The Real Effects of Sovereign Rating Downgrades1

Heitor AlmeidaUniversity of Illinois at Urbana Champaign

halmeida@illinois.edu

Igor CunhaNova School of Business and Economics

igor.cunha@novasbe.pt

Miguel A. FerreiraNova School of Business and Economics

miguel.ferreira@novasbe.pt

Felipe RestrepoBoston Collegerestrepf@bc.edu

January 22, 2014

1We thank Viral Acharya, Murillo Campello, Sergey Chernenko, Paolo Colla, Clifford Holder-ness, Darren Kisgen, Mitchell Petersen, Jun Qian, Rui Silva, Phil Strahan, Jerome Taillard, DavidThesmar, and Mike Weisbach for helpful comments.

Abstract

We study the effect of sovereign credit risk on firm investment and financial policy. We usethe differential effect of sovereign downgrades on on the ratings of firms at the sovereignbound versus firms below the bound due to sovereign ceiling policies followed by credit ratingagencies. We find that sovereign downgrades lead to greater increases in the cost of debt andgreater decreases in investment and leverage of firms that are at the sovereign bound relativeto similar firms that are below the bound. Our findings suggest that public debt managementgenerates negative externalities for the private sector and real economic activity.

JEL classification: G24, G28, G31, G32, H63Keywords: Credit rating, Sovereign bond, Sovereign ceiling, Rating downgrade, Cost ofcapital, Investment, Leverage

1 Introduction

Sovereign credit risk has become a significant problem for developed countries in the aftermath

of the 2007-2009 global financial crisis. An important question is how changes in sovereign

credit risk affect the private sector and real corporate outcomes. However, identifying em-

pirically the causal impact of sovereign credit risk on firm financial and investment policy

is difficult, because changes in sovereign credit risk are correlated with changes in firm fun-

damentals. We employ a novel identification strategy that relies on sovereign downgrades

and the sovereign ceiling policy followed by rating agencies to study the effects of sovereign

credit risk on firm policies. The ceiling policy implies that firms generally cannot have rat-

ings above the sovereign rating of the country where they are domiciled. (?) 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”. Following a sovereign rating downgrade, firms that are at the sovereign bound are

generally downgraded, while firms below the sovereign bound are not necessarily downgraded.

Moreover, bound firms are downgraded not because of a deterioration of their fundamentals,

but simply because of the sovereign ceiling policy.1

In this paper, we show that sovereign credit risk has an important effect on firm’s cost

of debt, investment and leverage through the sovereign ceiling channel. Our benchmark

empirical specification employs the Abadie and Imbens (2011) matching estimator of the

average effect of the treatment on the treated (ATT). We isolate firms at the sovereign bound

(treated firm) and then, from the population of other firms (non-treated firms), look for

control firms that best match the treated firm in multiple dimensions (country, industry, size,

investment, Tobin’s Q, cash flow, cash, leverage, and foreign sales). Using a difference-in-

differences estimator, we find that the treated firms face an increase in the cost of debt and

cut investment and leverage in the aftermath of a sovereign downgrade, when benchmarked

1While credit rating agencies have been gradually moving away from a policy of never rating a firmabove the sovereign, sovereign ratings still represent a strong upper bound of credit rating assigned to firms(Borensztein, Cowan, and Valenzuela (2013)).

1

against control firms.

Credit ratings are among corporate managers’ major concerns due to the discrete costs

and benefits associated with different ratings levels (Kisgen (2006, 2007, 2009) and Kisgen and

Strahan (2010)). First, a decline in credit ratings can affect a firm’s access to the bond and

commercial paper markets because of regulations on institutional investors. Some investors

such as pension funds often follow guidelines that restrict investments to investment-grade

bonds (Boot, Milbourn, and Schmeits (2006)). Second, credit ratings affect the capital re-

quirements that banks and insurance companies are subject to when investing in specific

firms.2 Third, credit ratings can convey information to the market about a firm’s credit

quality. Credit ratings can reduce the total certification cost supported by borrowers, who

gain access to the capital of less-informed investors, and are therefore able to raise more debt

financing (Faulkender and Petersen (2006)). Fourth, downgrades can trigger events such as

bond covenant violations, increases in bond coupon rates or loan interest rates, and forced

bond repurchases. Finally, ratings can impact customer and employees relationships and

business operations such as the ability to enter or maintain long-term supply and financial

contracts. Merger deals can also be contingent on the maintenance of rating levels.

We first establish the validity of the identification strategy using the sovereign downgrade

and ceiling as instrument. We show that the distribution of corporate ratings across sovereign

rating levels is systematically concentrated exactly at each country’s sovereign rating. Thus,

sovereign ratings are a strong upper bound for the ratings of corporate borrowers. More

importantly, we show that the effect of sovereign downgrades is asymmetric between treatment

and control groups. While ratings decrease nearly one-to-one for the treatment group at the

time of a sovereign rating downgrade, ratings for the control group decrease significantly

less. Similarly, the probability of a firm suffering a rating downgrade following a sovereign

downgrade is 1.5 times higher for treated firms than for control firms. We also show the

sovereign ceiling policy is associated with ratings that tend to be more pessimistic for treated

2Basel rules rely on external agency ratings to determine risk weights for the purpose of banks capitalrequirements for credit risk and such risk-weighting can affect the supply of bank capital to firms.

2

firms relative to control firms and lower probability of default (five-year window) for treated

firms. We exploit the discontinuity in credit ratings changes between the treatment and

control group in our analysis.

Next, we show that rating downgrades affect the cost of debt. We find that in the month

following a sovereign rating downgrade, yield spreads of treated firms increase by approx-

imately 54 basis points more relative to control firms. This differential effect is even more

pronounced as the post-event window widens, and remain statistically significant. Thus, firms

that are downgraded as a consequence of the sovereign downgrade find it more expensive to

raise debt in the aftermath of a sovereign downgrade.

This increase in the cost of debt appears to have real consequences. We find that treated

firms decrease investment in the year of the sovereign downgrade significantly more than

control firms. Treated firms investment goes from 28.7% to 18.5% of capital, implying a 10.2

percentage points decrease. This decrease in investment is much larger than that observed

for the control firms, which is only 2.8 percentage points and statistically insignificant. The

ATT is -16.8 percentage points, which is highly statistically and economically significant. We

also show that this relative decrease in investment for the treatment group happens only in

the year of the downgrade and that, prior to the downgrade, investment grows at about the

same rate for both treatment and control groups.

A lower supply of debt capital for treated firms after a sovereign downgrade would naturally

imply a decrease in the use of debt. We show that treated firms decrease long-term debt

following the sovereign downgrade significantly more than control firms. Treated firms long-

term debt goes from 25.3% to 23.7% of assets, implying a 1.6 percentage points decrease.

The control group actually shows an increase in their long-term debt of 1.2 percentage points.

The ATT is -3.8 percentage points, which is statistically and economically significant. The

decrease in total debt is lower than the decrease in long-term debt, which indicates that

treated firms substitute between long-term and short-term debt following a sovereign-related

downgrade. We only observe changes in leverage in the year after the sovereign downgrade,

which is consistent with the idea that leverage takes longer to adjust than investment (Leary

3

and Roberts (2005) and Lemmon, Roberts, and Zender (2008)). Additionally, there is an

immediate reaction in corporate liquidity as we find that treated firms cash holdings decrease

significantly more than control firms in the year of the sovereign downgrade.

The key assumption of our identification strategy is that sovereign downgrades must not

be related to the differences in cost of capital, investment and leverage across treatment and

control groups, through channels other than changes in credit ratings. A potential concern

with this identification assumption is that country-level variables may affect both firm policies

and sovereign ratings, thereby contaminating the results. For example, a deterioration of

macroeconomic fundamentals can cause sovereign downgrades, and also increase the cost of

external finance for firms. However, this possibility is unlikely to contaminate our results

because the treatment group contains firms with higher credit quality than those in the

control group. If anything, treated firms should be less sensitive to macroeconomic shocks

that are associated with sovereign downgrades when compared to control firms.

To further validate our exclusion restriction, we also conduct two placebo tests that can de-

tect the existence of differential effects of macroeconomic shocks not associated with sovereign

downgrades. First, we examine the relative change in investment for treatment and control

groups around recessions that are not accompanied by sovereign rating downgrades. This

placebo test can detect whether treated firms are more sensitive to macroeconomic shocks

than control firms, which may confound our main results. We find a relatively small decrease

in investment for both treated and control firms in recession years, and no difference across

the two groups. Second, we examine the consequences of sovereign upgrades for investment.

Sovereign ceiling policies should not matter as much in the case of sovereign upgrades.3 Con-

sistent with this idea, we find no differences in investment behavior between treated and

control firms following sovereign upgrades. In summary, the placebo tests support a causal

interpretation for our results.

An additional concern is that rating agencies may only downgrade firms with poor funda-

3Firms with rating at the sovereign bound are not necessarily upgraded following a sovereign upgrade.Additionally, the market reaction to upgrades is typically insignificant (e.g. Brooks et al., 2004; Gande andParsley, 2005; Ferreira and Gama, 2007).

4

mentals following a sovereign downgrade, and exempt higher-quality firms from the sovereign

ceiling rule. We believe this “selective downgrade” hypothesis does not explain our results.

The control group should pick up the effect of weaker fundamentals on cost of debt, investment

and leverage. Thus, a stronger sensitivity of the treated firms to the sovereign downgrade is

likely to be due to the lower sovereign ceiling and not to firm fundamentals.

There are other papers that examine the effects of credit ratings. Sufi (2009) finds that

the introduction of loan ratings by Moody’s in 1995 leads to an increase in investment and

debt for firms that obtain a rating, in particular those with lower credit quality and no rating

prior to 1995. Lemmon and Roberts (2010) find that as a consequence of the junk bond crisis

of 1989, junk-rated firms decrease investment and net debt more than unrated firms. Tang

(2009) finds that the Moody’s credit rating refinement in 1982 leads to an increase in debt

and investment of upgraded firms versus downgraded firms. Chernenko and Sunderam (2012)

show that the investment rate of firms with rating right below the investment-grade cutoff

is more sensitive to flows into high-yield mutual funds, when compared to a matched sample

of firms with rating right above the cutoff. Harford and Uysal (2013) find that firms with a

credit rating are more likely to make acquisitions than a matched sample of non-rated firms.

Some papers empirically study the effects of the transmission of sovereign risk to corporate

risk (Durbin and Ng (2005), Arteta and Hale (2008), Borensztein, Cowan, and Valenzuela

(2013), and Bedendo and Colla (2013)).4

This paper makes two contributions. First, it provides a causal estimate of the effect of

credit rating downgrades on firms’ cost of capital and investment and financial policy. More

specifically, rating downgrades lead to an increase in the cost of capital, causing firms to cut

investment and deleverage. Second, we provide the exact channel through which sovereign

credit risk produce real effects. Sovereign downgrades have important real effects through the

sovereign ceiling, and not only through fundamentals such as interest rates and crowding-out

effects. When the sovereign has a credit rating that is not at the high end of the scale, credit

4Researchers have also studied the stock and bond market reaction to credit rating downgrades (Hand,Holthausen, and Leftwich (1992), Goh and Ederington (1993) among others).

5

ratings for firms from that country will tend to suffer, regardless of their financial soundness.

Governments need to be aware of the potential adverse effects of rating downgrades on the

private sector and they should factor these negative externalities into public debt management

decisions.

2 Methodology and data

In this section we first describe our experimental design as well as the matching estimator

that we employ. We exploit the fact that sovereign ratings downgrades can create exogenous

variation in corporate credit ratings because of sovereign ceilings as a way to identify the

effect of credit rating on firms’ cost of capital and investment and financial policy. We then

describe the data and present summary statistics.

2.1 Sovereign downgrades and ceilings: Institutional backgroud

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 obliga-

tions. The three major agencies – Standard and Poor’s (S&P), Moody’s and Fitch – assign

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

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

foreign-currency, long-term issuer ratings, where agencies use a sovereign’s rating as a strong

upper bound on the credit ratings of firms that operate within each country. We prefer the

S&P foreign currency long-term rating history over other agencies’ rating history because

S&P tends to be more active in making rating revisions, and tends to lead other agencies

in re-rating (Kaminsky and Schmukler, 2002; Brooks et al., 2004; Gande and Parsley, 2005).

Foreign currency rating announcements by S&P also seem to convey a greater own-country

stock market impact and seem not to be fully anticipated by the market (Reisen and von

Maltzan, 1999; Brooks et al., 2004). S&P is also the agency least likely to assign corporate

ratings above the sovereign rating.

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

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.5 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 example,

S&P reports that there are only 54 non-financial corporations worldwide with rating that

exceeds the sovereign as of October 2012. Consistent with this policy, Borensztein, Cowan,

and Valenzuela (2013) show that sovereign ratings remain an important determinant of the

credit rating assigned to corporations.

Why do rating agencies use sovereign’ rating as a strong upper bound when rating cor-

porate issuers? There are two key factors rating agencies use when rating foreign-currency

corporate issues: the issuer’s inherent likelihood of repayment (which is the same as local

ratings); and 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 con-

vert 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. In general, rating

agencies 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. Additionally, rating agencies follows an implicit rule that corporations generally

cannot be rated more than two notches above the sovereign.

Even though the sovereign ceiling has typically represented a more important constraint for

firms in developing countries where sovereign’ ratings tend to be lower, the relation between

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

5Fitch and Moody’s followed suit in 1998 and 2001 respectively.

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following the recent downgrade of the United States, and the European sovereign debt crisis,

where several developed countries including Greece, Italy, Ireland, France, the Netherlands,

Portugal and Spain, experienced sovereign rating downgrades.

2.2 Identification strategy

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. We explicitly address this concern in our 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

not bound by it. We do this by exploiting two important empirical regularities associated

with the sovereign ceiling.

First, Figure 1 shows that the distribution of corporate ratings across sovereign rating

levels is strongly bound by each country’s sovereign rating. This is the direct implication of

the sovereign ceiling on corporate ratings. The sample includes 3,586 different firms (31,022

observations) with credit ratings from 80 countries for the 1990-2012 period (see data section

below for more details). The figure shows the relation between credit ratings granted to firms

and their government. The figure shows that only a few corporations’ ratings overcome the

sovereign ceiling and only by a limited degree. In our sample of rated firms, 89% of the firms

receive a rating lower than the sovereign, 6% receive the same rating, and just 5% receive a

rating higher than the sovereign. This confirms that even though rating agencies 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 in international markets.

Second, as Figure 2 shows, the probability of a corporate issuer obtaining a rating down-

grade in the same magnitude as its sovereign within the month of a sovereign rating downgrade

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

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zero). More precisely, the middle panel in Figure 2 shows that conditional on the event of a

sovereign rating downgrade, firms that are at the bound have a probability of approximately

69% of obtaining the same rating adjustment as the sovereign within a month, compared

to 13%, 10% and 5% for firms that are respectively one, two and three notches below the

sovereign rating. The left and right panels in Figure 2 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 our empirical strategy is that sovereign rating

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

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

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. The identification strategy requires that there is enough variation in

credit rating across firms. In particular, there must exist a significant group of firms that

have a spike (or discontinuity) in their credit rating right after the sovereign downgrade.

In our framework, the real effects of credit ratings (due to the exogenous variation created

by sovereign downgrades and ceilings) are not confounded with common macro effects. The

macroeconomic effects associated with sovereign rating downgrade should affect firms equally.

If there were any macro differential effects, better quality firms (our treatment group) should

be less affected than lower quality firms (our control group).

2.3 Matching approach

We test whether firms that are downgraded as consequence of a sovereign downgrade and

ceiling change investment and financial decisions in a significant way, as a consequence of an

increase in their cost of capital. If credit ratings were randomly assigned across firms, then it

would suffice to compare the outcomes of bound firms (i.e., with pre-downgrade credit rating

equal or above the sovereign rating) with those of non-bound firms (i.e., with pre-downgrade

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credit rating below the sovereign rating) around the time of the sovereign downgrade. Our

analysis, however, needs to account for the fact that we are relying on non-experimental data.

One way to tackle this issue is to estimate differences between plausibly counterfactual out-

comes and those that are observed in the data. A standard method is to use a parametric

regression where the outcome difference for the group of interest versus other observations is

estimated by the coefficient on the group dummy. The regression model is specified according

a linear representation of the outcome variable and controls may be added to the specifica-

tion to capture additional sources of firm heterogeneity. Estimation of group differences can

be improved by allowing for nonlinear and nonparametric methods when the groups being

compared have very different characteristics and control variables have poor distributional

overlap (Heckman, Ichimura, Smith, and Todd (1998), Roberts and Whited (2010)).

The strategy that we apply in our main tests is nonparametric. We conduct our anal-

ysis combining a natural experiment with the use of matching estimators. The idea of this

estimator is to first isolate treated observations (in our application, bound firms) and then,

from the population of non-treated (non-bound firms) observations, find observations that

best match the treated ones in multiple dimensions (covariates). In this framework, the set

of counterfactuals are restricted to the matched controls. In other words, it is assumed that

in the absence of the treatment (in our application, sovereign downgrades), the treatment

group would behave similarly to the control group. The matches are made so that treated

and control observations have distributions for the covariates that are as similar as possible to

each other, in the pre-treatment period. Inferences about the treatment of interest are based

on differences in the post-treatment outcomes between the treatment and control groups.

We employ the Abadie and Imbens (2011) estimator, as implemented by Abadie, Drukker,

Herr, and Imbens (2004). The Abadie-Imbens matching estimator minimizes the distance (the

Mahalanobis distance) between a vector of observed covariates across treated and non-treated

firms, finding control firms based on matches for which the distance between vectors is the

smallest. The estimator allows control firms to serve as matches more than once, which

compared to matching without replacement, lowers the estimation bias but can increase the

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variance. In our estimations we select one matched control observation for each treated

observation. The Abadie-Imbens estimator produces exact matches on categorical variables.

Naturally, the matches on continuous variables will not be exact (though they should be

close). The procedure recognizes this difficulty and applies a bias-correction component to

the estimates of interest.

Among the list of categorical variables that we include in our estimations are year, country,

industry, and whether a firm has a credit rating. Our non-categorical variables include firms’

size, investment, Tobin’s Q, cash flow, cash, leverage, and foreign sales. The estimation

implicitly account for all possible interactions between the included covariates.

We estimate the average effect of the treatment on the treated (ATT). We model the

outcomes in our experiments in differenced form by performing difference-in-differences es-

timations. Specifically, rather than comparing the levels of investment, leverage and cash

(outcome variables) of the treatment and control groups, we compare the changes in the out-

come variables between the groups around the sovereign downgrade. We do so because the

outcome levels of the treated and controls could be different prior to the event defining the

experiment, and continue to be different after that event, in which case our inferences could

be potentially biased by these uncontrolled firm-specific differences.

2.4 Sample and variable construction

Our sample of firms is taken from the WRDS-Factset Fundamentals Annual Fiscal (North

America and International) database and contains firms from 80 countries for the 1990-2012

period. We exclude financial firms (SIC codes 6000-6999) because these firms tend to have

significantly different investment and financial policies due to regulation. We drop any ob-

servation with negative total assets. We obtain firm accounting and market variables from

Factset and sovereign and corporate credit ratings from Bloomberg. We match firms in Fact-

set to Bloomberg using ISIN, SEDOL, CUSIP or company name. The sample includes 546,957

firm-year observations and 54,893 different firms. Only a small fraction of these firms have a

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credit rating (31,022 firm-year observations and 3,586 different firms).

In our experiments, the outcome variables are the annual change in firm investment, debt

and cash around a sovereign downgrade. Investment is defined as the ratio of annual capital

expenditures (Factset item FF CAPEX FIX) to the lag of net property, plant and equipment

(Factset item FF PPE NET). Long-Term Leverage is defined as the ratio of long-term debt

(Factset item FF DEBT LT) to total assets (Factset item FF ASSETS). Total Leverage is

defined as the ratio of total debt (Factset item FF DEBT) to total assets. Cash is defined as

the ratio of cash and short-term investments (Factset item FF CASH ST) to total assets.

The treatment group includes bound firms (those with a credit rating equal or above the

sovereign credit rating of the country where the firm is domiciled in the year prior to the

sovereign downgrade). Table 1 reports the number of treated firm-year observations by coun-

try and year. There are 66 observations in the treatment group in 12 different countries, which

have been downgraded a total of 22 times during the sample period. Of course, there have

been many more sovereign downgrades during our sample period but we just rely on those

for which we identify bound firms in the downgraded country. The list of countries includes

both developed markets (Ireland, Italy, Japan, Portugal, Spain and the United States) and

emerging markets (Argentina, Brazil, Indonesia, Mexico, Philippines and Thailand). There

are countries with multiples downgrades over the sample period such as Italy with four down-

grades, Argentina and Japan with three downgrades, and Indonesia, Portugal, Thailand with

two downgrades. The median sovereign rating downgrade is one-notch and the average is

two-notches. Finally, there are 11 downgrades during the post-2007 period corresponding to

the global financial crisis and euro-zone sovereign debt crises, but there are also a sizable

number of downgrades in earlier periods.

As discussed before, we match firms based on several covariates: size, investment, Tobin’s

Q, cash flow, cash, total leverage and foreign sales. Size is defined as the log of total as-

sets. Tobin’s Q is defined as the ratio of total assets plus market capitalization (Factset item

FF MKT VAL) minus common equity (Factset item FF COM EQ) to total assets. Cash flow

is defined as the ratio of annual operating income (Factset item FF OPER INC) plus depre-

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ciation and amortization (Factset item FF DEP AMORT EXP) to the lag of net property,

plant and equipment. Foreign sales is the ratio of foreign sales to total sales (Factset item

FF FOR SALES PCT). Investment, Leverage and Cash are defined as before. The matching

estimator uses the pre-treatment (year prior to the sovereign downgrade) value of the covari-

ates. In some tests, we also use return on assets (ROA), defined as the ratio of operating

income (Facset item FF OPER INC) to total assets. To minimize the impact of outliers on

these comparisons, we winsorize variables at the top and bottom 1% level.

In addition, we match firms on year and firms’ country of domicile and industry (two-digit

SIC codes). So we impose that the control firm should match exactly the country and year

of the treated firm such that we are effectively comparing outcomes within firms in the same

country and year. We also impose that control firms should have a credit rating as treated

firms are necessarily rated. Although we include the industry as a covariate in the matching,

we do not impose an exact match because in smaller countries would be difficult to find an

exact match. We are able to find an exact match by industry in about 50% of the cases. The

results are robust when we use alternative matching methods that impose an exact match by

industry but aggregate countries into geographic regions to find a matched control firm.6

2.5 Summary statistics

Table 2 compares summary statistics of the covariates between the 66 treated firm-years and

the remaining 24,851 non-treated firm-years (i.e., firms that are not assigned to the treatment

group) in treated years (i.e., years with sovereign downgrades). Since we require an exact

match of country and year (and existence of a credit rating), we restrict the group of non-

treated to countries that have least a treated firm over the sample period. The treated firms

are bigger and have higher investment, Tobin’s Q, cash flow and leverage than non-treated

firms. These differences are expected given that we are relying on observational data rather

than running a true experiment. The goal of the matching estimator techniques is to control

for these distributional differences, which could affect both the selection into the treatment

6We drop treated firms for which we were unable to find a match within the same country (15 cases).

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and the post-treatment outcomes.

Table 2 also shows summary statistics of the covariates for the matched control firms. The

Abadie-Imbens matching estimator identifies a match for each firm in the treatment group. We

have 66 firm-year observations in both groups, but since matching is done with replacement,

we have only 44 unique firms in the control group.7 The Pearson chi-square statistic tests

for differences in the medians of the variables of interest between the treatment and control

groups. After the matching procedure, there are no statistically significant differences in the

pre-downgrade median values of the covariates across treatment and control groups, with the

exception of Q and cash flow. The median Q and cash flow is higher for firms in the treatment

versus the control group. This difference cannot explain our findings since we expect firms

with higher Q or cash flow to be less affected, rather than more affected by the sovereign

downgrade.

Table 2 also compares the entire distributions of the various matching covariates (pre-

downgrade) across the three groups of firms. The Kolmogorov-Smirnov test of distributional

differences in the variables of interest between the treatment and control groups. These

statistics support the assertion that the matching estimator moves our experiment closer to

a test in which treatment and control groups differ only with respect to their post-treatment

outcomes (investment, leverage, and cash). Treated firms differ significantly from non-treated

firms. These differences disappear when we compare the treated firms to the group of matched

control firms, with the exception of Q and cash flow. Similarly to the median tests, treated

firms have higher Q and cash flow than control firm.

3 Effect on corporate ratings

In this section we study the link between corporate ratings and sovereign rating downgrades.

7We implement the matching with replacement to allow for the best possible match for each treatedobservation.

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3.1 Do sovereign rating ceilings lead to pessimistic ratings?

We test whether firms that are bounded by the sovereign ceiling (i.e., that their rating is equal

or above to the sovereign rating) have a more “pessimistic” rating relative to firms that are

not bounded 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,

we examine whether the sovereign ceiling policy is consistent with rating agencies providing

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

effect of being bound on a firm’s predicted rating.

We explore whether bound firms tend to be pessimistically rated using a two-step pro-

cedure. First, we 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.8 Using this sample of firms in countries with a AAA sovereign rating we estimate

a regression using a set of explanatory variables used in previous studies predicting credit

ratings (see Kisgen (2006) for a similar implementation). The dependent variable is a firm’s

credit rating converted into twenty-two numerical values, with 22 corresponding to the highest

rating (AAA) and one to the lowest (default). We estimate the following firm credit rating

regression:

Ratingi,t = β1ROAi,t + β2Leveragei,t + β3 Si zei,t (1)

+β4ROA2i,t + β5Leverage

2i,t + β Si ze2i,t + αt + αd + εi,t

where Ratingi,t is the credit rating (numerical value) for firm i in year t. The regression

8We 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 our data.

15

includes year fixed effects to control for time specific shocks common to all firms (αt) and

industry fixed effects (αd). We estimate the model above for firms in AAA countries and then

we use the estimated coefficients to calculate the predicted credit ratings for the sample of

firms in non-AAA countries (which we denote as Rating), where the sovereign rating ceiling

potentially represent a meaningful institutional friction.9

In the second step, we compare, for each actual corporate rating level, whether predicted

rating are systematically higher for firms that are bounded relative to other firms with the

same actual rating but that are not bounded by the sovereign ceiling. We estimate the

following regression:

Ratingi,t = β1(RatingDum)i,t + β2(RatingDum)i,t × (Bound)i,t

+β3(SovRatingDum)i,t + αt + εi,t (2)

where RatingDum is a set of corporate rating dummies for each rating level, Bound is a

dummy variable that takes a value of one for bound firms and zero otherwise, and SovRatingDum

is a set of sovereign rating dummies to control for differences in corporate ratings that vary

dependent on the overall level of creditworthiness of a sovereign. We include year fixed effects

(αt) to account for variations in corporate ratings through the business cycle. β2 is a vector

of coefficients that captures the differential effect, for each corporate rating level, of being

bounded 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).

9Estimating the model in equation (1) for the sample of firms in AAA countries results in an adjusted R2

of 0.51. Table A.1 in the appendix shows the estimated coefficients obtained from this regression.

16

Table 3 shows the estimates of the comparison of predicted ratings for bound versus non-

bound firms in equation (2). We find that bound firms tend to have a predicted rating that is

above the predicted rating of a non-bound firm with the same rating. For example, a bounded

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 only exceptions are two of the highest rating

levels (AA- and AA) where the sovereign ceiling rule represents a less meaningful restriction.

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. We examine if a firm’s bound status affects

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

logit model where the dependent variable is a dummy variable that indicates whether a firm

had been “close to default” during the last five years. Table A.2 in the Internet Appendix

show the results from estimating the logit model, which indicate that bound firms tend to

have a lower probability of transitioning into default, for a given rating, than non-bound firms.

3.2 Corporate ratings

We examine whether the effect of sovereign rating downgrades on corporate ratings differ be-

tween bound firms (treatment group) and non-bound (control group). We expect that treated

firms are more affected than otherwise similar firms at the time of a sovereign downgrade. In

contrast, spillovers or common macro shocks associated with the sovereign rating downgrade

should equally affect treated and control firms or, if anything, they should affect more the

control group than the treatment group.

Table 4 presents the results of difference-in-differences matching estimator for corporate

credit ratings. To perform this test we map the credit ratings into twenty-two numerical values

as before. The table shows the firm’s rating value in the year before the sovereign downgrade

17

and in the year of the sovereign downgrade. Not surprisingly, we see that the pre-downgrade

rating is significantly higher for treated firms than for control firms. The average treated firm

has a rating value of 16 (i.e., A-) and the average control firm has a rating value of 13 (i.e.,

BBB-). On average, there is a three notches difference between treated and control firms and

the latter are just one-notch above non-investment grade ratings.

We find that sovereign rating downgrades have a much stronger effect on treated firms

with a rating decrease of 1.66 notches, while control firms ratings decrease by only 0.89

notches. These estimates suggest that credit ratings decrease 0.77 notches more for bound

firms relative to otherwise similar firms that are not bounded by the sovereign ceiling. The

effect of the sovereign downgrade on treated firm ratings is nearly one-to-one (the average

sovereign downgrade is about two notches), while control firm ratings are much less sensitive to

sovereign downgrades. Table 4 also reports the differential change in ratings that is produced

by the Abadie-Imbens matching estimator of the average effect of the treatment on the treated

(ATT). The ATT difference is equal to -1.38 notches, indicating a significant asymmetry in

the reaction of treatment and control groups ratings to a sovereign downgrade.

Figure 3 plots the evolution of corporate credit ratings in the two years before and after

the sovereign downgrade for the treatment and control groups. The credit rating processes of

firms in the two groups follow parallel trends before the sovereign downgrade. Furthermore,

the ratings fall significantly more for the treatment group in the year of the downgrade than

for the control group.

In alternative test, we estimate a logit regression of the probability of a credit rating

downgrade using a firm-year panel of all firms with a credit rating in alternative to use a

numerical scale for ratings as in Table 4. Table A.3 in the Internet Appendix indicates that

the probability of a credit rating downgrade following a sovereign downgrade is significantly

higher for treated firms versus other (non-treated) firms. The marginal effect indicates that

the probability of a rating downgrade is more than 1.5 times higher for treated firms versus

non-treated firms when a sovereign downgrade hits the country where the firm is domiciled.

These findings confirm the results of the difference-in-differences matching estimator in Table

18

4.

4 Effect on investment and financial policy

In this section, we provide empirical evidence on the effect of credit rating downgrades on

firm investment and financial policy.

4.1 Investment

We examine the investment behavior of the treated and matched control firms around sovereign

downgrades. Table 5 presents the results of difference-in-differences matching estimator for in-

vestment as measured by annual capital expenditures as a percentage of capital (Investment).

The table shows the investment rates in the year before the sovereign downgrade and in the

year of the sovereign downgrade. Firms in the treatment groups are compared with closer

counterfactuals (matched controls). Not surprisingly, we see that the pre-downgrade invest-

ment levels of treatment and control firms are economically similar and statistically indistin-

guishable.

We find that the investment rates of the treated and control firms become significantly

different after a sovereign downgrade. For firms in the treatment group, the average invest-

ment drops to 18.53% of capital, a fall of 10.20 percentage points. In contrast, for control

firms, the investment falls only slightly to 19.22% of capital, a fall of 2.84 percentage points.

These estimates suggest that investment decreases by 7.35 percentage points more for firms

with pre-downgrade rating equal (or above) the sovereign rating, relative to otherwise similar

firms with rating below the sovereign rating. Table 5 also reports the differential change in

investment that is produced by the Abadie-Imbens matching estimator of the ATT. The ATT

difference is equal to -16.78 percentage points. It indicates that investment for the treated

firms following a sovereign downgrade fell by about half of its pre-downgrade investment

levels.

More generally, the estimates in Table 5 imply that credit rating downgrades lead to lower

19

investment. Given the similarity between firms in the treatment and control groups, the

evidence supports a causal effect of credit ratings on investment. Notice that treated firms

are of higher quality than control firms and therefore we expect them, if anything, to be

less affected by the sovereign downgrade. Contrary to this intuition, we find that treated

firms cut investment significantly more than control firms following a sovereign downgrade,

which cannot be explained by any firm-specific differences between the two groups. Thus, our

findings provide casual evidence that credit ratings matter and affect investment policy in an

important way.

A concern about inferences from the treatment-effects framework is whether the processes

generating the treatment and control group outcomes followed parallel trends prior to the

treatment. Differences in the post-treatment period can only be attributed to the treatment

when this assumption holds. The outcome variable of our study is the within-firm change in

investment. The best way to address this concern is to look at the evolution of the outcome

variable (changes in investment rates) in the years leading to the treatment separately for the

treatment and control groups.

Figure 4 plots the evolution of investment rates in the two years before and after the

sovereign downgrade. It is hard to argue that the investment processes of firms in the two

groups follow different trends before the downgrade. Furthermore, we can see that investment

falls dramatically for the treatment group in the year of the downgrade and only slightly for

the control group. In the two years following the downgrade, the investment processes again

follow similar dynamics. Thus, we identify an unique effect on investment at the time of the

sovereign downgrade.

4.2 Placebo test: Effect of recessions and sovereign upgrades on

investment

Another potential concern regarding our difference-in-differences approach is whether macro

factors other than sovereign downgrades affecting both treatment and control firms can ex-

20

plain the differential behavior in the post-treatment period (irrespective of any effects arising

from sovereign downgrades). This concern is valid when there are reasons to believe that

there are important, latent differences between treatment and control firms and that these

differences trigger sharp contrasts in the post-treatment period because of other changes in

the environment. An appealing feature of our identification strategy is that it is difficult to

find a story in which higher quality firms are more affected than lower quality firms.

In order to strengthen the interpretation of the results, we replicate exactly the same

experiment that we run for sovereign downgrades but using a placebo period. That is, we

use corporate and sovereign credit ratings information to sort firms into treatment and non-

treated groups and covariates to produce a matched control group of firms. We then compare

treated versus control firms investment behavior during periods without sovereign downgrades.

We consider two experiments: (1) recession periods without downgrades; and (2) sovereign

upgrades. These falsification tests can help to rule out alternative explanations for the results

reported in Table 7. For example, there could be unobservable firm characteristics that predict

both a higher credit rating and a drop in investment (characteristics that are not captured

by the matching estimator procedure).

Panel A of Table 6 presents the results of the placebo test using recession periods without

downgrades. This placebo addresses the concern that some macroeconomic shocks (e.g., a

demand shock) not associated with sovereign downgrades affects differently the treatment

and countrol groups. If this is the case, we should find differential investment effects be-

tween treatment and control groups during periods of recessions that are not accompanied by

sovereign downgrades. We identify recession periods using the OECD recession indicators for

each country drawn from the Federal Reserve Economic Data (FRED) database. The reces-

sion indicator is available for 38 countries with monthly frequency and we adopt the “From

the Period following the Peak through the Trough” definition. We aggregate the monthly

series into an annual series and classify a country as being in a recession in a given year if it

has more than six months of recession. For each country, we exclude recessions years in which

the country is downgraded.

21

We have 48 treated and control firms in this placebo test. Treated and control firms have

virtually identical investment dynamics before recessions. Treated firms display an investment

rate of 22.89% of capital in the year before the recession, while their control counterparts

investment rate is 19.37%. More important, there is no difference in investment behavior

between the two groups of firms in the post-treatment period. Both groups invest about

20% in the first recession year without sovereign downgrade and the difference-in-differences

estimator is -1.86 percentage points and statistically insignificant. The ATT effect in this

case is -0.74 percentage points, and is statistically insignificant. Simply put, our treatment-

control differences do not appear in recession periods that are not accompanied by sovereign

downgrades. Panel A of Figure 5 plots the evolution of investment rates in the two years

before and after recessions. As expected, investment rates for treated firms are higher than

those for control firms, but the investment processes follow similar dynamics around recessions

that are not accompanied by sovereign downgrades.

Panel B of Table 6 presents the results of the second placebo test. Sovereign ceiling

policies should not matter as much in the case of sovereign upgrades. Firms with rating at

the sovereign bound are not necessarily upgraded following a sovereign upgrade. Additionally,

the market reaction to upgrades is typically insignificant (e.g. Brooks et al., 2004; Gande and

Parsley, 2005; Ferreira and Gama, 2007). We identify years of sovereign upgrades and then

construct the treatment and control groups in a similar way to that in Table 5. We find no

difference in the investment behavior of the two groups following a sovereign upgrade. For

firms in the treatment group, the average investment increases by 0.83 percentage points of

capital, while for control firms the investment rate increases by 1.68 percentage points. These

estimates suggest that the investment rate increases by 0.85 percentage points less for treated

firms relative to control firms. This difference is economically and statistically insignificant.

The ATT gives consistent results with a statistically insignificant estimate of -0.57 percentage

points. Panel B of Figure 5 plots the evolution of investment rates in the two years before

and after sovereign upgrades. As expected, investment rates for treated firms are higher than

those for control firms, but the investment processes follow similar dynamics around sovereign

22

upgrades.

4.3 Financial policy

We also examine whether credit ratings affect differently the financial policy of the treated and

matched control firms around sovereign downgrades. We expect treated firms to deleverage

as they may face an increase in the cost of capital, while control firms are not affected.

Table 7 presents the results of difference-in-differences matching estimator for the long-term

debt-to-assets ratio (Long − Term Leverage) in Panel A and the total debt-to-assets ratio

(Total Leverage) in Panel B. Firms cannot adjust leverage immediately following a sovereign

downgrade so we examine the change in leverage in the subsequent year versus the year of

the downgrade.

We find that the capital structures of the treated and control firms become significantly

different after a sovereign downgrade. For firms in the treatment group, the average long-term

leverage drops to 23.68% of assets, a fall of 1.65 percentage points. In contrast, for control

firms, the long-term leverage increases to 26.68% of capital, an increase of 1.22 percentage

points. These estimates suggest that long-term leverage decreases by 2.87 percentage points

more for firms with a pre-downgrade rating equal (or above) to the sovereign rating relative

to otherwise similar firms with rating below the sovereign rating. The table also reports the

differential change in investment that is produced by the Abadie-Imbens matching estimator

of the ATT, which is equal to -3.78 percentage points. The effect is statistically significant.

Panel B of Table 7 presents the difference-in-differences matching estimator for total lever-

age. Interestingly, the differential effect between treated and control firms is smaller than the

one observed for long-term leverage. The ATT difference is equal to -2.74 percentage points.

This result suggests that treated firms substitute short-term debt to long-term debt following

a sovereign downgrade.

Finally, we analyze the effect of sovereign downgrades on the ratio of cash holdings-to-

assets ratio (Cash). In contrast to leverage, which reacts slowly to the downgrade, treated

23

firms immediately react decreasing cash in the year of the sovereign downgrade relative to

the previous year. Moreover, there is a differential reaction between treated and control firms

with a statistically significant ATT estimate of -2.14 percentage points.

Figure 6 plots the evolution of long-term and total leverage in the two years before and

after the sovereign downgrade for treatment and control firms. The leverage processes of

firms in the two groups follow similar trends before the downgrade. Furthermore, long-term

leverage falls for the treatment group in the year of the downgrade and actually increases for

the control group. Figure 8 also shows the evolution of cash in the two years before and after

the sovereign downgrade for treatment and control firms. Cash after the decrease in the year

of the sovereign downgrade, which is consistent with firms using internal cash to attenuate

the effect on investment, seems to increase in the following years. This behavior is consistent

with firms building up cash reserves for precautionary motives.

Overall, the evidence suggests that credit rating downgrades generate an increase in the

cost of capital, which leads to lower investment rates, less use of debt, and lower cash holdings

among affected firms. Given the similarity between firms in the treatment and control groups,

the evidence supports a causal effect of credit ratings on firm investment and financial policy.

4.4 Linear regression model

While the nonparametric matching approach is well-suited for our test strategy, it is useful

to show that our results also hold when we use a linear regression model. We first implement

reduced form regressions to examine whether firms’ investment rate decrease for those firms

that are bound by the sovereign ceiling following a contraction in the ceiling. The dependent

variable is the annual changes in the investment rate (∆Investment) in year t. The main

explanatory variables are a dummy variable that takes the value of one if a firm has a rating

equal to (or above) the sovereign rating in a year t − 1 (Bound), a dummy variable that

takes the value of one if a firm’s country rating is downgraded in year t (SovDown), and the

interaction term Bound × SovDown. The interaction term coefficient captures the difference

24

in the reaction of investment between firms with rating equal (or above) to the sovereign

rating (treated) versus other (non-treated) firms following a sovereign downgrade. We run a

pooled OLS regression using the sample of all firms in the 1990-2012 period.

Column (1) of Table 8 shows that treated firms cut their investment by 9.6 percent-

age points more than other firms as indicated by the interaction term coefficient Bound ×

SovDown. The group difference estimate is significant at the 5% level. Outside the sovereign

downgrade periods the difference between the two groups of firms is only 3 percentage points.

Interestingly, the SovDown coefficient is positive, indicating that firms with rating below the

sovereign increase investment but only slightly following a sovereign downgrade.

In column (2), we estimate the investment rate change regressions including the covariates

used in Table 5 (size, investment, Tobin’s Q, cash flow, cash holdings, leverage and foreign

sales). While these controls predict changes in investment in their own right, their inclusion

does not materially alter the coefficient on the interaction term. The estimated group-mean

difference increases slightly to 11.4 percentage points (significant at the 1% level) after we add

the controls. Columns (3)-(5) present additional estimates using combinations of year, indus-

try, country, and firm fixed effects. Column (6) presents estimates using firm and country-year

fixed effects so the effects are driven only by within-firm and within country-year variation.

The magnitude of the group difference estimates are similar at about 11 percentage points

and significant in all specifications. Furthermore, the difference between the two groups of

firms outside the sovereign downgrade period becomes statistically insignificant when we in-

clude country or firm fixed effects. The effect of the sovereign downgrade on investment also

becomes insignificant. Overall, the linear model regression estimates are fully consistent with

those reported under the matching estimator approach.

Next we estimate the effect of credit ratings on investment using instrumental variables

methods. The sample includes all rated firms in the 1990-2012 period. Table 9 presents

the results. Column (1) shows the estimates of an OLS regression of the annual changes

in investment rate on annual changes in firm’s credit ratings (using a numerical scale with

upgrades having a positive sign and downgrades having a negative sign). The regression also

25

includes control variables as well as year and firm fixed effects. We find that the credit rating

changes (∆Ratings) coefficient is positive and significant, which indicates that upgrades are

associated with increases in investment, while downgrades are associated with decreases in

investment. Of course, this coefficient is biased as rating changes are in general correlated

with changes in firm’s fundamentals. To correct for the endogeneity of rating changes, we use

an instrumental variables estimation method.

In the first stage, we run a regression of ∆Ratings on the instruments: Bound, SovDown,

and Bound × SovDown. The first stage regression estimates in column (2) show that the

interaction term coefficient is negative and significant. The interpretation is that treated firms

ratings decrease significantly more (nearly two notches more) than other firms ratings at the

time of a sovereign downgrade. This result using a linear model of ratings is consistent with

our evidence based on the matching estimator in Table 4.

In the second stage regression, the dependent variable is the annual change in investment

rate and the main explanatory variable is the predicted ∆Ratings estimated in the first stage.

In column (2) we find that the ∆Ratings coefficient is positive and significant. The point

estimate indicates that treated firms cut investment by 4.3 percentage point more than other

firms. Columns (3)-(6) show estimates of similar regressions including control variables, and

combinations of year, industry, country, and firm fixed effects. Column (7) presents estimates

using country-year fixed effects so the effects are driven only by within country-year variation.

The differential effect of sovereign downgrades on treated versus other firms is similar in all

specifications.

5 Effect on corporate yields

So far the evidence indicates that bound firms tend to be more unfavorably rated by rating

agencies and their ratings are more affected by sovereign downgrades. Thus, a 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. More precisely, it is possible

26

that contractions in the sovereign ceiling result in even a more pronounced increase in the

bond yields of firms bound by the sovereign rating. This increase in bonds yield, which proxy

for the cost of debt, can explain the documented impact in firms’ investment and financial

policy.

We collect data on long-term foreign-currency sovereign and bond (issue) ratings for USD-

denominated bonds, as well as its end of the month yield to maturity from Bloomberg. We also

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

ment and frequency and collateral type). Since bond pricing data is available from Bloomberg

starting in 1999, we construct the sample of sovereign ratings, corporate ratings and corporate

bond yields from 1999 to 2012.10

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

yields represent default risk, rather than currency risk ((?); (?)). Thus, we calculate corporate

yield spreads by subtracting the equivalent maturity US Treasury yield for each issue.11 We

eliminate a small number of observations with negative spreads, we 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, we

exclude from my sample countries and firms where sovereign ratings were unchanged between

1999 and 2012.

Table A.4 in the Internet Appendix, Panel A shows the number of bonds (CUSIPs), firms

and countries each year in the sample. Panel B displays the composition of firms by industry,

using the Dow Jones’s Industry Classification Benchmark (ICB). The final matched sample

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

A.5 in the Internet Appendix 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. The table also shows

10The bond yield tests use issue ratings rather than issuer ratings as the analysis is performed at the bondlevel.

11I obtain constant maturity U.S. treasury rates data from the Federal Reserve Economic Data (FRED)website: http://research.stlouisfed.org/fred2/

27

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). Table A.6 in the

Internet Appendix summarizes the coverage of the data as well as the number of sovereign

rating changes by country.

We first implement reduced form regressions to examine whether bonds yield increase for

those firms that are bound by the ceiling following a contraction in the sovereign ceiling. We

examine the change in bond spreads around sovereign rating downgrades for treated firms

relative to non-treated firms. That is, we use a firm’s bound status as an instrument to

estimate the effect of a contraction in the sovereign ceiling on corporate spreads. We estimate

a reduced form pooled regression where the dependent variable is the change in spread around

sovereign rating changes, i.e., the spread on a firm’s i bond j measured t months after each

sovereign event minus its spread s months prior to the event (∆Spreadi,j,t−s). The main

explanatory variables are a dummy variable that takes the value of one if a firm has a rating

equal to (or above) the sovereign rating in a month t − 1 (Bound), a dummy variable that

takes the value of one if a firm’s country rating is downgraded in month t (SovDown), and

the interaction term Bound × SovDown. The interaction term captures the differential effect

on bond yield of bound firms relative to firms that are near but not bound by the sovereign

ceiling.

We 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, we

constrain non-bound firms to be three rating notches or less below the sovereign. We also

limit my analysis to firms that have a rating of B- or higher.12 Because rating changes can

be anticipated, we perform event studies with different values of t around the time of the

sovereign rating announcements to capture the response of financial markets. Since firms can

have two or more bonds at any given point, we weight observations based on the number of

bonds observed for each firm at each point in time, and we cluster standard errors by each

12Only 0.1% of the observations in the sample have a corporate rating in the CCC, CC or D categories.

28

country event (sovereign downgrade).

Figure 7 shows the evolution of corporate spreads around sovereign rating changes. Panel

A of Table 10 in reports the results for event windows starting three months prior to a

sovereign rating change. When looking at the spread one month after the event, the average

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

non-bounded firms. As the event windows widens to three months after a sovereign downgrade

the differential effect increases to 117 bp.

We extend the specification to include country-event specific fixed effects (i.e., fixed effects

for each sovereign rating downgrade for each country). Including country-event fixed effects

equates to estimating the differential impact of the sovereign ceiling on the bond yield of

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

are not bound by the constraint. However, this approach reduces considerably the sample

size. Panel A of Table 10 also shows these estimates. Although including country-event fixed

effects dampens the overall magnitudes compared to the pooled OLS, the coefficients for the

interaction term remain statistically and economically significant. For example, the spread for

firms that are bounded by the sovereign ceiling increases by 54 bp more than for non-bounded

firms using the spread one month after the event.

Next we use a fuzzy regression discontinuity design (RDD) to instrument corporate rat-

ing changes directly related to the sovereign ceiling, using as an instrument the interaction

between a firm’s bound status with the sovereign rating downgrade. More precisely, we imple-

ment 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 downgrade or upgrade.

As it has been noted, rating agencies do not strictly follow the sovereign ceiling policy and

thus a sovereign rating change does not lead with a 100% probability to the same rating

change for bounded firms. Thus, the jump in the relationship between ∆Spread 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 ∆Spread outside of its influence

through treatment receipt.

29

The fuzzy RD design is described by the following equations:

∆ CorpDowni,j = γ1(∆SovDown)i,j × (Bound)i,j + αi (3)

∆Spreadi,j,t−s = β1(∆ CorpDown)i,j + αi + εi,j (4)

where αi is a country-event specific fixed effect. The first equation corresponds to the first

stage where the change in corporate ratings are estimated after a sovereign downgrade. These

are then used in the second stage regression. Panel B of Table 10 shows the estimation results

of the RDD setting obtained using 2SLS. The results from this set of regressions are 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.

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

differential effects on spreads. We estimate the same model with country-event fixed effects

as in Panel A of Table 10, with the only difference that we focus on the differential effect

of bound versus non-bound firms one year before the actual event. We report the results

of this test in Table A.7 in the Internet Appendix. Consistent with the hypothesis that the

contractions and relaxations in the sovereign ceiling are the main drivers behind the identified

differential changes in corporate spreads, we find that none of the coefficients reported are

statistically significant.

30

6 Robustness

In this section, we perform several robust checks of our primary findings on the effect of

sovereign downgrades on corporate investment. The results of these robustness tests are

shown in the Internet Appendix.

A first robustness consists in excluding firms with ratings above the sovereign from the

treatment group as these firms may be systematically different from firms exactly at the

bound even though including these firms works against finding a drop in investment for the

treatment group. Table A.8 in the Internet Appendix reports the results of the difference-in-

differences estimator of investment using this alternative treatment group. Not suprisingly,

the effect is even stronger than in Table 5. The differential change in investment produced by

the matching estimator of the ATT is -21.07 percentage points and is statistically significant.

A second robustness is related to the matching procedure used to create the control group.

We impose that control firms should be from the same country as treated firms. This creates a

difficulty in finding exact matches in the same industry, especially in smaller countries. In our

main results, we give priority to find an exact match by country as we are examining the effects

of sovereign downgrades (a country-level event). In alternative, we impose that a control firm

should be from the same industry (Fama-French ten industry classification) as the treated firm

but require only that the control firm is located in the same geographic region (Africa, Asia,

Eastern Europe, Japan, Latin America, North America, Northern Europe, Oceania, Southern

Europe, United Kingdom and Western Europe) as the treated firm, rather than in the same

country. We also include the other covariates used in Table 7. Using this alternative matching

specification, we construct a control group that matches exactly the industry of treated firms.

Table A.9 in the Internet Appendix reports the results of the difference-in-differences estimator

using this alternative matching specification. The results are consistent with those in Table

7. The differential change in investment produced by the matching estimator of the ATT is

equal to -7.46 percentage points and is statistically significant.

31

7 Conclusion

We provide causal evidence that credit ratings affect firm investment and financial policy using

sovereign rating downgrades as a natural experiment. Sovereign downgrades create exogenous

and asymmetric variation across corporate credit ratings because of rating agencies’ sovereign

ceiling policy that prevents firms from having ratings above the sovereign rating. We explore

this variation to show that firms with ratings equal to the sovereign ratings (treatment group)

face an increase in cost of debt, and cut investment and leverage more than otherwise similar

firms with rating below the sovereign rating following a sovereign downgrade. One important

feature of our identification strategy is that higher-quality firms are more affected than lower-

quality firms, which rules out several alternative explanations of the results.

Our study makes three contributions to the literature on credit ratings. First, we develop

a new strategy to identify the effects of credit ratings on firms’ cost of capital, investment

and financial policy. Our results show that credit ratings have causal effects on firm policies,

which are not confounded by variation in unobservable firm characteristics or macroeconomic

conditions. Second, we establish that sovereign rating downgrades and ceilings is a channel

through which public debt management has important real consequences for a country’s

economy, in addition to interest rate changes and crowding out effects. Finally, we identify

unintended consequence for real economic activity of sovereign rating ceiling policies that are

typically followed by credit rating agencies.

Our study is subject to the standard limitations associated with quasi-natural experiments.

We can only hope to measure the causal effect of ratings downgrades if we focus on firms

for which rating-ceiling policies are likely to bind following a sovereign downgrade. This

restriction forces us to focus on firms that had credit ratings equal to the sovereign rating

prior to the downgrade. As usual, this restriction limits the sample size. In addition, the

effect of the downgrade on treated firms may not directly translate to an average firm that

has different characteristics from treated firms.

In addition, our results cannot establish whether the investment cuts that follow ratings

32

downgrades are good or bad for the treated firms. Because of this, it may be interesting

to explore the consequences of ratings downgrades for firm performance. One can examine

the relative change in future profitability for treated and control firms (subject to the usual

caveats with long-term event studies). It may also be possible to conduct a short-term event

study around the dates of sovereign downgrades, provided that the actual downgrade is not

fully anticipated by investors.

33

References

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treatment effects, Journal of Business & Economic Statistics 29, 1–11.

Arteta, Carlos, and Galina Hale, 2008, Sovereign debt crises and credit to the private sector,

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coordination mechanisms, Review of Financial Studies 19, 81–118.

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Goh, Jeremy C, and Louis H Ederington, 1993, Is a bond rating downgrade bad news, good

news, or no news for stockholders?, Journal of Finance 48, 2001–08.

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Harford, Jarrad, and Vahap Uysal, 2013, Bond market access and investment, Journal of

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Quantitative Analysis 44, 1323–1344.

, and Philip E. Strahan, 2010, Do regulations based on credit ratings affect a firm’s

cost of capital?, Review of Financial Studies 23, 4324–4347.

Leary, Mark T., and Michael R. Roberts, 2005, Do firms rebalance their capital structures?,

Journal of Finance 60, 2575–2619.

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vestment to changes in the supply of credit, Journal of Financial and Quantitative Analysis

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book of the Economics of Finance 2.

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bank loan ratings, Review of Financial Studies 22, 1659–1691.

35

Tang, Tony T., 2009, Information asymmetry and firms’ credit market access: Evidence from

moody’s credit rating format refinement, Journal of Financial Economics 93, 325–351.

36

Figure 1: Sovereign Ceiling Rule

This figure presents the relationship between sovereign and corporate ratings. Panel A depicts the frequency

distribution of long-term foreign-currency (LT FC) corporate ratings by the LT FC sovereign rating of the

corresponding country of domicile. Observations for countries with AAA ratings are excluded as, by definition,

the sovereign ceiling policy does not represent a constraint for corporates 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 the Appendix. The bars in dark blue in the diagonal represent the sovereign rating

ceiling (i.e. where corporate and sovereign ratings equate). Panel B plots the distribution of the difference

between long-term foreign-currency (LT FC) corporate ratings and LT FC sovereign ratings for S&P, Moody’s

and Fitch. Observations for countries with a AAA rating are excluded from the graph, as well as for countries

where no sovereign rating changes are observed from any credit rating agency between 1999 and 2012.

CCC

CCC+

B-

B

B+

BB-

BB

BB+

BBB-

BBB

BBB+

A-

A

A+

AA-

AA

AA+

AAA

S&

P C

orpo

rate

Rat

ing

- F

C L

T

CCCCCC+ B- B B+

BB-BB

BB+BBB-

BBBBBB+ A- A A+

AA-AA

AA+

S&P Sovereign Rating - FC LT

Frequency of Corporate Ratings by Sovereign Rating

0.0

5.1

.15

.2D

ensi

ty

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

Figure 2: 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. Observations

are 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 the fraction

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 beyond these values.

1.0 2.3 1.13.0 2.8

0.73.2 4.4 5.9

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 Downgrade

0.94.0

12.2

4.2 5.4

12.6

63.0

46.3

22.7

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 Downgrade

1.1 0.5 1.1 1.5

7.3 6.58.8

19.0

14.2

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 Downgrade

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

38

Figure 3: Corporate Ratings Parallel Trends around SovereignDowngrade

This figure presents the parallel trends of corporate ratings of treatment and control groups around the

sovereign downgrade. Treated firms are firms with credit rating equal to the sovereign rating in the year

before a sovereign downgrade. Control firms are matched firms using the Abadie and Imbens matching

estimator. Corporate rating are converted to a numerical scale with 22 corresponding to the highest rating

(AAA) and one to the lowest (default).

1012

1416

18C

orpo

rate

Rat

ings

-2 -1 0 1 2Years from Sovereign Downgrade

Treatment Control

39

Figure 4: Investment Parallel Trends around Sovereign Downgrade

This figure presents the parallel trends of investment of treatment and control groups around the sovereign

downgrade. Treated firms are firms with credit rating equal to the sovereign rating in the year before a

sovereign downgrade. Control firms are matched firms using the Abadie and Imbens matching estimator.

Investment is defined as the ratio of annual capital expenditure to lag net property, plant and equipment..1

5.2

.25

.3In

vest

men

t Rat

e

-2 -1 0 1 2Years from Sovereign Downgrade

Treatment Control

40

Figure 5: Investment Parallel Trends Recession without Downgrade

This figure presents the parallel trends of investment of treatment and control groups around a recession in

which the sovereign rating is not downgraded. Treated firms are firms with credit rating equal to the sovereign

rating in the year before a sovereign downgrade. Control firms are matched firms using the Abadie and Imbens

matching estimator. Investment is defined as the ratio of annual capital expenditure to lag net property, plant

and equipment.

.14

.16

.18

.2.2

2In

vest

men

t Rat

e

-2 -1 0 1 2Years from Recession without Downgrade

Treatment Control

.14

.16

.18

.2.2

2In

vest

men

t Rat

e

-2 -1 0 1 2Years from Sovereign Upgrade

Treatment Control

.15

.2.2

5.3

.35

Inve

stm

ent R

ate

-2 -1 0 1 2Years from Currency Crisis without Downgrade

Treatment Control

41

Figure 6: Financial Policy Parallel Trends around Sovereign Down-grade

This figure presents the parallel trends of leverage and cash of treatment and control groups around the

sovereign downgrade. Treated firms are firms with credit rating equal to the sovereign rating in the year

before a sovereign downgrade. Control firms are matched firms using the Abadie and Imbens matching

estimator. Total leverage is defined as the ratio of total debt to total assets. Long-term leverage is defined as

the ratio of long-term debt to total assets. Cash is defined as the ratio of cash and short-term investments to

total assets..2

.22

.24

.26

.28

Long

-Ter

m L

ever

age

-2 -1 0 1 2Years from Sovereign Downgrade

Treatment Control

.3.3

5.4

.45

Leve

rage

-2 -1 0 1 2Years from Sovereign Downgrade

Treatment Control

.09

.1.1

1.1

2.1

3C

ash

-2 -1 0 1 2Years from Sovereign Downgrade

Treatment Control

42

Figure 7: Corporate spreads around sovereign rating changes

This 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 that are three notches or less

below the sovereign ceiling (“Below Bound Firms”). The dependent variable is the corporate bond spread,

regressed on event-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. We require that a bond has at least one observation in the pre-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 by country-event are used to calculate the 95% confidence interval of the

difference between the two groups.

-2.0

0.0

2.0

4.0(%

)

-1.0

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

(%)

-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

43

Table 1: List of Sovereign Downgrades

This table presents the sample of sovereign downgrades, and the number of treated firm-years observations

(i.e., firm-year observations with credit rating equal to the sovereign rating) using S&P long-term foreign

currency credit ratings.

Country Downgrade year Sovereign RatingNumber of treated

firm-yearsBefore

DowngradeAfter

Downgrade

Argentina 2001 BB- SD 42008 B+ B- 32012 B B- 1

Brazil 2002 BB- B+ 5Indonesia 1997 BBB BB+ 1

1998 BB+ CCC+ 5Ireland 2011 A BBB+ 4Italy 2004 AA AA- 1

2006 AA- A+ 22011 A+ A 22012 A BBB+ 2

Japan 2001 AAA AA 12002 AA AA- 42011 AA AA- 13

Mexico 2009 BBB+ BBB 4Philippines 2005 BB BB- 2Portugal 2010 A+ A- 1

2011 A- BBB- 2Spain 2012 AA- BBB- 2Thailand 1997 A BBB 1

1998 BBB BBB- 2United States 2011 AAA AA+ 4

Total 66

44

Table 2: Summary Statistics - Median and Mean Tests

This table presents the pre-treatment statistics comparison of non-treated, treated and control groups. Panel

A presents sample medians, and Panel B presents sample means. Treated firms are firms with credit rating

equal or above to the sovereign rating in the year before a sovereign downgrade. Non-treated firms are all

other firms in the sample. Control firms are matched firms using the Abadie and Imbens matching estimator.

There are 66 treated, 24,873 non-treated, and 66 control firm-year observations in treated years from countries

with at least one treated firm over the sample period. Pearson’s χ2 statistic test the difference in medians

between treated and control firms. Kolmogorov-Smirnov statistic test the difference in means of treated and

control firms. *, **, *** indicates significance at the 1%, 5% and 10% level respectively.

Panel A: MedianPearson χ2

p-valuePanel B: Mean

Kolmogorov-Smirnovp-value

Non-treated Treated Control Non-treated Treated Control

Size 5.30 9.43 9.20 0.60 5.32 8.91 8.71 0.65(0.013) (0.157) (0.170)

Investments 11.46 16.13 12.17 0.12 26.25 28.73 22.07 0.31(0.414) (5.523) (3.420)

Q 1.00 1.31 1.02 0.06* 2.06 1.50 1.15 0.00***(0.045) (0.097) (0.046)

CashF low 7.53 14.80 10.53 0.00*** 1.33 17.36 11.09 0.00***(0.362) (1.218) (0.768)

Cash 10.00 8.47 6.94 0.86 15.20 11.62 9.17 0.36(0.103) (1.317) (0.963)

Leverage 23.32 33.31 34.19 0.22 28.60 32.86 38.11 0.36(0.212) (2.284) (2.152)

Foreign Sales 0.00 0.00 2.00 0.6 11.48 19.19 21.84 0.65(0.148) (3.360) (3.067)

Gov. Ownership 0.00 0.00 0.00 0.66 0.23 1.55 1.55 1.00(0.022) (1.084) (0.785)

45

Table 3: Predicted Rating by Sovereign Bound Status

This table reports the estimated coefficients of the effect of being bound by the sovereign ceiling on a firm’spredicted rating, using the following regression model:

Rgi,t = β1(RgDummy)i,t + β2(RgDummy)i,t × (Bound)i,t + β3(SovRgDummy)i,t + αt + εi,twhere β2 is a coefficients vector that captures the differential effect, for each rating level, of being boundby the sovereign ceiling on a firm’s predicted rating. We weigh observations based on the number of bondsobserved each year for each firm. Standard errors clustered by firm.

Predicted Rating by Bound Status

Below Bound BoundDifference between ”Bound”and

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

AA 14.49 14.98 -0.49 0.263AA- 13.90 14.36 -0.46 0.101A+ 14.30 14.19 0.11 0.852A 14.47 14.02 0.45 0.153A- 13.89 13.64 0.25 0.430BBB+ 14.04 13.39 0.64* 0.051BBB 14.14 13.04 1.10*** 0.000BBB- 13.69 12.96 0.73*** 0.002BB+ 13.40 12.58 0.82*** 0.003BB 12.55 12.15 0.39 0.234BB- 12.57 11.44 1.13*** 0.006B+ 12.64 10.86 1.78*** 0.002B 11.20 10.00 1.21*** 0.003B- 11.40 9.97 1.43*** 0.003CCC+ 11.91 9.92 2.00*** 0.001

46

Table 4: Differences-in-Differences of Corporate Ratings around aSovereign Downgrade

This table presents the results of the credit ratings differences-in-differences and the Abadie and Imbens

matching estimator around the sovereign downgrade. Treated firms are firms with credit rating equal or

above to the sovereign rating in the year before a sovereign downgrade. There are 66 treated and 66 control

firm-year observations. Corporate rating are converted to a numerical scale with 22 corresponding to the

highest rating (AAA) and one to the lowest (default). Robust standard errors are reported in parentheses.*,

**, *** indicates significance at the 1%, 5% and 10% level respectively.

BeforeDowngrade

Year ofDowngrade

Difference

Treated Firms 16.41*** 14.75*** -1.66***(0.515) (0.665) (0.336)

Control Firms 13.09*** 12.20*** -0.89***(0.505) (0.652) (0.3125)

Difference 3.32*** 2.55***(0.273) (0.361)

Differences in Differences -0.77***(0.303)

Matching Estimator (ATT) -1.38***(0.309)

47

Table 5: Differences-in-Differences of Investment around aSovereign Downgrade

This table presents the results of the investment differences-in-differences and the Abadie and Imbens matching

estimator around the sovereign downgrade. Treated firms are firms with credit rating equal or above to the

sovereign rating in the year before a sovereign downgrade. There are 66 treated and 66 control firm-year

observations. Robust standard errors are reported in parentheses. *, **, *** indicates significance at the 1%,

5% and 10% level respectively.

BeforeDowngrade

Year ofDowngrade

Difference

Treated Firms 28.73*** 18.53*** -10.20**(5.523) (1.676) (5.104)

Control Firms 22.07*** 19.22*** -2.84(3.420) (3.127) (3.074)

Difference 6.66 -0.69(4.542) (2.943)

Differences in Differences -7.35*(4.424)

Matching Estimator (ATT) -16.78***(5.368)

48

Table 6: Placebo test: Differences-in-Differences of Investment around a Reces-sion without Downgrade

This table presents the results of the investment differences-in-differences and the Abadie and Imbens matching estimator for

investment rate. Panel A presents the estimates around the first year of recession without downgrade. In Panel A there are 51

treated and 51 control firm-year observations. Panel B presents the estimates around a sovereign upgrade. In Panel B there

are 48 treated and 48 control firm-year observations. Panel C presents the estimates around a currency crisis without sovereign

downgrade. Currency crisis is defined using the Reinhard and Rogoff currency crisis indicator. In Panel C there are 53 treated

and 53 control firm-year observations. Treated firms are firms with credit rating equal or above to the sovereign rating in the

year before a sovereign downgrade. Robust standard errors are reported in parentheses. *, **, *** indicates significance at the

1%, 5% and 10% level respectively.

Panel A: Recession without DowngradeBefore Recession

without DowngradeYear of Recession

without DowngradeDifference

Treated Firms 22.89*** 21.81*** -1.08(2.039) (1.794) (1.698)

Control Firms 19.37*** 20.15*** 0.78(1.585) (2.196) (2.060)

Difference 3.52* 1.66(1.991) (2.567)

Differences in Differences -1.86(2.428)

Matching Estimator (ATT) -0.74(2.700)

Panel B: UpgradeBefore Upgrade Year of Upgrade Difference

Treated Firms 21.11*** 21.90*** 0.83(1.277) (1.208) (0.745)

Control Firms 13.95*** 15.66*** 1.68(0.945) (1.144) (0.913)

Difference 7.16*** 6.23***(1.340) (1.368)

Differences in Differences -0.85(1.093)

Matching Estimator (ATT) -0.57(1.266)

Panel C: Currency CrisisBefore CurrencyCrisis without

Downgrade

First Year ofCurrency Crisis

without DowngradeDifference

Treated Firms 23.15*** 23.78*** 0.63**(2.078) (2.188) (1.360)

Control Firms 27.27*** 31.36*** 4.09(27.132) (6.203) (0.534)

Difference -4.12*** -7.58(2.841) (6.799)

Differences in Differences -3.46(6.497)

Matching Estimator -5.25(7.782)

Table 7: Differences-in-Differences of Financial Policy around aSovereign Downgrade

This table presents the results of the firms financial policy differences-in-differences and the Abadie and Imbens

matching estimator around the sovereign downgrade. Panel A presents long-term leverage estimates, Panel B

presents total leverage estimates and Panel C presents cash holdings estimates. Treated firms are firms with

credit rating equal or above to the sovereign rating in the year before a sovereign downgrade. There are 66

treated and 66 control firm-year observations. Robust standard errors are rreported in parentheses. *, **,

*** indicates significance at the 1%, 5% and 10% level respectively.

Panel A: Long-Term LeverageYear of

DowngradeYear afterDowngrade

Difference

Treated Firms 25.33*** 23.68*** -1.65*(2.346) (2.299) (0.943)

Control Firms 25.54*** 26.68*** 1.22(1.910) (2.225) (0.966)

Difference -0.20 -3.00(2.163) (2.479)

Differences in Differences -2.79*(1.497)

Matching Estimator (ATT) -3.78**(1.90)

Panel B: Total LeverageTreated Firms 34.99*** 34.21*** -0.78

(1.621) (2.926) (1.333)Control Firms 36.70*** 38.48*** 1.78

(2.426) (3.023) (1.446)Difference -1.71 -4.27

(2.167) (2.637)

Differences in Differences -2.54*(1.337)

Matching Estimator (ATT) -2.74*(1.601)

Panel C: CashBefore

DowngradeYear of

DowngradeDifference

Treated Firms 11.62*** 10.89*** -0.73(1.317) (1.304) (0.691)

Control Firms 9.43*** 9.63*** 0.20(0.963) (1.026) (0.634)

Difference ‘2.19** 1.26(0.979) (0.108)

Differences in Differences -0.925(0.735)

Matching Estimator (ATT) -2.14**(1.145)

Table 8: Linear Regressions of Investment

This table presents the results of the linear regression model of investment. The dependent variable is the

annual change in investment rate. Treated is a dummy variable that takes the value of one if a firm has a

credit rating equal to the sovereign rating in a year t− 1, and Sovereign Downgrade is a dummy variable that

takes the value of one if a firm’s country rating is downgraded in year t. Robust standard errors clustered

by firm, industry or country are reported in parentheses. *, **, *** indicates significance at the 1%, 5% and

10% level respectively.

Dependent Variable: ∆Investment(1) (2) (3) (4) (5) (6)

Treated x Sovereign Downgrade -0.096** -0.114*** -0.116** -0.107* -0.117*** -0.073*(0.038) (0.043) (0.050) (0.058) (0.032) (0.039)

Treated 0.030*** 0.021*** 0.020** -0.016 -0.020 0.005(0.005) (0.007) (0.008) (0.011) (0.015) (0.016)

Sovereign Downgrade 0.016*** 0.029*** 0.041*** 0.021 0.006(0.005) (0.005) (0.014) (0.016) (0.007)

Controls No Yes Yes Yes Yes YesYear FE No No Yes Yes Yes NoIndustry FE No No Yes No No NoCountry FE No No No Yes No NoFirm FE No No No No Yes YesCountry x Year FE No No No No No YesCluster Firm Firm Ind Country Firm Firm

Observations 391,702 357,229 355,670 357,229 357,229 357,229R2 0.00 0.01 0.02 0.02 0.04 0.01

51

Table 9: Instrumental Variable Regression of Investment

This table presents the results of the instrumental variable regression of investment. The dependent variable

is the annual change in investment rate. The endogenous variable is the change in credit ratings. Treated is a

dummy variable that takes the value of one if a firm has a credit raring equal to the sovereign rating in a year

t− 1, and Sovereign Downgrade is a dummy variable that takes the value of one if a firm’s country rating is

downgraded in year t. Robust standard errors are reported in parentheses. *, **, *** indicates significance

at the 1%, 5% and 10% level respectively.

Dependent Variable: ∆Investment(1) (2) (3) (4) (5) (6) (7)

OLS IV IV IV IV IV IVSecond Stage∆Rating 0.007*** 0.043** 0.051** 0.050** 0.049*** 0.050** 0.089**

(0.002) (0.019) (0.023) (0.022) (0.017) (0.023) (0.037)

First Stage

Treated x Sovereign Downgrade -1.920*** -1.631*** -1.529*** -1.472*** -1.374*** -0.735***(0.111) (0.111) (0.110) (0.111) (0.117) (0.138)

Treated -0.038 -0.064* -0.038 -0.099*** -0.305*** -0.146***(0.037) (0.036) (0.031) (0.034) (0.052) (0.036)

Sovereign Downgrade 0.035 0.039 -0.095*** -0.085** -0.086**(0.026) (0.026) (0.035) (0.037) (0.038)

Controls Yes No Yes Yes Yes Yes YesYear FE Yes No No Yes Yes Yes NoIndustry FE No No No Yes No No NoCountry FE No No No No Yes No NoFirm FE Yes No No No No Yes NoCountry x Year FE No No No No No No Yes

Observations 24,511 26,114 24,511 24,508 24,511 24,511 24,511R2 0.00 0.00 0.00 0.01 0.43 0.00 0.40

52

Table 10: Effect of sovereign rating downgrades on corporate spreadchanges

This table reports the estimation results of the effects of sovereign downgrade on corporate spreads. The

dependent variable is the yield spread change of a corporate bond around the time window specified in each

column. Treated 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 sovereign rating 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.Panel A. OLS with Country-Event Fixed Effects

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

Treated 0.531* 0.544* 0.847** 1.034(1.95) (1.83) (2.48) (1.68)

Country-Event FE Yes Yes Yes Yes

Observations 603 594 572 519r2 0.777 0.728 0.811 0.582

Panel B. 2SLS using bound status as an instrument

(1) (2) (3) (4)1mPost−-3mPre 2mPost−-3mPre 3mPost−-3mPre 6mPost−-3mPre∆Corp.Dwg (2SLS) 0.908** 0.961* 1.473** 1.710

(2.33) (1.98) (2.31) (1.46)Country-Event FE Yes Yes Yes Yes

Observations 603 594 572 519r2 0.776 0.727 0.809 0.577

53

The Real Effects of Sovereign Credit Risk

Heitor AlmeidaUniversity of Illinois at Urbana Champaign

halmeida@illinois.edu

Igor CunhaNova School of Business and Economics

igor.cunha@novasbe.pt

Miguel A. FerreiraNova School of Business and Economics

miguel.ferreira@novasbe.pt

Felipe RestrepoBoston Collegerestrepf@bc.edu

January 22, 2014

A Default Analysis

An alternative test to evaluate whether a firm’s “bound status” leads to a systematicallypessimistic rating is to examine whether bound firms are associated with a lower default ratethan non-bound firms, for the same actual rating. The power of any default based test isconstrained by the fact that actual defaults do not occur frequently. Out of the 566 firmsin the full sample of non-AAA countries, only 15 had at any time a default rating (“D”).Thus, to perform this particular test we extend this sample to include firms with a rating ofCCC+ and below, which more precisely proxy for being “close to default”. These firms arecharacterized by S&P as having “significant speculative characteristics, currently vulnerable”.The number of close-to-default firms in the sample is 64. We estimate a logit regression wherethe dependent variable is a dummy variable that indicates whether a firm had been “close todefault” during the last five years (Default). We examine if a firm’s bound status affects itsprobability of being close to default, after controlling for its credit rating by estimating thefollowing model:

Defaulti,t = β1(RatingDum)i,t + β2(RatingDum)i,t × (Bound)i,t

+β3(SovRatingDum)i,t + αt + εi,t (1)

where RatingDum is a set of corporate rating dummies for each rating level, Bound is adummy variable that takes a value of one for bound firms and zero otherwise, and SovRatingDumis a set of sovereign rating dummies. The β2 coefficient captures, for each corporate ratinglevel, whether being bound by the sovereign rating is associated with a lower default.

Table A.7 in the Internet Appendix show the results from estimating the logit model, whichrespectively report the estimated coefficients and the marginal effects. Note that there are noestimated coefficients for firms with ratings above A, as there are no firms with these higherratings that transition into “close-to-default” in the sample. The predicted probabilities inTable A.7 indicate that bound firms tend to have a lower probability of transitioning intodefault, for a given rating, than non-bound firms. For instance, the probability of a non-bounded firm with a B+ rating transitioning into “close-to-default” in a 5-year window is7.1%. This is significantly higher than the 4.3% probability of a firm also with a B+ ratingbut bounded by the sovereign ceiling of transitioning into “close-to-default”. The differencebetween bounded and non-bounded firms is more pronounced for firms in lower rating levels.

B Probability of downgrade

We present supporting evidence that the reaction of corporate ratings to sovereign ratingsdowngrades is asymmetric for firms in the treatment and control groups. We estimate a logitregression of the probability of a credit rating downgrade using a firm-year panel of all firmswith a credit rating in alternative to use a numerical scale for ratings as in Table 4. Themain explanatory variables are a dummy variable that takes the value of one if a firm has arating equal to (or above) the sovereign rating in a year t − 1 (Bound), a dummy variablethat takes the value of one if a firm’s country rating is downgraded in year t (SovDown),and the interaction term Bound × SovDown. The interaction term coefficient measures the

1

difference in the probability of a credit rating downgrade between treated and other firmswhen a sovereign downgrade hits the country where the firm is located.

Table A.8 in the Internet Appendix, column (1), shows that the interaction term Bound× SovDown coefficient is positive and significant, which indicates that the probability ofa credit rating downgrade following a sovereign downgrade is significantly higher for treatedfirms versus other (non-treated) firms. The marginal effect (shown at the bottom of the table)indicates that the probability of a rating downgrade is more than 1.5 times higher for treatedfirms versus non-treated firms when a sovereign downgrade hits the country where the firmis domiciled. As expected, the Bound coefficient is positive and significant as higher qualityfirms are less likely to be downgraded unconditionally. The SovDown coefficient is negativeand significant, which consistent with the idea that corporate ratings are negatively affectedby sovereign downgrades. The next columns of Table A.8 show additional specifications thatcontrol for firm characteristics. Columns (2)-(4) include control variables (size, investment,Tobin’s Q, cash flow, cash holdings, leverage and foreign sales), and combinations of year,industry, country, and firm fixed effects. The firm fixed effects estimate in column (4) is drivenonly by within-firm variation in credit ratings and therefore controls for any source of time-invariant unobserved heterogeneity. Column (5) presents estimates including country-yearfixed effects that controls for any country-specific macro shock. The results are consistentin all specifications. In short, the results of the logit regression confirm the results of thedifference-in-differences matching estimator in Table 4 that the reaction of corporate ratingsto sovereign downgrades is strongly asymmetric between treated and control firms.

C Example

One of the firms in our treatment group comes from the energy business: EDP Energias dePortugal. Portugal sovereign rating was downgraded on March 25, 2011 by S&P from A- toBBB and then on March 28, 2011 to BBB-. As a consequence, EDP was downgraded onMarch 28, 2011 from A- to BBB. The effect of sovereign downgrades on the firm credit ratingwas explained by Miguel Viana, Head of Investor Relations Office, in the 2011 year-end resultsconference call on March 9, 2012:

“In terms of credit ratings, EDP recently suffered with downgrades by S&P andMoody’s, penalized by the maximum notch differential allowed between EDP andPortugal Sovereign, so right now EDP is one notch above Portugal by S&P andtwo notches above Portugal by Moody’s. Nevertheless, we consider that these by-the-book credit agencies methodologies are unable to reflect EDPs distinct creditprofile, namely the geographical diversification, the high quality of our generationfleet, our resilient EBITDA, and the fact that our operations in Portugal have lowsensitivity to the economic cycle”.

The effect of the sovereign rating downgrade on the firm investment and financial policywas explained by the chief executive officer, Antonio Mexia, in the 2011 and 2012 year-endresults conference call:

“We are reducing CAPEX not only because of the evolution of the energy marketbut also to improve financials. The CAPEX fell 19% to less than 2.2 billion

2

euros, especially because of the lower additions in the US market. In the disposalsprogram we reached 440 millions euros in cash proceeds... I would also like tomention the fact that CAPEX were 2 billion euros, 7% lower on year-on-yearbasis, namely due to fewer expansion projects in wind power especially in the USmarket, and by the fact that we went down the road once again in what concernsthe deleveraging through disposals.”

The corporate managers quotes support that the link between the corporate and sovereignratings was due to ceiling policies and unrelated to firm fundamentals, as well as how the ratingdowngrade affected the firm’s investment and financial policy.

3

Table A.1: Variables Distribution Support

This table presents the distribution support of the main variables considered in the paper

5% 25% 50% 75% 95%Panel B: Treated vs Non-treated Groups

Size Treated 6.60 7.71 9.43 10.08 10.08Non-treated 2.02 4.14 5.30 6.57 8.76

Investment Treated 3.91 12.26 16.13 25.40 82.32Non-treated 0.43 4.49 11.46 24.22 83.58

Q Treated 0.85 1.03 1.31 1.59 2.78Non-treated 0.57 0.82 1.00 1.36 3.75

CashF low Treated 7.68 11.00 14.80 21.11 33.10Non-treated -18.79 2.90 7.53 13.43 27.60

Cash Treated 0.94 2.86 8.47 15.20 34.11Non-treated 0.54 4.18 10.00 20.31 48.73

Leverage Treated 0.72 19.03 33.31 46.98 62.41Non-treated 0.00 6.46 23.32 40.50 69.20

Panel B: Treated vs Control Groups

Size Treat 6.60 7.71 9.43 10.08 10.08Control 6.75 7.33 9.20 10.08 10.08

Investment Treat 3.91 12.26 16.13 25.40 82.32Control 4.20 8.01 12.17 23.75 106.09

Q Treat 0.85 1.03 1.31 1.59 2.78Control 0.75 0.90 1.01 1.27 1.64

CashF low Treat 7.68 11.00 14.80 21.11 33.10Control 1.83 6.98 10.53 14.55 23.35

Cash Treat 0.94 2.86 8.47 15.20 34.11Control 1.47 3.14 6.94 11.52 20.88

Leverage Treat 0.72 19.03 33.31 46.98 62.41Control 15.45 24.50 34.19 49.62 62.75

4

Table A.2: Estimation of coefficients from firms in AAA countriesto predict firm ratings in non-AAA countries

This table reports the estimated coefficients from firms in AAA countries that are used to predict ratings on

non-AAA countries. t-statistics are reported in parentheses below coefficients estimates; * indicates signifi-

cance at the 10% level, ** at the 5% level, and *** at the 1% level.Pooled OLS downgrades and upgrades

(1)Corp. Rating

ROA 6.798***(18.62)

Size 1.240***(4.01)

Leverage -7.525***(-20.14)

Square of ROA 1.097***(9.84)

Square of Size 0.010(0.49)

Square of Leverage 2.288***(10.33)

Year FE YesIndustry FE Yes

Observations 22005r2 0.596

5

Table A.3: Default and Bound Status: Logit Regression of “Close-to-Default” by Rating and Bound Status

This table reports the estimated coefficients from a logit model, which measure the effect of being bound 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 being

bound by the sovereign ceiling on a firm’s predicted rating. I weigh observations based on the number of

bonds observed each year for each firm. Standard errors clustered by firm.

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

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 0.000Bound * A- -0.327 0.991 0.741Bound * BBB+ -3.113 0.796 0.000Bound * BBB -1.143 0.517 0.027Bound * BBB- -1.523 0.488 0.002Bound * BB+ 0.104 0.620 0.866Bound * BB -0.368 0.463 0.427Bound * BB- -1.212 0.646 0.061Bound * B+ -0.525 0.439 0.232Bound * B -0.916 0.493 0.063Bound * B- -2.250 0.703 0.001

Sovereign Rating FE Yes

Observations 16 459Number of firms 556Pseudo R2 0.2895

6

Table A3 (Panel B): Default and Bound Status: Marginal Effectsfrom Logit Regression of “Close-to-Default” by Rating and BoundStatus

This table reports the predicted probabilities (marginal effects) from the logit model, whose estimated coeffi-

cients are reported in table A.3. The marginal effects indicate the predicted probability of a firm with a given

rating transitioning into “Close-to-Default” in a 5-year window, depending on whether the firm is bound or

not by the sovereign ceiling.

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

(1) (2) (3) = (1) - (2) p-valueA+ . 0.35% . .A 0.01% 0.74% -0.73% 0.030A- 0.16% 0.11% 0.04% 0.775BBB+ 0.61% 0.03% 0.59% 0.020BBB 0.42% 0.13% 0.28% 0.012BBB- 0.50% 0.11% 0.39% 0.035BB+ 1.02% 1.13% -0.11% 0.867BB 3.69% 2.59% 1.11% 0.376BB- 4.34% 1.33% 3.01% 0.004B+ 7.05% 4.29% 2.75% 0.168B 9.21% 3.90% 5.31% 0.015B- 26.37% 3.64% 22.73% 0.000

7

Table A.4: Probability of Corporate Downgrade FollowingSovereign Downgrade

This table presents the results of the logit regression of the probability of a credit rating downgrade. The

dependent variable is a dummy that takes the value of one if a firm credit rating is downgraded in a given

year. Treated firms are firms with credit rating equal to the sovereign rating in the year before a sovereign

downgrade. Robust standard errors are reported in parentheses. *, **, *** indicated significance at the 1%,

5% and 10% level respectively.

Dependent Variable: Corporate Downgrade(1) (2) (3) (4) (5)

Treated x Sovereign Downgrade 2.156*** 2.219*** 2.644*** 2.407*** 1.906***(0.297) (0.325) (0.291) (0.347) (0.405)

Treated -0.628*** -0.349** -0.446*** 0.192 -0.191(0.130) (0.144) (0.136) (0.210) (0.175)

Sovereign Downgrade 0.807*** 0.683*** 0.445*** 0.417***(0.167) (0.172) (0.115) (0.134)

Controls No Yes Yes Yes YesYear FE No No Yes Yes NoIndustry FE No No Yes No NoCountry FE No No No No NoFirm FE No No No Yes NoCountry x Year FE No No No No Yes

Observations 27,268 24,627 24,609 17,486 23,114

Marginal Effects

∂ Pr(CorporateDowngrade)∂Treated |SovereignDowngrade=1,X=X 1.528*** 1.870***

(0.267) (0.292)

8

Table A.5: Corporate Yields Sample Description

This table reports in panel A the number of observations over time included in the sample. Panel B shows

the 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 rating

and 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 YearYear Number of Observations Number 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

Number of distinct bonds: 1 935Number of distinct issuers: 566Number of distinct countries: 51

Panel B. Number of Firms by IndustryNumber of issuers by ICB industry

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

Total 566

9

Table A.6: Corporate yield spread summary statistics by rating

This table reports sample statistics on yield spreads for the sample of corporate bonds. The sample includes monthly data on all USD-denominated

bonds issued by non-US firms located in countries with at least one sovereign rating change between 1999 and 2012.

Yield spread: Summary Statistics by Corporate Rating and Bound StatusCorporate Rating Mean Std. Deviation Number of observations

Below Bound Bound Difference 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 1 140

BBB+ 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

10

Table A.7: Corporate Yields Country coverage

The first two columns in this table show the number of unique corporate bonds and firms by country used

in the sample. Columns 4, 5 and 6 report the number of total long-term foreign-currency (LT FC) sovereign

rating changes by credit rating agency in each country.

CountryNumber of

Corporate BondsNumber of Firms

Number of LTFC Sovereign

RatingChanges

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

Total 1 935 566 192

11

Table A.8: Falsification test: Effect of the sovereign ceiling on corporate spread changes oneyear before the actual event

This table reports falsification tests for the regressions in table ??. Specifically, the same regressions are estimated one year before each actual sovereign

downgrade or upgrade. The dependent variable is the yield spread change of a corporate bond around the time window specified in each column. Treated

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 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)1mPost−-3mPre 2mPost−-3mPre 3mPost−-3mPre 6mPost−-3mPre

Treated -0.766 -0.243 -0.382 -0.848(-0.87) (-0.37) (-0.65) (-1.05)

Country-Event FE Yes Yes Yes Yes

Observations 390 384 353 307r2 0.630 0.608 0.649 0.630

12

Table A.9: Differences-in-Differences of Investment around aSovereign Downgrade - Bounded Firms

This table presents the results of the investment differences-in-differences and the Abadie and Imbens matching

estimator around the sovereign downgrade restring our treated sample to bounded firms. Treated firms are

firms with credit rating equal or above to the sovereign rating in the year before a sovereign downgrade. There

are 66 treated and 66 control firm-year observations. Robust standard errors are reported in parentheses. *,

**, *** indicates significance at the 1%, 5% and 10% level respectively.

BeforeDowngrade

Year ofDowngrade

Difference

Treated Firms 30.47*** 18.56*** -11.91**(6.451) (1.751) (5.931)

Control Firms 19.82*** 20.94*** 1.12(3.159) (3.905) (3.725)

Difference 10.66 -0.24(4.934) (3.547)

Differences in Differences -13.03**(5.462)

Matching Estimator -21.07***(7.029)

13

Table A.10: Differences-in-Differences of Investment around aSovereign Downgrade using Regions

This table presents the results of the investment differences-in-differences and the Abadie and Imbens matching

estimator around the sovereign downgrade. Treated firms are firms with credit rating equal or above to the

sovereign rating in the year before a sovereign downgrade. There are 81 treated and 81 control firm-year

observations. Robust standard errors are reported in parentheses. *, **, *** indicates significance at the 1%,

5% and 10% level respectively.

BeforeDowngrade

Year ofDowngrade

Difference

Treated Firms 25.72*** 17.26*** -8.46**(4.570) (1.446) (4.181)

Control Firms 21.40*** 19.39*** -2.01(3.374) (2.219) (2.299)

Difference 4.32 -2.13(3.228) (2.167)

Differences in Differences -6.44*(3.849)

Matching Estimator -7.46***(2.796)

14

Table A.11: Differences-in-Differences of Returns on Assets

This table presents the results of the returns on assets (ROA) differences-in-differences and the Abadie and

Imbens matching estimator around the sovereign downgrade. Treated firms are firms with credit rating equal

or above to the sovereign rating in the year before a sovereign downgrade. There are 66 treated and 66 control

firm-year observations. Robust standard errors are reported in parentheses. *, **, *** indicates significance

at the 1%, 5% and 10% level respectively.

BeforeDowngrade

Year ofDowngrade

Difference

Treated Firms 14.28*** 13.71*** -0.57**(1.279) (1.339) (0.762)

Control Firms 10.34*** 9.98*** -0.36(0.825) (0.934) (0.534)

Difference 3.94*** 3.73(1.059) (1.282)

Differences in Differences -0.21(0.950)

Matching Estimator -0.05(0.107)

15

Table A.12: List o Treated Firms

Company Name Country Year

MetroGas SA Argentina 2001Telecom Argentina SA Argentina 2001Transportadora de Gas del Sur SA Argentina 2001YPF SA Argentina 2001Telecom Argentina SA Argentina 2008Transportadora de Gas del Sur SA Argentina 2008YPF SA Argentina 2008Transportadora de Gas del Sur SA Argentina 2012Aracruz Celulose SA Brazil 2002Centrais Eltricas Brasileiras SA Brazil 2002Cia Bebidas das Amricas - AMBEV Brazil 2002Klabin SA Brazil 2002Tele Norte Leste Participaes SA Brazil 2002PT Hanjaya Mandala Sampoerna Tbk Indonesia 1997PT Asia Pacific Fibers Tbk Indonesia 1998PT Barito Pacific Tbk Indonesia 1998PT Citra Marga Nusaphala Persada Tbk Indonesia 1998PT Daya Guna Samudera Indonesia 1998PT Hanjaya Mandala Sampoerna Tbk Indonesia 1998Accenture Plc Ireland 2011Cooper Industries Plc Ireland 2011Covidien Plc Ireland 2011Eaton Corp. Plc Ireland 2011Eni SpA Italy 2004Eni SpA Italy 2006TERNA Rete Elettrica Nazionale SpA Italy 2006Eni SpA Italy 2011TERNA Rete Elettrica Nazionale SpA Italy 2011Eni SpA Italy 2012TERNA Rete Elettrica Nazionale SpA Italy 2012Toyota Motor Corp. Japan 2001DENSO Corp. Japan 2002FUJIFILM Holdings Corp. Japan 2002Ito-Yokado Co. Ltd. Japan 2002Toyota Motor Corp. Japan 2002Canon, Inc. Japan 2011Chubu Electric Power Co., Inc. Japan 2011DENSO Corp. Japan 2011Electric Power Development Co., Ltd. Japan 2011NTT DoCoMo, Inc. Japan 2011Nippon Telegraph & Telephone Corp. Japan 2011Okinawa Electric Power Co., Inc. Japan 2011Osaka Gas Co., Ltd. Japan 2011Shikoku Electric Power Co., Inc. Japan 2011Takeda Pharmaceutical Co., Ltd. Japan 2011Tokyo Electric Power Co., Inc. Japan 2011Tokyo Gas Co., Ltd. Japan 2011Toyota Motor Corp. Japan 2011Coca-Cola FEMSA SAB de CV Mexico 2009Grupo Bimbo SAB de CV Mexico 2009Grupo Televisa SAB de CV Mexico 2009Kimberly-Clark de Mxico SAB de CV Mexico 2009Globe Telecom, Inc. Philippines 2005Universal Robina Corp. Philippines 2005Redes Energeticas Nacionais SA Portugal 2010EDP - Energias de Portugal SA Portugal 2011Redes Energeticas Nacionais SA Portugal 2011Enags SA Spain 2012Red Elctrica Corp. SA Spain 2012PTT Exploration & Production Plc Thailand 1997Advanced Info Service Public Co., Ltd. Thailand 1998PTT Exploration & Production Plc Thailand 1998Automatic Data Processing, Inc. United States 2011Exxon Mobil Corp. United States 2011Johnson & Johnson United States 2011Microsoft Corp. United States 2011

16

Table A.13: Firm Data Country Coverage

Country Number of Firm Years Country Number of Firm Years

Argentina 215 Lebanon 2Belgium 85 Sri Lanka 148Bulgaria 46 Lithuania 48Bahrain 14 Latvia 57Brazil 340 Macedonia 2Canada 275 Malta 8China 122 Mexico 139Colombia 29 Malaysia 465Cyprus 156 Nigeria 63Czech Republic 34 New Zealand 115Ecuador 1 Peru 45Estonia 12 Philippines 232Egypt 229 Pakistan 221Spain 451 Portugal 209Finland 109 Romania 30France 451 Serbia 9Greece 817 Russian Federation 186Hong Kong 220 Sweden 94Croatia 189 Slovenia 64Hungary 124 Slovakia 11Indonesia 473 Thailand 378Ireland 197 Tunisia 62India 255 Turkey 160Iceland 20 Taiwan 1336Italy 865 Ukraine 338Jamaica 16 United States 4081Japan 9712 Venezuela 54Kenya 23 Vietnam 475South Korea 251 South Africa 174Kazakhstan 2

Total 24939

17

Figure A.1: Returns on Assets Parallel Trends around SovereignDowngrade

This figure presents the parallel trends of returns on assets (ROA) of treatment and control groups around the

sovereign downgrade. Treated firms are firms with credit rating equal or above to the sovereign rating in the

year before a sovereign downgrade. Control firms are matched firms using the Abadie and Imbens matching

estimator. ROA is defined as the ratio of annual operating income minus taxes to assets.

.08

.1.1

2.1

4.1

6.1

8R

etur

ns o

n A

sset

s

-2 -1 0 1 2Years from Sovereign Downgrade

Treatment Control

18

Figure A.2: 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 ratings

and 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

19

Figure A.3: Distribution of sovereign rating changes by year.

The figure depicts the distribution of long-term foreign-currency (LT FC) sovereign ratings and rating changes

for the countries and period in the sample for S&P, Moody’s and Fitch.

0

4

8

12

16

20

Fre

quen

cy

1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012

S&P Sovereign Downgrades per Year

20