Ghosh Presentation - European Finance Association Conference, Copenhagen, Denmark

Cross-listed Bonds and Ratings Conservatism
Yigit Atilgan
Sabanci University
Aloke (Al) Ghosh#
Baruch College
Jieying Zhang#
University of Southern California
June 2012
# Corresponding Authors
(Aloke.Ghosh@baruch.cuny.edu, Jieying@marshall.usc.edu )
Acknowledgements: Our paper has benefited from the comments of Jay Dahya, Ozgur Demirtas, Mingyi
Hung, Bill Rees, Kishore Tandon, Joe Weintrop, and participants at the 6th Accounting Symposium of
the Accounting Research Network Netherlands (Leuven, Belgium) and, in particular, the discussant,
Carolina Salva. We are particularly grateful to Paquita Davis-Friday for her comments and suggestions
while the paper was initially being developed.

ABSTRACT
We investigate whether cross-listed bonds are rated more conservatively than U.S. domestic
bonds. We argue that because of the high information asymmetry of cross-listed bonds, investors
are more reliant on ratings, which in turn increases rating agencies’ exposure to reputation losses
when foreign issuers default but ratings indicate otherwise. We expect this heightened reputation
concerns to motivate rating agencies to be more conservative when rating cross-listed bonds than
when rating domestic bonds. Consistent with our expectations, we find that cross-listed bonds
have lower ratings at issuance and subsequently are less likely (and take longer) to be upgraded
than comparable U.S. domestic bonds. We also find that rating conservatism is more pronounced
for investment-grade cross-listed bonds, consistent with the higher reputational concerns for
investment-grade bonds. Finally we examine a competing explanation that rating agencies’
private information indicates higher default risk for cross-listed bonds. We find that the lower
ratings of cross-listed bonds are more likely to raise false alarms, less likely to miss future
defaults, and are also corrected by a lower spread at issuance. Collectively, the evidence suggests
that rating conservatism is a more plausible explanation for our finding than the private
information explanation.
Keywords: cross-listed bonds; credit ratings; rating conservatism; cost of debt
JEL classification: M41; G29; G38
Data Availability: The data used in this study are publicly available from the sources identified
in the text.

1
1 Introduction
While the theory and evidence on conservatism in financial reporting is extensive (Watts
(2003 a&b), the application of the conservatism principle in other areas such as credit ratings has
been limited. It is important to study rating conservatism because debtholders have asymmetric
payoff function and thus conservatism can be a desirable rating property benefiting debtholders.
However, most existing studies on credit ratings focus on the timeliness and accuracy of ratings
(e.g., Cheng and Neamtiu 2009). One exception is Beaver, Shakespeare and Soliman (2006),
which relies on the debt contracting role of certified credit ratings to explain rating conservatism.
In this paper, we propose information asymmetry as another explanation for rating conservatism.
Because information asymmetry is likely to be higher for cross-listed bonds than for U.S. bonds,
we investigate whether cross-listed bonds are rated more conservatively.
The maintained assumption in our study is that rating agencies tend to have an
asymmetric loss function, because their reputation costs are higher when an issuer defaults but
ratings indicate otherwise than when an issuer has a higher credit quality relative to its ratings
(Beaver et al. 2006). Consequently, rating agencies are likely to be conservative, i.e., they would
require a higher verification standard to report more favorable ratings than unfavorable ratings.
We expect that rating agencies are more conservative when information asymmetry is high for
the following reason. When information asymmetry is high, investors are more likely to rely on
ratings to assess the default risk of a bond issue for investment and contracting decisions (Ball et
al. 2008). Greater reliance on ratings for investment and contracting reasons increases rating
agencies’ exposure to reputational costs especially when an issuer defaults but ratings indicate
otherwise. Thus rating agencies are more likely to impose a higher standard when reporting

2
favorable ratings than when reporting unfavorable ratings for bonds with higher information
asymmetry, which suggests that ratings are more conservative when information asymmetry is
high.
Cross-listed bonds provide a unique setting to test our hypothesis that ratings are more
conservative when information asymmetry is high. Prior studies suggest that information
asymmetry is higher for cross-listed firms than for domestic firms because of several reasons.
First, the quality and quantity of public information is lower for cross-listed firms compared to
that of U.S. domestic firms (Leuz et al. 2003; Bradshaw et al. 2004; DeFond et al., 2006). For
instance, Leuz et al. (2003) find more pervasive earnings management in countries with weak
investor protection. DeFond et al. (2006) also show that earnings are less informative in
countries with poor accounting quality and weak insider trading law enforcements. Second,
although cross-listed bonds are subject to SEC’s jurisdiction, their public reports are still
influenced by home regulatory environment and managerial discretion. Consistent with the
premise that SEC’s oversight does not entirely overcome the effect of local market, Lang et al.
(2006) find that cross-listed reconciled earnings are subject to greater earnings management than
U.S. earnings. Third, the private information collection of rating agencies is unlikely to fully
offset the higher information asymmetry of cross-listed bond issuers. For example, even though
analysts also have incentives to engage in private information collection, we find that analysts
forecast dispersion of cross-listed bond issuers is higher than that of domestic issuers. Overall,
since cross-listed bonds are likely to have higher information asymmetry, which amplifies rating
agency’s asymmetric loss function, we hypothesize that cross-listed bonds are rated more
conservatively than U.S. domestic bonds.1
1
It is also possible that the private information collection of rating agencies may mitigate information asymmetry,
which works against finding our hypothesized relation. We discuss this possible alternative in detail in Section 2.

3
Using a sample of public debt issued in the U.S. by foreign firms between 1990 and
2009, we find evidence that on average cross-listed bonds have significantly lower ratings at
issuance compared to U.S. bonds with similar issuer and issue characteristics. Specifically, in a
regression of bond ratings on a cross-listed bond indicator variable and various issue, issuer,
country-level control variables, the coefficient on the cross-listed bond indicator variable is 1.25
and statistically significant at less than 1% level. Thus, cross-listed bond ratings are more than
one notch lower than comparable U.S. domestic bond ratings. Our results are consistent with the
hypothesis that cross-listed bonds are rated more conservatively than domestic bonds.
It is possible that the initial conservative rating for cross-listed bonds is only temporary
and that the bias reverses as more information becomes available once foreign registrants meet
the SEC filing requirements. Therefore, we investigate the likelihood, frequency, and timing of
subsequent rating changes. We find that following the initial rating assignment, cross-listed
bonds are less likely to be upgraded, are likely to receive fewer upgrades, and take longer to be
upgraded. These results suggest that the initial conservative ratings of cross-listed bonds do not
reverse in subsequent periods.
A key argument in our paper is that ratings are more conservative when reputation costs
are higher. While rating agencies have higher reputation cost from underestimating than
overestimating default risk (Beaver et al. 2006), the reputation cost of underestimating the
default risk is substantially higher for investment-grade bonds than for speculative-grade. Thus,
we expect that the investment-grade cross-listed bonds are rated more conservatively compared
to non-investment-grade cross-listed bonds. Consistent with our expectation, we find that rating
conservatism for cross-listed bonds is more pronounced for investment-grade bonds.

4
While our results are consistent with ratings conservatism, they are also consistent with
an alternative explanation that rating agencies possess private information about the default risk
of cross-listed bonds and that lower ratings accurately reflect the additional risk of cross-listed
bonds (Ederington et al. 1987; Reiter and Zeibart 1991). We call this the “private information”
explanation. 2
While our cross-sectional analysis with respect to investment-grade bond is
inconsistent with the “private information” explanation, we conduct two additional tests to
further differentiate between the rating conservatism and private information explanation.
Our first test directly compares the probability of ratings failing to predict a default, and
the probability of ratings raising a false alarm about a future default across cross-listed bonds and
U.S. domestic bonds. If the lower rating is due to rating conservatism, cross-listed bond ratings
are expected to have a higher probability of raising false alarms (Type I error) and a lower
probability of failing to predict future defaults (Type II error). In contrast, if the lower rating of
cross-listed bonds accurately reflects higher default risk, we do not expect the probability of
raising false alarms and failing to predict future defaults to vary between cross-listed and U.S.
domestic bonds. Our results indicate that ratings of cross-listed bonds are more likely to raise
false alarms and are less likely to fail to predict a future default than ratings of domestic bonds,
providing direct support for the conservatism explanation.
Our second test compares the spreads to a benchmark (defined as the yield to maturity
less a U.S. treasury yield with similar maturity) on the issuance day between cross-listed bonds
and U.S domestic bonds.3
If the bond market perceives ratings of cross-listed bonds as being
2
In a 2002 Moody’s global credit research report entitled “Understanding Moody’s Corporate Bond Ratings and
Rating Process”, Moody’s clearly points out that it uses confidential non-public information that issuers provide to
Moody’s only for the purpose of assigning ratings (p.5).
3
The underlying assumption of this test is that bond market is efficient, which is a reasonable assumption based on
prior literature (Hotchkiss and Ronen 2002; Covitz and Harrison 2003).

5
conservative, we expect a correction for the conservative bias, i.e., a lower spread for foreign
bonds compared to domestic bonds with comparable ratings. On the other hand, if the bond
market perceives that rating agencies’ private information captures the additional risk of a
foreign issuer, we do not expect the spread to vary between cross-listed bonds and domestic
bonds with comparable ratings. We find that the spread for cross-listed bonds is on average 37
basis points lower for cross-listed bonds than for U.S. domestic bonds after controlling for
ratings and various issue, issuer, and country characteristics.4
We also find that the price
correction is concentrated in investment-grade bonds, which is consistent with rating
conservatism being more pronounced among investment-grade bonds. Collectively, our second
test also suggests that the lower ratings associated with cross-listed bonds is consistent with
rating conservatism explanation instead of private information explanation.
Our study makes three contributions. First, our study advances the rating literature by
proposing information asymmetry as another explanation for rating conservatism, while Beaver
et al. (2006) provide initial evidence that contracting use of ratings explains rating conservatism.
Specifically, we provide evidence demonstrating that rating agencies tend to be more
conservative when information asymmetry is higher for cross-listed bonds.5
Second, our findings
also add to the small yet growing literature on the application of conservatism principle outside
financial reporting (Lu and Sapra 2009; Hugon and Muslu 2010). It is important to understand
rating conservatism because conservatism can be a desirable rating property benefiting
debtholders. While existing rating studies largely focus on rating attributes such as timeliness
4
This result partially explains why foreign bond issuers in the U.S. may not object to rating agencies imposing a
higher standard for cross-listed bonds because foreign firms do not appear to bear added borrowing costs.
5
We extend Beaver et al. (2006) in two additional aspects. Beaver et al. (2006) focus on the timeliness of rating
downgrades, and we study both initial rating assignments and subsequent rating revisions. Also, by differentiating
between conservatism and private information explanations, we rule out one potential alternative explanation for
rating conservatism that could also be applicable for the findings in Beaver et al. (2006).

6
and accuracy (Cheng and Neamtiu 2009), our paper extends the literature by highlighting a less
frequently examined yet intuitive rating property. Third, our paper adds to the very limited
research on foreign firms issuing debt in the U.S. by documenting the rating properties of cross-
listed bonds and their pricing implications.6
The remainder of the paper is organized as follows. Section 2 motivates the paper and
develops the hypotheses. Section 3 describes the data and research design and presents the
empirical results. Section 4 presents additional analyses and robustness tests. Section 5
concludes the paper.
2 Motivation and hypotheses
2.1 Rating conservatism and information asymmetry
The literature on the accounting conservatism suggests five explanations for conservatism
in financial reporting ― contracting, litigation, tax, regulation, and information asymmetry.
Empirical evidence is largely consistent with these explanations (e.g., Ball et al. 2000;
Holthausen and Watts 2001; Ahmed et al. 2002; LaFond and Watts 2008; Zhang 2008; Nikoleav
2010).7
In addition to financial reporting conservatism, researchers find that auditors and
analysts are also conservative when they issue audit opinions and earnings forecasts, respectively
(e.g., Lu and Sapra 2009; Hugon and Muslu 2010).
6
Additionally, examining cross-listed bonds is important in itself because relatively little attention is paid to
understanding the properties of cross-listed bonds, while the evidence regarding cross-listed equity is extensive
(Karolyi 2006). Understanding the rating properties of foreign bonds is especially important because foreign firms
raise significantly more debt than equity in the U.S. For example, Chaplinsky and Ramchand (2004) report that
while foreign firms are allowed to issue either debt or equity, the total amount of capital raised by debt is nearly
eight times the amount of raised by equity.
7
While there are various definitions of accounting conservatism, the definition proposed by Basu (1997), i.e., “a
higher degree of verification for recognizing good news than bad news in financial statements,” has gained
popularity in the past decade.

7
In stark contrast to the abundant research on conservatism in financial reporting,
relatively few studies examine whether rating agencies are conservative when rating debt
securities. Studying conservatism in credit ratings is important for several reasons. First,
conservatism can be a desirable rating property because the users of ratings, i.e., debtholders,
have an asymmetric payoff function. Even though debt contracting is the most frequently offered
explanation for conservative accounting (Watts 2003 a&b), and rating agencies have an
asymmetric payoff function similar to debtholders, existing rating studies largely focus on rating
attributes such as timeliness and accuracy (Cheng and Neamtiu 2009) while overlooking rating
conservatism. Second, contrary to the popular perception about a decline in the quality of credit
ratings, academic studies tend to find that rating agencies have raised their rating standards over
time (Cheng and Neamtiu 2009).8
Studying rating conservatism provides a better understanding
of the complex incentive structure of rating agencies and how those incentives affect the
properties of credit ratings.
To the best of our knowledge, Beaver et al. (2006) are the first to examine conservatism
in credit ratings. They attribute rating conservatism to the use of ratings in debt contracts.
Specifically, they argue that certified credit rating agencies impose a higher standard when
reporting favorable ratings than when reporting unfavorable ratings, i.e., they are conservative,
because the use of ratings in debt contracts makes certified rating agencies’ loss function
asymmetric. Beaver et al. (2006) provide an important initial step toward understanding rating
conservatism. Because ratings issued by certified rating agencies are primarily used for debt
contracting and those issued by non-certified rating agencies are mainly used for valuation
reasons, Beaver et al. (2006) propose that certified rating agencies issue more conservative
ratings than non-certified rating agencies.
8
Blume et al. (1998) also show that rating standards have become more stringent from 1978 through 1995.

8
In this paper, we explore an alternative source of rating conservatism, that is, high
information asymmetry.9
The maintained assumption in our paper is that rating agencies have an
asymmetric loss function, because their reputation costs are higher when an issuer defaults but
ratings indicate otherwise than when an issuer has a higher credit quality relative to its ratings
(Beaver et al., 2006). We argue that high information asymmetry exacerbates rating agencies’
asymmetric loss function for the following reason. When information asymmetry is high,
presumably because of insufficient public information, investors are more likely to rely on
ratings for investment and contracting decisions. For example, Ball et al. (2008) show that when
the quality of accounting information is lower and information asymmetry is higher, the
performance pricing provision in the debt contract is more likely to be based on ratings than on
accounting information. The greater reliance on ratings for investment and contracting reasons
increases rating agencies’ reputation cost when an issuer defaults and ratings indicate otherwise,
thereby making rating agencies’ loss function more asymmetric. To minimize reputation costs
from failing to predict a default, rating agencies are likely to impose a higher standard when
reporting favorable ratings than when reporting unfavorable ratings for bonds with high
information asymmetry. Therefore, when information asymmetry is severe, we expect bonds to
be rated more conservatively.
2.2 Cross-listed bonds, information asymmetry and conservative ratings
Measuring information asymmetry is not straightforward in the context of the debt
market. Information asymmetry proxies that are traditionally used in equity markets including
the probability of informed trading (PIN) are not applicable for debt markets (Bessembinder et
9
Our analysis in Section 4.4.1 shows that although debt contracts are more likely to contain rating-triggered
covenants for cross-listed bonds, the use of rating-triggered covenants is not prevalent for both cross-listed bonds
and U.S. domestic bonds, suggesting that the use of ratings in contracting is not the exclusive reason for rating
conservatism.

9
al. 2007; Edwards et al. 2007). One innovation in our study is that we test our hypothesis by
identifying a group of bonds that may have higher information asymmetry than another group,
i.e., cross-listed bonds vs. U.S. domestic bonds.10
Cross-listed bonds differ from domestic bonds on important dimensions that increase
information asymmetry. For instance, market participants have limited public information for
foreign firms than for U.S domestic firms. Even though foreign firms issuing public debt are
required to register with the Securities and Exchange Commission (SEC) and file a form 20-F
which includes reconciliations to U.S. GAAP, the reconciliations provide limited information for
U.S. investors relative to the amount of information available for U.S. firms (Bradshaw et al.
2004). While the 20-Fs reconcile the reasons for the differences in financial statements produced
under U.S. and home GAAP, there are significant differences in the information contained in the
notes to the financial statements.11
Further, reconciliations are less timely because they are
required on an annual basis rather than on a quarterly basis. Besides limited financial reporting,
information available on debt contracts is also limited in many foreign countries. For example, in
Germany and Japan, the details of the debt contracts such as debt covenants are not subject to
mandatory disclosure.
In addition to limited quantity of information, the quality of information for foreign
issuers might also be lower. Leuz et al. (2003) find that earnings management is more pervasive
in countries where the legal protection of outside investors is weak. DeFond et al. (2006) show
that earnings are less informative in countries with poor accounting quality and weak insider
10
We compare the ratings of cross-listed bonds to those of U.S. domestic bonds to find out the impact of
information asymmetry on rating conservatism. While it might be interesting to compare the ratings of foreign
bonds cross-listed in the U.S. with those listed in their domestic markets, such comparisons do not provide insights
into ratings conservatism.
11
The extent of the reconciliations and required disclosures varies according to whether firms complete Item 17 or
18, in Form 20-F. Item 17 is reserved for limited offerings and requires fewer disclosures than Item 18, which
essentially requires the non-U.S. issuer to provide the same disclosures as a U.S. issuer.

10
trading law enforcement. Although cross-listing may improve a foreign firm’s information
environment, Fernandes and Ferreira (2008) show that cross-listing in the U.S. improves the
information environment for firms that originate from developed markets but not those from
emerging markets. Moreover, although cross-listed bonds are subject to SEC’s jurisdiction, their
public reports are still influenced by the regulatory environment and managerial incentives in
their home countries. Consistent with the argument that SEC’s oversight does not entirely
overcome the effect of local market, Lang et al. (2006) find that reconciled earnings of cross-
listed firms are subject to more earnings management (smoothing, target management, lower
association with share price, less timely recognition of losses) than earnings of U.S. firms.
We consider the possibility that rating agencies collect private information to mitigate the
information asymmetry. Rating agencies could communicate with the management of the cross-
listed issuers and analyze their risk in detail. However, the private information collection is
unlikely to fully offset the information asymmetry of cross-listed bonds. For example, financial
analysts also have the incentive and ability to collect private information, yet we find that the
analysts forecast dispersion of cross-listed bond issuers is still higher than that of domestic
issuers. Later we analyze in detail whether private information collection is an alternative
explanation of our findings.
Because information asymmetry is higher for cross-listed bonds, investors are more likely
to rely on ratings of cross-listed bonds for debt contracting and investment decisions. Greater
reliance on ratings increases rating agencies’ exposure to losses when foreign issuers default but
ratings indicate otherwise, thereby making rating agencies’ loss function even more asymmetric.
Therefore, we hypothesize that the ratings of cross-listed bonds are more conservative than the
ratings of U.S. domestic bonds. In addition, we also expect rating conservatism of cross-listed

11
bonds to vary with rating agencies’ reputational cost. While rating agencies have higher
reputation cost from underestimating than overestimating default risk (Beaver et al. 2006), the
reputation cost of underestimating the default risk is larger for investment-grade bonds than for
speculative-grade. Thus, we also expect that the rating conservatism of cross-listed bonds is
more pronounced for investment-grade bonds.
Although the link between information asymmetry and rating conservatism appears to be
similar to that between information asymmetry and financial reporting conservatism (LaFond
and Watts 2008), there are key differences. First, in our setting, the economic entity providing
the conservative rating is an information intermediary with unique incentives. Although paid by
the issuers, rating agencies are subject to reputation costs if they compromise independence to
advance their own interests. Second, the information asymmetry studied in LaFond and Watts
(2008) is between shareholders and managers, while in our setting the information asymmetry
that prompts the conservative rating is between the borrower (shareholder and managers) and the
lender (debtholder). Lastly, conservatism in financial reporting could be costly for other
stakeholders such as equityholders who care about timely reporting of both positive and negative
news. In contrast, ratings primarily serve debtholders who mainly desire timely reporting of
negative news; however, rating conservatism could be costly to rating agencies in terms of lower
rating accuracy and timeliness; it also could be costly to firms in the form of higher cost of debt,
if the market fixates on ratings.
2.3. Differentiating between rating conservatism and private information explanations
A conservative rating bias for cross-listed bonds relative to U.S. domestic bonds is also
consistent with that rating agencies have access to private information that indicates higher
default risk for cross-listed bonds, and that the lower ratings for cross-listed bonds accurately

12
reflect rating agencies’ private information. When rating agencies have limited public
information, they are likely to acquire more private information to offset their information
disadvantage. Since private information collection quantifies unobservable firm- and country-
level default risk that cannot be captured in a research setting, a conservative rating bias could
potentially reflect the appropriate default risk of the cross-listed issuer.12
We note that higher
rating conservatism in investment-grade bonds would be inconsistent with the “private
information” explanation, because there are no reasons to presuppose that the private information
acquired by rating agencies indicates greater credit risk for investment-grade than for
speculative-grade bonds. Nevertheless, we conduct two additional tests to further differentiate
between the rating conservatism and private information explanation.
2.3.1 The probability of missing defaults and raising false alarms
A difference in ratings between cross-listed and U.S domestic bonds is only indirect
evidence of rating conservatism. We provide direct evidence on rating conservatism by
analyzing whether ratings agencies are accurate in predicting future default. Accordingly, we
directly test (1) the probability of ratings failing to predict defaults accurately, and (2) the
probability of ratings raising a false alarm about the probability of a default. If the lower cross-
listed bond rating reflects rating conservatism, we expect ratings of the cross-listed bonds to be
associated with more frequent false alarms and a lower probability of missing future defaults. In
contrast, if lower ratings capture higher intrinsic default risk of cross-listed bonds, we do not
expect the probabilities of false alarms and missing defaults to vary systematically between
cross-listed and U.S. domestic bonds, since the initial ratings are unbiased.
2.3.2 The bond market correction
12
Since the private information of the rating agencies cannot be captured by observable issuer-, issue-, and country-
characteristics, controlling for these characteristics would not eliminate this alternative explanation.

13
We also use the bond market to differentiate between rating conservatism and private
information explanation. Prior literature has established the bond market efficiency. For
example, Hotchkiss and Ronen (2002) conclude that the informational efficiency of corporate
bonds is similar to that of the underlying stocks. In addition, Covitz and Harrison (2003) find
that bond market preempts 75% of the information contained in downgrades. An efficient bond
market is expected to be unbiased and correct for a bias if there is any. Therefore, if bond
markets perceive ratings to be conservative for cross-listed bonds, the comparative cost of debt is
expected to be lower because the inherent quality of cross-listed bonds is superior to comparable
U.S. bonds. In contrast, if the bond market perceives ratings as containing private information
about the default risk of foreign issuers, the additional default risk is priced by the bond market.
In this case, the cost of debt would be the same for foreign and domestic bonds for the same
ratings category. Therefore, we expect a price correction under the rating conservatism
explanation, but not under the private information explanation.
In summary, under the conservatism explanation, we expect (1) a higher probability of
raising false alarms and a lower probability of failing to predict defaults for cross-listed bond
ratings than for domestic bond ratings, and (2) a bond market correction for the downward rating
bias on cross-listed bonds. In contrast, under the private information explanation, we expect (1)
no difference in the probability of raising false alarms and the probability of missing defaults
between cross-listed and domestic bonds, and (2) no bond market price correction for the
downward rating bias on cross-listed bonds.13
3 Data, Research Design, and Empirical Results
13
We do not claim that the rating conservatism and the private information explanations are mutually exclusive. It is
possible that a rating bias is an outcome of both explanations. Our tests are designed to tease out the dominating
explanation.

14
3.1 Data description
We obtain our sample bonds from the Global New Issues Database of the Securities Data
Corporation (SDC) and the Mergent Fixed Investment Securities Database (FISD). We include
non-governmental firms (both U.S. and non-U.S.) issuing fixed-rate public debt between 1990
and 2009. We exclude Over-the-Counter (OTC) issues and Rule 144A private placements.14
We
delete observations when the spread to benchmark is missing or when offer yield to maturity is
negative or coded by the words “Floats,” “Index,” “Market,” “Reset,” “Varies,” or “NA.” We
manually match non-U.S. issuers to Datastream to obtain issuer characteristics. For U.S. issuers,
we require necessary issuer-level data from Compustat Global. The final sample consists of
2,389 public debt issues by non-U.S. firms (treatment sample) and 11,345 public debt issues by
U.S. firms (control sample).
Table 1 presents the frequency distribution of cross-listed bonds by year. The number of
public debt issues by non-U.S. firms increased from 5 in 1990 to 597 in 2001. However, the last
eight years of the sample (2002 to 2009) exhibit a significant decline in the number of issues.
The number of cross-listed public debt issues in 2008 was down to 55. This is consistent with
the conjecture that the regulations imposed by Sarbanes-Oxley Act in 2002 have discouraged
foreign firms from raising capital in the U.S. Similarly, the number of foreign countries raising
debt in the U.S. increased from 2 to 21 during the period 1990 to 2001, but declined during the
subsequent years of the sample. During the period 1990 to 2009, non-U.S. firms in our sample
raised a total of $1.04 trillion in public debt in the U.S and the average size of the debt issued
was $437 million. While the number of issues has decreased since 2002, the average size of the
issue has increased steadily over the sample period from 1990 to 2009.
14
Rule 144A of the Securities Act of 1933, as amended, allows for the private resale of unregistered securities to
“qualified institutional buyers,” which are generally large institutional investors with assets exceeding $100 million.

15
Table 2 presents the frequency distribution, total public debt offering, and the average
issue size of debt raised by the country of origin. Firms from 34 different countries raised public
debt in the U.S during the sample period. Canadian firms raised debt most frequently in the U.S.
followed by firms from United Kingdom, Germany and Israel. The total amount of debt issued
in the U.S. was the largest for Dutch firms, followed by issuers from United Kingdom, Canada,
and Germany.
Table 3 reports the mean and median comparisons of various variables between cross-
listed bonds and U.S. domestic bonds. Since the inferences from the means and medians are
similar, we focus our discussion on the results from the mean comparisons. We find that the
issue size of the debt offerings is not significantly different between non-U.S. and U.S. firms.
Cross-listed bonds tend to have shorter maturities and they include special features such as
callability, puttability and sinking funds less often. Cross-listed bonds have lower costs of debt
compared to domestic bonds. Finally, non-U.S. firms raising debt in the U.S. are larger, less
levered and more profitable.
3.2. Initial rating of cross-listed bonds and rating conservatism
To test whether rating agencies are more conservative when rating cross-listed bonds, we
estimate the following ordered probit regression, with ratings at issuance as the dependent
variable.15
Rating = α + β1Non-US + β2Issue size + β3Maturity + β4 Seniority + β5Callability + β6Puttability
+ β7Sinking fund + β8Pay-in-kind + β9Default spread + β10Firm size + β11Leverage
+β12Profitability + β13Interest coverage + β14Capital expenditures + β15Emerging
+ β16Civil law + β17Rule of law + β18Creditor rights + β19Judicial efficiency + β20Ex-
ante self-dealing + β21Ex-post self-dealing + β22Anti-director rights + β23Public
enforcement + β24Disclosure requirements + β25Liability standards + β26Investor
protection + Year dummies + Industry dummies + ε (1)
15
We code ratings as 1 for the best rating category. Therefore, a lower value corresponds to a better rating. The
complete rating scheme that we use can be found in Appendix B of Cheng and Neamtiu (2009).

16
where:
Rating = one for firms with the best credit rating (AAA) and the value increase by
one for successively worse rating categories.16
Non-US = one when debt is issued by a non-U.S. firm and zero otherwise.
Issue size = the natural logarithm of the size of the debt issue in millions of dollars.
Maturity = the natural logarithm of the number of years to maturity.
Seniority = one when the debt is senior and zero otherwise.
Callability = one when the bond includes a call provision and zero otherwise.
Puttability = one if the bond includes a put provision and zero otherwise.
Sinking fund = one when the bond includes a sinking fund feature and zero otherwise.
Pay-in-kind = one if the bond pays in kind other than cash and zero otherwise.
Default spread = the yield difference between AAA- and BAA-rated corporate bonds.
Firm size = total assets of the issuer at the end of the fiscal year prior to bond issuance.
Leverage = total debt divided by total assets at the end of the fiscal year prior to bond
issuance.
Profitability = EBITDA divided by total assets in the fiscal year prior to bond issuance.
Interest coverage = EBIT divided by interest expense in the fiscal year prior to bond issuance.
Capital
expenditure
= Capital expenditure incurred by the issuer in the fiscal year prior to bond
issuance.
Emerging = one if the issuing country is defined as being part of an emerging market as
defined by Morgan Stanley Capital International.
Civil law = one if the legal origin of the issuing country is the civil law.
Rule of law = an index that assesses the extent to which investors have confidence in and
abide by the rules of the society, as defined in La Porta et al. (2006).
Creditor rights = an index between 0 and 4 that aggregates different creditor rights in case of
bankruptcy and reorganization, as defined in La Porta et al. (1998).
Judicial
efficiency
= an index between 0 and 10 that assesses the efficiency and integrity of the
legal environment as it affects business and reflects the investors’ assessment
of conditions in the country in question, as defined in La Porta et al. (1998).
Ex-ante self-
dealing &
Ex-post self-
dealing
= indices that range between 0 and 1 and measure the approval requirements
for managerial actions and the ease of proving wrongdoing against
managers, respectively, as defined in Djankov et al. (2008).
Anti-directors
rights
= an index between 0 and 6 that aggregates different investor rights against
directors, as defined in Spamann (2010).
Public
enforcement
= an index between 0 and 1 that aggregates various criminal sanctions against
various parties, as defined in La Porta et al. (2006).
Disclosure
requirements
= an index between 0 and 1 that assesses the strength of specific disclosure
requirements, as defined in La Porta et al. (2006).
Liability
standards
= an index between 0 and 1 that assesses the procedural difficulty in bringing
lawsuits against managers, distributors and accountants, as defined in La
Porta et al. (2006).
Investor
protection
= a comprehensive index between 0 and 1 that aggregates various legal
dimensions such as liability standards, investor rights and risk of
16
We define the initial rating as the first rating assigned to an issue during the first month after the offering date by
Standard and Poor’s, Moody’s or Fitch. If a bond is rated by multiple agencies, we assign the highest of the ratings
to the issue. Results do not change when we use the lowest rating or we use the average of all assigned ratings in the
regressions.

17
expropriation, as defined in La Porta et al. (2006).
Our variable of interest is Non-US. A positive coefficient indicates that ratings of cross-
listed bonds are more conservative than those of U.S. domestic bonds, after controlling for issue
and issuer characteristics and country-specific variables.
We include additional explanatory variables that are determinants of corporate bond
ratings (e.g., Kaplan and Urwitz 1979; Reiter and Ziebart 1992). These control variables fall into
three categories: issue characteristics, issuer characteristics, and country-specific variables. Prior
studies typically find that issue characteristics are key determinants of bond ratings. For
example, Bhojray and Sengupta (2003) posit that larger issues with shorter maturity have
superior ratings because larger offerings have more public information and shorter-term bonds
have less exposure to interest rate risks. They also conclude that callable bonds have worse
ratings because of a prepayment risk and that senior bonds with sinking fund provisions have
superior ratings because of a lower default risk. We also include indicator variables for puttable
and pay-in-kind bonds. Puttable bonds offer the option of forcing the company to repurchase the
bonds before maturity and pay-in-kind bonds allow the issuer the option of paying the
bondholder in additional securities rather than cash. We also control for economic conditions at
the time of the issue by including the default spread as an additional explanatory variable.
We also control for issuer firm characteristics because they are the underlying
determinants of the credit risk of a bond issue. We expect larger, less levered and more profitable
firms to have superior ratings. We also include the interest coverage ratio which is a key
determinant of the liquidity of a firm. Following Miller and Reisel (2011), we also control for
capital expenditures to proxy for investment opportunities.
Finally, we also control for country characteristics because country-specific default risk
affects ratings of foreign issuers (Covrig et al. 2007; Francis et al. 2007). Rajan and Zingales

18
(1995) find that there are large variations in the approaches taken by bankruptcy courts from
different countries and that institutional differences may play a role in determining ratings. Ferri
et al. (2001) find a strong linkage between sovereign ratings and firms’ private ratings for
developing countries. Purda (2003) finds that, in addition to the influence of firm-specific
variables on debt ratings, various country-specific factors predict debt ratings. Perraudin and
Taylor (2004) also find that firms domiciled in Japan, Europe and the U.S. pay different yields
for particular ratings categories. To control for the impact of country-specific factors on bond
ratings, we include indicator variables for the country of origin (emerging economy) and its legal
tradition (civil law). We also control for differences in legal environment as in La Porta et al.
(1998, 2006), Djankov et al. (2008), and Spamann (2010).
In Table 4, we examine whether, controlling for other factors, rating agencies assign
differential initial ratings for cross-listed bonds. The p-values reported in the table are associated
with robust t-statistics corrected for clustered errors. 17
We find a significantly positive
coefficient on Non-U.S. Specifically, the coefficient on Non-U.S. is 1.25 and is significant at less
than 1% level. Since higher values of the dependent variable correspond to worse ratings, the
result indicates that ratings for non-U.S. firms issuing public debt in the U.S. are more than one
notch worse than those for similar U.S. firms. Thus, our results suggest that rating agencies are
more conservative when assigning initial ratings to cross-listed bonds than to domestic bonds.
Consistent with prior literature, we find that ratings are superior for issues with shorter
maturity, higher seniority and puttability feature. We also find superior ratings are assigned to
17
All the regression analysis in this paper report t-statistics corrected for standard errors clustered by firm. We use
firm cluster because the large number of U.S. bonds lead to questionable large t-statistics using country cluster.
Wooldridge (2003) points out that the clustered standard errors approach is not appropriate when the number of
clusters is small relative to the number of observations in each cluster. Thus we use the more conservative approach
by clustering at firm level. However, we also conduct sensitivity tests using standard errors clustered by country and
find similar results.

19
larger, less levered and more profitable firms, and firms with higher interest coverage. As
expected, we find that ratings are better for foreign bonds from countries with stronger public
enforcement, higher liability standards, and stronger investor protection. Other country-specific
variables have no significant relationship with ratings.
3.3 Subsequent rating changes of cross-listed bonds
To examine whether the conservative rating is only confined to the initial rating
assignment, we also investigate various aspects associated with upgrade and downgrade
decisions subsequent to the bond issuance. In particular, we replace the dependent variable in
Model (1) with: a) an indicator variable for subsequent upgrades or downgrades; b) the number
of upgrades or downgrades divided by the total number of assigned ratings; c) the number of
days between the initial rating and the first upgrade or downgrade. If the initial conservative
ratings are persistent, then we expect the cross-listed bonds: a) to be less likely to receive an
upgrade and more likely to receive a downgrade, b) to receive less frequent upgrades and more
frequent downgrades, and/or c) to take longer to receive an upgrades and shorter to receive a
downgrade.
Table 5 reports the probit regression results when the dependent variable is Upgrade
(Downgrade), a dummy variable for subsequent rating changes. We find that the coefficient on
Non-US is significantly negative when the dependent variable is Upgrade. This result indicates
that foreign bonds are less likely to receive a rating upgrade within three years of the offering
date.
Table 6 reports the OLS regression results when the dependent variable is the relative
frequency of subsequent upgrades or downgrades. We find a significantly negative coefficient on
Non-US (-0.28, p = 0.00) when the dependent variable represents the frequency of subsequent

20
upgrades, indicating that cross-listed bonds receive 28% fewer upgrades. We also find a
significantly positive coefficient on Non-US (0.17, p = 0.04) when the dependent variable
represents the frequency of subsequent downgrades, suggesting that cross-listed bonds receive
17% more frequent downgrades.
Finally, Table 7 reports the OLS regression results when the dependent variable is the
time interval before the first upgrade or downgrade. We find a significantly positive coefficient
on Non-US when the dependent variable is the time interval before the first upgrade, suggesting
that it takes longer for a cross-listed bond to receive an upgrade, conditional on the existence of
an upgrade. On average, it takes more than half a year longer for a cross-listed bond to receive an
upgrade than what it takes a domestic bond to receive an upgrade. We find no significant
difference in the time interval before the first downgrade across cross-listed bonds and domestic
bonds.
Collectively, these results suggest that initial ratings of cross-listed bonds are more
conservative and that the conservative rating bias persists subsequent to the issuance.
3.4 Cross-sectional variation in the conservative ratings of cross-listed bonds
After establishing the initial lower ratings and subsequent persistence of these lower
ratings for cross-listed bonds, we explore the cross-sectional variation of the lower ratings among
cross-listed bonds. Specifically, we estimate Model (1) separately for investment- and
speculative-grade cross-listed bonds. Table 8 repeats the analysis in Table 4 for investment- and
speculative-grade bond subsamples. We observe that while the coefficients on Non-US are
positive and significant for both investment-grade and non-investment-grade debt, it is higher in
magnitude for investment-grade debt (1.34 vs. 0.55). Therefore, although on average lower
ratings for foreign bonds exist in the full sample, the conservative bias is more pronounced for

21
investment-grade bonds. Because rating agencies have higher reputation costs from failing to
predict the default of an investment-grade bond, they are more likely to be cautious when
attaching an investment-grade rating to a foreign issuer. Thus, the results from Table 8 are
consistent with reputational costs to ratings agencies being substantially higher for investment-
grade bonds.
While one may consider the conservative rating bias analogous to the “home bias”
documented in the accounting and finance literature, we highlight three important differences.18
First, home bias in the equity market refers to disproportionately higher holdings of the domestic
shares, but rating bias does not speak to the domestic holdings of foreign bonds. Second, credit
quality attestation by rating agencies is a distinct feature of the bond market and rating agencies
have their own incentives. While home bias and rating bias may share some common causes
such as limited access to information about foreign firms, rating bias has a unique determinant,
i.e. rating conservatism arising from rating agencies’ reputation cost. Third, while information
asymmetry has been the main explanation for home bias, rating agencies’ private information
collection may mitigate the information asymmetry.
3.5 Differentiating between rating conservatism and private information explanation
If the cross-listed bonds have different ratings compared to domestic bonds, it is
consistent with both the rating conservatism explanation and the private information explanation.
Under the private information explanation, the rating differences between comparable cross-
listed and U.S. bonds could indicate rating agencies’ assessment of the intrinsic default risk of
cross-listed bonds. We distinguish between the two competing explanations using the following
two tests.
18
A “home bias” refers to investors holding less than the optimal amount of foreign equities and requiring a higher
rate of return for foreign firms compared to domestic firms (French and Poterba (1991); Cooper and Kaplanis
(1994); Armstrong and Riddick (2000); and Bradshaw et al. (2004)).

22
3.5.1 The probability of missing defaults and raising false alarms
As in Cheng and Neamtiu (2009), we define an indicator variable for missed defaults and
an indicator variable for false alarms. For a sample of issuers with a default, Missed default
equals one if an issue defaults within one year from the rating date but the rating indicates a low
default risk (investment-grade), and zero otherwise. False alarm equals one if an issuer does not
default within one year from the rating date but the rating indicates a high default risk (non-
investment-grade), and zero otherwise.
In the regression using Missed default as a dependent variable, a negative coefficient on
Non-US indicates that rating agencies are less likely to miss predicting the default of cross-listed
bonds that are investment-grade than similar U.S. domestic bonds. In the regression using False
alarm as a dependent variable, a positive coefficient on Non-US indicates that rating agencies are
more likely to provide false warnings about the default risk of cross-listed bonds that are non-
investment-grade than similar U.S. domestic bonds. Taken together, a negative coefficient on
Non-US for Missed default regression and a positive coefficient on Non-US for the False alarm
regression are consistent with rating conservatism, i.e., rating agencies trade off more false
alarms for less missed defaults to reduce the costs associated with missing defaults for cross-
listed bonds. Under the private information explanation, there are no reasons for the coefficients
of Missed default and False alarm to vary between cross-listed and U.S. domestic bonds.
Table 9 presents the results from directly comparing the probabilities of missing defaults
and raising false alarms between cross-listed bonds and U.S. domestic bonds. In particular, we
regress the dummy variables for missing defaults and false alarms on the Non-US dummy along
with other control variables. The significantly negative coefficient on Non-US in the first column
of Table 9 suggests that rating agencies are less likely to miss predicting the default of an

23
investment-grade cross-listed bond compared to a similarly rated U.S. domestic bond.
Concurrently, the significantly positive coefficient on Non-US in the second column of Table 9
implies a higher probability of false alarms and indicates that non-investment-grade cross-listed
bonds are more likely to give false alarms than non-investment-grade U.S. domestic bonds.
These results indicate that rating agencies are willing to incur a higher cost by raising false
alarms to reduce the cost of missing a default, thereby providing direct evidence that rating
agencies are conservative in rating cross-listed bonds to reduce the cost associated with missing
defaults.
3.5.2 The bond market correction
We use the cost of debt on the issuance date as a second test to differentiate between the
conservative rating bias and private information explanations. We use Spread to benchmark to
proxy for the cost of debt.19
The spread to benchmark is defined as the yield to maturity on the
offer date (Offer yield) minus the yield of a U.S. Treasury security issued on the same date with
comparable maturity. We test for differences in the cost of debt between non-U.S. and U.S. firms
using the following regression:
Cost of debt = α + β1Non-US + β2Rating + β3Issue size + β4Maturity + β5 Seniority + β6Callability +
β7Puttability + β8Sinking fund + β9Pay-in-kind + β10Default spread + β11Firm size +
β12Leverage + β13Profitability + β14Interest coverage + β15Capital expenditures +
β16Emerging + β17Civil law + β18Rule of law + β19Creditor rights + β20Judicial
efficiency + β21Ex-ante self-dealing + β22Ex-post self-dealing + β23Anti-director rights
+ β24Public enforcement + β25Disclosure requirements + β26Liability standards +
β27Investor protection + Year dummies + Industry dummies + ε (2)
Our interest is on β1, the coefficient of Non-US, which captures the bond market’s
“correction” for a possible rating bias. A negative β1 indicates that, compared to U.S. firms with
similar ratings and issue characteristics, non-U.S. firms have a lower cost, consistent with the
19
Spread to benchmark is winsorized at the 1st
and 99th
percentiles to control for outliers. Our results are
qualitatively similar when we do not winsorize the variables.

24
bond market perceiving the rating bias as an outcome of rating conservatism. In contrast, an
insignificant β1 indicates that, compared to U.S. firms with similar ratings and issue
characteristics, non-U.S. firms have similar cost of debt which is consistent with the private
information hypothesis.
Because the control variables in the cost of debt regressions have been commonly used in
prior literature (e.g., Kidwell et al. (1984); Miller and Puthenpurackal (2002)), we provide a
limited discussion of these variables. We expect the cost of debt to be lower for bonds with
superior credit ratings (Ratings). Larger issues (Issue size) have lower cost of debt because these
issues tend to generate more public information. Bonds with longer maturity (Maturity) are
expected to have higher cost of debt because of greater interest rate risk. Bonds with Callability
(Puttability) feature give the issuer (bondholder) the option to force the bondholder (issuer) into
prepayment (repurchase) before the maturity date, which results in higher (lower) cost of debt.
Senior bonds (Seniority) are less risky than subordinated bonds and the presence of Sinking fund
provisions can reduce the default risk of an issue by requiring orderly payment of the principal
over the bond’s life which lowers the cost of debt. Bondholders might be more averse towards
Pay-in-kind bonds, increasing the cost of debt. We include the Default spread to control for the
economic conditions at the time of the issue. In addition, we expect larger, less levered and more
profitable firms, and firms with high interest coverage to have lower cost of debt. We again
control for capital expenditures to proxy for investment opportunities. Finally, as in the rating
regressions, we control for a large set of country-specific factors.
Table 10 presents the results from comparing the initial costs of debt for cross-listed and
U.S. bonds in the same rating group. Specifically, we run OLS regressions of Spread to

25
benchmark on Non-US after controlling for ratings and issue, issuer and country characteristics.20
We find that for the full sample, the coefficient on Non-US is significantly negative, consistent
with the expectation that the bond market corrects for the rating bias by assigning a lower spread
for the cross-listed bonds in the same rating group as the domestic bonds. After partitioning the
sample into investment and speculative-grade bonds, we find that on average, spreads of
investment-grade, cross-listed bonds are 42 basis points lower than the spreads of U.S. bonds
with similar ratings and issue characteristics, while we find no such results for speculative-grade
bonds. The correction for rating bias only in investment-grade debt is consistent with rating bias
being more pronounced among investment-grade bonds (Table 8). Overall, the results from the
cost of debt analyses are again consistent with the rating conservatism and inconsistent with the
private information explanation.
The results on most of the control variables from Table 10 are consistent with prior
studies. The coefficients on Rating and Maturity are significantly positive. Consistent with our
expectations, Callability (Puttability) has a significantly positive (negative) coefficient. Bonds
that pay-in-kind have higher costs of debt. Larger, less levered and more profitable firms and
firms with higher interest coverage have lower costs of debt. The adjusted-R2
is 65% for the full-
sample regression.
4 Additional analyses and robustness tests
4.1 Rating-based covenant
We argue that investors of cross-listed bonds are more dependent on ratings for
investment and contracting reasons. To substantiate this claim, we study the rating-based
20
We repeat the regressions with Offer yield as the dependent variable. Results are very similar and available from
the authors upon request.

26
covenants contained in the bond contracts. We find that 4.00% of the non-U.S. bonds in our
sample have the rating trigger covenant whereas only 0.98% of the domestic bonds contain
similar covenants. In an untabulated probit regression of the rating trigger covenant dummy on
the cross-listed dummy and control variables, the cross-listed dummy has a coefficient of 0.74,
which is highly significant, indicating that the cross-listed bonds are more likely to include rating
trigger covenants than U.S. domestic bonds. This analysis lends support to our argument that
ratings of cross-listed bonds are more likely to be used in debt contracting.
4.2 Analyst forecast dispersion
In order to present additional evidence on the higher information asymmetry for cross-
listed firms, we investigate the analyst forecasts dispersion because it has been used as a measure
of information asymmetry (Diether et al. 2002). We merge our full sample with the I/B/E/S
forecasts both for the nearest quarterly EPS forecast and the nearest annual EPS forecast prior to
the bond issuance. Since the standard deviation of the forecasts is scale dependent, we
standardize the measure by dividing the standard deviation by the absolute value of the mean
forecast. Then we test the equality of means for the dispersion measure between foreign and U.S.
firms on 7,330 U.S. and 753 non-U.S. observations for the nearest annual EPS forecast. The
means of the dispersion measure are 0.0708 and 0.1532 for U.S. vs. cross-listed firms,
respectively, and the difference is significantly different from zero. Similarly, for the nearest
quarterly EPS forecast, the means of the dispersion measure are 0.1121 and 0.1452 based on a
slightly smaller sample (7,030 U.S. and 455 non-U.S. observations) and the difference is again
significantly different from zero. These results provide corroborating evidence to our argument
that cross-listed firms have higher information asymmetry than U.S. firms even after private
information collection of analysts.

27
4.3 Upgrade and downgrade analysis
In the reported results in Tables 5 to 7, we focus on the three years after issuance to
identify upgrades and downgrades. However, our results remain the same if we change the
horizons to one or five years to identify rating changes.
4.4 Missing default and false alarm analysis
We use investment- and non-investment-grade dichotomy to define the dummy variables
for Missed default and False alarm analyses in Table 9. However, our results are robust to other
cutoff points such as CCC+ and CC.
4.5 Robust standard error clustered by country
In our main analysis we use standard errors clustered by firm because large number of
observations from U.S. renders questionable statistics using country cluster. Nonetheless, we
also conduct our analyses using country-cluster and all our results remain unchanged and even
strengthened in some cases. Thus, we conclude that firm-cluster is a more conservative approach.
4.5 The bond market analysis
We conduct a variety of sensitivity analyses to assess the robustness of our cost of debt
results. First, even though we report t-statistics from pooled regressions that are corrected for
clustered errors, the test statistics might be inflated if the residuals are cross-sectionally
correlated. Therefore, similar to the Fama and MacBeth (1973) procedure, we estimate our
regressions by year and the tests of significance are based on the distribution of the annual
coefficient estimates. We find that our results are qualitatively similar using the Fama-MacBeth
approach. For investment-grade debt, the time-series average of the coefficient of Non-US in the
spread to benchmark regression is -36.48 and highly statistically significant. The result remains

28
unchanged when we use Offer yield as another measure of the cost of debt.21
Second, we also
examine whether the inclusion or exclusion of firms from the neighboring countries of the U.S.
(Canadian and Mexican) affects our results. We find that our results are qualitatively similar and
even stronger when we exclude firms from Mexico and Canada. Finally, when we exclude bonds
with special features (callable, puttable, pay-in-kind and sinking funds), our results remain
unchanged. If we drop all bonds with special features from the investment-grade bond sample,
the coefficient of Non-US becomes -55 and is still highly significant.
5 Conclusion
We find that cross-listed bonds have more conservative ratings than comparable U.S.
domestic bonds. In particular, cross-listed bonds are associated with lower ratings at issuance
compared to similar U.S. domestic bonds, they are also less likely to be upgraded, are associated
with fewer upgrades, and take longer to receive an upgrade subsequent to the issuance. In
addition, we show that the lower rating is concentrated among investment-grade cross-listed
bonds, consistent with rating agencies bearing a higher reputation cost when they fail to predict
the default of an investment-grade bond.
To differentiate between the rating conservatism and private information explanations,
we provide direct evidence that compared to similar U.S. bonds, cross-listed bond ratings are
more likely to be associated with false alarms and are less likely to be associated with a missed
default. Moreover, we conclude that the bond market corrects for the rating bias in cross-listed
bonds because we find that the issuance yield spread is lower for cross-listed bonds than similar
21
Based on some of the recent evidence that the change in default premium affects the cost of capital rather than the
level of the default premium, we replace Default with ∆Default in equation (1), where ∆Default is defined as the
default premium for the current year less the number from the prior year. We find that our result is robust to this
modification.

29
for U.S. domestic bonds. Collectively, the results are consistent with the rating conservatism
explanation and inconsistent with the private information explanation.
Our paper extends the rating conservatism literature (Beaver et al. 2006) by identifying
information asymmetry as another explanation for rating conservatism. We provide evidence that
rating agencies are more conservative when information asymmetry is stronger. Our study
complements other academic studies on rating properties which tend to focus on accuracy and
timeliness (Cheng and Neamtiu 2009) by documenting conservatism in ratings as an additional
rating property. Our paper also contributes to a small yet growing literature on the application of
conservatism principle outside financial reporting (Lu and Sapra 2009; Hugon and Muslu 2010).
Lastly, our paper adds to the very limited research on foreign firms issuing debt in the U.S. by
documenting the rating properties of cross-listed bonds and their pricing implications.

30
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33
Table 1
Number, origin and size of cross-listed bonds over time
$ Million $ Million
Year Number of issues Number of countries Total debt issued Average issue size
1990 5 2 2,000 400
1991 19 4 5,350 282
1992 28 7 6,975 249
1993 40 9 11,320 283
1994 35 11 7,809 223
1995 56 12 14,246 254
1996 72 15 14,508 201
1997 86 16 19,117 222
1998 96 14 23,544 245
1999 195 19 46,824 240
2000 272 20 225,220 828
2001 597 21 245,568 411
2002 368 22 122,410 333
2003 177 16 53,600 303
2004 67 13 22,590 337
2005 45 10 24,225 538
2006 69 10 51,356 744
2007 87 14 71,422 821
2008 55 7 54,130 984
2009 20 7 21,700 1,085
Total 2,389 1,043,912 437
Table 1 presents the number of issues and the number of countries that issuing firms originate from each year and
the annual total and average size of fixed-rate non-governmental public debt issues in the U.S. by non-U.S. firms
between 1990 and 2009. The total and average issue sizes are in terms of millions of dollars. The data are
obtained from the SDC Global New Issues Database and Mergent Fixed Investment Securities Database. We
exclude observations if the spread to benchmark is missing, or the yield to maturity is negative, or the yield to
maturity is coded by non-numeric characters, such as “Floats”, “Index”, “Market”, “Reset”, “Varies” or “NA.”.
We also exclude observations if country-specific or firm-specific factors are missing for an issuer.

34
Table 2
Cross-listed bonds grouped by country of origin
Country Number of issues Total debt issued Average issue size
Argentina 20 3,800 190
Australia 87 38,866 447
Austria 22 12,768 580
Belgium 32 3,990 125
Brazil 15 11,530 769
Canada 663 186,415 281
Chile 14 3,160 226
Colombia 1 400 400
Denmark 23 4,260 185
Finland 9 3,175 353
France 161 95,875 595
Germany 218 132,782 609
Hong Kong 9 2,725 303
India 1 300 300
Indonesia 6 1,050 175
Ireland-Rep 9 2,434 270
Israel 209 2,718 13
Italy 24 18,166 757
Japan 7 8,500 1,214
Malaysia 2 630 315
Mexico 14 4,713 337
Netherlands 167 225,979 1,353
New Zealand 4 800 200
Norway 25 8,255 330
Philippines 12 2,200 183
Portugal 14 2,040 146
Singapore 3 1,375 458
South Africa 1 1,000 1,000
South Korea 22 5,961 271
Spain 33 17,498 530
Sweden 91 17,885 197
Switzerland 34 7,344 216
United Kingdom 433 214,918 496
Venezuela 4 400 100
Table 2 presents the number of issues and the total and average size of fixed-rate non-governmental public debt issues in
the U.S. for each foreign country that issuing firms originate from between 1990 and 2009. The total and average issue
sizes are in terms of millions of dollars. The data are obtained from the SDC Global New Issues Database and Mergent
Fixed Investment Securities Database. We exclude observations if the spread to benchmark is missing, or the yield to
maturity is negative, or the yield to maturity is coded by non-numeric characters, such as “Floats”, “Index”, “Market”,
“Reset”, “Varies” or “NA.” We also exclude observations if country-specific or firm-specific factors are missing for an
issuer.

35
Table 3
Descriptive statistics for cross-listed bonds and U.S. domestic bonds
Means Medians
Non-U.S. U.S. p-value Non-U.S. U.S. p-value
Issue characteristics
Issue size 5.118 5.155 0.22 5.298 5.371 0.26
Maturity 1.448 2.189 0.00 1.619 2.304 0.00
Seniority 0.953 0.938 0.00 1.000 1.000 0.00
Callability 0.207 0.259 0.00 0.000 0.000 0.00
Puttability 0.032 0.084 0.00 0.000 0.000 0.00
Sinking fund 0.002 0.007 0.00 0.000 0.000 0.01
Pay-in kind 0.000 0.000 0.03 0.000 0.000 0.03
Default spread 0.922 0.990 0.00 0.840 0.880 0.00
Rating 5.375 6.744 0.00 5.000 6.000 0.00
Spread to benchmark 86.185 154.378 0.00 71.000 113.000 0.00
Offer yield 5.708 6.529 0.00 5.930 6.381 0.00
Issuer characteristics
Firm size 0.156 0.0001 0.00 0.058 0.0001 0.00
Leverage 0.267 0.294 0.00 0.240 0.262 0.00
Profitability 0.051 0.024 0.00 0.022 0.020 0.00
Interest coverage 8.947 10.352 0.66 3.560 3.413 0.84
Capital expenditures 0.037 0.036 0.36 0.004 0.021 0.00
Country-specific variables
Emerging 0.141 0.000 0.00 0.000 0.000 0.00
Civil law 0.405 0.000 0.00 0.000 0.000 0.00
Rule of law 1.710 1.920 0.00 1.970 1.920 0.00
Creditor rights 2.163 1.000 0.00 2.000 1.000 0.00
Judicial efficiency 9.247 10.000 0.00 9.250 10.000 0.00
Ex-ante self-dealing 0.422 0.330 0.00 0.330 0.330 0.05
Ex-post self-dealing 0.747 0.980 0.00 0.900 0.980 0.00
Anti-director rights 4.259 2.000 0.00 4.000 2.000 0.00
Public enforcement 0.629 0.000 0.00 1.000 0.000 0.00
Disclosure requirements 0.696 1.000 0.00 0.667 1.000 0.00
Liability standards 0.629 1.000 0.00 0.660 1.000 0.00
Investor protection 0.597 1.000 0.00 0.594 1.000 0.00
Observations 2,389 11,345 2,389 11,345
Table 3 presents the descriptive statistics for cross-listed bonds and U.S. domestic bonds. Non-US equals one when debt is issued by a
non-US firm and zero otherwise. Issue size is the natural logarithm of the size of the debt issue in millions of dollars. Maturity is the
natural logarithm of the number of years to maturity. Seniority equals one when the debt is senior and zero otherwise. Callability
equals one when the bond includes a call provision and zero otherwise. Puttability equals one when the bond includes a put provision
and zero otherwise. Sinking fund equals one when the bond includes a sinking fund feature and zero otherwise. Pay-in-kind equals
one when the bond pays in kind and zero otherwise. Default spread is the yield difference between AAA- and BAA-rated corporate
bonds. Rating equals one for firms that have the best credit rating (AAA) and increases by one for successively lower rating
categories. Spread to benchmark is the difference between the offer yield and the yield of a U.S. Treasury security issued on the same
date with comparable maturity. Offer yield is the yield to maturity on the offer date. Firm size is equal to total assets. dollars.
Leverage is total debt to total assets. Interest coverage is EBIT to interest expenses. Profitability is EBITDA to total assets. Capital
expenditures is capital expenditures to total assets. Emerging equals one when the issuing country is defined as being part of an
emerging market as defined by Morgan Stanley Capital International. Civil law equals one when the legal origin of the issuing
country is the civil law. We also include variables capturing country-specific legal characteristics. These variables are Creditor rights
and Judicial efficiency as defined in La Porta et al. (1998), Ex-ante self-dealing and Ex-post self-dealing as defined in Djankov et al.
(2008), Anti-directors rights as defined in Spamann (2010) and Rule of law, Public enforcement, Disclosure requirements, Liability
standards and Investor protection as defined in La Porta et al. (2006). We also include industry dummies using Fama and French
(1997) industry definitions and year dummies.

36
Table 4
Initial rating of cross-listed bonds compared to U.S domestic bonds
Full sample
Independent Variables Coefficient (p-value)
Non-US 1.25 (0.00)
Issue size 0.04 (0.22)
Maturity 0.05 (0.08)
Seniority -0.81 (0.00)
Callability 0.05 (0.75)
Puttability -0.87 (0.00)
Sinking fund -0.11 (0.71)
Pay-in kind 0.39 (0.14)
Default spread -0.01 (0.92)
Firm size -1.70 (0.00)
Leverage 1.13 (0.00)
Profitability -4.78 (0.00)
Interest coverage -0.01 (0.05)
Capital expenditures -0.57 (0.57)
Emerging 0.25 (0.49)
Civil law -0.30 (0.43)
Rule of law -0.46 (0.17)
Creditor rights 0.05 (0.60)
Judicial efficiency 0.00 (0.97)
Ex-ante self-dealing -0.65 (0.10)
Ex-post self-dealing 0.02 (0.96)
Anti-director rights -0.17 (0.12)
Public enforcement -0.53 (0.01)
Disclosure requirements -0.93 (0.24)
Liability standards -1.11 (0.03)
Investor protection -2.50 (0.01)
Industry dummies Yes Yes
Year dummies Yes Yes
Pseudo R-squared 9.38%
Observations 13,734
Table 4 presents the results from cross-sectional regressions with Rating as the dependent variable. The reported results are based on
ordered probit estimation using robust standard errors clustered by issuer. Rating equals one for firms that have the best credit rating
(AAA) and increases by one for successively worse rating categories. Non-US equals one when debt is issued by a non-US firm and
zero otherwise. Issue size is the natural logarithm of the size of the debt issue in millions of dollars. Maturity is the natural logarithm of
the number of years to maturity. Seniority equals one when the debt is senior and zero otherwise. Callability equals one when the bond
includes a call provision and zero otherwise. Puttability equals one when the bond includes a put provision and zero otherwise. Sinking
fund equals one when the bond includes a sinking fund feature and zero otherwise. Pay-in-kind equals one when the bond pays in kind
and zero otherwise. Default spread is the yield difference between AAA- and BAA-rated corporate bonds. Firm size is equal to total
assets. Leverage is total debt to total assets. Interest coverage is EBIT to interest expenses. Profitability is EBITDA to total assets.
Capital expenditures is capital expenditures to total assets. Emerging equals one when the issuing country is defined as being part of
an emerging market as defined by Morgan Stanley Capital International. Civil law equals one when the legal origin of the issuing
country is the civil law. We also include variables capturing country-specific legal characteristics. These variables are Creditor rights
and Judicial efficiency as defined in La Porta et al. (1998), Ex-ante self-dealing and Ex-post self-dealing as defined in Djankov et al.
(2008), Anti-directors rights as defined in Spamann (2010) and Rule of law, Public enforcement, Disclosure requirements, Liability
standards and Investor protection as defined in La Porta et al. (2006). We also include industry dummies using Fama and French
(1997) industry definitions and year dummies.

37
Table 5
Likelihood of subsequent rating upgrades and downgrades
Upgrade Downgrade
Independent Variables Coefficient (p-value) Coefficient (p-value)
Non-US -0.72 (0.02) 0.14 (0.63)
Issue size -0.02 (0.51) -0.07 (0.00)
Maturity -0.05 (0.14) -0.01 (0.74)
Seniority 0.37 (0.00) 0.08 (0.19)
Callability 0.05 (0.41) 0.00 (0.97)
Puttability 0.29 (0.09) -0.03 (0.87)
Sinking fund -0.50 (0.04)
Default spread 0.09 (0.44) -0.35 (0.00)
Firm size -1.24 (0.05) -1.37 (0.01)
Leverage -0.33 (0.02) 0.28 (0.01)
Profitability -1.21 (0.33) -2.24 (0.01)
Interest coverage 0.02 (0.00) 0.00 (0.11)
Capital expenditures -0.04 (0.94) 0.47 (0.28)
Emerging -1.27 (0.08) 0.43 (0.38)
Civil law 1.17 (0.12) 0.33 (0.50)
Rule of law 2.24 (0.00) 0.36 (0.49)
Creditor rights 0.29 (0.14) -0.03 (0.81)
Judicial efficiency 0.56 (0.03) -0.04 (0.83)
Ex-ante self-dealing 0.84 (0.35) -0.14 (0.84)
Ex-post self-dealing 2.50 (0.02) 0.73 (0.37)
Anti-director rights 0.55 (0.06) -0.21 (0.15)
Public enforcement -0.12 (0.79) 0.14 (0.66)
Disclosure requirements -2.19 (0.09) -0.36 (0.71)
Liability standards 0.42 (0.66) -0.14 (0.84)
Investor protection 2.32 (0.13) -0.26 (0.81)
Industry dummies Yes Yes Yes Yes
Year dummies Yes Yes Yes Yes
Pseudo R-squared 9.15% 7.94%
Observations 6,114 6,114
Table 5 presents the results from cross-sectional regressions with indicator variables for upgrades and downgrades as the dependent
variables. In the UPGRADE (DOWNGRADE) regression, the dependent variable takes the value of one if the issue’s rating has
been upgraded (downgraded) by Standard and Poor’s at the end of the three years after the offering compared to the initial rating.
The reported results are based on probit estimation using robust standard errors clustered by issuer. Non-US equals one when debt
is issued by a non-US firm and zero otherwise. Issue size is the natural logarithm of the size of the debt issue in millions of dollars.
Maturity is the natural logarithm of the number of years to maturity. Seniority equals one when the debt is senior and zero
otherwise. Callability equals one when the bond includes a call provision and zero otherwise. Puttability equals one when the bond
includes a put provision and zero otherwise. Sinking fund equals one when the bond includes a sinking fund feature and zero
otherwise. Pay-in-kind equals one when the bond pays in kind and zero otherwise. Default spread is the yield difference between
AAA- and BAA-rated corporate bonds. Firm size is equal to total assets. Leverage is total debt to total assets. Interest coverage is
EBIT to interest expenses. Profitability is EBITDA to total assets. Capital expenditures is capital expenditures to total assets.
Emerging equals one when the issuing country is defined as being part of an emerging market as defined by Morgan Stanley
Capital International. Civil law equals one when the legal origin of the issuing country is the civil law. We also include variables
capturing country-specific legal characteristics. These variables are Creditor rights and Judicial efficiency as defined in La Porta et
al. (1998), Ex-ante self-dealing and Ex-post self-dealing as defined in Djankov et al. (2008), Anti-directors rights as defined in
Spamann (2010) and Rule of law, Public enforcement, Disclosure requirements, Liability standards and Investor protection as
defined in La Porta et al. (2006). We also include industry dummies using Fama and French (1997) industry definitions and year
dummies.

38
Table 6
Frequency of subsequent upgrades and downgrades
Upgrade Downgrade
Non-US -0.28 (0.00) 0.17 (0.04)
Issue size 0.01 (0.00) -0.02 (0.00)
Maturity 0.01 (0.00) -0.06 (0.00)
Seniority -0.05 (0.00) 0.03 (0.00)
Callability -0.02 (0.00) 0.09 (0.00)
Puttability 0.03 (0.00) 0.03 (0.19)
Sinking fund 0.06 (0.00) -0.23 (0.00)
Pay-in-kind 0.02 (0.10) -0.06 (0.00)
Default spread 0.11 (0.44) -0.35 (0.00)
Firm size 0.03 (0.68) -0.36 (0.00)
Leverage 0.00 (0.73) 0.03 (0.06)
Profitability 0.45 (0.00) -0.45 (0.00)
Capital expenditures -0.02 (0.69) -0.22 (0.00)
Emerging -0.01 (0.85) 0.14 (0.14)
Civil law 0.11 (0.08) 0.02 (0.86)
Rule of law 0.27 (0.00) -0.23 (0.03)
Creditor rights -0.04 (0.06) -0.01 (0.78)
Judicial efficiency 0.10 (0.00) -0.04 (0.40)
Ex-ante self-dealing 0.20 (0.01) 0.04 (0.74)
Ex-post self-dealing 0.24 (0.04) -0.23 (0.17)
Public enforcement 0.09 (0.00) -0.06 (0.26)
Disclosure requirements -0.07 (0.62) 0.37 (0.06)
Liability standards 0.41 (0.00) -0.17 (0.22)
Investor protection -0.46 (0.00) -0.10 (0.66)
R-squared 15.32% 23.47%
Table 6 presents the results from cross-sectional regressions with the relative frequencies of upgrades and downgrades as the
dependent variables. In the UPGRADE (DOWNGRADE) regression, the dependent variable is equal to the number of upgrades
(downgrades) divided by the total number of ratings assigned to an issue by Standard & Poor’s during the three years after the
offering. The reported results are based on OLS estimations using robust standard errors clustered by issuer. Non-US equals one when
debt is issued by a non-US firm and zero otherwise. Issue size is the natural logarithm of the size of the debt issue in millions of
dollars. Maturity is the natural logarithm of the number of years to maturity. Seniority equals one when the debt is senior and zero
otherwise. Callability equals one when the bond includes a call provision and zero otherwise. Puttability equals one when the bond
includes a put provision and zero otherwise. Sinking fund equals one when the bond includes a sinking fund feature and zero
otherwise. Pay-in-kind equals one when the bond pays in kind and zero otherwise. Default spread is the yield difference between
AAA- and BAA-rated corporate bonds. Firm size is equal to total assets. Leverage is total debt to total assets. Interest coverage is
EBIT to interest expenses. Profitability is EBITDA to total assets. Capital expenditures is capital expenditures to total assets.
Emerging equals one when the issuing country is defined as being part of an emerging market as defined by Morgan Stanley Capital
International. Civil law equals one when the legal origin of the issuing country is the civil law. We also include variables capturing
country-specific legal characteristics. These variables are Creditor rights and Judicial efficiency as defined in La Porta et al. (1998),
Ex-ante self-dealing and Ex-post self-dealing as defined in Djankov et al. (2008), Anti-directors rights as defined in Spamann (2010)
and Rule of law, Public enforcement, Disclosure requirements, Liability standards and Investor protection as defined in La Porta et
al. (2006). We also include industry dummies using Fama and French (1997) industry definitions and year dummies.

39
Table 7
Time interval before subsequent upgrades and downgrades
Upgrade Downgrade
Non-US 253.51 (0.03) 53.44 (0.29)
Issue size 53.85 (0.07) -87.71 (0.00)
Maturity 17.15 (0.67) 112.76 (0.00)
Seniority 74.15 (0.26) 256.08 (0.00)
Callability 39.80 (0.54) -201.11 (0.00)
Puttability 686.90 (0.00) 243.02 (0.08)
Sinking fund -373.97 (0.01) 418.70 (0.00)
Default spread 35.67 (0.68) 111.36 (0.12)
Firm size -754.02 (0.02) 1,099.96 (0.09)
Leverage 553.51 (0.00) -193.80 (0.03)
Profitability 2,085.56 (0.06) -81.51 (0.89)
Capital expenditures -1,857.16 (0.00) -229.42 (0.54)
Emerging 92.84 (0.75) -774.79 (0.03)
Civil law -1,310.03 (0.03) -93.46 (0.81)
Rule of law 1,039.35 (0.01) 120.48 (0.63)
Creditor rights -20.05 (0.92) -13.07 (0.93)
Judicial efficiency -179.63 (0.03) -51.15 (0.56)
Ex-ante self-dealing 510.43 (0.41) 455.22 (0.51)
Public enforcement -690.85 (0.11) 416.87 (0.01)
Disclosure requirements -975.38 (0.20) -1,510.29 (0.00)
Liability standards -780.03 (0.42) -300.58 (0.54)
Investor protection -750.43 (0.65) 423.13 (0.71)
R-squared 29.51% 24.65%
Table 7 presents the results from cross-sectional regressions with the days until upgrades and downgrades as the dependent
variables. In the UPGRADE (DOWNGRADE) regression, the dependent variable is equal to the number of days between the initial
rating assigned to an issue by Standard and Poor’s and the first subsequent upgrade (downgrade) conditional on the existence of a
rating change. The reported results are based on OLS estimations using robust standard errors clustered by issuer. Non-US equals
one when debt is issued by a non-US firm and zero otherwise. Issue size is the natural logarithm of the size of the debt issue in
millions of dollars. Maturity is the natural logarithm of the number of years to maturity. Seniority equals one when the debt is
senior and zero otherwise. Callability equals one when the bond includes a call provision and zero otherwise. Puttability equals one
when the bond includes a put provision and zero otherwise. Sinking fund equals one when the bond includes a sinking fund feature
and zero otherwise. Pay-in-kind equals one when the bond pays in kind and zero otherwise. Default spread is the yield difference
between AAA- and BAA-rated corporate bonds. Firm size is equal to total assets. Leverage is total debt to total assets. Interest
coverage is EBIT to interest expenses. Profitability is EBITDA to total assets. Capital expenditures is capital expenditures to total
assets. Emerging equals one when the issuing country is defined as being part of an emerging market as defined by Morgan Stanley
Capital International. Civil law equals one when the legal origin of the issuing country is the civil law. We also include variables
capturing country-specific legal characteristics. These variables are Creditor rights and Judicial efficiency as defined in La Porta et
al. (1998), Ex-ante self-dealing and Ex-post self-dealing as defined in Djankov et al. (2008), Anti-directors rights as defined in
Spamann (2010) and Rule of law, Public enforcement, Disclosure requirements, Liability standards and Investor protection as
defined in La Porta et al. (2006). We also include industry dummies using Fama and French (1997) industry definitions and year
dummies.

40
Table 8
Rating variation by investment and non-investment grade
Investment grade Non-investment grade
Non-US 1.34 (0.00) 0.55 (0.00)
Issue size 0.03 (0.44) -0.26 (0.00)
Maturity 0.12 (0.00) -0.41 (0.00)
Seniority -0.10 (0.68) -0.63 (0.00)
Callability 0.42 (0.01) 0.96 (0.00)
Puttability 0.41 (0.00) -0.03 (0.42)
Sinking fund 0.19 (0.51) -0.13 (0.01)
Pay-in kind 0.80 (0.00)
Default spread 0.16 (0.14) -0.41 (0.00)
Firm size -1.76 (0.00) -2.22 (0.00)
Leverage 0.52 (0.21) 0.68 (0.00)
Profitability -3.09 (0.01) -2.41 (0.00)
Interest coverage -0.01 (0.03) -0.02 (0.00)
Emerging 0.05 (0.89) -1.33 (0.39)
Civil law -0.14 (0.72) 1.01 (0.16)
Rule of law -0.50 (0.15) -0.85 (0.00)
Creditor rights 0.11 (0.22) -0.48 (0.00)
Judicial efficiency -0.01 (0.91) 0.67 (0.42)
Ex-ante self-dealing -0.44 (0.26) 7.66 (0.00)
Anti-director rights -0.17 (0.08) -2.24 (0.00)
Public enforcement -0.63 (0.00) -0.38 (0.00)
Disclosure requirements -0.45 (0.59) -1.21 (0.00)
Liability standards -1.26 (0.01) 2.60 (0.24)
Investor protection 2.47 (0.01) -2.82 (0.00)
Pseudo R-squared 7.18% 17.34%
Observations 12,484 1,250
Table 8 presents the results from cross-sectional regressions with Rating as the dependent variable. The regressions are separately run for
investment and speculative grade issues. The reported results are based on ordered probit estimation using robust standard errors
clustered by issuer. Rating equals one for firms that have the best credit rating (AAA) and increases by one for successively lower rating
categories. Non-US equals one when debt is issued by a non-US firm and zero otherwise. Issue size is the natural logarithm of the size of
the debt issue in millions of dollars. Maturity is the natural logarithm of the number of years to maturity. Seniority equals one when the
debt is senior and zero otherwise. Callability equals one when the bond includes a call provision and zero otherwise. Puttability equals
one when the bond includes a put provision and zero otherwise. Sinking fund equals one when the bond includes a sinking fund feature
and zero otherwise. Pay-in-kind equals one when the bond pays in kind and zero otherwise. Default spread is the yield difference
between AAA- and BAA-rated corporate bonds. Firm size is equal to total assets. Leverage is total debt to total assets. Interest coverage
is EBIT to interest expenses. Profitability is EBITDA to total assets. Capital expenditures is capital expenditures to total assets.
Emerging equals one when the issuing country is defined as being part of an emerging market as defined by Morgan Stanley Capital
International. Civil law equals one when the legal origin of the issuing country is the civil law. We also include variables capturing
country-specific legal characteristics. These variables are Creditor right and Judicial efficiency as defined in La Porta et al. (1998), Ex-
ante self-dealing and Ex-post self-dealing as defined in Djankov et al. (2008), Anti-directors rights as defined in Spamann (2010) and
Rule of law, Public enforcement, Disclosure requirements, Liability standards and Investor protection as defined in La Porta et al.
(2006). We also include industry dummies using Fama and French (1997) industry definitions and year dummies.

41
Table 9
Likelihood of missing defaults and raising false alarms
Missed default False alarm
Non-US -0.54 (0.03) 0.55 (0.01)
Issue size 0.33 (0.80) -0.05 (0.00)
Maturity 0.07 (0.90) -0.05 (0.00)
Seniority -0.52 (0.71) -0.43 (0.00)
Callability -0.93 (0.32) 0.11 (0.00)
Puttability -0.47 (0.01) 0.25 (0.00)
Sinking fund -0.48 (0.00) -0.23 (0.00)
Default spread -2.55 (0.00) 0.14 (0.00)
Firm size -17.89 (0.72) -0.35 (0.00)
Leverage 10.77 (0.04) 1.83 (0.00)
Profitability 12.09 (0.89) -6.79 (0.00)
Emerging 0.06 (0.81)
Civil law -0.77 (0.01)
Rule of law -0.45 (0.07)
Creditor rights 0.16 (0.06)
Judicial efficiency -0.03 (0.74)
Ex-ante self-dealing -1.22 (0.00)
Ex-post self-dealing -0.62 (0.12)
Anti-director rights 0.02 (0.81)
Public enforcement 0.22 (0.11)
Disclosure requirements -0.98 (0.03)
Liability standards -1.15 (0.00)
Investor protection 1.12 (0.03)
Table 9 presents the results from cross-sectional regressions of indicator variables for missed defaults and false alarms as the
dependent variables. Missed default is an indicator variable that takes the value of one for missed defaults and zero otherwise.
Specifically, for a sample of issuers that experience an event of default within one year from the rating date, this variable takes the
value of one if a debt issue is investment-grade and zero otherwise. False alarm is an indicator variable that takes the value of one for
false warnings and zero otherwise. Specifically, for a sample of issuers that do not experience an event of default within one year
from the rating date, this variable takes the value of one if a debt issue is non-investment grade and zero otherwise. In Missed default
regressions, the legal variables are dropped from the specification due to multicollinearity. Non-US equals one when debt is issued by
a non-US firm and zero otherwise. Issue size is the natural logarithm of the size of the debt issue in millions of dollars. Maturity is the
natural logarithm of the number of years to maturity. Seniority equals one when the debt is senior and zero otherwise. Callability
equals one when the bond includes a call provision and zero otherwise. Puttability equals one when the bond includes a put provision
and zero otherwise. Sinking fund equals one when the bond includes a sinking fund feature and zero otherwise. Pay-in-kind equals
one when the bond pays in kind and zero otherwise. Default spread is the yield difference between AAA- and BAA-rated corporate
bonds. Firm size is equal to total assets. Leverage is total debt to total assets. Interest coverage is EBIT to interest expenses.
Profitability is EBITDA to total assets. Capital expenditures is capital expenditures to total assets. Emerging equals one when the
issuing country is defined as being part of an emerging market as defined by Morgan Stanley Capital International. Civil law equals
one when the legal origin of the issuing country is the civil law. We also include variables capturing country-specific legal
characteristics. These variables are Creditor rights and Judicial efficiency as defined in La Porta et al. (1998), Ex-ante self-dealing
and Ex-post self-dealing as defined in Djankov et al. (2008), Anti-directors rights as defined in Spamann (2010) and Rule of law,
Public enforcement, Disclosure requirements, Liability standards and Investor protection as defined in La Porta et al. (2006). We also
include industry dummies using Fama and French (1997) industry definitions and year dummies.

Ghosh Presentation - European Finance Association Conference, Copenhagen, Denmark

Ghosh Presentation - European Finance Association Conference, Copenhagen, Denmark

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