Abstract
This paper asks whether rating agencies played a passive role or were an active driving force during Europe’s sovereign debt crisis. We address this by estimating relationships between sovereign debt ratings and macroeconomic and structural variables. We then use these equations to decompose actual ratings into systematic and arbitrary components that are not explained by previously observed procedures of rating agencies. Finally, we check whether systematic, as well as arbitrary, parts of credit ratings affect credit spreads. We find that both do affect credit spreads, which opens the possibility that arbitrary rating downgrades trigger processes of self-fulfilling prophecies that may drive even relatively healthy countries towards default.
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Notes
Our sample includes Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Poland, Portugal, South Korea, Spain, Sweden, Switzerland, United Kingdom, and the USA.
Data for 2010 are mostly estimates provided by the OECD Economic Outlook or the IMF World Economic Outlook database.
Since the data needed for our analysis were not available for all three agencies we settled for Fitch as a representative. Given the high correlation between the ratings of Fitch and Standard & Poor’s we do not expect this choice to bear on our main results.
Due to the high volatility of daily data we used December averages instead of the December 31st value for the end-of-year values.
Note that this must not be confused with the question whether a country actually declares default or not, see Eaton et al. (1986).
In fact, high values of government effectiveness seem to be a result rather than a cause of high income levels, since this index comprises elements like the quality of infrastructure, public schooling or public health care.
Afonso et al. (2009) also find a negative effect of income growth on sovereign debt ratings.
A Hausman Test strongly indicates the use of fixed effects rather than random effects.
We can do so since the underlying data series—government bond yields and sovereign debt ratings—are available on a daily basis.
As mentioned before, the credit spread of a country denotes the difference between its government bond yield and that of Germany. The rating spread denotes the difference between Germany’s rating and the respective country’s rating. Since Germany obtains a AAA rating in all observations, the rating spread is the distance of the current rating to the maximum rating.
Reisen and von Maltzan (1999) find similar results using Granger causality tests. However, they apply a slightly different method and use different data.
Both ratings and credit spreads are now end of year values.
Note that the rating markup for the PIGS countries during the crisis is always negative and actually represents a markdown.
Or that rating agencies use a model that economic theory has not deciphered yet.
Note the negative coefficients: A markdown or negative markup therefore leads to higher credit spreads.
Note that, as expected, the signs of the coefficients are opposite to the ones before, since higher credit spreads match with lower ratings. Also note that including \( {\overset{\lower0.5em\hbox{$\smash{\scriptscriptstyle\frown}$}}{R}}_{{i,t}}^X \) in this regression results in multicollinearity, as expected.
Compared to standard OLS, coefficients are virtually identical and significance levels are very much the same. Only the significance level of the PIGS × Crisis markup drops slightly from 99% to 95% when bootstrapping is applied.
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We are grateful to Andreas Kleiner, Simon Knaus and Giovanni Mellace for many helpful comments and suggestions.
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Gärtner, M., Griesbach, B. & Jung, F. PIGS or Lambs? The European Sovereign Debt Crisis and the Role of Rating Agencies. Int Adv Econ Res 17, 288–299 (2011). https://doi.org/10.1007/s11294-011-9302-7
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DOI: https://doi.org/10.1007/s11294-011-9302-7