metadata: authors: 0: author: Basu,Kaushik
1: author: De,Supriyo
2: author: Ratha,Dilip K.
3: author: Timmer,Hans
count: World,Philippines,Kazakhstan,Indonesia,Ethiopia,Azerbaijan
docna: 0: docna: Sovereign ratings in the post-crisis world :
            an analysis of actual, shadow and relative risk ratings
docty: Policy Research Working Paper
subtopic: Judicial System Reform,National Governance,Government Policies,Capital Markets and Capital Flows,Capital Flows,Youth and Governance,Inflation,Governance Indicators,Natural Disasters
teratopic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,Private Sector Development
abstracts: cdata!: This paper analyzes the evolution of
            sovereign credit ratings in the wake of the global financial
            crisis by studying changes in actual, shadow, and relative
            ratings between 2008 and 2012. For countries that do not
            have a rating from the major rating agencies, shadow ratings
            are estimated as a function of macroeconomic, structural,
            and governance variables. The shadow rating exercise
            confirms earlier findings in the literature that even after
            the financial crisis, many unrated countries appear to be
            more creditworthy than previously believed and can access
            international capital markets. The paper also develops a new
            rating scale called the "relative risk rating,"
            which ranks countries according to their actual or shadow
            ratings after controlling for changes in the world weighted
            average rating. When relative ratings in 2012 are compared
            with the first half of 2008, the world average rating is
            found to be weaker because of the financial crisis. The
            relative rating improved in developing economies such as
            Azerbaijan, Ethiopia, Kazakhstan, Indonesia, and the
            Philippines, whereas it deteriorated in crisis-affected
            high-income countries such as Cyprus, Greece, Spain,
            Portugal, Ireland, and Egypt. Interestingly, India, Jordan,
            Poland, and the United Kingdom had their rating outlook
            downgraded by the rating agencies, but their relative rating
            actually improved as other countries suffered even worse
            downgrades. A regression model is used to analyze the
            relative contributions of different variables to rating
            changes during 2008-2012, a helpful feature for policy
            makers interested in improving sovereign ratings.
geo_reg_key: 517191,517208,517198,545134,82689,119229
repnme: repnme: Sovereign ratings in the post-crisis world
            : an analysis of actual, shadow and relative risk ratings
display_title: Sovereign ratings in the post-crisis
            world : an analysis of actual, shadow and relative risk ratings
listing_relative_url: /research/2013/10/18360008/sovereign-ratings-post-crisis-world-analysis-actual-shadow-relative-risk-ratings
topicv3: Justice m1326106 840,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Capital Markets m1326967 332,Governance Indicators m1327488 834,Erosion m1503662 3202
publishtoextweb_dt: 2013-10-01T00:00:00Z
historic_topic: Macroeconomics and Economic Growth,Finance and Financial Sector Development,Private Sector Development
no_of_pages: 29
author_keys: 0: author_key: 86589
1: author_key: 1292393
2: author_key: 99413
3: author_key: 517433
model_extractions: WPS6641 Policy Research Working Paper 6641 Sovereign Ratings in the Post-Crisis World An Analysis of Actual, Shadow and Relative Risk Ratings _Kaushik Basu Supriyo De Dilip Ratha Hans Timmer_ The World Bank Development Economics Vice Presidency Office of the Chief Economist October 2013
Policy Research Working Paper 6641

# **Abstract**

This paper analyzes the evolution of sovereign credit ratings in the wake of the global financial crisis by studying changes in actual, shadow, and relative ratings between 2008 and 2012. For countries that do not have a rating from the major rating agencies, shadow ratings are estimated as a function of macroeconomic, structural, and governance variables. The shadow rating exercise confirms earlier findings in the literature that even after the financial crisis, many unrated countries appear to be more creditworthy than previously believed and can access international capital markets. The paper also develops a new rating scale called the “relative risk rating,” which ranks countries according to their actual or shadow ratings after controlling for changes in the world weighted average rating. When relative ratings in 2012 are compared with the first half of 2008, the world average rating is found to be weaker because of the financial crisis. The relative rating improved in developing economies such as Azerbaijan, Ethiopia, Kazakhstan, Indonesia, and the Philippines, whereas it deteriorated in crisis-affected high-income countries such as Cyprus, Greece, Spain, Portugal, Ireland, and Egypt. Interestingly, India, Jordan, Poland, and the United Kingdom had their rating outlook downgraded by the rating agencies, but their relative rating actually improved as other countries suffered even worse downgrades. A regression model is used to analyze the relative contributions of different variables to rating changes during 2008–2012, a helpful feature for policy makers interested in improving sovereign ratings.

This paper is a product of the Office of the Chief Economist, Development Economics Vice Presidency. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The authors may be contacted at Dratha@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Sovereign Ratings in the Post-Crisis World: An Analysis of Actual, Shadow and Relative Risk Ratings

## Kaushik Basu, Supriyo De, Dilip Ratha and Hans Timmer

Development Economics Vice Presidency (DEC) The World Bank, Washington DC 20433 _Keywords:_ _Sovereign rating, country risk, global financial crisis, development finance_

--------------------------------------------

The authors would like to thank participants of two brown-bag presentations at the World Bank Treasury and Corporate Finance Department, especially to Bernard Badré, Merli Baroudi, Miles Brennan, Vivian Hon, Lee Meddin, Sanket Mohapatra, and Ramesh Ramiah, for constructive comments. Thanks are also due to Bart Oosterfeld and colleagues at Moody’s Investor Service for helpful comments and suggestions. Also thanks to Gemechu Ayana Aga, David Horowitz, and Hazel Macadangdang for assistance with data. Views expressed in this paper the authors’ own, and not those of the World Bank or any other institutions they may be associated with.
# Sovereign Ratings in the Post-Crisis World: An Analysis of Actual, Shadow and Relative Risk Ratings

## 1. Introduction

Sovereign credit ratings assigned by the major rating agencies (such as Fitch, Moody’s and Standard and Poor’s) play a major role in determining the government’s access to international capital markets. Although sovereign ratings relate to debt and creditworthiness of the central government, in effect they serve as a barometer of confidence and a ceiling for creditworthiness for the private sector as well. They influence the borrowing costs of private entities and in a wider sense overall investment flows. The sovereign rating is often a benchmark and sub-sovereign entities, such as companies and banks based in developing countries, rarely get a rating higher than the sovereign’s. Furthermore, sub-sovereign ratings in developing and emerging markets are linked to sovereign rating movements (Ferri, Liu and Majnoni 2001).

Since the global financial crisis that began in the second half of 2008, there has been a major realignment of sovereign ratings that has especially affected advanced economies. At the same time, some large developing economies (for example, India) have had minor erosions in their ratings or outlooks. However, capital flows to these economies have remained robust, suggesting that these flows may be guided not so much by the absolute credit ratings, but by _relative_ ratings. Given the massive downgrades of certain highly rated economies, other economies that have not suffered these downgrades would become relatively more attractive, even if their absolute ratings remain the same or are slightly eroded (Basu et al. 2012).

This paper carries out a comprehensive analysis of sovereign rating developments from the pre-crisis (July 2008) to post-crisis (December 2012) period. It develops a new rating measure which we shall refer to as the “relative risk rating.” We then go on to compute the relative risk ratings for all countries for which data are available and use an econometric model to analyze the contribution of various factors to changes in the relative and absolute sovereign ratings of countries. Since nearly 50 developing countries do not have a rating from the major rating agencies, the paper uses a modified version of the methodology of Ratha, De and Mohapatra (2011) to develop shadow ratings for some countries.<sup>1</sup> A useful by-product of the shadow rating exercise is a confirmation that the rating model still works: despite the disturbance caused by the global financial crisis, a handful of macroeconomic, structural and governance variables are sufficient to predict nearly 90 percent of the variations in ratings. The model also revealed that many currently unrated

> 1 Even the ratings of the rated countries are reaffirmed only periodically – see Ratha, De and Mohapatra (2011). The rating process is costly in terms of the requirement of devoting executive time to meet the process requirements. Furthermore, the stigma and political cost associated with a low rating may make politicians hesitant in approaching ratings agencies. Consequently many countries, especially low income ones, avoid going in for a rating.

2
countries are not necessarily at the tail end of the rating spectrum; that is, some countries appear to be even investment grade and many are in the B or BB category, the rating range for emerging markets, and could potentially access international capital markets. During this period, the average rating of high-income countries deteriorated, while that of developing countries registered a slight improvement. Some of the largest improvements in relative ratings occur in developing economies, including newly rated ones such as Azerbaijan, unrated ones such as Ethiopia, and large emerging markets such as Kazakhstan, Indonesia and the Philippines. Peripheral European economies such as Cyprus and Greece perform poorly in terms of changes in relative and actual ratings. Politically troubled Tunisia and Egypt also face deteriorated ratings. The contributions of various factors to changes in ratings are also deconstructed.

This research advances the knowledge frontier in several ways and has significant policy implications:

- 1) By juxtaposing actual, shadow and relative ratings, it allows the study of sovereign ratings to cover (almost) all countries in the world, including many unrated countries.

- 2) To our knowledge, this is the first confirmation of the rating model after the disturbances of the global financial crisis; and it shows that cyclical factors (such as growth or inflation) have become somewhat less important than structural factors (such as the rule of law) in the rating determination process.

- 3) The empirical results show that relative risk ratings can improve even as the absolute rating is downgraded or put on negative credit watch.

- 4) By analyzing contributions to rating changes, it provides a means of directing policy efforts in developing countries in a manner whereby concrete steps can be taken to improve ratings.

The rest of the paper is organized as follows: Section 2 provides an overview of the literature; Section 3 analyzes the numerical conversion of alphabetical ratings and its characteristics; Section 4 reports empirical estimation of the rating model; Section 5 discusses predicted (shadow) ratings; Section 6 develops the formula for computing relative risk ratings; Section 7 discusses the contribution of various factors to relative and actual rating changes; and Section 8 concludes the paper.

# 2. Literature review

The seminal analysis of sovereign ratings is that of Cantor and Packer (1996). They describe the determinants and impact of sovereign credit ratings given by the major U.S. based rating agencies, Moody’s Investors Service and Standard and Poor’s. They state that both agencies use a relatively small set of well-defined criteria with similar weights to arrive at credit ratings. Sovereign debt yields broadly share the rankings assigned by the agencies. Ratings appear to have correlation with yields over and above publicly available information. Dadush and Dasgupta (2001) examine capital account liberalization risks in developing economies. They carry out a pioneering work in converting the alphabetic rating scores into numeric 3
ratings. Afonso, Gomes and Rother (2009) use ordered logit and probit plus random effects ordered probit methods with panel data to study determinants of sovereign ratings. They find that the last procedure is the best for panel data as it takes into account the additional crosssection error. Bissoondoyal-Bheenick (2005) analyzes quantitative determinants that influence the sovereign ratings given by the main rating agencies, Standard and Poor’s and Moody’s. The paper finds that current economic financial indicators alone do not determine the ratings. Furthermore, economic variables do not have the same importance for high-rated countries with a history of financial stability in comparison with low-rated countries subject to structural changes. Hauner, Jonas and Kumar (2010) examine whether rating agencies and investors perceive the sovereign risk of the new member states of the European Union as different from that of other emerging markets. Their results suggest a favorable treatment of new member states, probably because of higher policy credibility bestowed by EU membership. Gaillard (2009) studies Moody’s ratings for sub-national entities and provides a useful linear transformation of the ratings into numerical scores. Moody’s Investor Service (2011) provides a guide to the rating system of the agency.

A large body of work also reviews the performance of the rating agencies and their rating actions. Ferri, Liu and Stiglitz (1999) explore the role and reactions of the rating agencies following the East Asian Crisis of 1997. They state that the rating agencies aggravated the crisis. After failing to predict the crisis, they attempted to recover their reputation by being more conservative. So they downgraded economies more than what economic fundamentals warranted. Reinhart (2002) states that in emerging market economies there is a strong link between currency crises and default. The analysis suggests that sovereign credit ratings systematically fail to predict currency crises but do considerably better in predicting defaults. Tennant and Tracy (2013) examine whether controlling for macroeconomic and institutional factors, S&P has been less generous with sovereign ratings issued to developing countries when compared with those assigned to developed countries. Using an ordered-probit mixed-effects regression with annual data for around 70 countries across the 1999 to 2009 period, they find support for their hypothesis. Altman et al. (2004) find that rating agencies focus more on the long term and give less importance to short-term indicators of credit quality. Löffler (2005) finds that rating changes happen only when the difference between the actual agency rating and that predicted by a model exceeds a certain threshold. Furthermore, rating migrations are slow and partial. Bruner and Abdelal (2005) elucidate the role of credit rating agencies in global capital markets. They describe the host of problems that arise when their ratings are given the force of law and discuss public policy alternatives. Barua (2011) carries out a critical appraisal of the credit rating system in the wake of the global financial crisis. He finds that rating agencies face a “revenue versus reputation” conflict of interest. Furthermore, rating agencies are pro-cyclical and failed to provide appropriate early warnings. He also detects flaws in their methodology; the variables they use often do not capture important economic aspects, such as currency misalignment, that may turn out to be crucial factors in precipitating an adverse economic event. As a constructive remedy, he suggests a balance between the credit rating and early warning functions. Becker and Milbourn (2011) find that increase in competition from Fitch coincided with lower ratings from the incumbents, that is, rating levels went up. But the correlation 4
between ratings and market-implied yields fell and the ability of ratings to predict default deteriorated. Alsakka and Gwilym (2012) analyze the sovereign watch and outlook signals from Moody’s, Standard and Poor’s and Fitch. They demonstrate that each agency’s actions indicate or imply different policies. For instance, Standard and Poor’s has more emphasis on short-term accuracy while Moody’s actions are consistent with greater stability.

With regard to the impact of ratings, Kim and Wu (2008), analyze how sovereign credit ratings history provided by independent rating agencies affects domestic financial sector development and international capital inflows to emerging countries. They find strong indication that sovereign credit ratings affect financial intermediary sector developments and capital flows. Afonso, Furceri and Gomes (2012) use EU sovereign bond yields and CDS spreads daily data to carry out an event study analysis on the reaction of government yield spreads before and after announcements from rating agencies. Their results demonstrate significant responses of government bond yield spreads to changes in rating notations and outlook, particularly in the case of negative announcements. Alsakka and Gwilym (2012a) analyze how the foreign exchange markets reacted to sovereign credit events prior to (20002006) and during the crisis (2006-2010). They find that rating agencies’ signals do affect the own country exchange rate and identify strong spillover effects to other countries’ exchange rates in that region.

# 3. Characteristics and numerical analytics of sovereign ratings

Sovereign ratings are assigned by various agencies, but the three prominent ones are Standard and Poor’s, Moody’s and Fitch. The broad rating scales are similar, allowing for the classification of around 20 ratings with each divided into three outlooks, namely, “positive,” “stable,” and “negative.” These outlooks indicate, respectively, the possibilities of an upgrade, remaining at the same rating, or having a lower rating. Systems of converting the alphabetical ratings into numerical scores have been proposed in papers such as Dadush and Dasgupta (2001) and Gaillard (2009). We build on this system but introduce an innocuous cardinal change (Sen 1977; Basu 1983) of multiplying each number by 3, as done in Basu et al. (2012).<sup>2</sup> This modification allows for a richer analysis.<sup>3</sup> Since in addition to the grades, the agencies give three outlook classifications, positive, stable, and negative for each grade, we refine the conversion system further by subdividing each grade across the three outlook classes and assigning a numerical score corresponding to each. Furthermore, following Ratha, De and Mohaptra (2011), better scores are assigned smaller numbers since the scores for the lower-than-default category are indeterminate. The resulting scoring system is described in Table 1.

> 2 The measure proposed in Basu et al. (2012) is a non-cardinal, positive monotone transformation of the measure developed in this paper.

> 3 It should be recognized that rankings could be done in many different ways, satisfying different axiomatic properties, with the additional hazard of there being sets of reasonable axioms which are together inconsistent (see, for example, Pattanaik and Xu, 2007). We use a very simple form here but there is scope for developing this further in different ways.

5
**Table 1: Ratings, outlooks and numerical scores**

|**S&P**|**Fitch**|**Moody's**|**Outlook**|**Outlook adjusted score**|
|---|---|---|---|---|
|AAA|AAA|Aaa|P<br>S|Does not exist<br>1|
||||N|2|
|AA+|AA+|Aa1|P<br>|3<br>|
||||S|4<br>|
|AA|AA|Aa2|N<br>P<br>S|5<br>6<br>7|
||||N|8|
|AA-|AA-|Aa3|P|9|
||||S<br>|10<br>|
||||N|11|
|A+|A+|A1|P|12|
||||S<br>N|13<br>14|
|A|A|A2|<br>P<br>S|<br>15<br>16|
||||<br>N|<br>17|
|A-|A-|A3|<br>P|18|
||||S|19|
||||N|20|
|BBB+|BBB+|Baa1|P|21|
||||S<br>|22<br>|
||||N|23|
|BBB|BBB|Baa2|P<br>|24<br>|
||||S<br>N|25<br>26|
|BBB-|BBB-|Baa3|P|27|
||||S|28|
||||N|29|
|BB+|BB+|Ba1|P|30|
||||S|31|
||||N|32|
|BB|BB|Ba2|P|33|
||||S<br>|34<br>|
|BB-|BB-|Ba3|N<br>P|35<br>36|
||||S|37|
||||<br>N|<br>38|
|B+|B+|B1|P<br>|39<br>|
||||S|40|
||||N|41|
|B|B|B2|P<br>|42<br>|
||||S|43|
||||N|44|
|B-|B-|B3|<br>P|<br>45|
||||S|46|
||||N|47|
|CCC+|CCC+|Caa1|P|48|
||||S<br>N|49<br>50|
|CCC|CCC|Caa2|P|51|
||||S|52|
||||N|53|
|CCC-|CCC-|Caa3|P<br>|54<br>|
|CC|CC|Ca|S<br>N|55<br>56|
||||P|57|
||||S|58|
||||N|59|
|C|C|C||60|

Notes: Based on Gaillard (2009), Ratha, De and Mohapatra (2011) and Basu, et al. (2012), as adapted by the authors. ‘P’ signifies Positive, ‘S’ Stable and ‘N’ Negative.

The numerical values of the ratings for the high-income and developing countries are shown for the pre-crisis period (July 2008) and the post-crisis period (December 2012) in Figures 1–4. By and large there is a high degree of correlation among the ratings for the three agencies.

6
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agencies. This suggests that just as one sees evidence of herd behavior among corporations and investors, there may be herd behavior among rating agencies. Alternatively, the rating agencies respond to broadly similar variables and information. Whether that is true or not, the fact remains that analyzing the rating of any one agency gives a fair indication of the rating of the other two agencies. Explicit announcements from rating agencies indicate that they rely on certain key economic and structural variables for arriving at ratings. For instance, Moody’s assesses sovereigns on the basis of economic strength, institutional strength, fiscal strength and susceptibility to event risk. Furthermore, there have been realignments in rating methods following the crisis (Moody’s 2013b).

# 4. Empirical model for sovereign ratings

Based on the numerical assignment of scores for agency ratings, the following equation is used to analyze various macroeconomic, structural and governance factors determining ratings: _Sovereign rating = α + β1(GDP growth) + β2(log of GNI per capita), + β3(Reserves/ Imports + Short term debt) + β4(External debt/Exports + Remittances) + β5(GDP volatility) + β6(Rule of law) + β7(Inflation) + β8(Government debt) + β9(log of GDP) + β10(High income dummy) + error_ (1) This model is a modified version of the one used in Ratha, De and Mohapatra (2011), which in turn builds on Cantor and Packer (1996). The dependent variable is the numerical score described earlier encompassing both the rating and the outlook (1–59 for the available ratings scale with lower numbers assigned to better ratings; selective default and C rating excluded). On the right hand side, GDP growth captures medium-term economic performance, GNI per capita reflects market and economic development, the ratio of reserves to the sum of imports and short-term debt and the ratio of external debt to the sum of exports and remittances serve as indicators of short-run external liquidity and solvency, the rule of law encompasses the overall impact of governance, and total GDP indicates the size of the economy. The variables and data sources are listed in Table 2.

To avoid repetition, we confine the econometric analysis to S&P long-term foreign currency ratings. Given the high degree of correlation among the ratings of the three agencies, the results are expected remain qualitatively similar if ratings of other agencies were used. The explanatory variables are one period lagged (2007 explanatory variables for 2008 ratings and 2011 ones for 2012 ratings) values of GDP growth (3-year moving average), log of GNI per capita, ratio of reserves to sum of imports and short term debt, ratio of total external debt to sum of exports and remittances, GDP volatility (5-year standard deviation), rule of law (+2.5 to –2.5 from World Governance Indicators, Kraay, Kaufmann and Mastruzzi 2010), inflation, general government gross debt (% of GDP), log of GDP and a dummy for highincome countries. The use of lagged explanatory variables helps address, to some extent, the simultaneity problem.

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**Table 2: Variable composition and data sources**

|Variable|Composition|Data sources|
|---|---|---|
|Sovereign rating|Numerical values as in Table 1.|Standard & Poor’s.|
|GDP growth|3-year moving average of GDP growth.<br>Annual percentage growth rate of GDP<br>at market prices based on constant local<br>currency. Aggregates are based on<br>constant 2000 U.S.dollars.|World Bank, World<br>Development Indicators.|
|Log of GNI per<br>capita|Natural log of GNI per capita. GNI per<br>capita is the gross national income,<br>converted to U.S. dollars using the<br>World Bank Atlas method, divided by<br>the midyearpopulation.|World Bank, World<br>Development Indicators.|
|Reserves/ Imports +<br>Short term debt|Total reserves divided by sum of<br>imports and short-term debt. Total<br>reserves (includes gold, current US$).<br>Imports of goods and services (constant<br>2000 US$). Short-term external debt,<br>residual maturity.|World Bank, World<br>Development Indicators for<br>reserves and imports, and Bank<br>of International Settlements for<br>short-term debt.|
|External<br>debt/Exports +<br>Remittances|External debt divided by sum of exports<br>and remittances. External debt stocks<br>(US$), total of ‘Liabilities to BIS banks,<br>consolidated, total’, ‘International debt<br>securities, all maturities’, ‘Official<br>bilateral loans, total’ and ‘Multilateral<br>loans, total’. Exports of goods and<br>services (constant 2000 US$).<br>Remittances in US$.|Joint External Debt Hub for<br>constructing external debt.<br>World Bank, World<br>Development Indicators database<br>for exports and Global Migration<br>Report for remittances.|
|GDP volatility|5-year standard deviation of GDP<br>growth|World Bank, World<br>Development Indicators.|
|Rule of law|Rule of law with values from +2.5 to -<br>2.5 spanning the best and the worst<br>scores,respectively.|Estimates from World<br>Governance Indicators (Kraay,<br>Kaufmann and Mastruzzi,2010)|
|Inflation|Inflation, consumer prices (annual %).|World Bank, World<br>Development Indicators.|
|Government debt|General government gross debt (US$)|International Monetary Fund,<br>World EconomicOutlook.|
|log of GDP|Natural log of GDP|World Bank, World<br>Development Indicators.|

The model is run separately for data pertaining to ratings as of July 2008 (before the crisis set in) and December 2012. A pooled regression across the two periods is also carried out. Additional explanatory variables in the pooled model are a dummy for the year 2008 and a term interacting GDP volatility with the 2008 dummy (to check if the relationships were impacted by the financial crisis, see below).<sup>4</sup>

> 4 Following Ratha, De and Mohapatra (2011), we eliminate outliers where the residual is greater than one standard deviation of the dependent variable’s distribution. For the 2008 estimates there are no such outliers; for 2012, the outliers are Argentina, Cyprus and Spain; and for the pooled regression, the outliers are Argentina and Cyprus.

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The results are reported in Table 3.

**Table 3: Estimation results for determinants of sovereign ratings**

|**Dependent variable:**|**S&P 2008**|**S&P 2012**|**2008-2012**|**S&P 2008**|**S&P 2012**|
|---|---|---|---|---|---|
|**Sovereign rating**|**OLS**|**OLS**|**Pooled**|**O. Probit**|**O. Probit**|
|GDP growth|-0.43**<br>(0.23)|-.55**<br>(0.27)|-0.53***<br>(0.17)|-0.10<br>(0.06)|-0.13**<br>(0.06)|
|Log of GNI per capita|-3.66***|-3.60***|-3.82***|-0.80***|-0.87***|
||(0.81)|(0.86)|(0.58)|(0.26)|(0.20)|
|Reserves to import and|-4.07***|-3.78*|-3.92***|-0.97**|-0.70|
|ST debt|(1.21)|(1.99)|(1.03)|(0.39)|(0.45)|
|External debt to exports|0.59**|0.73|0.78***|0.16**|0.15*|
|& remittances|(0.25)|(0.51)|(0.27)|(0.06)|(0.09)|
|GDP volatility|1.58***|-0.13|-0.17|0.40**|-0.05|
||(0.56)|(0.33)|(0.25)|(0.16)|(0.07)|
|Rule of law|-6.34***|-7.91***|-7.46***|-1.74***|-1.64***|
||(1.03)|(0.91)|(0.71)|(0.27)|(0.23)|
|Inflation|0.48**|0.12**|0.19***|0.12**|0.04***|
||(0.20)|(0.05)|(0.05)|(0.05)|(0.01)|
|Govt. debt|0.08***|0.14***|0.11***|0.02***|0.03***|
||(0.02)|(0.03)|(0.02)|(0.004)|(0.006)|
|Log of GDP|-1.65***|-2.32***|-1.94***|-0.49***|-0.52***|
||(0.28)|(0.31)|(0.23)|(0.09)|(0.09)|
|High income dummy|-5.83***|-3.8*|-3.82**|-1.56***|-0.53|
||(1.84)|(2.10)|(1.51)|(0.52)|(0.41)|
|Intercept|96.47***<br>(7.68)|113.56***<br>(8.21)|107.48***<br>(5.77)|||
|GDP volatility X 2008<br>dummy|||1.61***<br>(0.57)|||
|2008 dummy|||-2.59**<br>(1.18)|||
|Observations|94|90|191|94|90|
|Adj. R-sq. (OLS)|0.93|0.87|0.90|||
|Pseudo-R-sq. (O. Probit)||||0.40|0.30|
|Logpseudo-likelihood||||-172.99|-202.81|

_* significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors in parenthesis._ The empirical estimates are arrived at using ordinary least squares (OLS). OLS is appropriate if we are willing to assume that the ratings scale is cardinal and adjacent rating/outlook states have equal distance. However, credit ratings are often estimated using ordered probit which relaxes this assumption and requires the scale to be merely ordinal. We therefore also report ordered probit estimates. But for estimating ratings for developing countries and shadow ratings for unrated countries, ordered probit suffers from some drawbacks. Its ability to estimate ratings at the lower end of the ratings spectrum where data 11
points are sparse is limited.<sup>5</sup> Furthermore, it cannot be used to infer ratings out of the range of the sample. On the other hand, the drawbacks of OLS in estimating ordered categorical data are somewhat mitigated when the number of categories increases (Larabee 2011). This is only to be expected because when the number of categories increases, the categorical variable behaves closer to a continuous one. In this trade-off, we find that OLS serves our purpose better because it can be used to estimate out-of-range or extremes-of-the-range values (for instance, very low rated countries) and has good in-sample predictive powers. The assumption of a linear ratings scale does not mean that default risk is linear in ratings. Rather empirical evidence indicates that default risk is non-linear in ratings (Moody’s 2013a). Similarly, spreads are non-linear in ratings with a distinct jump below investment grade (Jaramillo and Tejada 2011).

For 2008, the OLS estimates have 94 observations and yield an adjusted R-square of 0.93. All the explanatory variables are statistically significant and have expected signs. GDP growth, log of GNI per capita, the reserve ratio, rule of law, log of GDP and the high-income dummy all have negative signs indicating that increases in these values leads to better rating outcomes (lower is better in the numerical conversion scale since smaller numbers correspond to higher ratings). This matches usual economic logic. GDP growth captures economic performance, GNI per capita indicates level of income and economic development, the reserve ratio reveals ability to meet international financial commitments, rule of law reflects various governance aspects and GDP is linked to overall size of the economy.

The high-income dummy indicates that high-income countries are generally rated higher than other countries even after accounting for factors such as GDP and GNI per capita. However, this impact was larger in 2008. External debt to imports-remittances, GDP volatility, inflation and government debt have negative signs. Larger values of these variables have a detrimental effect on ratings (since larger numerical scores correspond to worse ratings). External debt to imports-remittances and government are indebtedness indicators, while GDP volatility and inflation reflect macroeconomic instability. While the other variables have been used previously (Ratha, De and Mohapatra 2011), GDP is a new addition and is seen to have a significant impact. Furthermore, in the pooled regression, a slope dummy for GDP volatility is introduced to test for structural changes in the post-2008 scenario.

For the 2012 OLS estimates, we have 90 observations with an adjusted R-square of 0.87. While the overall fit for 2008 is better, indicating the relatively stable economic circumstances and less divergence in rating outcomes, the explanatory power of the model remains substantial. All variables other than external debt to imports-remittances and GDP volatility are significant and have the right signs. The pooled 2008-2012 model has 191 observations and an adjusted R-square of 0.9. All explanatory variables other than GDP volatility are significant and have the correct signs. However, the GDP volatility-2008

> 5 Our estimates show that for the lower ends of the rating spectrum, ordered probit predicted ratings are not very accurate. The 2008 estimates generate 28 cut-offs while the 2012 estimates have 30 cut-offs. Faced with a similar situation, Afonso, Gomes and Rother (2009) gave up trying to distinguish ratings below B-.

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**<u>Table 4: Predictions within one-third of a notch</u>**

|**Country**|**S&P 2012**|**Predicted 2012**|**_S&P June 2013_**|
|---|---|---|---|
|India|BBB- o-|BBB- os||
|Colombia|BBB- o+|BBB o-|_BBB os_|
|Zambia|B+ os|B+ os||
|Lithuania|BBB os|BBB os||
|Belarus|B- os|B- os|_B-o+_|
|Georgia|BB- os|BB- os||
|Nigeria|BB- os|BB- os||
|Mexico|BBB os|BBB o-|_BBBo+_|
|Vietnam|BB- os|BB- o-|_BB- os_|
|Serbia|BB- o-|B+ o+|_BB-o-_|
|Cape Verde|B+ os|B+ o-|_B+ os_|
|Botswana|A- os|A- o-|_A- os_|

Notes: “os” stands for stable outlook, “o+” for positive outlook and “o-” for negative outlook for actual ratings. Nomenclature for predicted ratings are deliberately kept different and unabbreviated.

The distribution of divergences of the predicted ratings for 2012 from the actuals is displayed in Figure 5. This shows a strong central tendency and a longer tail for negative divergences related to cases where the predicted rating is better than the actual rating arising largely due to the persistence of the 2008 shock for peripheral European economies. In certain cases the predicted rating is within one-third of a notch (that is close to the precise outlook state) (see Table 4).

**Table 5: Prediction higher than actual rating**<sup>**6**</sup>

|**Country**|**S&P 2012**|**Predicted 2012**|**_S&P June 2013_**|
|---|---|---|---|
|Turkey|BB os|BBB+positive|_BB+os_|
|Portugal|BB o-|BBBpositive|_BB os_|
|Hungary|BB os|BBBpositive|_BBo-_|
|Costa Rica|BB os|BBB os|_BB os_|
|Greece|B- os|BB- negative|_B-os_|
|Iceland|BBB- os|BBB+positive|_BBB-os_|
|Saudi Arabia|AA- os|AA+positive|_AA- o+_|
|Ghana|B os|BB-positive|_Bos_|
|Uruguay|BBB- os|BBB+ os|_BBB- os_|
|Dominican Rep|B+ os|BB os|_B+os_|
|Ecuador|B os|BB- os|_B os_|
|Bahrain|BBB o-|BBB+positive|_BBB os_|

Notes: “os” stands for stable outlook, “o+” for positive outlook and “o-” for negative outlook for actual ratings. Nomenclature for predicted ratings are deliberately kept different and unabbreviated.

In some cases the predicted rating is higher than the actual rating (Table 5). Some of these cases pertain to unanticipated political shocks while others relate to the continuation of the European crisis. In the latter case, it is possible that rating agencies are lagging in restoring ratings, as has been discussed in the past in the literature (Ferri, Liu and Stiglitz

> 6 Shadow ratings for some countries could not be calculated due to unavailability of data on ‘general government gross debt’. Instead, if ‘government net lending/borrowing’ (from IMF World Economic Outlook) is used in the regressions, the resulting shadow ratings are: Bangladesh B positive for 2012, lower than the actual rating of BB- stable; Republic of Korea AA positive for 2012, higher than the actual rating of A+ stable; Sri Lanka BB- negative for 2012, higher than the actual rating of B+ stable; and Samoa BB stable shadow rating for 2012.

14
1999; Barua 2011). At the other end of the spectrum are the cases – such as Belgium, Canada, Luxembourg, Singapore, and Japan – where the predicted rating is lower than the actual rating (Table 6).

**Table** **<u>6: Prediction lower than actual rating</u>**

|**Country**|**S&P 2012**|**Predicted 2012**|**_S&P June 2013_**|
|---|---|---|---|
|Belgium|AA o-|A+ stable|_AAo-_|
|Bulgaria|BBB os|BB+positive|_BBB os_|
|Canada|AAA os|AApositive|_AAAos_|
|El Salvador|BB- o-|B stable|_BB- o-_|
|Guatemala|BB os|B+positive|_BBos_|
|Thailand|BBB+ os|BBB-positive|_BBB+os_|
|Kazakhstan|BBB+ os|BBB-positive|_BBB+ os_|
|Luxembourg|AAA o-|AA negative|_AAAos_|
|Fiji|B os|CCC+ stable|_B os_|
|Singapore|AAA os|AA stable|_AAAos_|
|United Kingdom|AAA o-|AA-positive|_AAA o-_|
|Kenya|B+ os|CCC+positive|_B+os_|
|Trinidad&Tob.|A os|BBBpositive|_Aos_|
|Japan|AA- o-|BBB+ negative|_AA- o-_|

Notes: “os” stands for stable outlook, “o+” for positive outlook and “o-” for negative outlook for actual ratings. Nomenclature for predicted ratings are deliberately kept different and unabbreviated.

**<u>Table 7: Shadow ratings for unrated countries*</u>**

|Algeria|A- negative|
|---|---|
|Mauritius|BBB negative|
|St. Vincent & the Grenadines|B+positive|
|Dominica|B+positive|
|Swaziland|B+ stable|
|Moldova|B+ negative|
|Seychelles|Bpositive|
|Armenia|Bpositive|
|Bhutan|B stable|
|Tanzania|B negative|
|Mali|B negative|
|Solomon Islands|B-positive|
|Maldives|B- stable|
|Malawi|B- negative|
|Guyana|B- negative|
|Nepal|CCC+positive|
|Ethiopia|CCC+positive|
|Belize*|CCC+ stable*|
|Lao PDR|CCC+ stable|
|Togo|CCC+ stable|
|Nicaragua|CCC+ negative|
|Yemen,Rep.|CCC+ negative|
|Haiti|CCC+ negative|
|Gambia,The|CCC-positive|
|Kyrgyz Rep|CCC- negative|
|Cote d'Ivoire|CC positive|
|Sierra Leone|CC stable|

*Unrated refers to countries not rated by S&P. Belize was in selective default in December 2012 and came back with a rating of B- stable, that is a notch above our prediction in March 2013.

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The predicted shadow ratings for the unrated countries are reported in Table 7. It can be seen that countries like Algeria that have little need for debt have shadow ratings well within investment grade. Mauritius has an investment grade shadow rating. A fairly large range of countries such as Dominica, Moldova, Armenia, Bhutan and Mali have below investment grade shadow ratings which are still in the middle of the B range or even better.

# 6. Relative risk ratings

With the vector of actual ratings for the rated countries and shadow ratings for the unrated countries, it is relatively easy to compute the relative risk rating of countries by sorting them in ascending or descending order. In this case, an improvement in relative rating of a country will usually be accompanied by a worsening of relative rating of another country. The complication arises when the average rating of the overall portfolio changes over time: in this case, it becomes important to compare rating changes of a country with the change in the average rating. Another source of complication is the choice of weight for a country for computing the world average. In our calculation, we use the GDP as weight, thus assuming that the larger a country, the greater is its impact on the world average rating. To avoid confusion that may arise from changes in the GDP over time, we use the data for 2008 to compute the weights.<sup>7</sup> The _relative risk rating_ , _RR_ , is then computed as: where rit stands for the rating of country _i_ in time _t_ and wi for the share of the country’s GDP in the world GDP in 2008. Since in our scoring system smaller numbers relate to better ratings, a negative _RR_ value would indicate a better than average rating performance. Similarly, when we compare 2008 and 2012, a higher positive value of relative risk rating, RR, would indicate a worse outcome.

The average ratings (not RR) for the world and separately for the high-income and the developing countries are shown for 2008 and 2012 in Figure 6. In 2008 the world average stood at 8.4 (approximately AA with negative outlook). By 2012 this had worsened to a score of 11.3 (AA- with a negative outlook), a downgrade of one rating notch. Most of this change arose due to a worsening of the weighted average of the high-income countries, from a score of 3.6 (AA+ stable) in 2008 to 7.5 (AA stable) in 2012. In contrast, developing countries had a slight improvement from 24.8 (BBB stable) to 24.2 (BBB positive).

> 7 This is an innocuous assumption when comparing ratings over two relatively close years as in the exercise below. As with the construction of index numbers a la Laspeyres and Paasche we can use the GDP weights of the start year or the end year. We prefer to use the start year for the pragmatic reason of more reliable data availability.

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# **Figure 6: Weighted averages for the world, high income and developing countries in 2008 and 2012.**

<!-- Start of picture text --> 30<br>24.8<br>24.2<br>25<br>20<br>15 2008<br>11.3<br>2012<br>10 8.4<br>7.5<br>5 3.6<br>0<br>World High Income Developing<br><!-- End of picture text --> In terms of relative ratings, some of the largest improvements were recorded by Azerbaijan and Ethiopia (both unrated) followed by rated countries such as Bolivia, Uruguay and Kazakhstan.<sup>8</sup> Philippines and Indonesia also recorded sizable improvements. Some of the worst performers in terms of relative ratings have been peripheral European economies, namely, Cyprus, Greece, Spain, Portugal and Ireland. Egypt, Hungary and Iceland have also not performed well (Table 8). In all these cases, the countries had rating downgrades and also did worse than the approximately one-notch downgrade in the world average.

Interestingly enough, between 2008 and 2012, 19 countries in the sample registered improvements in relative rating even though their actual ratings were unchanged. Another 20 countries had a slight lowering of outlook but still had relative rating improvements because this erosion was less than the deterioration of the world average. Interesting examples in this category are India, Jordan, Poland and the United Kingdom.

**Table 8: Actual, Predicted and Relative Risk Ratings for 2008 and 2012**

||**Country**|**Actual**<br>**2008**|**Actual**<br>**2012**|**Predicted 2008**|**Predicted 2012**|**RR**<br>**2008**|**RR**<br>**2012**|
|---|---|---|---|---|---|---|---|
|1|Azerbaijan||BBB- os|B stable|BB+ stable|34.7|16.7|
|2|Ethiopia|||CCpositive|CCC+positive|48.5|36.4|
|3|Bolivia|B- os|BB- os|B+ stable|B+positive|37.6|25.7|
|4|Uruguay|BB- os|BBB- os|BB+positive|BBB+ stable|28.6|16.7|
|5|Kazakhstan|BBB- o-|BBB+ os|BBpositive|BBB-positive|20.6|10.7|
|6|Estonia|A o-|AA- os|A+positive|A+ stable|8.6|-1.3|
|7|Indonesia|BB- os|BB+ o+|BBpositive|BBB- negative|28.6|18.7|
|8|Philippines|BB- os|BB+ o+|BBpositive|BB+ stable|28.6|18.7|

8 As mentioned earlier, ratings at default or below are difficult to quantify and compare. Hence we do not report relative rating changes for countries with shadow ratings of 60 and below. This happens for Tajikistan, Burundi and Guinea-Bissau with respect to their 2008 shadow ratings.

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|9<br>10|Panama<br>Paraua|BB+ os<br>B os|BBB os<br>BB- os|BB+ negative<br>B neative|BBB- stable<br>BB-ositive|22.6<br>346|13.7<br>257|
|---|---|---|---|---|---|---|---|
|<br>11|gy<br>Czech Republic|<br>A os|<br>AA- os|g <br>AA- stable|p <br>A+ stable|.<br>7.6|.<br>-1.3|
|12|Hong Kong SAR,<br>China|AA os|AAA os|AAA negative|AAA stable|-1.4|-10.3|
|13|Guyana|||CCC stable|B- negative|43.8|35.3|
|14|Moldova|||B- stable|B+ negative|37.9|29.6|
|15|Seychelles|B o-|NR|CCC+positive|Bpositive|38.6|30.5|
|16|Rwanda||B os|CCC+positive|B+ negative|39.7|31.7|
|17<br>|Togo<br>|||CCC-positive<br>|CCC+ stable<br>|46.0<br>|38.2<br>|
|18|Haiti|||CCC- stable|CCC+ negative|46.5|39.0|
|19|Maldives|||CCC+ negative|B- stable|41.3|34.4|
|20|Colombia|BB+ os|BBB- o+|BB+positive|BBB negative|22.6|15.7|
|21|Peru|BBB- os|BBB o+|BB+positive|BBB negative|19.6|12.7|
|22|Solomon Islands|||CCC+ stable|B-positive|40.6|33.8|
|23|Zambia||B+ os|B negative|B+ stable|35.4|28.7|
|24|Malawi|||CCC+ negative|B- negative|41.8|35.3|
|25|Mauritius|||BBB- negative|BBB negative|21.1|15.1|
|26|Brazil|BBB- os|BBB os|BBB-positive|BBB+ stable|19.6|13.7|
|27|Georgia|B+ os|BB- os|BB- negative|BB- stable|31.6|25.7|
|28<br>|Turkey<br>|BB- os<br>|BB os<br>|BBB negative<br>|BBB+positive<br>|28.6<br>|22.7<br>|
|29|Kuwait|AA- os|AA os|A+positive|AA stable|1.6|-4.3|
|30|Kenya|B os|B+ os|B- negative|CCC+positive|34.6|28.7|
|31<br>|Ecuador<br>|B- os<br>|B os<br>|B+positive<br>|BB- stable<br>|37.6<br>|31.7<br>|
|32|China|A+ os|AA- os|A- stable|A+ stable|4.6|-1.3|
|33|Israel|A o+|A+ os|AA- negative|AA- negative|6.6|1.7|
|34|Trinidad and Tobago|A- o+|A os|BBBpositive|BBBpositive|9.6|4.7|
|35|Morocco|BB+ os|BBB- o-|BBpositive|BBpositive|22.6|17.7|
|36|Kyrgyz Republic|||CC stable|CCC- negative|49.3|44.9|
|37<br>|Pakistan<br>|B o-<br>|B- os<br>|B+ negative<br>|B negative<br>|38.6<br>|34.7<br>|
|38|Dominican Republic|B+ o-|B+ os|BB- stable|BB stable|32.6|28.7|
|39|Swaziland|||B+ stable|B+ stable|31.9|28.3|
|40|Algeria|||A- negative|A- negative|11.7|8.3|
|41|Canada|AAA os|AAA os|AAA stable|AApositive|-7.4|-10.3|
|42|Norway|AAA os|AAA os||AAA stable|-7.4|-10.3|
|43|Saudi Arabia|AA- os|AA- os|AA-positive|AA+positive|1.6|-1.3|
|44|Malaysia|A- os|A- os|A- stable|BBB+positive|10.6|7.7|
|45|Serbia|BB- o-|BB- o-|BB- stable|B+positive|29.6|26.7|
|46|Senegal|B+ o-|B+ o-|B+ stable|B stable|32.6|29.7|
|47|Mozambique|B+ os|B+ os|B- stable|B negative|31.6|28.7|
|48|Thailand|BBB+ os|BBB+ os|BBB negative|BBB-positive|13.6|10.7|
|49|Switzerland|AAA os|AAA os|AAA stable||-7.4|-10.3|
|50|Denmark|AAA os|AAA os||AAA negative|-7.4|-10.3|
|51|Singapore|AAA os|AAA os|AA+ negative|AA stable|-7.4|-10.3|
|52|Sweden|AAA os|AAA os|||-7.4|-10.3|
|53|Nigeria|BB- os|BB- os|BB- stable|BB- stable|28.6|25.7|
|54|Germany|AAA os|AAA os|AAA negative|AA+ stable|-7.4|-10.3|
|<br>55|Australia|<br>AAA os|<br>AAA os|||<br>-7.4|<br>-10.3|
|56|Fiji|B os|B os|B stable|CCC+ stable|34.6|31.7|
|57|Cameroon|B os|B os|B+positive|B+ negative|34.6|31.7|
|<br>58|<br>Uganda||<br>B+ o-|<br>B+ negative|<br>Bpositive|<br>32.3|<br>29.7|
|59|Hd||B+|B+ tbl|B tbl|311|287|
|60|onuras<br>Nicaragua||os|sae<br>CCC+ stable|sae<br>CCC+ negative|.<br>408|.<br>385|
|<br>61|<br>Gambia,The|||<br>CCC negative|<br>CCC-positive|.<br>44.8|.<br>42.6|
|62|Finland|AAA os|AAA o-|AAA stable|AA+ stable|-7.4|-9.3|
|63|India|BBB- os|BBB- o-|BBB- negative|BBB- stable|19.6|17.7|
|64|Luxembourg|AAA os|AAA o-|AApositive|AA negative|-7.4|-9.3|
|65|Netherlands|AAA os|AAA o-|AA+positive|AApositive|-7.4|-9.3|
|66|Poland|A- o+|A- os|Apositive|Apositive|9.6|7.7|
|67|Latvia|BBB+ o-|BBB o+|BBB+ negative|BBB-positive|14.6|12.7|

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|68|Jordan|BB os|BB o-|BB+ negative|BB stable|25.6|23.7|
|---|---|---|---|---|---|---|---|
|69<br>70|Costa Rica<br>Guatemala|BB o+<br>BB o+|BB os<br>BB os|BBB- stable<br>B+ stable|BBB stable<br>B+positive|24.6<br>24.6|22.7<br>22.7|
|71|United Kingdom|AAA os|AAA o-|AAA stable|AA-positive|-7.4|-9.3|
|72|Lao PDR|||CCC+positive|CCC+ stable|39.6|38.0|
|73|Dominica|||BB- negative|B+positive|29.5|27.9|
|74|Nepal|||B- stable|CCC+positive|37.4|36.3|
|75|Vietnam|BB o-|BB- os|BB- stable|BB- negative|26.6|25.7|
|76|Romania|BBB- o-|BB+ os|BBB negative|BBB- negative|20.6|19.7|
|77|Benin|B o+|B o-|B+ stable|B- stable|33.6|32.7|
|78|Cape Verde||B+ os|BB- stable|B+ negative|28.8|28.7|
|79|Bulgaria|BBB+ os|BBB os|BB+positive|BB+positive|13.6|13.7|
|80|Cambodia|B+ os|B os|B-positive|B-positive|31.6|31.7|
|81|Ghana|B+ os|B os|B+ stable|BB-positive|31.6|31.7|
|82|Macedonia,FYR|BB+ os|BB os|BB stable|BB-positive|22.6|22.7|
|83|Botswana|A os|A- os|A- stable|A- negative|7.6|7.7|
|84|Mexico|BBB+ os|BBB os|BBB negative|BBB negative|13.6|13.7|
|85|New Zealand|AA+ os|AA os|AA+positive|AA+ negative|-4.4|-4.3|
|86|Albania||B+ os|BB- stable|Bpositive|28.5|28.7|
|87|Sierra Leone|||CCC- stable|CC stable|46.7|46.9|
|88|Bhutan|||B+ stable|B stable|31.7|32.0|
|89|Tanzania|||B+ negative|B negative|32.1|32.6|
|90|Mali|NR|NR|B+ negative|B negative|32.1|33.2|
|91|St. Vinc& Grenadns|||BB negative|B+positive|26.5|27.6|
|92|Japan|AA os|AA- o-|AApositive|BBB+ negative|-1.4|-0.3|
|93|Austria|AAA os|AA+ o-|AA+positive|AA stable|-7.4|-6.3|
|94|Ukraine|B+ os|B o-|B+ stable|B+ stable|31.6|32.7|
|95|South Africa|BBB+ os|BBB o-|BBB stable|BBB stable|13.6|14.7|
|96|Grenada|B- os|CCC+ o-|B- stable|CCC+positive|37.6|38.7|
|97|France|AAA os|AA+ o-|AA+ stable|AA negative|-7.4|-6.3|
|98|Russian Federation|BBB+ o+|BBB os|BBBpositive|BBB+ stable|12.6|13.7|
|99|Belgium|AA+ os|AA o-|AA negative|A+ stable|-4.4|-3.3|
|100|United States|AAA os|AA+ o-||AA+ stable|-7.4|-6.3|
|101|Lithuania|A- o-|BBB os|BBB+positive|BBB stable|11.6|13.7|
|102|Belize|B os|SD|B stable|CCC+ stable|34.6|37.3|
|103|Belarus|B+ os|B- os|BB- stable|B- stable|31.6|34.7|
|104|Croatia|BBB os|BB+ os|A- stable|BBB negative|16.6|19.7|
|105|Argentina|B+ o-|B- o-|BBB- stable|BBB stable|32.6|35.7|
|106|Cote d'Ivoire|||CCCpositive|CCpositive|42.3|45.7|
|107|El Salvador|BB+ os|BB- o-|BB- stable|B stable|22.6|26.7|
|108|Bahamas,The|A- os|BBB o-|A negative|BBB- negative|10.6|14.7|
|109|Yemen,Rep.|||B stable|CCC+ negative|34.6|38.8|
|110|Slovenia|AA os|A|A+positive|A negative|-1.4|4.7|
|111|Tunisia|BBB os|BB os|BBB- negative|BBpositive|16.6|22.7|
|112|Italy|A+ os|BBB+ o-|Apositive|BBB+ negative|4.6|11.7|
|113|Bahrain|A os|BBB o-|A+ negative|BBB+positive|7.6|14.7|
|114|Armenia|||BB+ negative|Bpositive|23.7|30.9|
|115|Bosnia&Herzegovina||B os|BBpositive|BB- negative|24.4|31.7|
|116|Hungary|BBB+ o-|BB os|A- negative|BBBpositive|14.6|22.7|
|117|Iceland|A o-|BBB- os|A+ stable|BBB+positive|8.6|16.7|
|118|Egypt,Arab Rep.|BB+ os|B- o-|BB- stable|BB- negative|22.6|35.7|
|119|Ireland|AAA os|BBB+ o-|AA+ stable|A- stable|-7.4|11.7|
|120|Portugal|AA- os|BB o-|Apositive|BBBpositive|1.6|23.7|
|121|Spain|AAA os|BBB- o-|AApositive|A+positive|-7.4|17.7|
|122|Greece|A os|B- os|A-positive|BB- negative|7.6|34.7|
|123|Cyprus|A+ os|CCC+ o-|<br>Apositive|BBB+ negative|4.6|38.7|

Notes: “os” stands for stable outlook, “o+” for positive outlook and “o-” for negative outlook for actual ratings. Nomenclature for predicted ratings are deliberately kept different and unabbreviated. Countries whose 2008 shadow values were beyond the rating scale (greater than 60) are not included in the table. These are Tajikistan (2008- 64.70; 2012- 40.15), Burundi (2008-66.62; 2012- 47.18) and Guinea-Bissau (2008- 61.55; 2012- 48.47).

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# 8. Conclusion

This paper analyzed changes in actual, shadow and relative sovereign ratings between 2008 and 2012. For the unrated countries, shadow ratings were predicted using a regression equation based on macroeconomic, structural and governance variables. It is revealed that some unrated developing countries, such as, Dominica, Moldova, Armenia, Bhutan and Mali, have shadow ratings below investment grade but still in the B range or better. If they were to obtain corresponding formal ratings, their access to international capital markets would improve. The paper then computed relative risk ratings to compare the actual or shadow ratings of a country against those of other countries after controlling for changes in the global average rating. Broadly, certain developing economies, such as Azerbaijan, Ethiopia, Kazakhstan, Indonesia and Philippines, have done well in terms of improvements in relative ratings after the crisis. Some economies, such as India, Jordan, Poland and the United Kingdom, suffered a rating downgrade or a negative outlook, but their relative ratings improved in 2012 compared with pre-crisis levels (of July 2008) as other countries suffered greater downgrades. Several European economies (such as Greece and Portugal) and countries facing political adjustments (such as Egypt and Tunisia) fared badly in terms of both relative and actual ratings.

The paper goes on to deconstruct factors contributing to changes in relative and actual or shadow ratings. While wide variances exist between countries, macro-stability and debt indicators seemed to have a major impact on rating outcomes either way.

This research advances the literature by comprehensively analyzing post-crisis sovereign rating developments and developing actionable policy implications based on the contributions of various factors to rating changes. It also opens up avenues for policy innovation and further research. The availability of shadow ratings allows unrated countries to be judged in terms of international benchmarks of credit risk. This can open ways for riskpooling and joint borrowing for regional development projects. The methodology developed in this paper may allow an analysis of the behavior of rating agencies during the recent financial crisis, a topic for future research. This could take into account stated changes in their methodologies. S&P and Moody’s are now also supplying market-based ratings for sovereigns (where market information, for example, credit default swaps, is translated into the traditional ratings scale used by the rating agencies). It may be useful to augment the analysis in the paper in the future by incorporating these ratings. Future avenues of research include studying sovereign rating transitions and analyzing the factors influencing subsovereign and supra-sovereign ratings.

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