Shadow sovereign ratings

Otaviano Canuto, Sanket Mohapatra, Dilip Ratha, 3 September 2011

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Sovereign ratings act as internationally comparable indicators of a country’s fiscal performance. They provide a basis for international investors and bondholders to assess the risks of a country’s ability to honour its public-debt obligations (Beers and Cavanaugh 2005). Assessments of sovereign creditworthiness are also important for other type of resource flows, for example in performance-based aid allocations. The sovereign rating often acts as a “ceiling” for the foreign currency rating of firms and banks located in the country (Borensztein et al 2007). However, as of mid-2011, 58 developing countries are not rated by any of the 3 international rating agencies, Standard and Poor’s, Moody’s, and Fitch. In recent research (Canuto et al 2011), we present an exercise to predict “shadow” sovereign ratings to estimate where unrated countries would lie on the credit spectrum if they were to be rated.

Sovereign credit ratings and the case for shadow ratings

Sovereign credit ratings in some form have been in existence for nearly a century. By 1929, 21 countries were rated by Poor’s Publishing, the predecessor to S&P (Bhatia 2002). After a decline during the Great Depression and World War II, ratings activity for sovereigns resumed slowly in the post-war period. The number of rated countries increased significantly during the 1980s and 1990s during the emerging-markets phenomen. By April 2011, 45 high-income countries and 90 developing countries were rated by one or more of the 3 premier agencies.

However, there is still a large group of 58 developing countries that are still not rated by any of the 3 agencies. Without a credit rating, these unrated countries – and especially private companies located in these countries – find it difficult to access international bond markets and resort to costly relationship-based borrowing from commercial banks or selling equity to foreign direct investors.

Several factors influence a country's reluctance or inability to get rated. Countries are constantly reminded of the risks of currency and term mismatch associated with market-based foreign-currency debt, and the possibility of sudden reversal of investor sentiment (Eichengreen et al 2005). The information required for the commercial rating process can be complex and not readily available in many countries. The institutional and legal environment governing property rights and sale of securities may be absent or weak, prompting reluctance on the part of politicians to be publicly “judged" by ratings analysts. The fact that the country has to request a rating (and has to pay a fee for that), but has no command over the final rating outcome, can also be discouraging. Also, Basel capital adequacy regulations that assign a lower risk weight (100%) to unrated entities than to those rated below BB- (150%) may discourage borrowing entities from being rated.

A predictive model for sovereign ratings

Many researchers have found that the ratings by major agencies are largely explained by a handful of macroeconomic variables (Cantor and Packer 1996, Canuto et al 2004, Rowland 2005). We follow the well-established literature to develop an econometric model for explaining ratings (see Ratha et al 2011; for a previous econometric exercise using fixed-effects methods, see Canuto et al 2004). We first convert the long-term foreign-currency letter rating from the 3 major agencies to a numerical equivalent. In our scale (see Table 1), 1 denotes the highest rating (corresponding to AAA for Standard and Poor's and Fitch, Aaa for Moody's) and 21 the lowest (C for all 3 agencies).

Table 1. Sovereign ratings - conversion from letter to numeric scale

Standard & Poor’s
Fitch
Moody’s
Numeric Grade
Investment Grade
Highest credit quality
AAA
AAA
Aaa
1
Very high credit quality
AA+
AA+
Aa1
2
AA
AA
Aa2
3
AA-
AA-
Aa3
4
High credit quality
A+
A+
A1
5
A
A
A2
6
A-
A-
A3
7
Good credit quality
BBB+
BBB+
Baa1
8
BBB
BBB
Baa2
9
BBB-
BBB-
Baa3
10
Speculative Grade
Speculative
BB+
BB+
Ba1
11
BB
BB
Ba2
12
BB-
BB-
Ba3
13
Highly speculative
B+
B+
B1
14
B
B
B2
15
B-
B-
B3
16
High default risk
CCC+
CCC+
Caa1
17
CCC
CCC
Caa2
18
CCC-
CCC-
Caa3
19
Very high default risk
CC
CC
Ca
20
C
C
C
21
 

Source: Standard and Poor’s, Moody’s Investors Service, and FitchRatings.

We then estimate the numeric equivalent of sovereign ratings for the rated developing countries as a function of macroeconomic variables, rule of law, debt and international reserves, and macroeconomic volatility, as identified in the literature. The signs of the explanatory variables are in the expected direction, and are significant at the 10% level or better (see Ratha et al 2011). All these variables together explain about 80% of the variation in ratings for the dated regression sample.

Shadow ratings for unrated developing countries

The benchmark model is used to predict ratings for the unrated developing countries. The detailed results for unrated countries are presented in Canuto et al (2011). It is rather striking to see that the predicted ratings for the unrated countries do not all lie at the bottom end of the rating spectrum, but are spread over a wide range (Figure 1).

Figure 1. Distribution of predicted ratings


Source: Authors’ calculations. The distribution is based on the lowest predicted rating.

Of the 47 unrated countries that are analysed, 7 countries are likely to be above investment grade, 10 are likely to be in the BB category, 20 in the B category, and 10 in the CCC or lower category. The countries just below the investment grade but at or above CCC are comparable to many emerging markets with regular market access. For example, the shadow rating for Standard and Poor's for Swaziland in our analysis is a range from B+ to BB, which puts it in a similar bracket as emerging markets such as Indonesia. There several other unrated developing countries (eg Algeria, Bhutan, Djibouti, Equatorial Guinea, Maldives, and Syria) have shadow ratings in the B category or above. We also find that among the countries that were rated for the first time during 2010-11, the shadow ratings are within one notch of the actual rating range for 5 out of the 6 countries.

Policy conclusions

The model-based shadow ratings can provide a benchmark for:

  • Evaluating unrated countries;
  • Evaluating rated countries that have not been rated for some time and might have improved sufficiently in the meantime to deserve an upgrade (or downgrade in some cases).

They also suggest a group of indicators that developing countries can improve to achieve a higher sovereign rating.

There is a role for the international donor community to help developing countries obtain ratings. The UN Development Program partnered with Standard and Poor’s to rate 8 African countries during 2003–06. Since then several of them have accessed international capital markets. The knowledge of shadow ratings for unrated countries can also be helpful for bilateral and multilateral donors interested in setting up guarantees and other financial structures to reduce project risks and mobilise private financing. One such innovative financing instrument being discussed is diaspora bonds to tap into the considerable wealth of the diaspora of developing countries (Okonjo-Iweala and Ratha 2011). These mechanisms can complement existing efforts to increase aid and improve aid effectiveness.

Editor’s note: The views here are those of the authors and not necessary those of their employers.

References

Beers, D., & Cavanaugh, M. (2005). Sovereign Credit ratings: A Primer. New York: Standard and Poor's.

Bhatia, A. (2002). Sovereign Credit Ratings Methodology: An Evaluation. IMF Working Paper 02/170, International Monetary Fund, Washington, DC.

Borensztein, E., Cowan, K., & Valenzuela, P. Sovereign ceilings' lite'? The impact of sovereign ratings on corporate ratings in emerging market economies. IMF Working Paper 07/75.

Canuto, Otaviano, Pablo Fonseca P. dos Santos, and Paulo C. de Sá Porto. 2004. “Macroeconomics and Sovereign Risk Ratings.” FEA-USP Seminar papers (available here).

Canuto, Otaviano, Sanket Mohapatra, and Dilip Ratha. 2011. “Shadow Sovereign RatingsEconomic Premise 63, World Bank.

Cantor, R., & Packer, F. (1996). Determinants and Impact of Sovereign Credit Ratings. Economic Policy Review, 2(2)

Eichengreen, Barry, Ricardo Hausmann, and Ugo Panizza (2005), "The Pain of Original Sin", in Barry Eichengreen and Ricardo Hausmann (eds.), Other People's Money, Chicago University Press.

Kaufmann, D. K., A. Kraay, and M. Mastruzzi. 2009. ‘Governance matters VIII: Aggregate and individual governance indicators, 1996-2008’. Policy Research Working Paper 4978

Moody’s. 2009. Moody’s Rating Symbols and Definitions. Moody’s Investor Service. (available here)

Okonjo-Iweala, Ngozi, and Dilip Ratha. 2011. “A Bond for the Homeland” Foreign Policy, May 24.

Ratha, Dilip, Prabal De and Sanket Mohapatra. 2011. “Shadow Sovereign Ratings for Unrated Developing Countries.” World Development 39(3). page 295-307.

Rowland, P. 2005. Determinants of spread, Credit Ratings and Creditworthiness for Emerging Market Sovereign Debt: A follow-up study using pooled data analysis. Working paper, Banco de la República, Bogota. 

Topics: International finance
Tags: ratings agencies, sovereign ratings

Vice President and Head, Poverty Reduction and Economic Management Network at the World Bank

Senior Economist at the Global Macroeconomics Team in the Development Prospects Group, World Bank

Dilip Ratha

Lead Economist and the Manager, Migration and Remittances Unit, World Bank