methodology 9
VII. Definition of Variables 12
VIII. Overview on Logistic Modeling 13
IX. Findings/Result Highlights 14
X. Conclusion 34
Annexes 35
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 4/50
I. INTRODUCTION
Sovereign debt, defined as debt incurred by governments, can take the form of
commercial loans or of bond issues. Particularly, developed countries are the largest
issuers of bonds in world capital markets. Moreover, the structure of private capital flow
to developing countries in the 1990s has dramatically changed since bond issues
exceeded bank lending. As a consequence, the demand for sovereign credit ratings, i.e.,
the risk assessments assigned by credit rating agencies to government bonds, has
significantly increased; all the more so as recent years have witnessed a significant
number of debt crises in developing countries. These credit ratings significantly influence
the terms and the extent to which, in developing countries especially, private and public
borrowers have access in international capital markets.
With the recent downgrade of the Philippines’ credit rating by global rating
agencies, Fitch IBCA and Standard & Poor’s (S&P), it is very important for policy
makers to know and identify the relevant determinants or factors that affect or influence
such credit rating organizations. Specifically, the London-based Fitch, which had been
the most optimistic about the Philippines’ prospects among global rating agencies, has
placed the country’s rating on a “negative” outlook since December last year, but
changed this to “stable” in May 2005 because of significant strides in fiscal reform,
including the passage of the expanded VAT law (e-VAT). Fitch believes that in the
context of the recent political crisis that the Philippines is undergoing, it is questionable
whether the weakened political leadership in the country will commit the necessary
political capital in the resolution of the e-VAT issue soon. On the other hand, United
States- based S&P voiced concern over the country’s ability to maintain the fiscal
consolidation needed to reduce the country’s high level of public and external
indebtedness. The combination of delayed fiscal consolidation, protracted politicalstalemate, and a possible change in economic policy has shifted the balance of risk on the
downside, making a “stable” outlook for the Philippines no longer justified, according to
S&P.
1
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 5/50
We see that global credit rating agencies use several quantitative and qualitative
variables (economic, social, and political) in order to assign a credit rating to a debtor or
debt instrument. As a result, a very relevant issue now is to identify the various factors
that are statistically significant in the determination of sovereign credit ratings. This
paper will attempt to answer this particular question.
2
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 6/50
II. OBJECTIVES OF THE STUDY
This paper aims to:
1) Identify the determinants of sovereign credit ratings; and
2) Identify which factors have the largest influence on the international rating
agencies’ decision on what rating to give to a particular country.
III. SCOPE AND LIMITATIONS
The paper focused mainly on the various economic/political indicators affecting
the sovereign credit rating of a given country. Although there are two types of sovereignratings – local and foreign currency sovereign ratings – only factors influencing the credit
ratings on foreign currency denominated debts was studied. Note that sovereign credit
ratings are an indication of a government’s capacity and willingness to repay principal
and interests on obligations as they fall due. A country may resort to extensive revenue-
generation measures in the form of strict tax collection or through money creation to
service its local currency denominated debt. However, it must exert its best efforts to
secure the foreign currency it needs in order to service its maturing foreign obligations.
Further, although there are other international credit rating agencies like Fitch and
Moody’s, only the ratings given by the Standard and Poor’s were included in the analysis.
Considering that foreign currency denominated debts constitute a large part of our
country’s total debt, it is deemed more important to examine the various factors which
have an effect on our sovereign foreign currency credit ratings which are usually the basis
of foreign creditors in deciding what terms and conditions to impose on our external
borrowings or whether or not they will still provide the financing we need given the risks
involved.
3
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 7/50
While it is likewise necessary to ensure that the government will not default on its
maturing local obligations, factors which have an effect on our sovereign credit rating on
domestic debts was not included in the analysis.
4
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 8/50
IV. SIGNIFICANCE OF THE STUDY
The study will identify the explanatory variables with the most significant
influence on sovereign credit ratings assigned to countries, and this is very important in
helping policy makers and relevant stakeholders in decision-making and charting policies
for economic development in general. In particular, it could also aid in implementing an
effective mechanism that could reduce vulnerability of emerging economies, i.e. the
Philippines, against external shocks.
Furthermore, the results of the study will be helpful in outlining a national
comprehensive economic development medium-term or long-term plan, especially in
fiscal planning and the achievement of fiscal balance of the Philippines. It will help
national fiscal executives anticipate, adjust and recommend legislative policies to achieve
and maintain acceptable and doable sovereign credit ratings that will benefit the nation as
a whole.
The paper’s results will help explain the future behavior of global credit rating
bodies, and it will help the Philip pines to manage effectively a “downgrade” or take
advantage of an “upgrade” of sovereign credit ratings.
The results of this study further underscore the relevance of the formal adoption
of inflation targeting in 2002 as the framework for monetary policy in the Philippines.
With the maintenance of price stability conducive to sustainable economic development
as the main thrust of the monetary authorities, this study would help aid the policymakers
in the formulation of measures that could help the country in attaining a higher credit
rating, thus improving the country’s access in the international capital markets as well as
reducing the vulnerability that goes with it.
5
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 9/50
V. REVIEW OF RELATED LITERATURE
The increased function of credit rating agencies is indeed crucial in the access of
financial resources by countries from the international capital markets. Said agencies seek
to assess the capacity and willingness of a government to timely service its debt, and in
accordance with pre-agreed conditions when the loans were made. Considering that
ratings are straightforward pronouncements by credit rating agencies, it helps reduce the
uncertainties involving government bonds over the near term (Canuto, et.al. 2004), thus
aids investors in managing the risk exposure of their investments. Leading agencies
include: S&P, Fitch and Moody’s.
Sovereign debt and default could be understood using two fundamental
approaches:
Willingness to pay maturing obligations
Countries are reinforced to pay their obligations on a pre-agreed maturity date
since this could translate into good reputation in the international capital markets, hence
greater access of financial resources. However, a small economy may renegotiate its debt
“if cash-in-advance contracts allow it to hedge future stochastic output and lending.”
Debt-servicing capacity approach
This model tackles the solvency and liquidity position of a country, referring to
the sovereign’s payment capacity.
As a result, studies on the determinants of sovereign ratings have been of high
importance to both developing and developed countries since these provide, among other
things, insights as to the improvements of factors that lead to the path towards earning
higher sovereign risk ratings.
The study of Mellios and Paget shows that sovereign credit ratings are particularly
affected by: per capita income, real exchange rate changes, government income, inflation
rate and default history. In addition to these factors, corruption index poses significant
influence on how credit rating agencies award sovereign evaluations.
6
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 10/50
A review of empirical and theoretical underpinnings of sovereign risk premium of
emerging markets and how they are affected by economic fluctuations suggest that the
most important country-specific predictors of sovereign spread and default probabilities
are liquidity and solvency variables, credit ratings, and indicators of the quality of
macroeconomic policy (Souza, 2004).
In the evaluation of Canuto, et.al. (2004), the researchers analyze the factors that
determine sovereign risk and the role of international credit rating agencies in the
appraisal of such risk. Parallel to other undertakings as contained in international
literature, the results further validate that high sovereign credit ratings are determined by
the following variables: per capita income, inflation as evidenced by CPI, economic
growth, total external debt/current account receipts ratio, central government grossdebt/total fiscal receipts ratio, absence of default events, level of trade openness as
indicated by the sum of total exports and imports as a percentage of GDP.
Using data from S&P’s and Moody’s for June 2001 covering 81 developed and
developing countries (29 developed countries and 52 developing countries as classified
by the IMF in 2001), this study shows that the variables that exert significant explanatory
power for the rating levels are GDP per capita, external debt as a percentage of exports,
the level of economic development, default history, real growth rate, and the inflation rate
(Alfonso, 2003).
The results of a study Cantor and Packer (1996) using Moody’s and S&P’s ratings
in September 1995 illustrate the factors that appear to largely influence sovereign ratings,
as follows: per capita income, GDP growth and inflation, external debt, extent of
economic development, and default. On the one hand, these ratings aim to guide financial
markets on macroeconomic fundamentals of participating sovereigns and thus, effectively
affecting bond yield movements. This further strengthens the findings of other empirical
studies on similar topic.
In sum, empirical findings point to a host of factors that significantly explain how
credit rating agencies assess sovereign risks: per capita income, inflation rate,
7
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 11/50
government income, default history, real exchange rate, and corruption perception index,
which is also an indicative of the quality of governance in a country.
8
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 12/50
VI. DATA SOURCES AND RESEARCH METHODOLOGY
The cross-section data that were used in this study include: 1) sovereign foreign
currency credit ratings of a sample of 61 countries as of December 31, 2004; and
2) economic and political indicators of such countries as of December 31, 2003. There is
a one-year lag in indicators used to allow us to determine the credit rating that will be
given in the current year based on what has transpired (actual observations) in the
previous year.1
Since credit ratings assigned by the Standard and Poor’s are discrete and in
alphabetical form ranging from as high as AAA to as low as D, the ratings were
subdivided into 4 groups and transformed into numbers using the following format: 0 was
assigned to the lowest ratings ranging from D to CCC+; 1 for ratings from B- to BB+; 2
for ratings from BB- to AA+; and lastly, 4 was assigned to the highest rating of AAA.2
Considering that sovereign foreign currency credit ratings are in ranks or ordinal
in nature, the ordered logistic model was used to determine the factors affecting a
country’s credit rating – our variable of interest.
Possible independent variables that were used for the analysis include:
1) gross domestic savings [% of gross domestic product (GDP)]; 2) gross national income
per capita (in USD); 3) consumer price index (% change); 4) trade openness;
5) corruption perceptions index; and 6) default history of countries.
As mentioned earlier, data on sovereign credit ratings came from S&P; default
history of countries from the World Bank’s Global Development Finance 2004; and
corruption perceptions index from Transparency International. The 2003 data on the rest
of the inde pendent variables came from the International Monetary Fund’s InternationalFinancial Statistics and World Bank’s World Development Indicators.
1Complete list of data is presented in Annex A.
2Description of the alphabetical sovereign credit ratings as well as their numerical equivalent are shown in
Annexes B and C, respectively.
9
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 13/50
Default history of countries was captured in a dummy variable wherein a country
which has defaulted or even rescheduled its debt was assigned a value of 1; and 0,
otherwise.
The corruption perceptions index from Transparency International was based on a
number of surveys conducted by independent institutions. Rampant corruption in the
government is perceived to hamper a country’s sustainable development as funds
intended to finance projects as well as the revenues generated therefrom go to the hands
of crooked and fraudulent officials. The rating system used by Transparency
International is as follows: the highest index of 10 is attributed to uncorrupted countries
while 0 corresponds to highly corrupt countries.
Given that the ratings are in ranks, the ordered logit model that was estimated is
as follows:
Ratingi* = β1*Savingsi + β2*GNIi + β3*CPIi + β4*Tradei + β5*Corruptioni +
β6*Defaulti
where:
Rating = 0, if the credit rating of a country is D to CCC+
= 1, if the credit rating is B- to BB+
= 2, if the credit rating is BBB- to AA+
= 3, if the credit rating is AAA
Savings - Gross domestic savings (% of GDP)
GNI - Gross national income per capita (in USD)
CPI - Consumer price index (% change)
Trade - Trade openness, [exports + imports (% of GDP)]
Corruption - Corruption perceptions index [values range from 0 (highly corrupt) to 10
(highly clean)]
Default = 1, if a country has defaulted or rescheduled its debt at least once in
history
= 0, otherwise
10
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 14/50
The null and alternative hypotheses tested were: H0: β j=0 versus H1: β j>0 for
j=1,2,4,5; H1: β j<0, for j=3,6, at 95% confidence level (or 5% level of significance).
Following are the rationale for the aforementioned a priori expectations with
respect to the relationship between the sovereign credit rating and the set of explanatory
variables:
An increase in gross domestic savings will lead to an improvement in the credit
rating because of the increase in available funds to pay maturing foreign obligations.
An increase in GNI per capita means a potential increase in tax receipts, which
will likewise improve the country’s repayment capacity and consequently, its sovereign
credit rating.
A substantial increase in percent change in consumer prices (or inflation rate)
would indicate that there is a somewhat relaxed monetary policy, which could not
maintain price stability. Because of this, it is expected that a high inflation rate will lead
to a decrease in the country’s credit rating.
An increase in the trade openness indicator, which is measured as the sum of
exports and imports as a percentage of GDP, is expected to lead to an upgrading of
sovereign credit ratings considering that countries will be able to generate the necessary
foreign currency for debt servicing. Further, countries open to external trade will most
probably exert best efforts not to default so as not to impair their trade relations with the
rest of the world.
An improvement in the corruption perceptions index of the country will likewise
lead to an increase in our sovereign credit rating as the former is viewed by credit rating
agencies as a measure of quality of governance.
Lastly, countries with default histories are perceived as high credit risks that are
most likely to default again; hence, a lower credit rating is assigned to such countries by
credit rating agencies.
11
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 15/50
VII. DEFINITION OF VARIABLES
Consumer Price Index
general measure of the average annual changes in the retail prices of commoditiescommonly purchased by households reckoned from a base year and weighted by
the consumption pattern or basket of the households.
Corruption Perceptions Index
relates to perceptions of the degree of corruption as seen by business people andcountry analysts and ranges between 10 (highly clean) and 0 (highly corrupt).
Exports and imports of goods and services comprise all transactions between residents of an economy and the rest of the
world involving a change in ownership of general merchandise, goods sent for
processing and repairs, nonmonetary gold, and services.
Gross Domestic Savings represent the difference between GDP and total consumption. Domestic savings
also satisfy the fundamental identity: exports minus imports equal domestic
savings minus capital formation
Gross National Income (GNI)
takes into account all production in the domestic economy (i.e., Gross DomesticProduct or GDP) plus the net flows of factor income (such as rents, profits, and
labor income) form abroad. The Atlas method smoothes exchange rate
fluctuations by using a three-year moving average, price-adjusted conversionfactor.
GNI per capita
GNI divided by the actual population in a given country.
Inflation Rate
annual rate of percentage change or the year-on-year change in the CPI. Itindicates how fast or slow the CPI increases or decreases.
Sovereign Credit Rating
an assessment of the capacity and willingness of a government to timely serviceits debt, and in accordance with pre-agreed conditions when the loans were made
Trade Openness
measured by the sum of exports and imports as percentage of GDP
12
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 16/50
VIII. OVERVIEW ON LOGISTIC MODELING
Logistic models are used when dealing with discrete dependent variables. Logistic
regressions relate the probability of occurrence of an event, dichotomous outcomes(binary models) or multinomial outcomes (multinomial models), to a host of explanatory
variables. Within the multinomial models, ordered logistic models are used when thedependent variable is an ordinal variable which measures ranks such as ratings.
It assumes that the ordered logistic model is well adapted for modeling sovereign
ratings, which are clearly ordinal variables. The goal of this model is to express the
probability of a rating score assigned to a country as a function of the economic andpolitical determinants of the said country.
The probability obtained by applying a logistic function to a score obtained by a
linear combination of independent variables. Only the most statistically significantvariables are observed.
Therefore, the model helps identify which independent variables have the largest
influence over the rating agency’s choices.
In ordered dependent variable models, the observed dependent variable Y denotesoutcomes representing ordered or ranked categories. We can model the observed
response by considering a latent variable Y* that depends linearly on the explanatory
variables Xs:
Y* =x’ +
where ’s are independent and identically distributed random variables. The observed Yis determined from Y* using the rule:
Y = 0 , if Y* 1
= 1 , if 1 < Y* 2
= 2 , if 2 < Y* 3 …
= M , if Y* > M
It is worth noting that the actual values chosen to represent the categories in are
completely arbitrary. All the ordered specification requires is for ordering to be preservedso that Yi<Yj implies that Yi*<Yj*.
It follows that the probabilities of observing each value of Y are given by
Pr(Y=0) = F (1 – x’)
Pr(Y=1) = F (2 – x’) - F (1 – x’)
Pr(Y=2) = F (3 – x’) - F (2 – x’) …
Pr(Y=M) = 1 - F (M – x’)
where F is the cumulative distribution function of .
13
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 17/50
IX. FINDINGS/RESULT HIGHLIGHTS
Descriptive Statistics
This study is aimed at determining the economic and political variables that have
the most influence on the sovereign credit ratings awarded by ratings agencies,
particularly Standard and Poor’s. The sample of the study includes 61 countries, nine of
which are Asian countries namely: China, India, Indonesia, Israel, Japan, Malaysia,
Philippines, Thailand and Vietnam. Cross-section data were used covering 2003
independent variables vis-à-vis the 2004 ratings by S&P’s awarded to respective
countries.
Rating
A scale of 0-3 (or 4 categories) was used in transforming the alpha ratings of S&P
to sample countries: “0” – lowest (D to CCC+); 1 (B- to BB+); 2 (BBB- to AA+); and
“3” - highest (AAA). Among the sample, three countries obtained the lowest rating of
“0”, namely Argentina, Dominican Republic and Ecuador. In Asia, the Philippines was
awarded a rating of 1 along with India, Indonesia and Vietnam; while China, Israel,
Japan, Malaysia and Thailand gained a rating of “2”.
Standard and Poor's Sovereign Credit Ratings for the Sample of 61 Countries
as of December 31, 2004
0
1
2
3
A r g e n t i n a
A u s t r a l i a
A u s t r i a
B e l g i u m
B o l i v i a
B r a z i l
B u l g a r i a
C a n a d a
C h i l e
C h i n a
C o l o m b i a
C o s t a R i c a
C r o a t i a
C z e c h
D e n m a r k
D o m i n i c a n
E c u a d o r
E g y p t
E l S a l v a d o r
E s t o n i a
F i n l a n d
F r a n c e
G e r m a n y
G h a n a
G r e e c e
H u n g a r y
I n d i a
I n d o n e s i a
I r e l a n d
I s r a e l
I t a l y
J a p a n
K a z a k h s t a n
L a t v i a
L i t h u a n i a
M a l a y s i a
M a l i
M e x i c o
M o z a m b i q u e
N e t h e r l a n d s
N e w
Z e a l a n d
N o r w a y
P a n a m a
P e r u
P h i l i p p i n e s
P o l a n d
P o r t u g a l
R o m a n i a
R u s s i a
S l o v a k i a
S o u t h A f r i c a
S p a i n
S w e d e n
T h a i l a n d
T u n i s i a
T u r k e y
U n i t e d K i n g d o m
U n i t e d S t a t e s
U r u g u a y
V e n e z u e l a
V i e t n a m
N
o t c h e s ( 0 - l o w e s t ; 3 - h i g h e s t )
14
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 18/50
Of the 61 countries, three fall in the lowest category of “0” rating, 20 in the
second higher category of “1”, 24 in category “2”, and 14 in the highest category of “3”
as shown in the one-way tabulation of the dependent variable Rating3. The counties,
which were given ratings in the lowest scale of “0” in 2004, include Argentina,
Dominican Republic and Ecuador. On the other hand, there were 14 countries which
received the highest rating of “3” (or AAA) for the same rating period, namely: Australia,
Austria, Canada, Denmark, Finland, France, Germany, Ireland, Netherlands, Norway,
Spain, Sweden, United Kingdom, and United States.
Tabulation of RATING
Number of categories: 4
Cumulative Cumulative
Value Count Percent Count Percent0 3 4.92 3 4.921 20 32.79 23 37.70
2 24 39.34 47 77.053 14 22.95 61 100.00
Total 61 100.00 61 100.00
The average rating received by the sample of 61 countries was 1.8. As expected,
since ratings were divided into 4 categories, the minimum rating was “0” and the
maximum, “3”.
3One-way tabulations for each of the variables are shown in Annex D.
0
5
10
15
20
25
0.0 0.5 1.0 1.5 2.0 2.5 3.0
Series: RATING
Sample 1 61Observations 61
Mean 1.803279
Median 2.000000Maximum 3.000000Minimum 0.000000Std. Dev. 0.852832
Skewness -0.102443Kurtosis 2.203760
Jarque-Bera 1.718108Probability 0.423563
15
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 19/50
The distribution of the series has a long left tail and is flat (platykurtic) relative to
the normal distribution as indicated by the negative value of the skewness statistics and a
value of less than 3 for the kurtosis.
Although the series Rating is not perfectly symmetric, the Jarque-Bera statistics
p-value of 0.423563 leads to the acceptance of the null hypothesis of normal
distribution.4
Gross Domestic Savings (% of GDP)
The average gross domestic savings as % of GDP was 21.65574%. This ratio
ranged from as low as 0% to as high as 47%. China had the highest savings relative to its
GDP and El Salvador, the lowest for the sample of 61 countries. On the other hand,
Bolivia, Bulgaria, Ghana, Israel and Mozambique had savings that ranged from 9% to
12% of their respective GDPs in 2003.
4Summary of descriptive statistics for all the variables included in the study is presented in Annex E.
16
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 20/50
The series Savings was automatically divided by the software Eviews into 25
categories. The highest number of countries with the same percentage of gross domestic
savings is 7 (22% savings).
Gross National Income per capita
0
2
4
6
8
10
12
14
16
0 10 20 30 40
Series: SAVINGSSample 1 61Observations 61
Mean 21.65574Median 22.00000
Maximum 47.00000Minimum 0.000000
Std. Dev. 7.852569Skewness 0.576137Kurtosis 4.880762
Jarque-Bera 12.36520Probability 0.002065
17
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 21/50
The gross national income per capita of the sample countries, on the average, was
USD10,608.52. We can also see that our sample includes a diverse group of countries
with different income per capita levels. Although the mean income was USD10,608.52,
note that the lowest per capita income was registered at USD210 while the highest was
USD43,400. The country with the highest income per capita was Norway, followed by
the United States, Japan and Denmark. On the other hand, Bolivia, Ghana, India,
Indonesia, Mali, Mozambique and Vietnam had per capita income of less than
USD1,000.
As shown in the following table, 65.57% of the sample or 40 out of the 61
countries included in the study registered a GNI per capita within the interval of [USD0,
USD10,000). On the other hand, only 1 country posted a GNI above 40,000.
Tabulation of GNI
Number of categories: 5
Cumulative CumulativeValue Count Percent Count Percent
[0, 10000) 40 65.57 40 65.57
[10000, 20000) 5 8.20 45 73.77
[20000, 30000) 12 19.67 57 93.44[30000, 40000) 3 4.92 60 98.36[40000, 50000) 1 1.64 61 100.00
Total 61 100.00 61 100.00
0
4
8
12
16
20
24
0 10000 20000 30000 40000
Series: GNI
Sample 1 61
Observations 61
Mean 10608.52Median 4360.000
Maximum 43400.00
Minimum 210.0000
Std. Dev. 11757.04
Skewness 1.070738
Kurtosis 2.816475
Jarque-Bera 11.74149
Probability 0.002821
18
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 22/50
Consumer Price Index (% change) - Inflation Rate
The movements in the consumer prices (inflation) indicate the sustainability of
monetary and exchange rate policies of a country, or it can be a proxy for economic
development. Venezuela emerged to have the highest inflation rate in 2003 at 31.06%,
while China showed a deflation in the same year at -1.56%. On the other hand, Indonesia
has the highest in Asia at 5.9%. The average change in the CPI of the countries included
was 5.64%.
0
4
8
12
16
20
24
0 10 20 30
Series: CPI
Sample 1 61
Observations 61
Mean 5.643279
Median 2.830000Maximum 31.06000
Minimum -1.560000Std. Dev. 7.280578
Skewness 2.023545
Kurtosis 6.415826
Jarque-Bera 71.28563Probability 0.000000
19
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 23/50
Out of the 61 countries, 48 (78.7%) had inflation rates that are greater than and
equal to 0% but less than 10%; three countries (which constitute 4.9% of the sample) had
2003 consumer price indices that are lower than the 2002 level. The remaining ten
countries had inflation rates of at least 10% in year 2003.
Tabulation of CPI
Number of categories: 5
Cumulative Cumulative
Value Count Percent Count Percent
[-10, 0) 3 4.92 3 4.92
[0, 10) 48 78.69 51 83.61
[10, 20) 6 9.84 57 93.44[20, 30) 3 4.92 60 98.36
[30, 40) 1 1.64 61 100.00
Total 61 100.00 61 100.00
Trade Openness
The average value for trade openness indicator was 79.88%. The percentage of
the sum of exports and imports with respect to GDP ranged from as low as 22.81% to
207.64%. Countries with percentages above 150% included Malaysia (which posted thehighest trade openness indicator value of 207.64%), Estonia, Slovakia and Ireland.
0
2
4
6
8
10
12
14
20 40 60 80 100 120 140 160 180 200
Series: TRADE
Sample 1 61
Observations 61
Mean 79.88148
Median 68.59000
Maximum 207.6400
Minimum 22.81000Std. Dev. 37.88177
Skewness 0.946551
Kurtosis 3.779534
Jarque-Bera 10.65342
Probability 0.004860
20
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 24/50
In contrast, note that United States and Japan were the two countries with the
lowest trade openness values of 23.18% and 22.81%, respectively. Although one may
argue that these two countries should have the highest values for trade openness, recall
that the indicator is the sum of the exports and imports of a given country as percentage
of GDP. Hence, these low values may be due to the fact that these two countries do not
rely heavily on imported goods and services, thus, a lower numerator value over the
denominator GDP.
As indicated in the table below, more than fifty percent of the sample sovereigns
recorded trade openness from 50 to 100 percent. This indicates how integrated the
countries in the world economy as measured by their respective total exports and imports
as a share of GDP.
Tabulation of TRADE
Number of categories: 5
Cumulative CumulativeValue Count Percent Count Percent
[0, 50) 12 19.67 12 19.67
[50, 100) 33 54.10 45 73.77
[100, 150) 12 19.67 57 93.44[150, 200) 3 4.92 60 98.36
[200, 250) 1 1.64 61 100.00
Total 61 100.00 61 100.00
21
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 25/50
Corruption Perceptions Index
Based on Transparency International 2003, Indonesia was perceived to be the
most corrupt country garnering a rating of 1.9 using a scale from 0 (highly corrupt) to 10
(highly uncorrupt), while Finland appeared to be the least corrupt country having a grade
of 9.7. Aside from Finland, only Denmark, New Zealand and Sweden received indices
above 9. On the other hand, Argentina, Bolivia, Ecuador, Kazakhstan, Philippines,
Venezuela and Vietnam got corruption perception indices ranging from 2.2 to 2.5.
Of the Asian countries included, the Philippines ranked third as highly corrupt
sovereign at 2.5, while Japan and Israel were seen to be the least corrupt Asian countries
at 7.0.
22
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 26/50
The maximum corruption index given in 2003 to a country included in the sample
was 9.7 (0.3 units away from the highest value of 10) and was, therefore, considered
highly clean/uncorrupt while the minimum index was 1.9 (which is close to the index of 0
for the most corrupt country. The average rating in this index was 5.05.
Based on the tabulation below, 50.82% or 31 countries had corruption perception
indices which ranged from 0 to 4, 0 being the most corrupt country while only 16.39%
(ten countries) had indices of at least 8.
Tabulation of CORRUPTIONNumber of categories: 5
Cumulative Cumulative
Value Count Percent Count Percent
[0, 2) 1 1.64 1 1.64[2, 4) 30 49.18 31 50.82
[4, 6) 10 16.39 41 67.21
[6, 8) 10 16.39 51 83.61[8, 10) 10 16.39 61 100.00
Total 61 100.00 61 100.00
Default History
Focusing on the country’s default or rescheduling of debt history, it may be noted
that 20 countries have defaulted or rescheduled on their debt, the Philippines included.
0
2
4
6
8
10
12
2 4 6 8 10
Series: CORRUPTIONSample 1 61Observations 61
Mean 5.045902Median 3.900000
Maximum 9.700000Minimum 1.900000
Std. Dev. 2.356309Skewness 0.599399Kurtosis 1.961636
Jarque-Bera 6.393099Probability 0.040903
23
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 27/50
The remaining 41 sovereigns, or 67.24 percent of the sample, do not have this history as
shown in the table below.
Without Default/Rescheduling History
With Default/Rescheduling
History
Australia Israel Argentina
Austria Italy Bolivia
Belgium Japan Brazil
Canada Kazakhstan Bulgaria
China Latvia Chile
Colombia Lithuania Costa Rica
Croatia Malaysia Ecuador
Czech Republic Mali Indonesia
Denmark Mexico Mozambique
Dominican Republic Netherlands Panama
Egypt New Zealand PeruEl Salvador Norway Philippines
Estonia Portugal Poland
Finland Slovakia Romania
France Spain Russia
Germany Sweden South Africa
Ghana Thailand Turkey
Greece Tunisia Uruguay
Hungary United Kingdom Venezuela
India United States Vietnam
Ireland
The mean default value for the sample was 0.327869. As expected, since the
variable default is a dummy, it will only take values of either “1” if a country has
defaulted on its debts or even rescheduled its loans or “0”, otherwise.
0
10
20
30
40
50
0.0 0.2 0.4 0.6 0.8 1.0
Series: DEFAULTSample 1 61Observations 61
Mean 0.327869Median 0.000000Maximum 1.000000Minimum 0.000000
Std. Dev. 0.473333Skewness 0.733352Kurtosis 1.537805
Jarque-Bera 10.90180
Probability 0.004292
24
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 28/50
The set of independent variables are all positively skewed. Only Savings, CPI
and Trade are leptokurtic. The rest of the explanatory variables are platykurtic relative to
the normal. Further, the Jarque-Bera statistics’ p-values of all these variables lead to the
rejection of the null hypothesis of a normally distributed series.
Ordered Logistic Model
The ordered logistic regression was performed to determine which among the
chosen independent variables have an effect on the sovereign credit rating assigned to a
given country.
The following table shows the results of the initial run of the model:
Dependent Variable: RATING
Method: ML - Ordered Logit (Quadratic hill climbing)
Sample: 1 61Included observations: 61
Number of ordered indicator values: 4
Convergence achieved after 16 iterationsCovariance matrix computed using second derivatives
Coefficient Std. Error z-Statistic Prob.
SAVINGS 0.05289 0.047918 1.10377 0.2697
GNI 0.000193 9.13E-05 2.114292 0.0345
CPI -0.128559 0.056463 -2.27687 0.0228TRADE 0.013382 0.011081 1.207639 0.2272
CORRUPTION 1.157246 4.35E-01 2.661828 0.0078
DEFAULT -1.145463 0.834437 -1.37274 0.1698
Limit Points
LIMIT_1:C(7) 0.955754 2.040921 0.468295 0.6396
LIMIT_2:C(8) 5.876975 2.141553 2.744259 0.0061
LIMIT_3:C(9) 14.78338 4.075983 3.626948 0.0003
Akaike info criterion 1.241632 Schwarz criterion 1.553072
Log likelihood -28.86976 Hannan-Quinn criteria. 1.363688
Restr. log likelihood -74.33273 Avg. log likelihood -0.47328LR statistic (6 df) 90.92593 LR index (Pseudo-R2) 0.611614
Probability(LR stat) 0.000000
25
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 29/50
Based on the results of the ordered logit analysis, our model for the latent variable
Rating*
is:
Rating* = 0.05289*Savings + 0.000193*GNI – 0.128559*CPI +
0.013382*Trade + 1.157246*Corruption – 1.145463*Default
As evidenced by the p-value of the LR statistic, the joint null hypothesis of
significant slope coefficients is rejected at 5% level of significance. This means that at
least one of the slope coefficients is not equal to zero.
However, note that only the variables GNI, CPI and Corruption are significant at
95% confidence level while the dummy variable Default is only significant at 10% levelof significance based on the p-values of the Z-statistic. This means that S&P’s credit
assessment of a country depends only on the gross national income per capita of each of
the individual countries, on the rate of increase in consumer prices, the corruption
perceptions index of businessmen on the country and the default risk of a country.
Based on the estimated equation, for every one-percentage point increase in the
variable Savings (as % of GDP), the estimated logit will increase by 0.05289, ceteris
paribus. For every USD1 increase in the GNI per capita, the estimated logit will increase
by 0.000193, holding all other variables constant. For every one-percentage point
increase in inflation rate and trade openness, the estimated logit will decrease by
0.128559 and increase by 0.013382, respectively. For every one-unit increase in the
corruption perceptions index, the estimated logit will increase by 1.157246, ceteris
paribus. The estimated logit will decrease by 1.145463 if a country has defaulted or
rescheduled its loans, ceteris paribus.
However, these changes don’t make any sense because these are just the effects
on the estimated logits for every one-unit change in the independent variables. A more
meaningful interpretation is in terms of the odds ratios.
26
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 30/50
Before moving any further, we verified first whether or not the proportional odds
assumption holds. According to this assumption, the independent variables’ effect on the
cumulative odds does not change from one cumulative odds to the next. This means that
the slopes remain the same for each category. The only thing that changes is the constant
term.
The Score test was carried out using the software SAS to validate the assumption:
Score Test for the
Proportional Odds Assumption
Chi-Square DF Pr > ChiSq
19.1761 12 0.0844
Since the Chi-square statistic is insignificant, this means that the proportional odds
assumption holds. This provides evidence that treating the dependent variable Rating as
ordered is consistent with the data used in the model specification.
To get the odds ratios, we have to take the antilog of the negative of the slope
coefficients [i.e., exp(-β j)]5. This is in contrast to the binary logit model wherein the odds
ratios are computed just by taking the antilog of the estimated slope coefficients [i.e.,
exp(β j)] since such model already includes the intercept in the estimated equation.
The odds ratios for the set of the independent variables are as follows:
Variable Odds Ratio
Savings 0.9485
GNI 0.9998
CPI 1.1372
Trade 0.9867
Corruption 0.3144
Default 3.1439
Hence, the odds of rating being equal to “0” versus all other ratings increases by
1.1372 (13.72%) for every one percentage point increase in the inflation rate. The odds
of rating being equal to “0” versus all other rating decreases by 0.3144 for every one-
5
This computation of the odds ratios only applies when we use the software Eviews wherein the intercepts
are separately estimated from the equation. However, if we use SAS wherein the intercepts are included
in the estimated equation, the same formula used in the binary logit model applies.
27
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 31/50
percentage point increase in the corruption perceptions index. The odds of rating being
equal to “0” versus all other ratings increases by 3.1439 (214.39%) for countries with
default histories
Since the proportional odds assumption holds, we will get the same odds ratios of
rating being equal to “0” or “1” versus ratings above it; or a rating being equal to “0” or
“1” or “2” versus a rating of “3”. For example, the odds in favor of rating being equal to
“0” or “1” (against that of being equal to “2” or “3”) increases by 3.1439 for countries
which have defaulted on its debts or have rescheduled/restructured its loans at least once
in history.
Limit Points
As discussed previously, limit points are the threshold parameters from which we
can determine the values of Rating. Shown below are the estimated limit points estimated
using the ordered logistic analysis:
LIMIT_1:C(7) 0.95575
LIMIT_2:C(8) 5.87698
LIMIT_3:C(9) 14.7834
Hence, this means that we can determine the observed value of the dependent variable
Rating by just examining within which interval the estimated latent variable Rating*
falls.
Rating = 0 , if Rating* 0.955754
= 1 , if 0.955754 < Rating* 5.876975
= 2 , if 5.876975 < Rating* 14.78338
= 3 , if Rating*
> 14.78338
For instance, given a country’s statistics/figures for the independent variables, if the estimated Rating
* is 5.95, then that country will receive a rating of “2” since 5.95 is
within the interval (5.876975, 14.78338].
28
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 32/50
Using this concept, among the 61 countries included in the sample, 47 have equal
actual and predicted ratings while the remaining 14 countries shown below were
incorrectly classified as having different ratings from the actual rating they received.6
Incorrectly classified countries
Argentina Kazakhstan
Belgium New Zealand
Bulgaria Panama
Dominican Republic Poland
Ecuador South Africa
France Spain
Japan Venezuela
In terms of probabilities, we can determine the value of Rating by getting the
probabilities associated with each of the four ordered categories from “0” to “3”. The
rank with the highest probability will be the category to which an observation/country
belongs.
For example, in the case of the Philippines, the category with the highest
probability is at Rating=1. This means that, based on the model, the predicted rating that
Philippines will receive is “1”. Comparison between actual and predicted ratings shows
that this country is correctly “rated” by the estimated model. In the case of the United
States, it has the highest computed probability of 0.876735 at Rating=3, while its lowest
probability is at Rating=0. Therefore, based on these probabilities, the United States is
least likely to receive a rating of “0” (but will most probably get a rating of “3”) in the
current year given its values for its economic and political indicators in the previous year.
However, as in any other models, there are also errors in the estimation. There
are countries, which received the maximum probability at a certain category that is not
the same as its actual rating. As expected, these are the same countries incorrectly
6Table on the actual ratings, estimated values for the latent variable Rating
*and the predicted ratings are
shown in Annex F.
29
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 33/50
classified using the “limit points” technique discussed previously. The one-notch
inconsistencies in the actual and predicted Rating for said 14 countries are as follows7:
Country Actual Predicted
Argentina 0 1Belgium 2 3
Bulgaria 2 1
Dominican Republic 0 1
Ecuador 0 1
France 3 2
Japan 2 3
Kazakhstan 2 1
New Zealand 2 3
Panama 1 2
Poland 2 1
South Africa 2 1
Spain 3 2
Venezuela 1 0
The following expectation-prediction table classifies observations on the basis of
the predicted response. There are two columns labeled "Error". The first measures the
difference between the observed count and the number of observations where the
probability of that response is highest. For example, 24 countries were rated “2”, while
only 20 had predicted probability that was highest for this value. The actual count minus
the predicted is 4; hence, an underestimation. The second error column measures the
difference between the actual number of countries rated “2” and the sum of the individual
probabilities for that value.
Prediction table for ordered dependent variable
Value CountCount of obs
with Max ProbError
Sum of all
ProbabilitiesError
0 3 1 2 2.908 0.0921 20 25 -5 20.09 -0.09
2 24 20 4 24.005 -0.005
3 14 15 -1 13.998 0.002
7The probabilities associated with each of the ordered categories for all the 61 countries are presented in
Annex G. Since these are probabilities, their sum for each country would all be equal to one.
30
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 34/50
Correlation Matrix
The correlation matrix was also obtained to determine if there are independent
variables that are highly correlated. Since there are no correlation coefficients greater
than the benchmark of 0.90, then we can say that there is no possible problem of
multicollinearity.
Correlation Rating Savings GNI CPI Trade Corruption Default
Rating 1.00000 0.24854 0.77465 -0.54842 0.14201 0.81321 -0.58073
Savings 0.24854 1.00000 0.15808 -0.15352 0.34421 0.14364 -0.15297
GNI 0.77465 0.15808 1.00000 -0.34844 -0.10404 0.86674 -0.48018
CPI -0.54842 -0.15352 -0.34844 1.00000 -0.14048 -0.38540 0.39123
Trade 0.14201 0.34421 -0.10404 -0.14048 1.00000 0.00092 -0.19684
Corruption 0.81321 0.14364 0.86674 -0.38540 0.00092 1.00000 -0.49789Default -0.58073 -0.15297 -0.48018 0.39123 -0.19684 -0.49789 1.00000
Redundant variables test
The redundant variables test allows us to test for the statistical significance of a
subset of the included variables. More formally, the test is for whether a subset of
variables in an equation all have zero coefficients and might thus be deleted from the
equation.
Since the estimated coefficients of the variables Savings and Trade are not
significant, this test was performed to know if we incorrectly included these variables in
the model.
Redundant Variables: SAVINGS TRADE
Log likelihood ratio 3.853068 Probability 0.145652Test Equation:
Dependent Variable: RATING
Method: ML - Ordered Logit (Quadratic hill climbing)Number of ordered indicator values: 4Convergence achieved after 12 iterations
Covariance matrix computed using second derivatives
Coefficient Std. Error z-Statistic Prob.
GNI 0.000205 0.000106 1.934498 0.0531
CPI -0.13489 0.054498 -2.47516 0.0133
31
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 35/50
CORRUPTION 1.085787 0.41485 2.617297 0.0089
DEFAULT -1.31003 0.7986 -1.6404 0.1009
Limit Points
LIMIT_1:C(5) -1.37174 1.564079 -0.87703 0.3805
LIMIT_2:C(6) 3.424061 1.553747 2.203745 0.0275
LIMIT_3:C(7) 12.32897 3.717755 3.31624 0.0009
Akaike info criterion 1.239223 Schwarz criterion 1.481454
Log likelihood -30.7963 Hannan-Quinn criteria. 1.334156
Restr. log likelihood -74.3327 Avg. log likelihood -0.50486LR statistic (4 df) 87.072860 LR index (Pseudo-R2) 0.585697
Probability(LR stat) 0.000000
Based on this test, the independent variables Savings and Trade may be deleted
from the model. The p-value of the Log likelihood ratio statistic leads to the acceptance
of the null hypothesis that these variables have zero coefficients (insignificant).
However, since we are only interested in determining whether or not each of the
explanatory variables influences the rating score of a given country, the two variables
will be retained in the model.
Test for Normality of Standardized Residuals
Based on the Jarque-Bera statistic with p-value of 0.18044, the standardized
residuals are normally distributed since the p-value leads to the acceptance of the null
hypothesis of normally distributed error terms.
0
2
4
6
8
10
12
14
-0.5 0.0 0.5 1.0
Series: Standardized Residuals
Sample 1 61
Observations 61
Mean -2.52E-11Median -0.038416
Maximum 0.910229
Minimum -0.864951
Std. Dev. 0.389805
Skewness 0.535422Kurtosis 3.448022
Jarque-Bera 3.424718Probability 0.180440
32
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 36/50
Graph of Standardized Residuals, Actual and Fitted Observations
As can be seen in the graph above, the estimated ordered logit model was able to
capture the actual values of the sovereign credit ratings assigned to given countries.
Hence, we can say that, except for minor deviations from the actual values of Rating, the
estimated model perfectly mimics the behavior of the credit ratings of the sample of 61
countries.
-1.0
-0.5
0.0
0.5
1.0 -1
0
1
2
3
4
10 20 30 40 50 60
Standardized ResidualsActualFitted
33
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 37/50
X. CONCLUSION
Based on the results of the analysis, only the GNI per capita, percentage change in
CPI and corruption perceptions index are significant at 95% confidence level while
default history of countries is only significant at 10% level of significance. However,
savings and trade openness are insignificant in determining the credit rating of a country.
Countries with higher GNI per capita and are considered to be uncorrupt tend to
receive higher ratings while those with higher inflation rates and have defaulted or
rescheduled their foreign loans get lower sovereign debt credit ratings.
In contrast, savings and the trade openness level of countries do not have much
bearing on the S&P’s assessment of a given country.
Factors that have the highest influence on a country’s sovereign credit rating is
the corruption perceptions index; followed by default history; and the percentage change
in the consumer price index or the inflation rate.
Therefore, a country that seeks to receive a high credit rating, particularly theemerging and low-income economies that rely on foreign borrowings to finance their
own development, should implement measures to mitigate corruption in government. It
also implies that good governance is the bottom line key that S&P essentially looks for in
assessing a country’s particular credit rating.
Likewise, countries should avoid defaulting or even rescheduling debts and loans
as it contributes to the perceived default risk of a country. Finally, a government should
also exert best efforts to maintain price stability in general.
34
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 38/50
ANNEX A
Data on Dependent and Independent Variables
Country Rating Savings GNI CPI Trade Corruption Default
Argentina 0 26 3810 13.49 40.28 2.5 1
Australia 3 22 21950 2.77 38.1 8.8 0
Austria 3 25 26810 1.36 103.72 8 0
Belgium 2 22 25760 1.59 138.1 7.6 0
Bolivia 1 10 900 3.41 48.91 2.3 1
Brazil 1 22 2720 14.67 29.94 3.9 1
Bulgaria 2 12 2130 2.11 116.29 3.9 1
Canada 3 25 24470 2.77 72.81 8.7 0
Chile 2 27 4360 2.83 68.29 7.4 1
China 2 47 1100 -1.56 65.91 3.4 0Colombia 1 14 1810 9.11 40.96 3.7 0
Costa Rica 1 18 4300 9.47 95.46 4.3 1
Croatia 2 21 5370 0.19 111.56 3.7 0
Czech Republic 2 25 7150 0.09 128.28 3.9 0
Denmark 3 26 33570 2.09 84.62 9.5 0
Dominican Republic 0 21 2130 27.4 108.66 3.3 0
Ecuador 0 23 1830 7.88 54.97 2.2 1
Egypt 1 15 1390 4.47 48.19 3.3 0
El Salvador 1 0 2340 2.08 70 3.7 0
Estonia 2 23 5380 1.37 158.59 5.5 0
Finland 3 26 27060 0.86 68.34 9.7 0
France 3 21 24730 2.1 51.62 6.9 0
Germany 3 22 25270 1.05 68.7 7.7 0
Ghana 1 11 320 26.67 96.69 3.3 0
Greece 2 18 13230 3.53 50.12 4.3 0
Hungary 2 22 6350 4.61 128.09 4.8 0
India 1 22 540 3.88 31.16 2.8 0
Indonesia 1 22 810 5.09 60.11 1.9 1
Ireland 3 41 27010 3.48 150.7 7.5 0
Israel 2 9 16240 0.72 78.61 7 0
Italy 2 20 21570 2.67 49.16 5.3 0Japan 2 26 34180 -0.25 22.81 7 0
Kazakhstan 2 33 1780 6.45 94.83 2.4 0
Latvia 2 21 4400 2.87 97.51 3.8 0
Lithuania 2 16 4500 1.2 110.69 4.7 0
Malaysia 2 42 3880 0.97 207.64 5.2 0
35
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 39/50
Country Rating Savings GNI CPI Trade Corruption Default
Mali 1 19 290 -1.36 50.92 3 0
Mexico 2 18 6230 4.57 58.53 3.6 0Mozambique 1 11 210 13.34 68.5 2.7 1
Netherlands 3 26 26230 2.11 119.13 8.9 0
New Zealand 2 23 15530 1.75 57.99 9.5 0
Norway 3 31 43400 2.48 68.59 8.8 0
Panama 1 27 4060 1.38 116.9 3.4 1
Peru 1 19 2140 2.25 35.54 3.7 1
Philippines 1 16 1080 3.45 96.93 2.5 1
Poland 2 14 5280 0.74 71.37 3.6 1
Portugal 2 18 11800 3.28 66.72 6.6 0
Romania 1 15 2260 15.23 80.35 2.8 1
Russia 1 31 2610 13.66 58.8 2.7 1Slovakia 2 24 4940 8.57 156.49 3.7 0
South Africa 2 19 2750 5.89 54.95 4.4 1
Spain 3 24 17040 3.03 57.81 6.9 0
Sweden 3 23 28910 1.9 81.12 9.3 0
Thailand 2 32 2190 1.76 125.19 3.3 0
Tunisia 2 21 2240 2.67 91.36 4.9 0
Turkey 1 20 2800 25.29 59.94 3.1 1
United Kingdom 3 13 28320 2.91 53.71 8.7 0
United States 3 14 37870 2.27 23.18 7.5 0
Uruguay 1 15 3820 19.43 51.49 5.5 1
Venezuela 1 25 3490 31.06 48.67 2.4 1
Vietnam 1 27 480 3.09 128.17 2.4 1
36
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 40/50
ANNEX B
Description of Sovereign Long-Term Foreign Currency Denominated Debt
Credit Ratings of Standard and Poor’s
AAA. An obligor rated ‘AAA’ has EXTREMELY STRONG capacity to meet its
financial commitments. ‘AAA’ is the highest Issuer Credit Rating assigned by Standard
& Poor’s.
AA. An obligor rated ‘AA’ has VERY STRONG capacity to meet its financial commitments. It differs from the highest rated obligors only in small degree.
A. An obligor rated ‘A’ has STRONG capacity to meet its financial commitments but is
somewhat more susceptible to the adverse effects of changes in circumstances andeconomic conditions than obligors in higher-rated categories.
BBB. An obligor rated ‘BBB’ has ADEQUATE capacity to meet its financialcommitments. However, adverse economic conditions or changing circumstances aremore likely to lead to a weakened capacity of the obligor to meet its financial
commitments.
BB. An obligor rated ‘BB’ is LESS VULNERABLE in the near term than other lower -rated obligors. However, it faces major ongoing uncertainties and exposure to adverse
business, financial, or economic conditions, which could lead to the obligor’s inadequate
capacity to meet its financial commitments. B An obligor rated ‘B’ is MORE
VULNERABLE than the obligors rated ‘BB’, but the obligor currently has the capacityto meet its financial commitments. Adverse business, financial, or economic conditions
will likely impair the obligor’s capacity or willingness to meet its financial commitments.
B. An obligation rated ‘B’ is more vulnerable to nonpayment than obligations rated‘BB’, but the obligor currently has the capacity to meet its financial commitment on the
obligation. Adverse business, financial, or economic conditions will likely impair the
obligor’s capacity or willingness to meet its financial commitment on the obligation.
CCC. An obligor rated ‘CCC’ is CURRENTLY VULNERABLE, and is dependent uponfavorable business, financial, and economic conditions to meet its financial
commitments.
CC. An obligor rated ‘CC’ is CURRENTLY HIGHLY-VULNERABLE.
SD and D. An obligor rated ‘SD’ (Selective Default) or ‘D’ has fail ed to pay one or more
of its financial obligations (rated or unrated) when it came due. A ‘D’ rating is assigned
when Standard & Poor’s believes that the default will be a general default and that theobligor will fail to pay all or substantially all of its obligations as they come due. An ‘SD’
rating is assigned when Standard & Poor’s believes that the obligor has selectivelydefaulted on a specific issue or class of obligations but it will continue to meet its
payment obligations on other issues or classes of obligations in a timely manner. Please
see Standard & Poor’s issue credit ratings for a more detailed description of the effects of a default on specific issues or classes of obligations.
37
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 41/50
ANNEX C
Linear Transformation of Credit Ratings of Standard and Poor’s
RatingNumerical Equivalent
(Notches)
-- 0
D 0
SD 0
C 0
CC 0
CCC- 0
CCC 0
CCC+ 0
B- 1
B 1
B+ 1
BB- 1
BB 1
BB+ 1BBB- 2
BBB 2
BBB+ 2
A- 2
A 2
A+ 2
AA- 2
AA 2
AA+ 2
AAA 3
38
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 42/50
ANNEX D
One-way Tabulations for the Dependent and Independent Variables
Tabulation of RATINGNumber of categories: 4
Cumulative CumulativeValue Count Percent Count Percent
0 3 4.92 3 4.92
1 20 32.79 23 37.70
2 24 39.34 47 77.053 14 22.95 61 100.00
Total 61 100.00 61 100.00
Tabulation of SAVINGS
Number of categories: 25
Cumulative Cumulative
Value Count Percent Count Percent
0 1 1.64 1 1.64
9 1 1.64 2 3.2810 1 1.64 3 4.92
11 2 3.28 5 8.20
12 1 1.64 6 9.8413 1 1.64 7 11.48
14 3 4.92 10 16.39
15 3 4.92 13 21.31
16 2 3.28 15 24.5918 4 6.56 19 31.15
19 3 4.92 22 36.07
20 2 3.28 24 39.3421 5 8.20 29 47.54
22 7 11.48 36 59.02
23 4 6.56 40 65.5724 2 3.28 42 68.85
25 4 6.56 46 75.41
26 5 8.20 51 83.61
27 3 4.92 54 88.52
31 2 3.28 56 91.8032 1 1.64 57 93.44
33 1 1.64 58 95.08
41 1 1.64 59 96.7242 1 1.64 60 98.36
47 1 1.64 61 100.00
Total 61 100.00 61 100.00
39
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 43/50
Tabulation of GNI
Number of categories: 5Cumulative Cumulative
Value Count Percent Count Percent
[0, 10000) 40 65.57 40 65.57[10000, 20000) 5 8.20 45 73.77
[20000, 30000) 12 19.67 57 93.44
[30000, 40000) 3 4.92 60 98.36[40000, 50000) 1 1.64 61 100.00
Total 61 100.00 61 100.00
Tabulation of CPINumber of categories: 5
Cumulative Cumulative
Value Count Percent Count Percent
[-10, 0) 3 4.92 3 4.92[0, 10) 48 78.69 51 83.61
[10, 20) 6 9.84 57 93.44
[20, 30) 3 4.92 60 98.36[30, 40) 1 1.64 61 100.00
Total 61 100.00 61 100.00
Tabulation of TRADE
Number of categories: 5
Cumulative CumulativeValue Count Percent Count Percent
[0, 50) 12 19.67 12 19.67
[50, 100) 33 54.10 45 73.77
[100, 150) 12 19.67 57 93.44[150, 200) 3 4.92 60 98.36
[200, 250) 1 1.64 61 100.00
Total 61 100.00 61 100.00
40
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 44/50
Tabulation of CORRUPTION
Number of categories: 5Cumulative Cumulative
Value Count Percent Count Percent
[0, 2) 1 1.64 1 1.64[2, 4) 30 49.18 31 50.82
[4, 6) 10 16.39 41 67.21
[6, 8) 10 16.39 51 83.61[8, 10) 10 16.39 61 100.00
Total 61 100.00 61 100.00
Tabulation of DEFAULTNumber of categories: 2
Cumulative CumulativeValue Count Percent Count Percent
0 41 67.21 41 67.21
1 20 32.79 61 100.00
Total 61 100.00 61 100.00
41
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 45/50
ANNEX E
Descriptive Statistics
Rating Savings GNI CPI Trade Corruption Default
Mean 1.803279 21.65574 10608.52 5.643279 79.88148 5.045902 0.327869
Median 2.000000 22.00000 4360.000 2.830000 68.59000 3.900000 0.000000
Maximum 3.000000 47.00000 43400.00 31.06000 207.6400 9.700000 1.000000
Minimum 0.000000 0.000000 210.0000 -1.560000 22.81000 1.900000 0.000000
Std. Dev. 0.852832 7.852569 11757.04 7.280578 37.88177 2.356309 0.473333
Skewness -0.102443 0.576137 1.070738 2.023545 0.946551 0.599399 0.733352
Kurtosis 2.203760 4.880762 2.816475 6.415826 3.779534 1.961636 1.537805
Jarque-Bera 1.718108 12.36520 11.74149 71.28563 10.65342 6.393099 10.90180
Probability 0.423563 0.002065 0.002821 0.000000 0.004860 0.040903 0.004292
Sum 110.0000 1321.000 647120.0 344.2400 4872.770 307.8000 20.00000
Sum Sq.
Dev.43.63934 3699.770 8.29E+09 3180.409 86101.70 333.1315 13.44262
42
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 46/50
ANNEX F
Estimated Sovereign Credit Ratings of the 61 Countries
Based on the Threshold Values for the Latent Variable Rating*
Estimated Rating
Country Actual Rating* Predicted
Argentina 0 2.662718 1
Australia 3 15.73638 3
Austria 3 16.96641 3
Belgium 2 16.57275 3
Bolivia 1 2.434898 1
Brazil 1 3.570914 1
Bulgaria 2 5.69842 1
Canada 3 16.73006 3
Chile 2 10.23751 2
China 2 7.715287 2
Colombia 1 4.748479 1
Costa Rica 1 5.672421 1
Croatia 2 7.897141 2
Czech Republic 2 8.92021 2
Denmark 3 19.71007 3
Dominican Republic 0 3.272194 1
Ecuador 0 2.692628 1
Egypt 1 4.950696 1El Salvador 1 5.402668 1
Estonia 2 10.56556 2
Finland 3 18.62567 3
France 3 14.28819 2
Germany 3 15.73463 3
Ghana 1 2.327714 1
Greece 2 8.69783 2
Hungary 2 9.065073 2
India 1 4.426246 1
Indonesia 1 2.523217 1
Ireland 3 17.62877 3Israel 2 12.66967 2
Italy 2 11.66778 2
Japan 2 16.40833 3
Kazakhstan 2 5.306046 1
43
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 47/50
Estimated Rating
Country Actual Rating* Predicted
Latvia 2 7.293146 2Lithuania 2 8.480582 2
Malaysia 2 11.64169 2
Mali 1 5.388866 1
Mexico 2 6.515941 2
Mozambique 1 1.80312 1
Netherlands 3 18.05871 3
New Zealand 2 15.7579 3
Norway 3 20.79651 3
Panama 1 6.387555 2
Peru 1 4.740521 1
Philippines 1 3.655889 1Poland 2 5.63982 1
Portugal 2 11.33786 2
Romania 1 2.441559 1
Russia 1 3.153059 1
Slovakia 2 7.496783 2
South Africa 2 5.460088 1
Spain 3 12.92634 2
Sweden 3 18.39839 3
Thailand 2 7.383011 2
Tunisia 2 8.092753 2
Turkey 1 1.590947 1
United Kingdom 3 16.56465 3
United States 3 16.74525 3
Uruguay 1 4.940971 1
Venezuela 1 0.285862 0
Vietnam 1 4.470525 1
44
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 48/50
ANNEX G
Predicted Probabilities Associated with the 4 Ordered Categories
of Sovereign Credit Ratings
Country Rating Rating=0 Rating=1 Rating=2 Rating=3 Sum
Argentina 0 0.153558 0.807809 0.038627 5.45E-06 1
Australia 3 3.81E-07 5.19E-05 0.278228 0.721719 1
Austria 3 1.11E-07 1.52E-05 0.101269 0.898715 1
Belgium 2 1.65E-07 2.25E-05 0.143127 0.85685 1
Bolivia 1 0.185557 0.783437 0.031002 4.34E-06 1
Brazil 1 0.068169 0.841209 0.090609 1.35E-05 1
Bulgaria 2 0.00864 0.535881 0.455366 0.000113 1
Canada 3 1.41E-07 1.92E-05 0.124896 0.875085 1
Chile 2 9.31E-05 0.012518 0.97689 0.0105 1
China 2 0.001158 0.136093 0.861898 0.000851 1Colombia 1 0.022038 0.733524 0.244395 4.38E-05 1
Costa Rica 1 0.008866 0.542095 0.448929 0.00011 1
Croatia 2 0.000966 0.116136 0.881877 0.001021 1
Czech Republic 2 0.000347 0.045163 0.951655 0.002834 1
Denmark 3 0 9.75E-07 0.007197 0.992802 1
Dominican Republic 0 0.089771 0.841398 0.068821 1.00E-05 1
Ecuador 0 0.14971 0.810531 0.039753 5.61E-06 1
Egypt 1 0.018076 0.698244 0.283627 5.37E-05 1
El Salvador 1 0.011579 0.604824 0.383513 8.43E-05 1
Estonia 2 6.71E-05 0.009049 0.976367 0.014517 1
Finland 3 0 2.88E-06 0.020991 0.979006 1
France 3 1.62E-06 0.000221 0.621105 0.378673 1
Germany 3 3.82E-07 5.20E-05 0.278581 0.721366 1
Ghana 1 0.202303 0.769754 0.027939 3.90E-06 1
Greece 2 0.000434 0.055774 0.941522 0.00227 1
Hungary 2 0.000301 0.039316 0.957109 0.003275 1
India 1 0.030164 0.779947 0.189858 3.18E-05 1
Indonesia 1 0.172578 0.793649 0.033768 4.74E-06 1
Ireland 3 0 7.82E-06 0.054912 0.94508 1
Israel 2 8.18E-06 0.001112 0.891107 0.107772 1
Italy 2 2.23E-05 0.003024 0.954485 0.042469 1Japan 2 1.95E-07 2.65E-05 0.164496 0.835477 1
Kazakhstan 2 0.012739 0.626239 0.360946 7.66E-05 1
Latvia 2 0.001766 0.193497 0.804179 0.000558 1
Lithuania 2 0.000539 0.068367 0.929265 0.001828 1
Malaysia 2 2.29E-05 0.003104 0.955453 0.04142 1
45
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 49/50
Country Rating Rating=0 Rating=1 Rating=2 Rating=3 Sum
Mali 1 0.011738 0.607923 0.380256 8.32E-05 1
Mexico 2 0.003833 0.341647 0.654263 0.000257 1
Mozambique 1 0.299986 0.683287 0.016725 2.31E-06 1Netherlands 3 0 5.09E-06 0.036422 0.963573 1
New Zealand 2 3.73E-07 5.08E-05 0.273929 0.72602 1
Norway 3 0 3.29E-07 0.00244 0.99756 1
Panama 1 0.004356 0.370702 0.624717 0.000226 1
Peru 1 0.02221 0.734819 0.242928 4.35E-05 1
Philippines 1 0.062965 0.839162 0.097858 1.47E-05 1
Poland 2 0.009157 0.549856 0.440881 0.000107 1
Portugal 2 3.10E-05 0.004201 0.964865 0.030903 1
Romania 1 0.184552 0.784241 0.031202 4.37E-06 1
Russia 1 0.099993 0.83843 0.061568 8.89E-06 1
Slovakia 2 0.001441 0.16379 0.834085 0.000684 1South Africa 2 0.01094 0.591798 0.397172 8.93E-05 1
Spain 3 6.33E-06 0.000861 0.864084 0.135049 1
Sweden 3 0 3.62E-06 0.026208 0.973789 1
Thailand 2 0.001614 0.179913 0.817862 0.000611 1
Tunisia 2 0.000795 0.097548 0.900416 0.001241 1
Turkey 1 0.346334 0.640093 0.013571 1.86E-06 1
United Kingdom 3 1.66E-07 2.27E-05 0.144124 0.855854 1
United States 3 1.39E-07 1.89E-05 0.123246 0.876735 1
Uruguay 1 0.018249 0.700043 0.281655 5.31E-05 1
Venezuela 1 0.661479 0.334804 0.003717 5.06E-07 1
Vietnam 1 0.028895 0.774311 0.196761 3.32E-05 1
46
7/31/2019 Determinants Of Sovereign Credit Ratings
http://slidepdf.com/reader/full/determinants-of-sovereign-credit-ratings 50/50
References
Alfonso A. (2003), “Understanding the Determinants of Sovereign Debt Ratings:Evidence of the Two Leading Agencies”, Journal of Economics and Finance, 27, 56-74.
Bissoondoyal-Bheenick, Emawtee, Brooks, Robert and Yip, Angela Y.N. “Determinantsof Sovereign Ratings: A Comparsion of Case-Based Reasoning and Ordered Probit
Approaches.” Monash University Working Paper 9/05 (May 2005).
Cantor, Richard and Packer, Frank. “Determinants of Sovereign Credit Ratings.” FRBNY
Economic Policy Review (October 1996).
Canuto, Otaviano, Dos Santos, Pablo Fonseca P. and De Sa Porto, Paulo C. “Macroeconomics
and Sovereign Risk Ratings.” (Washington: January 2004).
Gande, Amar and Parsley, David. “Sovereign Credit Ratings and International Portfolio Flows.”
(Tennessee: October 2004).
Hilscher, Jens and Nosbusch, Yves. “Determinants of Sovereign Risk.” (November 2004).
Kraussl, Roman. “Sovereign Credit Ratings and their Impact on Recent Financial Crises.”Center for Financial Studies Working Paper No. 2000/04 (April 2000).
Kraussl, Roman. “Do Changes in Sovereign Credit Ratings Contribute to Financial
Contagion in Emerging Market Crises?” Center for Financial Studies Working Paper No.2003/22 (August 2003).
Mellios, Constantin and Paget-Blanc, Eric. “Which Factors Determine Sovereign Credit
Ratings.”
Reinhart, Carmen M. “Default, Currency Crises, and Sovereign Credit Ratings.”
Souza Sobrinho, N. “Sovereign Risk in Developing Countries” (UCLA: 2004).
Standard and Poor’s. Sovereign Credit Ratings: A Primer. (March 15, 2004).
Transparency International. Corruption Perceptions Index 2003.
World Bank. Global Development Finance 2004.
World Bank. World Development Indicators.
Recommended