The study investigates the relationship between public debt and sovereign credit ratings and spreads, with a particular emphasis on Chile’s position. Drawing on several alternative analytical tools, it finds that higher debt is likely to result in weaker sovereign credit ratings, higher probability of downgrade, and wider sovereign spreads. Thereby, the findings in the paper underscore the potential for fiscal consolidation in helping to regain a better credit rating and move to a higher investment grade category.

Abstract

The study investigates the relationship between public debt and sovereign credit ratings and spreads, with a particular emphasis on Chile’s position. Drawing on several alternative analytical tools, it finds that higher debt is likely to result in weaker sovereign credit ratings, higher probability of downgrade, and wider sovereign spreads. Thereby, the findings in the paper underscore the potential for fiscal consolidation in helping to regain a better credit rating and move to a higher investment grade category.

Impact of Debt on Sovereign Credit Ratings and Spreads1

The study investigates the relationship between public debt and sovereign credit ratings and spreads, with a particular emphasis on Chile’s position. Drawing on several alternative analytical tools, it finds that higher debt is likely to result in weaker sovereign credit ratings, higher probability of downgrade, and wider sovereign spreads. Thereby, the findings in the paper underscore the potential for fiscal consolidation in helping to regain a better credit rating and move to a higher investment grade category.

A. Introduction

1. There is a negative relationship between debt levels and sovereign credit ratings. In general, higher levels of public debt increase fiscal vulnerabilities and raise concerns about the capacity to service obligations. Therefore, higher debt levels are expected to be associated with perceptions of lower creditworthiness and weaker sovereign credit ratings. This relationship is illustrated in Figure 1 using credit rating data from Fitch Ratings and government gross debt. As expected, countries with higher levels of debt have lower credit ratings. Nonetheless, there are clear differences between Emerging Markets and Developing Economies (EMDEs) and Advanced Economies (AEs)—for the same level of debt AEs typically have higher credit ratings than EMDEs—as well as within each country grouping.

Figure 1.
Figure 1.

Gross Debt and Credit Ratings

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A002

Note: Panel dataset for AEs and EMDEs over the period 1998‐2014.Source: IMF WEO database and Fitch Ratings data.

2. Deeper insights into the impact of debt on credit ratings is particularly relevant for Chile in light of the recent credit downgrades. In particular, the continued increase in public debt over the past decade has been emphasized as one of the key reasons for the negative credit actions undertaken by several major credit rating agencies since mid-2017.

3. Chile has a relatively low debt level and strong credit ratings compared to the group of EMDEs. Notwithstanding the recent downgrade, the sovereign rating remains among the highest across EMDEs, and higher than some AEs. At the same time, despite the significant increase of almost 20 percentage points of GDP between 2007 and 2017, government gross debt at about 24 percent of GDP in 2017 remains moderate compared to other emerging markets.

4. Net debt displays a stronger negative relationship with credit ratings compared to gross debt. Figure 2 highlights the role of financial assets by showing that countries with lower net debt generally receive better credit ratings, and the relation appears stronger than for gross debt. Most of the countries with assets above gross debt have high investment-grade ratings. While Chile’s net debt increased considerably over the last decade, it amounts to about 5 percent of GDP in 2017, remaining below many countries with similar credit ratings.

Figure 2.
Figure 2.

Net Debt and Credit Ratings

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A002

Note: Panel dataset for AEs and EMDEs over the period 1998‐2014.Source: IMF WEO database and Fitch Ratings data.

5. The rest of the paper formally investigates the relationship between public debt and sovereign credit ratings, with a particular emphasis on Chile’s position. Having shown some indicative evidence about the relationship between public debt and credit ratings in this section, the analysis turns to a more formal approach, employing various empirical procedures and techniques (based on Hadzi-Vaskov and Ricci, forthcoming). Section B briefly describes the dataset. Section C presents results from ordered probit regressions. Section D draws on findings from standard panel regressions. Section E calculates transition probabilities of sovereign downgrade/upgrade. Section F complements the analysis of credit ratings by providing a glimpse on debt’s impact on Chile’s sovereign spreads. Finally, Section G offers some concluding remarks.

B. Dataset and Empirical Strategy

6. The analysis is based on widely available data sources. Data on general government gross and net debt (as percent of GDP) comes from the IMF’s World Economic Outlook (WEO) database. Data on sovereign credit ratings comes from Fitch Ratings. As an alternative measure we also use the Institutional Investor Index, obtained from Institutional Investor, Inc; this indicator is based on information on likelihood of default provided by senior economists and sovereign-risk analysts at leading global banks and money management as well as securities firms. Among the control variables used in the analysis, GDP and inflation come from WEO database, while 10-year U.S. interest rates and the implied volatility index VIX are retrieved from Bloomberg. Series on sovereign bond spreads come from JP Morgan’s Emerging Market Bond Index Global (EMBIG). The indicator for quality of institutions comes from the World Economic Forum’s database. The analysis covers annual data over the period 1998–2014, unless stated otherwise. Both advanced economies and emerging and developing economies are included in the analysis of ratings (while the work on spreads further below is based only on emerging markets).

C. Ordered Probit Regressions

7. The ordered probit regressions are based on the following specification:

yit*=βDit+γXit+ui+ϵit

Where the dependent variable yit* is the country i’s credit rating category that takes three values:

yi*={1ifyiNIG2ifyiLIG3ifyiHIG

where NIG stands for non-investment grade credit rating, LIG for low investment grade, and HIG for high investment grade, based on sovereign credit ratings by Fitch. The overview and definition of these three categories across credit ratings are provided in Annex Table 1. Dit stands for (gross or net) debt to GDP ratio of country i in year t, and X stands for the set of control variables for country i in year t. The regression encompasses fixed effects (ui).

8. The results presented in Table 1 indicate that both higher gross and net debt lower the probability of being in a better rating category and this effect is statistically significant at conventional levels. In addition, net debt seems to have a somewhat larger effect than gross debt, most likely reflecting the credit agencies’ attention to governments’ financial assets, besides gross debt figures, in their rating decisions. As expected, higher inflation and tighter global market conditions (captured by U.S. interest rates) lower the probability of being in a better rating category, while stronger GDP growth raises this probability (in the specification with gross debt).

Table 1.

Ordered Probit Results 1/

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p-val in parentheses*** p<0.01, ** p<0.05, * p<0.1Note: NIG stands for non‐investment grade, LIG for low investment grade, and HIG for high investment grade cases.

Regression results remain qualitatively unchanged when a diversification index and GDP per capita (PPP‐adjusted) are included as additional control variables in the specifications.

9. The empirical results suggest that an increase in debt lowers the probability of being classified in the higher rating category. On the basis of the ordered probit regressions, Tables 2a and 2b present calculations (using results based on gross debt and net debt, respectively) for average/marginal probabilities of being placed in each of the three credit rating categories. Calculations evaluated at Chile’s current values suggest that an increase in Chile’s debt by 10 percent of GDP will result in 6 percent lower probability of being placed in the high investment grade category. The same result holds for both gross and net debt calculations.

Table 2a.

Probabilities Implied by Ordered Probit Results

(Gross debt)

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Note: NIG stands for non-investment grade, LIG for low investment grade, and HIG for high investment grade cases.
Table 2b.

Probabilities Implied by Ordered Probit Results

(Net debt)

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Note: NIG stands for non-investment grade, LIG for low investment grade, and HIG for high investment grade cases.

D. Panel Regressions

10. This section complements the results from the ordered probit regressions with standard panel estimations allowing for country-specific fixed effects. For this purpose, the categorical dependent variable is converted into integers from 1 to 23, each of them corresponding to a different credit rating (see Annex Table 1), with the highest (23) indicating the best rating (AAA). The empirical specification is similar to the one used in the ordered probit, with the key difference being the transformation of the dependent variable y:

y=βDit+γXit+ui+ϵit

The results from the full sample, and separate subgroups (corresponding to the rating categories NIG, LIG, and HIG) are presented in Table 3.

Table 3.

Panel Regression Results for Credit Ratings

(Fixed effects)

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p-val in parentheses*** p<0.01, ** p<0.05, * p<0.1Note: NIG stands for non-investment grade, LIG for low investment grade, and HIG for high investment grade cases.

11. The results suggest that an increase of gross debt by 10 percent of GDP is associated with half a notch lower credit rating in the full sample. Similarly, an increase of net debt by 10 percent of GDP is associated with close to half a notch lower rating. The effect corresponds to about 10–15 percent of one standard deviation of the categorical rating variable. In both cases, the middle category LIG seems to be the most sensitive to changes in debt.2

12. An alternative way of capturing investors perception about countries’ sovereign risk is through the Institutional Investors Index, which has been compiled since the 1970s. Hence, Table 4 contains results from regressions that employ this index as the dependent variable, which ranges from 0 (worst) to 100 (best). The results suggest that a debt increase of 10 percent of GDP is associated with a decline in the index by close to 3 units, or about 15–20 percent of one standard deviation. Such results are similar to the results that use credit ratings as dependent variable.

Table 4.

Panel Regression Results

(Fixed effects)

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p-val in parentheses*** p<0.01, ** p<0.05, * p<0.1Note: Dependent variable is the Institutional Investor Index of country sovereign risk.

E. Transition Probabilities

13. This section aims to shed light on the impact of debt changes on the probability of moving from one credit rating category to another one. This analysis complements the one in the previous sections, which demonstrated the negative effects of higher debt levels on credit ratings, and on the probabilities to be placed in a certain rating category.3

14. An increase in debt raises the probability of moving to a worse credit rating category. Table 5 contains four matrices with transition probabilities calculated on the basis of the same dataset used in the previous sections. The top matrices show the general transition probabilities of moving from one category to another calculated on the basis of the entire dataset—when debt goes up, there is some probability of moving into lower credit rating bracket (between 3 and 14 percent), and much lower probability of moving to a better bracket (most probabilities equal 0, though some go to 3–7 percent). In addition, the lower panel shows the results for a 10 percent of GDP increase in debt, instead of the general (unspecified) increase in the debt level considered above. In that case, there is 17–50 percent probability of moving into a lower rating category, and zero probability of moving into a better category. Particularly sensitive to a deterioration in debt appears to be the best HIG category, with 33–50 percent probability of moving to LIG, and 33 percent probability of moving into NIG with a 10 percent of GDP increase in debt.

Table 5.

Transition Probability Matrices

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Source: IMF staff calculations based on Fitch Ratings data.Note: Matrices contain transition probabilities of moving from credit rating category marked by row to category marked by column. NIG stands for non-investment grade, LIG for low investment grade, and HIG for high investment grade. Green areas indicate probabilities of improving credit rating, red areas indicate probabilities of worsening credit rating, and the diagonal contains probabilities of remaining in the same category.

F. Debt and Sovereign Spreads

15. There are several arguments why higher debt is associated with higher sovereign spreads and borrowing costs. For instance, an increase in public debt raises the default risk for the sovereign, thereby leading to higher spreads in order to compensate investors for the higher risk of the securities they are holding. In addition, rapid expansion of debt creates an excess supply of certain securities relative to the portfolio benchmarks followed by investors, thereby necessitating a higher return to make the investors willing to deviate from the original (preferred) portfolio. Among others, Gruber and Kamin (2012) provide various theoretical arguments underpinning the positive debt-spreads relationship.

16. Debt increase of 10 percent of GDP is associated, on average, with 110–120 basis points higher sovereign spreads in emerging markets. Based on the empirical analysis provided in Hadzi-Vaskov and Ricci (2016), Table 6 illustrates that the effects of gross and net debt are similar in magnitude, while the standard control variables have the expected signs (higher growth lowers spreads, while higher inflation and market uncertainty raise them). More generally, the findings in Table 6 are consistent with the regression results for credit ratings in the previous sections.

Table 6.

Panel Regression Results for Spreads

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p-val in parentheses ***p<0.01,**p<0.05, *p<0.1Source: Hadzi-Vaskov and Ricci (2016).Note: Dependent variable is EMBI spread.

17. The adverse effect of debt on sovereign spreads is lower in countries with stronger institutions, such as Chile. Figure 3 shows the impact of debt as a function of the institutional quality, measured by WEF’s index. It indicates that the effect for Chile drops to 80–90 basis points, down from 110–120 basis points found for the typical emerging economy included in the sample. Table 7 summarizes the effect of debt on spreads for countries with different institutional quality, which range from 80 basis points for the strongest institutions to 140 basis points for countries with the weakest institutions.

Figure 3.
Figure 3.

Impact of Debt on Spreads Conditional on Institutional Quality

Citation: IMF Staff Country Reports 2018, 312; 10.5089/9781484383919.002.A002

Source: Bloomberg, WEF, and Fund staff calculations.
Table 7.

Impact of Debt on Spreads Conditional on Institutions

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Note: Effect of 1 percentage point higher debt‐to‐GDP ratio on EMBI spread (in percent).

G. Concluding Remarks

18. Higher debt is likely to result in weaker sovereign credit ratings, higher probability of downgrade, and wider sovereign spreads. Drawing on several alternative analytical tools, this paper presents consistent results about the adverse impact of increase in public debt for sovereign credit position and financing costs. This suggests that, going forward, the announced fiscal consolidation—by eventually contributing to a decline in the debt-to-GDP ratio over the medium term (see the Debt Sustainability Analysis accompanying the 2018 Article IV Staff Report)—has the potential to allow Chile to regain a better credit rating and move to the high investment grade category (of course, other factors may contribute to such outcome). Such a strategy is likely to result in lower sovereign spreads as well, providing an opportunity for Chile to move to a virtuous cycle of lower debt and lower financing costs.

Annex I. Credit Rating Categories

Table 1.

Credit Rating Categories

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Note: Chile’s current credit rating marked in red.

References

  • Gruber, J. W. and S.B. Kamin, 2012, “Fiscal Positions and Government Bond Yields in OECD Countries”, Journal of Money, Credit, and Banking, Vol. 44 (8).

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  • Hadzi-Vaskov, M. and L. A. Ricci, 2016, “Does Gross or Net Debt Matter More for Emerging Market Spreads?”, IMF Working Paper No. 16/246 (Washington: International Monetary Fund).

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  • Hadzi-Vaskov, M. and L. A. Ricci (forthcoming), “Public Debt and Sovereign Credit Ratings”, IMF Working Paper (Washington: International Monetary Fund).

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  • WEF, 2017, The Global Competitiveness Report 201718.

1

Prepared by Metodij Hadzi-Vaskov.

2

The estimated relation is valid on average across the whole sample and not always observed in specific cases (such as in Chile in 2011 when the rating improved while debt was rising).

3

The probabilities refer to transitions between rating categories and not to changes in the outlook (negative, stable, positive), which generally anticipate the movement between rating categories.

Chile: Selected Issues Paper
Author: International Monetary Fund. Western Hemisphere Dept.