Determinants of Sub-Sovereign Government Ratings In Europe

Abstract

The aim of this paper is to identify the determinants of the rating assigned to sub-sovereign entities in Germany, Austria, Belgium, France, Italy and Spain, using a total of 92 territorial entities for the 1989-2012 period. Multinomial ordered probit estimation models were estimated for each specifi cation and agency.We conclude that the country's rating is one of the most important determinants of regional government's ratings with a positive infl uence (as expected), and that the country debt/GDP ratio is a stronger determinant for regions than their own indebtedness with a negative sign. Other relevant variables are population growth rate, unemployment rate, elderly people weight, regional public expenditure weight and size. Additionally, economic variables, such as country's rating and population growth are more important to Fitch; whereas budget variables and size variables are more relevant to Moody's. Debt variables and elderly people ratio are more important to S&P.

Financial centre bias in sub-sovereign credit ratings

“…In line with literature and Moody's methodology we observe the variable to be significant at the 99% level, across all specifications, with a parameter value between 0.6 and 0.7 in most cases. This is close to what is reported in previous research (see for instance Jannone-Bellot et al, 2017).…”

“…We therefore opt for a random-effects panel ordered probit model, a choice also in accordance with the well-established econometric tradition in credit rating modelling (e.g. Gaillard, 2009;Afonso et al, 2011;Jannone-Bellot et al, 2017). Effectively, this is a generalisation of the simple probit model, where instead of two we have n possible outcomes, corresponding to the 21 different credit rating scores.…”

“…An alternative numerical conversion, with bottom ratings pooled together into one category, is saved as a robustness check (see discussion below). For deciding which control variables to include in the time-varying vector X it we follow the recent most cited papers on the determinants of subsovereign ratings (Gaillard, 2009;Jannone-Bellot et al, 2017), as well as Moody's own methodology reports (Moody's, 2005;2018). First and foremost, we take into account the overarching sovereign rating of every locality.…”

“…The share of population is used as a proxy for the size of a city or region, based on the hypothesis that size might positively influence credit ratings, e.g. due to the higher capacity of an area to diversify its economic activity and achieve economies of scale (Jannone-Bellot et al, 2017). The consideration of a country's legal framework is based on the legal indicators database of La Porta et al (1998) and is in line with research showing that credit ratings are biased in favour of countries culturally and institutionally similar to the home country of the CRA (Fuchs and Gehring, 2017;Yalta and Yalta, 2018).…”

“…More recently, Gaillard (2009) studies Moody's ratings for a large group of non-US regions and cities and uncovers that sovereign rating scores, GDP per capita and net direct debt are statistically significant predictors of subsovereign credit ratings. Jannone-Bellot et al (2017) investigate sub-sovereign credit ratings in Europe and identify local population growth, unemployment rate, regional public spending, capital expenditures, as well as the relative size of an area (measured in terms of its population share) as additional determinants. Furthermore, Liu and Sun (2016) highlight the positive role of a country's economic and financial openness.…”