Equity Cross-Listings in the U.S. and the Price of Debt ANNUAL MEETINGS...wealth transfers from debt to equity holders that partially offsets the debt-market benefits. Finally, to

Equity Cross-Listings in the U.S. and the Price of Debt*

Ryan T. Ball

Booth School of Business University of Chicago

Luzi Hail

The Wharton School University of Pennsylvania

Florin P. Vasvari

London Business School

December 2009

(First version: April 2009)

Abstract

This paper examines whether foreign firms can raise public debt capital at a lower cost after their equity shares are cross-listed in the U.S., and the sources of these potential debt market benefits. Employing a large global sample of public bonds, we find strong evidence that firms with shares cross-listed on U.S. exchanges or in the over-the-counter market can lower their offering yield spreads by about 48 basis points. Consistent with legal bonding, the reduction in bond spreads is larger for firms from countries with lax disclosure regulation, higher private control benefits and underdeveloped local debt markets. However, equity cross-listings do little to overcome weak debt-market institutions in the country of domicile as firms from countries with poor creditor protection pay significantly higher spreads. Moreover, consistent with greater risks of wealth transfer from debt to equity holders, firms likely to face substantial agency conflicts exhibit higher bond spreads after U.S. cross-listings. In further analyses, we show that the public bond results do not extend to the syndicated loan market, suggesting that private communication and tight monitoring in private debt markets play a mitigating role.

JEL classification: F34, G12, G15, G38, K22

Key Words: Corporate Governance, Bonding hypothesis, Debt financing, Disclosure, Law and

finance, International finance

* We thank Anne Beatty, Phil Berger, Günther Gebhardt, Wayne Guay, Bob Holthausen, Oguzhan Ozbas, René Stulz,

and workshop participants at the 2009 Global Issues in Accounting Conference, 2009 INTACCT international

workshop in Porto, 2009 Verein für Socialpolitik Accounting Section meeting, 2009 Conference on Empirical Legal

Studies, University of Chicago, Erasmus University, Goethe University, INSEAD, New York University, Ohio State University, University of Pennsylvania, University of Rochester, and Stanford University for helpful comments. Ryan

Ball gratefully acknowledges the financial support by the Neubauer Family Fellowship. Luzi Hail gratefully

acknowledges the financial support by the Golub Faculty Scholar Award Fund. Florin Vasvari gratefully acknowledges

the financial support of the London Business School RAMD Fund.

1

1. Introduction

Does cross listing equity shares in the United States affect foreign firms’ access to more and

cheaper debt financing? Debt markets are traditionally a greater source of external finance for

international firms than equity markets (e.g., Rajan and Zingales, 1995; Henderson, Jegadeesh, and

Weisbach, 2006). However, extant literature almost exclusively focuses on the costs and benefits of

cross-listing equity to shareholders (Karolyi, 1998, 2006). In particular, firms domiciled in countries

with poor protection of minority shareholders, limited availability of equity capital and segmented

markets can overcome these shortfalls by subjecting themselves to U.S. securities regulation and

oversight (Coffee, 1999; Stulz, 1999).1 While equity market effects are important and ultimately

serve to justify the cross-listing decision, there is limited evidence on the debt market implications

of cross-listing equity in the U.S. (e.g., Miller and Puthenpurackal, 2002; Lins, Strickland, and

Zenner, 2005).2

Much of the equity benefits of cross listing in the U.S. draw on the comparative advantages of

the U.S. judicial system, a more transparent disclosure regime and greater scrutiny from regulators

and market forces. This notion of legal bonding, in principle, should also benefit the lenders of the

firm as it facilitates access to information, and enables them to better negotiate and monitor debt

agreements. In return, lenders should reduce their price protection, lowering the borrowing costs of

the firm (Hart, 1995). However, the realization of these debt-related benefits after firms cross list

their shares in the U.S. is far from certain.

1 Consistent with this notion, prior evidence suggests that firms cross listing shares on a U.S. exchange obtain

higher equity valuations (e.g., Foerster and Karolyi, 1999; Doidge, Karolyi, and Stulz, 2004), lower costs of equity

capital (e.g., Errunza and Miller, 2000; Hail and Leuz, 2009), improved liquidity of the traded equity (e.g., Baruch,

Karolyi, and Lemmon, 2007), an expanded investor base (e.g., Ammer et al., 2008; King and Segal, 2009), and raise equity capital more frequently (e.g., Reese and Weisbach, 2002).

2 We refer to a foreign firm’s U.S. cross-listed equity as “ADR,” regardless of whether it is an exchange-listed

American Depositary Receipt (Level II or III), a direct listing (e.g., for Canadian firms), a globally or New York

registered share, a share traded in the over-the-counter markets (OTC), either on the OTC Bulletin Board or in the

Pink Sheets, or a private placement under Rule 144A.

2

Most notably, the effectiveness of debt enforcement and the level of creditor protection in the

country of domicile also likely affect the availability and terms of debt capital, because the physical

location of firms’ assets (that could serve as a collateral) typically determines the legal procedures

in case of default and the applicability of bankruptcy laws (La Porta et al. 1997; Qian and Strahan,

2007). Moreover, due to improvements in firms’ growth opportunities associated with the cross-

listing, lenders could face significantly greater agency costs because of controlling shareholders’

tendency to opportunistically select investment projects that maximize shareholder value rather than

total firm value. Finally, the type of market setting (i.e., public vs. private debt) also likely

influences the structure of debt contracts. In arm’s length transactions, public lenders rely more on

country-level governance institutions, in the host country and the country of domicile. Private bank

lenders, on the other hand, get privileged access to information, more closely monitor the borrower,

and have multiple levers at their disposal, not just interest rates, when setting or renegotiating

contract terms (Gigler et al., 2009). Thus, in light of these opposing forces, the debt market effects

of equity cross-listings in the U.S. are ultimately an empirical question.

We examine the above issues for a large international sample of public bonds. We begin with

an analysis of the propensity of issuing debt, and find that foreign firms with U.S. equity cross-

listings are more likely to issue bonds, and seem to shift from private to public debt financing.

Going beyond a descriptive analysis, we then turn to the economic consequences, namely an

analysis of the offering yield spreads. Our sample comprises 3,633 public bond issues over the years

1992 to 2005 from 31 countries, of which 671 come from firms with an active U.S. cross-listing.

We find that offering yield spreads of public bonds are, on average, lower by about 48 basis points

for firms with either exchange listings on NYSE, Nasdaq, and Amex or over-the-counter listings in

the Pink Sheets and on the OTC Bulletin Board. This translates into yearly cost savings of about

US$ 0.85 million based on the average bond size of US$ 177 million. In contrast, we find no effects

3

for firms with private equity placements under Rule 144A. The results are robust to including a

comprehensive set of bond-specific variables, firm attributes, macroeconomic factors, and

controlling for the time-invariant unobserved heterogeneity across countries and industries, and

within years.

Next, we explore the sources of the observed net benefits to public bondholders. First, we find

that the average reduction in offering yield spreads after equity cross-listings is about two times

larger for firms domiciled in countries with lax disclosure regulation, higher private benefits of

control, and underdeveloped local debt markets, consistent with the legal bonding argument. Second,

our evidence indicates that an equity cross-listing does little to overcome poor home country

institutions related to the enforcement of debt holder rights. This finding is in line with Miller and

Puthenpurackal (2002), and underscores that legal procedures protecting public bond investors are

less fungible than legal procedures protecting shareholders. Third, we find that firms with elevated

agency conflicts between debt and equity holders (i.e., firms with high levels of and changes in

leverage, growth opportunities, ownership concentration, earnings variability and stock return

variability) pay higher bond yields after cross listing. This result points to an increased likelihood of

wealth transfers from debt to equity holders that partially offsets the debt-market benefits.

Finally, to assess the impact of market structure, we analyze 2,828 syndicated loans from 38

countries between 1992 and 2005, of which 663 are from firms with U.S. equity cross-listings.

Contrary to public borrowing, we find that offering spreads of syndicated loans are not affected by

firms’ cross-listing status, nor by the presence of greater agency conflicts. These results are

consistent with the idea that bank lenders rely more on private monitoring and information sharing

as opposed to country-level governance mechanisms to mitigate risks of wealth expropriation.

4

To our knowledge, this is the first large sample study to explicitly examine the effects of

equity cross-listings on public and private debt securities issued by the firm.3 Even though the

primary means to cross list is via equity shares, debt financing remains the main source of external

funding for many firms. Evidence that firms can issue bonds in an arm’s length transaction at a

lower cost after cross-listing shares on a U.S. exchange is an important and new finding that adds to

the extensive literature on the equity market benefits of cross-listing. We also contribute to the

literature on the bonding hypothesis by showing that in debt markets legal bonding does not

uniformly apply to all types of monitoring. While the importance of firms’ country of domicile for

the enforcement of debt contracts has been shown in other settings, the role of a credible

commitment to more transparent reporting and greater scrutiny by market forces via cross listing in

the U.S. for the pricing of public debt contracts should be of interest to regulators and policy makers.

We also provide evidence on the effect of cross-listings on the agency conflicts between debt and

equity holders, which previously has been unexplored in the literature. Finally, we add to the

literature on the interaction of country-level and firm-level corporate governance mechanisms (e.g.,

Klapper and Love, 2004; Doidge, Karolyi, and Stulz, 2007) by showing that in markets with private

monitoring, legal bonding effects are not reflected in interest rates.

Section 2 contains the hypothesis development. In Section 3, we discuss the sample, introduce

the primary variables, and provide descriptive statistics. Section 4 presents the propensity analysis

of issuing public bonds, and discusses our main findings on the net benefits of public borrowing

following a U.S. equity cross-listing. In Section 5 we report the cross-sectional analyses in which

we partition the ADR observations using country- and firm-level variables. It also presents an

analysis of the offering yields of syndicated loans. Section 6 concludes.

3 In related work, Miller and Puthenpurackal (2002) focus on the benefits and wealth effects of equity cross-listings

for the much smaller segment of public Yankee bonds. Lins, Strickland, and Zenner (2005) examine whether firms

use ADR listings to relax capital constraints and improve the overall cash flow sensitivity.

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2. Hypothesis Development and Related Literature

The main theories underlying the cross-listing decision are the market segmentation

hypothesis (Stulz, 1981), the investor recognition hypothesis (Merton, 1987), and the bonding

hypothesis (Stulz, 1999; Coffee, 1999, 2002). They suggest that companies choose to cross-list in

the U.S. to broaden their shareholder base, to increase their visibility, and to credibly signal their

commitment to protect minority interests. Existing evidence suggests that, on average, firms are

successful along these dimensions. In particular, the bonding hypothesis, which builds on

comparative advantages of the U.S. judicial system, disclosure regime and greater scrutiny from

regulators, analysts, media and investors, has received strong empirical support in equity markets.

Legal bonding also implies similar benefits in public debt markets, as U.S. institutions

arguably provide a series of cost savings mechanisms to bondholders. First, stringent disclosure and

listing requirements for foreign registrants such as the provisions of the U.S. Securities and

Exchange Commission (SEC) decrease bondholders’ information acquisition costs by lowering ex

ante information asymmetries and allowing them to detect credit problems earlier during the post

contracting period. These benefits are particularly pronounced in transactions conducted at arm’s

length, because of the potential free-rider problems.4 Second, in case of corrective actions against

borrowers, even if only threatened, bondholders are able to pursue them through the mechanisms of

the U.S. legal system, which might not be available or come at a higher cost in the home country

(e.g., class action lawsuits). Third, efficient debt enforcement in the U.S. (e.g., Djankov et al., 2008)

helps deter strategic defaults, which occur when potentially solvent borrowers are unwilling to

repay the debt. Lower expected default rates, in turn, reduce the costs of re-contracting and increase

the likelihood that bond investors will recover the face value of the debt. Finally, regulatory bodies

4 An arm’s length transaction puts all investors on an equal footing, i.e., they have access to the same set of

information. Because public bond ownership is dispersed and individual bond investors do not have a significant

informational advantage, they rely on other bond investors to pursue monitoring and information acquisition tasks.

Thus, in equilibrium, no bond investor has incentives to monitor the bond contract.

6

in the U.S. such as the SEC as well as reputational intermediaries like auditors, financial analysts or

the media, facing a threat of U.S. litigation, limit the ability of management or controlling

shareholders to expropriate resources from outside investors. As a side effect, this might also

benefit bond investors in terms of reducing their monitoring costs or increasing the equity value of

the firm.5 Overall, easier access to information and the ability to better negotiate or monitor debt

agreements should reduce the price protection required by lenders and translate into lower

borrowing costs for firms with equity cross-listings in the U.S. (Hart, 1995).

However, the realization of debt-related benefits after foreign firms cross list their shares in

the U.S. is far from certain. The effectiveness of debt enforcement and the level of creditor

protection in the country of domicile are also likely to affect the availability, structure and terms of

debt contracts (La Porta et al., 1997; Qian and Strahan, 2007). This applies even to Yankee bonds,

i.e., public debt issued by foreign firms in the United States. Miller and Puthenpurackal (2002) and

Miller and Reisel (2009) show that Yankee bond investors require higher yield spreads and impose

more restrictive debt covenants on issuers located in countries with weak creditor rights protection.6

This concern might be magnified for debt issued outside of the U.S., because the physical location

of firms’ assets typically determines the applicability of bankruptcy laws.7 Hence, bondholders are

likely to demand higher risk premiums for debt issued by firms located in countries that do not

protect their rights despite the U.S. cross-listing, simply because they expect that in case of default

the protection provided by the U.S. regulatory system might not fully apply to them (e.g., Licht,

2003; Siegel, 2005).

5 The effects of strengthening shareholder protection on debt holders might be ambiguous due to agency conflicts

between the two parties. Thus, we argue that, at the margin, stronger shareholder rights could actually increase the

agency costs for the debt holders of the firm. 6 Atitlan, Davis-Friday, and Ghosh (2007) confirm the importance of home country institutions when comparing a

sample of foreign firms that issue debt in the U.S. to their U.S. counterparts. 7 In our sample, only about 4% of the public debt observations consist of Yankee bonds.

7

In addition, cross-listings can significantly exacerbate the agency costs of debt, which result

from the conflicts of interest between shareholders and bondholders (e.g., Jensen and Meckling,

1976; Myers, 1977). Since cross-listings typically occur in association with an improvement in

firms’ growth opportunities (Doidge, Karolyi, and Stulz, 2004; Hail and Leuz, 2009), firms might

exploit these growth options by making investments that maximize shareholders’ wealth rather than

total firm value. More specifically, given the larger investment opportunity set combined with

additional external funds from an increase in equity offerings following cross-listings (Reese and

Weisbach, 2002; Lins, Strickland, and Zenner, 2005), they could intensify the tendency to avoid

safe positive net present value projects in favor of risky, but negative net present value projects.

Moreover, cross listing in the U.S. increases firms’ visibility and strengthens shareholder control,

particularly, if U.S. institutional investors use it to extend their blockholdings (e.g., Bradshaw,

Bushee, and Miller, 2004; Ammer et al., 2008). Greater shareholder control better aligns the

interests of managers with outside equity investors, potentially to the detriment of debt holders. It

also increases the likelihood of a takeover (Shleifer and Vishny, 1986; Cremers and Nair, 2005).

Similarly, cross listing in the U.S. exposes firms to a more active market for corporate control, and

facilitates the acquisition of U.S. targets (Tolmunen and Torstila, 2005; Kumar and Ramchand,

2008).8 Acquisitions and takeovers, disciplinary or not, can benefit shareholders at the expense of

bondholders by increasing the riskiness of equity claims and diverting cash flows from (less

protected) lenders.9 Bondholders, well aware of these agency conflicts, should require higher yields

after an equity cross-listing to price protect themselves against the expected costs of opportunistic

8 Kumar and Ramchand (2008) argue that one way to reduce dominant shareholder control is to issue new equity.

However, debt holders’ position does not necessarily improve by means of controlling shareholder dilution,

because acquisitions, by definition, also involve new and potentially risky uses of funds. 9 For instance, firm leverage generally increases after takeovers (Kim and McConnell, 1977; Ghosh and Jain, 2000).

This can reduce the value of the outstanding debt by increasing the probability and the deadweight costs of a

possible future bankruptcy and by reordering the priority of claims in case of default (e.g., issuance of more senior

debt). Furthermore, protective actions by incumbent managers might induce economic losses for bondholders even

before a takeover occurs (e.g., via recapitalizing the firm, or increasing the payouts to shareholders).

8

behavior by the shareholders. Yet, the extent of agency conflicts between debt and equity holders

likely varies substantially across firms (e.g., Parrino and Weisbach, 1999).

Finally, the type of market setting also likely affects the realization of debt related benefits of

equity cross-listings. Public debt holders typically exercise limited control over the decisions of

borrowers by not taking on an active monitoring role. They generally rely only on publicly available

information, and, due to the free rider problem, rarely renegotiate the contract if credit problems

arise (e.g., Diamond, 1984, 1991). Hence, when providing debt capital in arm’s length transactions,

lenders likely rely more on country-level corporate governance institutions, either in the host

country or the country of domicile, in specifying the contract terms (e.g., Doidge, Karolyi, and

Stulz, 2007). On the other hand, in markets with private monitoring and privileged access to

information about the borrower by the primary lender (e.g., bank lending), debt is more closely

monitored, significantly lowering information asymmetries and moral hazard problems.10 This

allows bank lenders to be more effective monitors and hence, the differential between home country

institutions and the U.S. regulatory system is less likely to affect the cost of private debt.11 Also,

bank lenders are likely in a better position to deal with greater agency costs after equity cross-

listings. Often, they are directly involved with the borrowing company (i.e., relationship lending),

and have extensive protection features attached to the lending agreement (e.g., covenants, seniority,

collateral requirements, etc.). Thus, in such a setting, lower interest rates do not uniquely identify

more efficient debt agreements (Gigler et al., 2009).12 Moreover, banks are less likely to suffer from

10 Banks have bargaining power over the firm’s profits due to extensive monitoring (Rajan, 1992). Berger and Udell

(1995) and Petersen and Rajan (1994) show that small U.S. firms with close banking ties have easier access to

credit at lower costs due to the fact that bank monitoring decreases agency costs. Bank lenders are usually able

exploit their privileged position with the borrowers, especially if their clients lack reputation, and can recoup

monitoring costs from borrowers via financing terms (Fama 1985). 11 Yet, the institutional environment also plays a role for private debt. For instance, Esty and Megginson (2003) or

Bae and Goyal (2009) show that cross-border differences in creditor rights and legal enforcement affect risk-

sharing arrangements by banks and important syndicated loan characteristics such as spreads, size and maturity. 12 Consistent with bank lenders being better able to mitigate agency issues than public debt holders, Harvey, Lins,

and Roper (2004) find that equity returns around the issuance of syndicated bank loans (but not public bonds) are

9

wealth expropriation arising from an increased mergers and acquisition activity associated with

cross-listings because in most cases they provide advisory services on these transactions.

3. Data Sources and Sample Description

In this section, we provide institutional background information on the structure of public

lending contracts and discuss the sample selection criteria. We then describe our two main variables

of interest: the bond offering yield spread and the U.S. cross-listing status. Finally, we introduce a

comprehensive set of bond attributes, various firm characteristics and macroeconomic factors used

as control variables in our tests and provide distributional statistics for each of these variables.

3.1. Sample Selection

The starting point for our sample construction is the availability of data on public lending

contracts. We obtain the bond data from two sources. We first collect all observations from

Thompson Deals (part of Thompson One Banker), and then augment this data set by observations

from the Mergent Fixed Income Securities Database (FISD). These sources provide data on bond-

issue size and type, issue date, bond features, bond ratings, coupon rates and borrower information.

We note that for many data items the availability is much sparser for international bonds than for

U.S. bonds. Bond issues are only retrieved from Mergent FISD if they do not overlap with the bond

issues from Thompson Deals. An overlapping observation is a bond issued by the same firm on the

same date that has the same coupon rate, maturity and face value. Public bonds are regularly traded

on over-the-counter markets (only few are traded on exchanges), and therefore are subject to limited

disclosure requirements. They include convertible bonds, callable bonds, bonds with collateral,

subordinated bonds, etc. Bonds are frequently issued in the issuer’s country of domicile. In addition,

positively associated with management’s separation of ownership and control and with the extent of assets in place

that can be exploited by the management.

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bonds are sometimes denominated in a currency different from the local currency, with U.S. Dollars,

Euros, Japanese Yens and Swiss Francs being the primary choices.

Next, we manually match the bond data to firm-level financial information in Worldscope via

the issuing firm’s name, country of domicile and 4-digit SIC code.13 Due to the nature of the

business and the existence of industry specific regulations, we exclude financial firms from the

analyses (i.e., one-digit SIC code equal to 6). We also require that issuing firms are publicly traded,

and firms raise at least US$ 10 million of new debt capital. We limit the sample to observations

from countries with at least one American Depositary Receipt (ADR) or direct listing in the U.S.

outstanding. Furthermore, we require bond year observations to have data available for all the

control variables used in the analyses. Finally, if a firm issues multiple bonds in a given year, we

retain only the bond issue with the largest principal amount.

Our final sample contains 3,633 publicly issued bonds from 31 countries over the period 1992

to 2005. Table 1 provides an overview of the sample composition by country and year. The table

presents the number of unique firms, the total number of firm-year observations and the proportion

of ADR firm-year observations. It also contains descriptive information on the bond spreads, which

we discuss in Section 3.2.1. In Panel A, we note a large percentage of bonds issued by Japanese

firms (58%). Thompson Deals and Mergent FISD have a bias towards Japanese bonds (about 30%

of all bond issues outside the U.S. are from Japanese borrowers), and this bias is further

strengthened by the comprehensive coverage of Japanese firms in Worldscope. Moreover, prior

evidence suggests that Japanese firms moved away from bank debt towards public debt financing in

the 1990s (Hoshi et al., 1993). Aside from Japan, only Canada, South Korea and the U.K. comprise

13 This matching procedure does not allow us to identify bonds that are issued by a subsidiary of the firm if this

subsidiary is incorporated under a different name, domiciled in a different country or belongs to a different

industry than its parent company. Even if we were to include bonds issued by the subsidiary, we would not be able

to tell whether the parent company provides implicit guarantees or what country legal environment is relevant

(country of the parent or country of the subsidiary). As a result, it would be difficult to choose the necessary firm

specific controls in our empirical tests.

11

more than 5% of the bond sample. No unusual pattern is apparent in the bond sample over time

(Table 1, Panel B).

3.2. Variable Definitions and Descriptive Statistics

3.2.1. Bond Spreads and Cross-Listing Variables

On a conceptual level, we are interested in the price of raising new public debt capital after

U.S. equity cross-listings. We measure the cost of public debt, our primary dependent variable, as

the offering yield spread of bonds. The Bond Spread is defined as the yield-to-maturity at issuance

minus the contemporaneous yield of a U.S. Treasury security with the same maturity and the closest

coupon rate.14

We draw the cross-listing status variables, our primary independent variables, from a

comprehensive data set of active and inactive U.S. equity cross-listings using information from

Citibank, JP Morgan, Bank of New York, Datastream and Bloomberg (see Hail and Leuz, 2009).

We use this panel to construct binary variables indicating the existence and type of a U.S. equity

cross-listing in a given year.15 We differentiate between three ADR types: (1) exchange listings on

NYSE, Nasdaq, and Amex (EXCH), (2) over-the-counter listings in the Pink Sheets or the OTC

Bulletin Board (OTC), and (3) private placements under Rule 144A (PP). This distinction reflects

different regulatory consequences. Foreign firms with a U.S. exchange listing have to file Form 20-

F with the SEC, requiring extensive disclosures and, during our sample period, a reconciliation of

14 We adjust for U.S. treasury rates to control for shifts in the interest rates and the time preference for money. As a

result of using U.S. risk-free rates, about half of the bond spreads are negative, in particular, in countries with

prime rates below those in the U.S. (e.g., Japan). To test the sensitivity of our results to this research design choice,

we also run the regression analyses using alternative dependent variables (see Section 4.2.3). These alternative measures for the cost of debt do not affect the inferences from our tests.

15 This coding, in principle, accounts for the sequence of U.S. cross-listings for a given firm. However, because of

the imbalanced structure of the public bond data, our sample only includes cross-listed firms that exhibit no

change in ADR type over time. Including all ADR firms (i.e., allowing for changes in ADR type) does not unduly

affect the inferences from our tests (analyses not reported).

12

foreign financial statements to U.S. GAAP.16 Moreover, by virtue of filing with the SEC, firms are

subject to SEC enforcement and could face legal liabilities from shareholder litigation. Cross-

listings in the OTC markets do not require a 20-F filing, but have to file a registration statement

using Form F-6 and home-country disclosures to the SEC. They are also subject to Rule 10b-5 and

the Foreign Corrupt Practices Act, under which SEC enforcement actions and private securities

litigation can be brought. Private placements do not require any registration with the SEC or any

additional disclosures, but are only made available to qualified institutional investors.

Finally, we recognize that cross listing represents a voluntary choice on the part of the firm.

To address this potential self-selection issue, we construct a Cross-listing Firm indicator variable

marking the entire time-series of ADR firms regardless whether the ADR has already been issued or

not. The purpose of this variable is to control for time-invariant selection effects, i.e., potentially

significant differences between firms that choose to cross list and firms that do not, which could

induce a spurious correlation between the variables of interest.17 Including this variable in the

regression model, in the spirit of a difference-in-differences analysis, allows us to identify the cross-

listing effect by comparing the post-cross-listing observations to the pre-cross-listing observations

of the same firms as well as to observations from firms that never cross list.

As reported in Table 1, the main sample comprises 671 bond-year observations (or about

18.4% of the bond sample) from firms with contemporaneous U.S. equity cross-listings. The table

also provides univariate evidence on the price of debt differential between firms with and without

ADRs. For 19 out of the 31 countries the average bond spread is larger for non-ADR firms.

16 We include Canadian firms in this group because they can directly list their shares on U.S. exchanges without

using depository receipts and, at the same time, are exempted from certain U.S. reporting requirements under the

Multi-Jurisdictional Disclosure System. 17 When we replace the aggregate Cross-listing Firm indicator by three distinct firm indicators for each ADR type,

the results remain largely unchanged.

13

3.2.2. Bond-Specific Control Variables

In our multivariate regression analyses we control for a large set of bond characteristics that,

as suggested by prior literature, potentially affect the offering yield spreads.18 Bond Maturity

measures the number of months from the date of issuance until maturity. Longer maturities increase

the risk and, therefore, should require higher yields. Bond Size equals the principal amount at the

date of issuance, denominated in US$ million. Larger bonds increase the risks of default, but at the

same time are expected be more actively traded thereby lowering the liquidity premium. To capture

a bond’s default risk, we create a binary indicator variable (Investment Grade) equal to 1 if the

bond’s credit rating is BBB- or higher (Standard & Poor’s) or Baa3 or higher (Moody’s). If issue-

specific credit ratings are missing (i.e., for about 75% of the sample), we use Altman’s (1968) Z-

score to determine firms’ investment grade status in a given year.19 Riskier firms should pay larger

spreads. Callable, Convertible and Subordinated are three indicators set equal to 1 if the issuer

retains the privilege of redeeming the bond before maturity (positive effect on yield), the bond can

be converted into shares of the issuing firm (negative effect), and the bond ranks after other debt

instruments in case of liquidation (positive effect), respectively. Finally, in an attempt to measure

firms’ reputation in the market, we define a Previous Bond Issues indicator that assumes the value

of 1 if the firm has issued other bonds over the last two fiscal years. Firms with a reputation have

already shared information with market participants and, hence, should face lower information

asymmetries. Table 2 provides descriptive statistics for the various public bond attributes.

18 For our analyses, we truncate all dependent and independent variables at the first and 99th percentile, except for

variables with natural lower or upper bounds, and use the natural logarithm where indicated in the tables. 19 Using Worldscope financial data, we compute the Z-score as (1.2*working capital + 1.4*retained earnings +

3.3*EBIT + 0.999*sales)/total assets + (0.6*market value of equity/book value of total liabilities). We then assign

investment grade status based on a cutoff value of 2.675 as defined by Altman (1968). We find high and

significant correlations between the investment grade indicator based on Altman’s Z-score and the one based on

ratings for the subsample of bonds that have ratings available.

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3.2.3. Firm-Specific and Macroeconomic Control Variables

We further control for a series of firm characteristics that are likely associated with debt

funding needs and offering yield spreads. We measure firm size by using Total Assets in US$

million. Larger firms should obtain more favorable debt terms given their reputation and tangible

asset base. In addition, size is a proxy for information asymmetry between firms and investors.

Market-to-Book is the ratio of market value of equity to book value of equity. Firms with valuable

growth options (i.e., high market-to-book ratios) need more financing, and are more likely to

generate sufficient cash flows to repay the principal, but might also be more risky. An increase in

Leverage, measured as the ratio of long-term debt divided by total assets, increases the probability

of future default, and therefore leads lenders to require higher returns. We define Return on Assets

as the ratio of operating income divided by average total assets. Firms with low profitability should

have to pay higher interest rates. Tangibility stands for the quality of assets available as collateral,

and equals firms’ book value of property, plant and equipment scaled by total assets.

Our final set of control variables attempts to capture factors in the macroeconomic

environment that influence the price of debt. We measure Inflation as the median monthly

percentage change in the consumer price index in a given country and year (source: Datastream).

High inflation typically translates into higher interest rates on government securities and, as a result,

higher rates for corporate debt. We also control for countries’ financial development and long-run

growth prospects by including the annual gross domestic product GDP (measured in constant US$)

and the GDP Growth (source: World Bank). In addition, we include Country Creditworthiness

reflecting the credit ratings of countries’ sovereign debt, which serves as benchmark for corporate

bonds. This variable, measured annually, is based on a survey of leading international bankers by

Institutional Investor. It ranges from 0 to 100, with higher values reflecting greater creditworthiness.

Finally, we account for the fact that most international bonds are denominated in currencies other

15

than US$. Hence, an adjustment of the bonds’ offering yields-to-maturity by U.S. Treasury rates

might be insufficient. We include Exchange Rate Volatility measured as the coefficient of variation

of daily US$ to local currency exchange rates in a given year (source: Datastream). Table 2 presents

descriptive statistics on the firm characteristics and macroeconomic factors for the sample.

4. Net Benefits of U.S. Equity Cross-Listings in Public Debt Markets

In this section, we first test if and how firms’ propensity to issue bonds changes after equity

cross-listings in the U.S. This allows an initial assessment of the relevance of our research question.

We then examine the average economic consequences related to this choice, i.e., whether the

offering yield spreads are affected by the cross listing decision, and how the debt market effects

vary across cross-listing type.

4.1. Change in Propensity to Issue Public Debt

4.1.1. Research Design

According to Myers (1984), firms that face high costs of asymmetric information use external

funds only when internally generated funds are not adequate. If external funds are required, firms

will issue debt first, and then equity.20 One of the advantages of private lenders is their preferred

access to inside information as well as their superior monitoring and screening functions. However,

when companies cross list in the U.S. and subject themselves to higher disclosure standards and a

more rigorous enforcement regime, the informational advantage of private lenders declines.

Therefore, private debt financing becomes relatively more costly than public debt, and firms should

increasingly rely on arm’s length debt financing. To test these assertions, we specify the following

probit model, which estimates the propensity of raising new capital in public debt markets after U.S.

equity cross-listings:

20 The value of debt changes least when inside information is revealed to the market (Myers, 1984).

16

Public debt issuancei,t = 0 + 1aPPi,t + 1bOTCi,t + 1cEXCHi,t + 2Cross-listing firmi +

jFirm-specific controlsi,t + kCountry, industry and year fixed effectsi,t + i,t. (1)

The dependent variable is a public debt issuance indicator that takes on the value of 1 if a firm

issues bonds in a given year and zero otherwise. The primary variables of interest are the three ADR

types (PP, OTC, EXCH). The coefficient estimates on these variables reflect the marginal change in

the propensity to issue debt compared with the pre-cross listing period of the same firms and a

benchmark sample consisting of firms that do not cross list. To control for potential selection effects

in the pre-period, we include the Cross-listing Firm variable in the model. We also control for a

series of firm attributes that have been shown to be associated with debt funding needs.

We include Total Assets, Leverage, Tangibility, Return on Assets, Market-to-Book and the Z-

score, as previously described in Section 3.2. In addition, we include the following variables:

Negative Earnings takes on the value of 1 if the firm reports operating losses in a given year, and 0

otherwise. Loss firms are expected to face more scrutiny from investors, reducing their propensity

to issue debt. We define Funding Needs as net cash flows from operations divided by total assets.

We multiply this variable by -1 so that higher values stand for greater funding needs (i.e., more

external financing). Return Variability is a proxy for the firm’s riskiness, and is computed as the

annual standard deviation of monthly stock returns, using Datastream stock price information.

Finally, we control for time-invariant unobserved heterogeneity across firms by including country,

one-digit SIC industry and year fixed effects. To further alleviate concerns about unobserved

within-firm correlations, we compute the statistical significance levels of the coefficient estimates

based on standard errors that are clustered by firm.

4.1.2. Results

Table 3 reports the results on the propensity of public debt issuance after U.S. equity cross-

listings. The sample for this analysis consists of up to 93,234 firm-year observations representing

17

the entire Worldscope universe with sufficient data to compute all control variables. Out of these

observations about 6 percent represent years with public bond issues. Models 1 and 2 vary

depending on whether we include all firms with data available or only the cross-listing firms. In

Model 3 we limit the sample to firms that at some point during the sample period either issued

public bonds or private debt in the form of syndicated loans.21 This sample choice conditions our

propensity analysis to only include debt-issuing firms, and gets at the issue of whether firms are

switching from one form of debt financing to another. In Model 4, to investigate the choice between

public and private debt financing in more detail, we limit the sample to the firm-year observations

with actual public bond or syndicated loan issuances. In this analysis, by construction, a value of

zero of the dependent variable represents years in which firms tapped into the syndicated loan

market, while a value of one stands for public bond financing. The table reports coefficient

estimates from probit regressions together with z-statistics (in parentheses).

We find significantly positive coefficients on EXCH for all specifications. PP is also

significant in all models except when we focus on the firms’ decision to issue a bond or a

syndicated loan (Model 4). The OTC coefficient is positive, but significant only in the last

specification.22 Overall, our evidence indicates that firms tend to raise more debt after an equity

cross-listing in the U.S., and seem to substitute private debt for public debt.23 This shift in the debt

structure suggests a decrease in information asymmetry between the firm and its lenders, consistent

with the argument of Leland and Pyle (1977), Diamond (1984) or Fama (1985) that firms with a

lower degree of information asymmetry prefer public debt to bank debt. The control variables

generally have the expected sign. Consistent with Houston and James (1996) or Cantillo and Wright

21 We draw the syndicated loan data from Dealscan. See also Section 5.2. 22 The magnitude of the coefficient estimates suggests that cross listing in the U.S. increases the probability that a

company will issue public debt from 0.4 percent (Model 1) up to 18 percent (Model 4). 23 Unreported analyses in which we use a private debt issuance indicator as dependent variable confirm this finding

(i.e., the dependent variable takes on the value of 1 if a firm issues a syndicated loan in a given year). Significantly

negative coefficients on OTC and EXCH throughout Models 1 to 4 suggest that the propensity to take on

syndicated loans declines after U.S. equity cross-listings.

18

(2000), we find that larger and more leveraged companies issue more debt. Also, companies with

greater growth opportunities and funding needs raise more debt. Tangibility is not significant,

probably because it is highly correlated with firm size. Contrary to Denis and Mihov (2003), we

find in Models 1 and 2 that foreign firms with higher credit quality (high Z-Scores) are less likely to

issue public debt.

Overall, the results in Table 3 suggest that cross-listings clearly represent an important factor

affecting firms’ optimal capital structure, and that public debt becomes relatively more attractive.

While the positive coefficients on EXCH and, to a lesser degree, on OTC are consistent with the

U.S. legal environment providing some bonding benefits, the results, in particular with respect to

private placements, also suggest that other factors such as an expansion in growth opportunities or

firm-specific investments go hand-in-hand with U.S. cross-listings (e.g., Hail and Leuz, 2009).

4.2. Effects on Offering Yield Spreads of Public Debt

4.2.1. Research Design

Moving beyond descriptive analyses, our main tests focus on the economic consequences of

debt financing in conjunction with an equity cross-listing in an environment with extensive

disclosure rules and tight enforcement mechanisms. To empirically investigate whether firms cross-

listing in the U.S. obtain public debt related benefits, we estimate the following ordinary least

squares (OLS) regression model:

Bond Spreadsi,t = 0 + 1aPPi,t + 1bOTCi,t + 1cEXCHi,t + 2Cross-listing firmi +

jBond-specific controlsi,t + kFirm-specific and macroeconomic controlsi,t +

lCountry, industry and year fixed effectsi,t + i,t. (2)

Our primary variables of interest are the three ADR types (PP, OTC, EXCH). The coefficient

estimates represent the marginal effects of U.S. equity cross-listings on the offering yield spread for

public bonds after controlling for all the bond attributes, firm characteristics, macroeconomic

19

factors and fixed effects (see Section 3.2 for variable descriptions). The identification of the cross-

listing effects stems from the bond-years before the cross listing as well as the firms that do not

cross list. Again, we attempt to control for potential selection effects by including the Cross-listing

Firm indicator.24

4.2.2. Results

Table 4, Panel A, presents our main results, i.e., the effects of U.S. equity cross-listings on

public debt offering yields. In these analyses our sample is limited by the existence of debt data,

reducing the number of firm-year observations to 3,633 bonds from 31 countries between 1992 and

2005 (see Table 1). The four columns present results utilizing the largest bond per firm-year. That is,

if a firm has multiple debt issues in a given year, we retain only the bond with the largest principal

amount. We present results for three stages of the regression model. In Model 1 we only include the

bond controls. Next, we recognize that bond features such as maturity or covenants likely are

determined simultaneously with offering yields, and therefore might produce biased estimates when

included as explanatory variables. Thus, we estimate Model 2 in a reduced form that only includes

the (exogenous) firm and macroeconomic controls (e.g., Qian and Strahan, 2007; Miller and Reisel,

2009). Model 3 corresponds to the full specification. Finally, in Model 4 we show results replacing

the OTC and EXCH variables with an aggregate XLIST variable. The table reports OLS coefficient

estimates, and t-statistics based on robust standard errors that are clustered by firm (in parentheses).

The results in Table 4 indicate that the yield spreads of public bonds are lower after firms

cross list their shares in the U.S., but only for exchange listings and OTC instruments. This result

holds regardless of model specification. It also suggests, in line with legal bonding, that the host

country’s institutional environment and the regulatory consequences related to the cross-listing are

24 Note that since we do not have a balanced panel, we cannot sensibly run firm-fixed effects regressions for our

sample. However, by clustering standard errors at the firm level, we implicitly control for unobserved within-firm

correlation over time. Moreover, the results remain virtually the same when we apply two-way clustering by firm

and year.

20

important for public borrowing. For U.S. exchange listings, the coefficient estimates imply a

decrease in bond offering yield spreads of about 48 basis points. This translates into yearly cost

savings of US$ 0.85 million based on the average bond size of US$ 177 million. The results are

similar in magnitude and significance levels for firms with OTC cross-listings.25 Consistent with the

bonding arguments, the debt benefits are nonexistent for firms with private equity placements. The

control variables are mostly significant and have the expected sign. Firms with larger issue amounts,

non-convertible and subordinated debt or firms that did not access the bond market previously

exhibit larger spreads. Also, small, leveraged and less profitable firms as well as firms domiciled in

countries with poorer economic growth, lower creditworthiness and more volatile exchange rates

display significantly higher bond spreads.

4.2.3. Sensitivity Analyses

To assess the sensitivity of our results, we conduct several robustness checks and report

results in Panel B of Table 4. The table presents only the coefficient estimates of the cross-listing

variables, but we include all the bond-specific, firm and macroeconomic control variables in the

estimation (see Model 3 in Table 4, Panel A). First, we assess the sensitivity of the results to our

adjustment of the bond offering yields by congruent U.S. Treasury rates. In the first row we present

results with bond spreads adjusted by risk-free rates on government securities in the issuing firm’s

country of domicile, matched by maturity. This takes into account factors like the country’s credit

risk rating or inflation, which likely affect local interest rate term structures. In the next

specification, we adjust the raw bond yields by interest rates on government securities from

countries with the same currency as the bond issue (again, matched by maturity). For instance, if a

French firm issues a bond in US$ we adjust by the corresponding U.S. Treasury rate thereby

25 Contrary to the equity cross-listing literature (e.g., Doidge, Karolyi, and Stulz, 2004, 2009; Hail and Leuz, 2009),

we do not find a larger effect of U.S. exchange listings relative to OTC instruments. This might be due to the

nature of debt contracts, or institutional forces differentially affecting debt compared to equity (see also Section 5).

21

controlling for exchange rate effects. In further tests (not reported), we also use the raw yields-to-

maturity as a dependent variable on the left-hand side and include the risk-free rates as an additional

control variable. This prevents the model from forcing a coefficient of -1 on the adjustment factor.

Regardless of the adjustment mechanism, the cost-of-debt effects of U.S. equity cross-listings are

similar if not stronger compared with those reported in our main analyses.

Second, we investigate whether our main public bond results are sensitive to different sample

selection choices. Instead of picking the largest bond issue per year when a firm issues multiple

bonds, we first randomly select a bond per year (row 3). Allowing for multiple firm-year

observations, we then include all bonds in the sample (row 4). In both cases the results remain

largely unchanged. Next, we assess the impact of the country with the largest number of sample

observations, Japan, on the results. We do so by randomly selecting only 300 Japanese firm-year

observations to reduce Japan’s weight to a level comparable with other large sample countries like

Canada or South Korea (row 5). While the coefficient estimates are similar in magnitude and

consistent with the main tests, the results are weaker indicating a drop in power due to the large

reduction in sample size (from 3,633 to 1,796). In row 6, to get closer to a true difference-in-

differences design, we require each ADR firm to have observations in both the pre- and post-cross-

listing period. Finally, we remove the bonds with a convertibility feature because they might have a

significantly different payoff structure than straight bonds (row 7). The results in these last two

specifications remain largely unchanged.

In additional sensitivity tests (not reported), we add control variables for whether a bond is

classified as a so-called Yankee bond (i.e., is issued and cross-listed in the U.S. by a foreign firm),

or the firm has cross-listings in other countries outstanding, namely on the London Stock Exchange

(LSE). None of these additional binary indicators affects the interpretation of the U.S. cross-listing

variables in our model. While Yankee bonds only comprise a small portion of the sample, the

22

positive but insignificant coefficients on the LSE variable suggest that legal bonding primarily

applies to the U.S. regulatory system (Doidge, Karolyi, and Stulz, 2009; Hail and Leuz, 2009).

Similarly, when examining whether the effects are still present after the passage of the Sarbanes–

Oxley Act (SOX) in 2002, we find that, after including an indicator variable marking the post-SOX

years of ADR observations, the debt market benefits become even more pronounced. Finally, we

also address the possibility that public debt holders anticipate the cross listing decision and require

lower yields even before the firm’s shares trade in the U.S. While this effect should generally bias

against finding results, we re-run the analyses after eliminating a two-year window around the

cross-listing. Our inferences remain the same.

5. Cross-Sectional Variation in Debt Market Benefits of U.S. Equity Cross-Listings

In this section, we investigate whether the, on average, lower offering yield spreads for public

bonds after U.S. equity cross-listings vary along the institutional features of the issuing firms’

country of domicile. We also examine the variation across firm-specific characteristics reflecting

changes in the agency costs between debt and equity holders. Finally, we assess the impact of

market structure, and investigate whether the benefits shown for public debt extend to syndicated

loans. These analyses attempt to shed light on the underlying forces driving our results.

5.1. Country and Firm Characteristics Affecting Offering Yield Spreads of Public Debt

5.1.1. Research Design

The bonding argument stipulates that firms located in a weak institutional environment should

benefit the most from subjecting themselves to extensive disclosure requirements and effective

enforcement mechanisms. As long as legal bonding comes at a cost (e.g., in terms of heightened

threats of litigation or greater pecuniary costs of financial misreporting), it allows firms to credibly

commit to improved transparency and enhanced minority investor protection, and hence, should

23

reduce the agency costs of controlling shareholders. Yet, debt holders primary concerns are the

timely receipt of interest and principal payments, particularly in case of financial distress. In such

instances, the formal legal procedures to restructure distressed debt in the country of domicile of the

issuing firms as well as the location of the assets serving as collateral could mitigate the monitoring

and information benefits that come with U.S. equity cross-listings. Furthermore, cross listing equity

might increase the risk of wealth transfers from bondholders to shareholders (e.g., via more risky

and opportunistic investment decisions), increasing the degree of price protection by lenders.

To empirically test these cross-sectional predictions, we adjust the OLS regression model

provided in equation (2) as follows:

Bond Spreadsi,t = 0 + 1aPPi,t + 1bXLISTi,t + 1cXLISTi,t * Partitioning Variablei +

2Cross-listing firmi + jBond-specific controlsi,t + kFirm-specific and

macroeconomic controlsi,t + lCountry, industry and year fixed effectsi,t + μi,t. (3)

In equation (3), we split the exchange-listed and over-the-counter traded ADR firms into two

groups by interacting the aggregate XLIST variable with a binary partitioning variable. This lets us

separately analyze the marginal effects of U.S. equity cross-listings for the two subgroups. While

the coefficient on the main effect ( 1b) compares the bond offering yield spreads of the base group

to the pre-cross listing years of the same firms as well as the firms that do not cross list, the

coefficient on the interaction term ( 1c) represents the incremental effect for the second group of

cross-listed firms (over and above the base group). The rest of the model remains the same as

previously defined (see Sections 3.2 and 4.2.1).26

We group the partitioning variables into three main categories. The first category tries to

capture the legal bonding arguments, provided they do not pertain directly to the enforcement of

26 In supplementary analyses we also allow for differential effects of U.S. equity cross-listings on all three ADR

types (i.e., we separately interact the PP, OTC and EXCH variables with the partitioning variables). Since the

results are largely the same and the inferences drawn do not differ compared with our aggregate specification, we

only report the results of the simplified specification in the text.

24

debt contracts. To measure the quality of local Disclosure Regulation, we draw on La Porta, Lopez-

de-Silanes, and Shleifer (2006). Higher values of their index of disclosure requirements in securities

offerings represent more transparent disclosures. The lower the quality of local disclosure rules, the

more should debt holders benefit from extensive U.S. filing and disclosure requirements in reducing

the ex ante and ex post costs of debt contracting. Next, we use the average block premiums paid in

control transactions as a proxy for the existence of Private Benefits of Control (Dyck and Zingales,

2004). Higher values indicate more expropriation of minority investors. In turn, minority investors

are reluctant to provide additional funding. This reluctance should extend to the providers of debt

capital, unless they can avoid wealth expropriation by improved monitoring or designing the debt

contracts in a way that better protects their interests. Finally, we measure the relative importance of

public debt financing by computing the Equity-to-Bond Market ratio as countries’ aggregate market

capitalization of stocks divided by the market capitalization of bonds (source: World Bank). In

countries with dominant equity markets the financial reporting system and the legal institutions

might not be geared towards the informational demands and procedural needs of debt holders (e.g.,

Ball, Robin, and Sadka, 2008). In addition, firms domiciled in countries with less developed bond

markets might benefit from an increase in visibility and higher liquidity after U.S. cross-listings.

The second category of partitioning variables focuses on debt-specific institutions in the

country of domicile, which likely affect the availability, structure and terms of debt contracts (La

Porta et al., 1997; Qian and Strahan, 2007). Our first proxy, Legal Tradition, distinguishes between

code law and common law countries (La Porta et al., 1997). Under common law regimes,

accounting practices are determined primarily in the private sector. Thus, investors express a high

demand for timely and conservative accounting information, leading income to exhibit substantial

volatility over time (Ball, Kothari, and Robin, 2000). In addition, better legal protection under

common law allows providers of debt to offer better terms, fostering the widespread use of public

25

debt financing (La Porta et al., 1997). Next, we use the updated Creditor Rights index from

Djankov, McLiesh, and Shleifer (2007), with higher values indicating better creditor protection.

This index aggregates several rights that secured creditors have in the case of liquidation and debt

reorganization. Finally, we employ the Debt Enforcement Efficiency score from Djankov et al.

(2008), which is measured as the discounted terminal value of a typical firm after bankruptcy costs

(in percent of firm value before the firm entered the bankruptcy proceedings). Higher efficiency

scores suggest better chances of debt recovery and quicker resolution of uncertainty.27

Our third category of partitioning variables attempts to capture the extent of the agency

conflicts between debt and equity holders. First, we use the Conversion Option present in some

bond contracts, which significantly reduces lenders’ risk arising from asset substitution and wealth

expropriation. When bondholders convert their debt securities into stock, their incentives become

fully aligned with external shareholders. Thus, in light of rising agency conflicts, the conversion

feature should become more valuable after equity cross-listings. Next, we use observable firm

attributes to create an Agency Factor capturing potential conflicts of interest between debt and

equity holders. Namely, we extract the first and primary factor using financial leverage (long-term

debt divided by total assets), growth opportunities (market-to-book ratio), ownership concentration

(percentage of closely held shares), earnings volatility (standard deviation of annual earnings per

share over the last five years), and stock return volatility (annual standard deviation of monthly

stock returns). The factor loadings suggest that shareholders’ incentives to expropriate wealth from

lenders increase with growth opportunities, their controlling share in the firm, the volatility of

earnings and stock returns, and decrease with leverage. To remove country effects and time trends

in the data, we then subtract country-year medians from each factor score. We construct our binary

partitioning variable by splitting the ADR firms by the median factor score (i.e., a value of 1 stands

27 When we repeat the country-level analyses using the country of debt issuance instead of the country of domicile to

partition the ADR observations, the results are very similar and none of the inferences changes.

26

for higher agency conflicts). Because the pricing of debt likely is also affected by changes in agency

conflicts around U.S. equity cross-listings, we define a Change in Agency Factor variable by

comparing the mean pre-cross-listing factor scores to the factor scores at the time of the debt

issuance after cross-listing. Again, we assign the value of 1 to ADR firms with above median

changes.

Table 5 presents descriptive statistics on the country-specific institutional factors. For the

regression analyses, we transform the continuous variables into binary indicators using the sample

median as a cut-off. Moreover, for the institutional factors that themselves might reflect the generic

characteristics of the legal system (i.e., disclosure regulation, private benefits of control, creditor

rights, and debt enforcement efficiency), rather than using raw values, we partition the ADR

observations based on the residuals from a regression of the institutional variable on countries’ legal

origin (La Porta et al., 1998) and the log transformed gross domestic product per capita.28

5.1.2. Results

We present the results of the cross-sectional analyses in Table 6. The table reports only the

ADR variables together with the interaction terms, but the full set of bond attributes, firm-specific

and macroeconomic control variables, and the various fixed effects are included in the regressions.

The table reports OLS coefficient estimates, and (in parentheses) t-statistics based on robust

standard errors that are clustered by firm.

In Panel A, we consider institutional factors that do not pertain directly to debt enforcement,

and find evidence corroborating the notion of legal bonding. All the coefficients on the interaction

terms of the partitioning variables with XLIST point in the right direction and are significantly

different from zero. The findings suggest that firms domiciled in countries with lax disclosure

28 We note that our results are consistent, albeit slightly weaker, when we use the raw values of the institutional

variables to partition the set of cross-listing observations.

27

regulation, higher private benefits of control and underdeveloped local debt markets benefit the

most from a certification role of U.S. equity cross-listings. These firms are able to raise public debt

at a lower cost compared to cross-listed firms from countries with transparent reporting regimes,

small control benefits, and well-developed debt markets. The latter firms still display better public

borrowing terms compared to firms without a cross-listing (significant in two out of three cases).

Consistent with our main analyses, we find that the institutional background only matters for firms

with a U.S. exchange listing or whose shares are traded in the over-the-counter markets, but not for

firms with a private equity placement.

In Panel B, we report results on the debt-specific enforcement institutions. Contrary to the

more general institutional factors and to what one would expect under the legal bonding argument,

we find that only firms domiciled in countries with common law legal tradition and strong creditor

protection receive lower bond spreads at the date of issuance. The coefficients on the XLIST

interaction term are negative and highly significant for these two partitions. In the case of the debt

enforcement variable, the offering yield spreads between the two groups of ADR firms do not differ

in a statistical sense. The results suggest that, when it comes to creditor protection and the formal

procedures dealing with debt recovery and insolvency, cross listing stock in the U.S. does little to

overcome weak institutions in the country of domicile. This finding is consistent with Miller and

Puthenpurackal (2002), and underscores that legal procedures protecting borrowers are less fungible

than legal procedures protecting shareholders.

With respect to the agency conflicts (Panel C), we find, consistent with expectations, that

equity cross-listings in the U.S. exacerbate agency conflicts between debt and equity holders. In the

first column, we report results partitioning the ADR observations based on the existence of a

conversion option. The results suggest that convertible bonds have significantly lower spreads after

cross-listing, not only compared to straight bonds, but also compared to convertible bonds before

28

the cross-listing. At the same, straight bonds continue to benefit from lower offering yield spreads

after equity cross-listings. Because the results imply that the conversion feature effectively allows

debt holders to align their interests with shareholders, we drop the convertible bonds from the

remaining analyses. Regardless of whether we use the Agency Factor or the Change in Agency

Factor to partition the ADR observations, bondholders seemingly demand higher yields after cross

listing when agency conflicts are elevated.29 The coefficients on the interaction terms of XLIST with

the partitioning variables are in both cases positive and significant. Thus, the evidence points to

increased agency costs for providers of public debt after U.S. equity cross-listings, consistent with

greater risks of wealth expropriation. It seems that country-level governance factors are not able to

completely overcome firm-level agency issues, and that the latter influence the interest rate terms of

public debt contracts (e.g., Klapper and Love, 2004; Doidge, Karolyi, and Stulz, 2007).

5.2. Effects of U.S. Equity Cross-Listings in Private Debt Markets

5.2.1. Research Design

As an additional validity check, we extend our analysis to a different market setting, namely

syndicated loans. Syndicated loans are private lending contracts. Similar to public debt, they have

credit ratings and trade in secondary markets. They are usually structured in packages of multiple

loans with varying maturities. Syndicate members can be either senior participants (lead arrangers)

or junior participants. Lead arrangers are responsible for designing the loan contract and monitoring

the borrower’s compliance with its terms. In exchange they receive significant fees. Lenders utilize

a number of protective features in the contract design and, if needed, renegotiate contractual terms

with borrowers at relatively low cost. For instance, they can simply adjust the publicly disclosed

accounting numbers and the covenant ratios to their needs (e.g., Leftwich, 1983; Beatty, Weber, and

29 In untabulated analyses, we re-run the models using each of the five agency proxies underlying the factor analysis,

and find generally weaker but consistent results to those reported in the text.

29

Yu, 2009). Bank lenders also have access to private information and often build strong ties with

borrowers through additional (advisory and financing) services. As a result, one would expect

interest rates of private debt contracts to be less affected by cross-listing decisions, country-level

governance mechanisms, and firm-specific agency conflicts (e.g., Gigler et al., 2009).

To examine these conjectures, we employ a similar sample selection procedure and research

design as in the case of the public bond data. We start with retrieving syndicated loan data from

Dealscan, and then manually match them to financial information in Worldscope.30 Next, we

remove financial firms, require loan facility amounts of at least US$ 10 million, and only retain the

loan issue with the largest facility amount in a given year. These criteria lead to a final sample of

2,828 syndicated loans from 38 countries over the period 1992 to 2005, of which 663 loan-year

observations come from firms with U.S. equity cross-listings outstanding.

We estimate the same OLS regression model as presented in equation (2), but replace the

bond-specific variables with a comprehensive set of loan characteristics. That is, we define the

dependent variable, Loan Spread, as the all-in-spread provided by Dealscan, i.e., the amount the

borrower pays (including annual fees) over LIBOR or an equivalent rate for each dollar drawn

down. We use the following loan-specific control variables: Loan Maturity, Loan Size, and

Investment Grade are defined similarly as for the public bond sample, and should assume the same

signs. Term Loans measures the percentage of individual loans in the loan package with a fixed

repayment schedule and maturity. Because of their long-term nature, term loans should be

positively related to yield spreads. The Number of Lenders equals the number of individual banks

participating in the deal syndicate. More syndicate members allow for better risk sharing thereby

lowering the expected spreads. Previous Loan Issues, equal to the number of syndicated loan

30 Syndicated loans represent a significant source of financing. Over the last few years these types of loans have

generated more underwriting revenue than either the equity or the bond market (e.g., Altunbas et al., 2006).

Dealscan collects syndicated loan data for public firms from local regulatory filings, SEC filings (if the firm is

listed in the U.S.), or through its connections with the major banks in the syndicated loan market.

30

packages previously taken by the same borrower, proxies for firms’ reputation in the syndicated

loan market. Borrowers with good reputation likely receive better terms. Performance Pricing and

Revolver are two indicator variables reflecting whether the interest rate of the loan is tied to a firm’s

performance and credit rating, or the loan comprises a renewal option. Both features should

decrease monitoring costs. Finally, we include three Purpose of Loan indicators that mark if the

loan was taken to repay existing debt, invest for corporate purposes, or finance working capital

needs.

5.2.2. Results

In Table 7, we report results for the syndicated loan analyses. Contrary to the public debt

sample, we find that offering yield spreads of syndicated loans are not affected by U.S. equity cross-

listings. None of the ADR coefficients in Models 1 and 2, which correspond to the analyses of the

average offering yield effect, are statistically significant. This suggests that in markets with private

monitoring and communication opting out of the local institutional environment does not improve

the lender’s position. The members of the loan syndicate are not adjusting the required interest rate

consistent with being already in a position to properly monitor the risks and, if needed, make other,

non-price adjustments to the loan contracts.31 Models 3 and 4 contain the results of the cross-

sectional analyses, in which we split the ADR firms by the Agency Factor and Change in Agency

Factor variables. Again, none of the cross-listing variables is significant, indicating that private

lenders are already protected against elevated agency cost levels after U.S. equity cross-listings, or

alternatively use other means than interest rate terms to renegotiate or adjust lending contracts. Such

an outcome is expected for this type of debt as it puts less weight on external monitoring

mechanisms like a general improvement in disclosure requirements and enforcement institutions.

31 Following the arguments of Gigler et al. (2009), bank lenders likely change other terms of the loan contract that

enhance their ability to monitor the borrowers as a result of a U.S. cross listing (e.g., increase the number of

protective covenants, add performance pricing features or require more revolving loans). Unfortunately, the

information on Dealscan is incomplete and does not allow us to pursue these alternative channels empirically.

31

The private debt results are robust to the inclusion of an extensive list of loan characteristics,

firm attributes, macroeconomic factors and various fixed effects that in total explain about 40% of

the variation in the dependent variable. Most of the control variables assume the correct sign and are

statistically significant. Overall, the results emphasize that in this setting lender specific monitoring

trumps the certification role of cross-listing equity shares in the U.S.

6. Conclusions

In this paper, we examine whether cross listing equity shares in the U.S. grants foreign firms

access to more and cheaper debt financing. Prior literature shows extensive equity market benefits

associated with U.S. cross-listings, in particular for firms domiciled in countries with poor minority

shareholder protection. However, it is not clear whether these benefits extend to security categories

other than the one cross-listed in the U.S., namely publicly traded debt instruments of the firm. This

is important because, on a global basis, debt markets are a much larger source of external finance

than equity markets. Also, issues such as the legal procedures in the case of default, or the shift in

agency conflicts between debt and equity holders after cross listing shares in the U.S. indicate that

results obtained from equity markets might not apply to debt financing.

We explore the above questions by employing a large global sample of public bonds, of which

about a fifth were issued after firms had cross-listed their equity shares in the U.S. We start with

analyzing the capital structure and the venue of debt financing, and find that ADR firms are more

likely to issue bonds, and seem to shift from private to public debt financing after cross-listing.

With regard to the economic consequences, we find strong evidence that firms with shares cross-

listed on U.S. exchanges or in the over-the-counter markets exhibit lower offering yield spreads.

Private equity placements as well as borrowing via bank debt display no such reaction. The results

are consistent with legal bonding generally applying to other types of securities, but playing a less

important role, if at all, in settings that allow private communication and monitoring.

32

When examining cross-sectional differences, we find that the reduction in bond spreads is

larger for ADR firms from countries with lax disclosure regulation, higher private control benefits

and underdeveloped local debt markets, but that equity cross-listings do little to overcome weak

creditor rights in the country of domicile. We also provide evidence that ADR firms with relatively

greater agency conflicts pay higher bond yields after equity cross-listings. Overall, our findings

underscore that legal procedures protecting public debt holders are less transferable across borders

than legal procedures protecting shareholders, and that equity cross-listings can create negative

spillover effects for the public debt investors in the firm due to a shift in agency conflicts.

Finally, several caveats are in order. First, our analysis focuses on two out of many possible

external-funding sources available to firms. We therefore only capture a very incomplete picture of

the complex issues firms face when selecting their capital structure. This also points to concerns

about self-selection, even though we attempt to control for the fact that debt-issuing firms with and

without equity cross-listings might differ along various dimensions. Second, our evidence suggests

that having shares cross-listed in the U.S., under certain conditions, can create public debt market

benefits. However, our analyses cannot preclude that the causality runs the other way, i.e., that

cross-listings are undertaken in light of already lower costs of public debt financing. In this case,

though, it would be hard to imagine that the results systematically vary across ADR types, countries

of domicile, agency conflicts and market structure along our predictions. Third, the reasons for why

legal bonding seems to work for some institutional features but not others are not yet well

understood. Our evidence points to the type of market (arm’s length vs. private monitoring) and the

nature of governance (e.g., commitment to disclosure vs. legal bankruptcy procedures), but more

evidence on the exact mechanism is needed. We leave this to future research.

33

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TABLE 1

Sample Composition and Descriptive Statistics by Country and Year

Panel A: Number of Observations and Spread Information for Public Debt Sample by Country

Firm-Years Mean Bond Spread

Country

Unique

Firms Total ADRs Non-ADRs ADRs

Argentina 10 24 12 4.38% 3.01%

Australia 30 50 21 2.00% 0.83%

Belgium 5 5 1 0.27% 0.15%

Brazil 11 11 5 4.95% 3.70%

Canada 131 259 90 2.08% 1.42%

Chile 7 8 4 3.55% 2.81%

Finland 11 18 8 3.27% 0.62%

France 67 160 45 -0.66% -0.10%

Germany 40 62 17 0.68% -0.69%

Greece 2 2 1 2.40% 0.39%

Hong Kong 15 17 4 0.92% -1.27%

India 14 20 11 2.87% 1.56%

Italy 14 24 12 1.04% 0.91%

Japan 786 2,137 142 -3.48% -3.02%

Korea (South) 126 261 63 1.31% 1.93%

Luxembourg 5 10 5 0.69% -0.96%

Malaysia 28 38 4 0.68% -0.10%

Mexico 13 16 13 3.80% 3.60%

The Netherlands 15 27 19 0.22% 1.49%

New Zealand 7 9 2 2.56% 1.05%

Norway 6 8 7 3.83% 0.05%

Peru 6 17 8 3.17% 4.12%

Philippines 5 8 3 2.96% 3.05%

Portugal 3 3 1 0.18% -0.55%

Singapore 22 36 9 -0.91% 0.11%

South Africa 4 4 2 5.10% 0.87%

Spain 10 23 7 1.11% 1.02%

Sweden 15 33 23 1.22% 0.65%

Switzerland 39 66 1 -1.84% -3.08%

Taiwan 62 94 29 -1.12% -0.79%

United Kingdom 88 183 102 1.35% 0.83%

Total 1,597 3,633 671 -2.06% 0.17%

(continued)

TABLE 1 — Continued

Panel B: Number of Observations and Spread Information for Public Debt Sample by Year

Firm-Years Mean Bond Spread

Year Total ADRs Non-ADRs ADRs

1992 247 14 -1.57% 1.19%

1993 331 25 -2.18% 0.37%

1994 262 19 -4.01% -2.08%

1995 232 27 -3.24% -0.49%

1996 346 47 -4.06% -0.69%

1997 240 40 -2.94% -0.30%

1998 270 43 -2.36% -0.25%

1999 213 41 -2.36% -0.53%

2000 232 54 -2.93% -0.68%

2001 243 76 -0.79% 0.49%

2002 225 67 0.21% 1.08%

2003 283 84 0.30% 1.16%

2004 261 71 0.09% 0.56%

2005 248 63 -0.78% 0.11%

Total 3,633 671 -2.06% 0.17%

The main sample comprises a maximum of 3,633 firm-year observations from 31 countries between 1992 and 2005

for which sufficient bond-specific data from Thompson Deals and Mergent, and firm-specific data from Worldscope

is available. We exclude financial firms (one-digit SIC code equal to 6), require a minimum bond amount of 10 US$ million, and limit the sample to observations from firms domiciled in countries with at least one American

Depositary Receipt (ADR) or direct listing in the U.S. If a firm has multiple issues in a given year, we retain only

the bond with the largest principal amount. The ADR observations comprise placements under Rule 144A, traded

shares in the over-the-counter markets, and NYSE, Nasdaq or Amex exchange listings of non-U.S. firms. We only

retain ADR firms with a single cross-listing type over the sample period. The table reports the number of unique

firms, total and ADR firm-year observations, and ADR and non-ADR mean spread values by country (Panel A) and

year (Panel B). The Bond Spread is the yield-to-maturity measured at the issuance of the bond minus the

contemporaneous yield of U.S. Treasury securities with equal maturity and similar coupon rate. We truncate the

spread variable at the first and 99th percentile.

TABLE 2

Descriptive Statistics for Variables Used in Main Regression Analyses

Variables (N=3,633) Mean Std. Dev. P1 P25 Median P75 P99

Dependent variable:

Bond spread (percent) -1.65% 3.07% -6.39% -3.75% -2.57% 0.44% 7.03%

Bond-specific control variables:

Bond maturity (months) 77.6 43.0 24.0 48.8 61.2 88.8 241.3

Bond size (US$ million) 177.1 200.1 12.2 53.3 100.0 215.8 1,000.0

Investment grade (indicator) 0.51

Callable (indicator) 0.26

Convertible (indicator) 0.20

Subordinated (indicator) 0.01

Previous bond issues (indicator) 0.53

Firm-specific and macroeconomic control variables:

Total assets (US$ million) 10,057.9 20,392.9 103.5 901.1 2,906.7 10,365.6 107,169.7

Market-to-book (ratio) 2.170 1.976 0.343 1.176 1.702 2.488 10.835

Leverage (ratio) 0.242 0.134 0.000 0.149 0.222 0.316 0.612

Return on assets (ratio) 0.051 0.048 -0.077 0.026 0.045 0.071 0.192

Tangibility (ratio) 0.405 0.224 0.036 0.233 0.373 0.549 0.916

Inflation (percent) 1.25% 1.52% -0.88% 0.05% 1.22% 1.98% 6.19%

GDP (log US$) 28.265 1.190 24.814 27.244 29.090 29.145 29.239

GDP growth (percent) 2.07% 2.23% -1.76% 0.97% 1.90% 2.94% 9.17%

Country creditworthiness (rating) 85.0 10.6 39.3 82.3 89.6 91.3 94.1

Exchange rate volatility (ratio) 0.034 0.022 0.000 0.020 0.033 0.045 0.076

The main sample comprises a maximum of 3,633 firm-year observations from 31 countries between 1992 and 2005 for which sufficient bond-specific data

from Thompson Deals and Mergent, and firm-specific data from Worldscope is available. The table reports descriptive statistics for the dependent and various

independent variables used in the main regression analyses. The dependent variable, Bond Spread, is the yield-to-maturity measured at the issuance of the bond

minus the contemporaneous yield of U.S. Treasury securities with equal maturity. We use the following bond-specific control variables: Bond Maturity is

measured in months at the date of the issuance. Bond Size equals the principal amount in US$ million. Investment Grade is an indicator variable equal to 1 if

the bond’s credit rating is BBB- or higher by Standard & Poor's or Baa3 or higher by Moody's. If credit ratings are missing, we compute Altman’s (1968) Z-score as (1.2*working capital + 1.4*retained earnings + 3.3*EBIT + 0.999*sales)/total assets + (0.6*market value of equity/book value of total liabilities), and

use 2.675 as cutoff value to assign investment grade status. Callable, Convertible and Subordinated are indicator variables set equal to 1 if the issuer of the

bond retains the privilege of redeeming the bond before maturity, if the bond can be converted into shares of stock in the issuing company, and if the bond

ranks after other debts in case of liquidation, respectively. Previous Bond Issues is an indicator variable equal to 1 if the firm has publicly issued another bond

within the last two fiscal years. We use the following firm-specific and macroeconomic control variables: Total Assets are denominated in US$ million.

Market-to-Book is the ratio of market value of equity divided by book value of equity. We compute Leverage as the ratio of total long-term debt divided by

total assets. Return on Assets is the ratio of operating income divided by average total assets. Tangibility is measured as the ratio of the book value of property,

plant and equipment divided by total assets. Inflation is the yearly median of country-specific, realized monthly percentage changes in the consumer price

index as reported in Datastream. GDP and GDP Growth is the natural log of countries’ annual gross domestic product (in constant US$), and the yearly

percentage change in real GDP, respectively, as reported by the World Bank. Country Creditworthiness is Institutional Investor’s yearly survey-based country

credit rating. The value of 100 represents maximum creditworthiness. Exchange Rate Volatility is the coefficient of variation of daily exchange rates (US$ to

local currency) in a given year. Accounting data and market values are measured as of the fiscal-year end. Except for variables with natural lower or upper bounds, we truncate all variables at the first and 99th percentile.

TABLE 3

Change in Propensity of Public Debt Issuance after U.S. Equity Cross-Listings

Variables Model 1 (all firms)

Model 2 (ADR firms only)

Model 3 (debt issuing firms

only)

Model 4 (firm-years with debt

issuances only)

Cross-listing variables:

PP 0.230** 0.271*** 0.223** 0.195

(2.41) (2.67) (2.30) (1.14)

OTC 0.103 0.121 0.146 0.392***

(1.16) (1.38) (1.64) (2.71)

EXCH 0.201** 0.178** 0.213** 0.510***

(2.35) (2.10) (2.52) (3.58)

Cross-listing firm -0.042 – -0.081 -0.192

(-0.53) (-1.02) (-1.47)

Firm-specific control variables:

Log(total assets) 0.344*** 0.267*** 0.259*** 0.139***

(34.17) (11.47) (21.93) (7.91)

Leverage 1.881*** 2.065*** 1.619*** 0.888***

(18.03) (9.31) (13.41) (4.46)

Tangibility -0.029 0.04 0.047 -0.034

(-0.37) (0.22) (0.56) (-0.26)

Return on assets -0.046 0.689 -0.309 -1.161**

(-0.15) (1.12) (-0.86) (-2.12)

Negative earnings -0.001 0.066 0.025 0.126

(-0.02) (0.65) (0.50) (1.50)

Funding needs 0.767*** 0.742* 1.037*** 1.108***

(3.87) (1.71) (4.31) (2.87)

Market-to-book 0.029*** 0.037*** 0.032*** 0.059***

(5.75) (4.18) (5.30) (5.13)

Return variability -0.161 -0.909** -0.345 -0.913**

(-0.77) (-1.99) (-1.47) (-2.51)

Z-score -0.013* -0.039*** 0.001 0.001

(-1.75) (-2.75) (0.11) (0.04)

Country, industry and

year fixed effects included included included included

Pseudo R2 32.31% 24.05% 17.53% 18.77%

N 92,234 8,523 28,154 7,143

(continued)

TABLE 3 — Continued

The propensity of debt issuance sample comprises a maximum of 92,234 firm-year observations from non-U.S.,

non-financial firms (one-digit SIC code not equal to 6) across 50 countries between 1992 and 2005 for which

sufficient Worldscope financial data exist. We use a binary public debt issuance indicator as dependent variable that takes on the value of 1 if a firm issued bonds in a given year and 0 otherwise (source: Thompson Deals and

Mergent). We have up to 5,540 firm-years with public debt issuances in our sample. The cross-listing variables

consist of the following binary indicators: PP is equal to 1 if the firm has a private placement under Rule 144A in

the U.S., OTC is equal to 1 if firm shares trade in the U.S. over-the-counter markets, and EXCH is equal to 1 if firm

shares are listed on the NYSE, Nasdaq or Amex. We set the Cross-listing Firm variable equal to 1 for the entire time

series if the firm has ADRs outstanding at any given time during the sample period. We include the following firm-

specific control variables: Total Assets are denominated in US$ million and transformed using natural logs.

Leverage is the ratio of total long-term debt divided by total assets. Tangibility is measured as the ratio of the book

value of property, plant and equipment divided by total assets. Return on Assets is the ratio of operating income

divided by average total assets. Negative Earnings is an indicator variable equal to 1 if the firm reports negative

operating income in a given year. Funding Needs are computed as net cash flows from operations divided by total assets. We multiply this measure by -1 so that higher values indicate higher funding needs. Market-to-Book is the

ratio of market value of equity divided by book value of equity. Return Variability is the annual standard deviation

of monthly stock returns, computed using Datastream stock price information. We compute Altman’s (1968) Z-

score as (1.2*working capital + 1.4*retained earnings + 3.3*EBIT + 0.999*sales)/total assets + (0.6*market value of

equity/book value of total liabilities). Accounting data and market values are measured as of the fiscal-year end.

Except for variables with natural lower or upper bounds, we truncate all variables at the first and 99th percentile. We

include country, one-digit SIC industry, and year fixed effects in the regressions, but do not report the coefficients.

Model 1 uses all Worldscope observations. Model 2 limits the sample to ADR firms. In Model 3 we only include

firms that at some point during the sample period issued public bonds or syndicated loans (i.e., debt issuing firms).

Syndicated loan information is from Dealscan. In Model 4 we limit the sample to firm-years with actual bond or

loan issuances. That is, a value of 1 of our dependent variable stands for the issuance of public bonds and a value of

0 for the issuance of syndicated loans. The table reports coefficient estimates from probit regressions, and (in parentheses) z-statistics based on standard errors that are clustered by firm. ***, **, and * indicate statistical

significance at the 1%, 5%, and 10% levels (two-tailed), respectively.

TABLE 4

Average Cost of Debt Effects of U.S. Equity Cross-Listings

Panel A: Regression Analysis of Bond Spreads (Base Specification)

Variables (N=3,633) Model 1 Model 2 Model 3 Model 4

Cross-listing variables:

PP 0.186 0.329 0.188 0.189

(0.59) (0.88) (0.58) (0.58)

OTC -0.455 ** -0.469 * -0.523 ** –

(-2.12) (-1.86) (-2.42)

EXCH -0.581 *** -0.452 * -0.455 ** –

(-2.83) (-1.89) (-2.21)

XLIST – – – -0.478 **

(-2.50)

Cross-listing firm 0.083 0.087 0.233 0.230

(0.50) (0.41) (1.31) (1.29)

Bond-specific control variables:

Log(bond maturity) -0.128 – -0.061 -0.060

(-1.43) (-0.69) (-0.68)

Log(bond size) 0.058 – 0.194 *** 0.194 ***

(1.39) (3.48) (3.49)

Investment grade -0.243 *** – -0.080 -0.079

(-3.41) (-1.08) (-1.08)

Callable 0.401 *** – 0.184 0.186

(3.21) (1.61) (1.64)

Convertible -2.281 *** – -2.181 *** -2.181 ***

(-17.89) (-18.44) (-18.45)

Subordinated 1.146 ** – 0.916 * 0.915 *

(2.55) (1.93) (1.93)

Previous bond issues -0.145 ** – -0.198 *** -0.198 ***

(-2.49) (-3.31) (-3.31)

Firm-specific and macroeconomic control variables: Log(total assets) – 0.055 * -0.162 *** -0.162 ***

(1.92) (-4.07) (-4.07)

Market-to-book – -0.039 * -0.023 -0.022

(-1.71) (-1.15) (-1.12)

Leverage – 2.180 *** 2.113 *** 2.104 ***

(5.94) (6.48) (6.50)

Return on assets – -3.443 *** -3.508 *** -3.505 ***

(-3.60) (-3.72) (-3.72)

Tangibility – -0.118 -0.122 -0.123

(-0.51) (-0.58) (-0.58)

Inflation – 2.192 3.675 3.725

(0.34) (0.60) (0.61)

Log(GDP) – 10.409 *** 7.892 *** 7.896 ***

(7.12) (6.62) (6.62)

GDP growth – -9.495 *** -5.763 ** -5.751 **

(-2.99) (-2.10) (-2.10)

Country creditworthiness – -0.133 *** -0.097 *** -0.097 ***

(-6.66) (-5.44) (-5.43)

Exchange rate volatility – 4.568 * 8.467 *** 8.435 ***

(1.83) (3.77) (3.75)

Country, industry, and

year fixed effects included included included included

Adj. R2 72.57% 69.43% 74.87% 74.88%

(continued)

TABLE 4 — Continued

Panel B: Sensitivity Analyses

Variables

Models N PP OTC EXCH Cross-listing firm

(1) Bond spreads adjusted 3,573 0.167 -0.668 *** -0.536 *** 0.292 *

for local rf (0.50) (-3.07) (-2.71) (1.68)

(2) Bond spreads adjusted 3,562 -0.055 -0.628 *** -0.406 * 0.255

for currency-matched rf (-0.19) (-2.82) (-1.91) (1.34)

(3) Randomly selected 3,633 0.331 -0.468 ** -0.437 ** 0.182

bond per year (1.02) (-2.19) (-2.14) (1.03)

(4) Multiple bonds per year 6,400 0.053 -0.533 ** -0.300 * 0.307 *

(0.19) (-2.50) (-1.78) (1.70)

(5) Only 300 firm-years 1,796 0.283 -0.567 * -0.419 0.251

from Japan (0.79) (-1.83) (-1.49) (0.96)

(6) Only ADRs with pre- 3,195 0.035 -0.561 ** -0.384 * 0.311

and post-cross-listing data (0.10) (-2.48) (-1.70) (1.63)

(7) No convertible bonds 2,913 -0.337 -0.435 ** -0.481 ** 0.278

(-0.99) (-2.14) (-2.34) (1.54)

The table reports results from regression analyses of bond spreads on firms’ cross-listing status and various control variables. If not stated otherwise, we use Bond Spreads as dependent variable, i.e., the yields-to-maturity of the

bonds minus the yields of U.S. Treasury securities with equal maturity and similar coupon rates. The cross-listing

variables consist of the following binary indicators: PP is equal to 1 if the firm has a private placement under Rule

144A in the U.S., OTC is equal to 1 if firm shares trade in the U.S. over-the-counter markets, and EXCH is equal to

1 if firm shares are listed on the NYSE, Nasdaq or Amex, either directly or in the form of American Depositary

Receipts. For some specifications, we aggregate the OTC and EXCH variables into a single XLIST variable. We set

the Cross-listing Firm variable equal to 1 for the entire time series if the firm has ADRs outstanding at any given

time during the sample period. For a description of the remaining bond-specific, firm-specific and macroeconomic

control variables see Table 2. We include country, one-digit SIC industry, and year fixed effects in the regressions,

but do not report the coefficients. In Panel A, the sample comprises 3,633 firm-year observations representing

publicly issued bonds from 31 countries between 1992 and 2005 (see Table 1). In Panel B, we only report the coefficient estimates of the binary cross-listing variables, but the full set of control variables is included (see Model

3 in Panel A). We conduct the following sensitivity analyses: (1) we replace our dependent variable by bond spreads

that are adjusted for risk-free interest rates (rf) in the issuing firms’ countries of domicile. (2) Similar to (1), but we

adjust by rf in the same currency as the publicly issued bonds. (3) In case of multiple debt issues in a given year, we

randomly select a bond instead of choosing the bond with the largest principal amount. (4) We allow for multiple

debt issues in a given year. (5) We limit the observations from Japan, the largest sample country, to 300 randomly

selected firm-years. (6) We require ADR firms to have observations in both the pre- and post-cross-listing period.

(7) We exclude convertible bonds from the sample. The table reports OLS coefficient estimates, and (in parentheses)

t-statistics based on robust standard errors that are clustered by firm. ***, **, and * indicate statistical significance at

the 1%, 5%, and 10% levels (two-tailed), respectively.

TABLE 5

Institutional Characteristics of U.S. Equity Cross-Listing Firms’ Home Countries

Legal Bonding Arguments Debt Enforcement in Country of Domicile

Country Disclosure

Regulation

Private Benefits

of Control

Equity-to-Bond

Market Legal Tradition Creditor Rights

Debt Enforcement

Efficiency

Argentina -0.01 (1) 0.07 (1) 1.68 (1) 0 -0.44 (0) -16.44 (0)

Australia 0.08 (1) -0.03 (1) 1.87 (1) 1 0.36 (1) 2.90 (0)

Belgium 0.08 (1) n.a. n.a. 0.40 (0) 0 0.36 (1) 20.75 (1)

Brazil 0.23 (1) 0.44 (1) 0.75 (0) 0 -0.35 (0) -31.32 (0)

Canada -0.09 (0) -0.04 (0) 1.10 (0) 1 -1.63 (0) 8.95 (1)

Chile -0.10 (0) -0.06 (0) 1.90 (1) 0 0.64 (1) -4.09 (0)

Finland 0.06 (1) -0.02 (1) 1.47 (0) 0 -0.73 (0) 7.50 (1)

France -0.25 (0) -0.17 (0) 0.70 (0) 0 -1.64 (0) -15.75 (0)

Germany 0.18 (1) -0.01 (1) 0.48 (0) 0 0.63 (1) -24.86 (0)

Greece 0.16 (1) n.a. n.a. 0.58 (0) 0 -0.50 (0) -4.26 (0)

Hong Kong -0.08 (0) -0.04 (0) 13.65 (1) 1 1.35 (1) 2.77 (0)

India -0.12 (0) n.a. n.a. 1.53 (1) 1 -0.02 (0) n.a. n.a.

Italy -0.17 (0) 0.18 (1) 0.30 (0) 0 0.42 (1) -19.78 (0)

Japan -0.15 (0) -0.15 (0) 0.63 (0) 0 -0.42 (0) 9.38 (1)

Korea (South) -0.16 (0) 0.04 (1) n.a. n.a. 0 0.80 (1) 21.02 (1)

Luxembourg n.a. n.a. n.a. n.a. n.a. n.a. 0 n.a. n.a. n.a. n.a.

Malaysia -0.10 (0) 0.01 (1) 1.88 (1) 1 0.61 (1) -14.19 (0)

Mexico -0.10 (0) 0.14 (1) 2.26 (1) 0 -1.31 (0) 31.31 (1)

The Netherlands 0.00 (1) -0.17 (0) 1.06 (0) 0 1.36 (1) 25.01 (1)

New Zealand 0.16 (1) -0.03 (1) 1.54 (1) 1 1.40 (1) 9.34 (1)

Norway -0.02 (1) -0.03 (1) 0.88 (0) 0 0.23 (1) 3.72 (1)

Peru 0.14 (1) -0.07 (0) 3.65 (1) 0 -1.24 (0) 6.62 (1)

Philippines -0.36 (0) -0.09 (0) 1.81 (1) 0 -0.14 (0) -8.48 (0)

Portugal 0.08 (1) 0.00 (1) 0.50 (0) 0 -0.50 (0) 24.79 (1)

Singapore -0.17 (0) -0.02 (1) 4.12 (1) 1 0.35 (1) 9.88 (1)

South Africa -0.02 (1) -0.05 (0) 3.04 (1) 1 0.62 (1) -22.35 (0)

Spain -0.01 (1) -0.15 (0) 0.99 (0) 0 0.45 (1) 20.09 (1)

Sweden -0.02 (0) 0.02 (1) 1.06 (0) 0 -0.74 (0) 0.56 (0)

Switzerland -0.06 (0) -0.04 (0) 3.02 (1) 0 -1.43 (0) -26.77 (0)

Taiwan -0.16 (0) -0.12 (0) 2.51 (1) 0 -0.22 (0) 24.68 (1)

United Kingdom 0.00 (1) -0.04 (0) 2.75 (1) 1 1.37 (1) 8.44 (1)

(continued)

TABLE 5 — Continued

The table presents raw values and (in parentheses) dichotomized indicator values of the institutional variables used in the cross-sectional analyses to partition

the ADR observations into two distinct groups. We use the following country-level partitioning variables: (i) we measure Disclosure Regulation by the index

of disclosure requirements in securities offerings from La Porta, Lopez-de-Silanes, and Shleifer (2006). We multiply the raw values by -1 so that higher values

stand for more opaque disclosure rules. (ii) We measure Private Benefits of Control by the average block premiums paid in control transactions (Dyck and

Zingales, 2004). Higher values indicate more expropriation of minority investors. (iii) We compute the Equity-to-Bond Market ratio as the aggregate market

capitalization of stocks divided by the aggregate market capitalization of public bonds (source: World Bank). The table reports sample period means. Higher

values indicate countries in which bond markets are relatively less important. (iv) We distinguish between code law vs. common law countries (equal to 1),

reflecting countries’ Legal Tradition (La Porta et al., 1997; Ball, Kothari, and Robin, 2000). (v) The Creditor Rights index from Djankov, McLiesh, and

Shleifer (2007), with higher values for better creditor protection. (vi) We use the Djankov et al. (2008) Debt Enforcement Efficiency score, measured as the discounted terminal value of a typical firm after bankruptcy costs, and expressed as a percentage of firm value before entry into bankruptcy proceedings.

Higher values stand for countries with more efficient debt enforcement. Note that for the variables (i), (ii), (v) and (vi), rather than using raw values, we

partition ADR observations based on the residuals from a regression of the institutional variables on countries’ legal origin (La Porta et al., 1998) and the log

transformed average GDP per capita in constant US$ over the nineties (source: World Bank). For our analyses, we transform the continuous variables into

binary variables splitting by the sample median.

TABLE 6

Cross-Sectional Analysis of the Cost of Debt Effects of U.S. Equity Cross-Listings

Panel A: Legal Bonding Arguments (Country-Level Partitions)

Variables

Disclosure

Regulation

(1=More Opacity)

Private Benefits

of Control

(1=Higher Benefits)

Equity-to-Bond Market

(1=Low Importance of

Bond Market)

Cross-listing variables:

PP 0.160 0.079 0.096

(0.48) (0.24) (0.24)

XLIST -0.344 * -0.371 * -0.292

(-1.75) (-1.90) (-1.49)

XLIST*Partitioning Variable -0.613 *** -0.870 *** -0.502 **

(-2.58) (-3.43) (-1.98)

Cross-listing firm 0.262 0.323 * 0.194

(1.46) (1.83) (1.12)

Bond-specific controls included included included

Firm-specific and macro-

economic controls included included included

Country, industry and year fixed effects

included included included

Adj. R2 74.97% 75.15% 76.48%

N 3,623 3,596 3,362

N (XLIST) 544 541 532

N (XLIST, part. variable = 1) 200 131 175

Panel B: Debt Enforcement in Country of Domicile (Country-Level Partitions)

Variables Legal Tradition

(1=Common Law)

Creditor Rights

(1=Strong Protection)

Debt Enforcement

(1=High Efficiency)

Cross-listing variables:

PP 0.225 0.146 0.125

(0.69) (0.44) (0.37)

XLIST -0.174 -0.312 -0.635 *

(-0.85) (-1.60) (-1.93)

XLIST*Partitioning Variable -0.819 *** -0.658 *** 0.102

(-3.76) (-2.92) (0.33)

Cross-listing firm 0.228 0.268 0.322 *

(1.26) (1.50) (1.83)

Bond-specific controls included included included

Firm-specific and macro-

economic controls included included included

Country, industry and year

fixed effects included included included

Adj. R2 75.03% 74.99% 74.99%

N 3,633 3,623 3,603

N (XLIST) 545 544 543

N (XLIST, part. variable = 1) 230 212 417

(continued)

TABLE 6 — Continued

Panel C: Agency Conflicts between Debt Holders and Equity Holders (Firm-Level Partitions)

Variables Conversion Option

(1=Yes)

Agency Factor

(1=High Level)

Agency Factor

(1=Large Change)

Cross-listing variables:

PP 0.194 -0.331 -0.417

(0.60) (-0.96) (-1.22)

XLIST -0.414 ** -0.642 *** -1.126 ***

(-2.13) (-3.23) (-4.24)

XLIST*Partitioning Variable -0.604 ** 0.341 * 0.634 **

(-1.98) (1.91) (2.24)

Cross-listing firm 0.227 0.258 0.284

(1.27) (1.44) (1.56)

Bond-specific controls included included included

Firm-specific and macro-

economic controls included included included

Country, industry and year fixed effects

included included included

Adj. R2 74.92% 75.68% 76.57%

N 3,633 2,818 2,538

N (XLIST) 545 395 115

N (XLIST, part. variable = 1) 55 170 55

The sample comprises a maximum of 3,633 firm-year observations representing publicly issued bonds from 31

countries between 1992 and 2005 (see Table 1). The analyses use Bond Spreads as dependent variable, i.e., the

yields-to-maturity of the bonds minus the yields of U.S. Treasury securities with equal maturity. The cross-listing

variables consist of the following binary indicators: PP is equal to 1 if the firm has a private placement under Rule 144A in the U.S., XLIST is equal to 1 if firm shares either trade in the U.S. over-the-counter markets (OTC), or are

listed on the NYSE, Nasdaq or Amex (EXCH). We set the Cross-listing Firm variable equal to 1 for the entire time

series if the firm has ADRs outstanding at any given time during the sample period. For the cross-sectional partitions

we classify the XLIST observations into two distinct categories using binary indicator variables. In Panel A and

Panel B, we report results using the country-level partitioning variables described in Table 5. In Panel C, we use the

following firm-level partitioning variables: (i) the Conversion Option variable takes on the value of 1 if an ADR

firm issues convertible bonds, i.e., if the debt holder can convert the bond into shares of stock in the issuing

company. (ii) Using factor analysis, we summarize the following firm-level attributes into a single Agency Factor

indicating the potential agency conflicts between debt holders and equity holders: leverage defined as long-term debt

divided by total assets, market-to-book, percent of closely-held shares, earnings variability equal to the standard

deviation of annual earnings per share over the last five years scaled by total assets per share, and return variability

measured as the annual standard deviation of monthly stock returns. We then subtract country-year medians from each factor score to account for country characteristics and time trends, and assign a value of 1 to ADR firms with

above median scores. (iii) We compute the Change ( ) in Agency Factor by comparing the mean pre-cross-listing

Agency Factor scores with the contemporaneous scores, and assign a value of 1 to ADR firms with above median

changes. In specifications (ii) and (iii), we exclude convertible bonds from the analysis. The table reports only the

cross-listing variables together with the interactions with the partitioning variables, but the full set of bond-specific,

firm-specific and macroeconomic controls as well as the various fixed effects are included in the regressions. See

Model 3 in Table 4, Panel A, for details. The table reports OLS coefficient estimates, and (in parentheses) t-statistics

based on robust standard errors that are clustered by firm. ***, **, and * indicate statistical significance at the 1%,

5%, and 10% levels (two-tailed), respectively.

TABLE 7

Cost of Debt Effects of U.S. Equity Cross-Listings – Private Debt Sample

Variables Model 1 (all firms)

Model 2 (all firms)

Model 3 (Agency Factor)

Model 4 ( Agency Factor)

Cross-listing variables: (N=2,828) (N=2,828) (N=2,731) (N=2,476)

PP 0.171 0.173 0.161 0.160

(0.72) (0.73) (0.68) (0.67)

OTC 0.000 – – –

(0.00)

EXCH 0.197 – – –

(0.94)

XLIST – 0.124 0.038 0.111

(0.65) (0.19) (0.57)

XLIST*Partitioning Variable – – 0.175 -0.001

(1.07) (0.00)

Cross-listing firm -0.046 -0.050 -0.055 -0.073

(-0.26) (-0.28) (-0.31) (-0.41)

Loan-specific control variables:

Log(loan maturity) 0.284 *** 0.285 *** 0.288 *** 0.249 ***

(5.49) (5.49) (5.42) (4.35)

Log(loan size) 0.212 *** 0.212 *** 0.230 *** 0.241 ***

(4.21) (4.23) (4.51) (4.41)

Term loans 0.980 *** 0.979 *** 0.955 *** 0.922 ***

(9.94) (9.95) (9.62) (9.04)

Log(number of lenders) -0.520 *** -0.521 *** -0.525 *** -0.519 ***

(-12.09) (-12.10) (-11.97) (-11.24)

Log(previous loan issues) -0.042 -0.044 -0.048 -0.055

(-1.04) (-1.07) (-1.17) (-1.30)

Investment grade -0.208 *** -0.206 *** -0.221 *** -0.223 ***

(-3.06) (-3.03) (-3.20) (-3.01)

Performance pricing -0.059 -0.056 -0.056 -0.019

(-1.00) (-0.96) (-0.95) (-0.29)

Revolver 0.061 0.058 0.036 0.035

(0.79) (0.75) (0.47) (0.43)

Purpose of loan indicators included included included included

Firm-specific and macroeconomic control variables: Log(total assets) -0.098 *** -0.096 *** -0.091 *** -0.088 **

(-3.01) (-2.96) (-2.77) (-2.49)

Market-to-book 0.001 0.002 -0.008 -0.001

(0.06) (0.13) (-0.51) (-0.07)

Leverage 0.846 *** 0.822 *** 0.709 *** 0.562 **

(3.18) (3.12) (2.80) (2.12)

Return on assets -1.055 ** -1.067 ** -0.823 * -0.773

(-2.16) (-2.17) (-1.77) (-1.55)

Tangibility -0.289 ** -0.281 ** -0.243 * -0.204

(-2.09) (-2.03) (-1.70) (-1.37)

Inflation 3.569 3.608 3.420 3.029

(1.47) (1.49) (1.35) (1.19)

Log(GDP) -1.592 * -1.580 * -1.662 * -1.601 *

(-1.75) (-1.73) (-1.81) (-1.70)

GDP growth 1.286 1.290 1.495 1.414

(0.72) (0.72) (0.82) (0.75)

Country creditworthiness -0.034 *** -0.034 *** -0.034 *** -0.034 ***

(-2.90) (-2.89) (-2.87) (-2.71)

Country, industry, year FE included included included included

Adj. R2 41.08% 41.06% 41.82% 42.37%

(continued)

TABLE 7 — Continued

The sample comprises a maximum of 2,828 firm-year observations representing syndicated loans from 38 countries

between 1992 and 2005 for which sufficient loan-specific data from Dealscan, and Worldscope financial data exist.

We exclude financial firms (one-digit SIC code equal to 6), require a minimum loan amount of 10 US$ million, and

limit the sample to observations from firms domiciled in countries with at least one ADR. If a firm has multiple

issues in a given year, we retain only the loan with the largest facility amount. We use Loan Spreads as dependent

variable, i.e., the amounts the borrowers pay (including annual fees) over LIBOR for each dollar drawn down. The

cross-listing variables consist of the following binary indicators: PP is equal to 1 if the firm has a private placement

under Rule 144A in the U.S., OTC is equal to 1 if firm shares trade in the U.S. over-the-counter markets, and EXCH

is equal to 1 if firm shares are listed on the NYSE, Nasdaq or Amex. For some specifications, we aggregate the OTC and EXCH variables into a single XLIST variable. We set the Cross-listing Firm variable equal to 1 for the entire

time series if the firm has ADRs outstanding at any given time during the sample period. The models contain the

following loan-specific variables: Loan Maturity is measured in months at the date of the issuance. Loan Size equals

the facility amount in US$ million. Term Loans represents the percentage of individual loans in a loan package

(measured using the facility amount) with a specified repayment schedule and a fixed maturity. The Number of

Lenders is the number of participants in the deal syndicate. Previous Loan Issues indicates the number of previous

syndicated loans taken by the borrower. Investment Grade is an indicator variable equal to 1 if the loan’s credit

rating is BBB- or higher by Standard & Poor's or Baa3 or higher by Moody's. If credit ratings are missing, we

compute Altman’s (1968) Z-score as (1.2*working capital + 1.4*retained earnings + 3.3*EBIT + 0.999*sales)/total

assets + (0.6*market value of equity/book value of total liabilities), and use 2.675 as cutoff value to assign

investment grade status. Performance Pricing and Revolver are indicator variables set equal to 1 if the loan facility uses performance pricing, and if the loan gets renewed automatically upon maturity, respectively. We include three

Purpose of Loan indicator variables marking the repayment of existing debt, corporate investments, or working

capital needs, but do not report the coefficients. For a description of the remaining firm-specific and macroeconomic

control variables see Table 2. We include country, one-digit SIC industry, and year fixed effects (FE) in the

regressions, but do not report the coefficients. In Models 3 and 4, we classify the XLIST observations into two

distinct categories applying the Agency Factor and Change ( ) in Agency Factor partitioning variables (see Table 6

for details). The table reports OLS coefficient estimates, and (in parentheses) t-statistics based on robust standard

errors that are clustered by firm. ***, **, and * indicate statistical significance at the 1%, 5%, and 10% levels (two-

tailed), respectively.