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92 Economic Survey 2020-21 Volume 1

institutional determinants of sovereign debt ratings, unobserved country-specific fixed effects and the
CRA’s desire for rating stability”.
Fuchs and Gehring (2017) examined the evidence of “home bias” in sovereign credit ratings by CRAs
based on data of 143 sovereigns from nine agencies based in six countries. Their findings suggested
that respective home country, countries with linguistic and cultural similarity, and countries with higher
home-bank exposures received higher ratings than justified by their political and economic fundamentals.

Hadzi-Vaskov and Ricci (2019), in their study of 106 countries during 1998-2014, found further
evidence of bias and subjectivity in sovereign credit ratings. They observed a non-linear negative
relation between public debt and sovereign credit ratings, which further depends on the rating grade.
This non-linear effect is strongest in the low investment grades, smallest in the non-investment
grades, and intermediate for high investment grades. For instance, through an ordered probit and
logit model, they found that a debt increase by ten per cent of GDP was associated with a five per
cent higher probability of being downgraded within a window of five adjacent grades for countries
rated in the low investment grades while it was almost zero for countries with the lowest ratings in the
non-investment grade, and three percent for best rated countries in the higher investment grade. They
found that this non-linear relationship between public debt and sovereign credit ratings of advanced
and emerging market economies explained the varied effect of debt on sovereign credit ratings
between these countries, even when controlling for income and other macroeconomic parameters.

Tennant, Tracey and King (2020), through a heterogeneous middle-inflated ordered model, found a
statistical bias in sovereign credit ratings against poor countries whenever their fundamentals change,
highlighting a cause of concern since such biases can have self-fulfilling consequences as suggested
by second-generation crisis models.

3.6 Figure 1 and 2 suggest evidence of bias in sovereign credit ratings (see Box 2) against
emerging giants. It may be seen that sovereign credit ratings of the fifth largest economy in
current US$ terms and that of the third largest economy in PPP $, dip sharply with the entry of
China and India in this category.

Box 3: Cohort for Examining whether Sovereign Credit
Ratings reflects India’s Fundamentals

A cohort of 33 countries (including India) is used for examining whether sovereign credit ratings
reflect India’s fundamentals across different dimensions. This cohort has sovereign credit ratings
between A+/A1 to BBB-/Baa3 for S&P/ Moody’s.
For purposes of graphical analysis, we use average sovereign credit rating across S&P and Moody’s,
where we set ratings below BBB-/Baa3 = 0, BBB-/Baa3 = 1, BBB/Baa2 = 2, BBB+/Baa1 = 3, A-/A3
= 4, A/A2 = 5, A+/A1 = 6 and ratings above A+/A1 = 7.

3.7 Figures 5-16 show correlations between sovereign credit ratings and different parameters
for India’s sovereign credit ratings cohort (see Box 3). Figure 5 shows a positive correlation
between sovereign credit ratings and GDP growth rate across India’s cohort. India is clearly a
negative outlier i.e. it is currently rated much below expectation for its level of GDP growth.

3.8 A negative correlation is observed between sovereign credit ratings and Consumer Price