Credit Ratings and the Cost of Debt:The Sovereign Ceiling Channel

Felipe Restrepo∗

Carroll School of ManagementBoston Collegerestrepf@bc.edu

https://www2.bc.edu/felipe-restrepogomez

This Draft: October 30, 2013Please do not distribute

Abstract

A sovereign’s credit rating generally represents the highest attainable rating by most is-suers domiciled within their respective country. In this paper I show that the sovereignceiling represents a meaningful institutional friction, and that through this channelcredit ratings have an important effect on borrowing costs in the private sector. Specif-ically, I estimate the differential effect of contractions and relaxations in the sovereignceiling on the bond spreads of firms that are exactly at the sovereign bound, relative toother firms that are near but not at the bound. I find that following a sovereign down-grade, the spreads of bound firms increase significantly more relative to non-boundfirms. I also show that firms that are bound tend to be rated more unfavorably andtheir default rates tend to be lower relative to non-bound firms.

Keywords: Sovereign Ceiling; Corporate Ratings; Cost of Debt; Event Study, FuzzyRegression Discontinuity Design.

JEL Classifications: G15, G23, G32.∗I would like to especially thank Phil Strahan for his advice and support. I would also like to thank

Clifford Holderness, Darren Kisgen, Jun Qian (QJ), Jérôme Taillard, and seminar participants at BostonCollege for helpful comments and suggestions.

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1 Introduction

Credit rating agencies (CRAs) play a central role in providing information about the ability

and willingness of issuers, including governments and firms, to repay their debts. A crucial

link between sovereign and corporate credit ratings is the “sovereign ceiling”, a policy still

strongly implemented by CRAs whereby a government’s foreign-currency rating generally

represent the highest attainable rating by corporate issuers within that country.1 CFO

Magazine (2013) summarized the key implication of the sovereign ceiling as follows: “If a

company is a better credit risk than its home country, it might still have trouble getting a

credit rating agency to recognize that fact”. In this paper I show that the sovereign ceiling

represents a meaningful institutional friction, and that through this channel credit ratings

have an important effect on borrowing costs in the private sector. Specifically, I estimate the

differential effect of contractions and relaxations in the sovereign ceiling on the bond spreads

of firms that are exactly at the sovereign bound, relative to other firms that are near but

not at the bound.

The main hypothesis I test in this study is whether the sovereign ceiling policy by

CRAs represents a meaningful friction that affects the cost of debt of firms issuing USD-

denominated bonds in international markets. I evaluate two main implications of this hy-

pothesis. First, if sovereign ratings represent a meaningful constraint for firms that are

exactly bound by the sovereign ceiling, then being bound by the ceiling should be associated

with credit ratings that are more pessimistic relative to firms that are not bound. Second,

if the sovereign ceiling is an important channel through which credit ratings affect a firm’s

cost of debt, then contractions and relaxations in the ceiling (i.e. sovereign downgrades and

upgrades respectively) should lead to more pronounced changes in the yield spreads of those

firms that are exactly at the sovereign bound, relative to non-bound firms. The main chal-1Rating agencies assign different types of ratings depending on the maturity (short term or long term)

and currency denomination (local currency or foreign currency) of an issuance. The focus of this study ison foreign-currency long-term ratings, where CRAs use a sovereign’s rating as a strong upper bound on theratings of bonds issued by firms domiciled within each country.

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lenge when tracing the effect of the sovereign ceiling on corporate outcomes is the inherent

endogeneity between a sovereign’s credit quality and the creditworthiness of firms in that

country. I explicitly address this concern in my empirical design by focusing on the differen-

tial effect stemming from contractions and relaxations in the sovereign ceiling on firms that

are at the sovereign bound, relative to other firms in the same country that are near but not

at the bound.

To evaluate these testable implications of the sovereign ceiling channel hypothesis, I

exploit two important empirical regularities associated with the sovereign ceiling. First, I

document that the distribution of corporate ratings across sovereign rating levels is system-

atically concentrated exactly at each country’s sovereign rating. As Borensztein et al. (2013)

point out, this implies that even though CRAs have moved away from fully enforcing the

sovereign ceiling over the last two decades, sovereign ratings still represent a strong upper

bound for the ratings of corporate borrowers. Second, I show that within the month of a

sovereign rating change, the probability of a corporate issuer obtaining a rating adjustment

in the same direction and magnitude as its corresponding sovereign jumps exactly at the

sovereign rating bound. Specifically, conditional on the event of a sovereign rating change,

firms that are at the bound have a probability of approximately 60% of obtaining the same

rating adjustment as the sovereign within a month, compared to less than 10% for firms that

are within three notches of the sovereign rating. This jump in the probability of obtaining

a corporate rating change allows me to use a firm’s bound status as an instrument to esti-

mate the effect of a rating adjustment directly related to the sovereign ceiling channel. Since

sovereign rating changes provide no additional firm-level private information, any differential

effect between bound and non-bound firms should result from a jump in the probability of

a rating change for those firms that are at the sovereign ceiling bound. My identification

strategy assumes that changes in fundamentals are the same for treatment (firms bound by

the ceiling) and control firms (firms near but not bound by the ceiling). I focus on a large

sample of bond and firm level data between 1999 and 2012 for 51 countries that had at least

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one sovereign rating change between this period. The sample includes not only developing

countries (29) but also developed economies (22).

I first find that the sovereign ceiling policy is associated with ratings that tend to be more

pessimistic for firms that are bound by the sovereign ceiling, relative to firms not bound by it.

Specifically, I use as a benchmark financial statements data on firms in AAA countries (where

the sovereign ceiling does not represent a constraint) to estimate the predicted ratings of

firms in non-AAA countries, and find that bound firms tend to be rated more pessimistically

relative to non-bound firms. Similarly, I find that the probability of transitioning into default

during a 5-year window is lower for firms that are bound compared to non-bound firms,

conditional on their rating. I also show that investors, to some extent, recognize that bound

firms are on average of better credit quality when compared to non-bound firms, and thus

their yield spreads tend to be lower for a given corporate rating.

Furthermore, even though the market appears to partially incorporate the fact that bound

firms tend to be more pessimistically rated, my main results indicate that the sovereign

ceiling still has a sizable impact on the cost of debt of firms bound by it. I find that in

the month following a sovereign rating downgrade, the spread of bound firms increases by

approximately 54 bp more relative to other firms in the same country that are near but

below the sovereign ceiling. This differential effect is even more pronounced as the pre

and post event window widens, while remaining statistically significant. Although sovereign

upgrades tend to be associated with a decrease in the spreads of bound relative to non-

bound firms, the magnitude and statistical significance of their effect is lower. Finally, I also

find evidence suggesting that through the sovereign ceiling channel firms’ investment policies

are affected following a sovereign rating downgrade. In particular, in the year following a

sovereign downgrade, capital expenditures scaled by total assets decline by 3.2% more for

bound firms relative to non-bound firms. I perform a falsification test of these results by

examining whether the effect on both corporate spreads and investment are also present one

year before each actual sovereign rating change event in my sample, and find no evidence

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supporting the alternative explanation that pre-event trends are driving the results.

Overall, the results from this paper are consistent with the hypothesis that the sovereign

ceiling policy is a meaningful institutional friction for firms that issue USD-denominated debt

in international markets, and that through this channel sovereign ratings have an important

effect on borrowing costs in the private sector. The findings of this paper also indicate that

since corporate debt costs can increase when public finances deteriorate, policies that affect

a sovereign’s creditworthiness have a potentially important impact on the cost of external

financing in the private sector through the sovereign rating channel.

2 Literature Review and Relative Contribution

Previous research documents that credit ratings affect a firm’s cost of capital (e.g. Kliger

and Sarig (2000); Jorion et al. (2005); Kisgen and Strahan (2010)), and that sovereign

ratings are an important determinant of corporate ratings (e.g. Borensztein et al. (2013);

Williams et al. (2012)). I extend the existing literature by identifying an unexplored di-

mension, the sovereign ceiling channel, through which credit ratings affect a firm’s cost of

debt. Furthermore, I contribute to prior research examining the link between sovereign and

corporate credit risk by implementing an identification strategy that allows me to estimate

the impact of the sovereign ceiling on corporate bond spreads, while explicitly controlling

for the endogenous component of the country-level information contained in sovereign rating

changes.

2.1 The Sovereign Ceiling and Credit Ratings

This paper is related to prior research that examines the link between sovereign and corporate

ratings. Borensztein et al. (2013) show that, even though CRAs have moved away from

a policy of never rating a firm above the sovereign ceiling, sovereign ratings still have a

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significant effect on corporate ratings after controlling for country specific and firm-level

characteristics. Williams et al. (2012) analyze the effect of sovereign rating changes on

banks’ ratings in a sample emerging markets countries between 1999 and 2009, and find that

sovereign rating upgrades (downgrades) are strongly associated with bank rating upgrades

(downgrades). The evidence from these studies indicates that the sovereign ceiling still

represents a strong bound for the credit ratings of firms that issue USD-denominated bonds

in international markets. However, these studies stop short of tracing any causal link between

the sovereign ceiling policy and a firm’s cost of debt or corporate policies, which is the main

goal of my study. Relative to these

Researchers have also examined the relationship between sovereign and corporate credit

risk using bond spreads data. Durbin and Ng (2005) compare the yield spreads of foreign

currency denominated corporate bonds to those of bonds issued by their respective home

government. Although Durbin and Ng are limited by a small sample size, they show that

11 corporate bonds out of 108 in their sample have yields that are below their compara-

ble government spreads. They find that these bonds tend to be issued by firms that are

export driven or that have a close relationship with either a foreign firm or the home gov-

ernment. Similarly, Lee et al. (2013) use credit default swaps (CDS) data on a larges sample

of 2,364 companies in 54 countries to examine the characteristics under which firms obtain

lower CDS spreads relative to their respective sovereigns. They find that firms exposed to

better property rights institutions and firms listed on stock exchanges with stricter disclo-

sure requirements are more frequently able to obtain CDS spreads below their sovereign

counterparts. These studies show that firms that manage to “pierce” the sovereign ceiling

are typically export driven, are exposed to better property rights institutions through their

foreign assets positions, and have their stocks listed on exchanges with stricter disclosure

requirements.2 However, these papers do not examine whether the sovereign ceiling still2The findings from these papers are overall consistent with the key features of the relaxation of the

sovereign ceiling by CRAs. For instance, Fitch (2004) explained that: “. . . exceptional banks and corporatescan be rated above the sovereign ceiling if their ’stand-alone’ credit fundamentals imply that they are morecreditworthy than the sovereign and they are shielded from the risk of exchange controls by substantial

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affects corporate spreads. That is, even if a corporate that is constrained by the sovereign

rating ceiling has a lower spread than its corresponding sovereign, the fact that the firm is

bound by the ceiling could still be hindering that issuer from obtaining an even lower cost

of debt. I explicitly test this hypothesis in my main empirical analysis.

2.2 Credit Ratings, Capital Structure and the Cost of Capital

Existing literature also provides strong evidence that corporate credit ratings affect firms’

financial policies. Kisgen (2006) finds that firms near a rating change issue less debt relative

to equity than firms not near a rating adjustment. Kisgen (2009) shows that following

a rating downgrade, a firm is more likely to reduce leverage. Sufi (2009) shows that the

introduction of syndicated bank loan ratings in 1995 by S&P and Moody’s resulted in an

increase in the use of debt by firms, and also in an increase in cash acquisitions and investment

in working capital.

This paper also relates to papers examining how ratings affect a firm’s cost of capital.

Kliger and Sarig (2000) use the refinement of Moody’s ratings in April, 1986, and find that

corporate rating changes contain additional information that help explain bond spreads.

By focusing on this specific event, Kliger and Sarig (2000) are able to examine the effect of

rating changes that exclusively reflect rating information and not fundamental changes in the

issuer’s risk. Tang (2009) also exploit Moody’s 1982 rating refinement to identify the impact

of information asymmetry on real outcomes: firms with refinement upgrades experience an

additional decrease in their cost of debt compared with firms with downgrades. Tang also

show that this lower cost of debt ultimately leads firms to issue more debt, to invest more and

to hold less cash. Jorion et al. (2005) find that following the implementation of Regulation

Fair Disclosure (Reg FD) in October, 2000, rating downgrades are related to larger declines

in stock prices relative to the pre-Reg FD period. They also find that rating upgrades,

foreign exchange revenues, off-shore assets or more highly rated foreign partners/parents willing to providefinancial support.”

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which are found to be generally insignificant in previous studies, are associated with greater

increases in stock prices.

Finally, recent literature also shows that credit ratings are important because securi-

ties rules, regulations and institutional investors’ investment policies often depend on them.

Kisgen and Strahan (2010) examine the introduction in the U.S. of DBRS, a fourth credit

rating agency, and show that investments rules and regulations related to bond ratings also

have an impact on corporate borrowing costs. Bongaerts et al. (2012) find that additional

credit ratings matter mainly for regulatory purposes, and they do not appear to add signif-

icant additional information related to credit quality.

3 The Sovereign Ceiling and Corporate Ratings: In-

stitutional Background

Credit rating agencies play a crucial role in providing information about the ability and

willingness of issuers, including governments and private firms, to meet their financial obli-

gations. The three major CRAs -Standard and Poor’s, Moody’s and Fitch- assign different

types or ratings depending on the maturity (short term or long term) and currency de-

nomination of an issuance (foreign currency or local currency). This study focuses on the

foreign-currency, long-term borrowing space, where CRAs use a sovereign’s rating as a strong

upper bound on the credit ratings of firms that operate within each country. Even though

the sovereign ceiling has typically represented a more important constraint for firms in devel-

oping countries where sovereign’ ratings tend to be lower, the relationship between the credit

risk of a sovereign and private sector borrowers has received increased attention following the

recent European sovereign debt crisis, where several developed countries including Greece,

Italy, Ireland, France, Portugal and Spain experienced sovereign rating downgrades.

Until 1997, rating agencies strictly followed the policy of not granting a private company a

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rating higher than the sovereign rating. In April of that year, S&P first relaxed its sovereign

ceiling rule in three dollarized economies: Argentina, Panama, and Uruguay.3 Fitch and

Moody’s followed suit in 1998 and 2001 respectively. Although rating agencies have moved

away from strictly enforcing the sovereign ceiling over the last two decades, corporate ratings

that “pierce” the ceiling are still not common. For instance, in 2012, S&P reported, that

only 114 corporate and local government FC LT ratings in 20 countries exceeded the ratings

on their corresponding sovereign (S&P’s Rating Services, 2012).4 The limited number of

firms above the sovereign ceiling coupled with the fact that prior research shows that firms

that pierce the ceiling are systematically different than firms at or below the ceiling, are the

central reasons I do not use these firms as part of the control group in my empirical analysis.

The key credit rating implication of the sovereign ceiling is shown in figure 1, where I

plot the distribution of corporate credit ratings by each sovereign rating level. This figure

show that although there are corporate issues that manage to “pierce” the ceiling, almost

systematically the largest concentration of ratings is located exactly at the sovereign rating.

Evidence from figure 1 also suggests that, as expected, the sovereign ceiling policy represents

less of a rating constraint for firms in countries with the highest sovereign ratings (i.e. there

is a lower concentration of corporates at the ceiling in countries with AA-, AA, and AA+

ratings).

Why do CRAs use sovereign’ rating as a strong upper bound when rating corporate

issues? The central argument by rating agencies is that a sovereign default may result

in the government imposing exchange controls and other restrictive measures that limit a

firm’s access to the foreign currency necessary to service their financial obligations. Moody’s

explains that “most non-structured locally-domiciled issuers are rated at or below the level of

the sovereign because they operate in the same economic and financial environment and are

therefore vulnerable to the same broad credit pressures as the sovereign” (Moody’s Investor3For instance, in the case of Argentina S&P increased the credit rating of 14 firms above the sovereign

rating of BB.4The focus of this study is solely on corporate ratings and not on local or state governments issues.

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Service (2012)). Similar arguments are also made by S&P and Fitch (S&P’s Rating Services,

2012: FitchRatings, 2012). However, the extent to which the sovereign ceiling policy is

implemented, although similar, is not identical across CRAs. S&P is the least likely to

assign a corporate rating above the sovereign, followed by Fitch and Moody’s respectively.

As depicted in figure 2, the percentage of firms with a rating at or below the ceiling in

countries with a sovereign rating below AAA is 90.7% for S&P, 79.3% for Moody’s, and

85.2% for Fitch.5

There are two key factors CRAs use when rating foreign-currency corporate issues: 1)

the issuer’s inherent likelihood of repayment (which is the same as local ratings), and 2) the

issuer profile after taking into account the risk of exchange controls being imposed by the

government that would hinder the ability of non-sovereign issuers to convert local currency

into foreign currency to meet their financial obligations. Thus, firms that “pierce” the ceiling

are particularly strong corporates whose exposure to the risk of not been able to meet their

foreign currency obligations in the case of a sovereign default is clearly very limited. Firms

with foreign assets, high export earnings and foreign parents tend to have a higher probability

of being rated above their corresponding sovereign.6 In general, CRAs only grant an issuer

a rating above the sovereign if it is able to demonstrate a strong resilience and low default

dependence with the sovereign, as well as a degree of insulation from the domestic economic

and financial disruptions that are typically associated with sovereign defaults. Note however

that there is not a clear reason why the creditworthiness of firms that are exactly at the bound

should be affected more by a change in a sovereign’s credit quality than other firms also near

the sovereign ceiling but not exactly at it, which is a key component of my identification

strategy.5Moody’s for instance explains that when rating corporate debt in foreign currency, they are willing to

assign a rating that is up two to notches above the sovereign (Moody’s Investor Service, 2012).6The sovereign ceiling can also be pierced if bonds are offered with sufficient collateral. For example, The

Economist (2006) reported that in 2005 the Emirates National Securitization Corporation (ENSeC) issuednotes for $350m, using as collateral property leases in The Palm, a property development on islands shapedlike a palm tree, as well cash collateral. The notes received a rare AAA rating by both S&P and Moody’s.

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4 Data and Summary Statistics

Since the goal of this paper is to examine how, through the sovereign ceiling channel, credit

ratings affect the cost of debt of firms, I start my sample construction by collecting data on

long-term foreign-currency sovereign ratings directly from S&P, Moody’s and Fitch. Sim-

ilarly, I obtain from Bloomberg corporate ratings for USD-denominated bonds by non-US

firms, and I match each issue with its corresponding sovereign data. I obtain a bond’s S&P,

Moody’s and Fitch ratings, as well as its end of the month yield where available. I also

collect issue specific information (issuance and maturity dates, amount issued, coupon pay-

ment and frequency and collateral type), firm-level information (e.g. issuer ticker, industry

classification) and financial statements data where available. I convert both sovereign and

corporate ratings to a numerical scale ranging from 0 to 21, where 21 represents a AAA

rating (see table A.1 in the Appendix for the numerical conversion). Since bond pricing data

is available from Bloomberg starting in 1999, I construct my matched sample of sovereign

ratings, corporate ratings and corporate bond yields from 1999 to 2012.

The fact that I use USD denominated bonds implies that spreads above US treasury

yields represent default risk, rather than currency risk (Domowitz et al. (1998); Durbin

and Ng (2005)). Thus, I calculate corporate yield spreads by subtracting the equivalent

maturity US Treasury yield for each issue.7 I eliminate a small number of observations with

negative spreads, I require that yields for consecutive months are not equal, and I winsorize

at the 1% level to reduce the influence of outliers. Since my empirical strategy exploits

sovereign rating changes for identification, I exclude from my sample countries and firms

where sovereign ratings for all three CRAs were unchanged between 1999 and 2012.

Table 1, panel A shows the number of bonds (CUSIPs), firms and countries each year

in my sample. Panel B in table 1 displays the composition of firms by industry, using the

Dow Jones’s Industry Classification Benchmark (ICB). The final matched sample consists7I obtain constant maturity U.S. treasury rates data from the Federal Reserve Economic Data (FRED)

website: http://research.stlouisfed.org/fred2/

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of rating history data for 51 countries, 566 firms and 1,935 distinct issues. Table 2 provides

corporate yield spreads summary statistics by rating category, and shows that conditional

on a firm’s corporate rating, yield spreads tend to be on average 90 bp lower if the firm is

bound by the sovereign ceiling. Table 2 also shows that this difference is generally more

pronounced for lower ratings (e.g. the bound vs. below bound difference is -1.7% for B+

firms) and less important for higher ratings (e.g. for A+ firms the difference is +0.2%,

although it is statistically no different to zero).8 Table 4 summarizes the coverage of the

data as well as the number of sovereign rating changes by country. In table 3 I report several

bond and firm level characteristics for bound and non-bound firms, and compare whether

their averages are statistically different from each other. I find that bonds issued by bound

firms do not have a statistically different maturity when issued, although they do appear to

be issued with a higher face value (the average issue for a bound firm has a face value of

436million, vs.385 million for non-bound firms). In terms of firm-level characteristics, bound

firms on average have a leverage ratio of 60%, which is slightly higher than the 56% ratio

for non-bound firms. A firm’s EBITDA over Assets, Capex over Assets, and Total Debt

Issuance over Assets are not statistically different from each other depending on a firm’s

bound status.

5 Methodology and Results

The fundamental hypothesis of this paper is that the sovereign ceiling represents a meaningful

institutional friction that affects the cost of debt of firms that issue USD-denominated bonds

in international markets. I evaluate two main empirical implications of this hypothesis.

First, if sovereign ratings do in fact represent a meaningful constraint for firms that are at

the sovereign ceiling, then being bound should be systematically associated to credit ratings

that are more pessimistic relative to firms that are not bound. Second if the sovereign8I perform a more formal test of the bound vs. below bound effect on bond spreads in the following

section to also account, for instance, for time variation in spreads.

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ceiling is an important channel through which credit ratings affect firms’ cost of debt, then

contractions and relaxations in the ceiling should lead to more pronounced changes in the

yield spreads of those firms that are at the sovereign bound, relative to non-bound firms.

The main challenge when tracing the effect of sovereign ceiling contractions or relaxations

on corporate outcomes is the inherent endogeneity between a sovereign’s credit quality and

the creditworthiness of firms in that country. I explicitly address this concern in my empirical

strategy by examining the differential effect stemming from sovereign rating changes on firms

that are bound by the sovereign ceiling, relative to other firms in the same country that are

near but not bound by it. I do this by exploiting two important empirical regularities

associated with the sovereign ceiling. First, as discussed earlier and depicted in figure 1, the

distribution of corporate ratings across sovereign rating levels is systematically concentrated

exactly at each country’s sovereign rating. This implies that even though CRAs have moved

away from fully enforcing the sovereign ceiling over the last two decades, sovereign ratings

still represent a meaningful upper bound for corporate borrowers issuing USD-denominate

bonds in international markets. Second, as figure 3 shows, the probability of a corporate

issuer obtaining a rating adjustment in the same direction and magnitude as its sovereign

within the month of a sovereign rating change is also discontinuous exactly at the sovereign

rating bound (where a firm’s “distance-from-sovereign”, the difference between a firm’s rating

and its corresponding sovereign, is equal to zero). More precisely, the middle panel in figure 3

shows that conditional on the event of a sovereign rating change, firms that are at the bound

have a probability of approximately 60.3% of obtaining the same rating adjustment as the

sovereign within a month, compared to 9.9%, 5.0% and 2.5% for firms that are respectively

one, two and three notches below the sovereign rating. The left and right panels in figure 3

also show that this disparity in the response of corporate of ratings is not observed either

the month before or the month after the sovereign change.

As a result, the key identifying assumption in my empirical strategy is that sovereign

rating changes do not provide additional firm-specific information, and thus the differential

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effect on corporate yields between bound and non-bound firms in the event of a contraction

or relaxation of the sovereign ceiling should be stemming from an increased probability of

obtaining a corporate rating change in the same direction as the sovereign for those firms

that are exactly at the ceiling bound. Consequently, if the sovereign ceiling constrains firms

from potentially obtaining higher ratings, and if this is not fully incorporated in market

prices, then following an upgrade in a country’s sovereign rating, corporate borrowing costs

should decrease more for those firms that are bound by the sovereign ceiling, relative to firms

in the same country that are not bound by the constraint. Conversely, if a contraction in the

sovereign ceiling results in firms at the bound obtaining a lower rating, then yield spreads

should increase more for bound firms relative to non-bound firms.

5.1 Does being bound by the sovereign rating lead to a pessimistic

rating?

I first test whether firms that are bound by the sovereign ceiling (i.e. that their rating

is equal to the sovereign rating) have a more “pessimistic” rating relative to firms that

are not bound by it. If the sovereign ceiling represents a meaningful friction and not just

an unbiased and accurate assessment of a firm’s creditworthiness, then this rating practice

should be systematically associated with ratings that are more pessimistic for bound firms

relative to other firms with the same actual ratings but that are not bound by the sovereign

ceiling. Thus, I first examine whether the sovereign ceiling policy is consistent with CRAs’

providing an unbiased assessment on the creditworthiness of borrowers by comparing the

differential effect of being bound on a firm’s predicted rating, as well as on its probability of

transitioning into default.

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5.1.1 Rating Analysis: The effect of “bound status” on ratings

I explore whether bound firms tend to be pessimistically rated using a two-step procedure.

First, I use as a benchmark annual financial data on rated firms that issue USD-denominated

debt in AAA countries (where this friction does not matter) to predict the corporate ratings

of firms in non-AAA countries, where the sovereign ceiling rule potentially matters.9 Using

this sample of firms in countries with a AAA sovereign rating I estimate a regression using

a set of explanatory variables used in previous studies predicting credit ratings (see Kisgen,

2006 and Horrigan, 1966 for a similar implementation). The dependent variable is a firm’s

credit rating, which takes a value of 1 for a rating of D, up to a value of 21 to AAA (see

table A.1 in appendix for the numerical conversion). I estimate the following regression for

firms in AAA countries:

Rtgi,t =β1(NI/Ai,t) + β2(D/TotCapi,t) + β3Ln(Ai,t) + β4Square(NI/Ai,t)

+ β5Square(D/TotCapi,t) + β6Square[Ln(Ai,t)] + TimeFEt + SectorFEi + εi,t

(1)

For firm i and year t. NI/A is net income over assets, D/TotCap is the ratio of debt to

total capitalization and Ln(A) is the log of total assets. I include year fixed effects to control

for time specific shocks common to all firms, as well as sector fixed effects. I estimate the

model above for firms in AAA countries and then I use the estimated coefficients to calculate

the predicted credit ratings for the sample of firms in non-AAA countries (which I denote

as Rtg), where the sovereign rating ceiling potentially represent a meaningful institutional

friction.10

In the second step, after having calculated the predicted rating for the sample of non-

AAA countries, I compare, for each actual corporate rating level, whether predicted rating

are systematically higher for firms that are bound relative to other firms with the same

actual rating but that are not bound by the sovereign ceiling. Thus, I estimate the following9I do not include data for U.S. firms, as the ratings for USD-denominated debt are in that case local-

currency, and not foreign-currency as they are elsewhere in my data.10Estimating the model in equation 1 for the sample of firms in AAA countries results in an adjusted R2

of 0.51. Table A.2 in the appendix shows the estimated coefficients obtained from this regression.

15

regression:

Rtgi,t = β0 + β1RtgFEi,t + β2(RtgFEi,t ∗Boundi,t) + Sov.RatFEi,t + TimeFEt + εi,t (2)

Where β2 is a vector of coefficients that captures the differential effect, for each rating

level (denoted by Rtg.FE), of being bound by the sovereign ceiling on a firm’s predicted

rating. If firms that are bound are rated fairly relative to firms that are not at the ceiling

bound, then predicted ratings should not systematically differ based on whether firms are

below or at the sovereign bound. On the other hand, if firms that are bound tend to be

pessimistically rated, then their predicted rating should be higher relative to other firms with

the same actual corporate rating but that are not bound by the sovereign rating. ). I focus

only on firms that have a corporate rating below the sovereign rating. As highlighted earlier,

previous research and CRAs themselves indicate that firms that are above the ceiling have

typically strong ties to more financially developed countries and other specific characteristics

that make them fundamentally different than other firms that do not pierce the ceiling.

Table 5 shows that when comparing the predicted rating for bound versus below bound

firms by estimating equation 2, bound firms tend to have a predicted rating that is above the

predicted rating of the latter. For example, a bound firm with a B+ rating has a predicted

rating that is 1.1 notches higher than a firm that is also rated B+ but that is not bounded by

the sovereign ceiling. The difference between the predicted ratings of bound vs. below bound

firms is positive and statistically significant in 12 of the 14 actual rating levels evaluated.

The difference between the predicted rating of bound versus below bound firms is only not

positive in two of the highest rating levels (AA- and AA) where, as indicated before, the

sovereign ceiling rule represents a less meaningful restriction.11

11I also perform this comparison for bound firms relative to above-bound issuers in unreported tests andfind similar results. Note however that due to the limited number of observations for firms above the sovereignceiling, the power of the tests is significantly smaller.

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5.1.2 Default analysis: The effect of “bound status” on transitioning into de-

fault

An alternative test to evaluate whether a firm’s “bound status“ leads to a systematically

pessimistic rating is to examine whether bound firms are associated with a lower default

rate than non-bound firms, for the same actual rating. Specifically, I asses the unbiasedness

of credit ratings in predicting that a firms transitions into default, depending on whether

the firm is bound by the sovereign rating or not. The power of any default based test is

constrained by the fact that actual defaults do not occur frequently. Out of the 566 firms

in my full sample of non-AAA countries, only 15 had at any time a default rating (“D”).

Thus, to perform this particular test I extend this sample to include firms with a rating of

CCC+ and below, which more precisely proxy for being “close to default”. These firms are

characterized by S&P as having “significant speculative characteristics, currently vulnerable”.

The number of close-to-default firms in the sample is 64. I estimate a logit regression where

the dependent variable is a dummy variable that indicates whether a firm had a rating of

CCC+ or below during the last five years. I examine if a firm’s bound status affects its

probability of being close to default, after controlling for its credit rating by estimating the

following model:

Close-to-defaulti,j,[t,t+T ] =β0 + β1RtgFEi,j,t + β2(RtgFEi,j,t ∗Boundi,j,t)

+ SovRatFEi,t + TimeFEt + εi,j,t

(3)

where the coefficients vector β2 measures the interaction between each corporate rating

and Bound, a dummy variable that takes a value of one for bounded firms and zero other-

wise. β2 captures, for each corporate rating, whether being bound by the sovereign rating is

associated with a lower default. I include time (year) fixed effects to account for variations in

default rates through the business cycle. Similarly, I include sovereign rating fixed to control

for differences in default rates that vary dependent on the overall level of creditworthiness

17

of a sovereign.

The results from estimating the default model in equation 3 are shown in tables 6 and

7, which respectively report the estimated coefficients and the marginal effects from the

estimated logit model. Note that there are no estimated coefficients for firms with ratings

above A, as there are no firms with these higher ratings that transition into “close-to-default”

in my sample. Consistent with the finding from the previous subsection that bound firms

tend to be rated more pessimistically, the predicted probabilities in table 7 indicate that

bound firms tend to have a lower probability of transitioning into default, for a given rating,

than non-bound firms. For instance, the probability of a non-bound firm with a B+ rating

transitioning into “close-to-default” in a 5-year window is 7.1%. This is significantly higher

than the 4.3% probability of a firm also with a B+ rating but bound by the sovereign ceiling

of transitioning into “close-to-default”. As it was the case in the predicted ratings test, the

difference between bound and non-bound firms is more pronounced for firms in lower rating

levels.

5.1.3 Are bound firms perceived as more creditworthy by the market?

The evidence from the previous two tests indicates that bound firms tend to be more un-

favorably rated by CRAs and tend to have lower probabilities of transitioning into default

than non-bound firms with the same rating. Thus, the next natural question is whether

investors in the corporate bond market “follow the ratings”, or do they see past them and

recognize bound firms’ relatively higher credit quality. To test whether the market actually

recognizes bound firms’ potentially higher credit quality, I estimate the following regression:

Spreadi,j,t =β0 + β1RtgFEi,j,t + β2(RtgFEi,j,t ∗Boundi,j,t)

+ Sov.RatFEi,t + TimeFEt + εi,j,t

(4)

where the dependent variable is a bond’s yield spread, and the coefficients vector β2

18

measures the interaction between each corporate rating and Bound, a dummy variable that

takes a value of one for bound firms and zero otherwise. The coefficients vector β2 captures,

for each corporate rating, whether being bound by the sovereign rating or not results in

differences in corporate spreads for a given rating level, after including sovereign rating fixed

effects and time (month) fixed effects. Table 8 shows the estimation results from model 4.

Overall, the average spread of bound firms, after being demeaned by sovereign rating and

time effects, tends to be lower for each rating category when compared to non-bound firms.

For instance, a B+ rated firm bound by the sovereign ceiling, the average, time-demeaned

spread is 7.13%, 66 basis points higher than the average spread of 6.46% for a non-bound

B+ rated firm. This suggest that investors do incorporate, to an extent, the better quality

and lower probability of default of bound firms relative to non-bound firms given the same

corporate rating.

5.2 The Effect of the Sovereign Ceiling Channel on Corporate

Yields

The results from the empirical tests in the previous subsection show that bound firms tend to

be rated more pessimistically, have lower probabilities of transitioning into default, and are

often priced at lower yield spreads by the market than non-bound firms. However, the fact

that investors price the debt of bound firms at lower yields does not in itself mean that the

sovereign ceiling policy has no impact on firms affected by it. More precisely, it is possible

that relaxations or contractions in the sovereign ceiling result in even a more pronounced

reduction or increase in the cost of debt of firms bound by the sovereign rating. To test this,

in this subsection I first implement reduced form regressions to examine whether firms’ bor-

rowing costs increase (decrease) more for those firms that are bound by the ceiling following

a contraction or a relaxation in the sovereign ceiling. I then use a fuzzy regression discon-

tinuity design (RDD) to instrument a one-notch corporate rating change directly related to

19

the sovereign ceiling channel, using as an instrument the interaction between a firm’s bound

status and a sovereign rating change.

5.2.1 Reduced Form Regressions

I examine the change in corporate spreads around sovereign rating downgrades and upgrades

for firms that are bound by the sovereign ceiling, relative to firms that are near but not

bound by it. That is, I use a firm’s bound status as an instrument to estimate the effect of

a contraction or a relaxation in the sovereign ceiling on corporate spreads. I estimate the

following reduced form pooled regression for sovereign downgrades and upgrades:

∆Spreadi,j,[t−s] =β1Sov.Downi,j + β2Sov.Upi,j + β3 (Sov.Downi,j ∗Boundi,j)

+ β4 (Sov.Upi,j ∗Boundi,j) + εi,j

(5)

where the dependent variable is the change in spread around sovereign rating changes:

the spread on a firm’s bond t months after each sovereign event minus its spread s months

prior to the event. I focus on the differential effect on bound firms relative to firms that

are near but not bound by the sovereign ceiling. The main coefficient of interests are β3

and β4, the interaction terms between sovereign downgrades and upgrades respectively, and

the bound status identifier. I focus only on firms that have a corporate rating below the

sovereign rating, as firms that pierce the ceiling tend to have certain specific characteristics

that make them fundamentally different. I then face a trade-off between using as controls

firms that are not bound by the sovereign rating but that are not too far away from it,

and having enough firms as controls. Thus, I constrain non-bound firms to be three rating

notches or less below the sovereign. I also limit my analysis to firms that have a rating

of B- or higher.12 Because rating changes can be anticipated, I perform event studies with

different values of t around the time of the sovereign rating announcements to capture the

response of financial markets. The indicator variables Sov.Down takes a value of one the12Only 0.1% of the observations in my sample have a corporate rating in the CCC, CC or D categories.

20

month of a sovereign downgrade. Similarly, the dummy variable Sov.Up takes a value of

one the month of a sovereign upgrade. The dummy variable Bound takes a value of one if

a corporate issue has the same rating as the corresponding sovereign prior to a sovereign

rating change, and zero if a firm is three notches or less below the sovereign bound. Since

firms can have two or more bonds at any given point, I weight observations based on the

number of CUSIPs observed for each firm at each point in time, and I cluster standard errors

by each country-sovereign event. If the sovereign ceiling represents a meaningful constraint

for firms, I expect the interaction coefficients β3, which measures the differential effect of

sovereign downgrades on the change in spreads of bound firms, to be positive, indicating a

higher increase in yields for bound firms. Similarly, I expect β4, which measures the added

effect of sovereign upgrades on bound firms, to be negative.

Table 9, panel A reports the results from estimating the model in eq. 5 for event windows

starting three months prior to a sovereign rating change. Results from panel A indicates

that following a sovereign rating downgrade, and when comparing the change in spread one

month after the event vs. the spread three months prior to the sovereign rating adjustment,

the average spread for firms that are bound by the sovereign ceiling increases by 103 bp more

than for firms that are not bound by the sovereign ceiling. As the event windows widens

to three months after a sovereign downgrade the differential effect increases to 117 bp,

while remaining statistically significant. Results for the upgrade interaction coefficient are

statistically significant although economically weaker: the spread for bound firms decreases

by 27 bp more following a sovereign upgrade. The fact the differential effect of sovereign

rating changes is stronger for downgrades than for upgrades is consistent with previous

studies who also find and asymmetry in the response to each type of event (e.g. Brooks

et al., 2004; Gande and Parsley, 2005; Ferreira and Gama, 2007)

Since I focus on the change in spreads around sovereign events for bound firms and firms

that are not bound but still close to the bound, the empirical design I set up in equation 5

should not require the use for other baseline covariates; that is, their inclusion should not

21

affect the consistency of the parameters of interest β3 and β4. Nevertheless, to further pin

down the identification of the effect of changes in the sovereign rating ceiling on spreads I

extend the specification in model 5 by including country-event specific fixed effects (i.e. fixed

effects for each sovereign rating downgrade or upgrade for each country). This approach,

which reduces the likelihood that the coefficient estimates β3 and β4 will be biased by a

correlation between country-time specific effects and spread changes, has a high cost in

terms of lost degrees of freedom. Including country-event fixed effects equates to estimating

the differential impact of the sovereign ceiling on the cost of debt of firms that are bound

by the sovereign ceiling, relative to firms in the same country that are not bound by the

constraint. I estimate the following model:

∆Spreadi,j,[t−s] = β1 (Sov.Downi,j ∗Boundi,j) + β2 (Sov.Upi,j ∗Boundi,j)

+ CountryEventFE + εi,j

(6)

where the main coefficients of interest are β1 and β2 (note that including country-event

specific fixed effects absorbs both the constant term and the Sov.Down term included in

the pooled regression). Panel B in table 9 shows the estimation results of this specification.

Although including country-event fixed effects dampens the overall magnitudes compared to

the pooled OLS results in panel A, the coefficients for the interaction term Sov.Downi,j,t ∗

Boundi,j,t remain statistically and economically significant. For example, the spread for

firms that are bound by the sovereign ceiling increases by 54 bp more than for firms that

are not bound by the sovereign ceiling using a one month after the event. When using a

three-month pre vs. one month post window, the effect of a sovereign rating upgrade on

firms bound by the ceiling is a decrease of 12 bp relative to non-bound firms. However,

results for sovereign upgrades yield no statistical significance as the event window widens

once country-event fixed effects are introduced.

22

5.2.2 Fuzzy Regression Discontinuity Design (RDD)

In this subsection I implement a fuzzy RDD to instrument a one-notch corporate rating

change directly related to the sovereign ceiling channel, using as an instrument the interac-

tion between a firm’s bound status with a sovereign rating downgrade (or upgrade). More

precisely, I implement a fuzzy RDD where the effect evaluated is the impact on a firm’s yield

spread resulting from a corporate credit rating change directly related to a sovereign ceiling

contraction or relaxation. As it has been noted, CRAs do not strictly follow the sovereign

ceiling policy and thus a sovereign rating change does not lead to a 100% probability in

bound firms obtaining the same rating change, as it would be required for a “sharp” RDD.

However, as previously shown in figure 3, the probability of treatment jumps sharply from

less than 10% for firms that are three notches or less below the sovereign ceiling to just above

60 % for firms that are exactly at the bound.

The reduced form estimated using equations 5 and 6 and the fuzzy RD are closely related

to each other. However, since the probability of a firm obtaining the same rating change as

the sovereign is less than one at the sovereign bound, the jump in the relationship between

δSpread and Bound can only be interpreted as the average treatment effect of a corporate

rating change stemming from the sovereign ceiling channel if Bound does not affect the

changes in spreads outside of its influence through treatment receipt. As Lee and Lemieux

(2010) point out, the treatment effect can still be obtained by instrumenting the treatment

dummy (obtaining a corporate downgrade or upgrade due to the sovereign ceiling) with a

firm bound’s status. Thus, I implement the following fuzzy RD design described by the

following equation system:

∆Corp.Downi,j = α1(Boundi,j ∗ ∆Sov.Downi,j) + CountryEventFE + εi,j (7a)

∆Corp.Upi,j = γ2(Boundi,j ∗ ∆Sov.Upi,j) + CountryEventFE + εi,j (7b)

23

∆Spreadi,j,[t−s] = β1 ∆Corp.Downi,j + β2 ∆Corp.Upi,j + CountryEventFE + εi,j (8)

where the first two equations correspond to the first stage where the change in corporate

ratings are estimated after a sovereign downgrade or upgrade respectively. These are then

used in the second stage regression where the main coefficients of interest are β1 and β2.

Table 10 shows the estimation results of the RDD setting, which I obtain using 2SLS. The

results from this set of regressions are overall consistent with previous results from the

reduced form regressions, but more precisely identify the effect of a one-notch corporate

rating change directly related to the sovereign ceiling channel. For example, the effect of a

one-notch corporate rating downgrade directly stemming from the sovereign ceiling channel

is 88 bp using a one month post vs. three month pre-event window. As before, the effect of

downgrades remains economically and statistically significant as the event window widens.

5.2.3 Effect on a Firm’s Investment and Financing

The results above indicate that through the sovereign ceiling channel credit ratings have an

important effect on a firm’s cost of debt. Thus, in this subsection I examine whether a firm’s

investment and financing policies are affected, for the subset of non-financial firms in my

sample. I focus my analysis around contractions and relaxations in the sovereign ceiling, but

now I examine the effect on corporate policies one year after a sovereign event relative to the

year prior to the event. I estimate the following regression using the instrumented variables

Corp.Downi,t and Corp.Upi,t estimated as above in the RDD eq. 8:

Corp.Outcomei,t = β1 δCorp.Downi,t + β2 δCorp.Upi,t

+ CountryEventFE + SectorFE + εi,t

(9)

Where Corp.Outcome is one of five dependent variables examined: capital expenditures,

net issuance of long-term debt, net issuance of short-term debt, total net debt issuance,

24

and net equity issuance. All variables are scaled by beginning of the year total assets.

Since financial statements data are only available for a subsample of firms, the number

of observations in these regressions is significantly lower. Table 11 reports the estimation

results from estimating the model in eq. 9 for each the evaluated corporate outcomes.

Panel A focuses on sovereign downgrades and Panel B reports the effect around sovereign

upgrades. The evidence around sovereign downgrades suggests that through the sovereign

ceiling channel, a firm’s capital expenditures decline by 3.2%. However, evidence in terms of

financial policies is not statistically significant. Similarly, there is little statistical evidence

that sovereign upgrades affect a firm’s investment or financing policies, which is consistent

with the findings above on the low effect on corporate spread changes around sovereign

upgrades.

5.3 Robustness Tests

5.3.1 Falsification test: Effect of the sovereign ceiling one year before the actual

event

I perform a falsification test to addresses concerns for pre-event trends driving the differential

effects on spreads. I estimate the same model with country-event fixed effects as in panel

B in table 9, with the only difference that I focus on the differential effect of bound versus

non-bound firms one year before the actual event. I report the results of this test in table

12. Consistent with the main hypothesis in this paper that the contractions and relaxations

in the sovereign ceiling are the main drivers behind the identified differential changes in

corporate spreads, I find that none of the coefficients reported in table 12 are statistically

different from zero.

Similarly, in table 13 I perform a falsification test that evaluates the concern for pre-

event trends driving the results on firms’ investment policies. There, I estimate the same

model as in table 11, with the only difference that I focus on the differential effect of bound

25

versus non-bound firms one year before the actual event. As it was the case with spreads,

the coefficients reported in table 13 indicate that for instance, the differential effect found

around sovereign downgrade on capital expenditures are not found one year prior to the

actual sovereign rating adjustment.

6 Conclusions

In this paper I investigate how, through the sovereign ceiling channel, credit ratings af-

fect the cost of debt of firms that issue USD-denominated bonds in international markets.

Specifically, I estimate the differential effect of contractions and relaxations in the sovereign

ceiling on firms that are bound by the sovereign rating ceiling relative to firms that are not

constrained by it. The empirical design and results of this paper add to the credit ratings

literature by identifying an unexplored dimension through which credit ratings affect firms,

and by exploiting an identification strategy that allows me to isolate the effect of contractions

and relaxations in the sovereign ceiling on corporate outcomes.

I first show that the sovereign ceiling policy is associated with pessimistically biased

ratings for firms that are bound by the sovereign ceiling, compared to non-bound firms.

Similarly, I find that the probability of bound firms transitioning into default using a 5-year

window is lower for bound firms. Consistent with these findings that suggest that bound

firms tend to be of better quality than non-bound firms, bond market participants tend to

price at lower yield spreads the bonds of firms that are bound by the sovereign ceiling, given

the same corporate rating. Although this might indicate that the market, at least partially,

sees through this rating policy and recognizes the higher credit quality of bound firms, I

find evidence indicating that the sovereign ceiling channel still has a sizeable effect on firms

affected by it. Specifically, I show that following a sovereign downgrade, the cost of debt

increases significantly more for firms that are bound by the sovereign rating, compared to

firms with ratings below the ceiling.

26

The results of this paper also highlight a particular link through which corporate debt

costs can be increased when public finances deteriorate. My findings suggest that through

the sovereign ceiling channel, policies that affect a sovereign’s creditworthiness might have

an important impact on the cost of external financing for firms in the private sector. This

represents a potential externality of public debt on private borrowers that have the same

rating as their government, evidence of which has not been previously been identified in the

literature.

27

References

Bongaerts, D., Cremers, K., and Goetzmann, W. N. (2012). Tiebreaker: Certification andmultiple credit ratings. The Journal of Finance, 67(1):113–152.

Borensztein, E., Cowan, K., and Valenzuela, P. (2013). Sovereign ceilings “lite”? the impactof sovereign ratings on corporate ratings. Journal of Banking & Finance. Forthcoming.

Brooks, R., Faff, R. W., Hillier, D., and Hillier, J. (2004). The national market impact ofsovereign rating changes. Journal of banking & finance, 28(1):233–250.

CFO Magazine (2013). Corporate, sovereign debt ratings closely linked: S&P. Apr 29, 2013.

Domowitz, I., Glen, J., and Madhavan, A. (1998). Country and currency risk premia in anemerging market. Journal of Financial and Quantitative Analysis, 33(2).

Durbin, E. and Ng, D. (2005). The sovereign ceiling and emerging market corporate bondspreads. Journal of International Money and Finance, 24(4):631–649.

Ferreira, M. A. and Gama, P. M. (2007). Does sovereign debt ratings news spill over tointernational stock markets? Journal of Banking & Finance, 31(10):3162–3182.

FitchRatings (2012). Rating corporates above the country ceiling.

Gande, A. and Parsley, D. C. (2005). News spillovers in the sovereign debt market. Journalof Financial Economics, 75(3):691–734.

Horrigan, J. O. (1966). The determination of long-term credit standing with financial ratios.Journal of Accounting Research, pages 44–62.

Jorion, P., Liu, Z., and Shi, C. (2005). Informational effects of regulation FD: Evidence fromrating agencies. Journal of Financial Economics, 76(2):309–330.

Kisgen, D. J. (2006). Credit ratings and capital structure. The Journal of Finance,61(3):1035–1072.

Kisgen, D. J. (2009). Do firms target credit ratings or leverage levels? Journal of Financialand Quantitative Analysis, 44(6):1323.

Kisgen, D. J. and Strahan, P. E. (2010). Do regulations based on credit ratings affect afirm’s cost of capital? Review of Financial Studies, 23(12):4324–4347.

28

Kliger, D. and Sarig, O. (2000). The information value of bond ratings. The Journal ofFinance, 55(6):2879–2902.

Lee, D. S. and Lemieux, T. (2010). Regression discontinuity designs in economics. TheJournal of Economic Literature, 48(2):281–355.

Lee, J., Naranjo, A., and Sirmans, S. (2013). The exodus from sovereign risk: Sovereignceiling violations in credit default swap markets. Unpublished Working Paper.

Moody’s Investor Service (2012). How sovereign credit quality may affect other ratings.

S&P’s Rating Services (2012). Corporate and government ratings that exceed the sovereignrating.

Sufi, A. (2009). The real effects of debt certification: Evidence from the introduction of bankloan ratings. Review of Financial Studies, 22(4):1659–1691.

Tang, T. T. (2009). Information asymmetry and firmsâĂŹ credit market access: Evidencefrom moody’s credit rating format refinement. Journal of Financial Economics, 93(2):325–351.

The Economist (2006). Where angels fear to tread. Apr 11, 2006.

Williams, G., Alsakka, R., and ap Gwilym, O. (2012). The impact of sovereign rating actionson bank ratings in emerging markets. Journal of Banking & Finance, 37(2):536–577.

29

Figure 1: Distribution of corporate credit ratings by sovereign rating.This figure depicts the frequency distribution of long-term foreign-currency (LT FC) corporate ratings bythe LT FC sovereign rating of the corresponding country of domicile. Observations for countries withAAA ratings are excluded as, by definition, the sovereign ceiling policy does not represent a constraint forcorporates when the sovereign has the maximum attainable rating. The figure includes ratings from S&P,Moody’s and Fitch which are homogeneized using the numerical conversion in Table A.1 in the Appendix.The bars in dark blue in the diagonal represent the sovereign rating ceiling (i.e. where corporate and sovereignratings equate).

CCC+

B-

B

B+

BB-

BB

BB+

BBB-

BBB

BBB+

A-

A

A+

AA-

AA

AA+

AAA

Cor

pora

te R

atin

g -

FC

LT

CCC+ B- B B+BB-

BBBB+

BBB-BBB

BBB+ A- A A+AA-

AAAA+

Sovereign Rating - FC LT

Frequency of Corporate Ratings by Sovereign Rating

30

Figure 2: Distribution of corporate bonds’ “Distance-from-Sovereign” (difference be-tween a corporate ratings and its corresponding sovereign rating)This figure plots the distribution of the difference between long-term foreign-currency (LT FC) corporateratings and LT FC sovereign ratings for S&P, Moody’s and Fitch. Observations for countries with a AAArating are excluded from the graph, as well as for countries where no sovereign rating changes are observedfrom any credit rating agency between 1999 and 2012.

0.1

.2.3

Den

sity

-16 -12 -8 -4 0 4 8S&P Corporate Rating minus Sovereign Rating

0.1

.2.3

Den

sity

-16 -12 -8 -4 0 4 8Moody's Corporate Rating minus Sovereign Rating

0.1

.2.3

Den

sity

-16 -12 -8 -4 0 4 8Fitch Corporate Rating minus Sovereign Rating

31

Figure 3: Proportion of corporate rating changes around sovereign rating changes by “distance-from-sovereign”This figure plots the fraction of corporate rating changes the month before, the month of, and the month after a sovereign rating change. Observationsare grouped according to each corporate’s “distance-from-sovereign” (the difference between the corporate rating and its corresponding sovereign).For example, a “distance-from-sovereign” of zero means that the corporate rating is equal to the sovereign rating. The value of each bar indicates thefraction of corporate issues in that group whose rating changes in the same direction and magnitude as the sovereign change. Values of “distance-from-sovereign” lower than -6 and greater than +2 are grouped at the “<= -6” and “>= +2” bins respectively due to limited observations beyondthese values.

1.2 1.9 1.9 0.4 0.4 1.7 1.1 1.73.4

0

10

20

30

40

50

60

70

80

(%)

<= -6 -5 -4 -3 -2 -1 0 +1 >= +2Distance-from-Sovereign

One Month Before a Sovereign Change

1.0 0.43.8 2.5

5.0

9.8

60.2

22.4

14.9

0

10

20

30

40

50

60

70

80

(%)

<= -6 -5 -4 -3 -2 -1 0 +1 >= +2Distance-from-Sovereign

The Month of a Sovereign Change

1.3 1.8 2.40.9

2.5 3.1 3.14.8

1.8

0

10

20

30

40

50

60

70

80

(%)

<= -6 -5 -4 -3 -2 -1 0 +1 >= +2Distance-from-Sovereign

One Month After a Sovereign Change

Percentage of Corporate Rating Changes Around Sovereign Changesby Distance-from-Sovereign

32

Figure 4: Corporate spreads around sovereign rating changesThis figure depicts the regression coefficients of corporate spreads on monthly time dummies around sovereign downgrades (left panel) and upgrades(right panel) for two groups: firms that at the time of the sovereign event are exactly at the sovereign ceiling (“Bound Firms”) and firms thatare three notches or less below the sovereign ceiling (“Below Bound Firms”). The dependent variable is the corporate bond spread, regressed onevent-time dummies (months relative to the sovereign rating change), a dummy variable for each country-event (i.e. each sovereign rating change),and bond fixed effects. Thus, the plotted coefficients can be interpreted as the change in bond spreads through time around sovereign rating changes.The vertical dotted line between zero and one represents the event occurrence, which happens after the end of the month at t=-1 and before t=0.The base period for each group’s corporate rate changes is 10 months prior to each event. I require that a bond has at least one observation in thepre-event period and one in the post-event period. The regression estimated is:

Spreadi,j,t = β1EventT imeFE + β2 [EventT imeFE ∗Boundi,j,t] + Firm&EventFE + εi,j,t

where the estimated coefficient vectors β1 and β2 are used to plot the changes in spreads in event time. Standard errors clustered bycountry-event are used to calculate the 95% confidence interval of the difference between the two groups.

-1.00.01.02.03.0

(%)

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

(%)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9Event Time (months)

Bound Firms Bounds vs. Below Bound Difference

Below Bound Firms 95% Conf. Interval

Sovereign Downgrades

-2.0-1.00.01.02.0

(%)

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

(%)

-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9Event Time (months)

Bound Firms Bound vs. Below Bound Difference

Below Bound Firms 95% Conf. Interval

Sovereign Upgrades

Corporate spreads around sovereign rating changes

33

Table 1: Sample DescriptionThis table reports in panel A the number of observations over time included in the sample. Panel B showsthe distribution of firms in the sample by industry, using the Dow Jones’s Industry Classification Benchmark(ICB). The sample includes USD denominated bonds issued by non-US firms with at least one credit ratingand located in countries with at least one sovereign rating change between 1999 and 2012.

Panel A. Number of Bonds, Firms and Countries in the Sample by Year

YearNumber of

ObservationsNumber of Bonds Number of Issuers Number of Countries

1999 1,369 176 92 142000 1,967 197 110 182001 1,613 226 128 182002 2,161 225 126 202003 1,617 211 115 192004 2,463 271 145 172005 2,942 237 114 162006 2,111 220 119 182007 2,191 194 101 162008 1,893 221 109 222009 2,731 378 133 222010 6,150 567 203 272011 10,041 865 255 322012 12,529 932 265 34

1,93556651

Number of distinct bonds:Number of distinct issuers:Number of distinct countries:

Panel B. Number of Firms by Industry

Oil & Gas 45Basic Materials 37Industrials 56Consumer Goods 46Health Care 5Consumer Services 24Telecommunications 40Utilities 64Financials 243Technology 6

Total 566

Number of issuers by ICB industry

34

Table 2: Corporate yield spread summary statistics by ratingThis table reports sample statistics on yield spreads for the sample of corporate bonds. The sample includes monthly data on all USD-denominatedbonds issued by non-US firms located in countries with at least one sovereign rating change between 1999 and 2012.

Below Bound Bound Below Bound Bound Below Bound Bound(1) (2) (3) = (2) - (1) p-value (4) (5) (6) (7)

AA+ 2.1% 1.7% -0.4% 0.257 1.4% 1.5% 202 519AA 1.5% 1.7% 0.2% 0.636 1.0% 1.4% 936 271AA- 1.2% 1.4% 0.2% 0.465 0.9% 1.2% 822 522A+ 1.5% 1.8% 0.3%*** 0.007 0.9% 0.7% 1,317 2,380A 2.1% 1.6% -0.5%*** 0.003 1.0% 1.0% 1,111 1,713A- 1.9% 1.5% -0.4%** 0.043 1.2% 1.2% 2,088 1,140BBB+ 2.3% 2.0% -0.3% 0.210 1.4% 1.3% 3,157 1,303BBB 2.6% 2.3% -0.3% 0.196 1.3% 1.7% 2,677 1,688BBB- 2.8% 2.9% 0.1% 0.842 1.4% 1.9% 1,292 1,653BB+ 4.2% 3.2% -1.0%** 0.028 2.7% 2.2% 964 970BB 5.3% 3.3% -2.0%*** 0.000 3.4% 1.6% 1,590 644BB- 5.9% 3.9% -2.0%*** 0.001 3.3% 3.3% 1,833 598B+ 6.8% 5.1% -1.7%** 0.027 3.7% 3.4% 1,027 633B 8.8% 7.0% -1.8%** 0.021 3.8% 3.8% 714 528B- 7.6% 5.6% -2.0%** 0.014 3.8% 3.5% 254 175<= CCC+ 9.6% 5.7% -3.9%*** 0.003 4.7% 4.2% 215 200

All 3.5% 2.5% -0.9%*** 0.000 3.0% 2.3% 20,211 14,938

Yield spread: Summary Statistics by Corporate Rating and Bound StatusStd. Deviation Number of observations

Corporate Rating

MeanDifference

35

Table 3: Financial Ratios Summary Statistics: Bound vs. Non-Bound FirmsThis table reports annual financial ratios sample statistics for bound and non-bound firms. Standard errorsare clustered by firm; * indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.

Below Bound Bound(1) (2) (3) = (2) - (1) p-value

Bond-level characteristicsYrs to Maturity at issuance 10.431 10.453 0.022 0.973ln(Issue Amount) 19.291 19.419 0.128 0.208Amount Issued (USD millions) 385.097 436.393 51.296* 0.08

Firm-level characteristicsBV Debt / BV Assets 0.557 0.602 0.045* 0.065Net Income / Assets 0.003 0.025 0.022 0.502EBITDA / Assets 0.162 0.216 0.054 0.222Capex / Assets 0.078 0.085 0.007 0.327Total Debt Issuance / Assets 0.032 0.031 -0.001 0.924

Difference

36

Table 4: Country coverageThe first two columns in this table show the number of unique corporate bonds and firms by country usedin the sample. Columns 4, 5 and 6 report the number of total long-term foreign-currency (LT FC) sovereignrating changes by credit rating agency in each country.

S&P Moody’s FitchArgentina 64 28 12 4 1Australia 197 28 2 1 1Austria 14 3 1 0 0Azerbaijan 1 1 1 1 0Bahrain 3 3 1 2 1Belgium 3 1 1 1 3Bermuda 59 19 1 1 2Bolivia 1 1 3 3 2Brazil 194 71 7 7 8Canada 56 36 1 2 2Chile 128 25 3 3 2Hong Kong 30 17 4 3 1Colombia 7 3 4 3 4Croatia 4 2 1 0 1Cyprus 9 5 9 6 3Czech Republic 6 2 2 1 3Denmark 3 3 1 1 1Dominican Republic 2 1 3 2 1El Salvador 2 1 1 3 0Finland 8 3 2 0 0France 178 30 1 1 0Georgia 1 1 2 0 1Greece 2 2 4 2 4India 20 11 2 2 2Indonesia 15 6 12 6 6Ireland 115 34 6 5 3Israel 17 1 2 2 1Italy 16 4 4 4 1Jamaica 3 2 5 1 1Japan 70 29 5 3 1Kazakhstan 48 12 8 4 6Lebanon 1 1 6 4 2Malaysia 25 8 3 3 3Malta 2 1 2 4 0Mexico 166 47 5 1 5New Zealand 13 5 1 1 1Panama 7 4 4 2 2Peru 10 5 5 4 5Philippines 36 10 4 5 1Republic of Korea 278 44 5 6 4Russian Federation 7 7 8 4 7Singapore 13 4 0 1 1Slovakia 1 1 0 4 4South Africa 10 5 2 3 0Spain 27 9 8 4 2Sweden 24 9 1 2 2Thailand 14 9 2 2 4Turkey 10 5 7 2 7Ukraine 2 2 7 1 1Venezuela 11 4 10 2 5Viet Nam 2 1 1 1 1

Total 1,935 566 192 130 119

Number of LT FC Sovereign Rating ChangesNumber of Firms

Number of Corporate Bonds

Country

37

Table 5: Predicted Rating by Sovereign Bound StatusThis table reports the estimated coefficients from the regression model in eq. 2, which measure the effect ofbeing bound by the sovereign ceiling on a firm’s predicted rating:

Rtgi,t = β0 + β1RtgFEi,t + β2(RtgFEi,t ∗Boundi,t) + Sov.RatFEi,t + TimeFEt + εi,t

where β2 is a coefficients vector that captures the differential effect, for each rating level, of beingbound by the sovereign ceiling on a firm’s predicted rating. I weigh observations based on the number ofbonds observed each year for each firm. Standard errors clustered by firm.

Below Bound Bound(1) (2) (3) = (2) - (1) P-value

AA+ 14.25 15.26 1.01*** 0.003AA 15.89 15.12 -0.77*** 0.010AA- 16.06 15.95 -0.11 0.668A+ 14.64 16.40 1.76*** 0.000A 14.43 15.77 1.34*** 0.000A- 14.47 15.25 0.79*** 0.000BBB+ 14.73 15.14 0.41** 0.034BBB 14.63 15.23 0.61*** 0.001BBB- 14.06 15.10 1.04*** 0.000BB+ 13.57 15.17 1.6*** 0.000BB 13.62 14.87 1.25*** 0.000BB- 13.17 14.24 1.06** 0.044B+ 12.17 13.29 1.11*** 0.000B 11.07 13.06 1.99*** 0.001B- 10.66 13.28 2.62*** 0.000

Difference between "Bound" Predicted Rating by Bound Status

38

Table 6: Default and Bound Status: Logit Regression of “Close-to-Default” by Ratingand Bound StatusThis table reports the estimated coefficients from the logit model in eq. 3, which measure the effect of beingbound by the sovereign ceiling on a firm’s probability of transitioning into a “Close-to-Default” status:

Close-to-defaulti,j,[t,t+T ] = β0 +β1RtgFEi,j,t +β2(RtgFEi,j,t ∗Boundi,j,t)+SovRatFEi,t +TimeFEt + εi,j,t

where β2 is a coefficients vector that captures the differential effect, for each rating level, of beingbound by the sovereign ceiling on a firm’s predicted rating. I weigh observations based on the number ofbonds observed each year for each firm. Standard errors clustered by firm.

Coeff. S.E. p-valueA+ 2.953 0.759 0.000A -0.609 0.860 0.479A- 2.168 1.131 0.055BBB+ 3.525 0.736 0.000BBB 3.137 0.628 0.000BBB- 3.321 0.678 0.000BB+ 4.038 0.714 0.000BB 5.350 0.634 0.000BB- 5.519 0.621 0.000B+ 6.032 0.626 0.000B 6.322 0.621 0.000B- 7.584 0.624 0.000

Bound * A+ 0.000 . .

Bound * A 4.319 0.772 0.000

Bound * A- -0.327 0.991 0.741

Bound * BBB+ -3.113 0.796 0.000

Bound * BBB -1.143 0.517 0.027

Bound * BBB- -1.523 0.488 0.002

Bound * BB+ 0.104 0.620 0.866

Bound * BB -0.368 0.463 0.427

Bound * BB- -1.212 0.646 0.061

Bound * B+ -0.525 0.439 0.232

Bound * B -0.916 0.493 0.063

Bound * B- -2.250 0.703 0.001

Sovereign Rating FE Yes

Observations 16,459

Number of firms 556

Pseudo R2 0.2895

Outcome: Close-to-Default(5-year realization window)

39

Table 7: Default and Bound Status: Marginal Effects from Logit Regression of “Close-to-Default” by Rating and Bound StatusThis table reports the predicted probabilities (marginal effects) from the logit model 3, and whose estimatedcoefficients are reported in table 6. The marginal effects indicate the predicted probability of a firm witha given rating transitioning into “Close-to-Default” in a 5-year window, depending on whether the firm isbound or not by the sovereign ceiling.

Non-Bound Bound Difference p-valueA 0.01% 0.74% 0.73% 0.030 A- 0.16% 0.11% -0.04% 0.775 BBB+ 0.61% 0.03% -0.59% 0.020 BBB 0.42% 0.13% -0.28% 0.012 BBB- 0.50% 0.11% -0.39% 0.035 BB+ 1.02% 1.13% 0.11% 0.867 BB 3.69% 2.59% -1.11% 0.376 BB- 4.34% 1.33% -3.01% 0.004 B+ 7.05% 4.29% -2.75% 0.168 B 9.21% 3.90% -5.31% 0.015 B- 26.37% 3.64% -22.73% 0.000

Predicted Probability of Close-to-Default (5-year window)

40

Table 8: Corporate Yield Spreads and Bound StatusThis table reports the estimated coefficients from the regression model in eq. 4, which measure the effect ofbeing bound by the sovereign ceiling on a firm’s predicted rating:

Spreadi,j,t = β0 + β1RtgFEi,j,t + β2(RtgFEi,j,t ∗Boundi,j,t) + Sov.RatFEi,t + TimeFEt + εi,j,t

where β2 is a coefficients vector that captures the differential effect, for each rating level, of beingbound by the sovereign ceiling on a bond’s yield spread. I weigh observations based on the number of bondsobserved each month for each firm. Standard errors clustered by firm.

Coeff. S.E. p-valueAA+ -0.771 0.567 0.174AA -0.462 0.550 0.401AA- -0.216 0.557 0.697A+ -0.399 0.532 0.453A -0.085 0.505 0.866A- 0.074 0.531 0.890BBB+ 0.552 0.529 0.297BBB 0.828 0.536 0.122BBB- 1.345 0.535 0.012BB+ 2.636 0.556 0.000BB 3.163 0.545 0.000BB- 4.070 0.541 0.000B+ 5.258 0.553 0.000B 6.299 0.574 0.000B- 7.391 0.596 0.000

Bound * AA+ -0.226 0.272 0.406Bound * AA 0.406 0.491 0.409Bound * AA- -0.072 0.296 0.807Bound * A+ 0.286 0.105 0.007Bound * A 0.061 0.131 0.644Bound * A- 0.080 0.102 0.431Bound * BBB+ -0.282 0.119 0.018Bound * BBB -0.194 0.158 0.219Bound * BBB- -0.330 0.117 0.005Bound * BB+ -0.878 0.289 0.002Bound * BB -1.210 0.262 0.000Bound * BB- -2.023 0.362 0.000Bound * B+ -0.694 0.773 0.370Bound * B -0.801 0.491 0.103Bound * B- -1.303 0.513 0.011

Sovereign Rating FE YesTime FE YesObservations 162,140R2 0.657

Outcome: Spread

41

Table 9: Effect of sovereign rating changes on corporate spread changes by “bound” statusThis table reports the estimation results of eq. 5. The dependent variable is the yield spread change of a corporate bond around the time windowspecified in each column. For instance, the dependent variable in column (1) is the change in the spread 1 month after the sovereign rating changeversus 1 before the event. Bound is a dummy variable that takes a value of one if a corporate issue has the same rating as the corresponding sovereignprior to a sovereign rating change. Standard errors are clustered by firm. t-statistics are reported in parentheses below coefficients estimates; *indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.

Panel A. Pooled downgrades and upgrades(1) (2) (3) (4) (5) (6)

1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre

Constant -0.026 -0.043 0.145 0.121 0.263 0.476*(-0.25) (-0.35) (0.91) (0.64) (1.29) (1.75)

Sov.Down 0.090 0.041 -0.061 0.004 -0.009 -0.191(0.49) (0.18) (-0.20) (0.01) (-0.03) (-0.51)

Sov.Down * Bound 1.031*** 0.777*** 1.171*** 1.213*** 1.226*** 0.882**(3.33) (3.51) (3.06) (3.14) (3.24) (2.52)

Sov.Up * Bound -0.271*** -0.240** -0.385*** -0.340** -0.449** -0.486**(-3.28) (-2.15) (-2.79) (-2.15) (-2.57) (-2.18)

Observations 2319 2245 2137 2066 1834 1687r2 0.0974 0.0554 0.0685 0.0708 0.0720 0.0323

Panel B. Pooled downgrades and upgrades with Country-Event Fixed Effects(1) (2) (3) (4) (5) (6)

1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre

Sov.Down * Bound 0.537*** 0.643*** 0.658** 0.723** 0.746** 0.768*(3.02) (3.16) (2.34) (2.30) (2.10) (1.72)

Sov.Up * Bound -0.122* -0.003 -0.129 -0.002 -0.141 0.042(-1.75) (-0.04) (-1.17) (-0.02) (-1.07) (0.47)

Country-Event FE Yes Yes Yes Yes Yes Yes

Observations 2319 2245 2137 2066 1834 1687r2 0.700 0.737 0.764 0.788 0.751 0.760

42

Table 10: Effect of corporate rating changes on corporate spreads: 2SLS using sovereign rating change * bound statusas an instrumentThis table reports the estimation results of eq. 5. The dependent variable is the yield spread change of a corporate bond around the time windowspecified in each column. For instance, the dependent variable in column (1) is the change in the spread 1 month after the sovereign rating changeversus 1 before the event. Bound is a dummy variable that takes a value of one if a corporate issue has the same rating as the corresponding sovereignprior to a sovereign rating change. Standard errors are clustered by firm. t-statistics are reported in parentheses below coefficients estimates; *indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.

(1) (2) (3) (4) (5) (6)1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre

∆Corp.Dwg (2SLS) 0.883*** 0.946*** 0.982*** 1.359*** 1.359*** 1.626***(4.86) (5.74) (3.58) (4.10) (4.21) (4.02)

∆Corp.Upg (2SLS) -0.310 -0.013 -0.366 0.007 -0.413 0.053(-1.54) (-0.06) (-1.13) (0.03) (-1.08) (0.28)

Country-Event FE Yes Yes Yes Yes Yes Yes

Observations 2319 2245 2137 2066 1834 1687r2 0.704 0.743 0.767 0.797 0.760 0.77243

Table 11: Effect on a Firm’s Investment and FinancingThis table reports the estimation results of eq. 5 for each of the corporate outcomes at the top of each column. Standard errors are clustered by firm.t-statistics are reported in parentheses below coefficients estimates; * indicates significance at the 10% level, ** at the 5% level, and *** at the 1%level.

Panel A. Sovereign downgrades(1) (2) (3) (4)

Capex LT Debt Iss. ST Debt Iss. Tot. Equity Iss.

∆Corp.Down (2SLS) -0.032** -0.030 0.005 0.011(-2.16) (-0.75) (0.98) (0.59)

Country-Event FE Yes Yes Yes YesSector FE Yes Yes Yes Yes

Observations 164 147 147 88r2 0.460 0.501 0.206 0.437

Panel B. Sovereign upgrades(1) (2) (3) (4)

Capex LT Debt Iss. ST Debt Iss. Tot. Equity Iss.

∆Corp.Up (2SLS) 0.022 0.006 0.005 0.006(1.39) (0.19) (0.37) (0.37)

Country-Event FE Yes Yes Yes YesSector FE Yes Yes Yes Yes

Observations 659 591 599 444r2 0.473 0.299 0.235 0.329

44

Table 12: Falsification test: Effect of the sovereign ceiling on corporate spread changes one year before the actual eventThis table reports falsification tests for the regressions in table 9. Specifically, the same regressions are estimated one year before each actual sovereigndowngrade or upgrade. The dependent variable is the yield spread change of a corporate bond around the time window specified in each column.Bound is a dummy variable that takes a value of one if a corporate issue has the same rating as the corresponding sovereign prior to a sovereignrating change. Standard errors are clustered by firm. t-statistics are reported in parentheses below coefficients estimates; * indicates significance atthe 10% level, ** at the 5% level, and *** at the 1% level.

(1) (2) (3) (4) (5) (6)1mPost−3mPre 2mPost−3mPre 3mPost−3mPre 4mPost−3mPre 5mPost−3mPre 6mPost−3mPre

Sov.Down * Bound 0.146 0.134 0.164 0.138 0.266 0.105(0.61) (0.50) (0.75) (0.63) (0.70) (0.29)

Sov.Up * Bound -0.221 -0.323 -0.330 -0.287 -0.187 -0.228(-1.63) (-1.36) (-1.52) (-1.45) (-0.90) (-1.09)

Country-Event FE Yes Yes Yes Yes Yes Yes

Observations 1333 1295 1221 1162 1020 934r2 0.508 0.521 0.523 0.536 0.551 0.616

45

Table 13: Falsification Test: Effect on a Firm’s Investment and FinancingThis table reports falsification tests for the downgrade regressions in table 11. Specifically, the same regres-sions are estimated one year before each actual sovereign downgrade. Standard errors are clustered by firm.t-statistics are reported in parentheses below coefficients estimates; * indicates significance at the 10% level,** at the 5% level, and *** at the 1% level.

(1) (2) (3) (4)Capex LT Debt Iss. ST Debt Iss. Tot. Equity Iss.

∆Corp.Down (2SLS) -0.024 -0.024 -0.008 0.025(-1.02) (-0.50) (-0.59) (0.68)

Country-Event FE Yes Yes Yes YesSector FE Yes Yes Yes Yes

Observations 81 77 77 38r2 0.434 0.373 0.0741 0.323

46

A First Appendix

Figure A.1: Issuance of rated USD corporate bonds by non-US firms per year.This figure plots the annual total issuance of rated USD-denominated bonds by non-US firms. Own calcu-lations using data from Bloomberg.

0

100

200

300

400

500

600

US

D B

illio

ns

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Issuance of Rated USD Corporate Bonds by Non-US Firms

47

Figure A.2: Average Corporate Spread by Broad Credit Rating Category.This figure plots the average yield spread of USD-denominated corporate bonds issued by non-US firms.The yield spread for each bond is calculated by subtracting the yield of a Treasury bond with an equivalentmaturity. Each line represents the monthly average of all corporate yield spreads within each broad ratingcategory.

0

5

10

15

20

%

1999

2000

2001

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

2013

B BB BBB A AA AAA

Average Spread by Broad Rating Category

48

Figure A.3: Distribution of sovereign ratings by rating category.The figures in Panel A and B depict the distribution of long-term foreign-currency (LT FC) sovereign ratingsand rating changes for the countries and period in the sample for S&P, Moody’s and Fitch.

0.0

5.1

.15

.2D

ensi

ty

D/C CC

CC

C-

CC

C

CC

C+ B- B

B+

BB

-

BB

BB

+

BB

B-

BB

B

BB

B+ A- A

A+

AA

-

AA

AA

+

AA

A

S&P Sovereign FC LT Rating0

.05

.1.1

5.2

Den

sity

D/C CC

CC

C-

CC

C

CC

C+ B- B

B+

BB

-

BB

BB

+

BB

B-

BB

B

BB

B+ A- A

A+

AA

-

AA

AA

+

AA

A

Moody's Sovereign FC LT Rating

0.0

5.1

.15

.2D

ensi

ty

D/C CC

CC

C-

CC

C

CC

C+ B- B

B+

BB

-

BB

BB

+

BB

B-

BB

B

BB

B+ A- A

A+

AA

-

AA

AA

+

AA

A

Fitch Sovereign FC LT Rating

49

Figure A.4: Distribution of sovereign rating changes by year.The figures in Panel A and B depict the distribution of long-term foreign-currency (LT FC) sovereign ratingsand rating changes for the countries and period in the sample for S&P, Moody’s and Fitch.

0

10

20

30

Fre

quen

cy

1999 2001 2003 2005 2007 2009 2011

S&P Sovereign FC LT Rating

0

10

20

30

Fre

quen

cy

1999 2001 2003 2005 2007 2009 2011

Moody's Sovereign FC LT Rating

0

10

20

30

Fre

quen

cy

1999 2001 2003 2005 2007 2009 2011

Fitch Sovereign FC LT Rating

Sovereign Downgrades per Year

0

10

20

30

Fre

quen

cy

1999 2001 2003 2005 2007 2009 2011

S&P Sovereign FC LT Rating

0

10

20

30

Fre

quen

cy

1999 2001 2003 2005 2007 2009 2011

Moody's Sovereign FC LT Rating

0

10

20

30

Fre

quen

cy

1999 2001 2003 2005 2007 2009 2011

Fitch Sovereign FC LT Rating

Sovereign Upgrades per Year

50

Table A.1: Credit ratings numerical conversion by CRACredit ratings are translated into the following numerical rating scale ranging from 1 (C/D) to 21 (AAA).

Numerical Rating S&P Moody’s Fitch21 AAA Aaa AAA20 AA+ Aa1 AA+19 AA Aa2 AA18 AA- Aa3 AA-17 A+ A1 A+16 A A2 A15 A- A3 A-14 BBB+ Baa BBB+13 BBB Baa BBB12 BBB- Baa BBB-11 BB+ Ba1 BB+10 BB Ba2 BB9 BB- Ba3 BB-8 B+ B1 B+7 B B2 B6 B- B3 B-5 CCC+ Caa1 CCC+4 CCC Caa2 CCC3 CCC- Caa3 CCC-2 CC Ca CC1 C/D C C /D

51

Table A.2: Estimation of coefficients from firms in AAA countries to predict firmratings in non-AAA countriesThis table reports the estimation results of model 1. t-statistics are reported in parentheses below coefficientsestimates; * indicates significance at the 10% level, ** at the 5% level, and *** at the 1% level.

Pooled OLS downgrades and upgrades(1)

Corp. Rating

Constant 0.633(0.15)

Net Income / Assets 5.852***(3.66)

Debt / Total Capital -0.962***(-3.07)

ln(Assets) 2.876***(4.32)

Square of Net Income / Assets 5.298(1.53)

Square of Debt / Total Capital 0.030***(3.58)

Square of ln(Assets) -0.101***(-3.34)

Time FE YesSector FE YesCollat. FE Yes

Observations 17191r2 0.515

52