A sovereign credit rating is a function of hard and soft information that should reect the creditworthiness and the probability of default of a country. We propose an alternative characterisation for the subjective component of a sovereign credit rating the parts related to the ratee's lobbying eort or its familiarity from a United States point of view and apply it to S&P, Moody's and Fitch ratings, using both traditional ordered-logit panel models and machine learning techniques. This subjective component turns out to be large, especially for the low-rated countries. Countries that are rated as investment grade tend to be positively inuenced by it, and vice versa. Subjective judgment in credit ratings does have predictive value: it helps in identifying chances of sovereign defaults in the short-term. Still, the impact of subjectivity in sovereign ratings on borrowing costs is very limited on average.

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