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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