Determinants Of Sovereign Credit Ratings

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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 [email protected].



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.



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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



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


17



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


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



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


19



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



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



20



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



[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



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



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



[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



23



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




24



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



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



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



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



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



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



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



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


32



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



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



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



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



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



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



ANNEX D

One-way Tabulations for the Dependent and Independent Variables

Tabulation of RATINGNumber of categories: 4


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




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



Tabulation of GNI

Number of categories: 5Cumulative Cumulative


[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



[-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



[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



Tabulation of CORRUPTION

Number of categories: 5Cumulative Cumulative


[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


0 41 67.21 41 67.21

1 20 32.79 61 100.00

Total 61 100.00 61 100.00

41



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



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



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



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



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



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