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Outside Blockholders’ Monitoring of Management and Debt Financing

Scott Liao

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Liao, S., 2015. Outside blockholders' monitoring of management and debt financing. Contemporary Accounting Research, 32(4), pp.1373-1404.

Publisher’s Statement This is the peer reviewed version of the following article: [Liao, S., 2015. Outside blockholders' monitoring of management and debt financing. Contemporary Accounting Research, 32(4), pp.1373-1404.], which has been published in final form at

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Electronic copy available at: http://ssrn.com/abstract=1512706

Outside Blockholders’ Monitoring of Management and

Debt Financing

Scott Liao

scott.liao@rotman.utoronto.ca, 416-946-8599

Rotman School of Management

University of Toronto

105 St. George St.

Toronto, ON M5S 3E6

September 24, 2012

This study was previously titled, “Dedicated Investor and Debt Financing.”

I would like to thank Anne Beatty (committee chair), Joy Begley (the editor), Jennifer Altamuro, Linda

Bamber, Francesco Bova, Gus De Franco, Alex Edwards, Richard Frankel, David Hershleifer, Ole-Kristian

Hope, Doug Skinner, Cliff Smith, Rene Stulz, Siew Hong Teoh, Joe Weber, Helen Zhang, and Jerry

Zimmerman. I also appreciate the inputs from two anonymous reviewers and the participants of workshops

at Ohio State University, Michigan State University, University of Georgia, University of Houston,

Washington University in St. Louis, UC Irvine, University of Toronto, University of Michigan, London

Business School, University of Rochester and Purdue University.

Electronic copy available at: http://ssrn.com/abstract=1512706

Abstract

This study examines how outside large shareholders’ monitoring of management as a proxy for

governance mechanisms that exacerbate agency conflicts of debt, affects firms’ debt financing

choices, i.e., the choice between public debt and bank loans. Consistent with the notion that

banks have a superior ability to monitor the firm and reduce agency costs of debt, I find that

firms with higher outside blockholdings are more inclined to use bank loans relative to public

debt when they decide to enter the debt markets. I also find that price protection against

increased agency risk associated with outside blocks is higher in corporate bonds than in bank

loans. Corroborating these findings, I document that firms with higher outside blockholdings are

more likely to employ accounting-based covenants and dividend restriction provisions in their

bank loans, but I do not find blockholdings correlated with public debt covenants, supportive of

banks’ superior monitoring role countering agency risk of debt caused by blockholders. This

study extends prior research that associates governance mechanisms with agency costs of debt,

by incorporating lenders’ differential monitoring mechanisms in the overall corporate

governance system.

1

1. Introduction

Firms adopt various corporate governance mechanisms to address the management-

shareholder agency problems (Jensen and Meckling, 1976). However, some governance

mechanisms designed to mitigate this principal-agent problem can result in an increase in agency

conflicts between shareholders and creditors, because these governance mechanisms can lead

management to act in shareholders’ interests to the detriment of creditors, e.g., risk-shifting

(John and John, 1993). Consistent with this argument, previous studies document that firms with

certain corporate governance mechanisms such as blockholdings face higher costs of debt (e.g.,

Cremers et al., 2007). This study aims to extend this literature by exploring whether firms’ debt

financing policy and debt contracting are affected by the interaction between lenders’ differential

monitoring effectiveness and governance structures accentuating the agency costs of debt.

Firms with governance that exacerbates the agency conflicts of debt may prefer bank debt

financing versus public debt to lower the financing costs when they decide to raise debt capital.

Prior literature argues that banks have informational and monitoring advantages over public

debtholders in mitigating the agency problems of debt (e.g., Fama, 1985, and Diamond, 1984). In

particular, banks have a comparative advantage in writing and enforcing debt covenants that

alleviate moral hazard problems and reduce the cost of debt financing. Therefore, I hypothesize

that firms with governance mechanisms aggravating agency risk of debt are more inclined to

borrow from banks than from the public debt market to lower the financing costs, when they

decide to access the debt markets. Further, I hypothesize that because of banks’ superior

monitoring ability, banks demand lower price protection against governance structures causing

higher agency problems of debt compared to public debtholders. Finally, to provide

corroborating evidence that banks do provide more monitoring of management, I also examine

2

whether bank debt contracts of firms with debt-value deteriorating governance structures are

more likely to contain debt covenants constraining the agency risk of debt (Smith and Warner,

1979).

I rely on previous studies to identify governance mechanisms that intensify agency

conflicts of debt. Ashbaugh-Skaife et al. (2006) find that of the four broad corporate governance

categories framed by Standard & Poor’s (2002), blockholdings significantly weaken firms’

creditworthiness while other governance mechanisms either improve credit ratings or have no

significant relations with credit ratings.1 Bhojraj and Segupta (2003) also document that the cost

of public debt increases with blockholdings but decreases with effective boards. Based on these

studies, I examine the relation between firms’ debt financing choices and outside blockholdings

as the proxy for governance mechanisms exacerbating the agency problems of debt.2

To test these hypotheses, I merge Dlugosz et al.’s (2006) blockholding data with bank

loan data from the LPC (Loan Pricing Corporation) Dealscan database and public debt data from

the FISD (Fixed Income Securities Database). To avoid over fitting data, I also limit my sample

to have only one representative debt financing per firm-year (public or bank debt) by retaining

1While some governance designs encourage risk taking resulting in higher agency conflicts with creditors, other

governance mechanisms limiting managerial opportunism may also align with creditors’ interests. Of more than a

dozen of governance mechanisms, shareholder right score (G-Score) is the other one that also weakens credit ratings,

although the relation is only marginal. 2While Ashbaugh-Skaife et al. (2006) do not find a significant relation between insider ownership and the cost of

debt, other studies e.g., Ortiz-Molina (2006) find that equity incentives provided to executives may increase the

agency costs of debt. Hence, management equity ownership may be a potential proxy for my study; however, Denis

and Mihov (2003) find that managerial ownership only affects debt financing in a very limited specification for

small firms. More details are provided in the following sections.

3

the larger type of debt financing of the year.3 I identify 284 corporate bonds and 241 bank loans,

representing 278 unique firms.

Similar to most studies on corporate governance, one important challenge that this study

encounters is endogeneity due to the omitted variables or selection issues. For example, Gerken

(2009) finds that blockholders tend to target smaller, more leveraged firms. To the extent that

these firm characteristics also determine firms’ debt financing choices, endogeneity needs to be

addressed. To deal with endogeneity, I employ the instrumental variable (i.e., IV) approach. I

choose instrumental variables based on theories developed by Maug (1998) and Kahn and

Winton (1998). Maug (1998) models that market liquidity facilitates blockholders to trade on

private information to cover the costs of intervention, thereby reducing the free-rider problem

and increasing blockholders’ intervention. Kahn and Winton’s (1998) model suggests that large,

well-established firms with numerous analysts that have underperformed attract more investor

intervention because intervention is likely to generate more profits vis-à-vis informed trading.

Directly motivated by these two theories, I use market illiquidity and an indicator variable for

firms that are well covered by analysts and have underperformed for two years as instruments.

The empirical results are consistent with my hypotheses. Using both Probit and IV Probit

models, I find that firms with larger outside blockholdings are more likely to use bank debt

financing versus public debt. I also document that banks require lower price protection against

the agency risk caused by blockholders: the increase in interest spreads associated with outside

blockholdings is only significant for corporate bonds, significantly higher than that for bank debt.

3 I first combine all public debt and bank debt in each firm-year, respectively, and then compare the combined public

debt and bank loans to determine the larger debt type to retain in the sample. Details of the sample selection are

discussed in the following sections.

4

These results are consistent with the notion that banks are better able to monitor management

and counter the debtholder-shareholder conflicts by demanding less price protection, thereby

leading firms with higher outside blockholdings to choose bank debt. In addition, I find that bank

debt issued by firms with larger outside blockholdings have a greater propensity to contain

financial covenants (both capital and performance covenants, classified by Christensen and

Nikolaev, 2012 and Demerjian, 2011) and dividend restrictions to mitigate the agency risk of

debt. In contrast, I do not find that the use of covenants in public debt is associated with

blockholdings. These findings further support the argument that banks provide more monitoring

mechanisms to address the agency problems of debt caused by large shareholders’ monitoring of

management.

To further address the endogeneity issue, I conduct a change analysis where I investigate

whether firms with larger increases in outside blockholdings are more likely to take on bank

loans relative to public debt. In addition, I analyze whether the positive association between the

change in interest rate and the change in blockholdings is more pronounced for firms that issue

public debt versus bank loans. I find results consistent with the level analysis. The use of bank

debt is positively associated with the increase in external blockholdings and the increase in

interest rate in response to increased blockholdings is concentrated in firms that issue public debt.

This finding again suggests that banks provide better monitoring and reduces price protection

against moral hazard concerns caused by large investors’ monitoring. In an untabulated

supplemental analysis, I also find supporting evidence of increased use of financial covenants in

response to increased outside blockholdings. In addition to this change specification, I also

conduct the main analysis using a propensity score matched sample, where propensity scores are

calculated using the two instrumental variables as determinants. While the sample declines due

5

to the matching procedure, I continue to find that firms with higher blockholdings tend to borrow

from banks rather than the public debt market. This propensity score matching result further

reduces the concern of endogeneity.

To further explore the relation between governance that accentuates agency conflicts of

debt and firms’ propensity of using bank debt versus public debt, I investigate whether

governance structures that improve debtholder-shareholder alignment are also positively

associated with the choice of bank debt financing as a counterfactual analysis. Specifically,

based on Ashbaugh-Skaife et al. (2006) and Bhojraj and Sengupta (2003) who find that board

structure that aligns management’s and shareholders’ interests reduces the agency costs of debt, I

use the first principal component of 3 board characteristics to proxy for such governance

mechanisms, namely the ownership of independent directors, the percentage of independent

directors on board, and whether 50% or more audit committee members are independent (Klein,

2002). I find that the propensity of taking on bank loans versus public debt is marginally

negative associated with this board structure variable. This counterfactual test provides further

support for the evidence that firms with governance structures that damage creditors’ interests

tend to rely on bank debt financing to take advantage of banks’ superior monitoring mechanisms.

This finding also provides insights that the association between debt financing and governance

mechanisms may vary because not all governance mechanisms affect agency conflicts of debt in

the same way.

This study makes several contributions to the corporate governance and debt literatures.

First, it directly responds to Weber’s (2006) call for research that incorporates lenders’

(differential) monitoring roles into the overall corporate governance system. This study provides

evidence that firms’ debt financing policies are driven by lenders’ differential monitoring

6

effectiveness in addressing moral hazards, and that monitoring by banks and monitoring by

external blockholders seem to work as complements rather than substitutes to enhance the

overall corporate governance. This study also provides a reason why firms that have access to

both public and bank debt markets may choose to borrow in the bank debt market despite the fact

that public debt often has lower transaction costs due to the economies of scale and that public

debt can give firms more flexibility in operations.

This study also contributes to the literature on the association between corporate

governance and agency costs of debt. First, it extends John and John (1993) and Begley and

Feltham (1999) by introducing lenders’ differential monitoring efficiency in addressing the

resultant agency problems of debt from governance structures designed to solve the

management-shareholder conflicts. Second, it directly extends the findings of Cremers et al.

(2007), Bhojraj and Sengupta (2003) and Ashbaugh-Skaife et al. (2006) by investigating the

effect of corporate governance (i.e., blockholdings) on debt financing decisions. In addition to

documenting that external blocks increase the reliance on bank debt, I also show that governance

that aligns debtholders’ interests do not have the same effect on debt financing.

This study further makes contributions to the debt covenant literature by showing that

covenants are endogenously determined by corporate governance mechanisms; that is, banks

employ various financial and non-financial covenants to contain agency problems of debt

exacerbated by outside blockholders’ monitoring.4

Finally, this study complements the

blockholder literature. While Cronqvist and Fahlenbrach (2009) find that blockholders affect

4 As noted by Begley and Feltham (1999), accounting studies that use debt covenant variables to explain accounting

choice have treated covenants as exogenous. To better understand the role of financial covenants in firms’ choice or

behavior, it is important to understand the factors that impact the choice of debt covenants.

7

various corporate policies, they do not explore how blockholders affect firms’ debt financing

choices, i.e., the use of debt covenants and public/private debt choices.

The next section discusses the motivation and hypothesis development. Section 3

describes the data and methodology. I present the empirical results in Section 4 and provide

supplemental and robustness checks in Section 5. Section 6 concludes.

2. Motivation and Hypothesis Development

2.1 Corporate Governance and Agency Costs of Debt

Within the Jensen and Meckling’s (1976) agency theory framework, some corporate

governance mechanisms designed to address the agency conflicts between management and

shareholders can heighten the agency risk of debt due to increased risk taking and wealth transfer

(e.g., John and John, 1993). Ashbaugh-Skaife et al. (2006) argue that not all governance

mechanisms designed to reduce the agency conflict between managers and shareholders are

detrimental to debtholders. For example, governance mechanisms that promote better managerial

decision making and limit opportunistic management behavior should also benefit debtholders.

Based on the four broad corporate governance categories framed by Standard & Poor’s (2002),

Ashbaugh-Skaife et al. (2006) examine the relation between credit ratings and various

governance mechanisms. They find that most governance mechanisms either improve credit

ratings or have unclear relations with credit ratings, except for blockholdings that significantly

weaken credit ratings. The strongest adverse impact of blockholdings on creditors is potentially

consistent with Shleifer and Vishny (1997) who argue that blockholding is the most direct and

effective way to align the cash flow and control rights of outside investors to mitigate the

management-shareholder conflicts.

8

Because of their large investments in the firm, outside blockholders have incentives to

monitor managers, thereby avoiding the conventional free-rider problem in diffusely-held firms

(Shleifer and Vishny, 1986). Blockholders can pressure management to achieve their goals or to

improve firm performance in many ways, including writing open letters to management and/or

the board, requesting special disclosures by the firm, suing directors and conducting proxy

contests.5 However blockholders’ monitoring may cause intensified agency conflicts between

shareholders and debtholders. For example, Becker et al. (2008) find that blockholders increase

firms’ payouts. Berger et al. (1997) find that firms with blockholders tend to have higher

leverage, thereby increasing bankruptcy risk. In addition, Klein and Zur (2009) find that hedge

funds, as large active investors, increase firms’ risk taking and their debt level, thereby

amplifying debtholders’ concern for wealth transfers. Further, Shleifer and Vishny (1986) and

Cremers et al. (2007) argue that large shareholders increase the probability of a takeover, which

may damage debtholders’ interests by potentially reordering the priority of claims in bankruptcy.

Finally, Ashbaugh-Skaife et al. (2006) and Bhojraj and Sengupta (2003) argue that blockholders

may also accrue private benefits of control at the expense of debtholders.6 Based on these

unambiguous relations between outside blockholdings and agency risk of debt, I examine how

5For example, Knight Vinke Asset Management LLC (2003) made a statement that, as active investors, they tailor

the strategy to improve a firm’s performance based on individual circumstances. To accomplish their goals, in

addition to taking the actions mentioned in the text, they may also hold public meetings inviting other large

shareholders, board members and management to attend, or contact potential acquirers for the business and make

their interest known to the board, to the press and to other large investors. Consistent with Shleifer and Vishny

(1997), Becker et al. (2008) find that blockholders increase firm profitability and reduce executive compensation. In

addition, Berger et al. (1997) suggest that external blockholders mitigate management entrenchment. 6 Consistent with these arguments, Cremers et al. (2007) find that bond spreads increase with institutional

blockholdings. Klein and Zur (2009) show that bondholders experience negative returns around the initial 13D filing

date. In addition, Chava et al. (2009) document that firms with a lower takeover defense pay a higher interest spread

on their bank loans.

9

outside blockholdings as the proxy for corporate governance exacerbating agency conflicts of

debt affects debt financings and debt structures.

Weber (2006) argues that this growing body of literature on the association between

corporate governance and agency costs of debt mostly ignores lenders’ roles as monitors to

reduce the debtholder-shareholder conflicts. In particular, he suggests that the difference in the

monitoring quality provided by different lenders may play an important part in a firm’s corporate

governance system. This paper aims to shed light on whether lenders’ monitoring roles interact

with corporate governance mechanisms that increase the agency conflicts with debtholders,

proxied by outside blockholdings, in affecting a firm’s choice of debt financing policies.

2.1.1 Managerial Ownership and Cost of Debt

Although Ashbaugh-Skaife et al. (2006) do not find a significant relation between insider

ownership (i.e., executive and director ownership) and credit ratings. There is a literature that

documented that executive ownership or equity incentive provided by executive compensation

increases management risk taking, thereby accentuating the agency problems of debt. For

example, John and John (1993) model that the optimal executive compensation scheme takes

into account the potential increase in the cost of debt caused by compensation schemes

incentivizing risk taking. On a relevant note, Begley and Feltham (1999) find that the demand for

bond covenants in corporate bonds increases with executive ownership. In addition, Ortiz-

Molina (2006) also finds similar results that bond yield spreads increase with managerial

ownership and option grants. A relevant study by Denis and Mihov (2003) examines the relation

between managerial ownership and debt structure. They argue that while management with

higher ownership may prefer bank debt to maximize firm value, management with low

10

ownership may also prefer closer monitoring provided by bank debt to signal that they are

committed to optimal investment policy. With these contradicting forces, they only find the

positive effect of managerial ownership on the choice of using bank loan versus public debt in

certain specifications for small firms.

2.2 Banks’ Monitoring Role and the Choice of Debt Financing

To mitigate the increased agency risk of debt arising from governance mechanisms that

intensify agency conflicts of debt, firms can rely on banks’ monitoring mechanisms. Prior

literature argues that, compared to public debtholders, banks have both greater access to private

information (Fama, 1985) and a comparative advantage in monitoring firms (Diamond, 1984 and

Smith and Warner, 1979). In particular, it is less costly for private lenders to write and enforce

covenants and to renegotiate debt contracts when covenants are violated. For example, in

covenant violations, approval is needed from debtholders that represent two-thirds of the total

principal to make changes to the covenants. All debtholders must agree to make any change to

the principal amount or maturity of the debt. Scattered ownership of public debt makes revisions

and renegotiations of debt contracts practically impossible.7 Therefore, covenants in bank loans

are more effective and more likely to be enforced to mitigate agency problems of debt.

I argue that when faced with governance mechanisms intensifying agency conflicts of

debt, proxied by external blockholdings, firms deciding to raise debt capital would try to mitigate

the agency costs of debt by choosing a lender who can monitor the firm and ensure that there are

no wealth transfers from debtholders to shareholders to maximize firm value. Consistent with

this argument, Knight Vinke Asset Management LLC (2003) states that, as an active large

7 The renegotiation is especially difficult for diffusely-held public debt given that the Trust Indenture Act of 1939

provides the trustees in public debt issues with only limited discretion during renegotiation outside of bankruptcy.

11

investor, they may hire investment bankers to prepare fairness opinions and advise on debt

structure and covenants to maximize firm value. Based on the discussion above, the first

hypothesis of this paper is stated as follows.8

H1: Firms with governance structures that increase agency risk of debt proxied by greater

outside blockholdings are more likely to choose bank loan financing over public debt

financing.

Following the first hypothesis, if the moral hazard problems attributable to governance

mechanisms i.e., outside blockholders can be mitigated via ex post monitoring by banks, banks

can demand less price protection against the agency problem of debt. That is, the adverse effect

of such governance mechanisms on interest rate should be lower in bank debt than in public debt.

The second hypothesis is based on this argument:

H2: Banks demand less price protection than bondholders from the agency problems

caused by governance structures that increase agency risk of debt proxied by outside

blockholdings.

To provide corroborating evidence that banks provide more monitoring compared to

public debt in response to governance mechanisms that exacerbates the agency problems of debt

proxied by blockholdings, I also examine the relation between blockholdings and debt covenants

in bank loans. Although corporate bonds can also contain some covenants to reduce the agency

conflicts caused by outside blockholders, these covenants tend to be less effective.9 In addition,

Begley and Freedman (2004) argue that bondholders’ use of financial covenants to lower agency

costs of debt declined drastically during and after the1990s.

8 Although banks reduce the agency problems of debt through short maturities and debt covenants (Myers, 1977 and

Smith and Warner, 1979), these features impose costs. Shorter maturities increase firms’ liquidity risk, and debt

covenants can prevent management from taking equity value-maximizing actions. 9For instance, Chava et al. (2010) find that the likelihood of using covenants to restrict investment, dividend

distribution or merger and acquisition activities does not vary with blockholdings.

12

On the other hand, Dichev and Skinner (2002) document that, in addition to general

covenants, bank loans contain a few financial covenants that assist banks to monitor the firm.

Banks can employ various covenants to restrict firms’ activities that concern lenders due to

blockholders’ monitoring. First, to reduce the asset substitution problems, banks can require

collaterals to protect their interests. Second, based on Christensen and Nikolaev (2012), to

constrain agency problems of debt through the transfer of control to lenders in states where the

value of their claim is at risk, banks can use “performance financial covenants” based on income

statement financial ratios as trip wire mechanisms, e.g., debt to EBITDA or interest coverage

covenants.10

In addition, banks can also use “capital financial covenants” based on balance sheet

accounts to directly control agency problems of debt by aligning debtholders’ and shareholders’

interests, e.g., net worth or leverage covenants.11

Finally, to avoid excessive dividend payouts

that preempt debtholders’ claims, bank loans can include covenants that restrict dividend

distributions. Accordingly, my third hypothesis is stated as follows:

H3: Bank loans contain more general and financial covenants in debt contracts to address

agency conflicts of debt caused by governance structures that increase agency risk of debt

proxied by outside blockholders’ monitoring of management.

3. Data and Research Design

3.1 Data

To measure the main variable of interest, outside large shareholders, I use the data

collected by Dlugosz et al. (2006), instead of the Compact Disclosure (CD) database for the

following reasons. First, Dlugosz et al. (2006) find that there are many mistakes and biases in the

CD database. For example, in firms where the CD database reports that the aggregate

10 Demerjian (2011) names this type of covenants as income statement covenants.

11 Demerjian (2011) calls this type of covenants balance sheet covenants.

13

blockholdings are greater than 50%, the blockholdings are overstated by almost 30% on average

due to overlapping. These biases and mistakes can result in biased inferences. Second, Dlugosz

et al.’s database further classifies the role of each blockholder in the firm. Based on their

classification, blockholders can be officers, directors, affiliated entities or outsiders. This

classification is important because the effect of outside blockholders on agency conflicts of debt

and debt financing can differ from that of inside blockholders.12

Dlugosz et al.’s (2006) database

ranges from 1996 to 2001, covering about 1,500 firms per year and representing 90% of the

market value for the NYSE/AMEX/NASDAQ markets. Their sample consists of firms that are

covered by the Investor Responsibility Research Center (IRRC), of which the universe is drawn

from the S&P500, and the annual lists of the largest corporations in the publications of Fortune,

Forbes, and Business Week.

Bank loan information is collected from the LPC Dealscan database. Corporate bond

information is collected from the Mergent database, which is based on the Fixed Investment

Securities Database. In merging COMPUSTAT and debt information, I require a minimum of

three months between the fiscal year end and the bond/loan initiation date to ensure that

debtholders have the necessary information available for debt contracting, including

blockholding information.13

To be more specific, in the merged data, all test and control

variables are measured before debt issuances: if debt is issued in fiscal year t, then blockholdings

and other control variables are measured at fiscal year t-1. Further, I only include observations

12Based on Morck et al. (1988), when management and officers hold more than 5% of ownership, management is

more likely to be entrenched and stock ownership becomes less effective in aligning management’s and shareholders’

interests. Therefore, the relation between outside blockholdings and agency costs of debt due to management-

shareholder alignment may not hold for inside blockholdings. 13

I also run the analysis requiring a minimum of four months between fiscal year end and bond/loan active date. All

of the results reported in the following sections continue to hold.

14

in the sample of debt issuances when the firm has previously issued bonds in the public debt

market to ensure that all firms in the sample have the access to public debt.14

Finally, to avoid

having duplicate bank loans in the sample because Dealscan often includes renegotiated debt in

the database, I remove the bank loans of interest if Dealscan indicates that the firm has issued

loans of the same type syndicated by the same lead arranger in the previous three years.15

3.2 Model Specification for Debt Choice and Sample

Following Bharath et al. (2008), Denis and Mihov (2003), and Hadlock and James (2002),

I adopt an incremental approach, instead of using the mix of debt, to analyze the determinants of

debt financing choices and interest spreads. Denis and Mihov (2003) argue that this approach

links the borrowing decisions and debt costs with variables measured immediately prior to

entering the debt contract and therefore is better suited to testing borrowing decisions based on

time-varying firm characteristics.16

This approach also allows comparisons of characteristics of

different types of debt financings and investigation of firms’ debt financing decisions that have

no debt outstanding at the time of issuance. Because I examine the effect of outside

blockholdings and the change in blockholdings on debt financing that are time-varying

characteristics, and because I also examine debt characteristics, such as covenants and pricing,

this incremental approach seems to be appropriate.

To avoid over fitting data in analyzing the effect of blockholdings on debt financing, I

only allow one representative debt financing in each year. Specifically, I first accumulate bank

14 To avoid losing too many observations, the criterion is that as long as the firm has ever issued public debt and is

covered by FISD before the debt issue of interest, that debt issue is included in the sample. 15

The most used loan types include revolvers, various term loans, bridge loans, and short-term facilities. 16

A caveat is attached to this approach: an incremental borrowing decision can merely reflect a temporary deviation

from firms’ optimal mix of debt claims.

15

loans and public debt that are issued in the same fiscal year, respectively, and then select the debt

financing with the higher total amount as the representative debt of the firm-year. The dependent

variable is set to be one if that representative type is bank loan, zero for public debt.17

Based on

this procedure, I identify 6,390 firm-years with a representative debt issue, with 2,845 bonds and

3,545 loans from Dealscan and FISD. After merging with Dlugosz et al.’s (2006) blockholdings

data, there are 965 debt issues remained, with 390 corporate bonds versus 579 bank loans.

Subsequent to merging with COMPUSTAT and requiring non-missing data on test and control

variables, I end up having 525 debt issues from 1996 to 2001, including 284 corporate bonds and

241 bank loans.18

I used the following Probit model to test whether outside blockholdings affect firms’

financing choices between corporate bonds and bank loans:

Choice of Bank Loan

versus Public Debt = β0 + β1OBLK + β2Size + β3MTB + β4CFO + β5Payout +β6Leverage +

β7Tangibility + β8Zscore + β9Current Ratio +ε, (1)

where OBLK is measured as the quintile ranking of outside blockholdings from Dlugosz et al.’s

(2006) database. OBLK is expected to have a positive relation with firms’ tendency to use bank-

debt financing based on H1. Following Krishnaswami et al. (1999) who use firm size to capture

the economies of scale in debt issuance costs, I include Size, measured as natural log of market

value of equity, to control for the size effect. Consistent with Krishnaswami et al. (1999), I

predict that larger firms tend to choose public debt over bank debt. MTB is measured as the

17Note that all of the results are not driven by this research design. That is, all results continue to hold if I allow

every firm-year to have multiple debt issues. 18

In Dealscan, the information on whether the bank loan requires collateral and whether there is a dividend

restriction is often missing. If both covenants are missing, then I delete the observation because it is likely that

Dealscan does not have the covenant information. However if only one is missing, then I view the debt contract does

not contain that covenant. Treating it otherwise does not change the results.

16

market-to-book ratio of the total assets, capturing a firm’s growth opportunities. I expect a

positive coefficient on MTB based on Krishnaswami et al.’s (1999) finding that firms with higher

growth options are less likely to use public debt. As for other control variables, I expect a

negative coefficient on CFO, assuming that firms with better performance have lower agency

risk. I include Leverage, measured as the ratio of long-term debt to total assets, to control for the

firm’s borrowing capacity. Following Bharath et al. (2008), I also control for bankruptcy risk by

including Altman’s Z-score in the model. Finally, I control for total payout and current ratios.

Detailed definitions of each variable are presented in the Appendix.

3.3 Endogeneity and IV Probit Model

Like most corporate governance studies, one important challenge that this study encounters

is endogeneity due to either correlated omitted variables or self-selection. For example, Gerken

(2009) finds that blockholders tend to target more leveraged and small firms. To the extent that

agency costs of debt vary with these firm characteristics, endogeneity needs to be considered. In

addition, Stepanov and Suvorov (2009) argue that managers may choose to attract blockholders

via investor relations to signal to the market that they are less likely to extract private benefits.

This argument again suggests that underlying factors (e.g., management quality) can affect

blockholdings and debt financing simultaneously.

I first use the IV approach to address endogeneity. Following Larcker and Rusticus’ (2010)

suggestion that instruments should be justified by economic theory, I choose these instruments

directly from the models developed by Maug (1998) and Kahn and Winton (1998). Specifically,

I use market illiquidity (ILLIQ) and an indicator (KW) for whether firms are well covered by

analysts and have underperformed for years as instruments.

17

The use of market illiquidity as the first instrument is based on Maug (1998) who argues

that large investors’ intervention decisions decreases with market illiquidity. In his model, large

investors’ intervention depends on the capital gain from monitoring and improving a firm’s

performance and the profits from trading on private information they acquire in the monitoring

process. In the equilibrium where large investors monitor management, their initial stake will be

small such that the capital gain on this stake is insufficient to cover the monitoring costs to avoid

the free-rider problem. When the equity market is liquid, large investors can cover the

monitoring costs by informed trading based on the knowledge acquired from intervention,

thereby incentivizing blockholders to intervene. Therefore, investors’ decision to purchase large

enough stakes initially and to intervene in operations depends on market liquidity.

The second instrument is based on Kahn and Winton’s (1998) model where they explore

the tension between large investors’ ability to trade on private information and their use of the

information to intervene in poorly performing firms. Claiming their model complements Maug’s

(1998), Kahn and Winton model that as the information environment improves, more informed

traders enter the market, thereby reducing the importance of trading profits in the intervention

decision. In addition, when the intervention is less likely to fail or when the delay between

intervention and success decreases (e.g., larger, older, and well know firms), the trading

consideration becomes lower, thereby encouraging intervention. According to their model, they

argue that the targets of intervention should be “large, well-established firms that had performed

badly for years, with relatively clear inefficiencies and mistakes,” and “well-established firms

with numerous analysts and extensive reporting in the press” where trading concerns are less

important. Again, the choice of these two instruments is directly adopted from their models and

predictions, which satisfies Larcker and Rusticus’ (2010) first criterion for instruments.

18

Incorporating these two instrument variables, the first-stage ordered Probit estimation is as

the following:

OBLK = β0 + β1ILLIQ + β2KW + β3Size + β4MTB + β5CFO + β6Payout

+ β7Leverage + β8Tangibility + β9Zscore + β10Current Ratio +ε (2)

Following Gaspar and Massa (2007) and Gerken (2009), ILLIQ is measured as the three-

year average of 1000* VolumeTradingDollarreturndaily __/|_| (using daily data) to capture

market illiquidity, where returns data is obtained from CRSP. ILLIQ is measured in the year

before blockholder ownership is measured. That is, if blockholding is measured at year t-1, then

ILLIQ is measured at year t-2. ILLIQ reflects the impact of order flow on price: the discount that

a seller concedes or the premium that a buyer pays when executing a market order that results

from adverse selection costs and inventory costs.19

To capture that notion that well-covered firms with enduring low performance that are

likely to be a target for blockholders’ intervention (Kahn and Winton, 1999), I employ an

indicator variable KW equal to one if the number of analysts covering the firm is greater than the

sample median and when the firm’s ROE has been in the lowest tercile of the sample for two

consecutive years, zero otherwise. Again, this variable is measured one year before the

blockholding information is measured to avoid endogeneity. The number of analysts following

the firm is collected from the IBES database. Based on the theories discussed above, β1 is

expected be negative and β2 is expected to positive in Equation (2). Other control variables are

defined in the Appendix.

3.4 Interest Spreads

19Amihud (2002) argues that because market makers cannot distinguish between order flow that is generated by

informed traders and by liquidity noise traders, they set prices that are an increasing function of the imbalance in the

order flow which may indicate informed trading.

19

To test H2, the following OLS model is estimated.

Interest Spread = β0 + β1 OBLK + β2Size + β3MTB+ β4CFO + β5Payout +β6Leverage +

β7Tangibility + β8Zscore + β9Current Ratio + β10Debt Size+ β11Maturity+

β12Security + β13Fin Cov + β14Restriction + Σ βOther Covenants+ ε. (3)

In Equation (3), for a bank loan, Interest Spread is the rate above LIBOR that a borrower is

required to pay on a loan; while for corporate bonds Interest Spread is the difference between a

bond’s stated interest rate and the yield to maturity on a treasury-note with a similar term to

maturity.20

Note that, because I accumulate debt issued in the same year, Interest Spread is

measured as the weighted average of these debt issues based on the size of each debt issue.21

I

run this OLS estimation separately for loan and bond samples to allow the coefficients on all

variables to differ for the two samples. Following Bharath et al. (2008), I also include debt

characteristics (Debt Size and Maturity) as control variables in Equation (3). Based on prior

studies, I expect a positive coefficient on OBLK in both samples, and based on H2, I expect the

coefficient on OBLK to be greater for the bond sample than the bank loan sample.

For both samples, following prior literature, I also control for collateral requirements

(Security), whether there is a financial covenant (Fin Cov), and dividend restriction provision

(Restriction) that have potentials reducing agency conflicts of debt. In my sample, because

public debt very rarely include a security requirement, financial covenants or dividend

restrictions, I include two additional covenants as control variables that are more likely to be

used in public debt: Cross-Acceleration and Cremers Cov. Based on Beatty et al. (2012), roughly

40% of public debt issues, instead of employing financial covenants to monitor borrowers, free-

20When there is no matching treasury-note, FISD sets the treasury spread to be zero. I remove these observations

from the sample. 21

Maturity and Debt Size are both measured as the weighted average as well.

20

ride on other private lenders’ monitoring by including cross-acceleration provisions. Therefore, I

also control for Cross-Acceleration in the bond sample. Second, Cremers et al. (2007) suggest

that poison puts, covenants restricting leverage, and net worth covenants may decrease the

adverse effect of blockholdings in bond yields. Therefore, I include an indicator (Cremers Cov)

equal to one for bonds that include any of the three types of covenants, zero otherwise.22

3.5 Covenant Analysis

To test H3, the following Probit model is estimated:

Various Covenants= β0 + β1OBLK + β2Size + β3MTB + β4CFO+ β5Payout +β6Leverage +

β7Tangibility + β8Zscore+ β9Current Ratio + β10Debt Size +β11Maturity + ε.

(4)

For the bank loan sample, I consider four types of covenants as dependent variables:

collateral requirements (Security), CCov (i.e., capital financial covenants or balance sheet

financial covenants), PCov (i.e., performance financial covenants or income statement financial

covenants) and dividend restrictions (Restriction). These covenants are expected to constrain

firms’ various activities that can adversely affect lenders’ interests. For example, if a loan

contains a collateral requirement, firms’ asset substitution may be constrained. Further, based on

Christensen and Nikolaev (2012), banks can also use financial covenants based on balance sheet

accounts to directly control agency problems of debt by aligning debtholders’ and shareholders’

interests. Specifically, if a bank loan contains quick ratio, quick ratio, debt-to-equity ratio, debt

to tangible net worth ratio, leverage ratio, senior leverage ratio, net worth or tangible net worth

ratio covenants, then CCov is equal to one, zero otherwise.

22If I only consider poison put covenants in measuring this indicator variable, all results continue to hold. In addition,

the results for the bond sample also continue to hold without these two additional control variables. Cremers et al.

(2007) do not directly test whether these covenants are more likely in the presence of higher blockholdings.

21

Next, to limit agency problems via the transfer of control rights to lenders in states where

the value of their claim is at risk, banks can use financial covenants based on income statement

financial ratios as trip wire mechanisms (Christensen and Nikolaev, 2012). Specifically, if a bank

loan includes either cash interest coverage ratio, debt service coverage ratio, EBITDA, senior

debt to EBITDA, fixed-charge coverage ratio, interest coverage ratio, or debt to EBITDA

covenants, then PCov is equal to one, zero otherwise. Finally, I also consider dividend

restriction provisions that may directly constrain a firm’s excessive dividend payout. Note that

because I accumulate all debt issued in the same year, as long as any of these debt issues

includes the covenant of interest, the dependent variable is set to be one, zero otherwise. Based

on H3, I expect the coefficients on OBLK to be positive in Equation (4) for all these covenants.

Following prior studies (e.g., Asquith et al., 2005), I also control for loan characteristics such as

the size and maturity of the loan (Debt Size and Maturity). Detailed definitions are provided in

the Appendix.

4. Empirical Results

4.1 Univariate Results

Table 1 provides firm and debt characteristics partitioned by whether or not the firm’s

external blockholdings are higher than the sample median. On average, high (low) blockholding

firms have 26.6 % (4.1%) shares owned by outside blockholders. In general, firms with high

blockholdings are smaller, less profitable, have a lower market-to-book ratio, have a higher

leverage ratio, and have lower Z-scores.

Among bank loan users, firms with high blockholdings pay higher interest rates. Their

bank loans also tend to be larger for high blockholdings firms. In addition, these firms with more

22

blockholdings are more inclined to include collateral requirements, capital covenants,

performance covenants and dividend restrictions in the debt contracts. For firms that issue public

debt, those with high blockholdings pay higher interest rates as well. However, none of the

covenants in public debt seems to be correlated with blockholdings.

Table 2 compares firm characteristics between bond and loan samples.23

Consistent with

H1, firms that use bank loans have higher external blockholdings than public debt issuers. Firm-

years where loan issues are predominant have 17.8% outside block ownership on average,

compared to 13.3% for firm-years with predominant public debt issues, with the difference being

significant statistically. Consistent with expectations, in the firm-years where bank debt is

predominant 92% of debt issue is bank debt, and in the firm-years where public debt is

predominant only 7% of debt issue is bank debt. In general, firms with more bank debt issues are

smaller, less profitable, and more levered. They also have lower creditworthiness and lower

market-to-book ratios.

4.2 Multivariate Results

Model 1 in Table 3 presents the results of the Probit estimation from Equation (1). I find

that the likelihood of using bank debt relative to public debt increases with outside blockholdings.

The effect of blockholdings on the choice of debt financing is also economically significant. For

example, moving from the bottom quintile to the top quintile in blockholdings increases the

likelihood of using bank debt by 12%, which is considerably significant given the proportion of

bank debt in my sample is 46%.

23 Note that firms classified in the bond sample may still have issued bank debt in that firm-year. Based on my

research design, this classification only represents firm-years where either public debt or bank loan dominates in

total issued amount.

23

Based on Larcker and Rusticus (2010), for IV strategies to be effective in addressing

endogeneity, the chosen instruments should correlate with OBLK but only affect the debt

financing choice via OBLK; that is, these instruments should not correlate with the error term in

the second-stage model. The first-stage model shows that these two instruments are significantly

associated with outside blockholdings as predicted. In Table 4, outside blockholdings decrease

with ILLIQ, significant at the 5% level. This result is consistent with Maug (1998) that market

liquidity enables blockholding. In addition, outside blockholdings increase with KW, significant

at the 1% level, consistent with Kahn and Winton (1998).

More importantly, the partial F-stat (13.78) of whether the instruments are jointly zero is

greater than the required F-stat 11.59 based on Larcker and Rusticus’ (2010) weak instrument

criteria, suggesting the instruments do not have the weak-instrument problem. In addition, I

conduct an over-identification test after the two-stage estimation, and p-value of the test is

greater than 10%, suggesting that two instruments are not correlated with the error term of the

second stage at the conventional levels. All of these findings suggest that the instruments for the

IV approach should be suitable to address endogeneity.

I report the results of the second-stage model in Model 2 of Table 3. I continue to find that

outside blockholdings increase the likelihood of firms using bank debt financing, significant at

the 5% level. This suggests that the finding from the Probit estimation as presented in Model 1 is

not driven by endogeneity.

In Table 5, I provide results of the effect of blockholdings on interest spreads. I find that, in

both OLS or 2SLS estimations, the coefficients on OBLK for the public debt sample are

significantly positive, consistent with prior studies (e.g., Cremers et al., 2007). However, I do not

find the same results for the bank loan sample, suggesting that monitoring provided by banks

24

may be effective in reducing the agency costs of debt resulting from blockholders’ monitoring of

management. At the bottom of Table 5, I present results consistent with H2 that monitoring by

banks results in a lower demand for price protection from the agency problems arising from large

investors’ monitoring.

Finally, Table 6 presents results mostly consistent with H3 that banks employ various

covenants to monitor management. Although the requirement of collateral does not seem to be

significantly increasing with blockholdings, I find that firms with higher external blockholdings

are more likely to include capital and performance financial covenants in debt contracts. I also

find that these firms are more likely to use dividend restrictions to constrain payouts to

shareholders (marginally significant).24

For completeness and to understand whether public debt includes debt covenants to counter

the adverse effect caused by blockholdings, I conduct a similar Probit estimation where the

dependent variables include whether the bond includes financial covenants, dividend restriction

provisions, cross-accelerations and covenants that Cremers et al. (2007) argue can mitigate

agency costs of debt (Cremers Cov).25

Consistent with the expectations that public debtholders

exert less influence and monitor management to a lesser degree relative to banks, I find none of

the these covenants is positively associated with outside blockholdings.

5. Supplemental Analyses and Robustness Checks

5.1 Change Model

24 I also test the effect of blockholdings on the use of debt covenants in the bank sample using the 2SLS approach.

Both financial covenants continue to have a significant correlation with blockholdings. 25

I do not test the effect of blockholdings on collateral requirements because in my sample, very few (less than 1%)

bonds are secured. In addition, I do not further partition financial covenants because the number of financial

covenants in public debt is low and only around 4% of the bonds in my sample use any financial covenant.

25

To further address endogeneity, I investigate whether the debt financing decision depends

on the change in external blockholdings. The incremental approach (of debt issuance) used in

this study is particularly suitable for this change analysis because it captures the effect of time-

varying characteristics (in this case, change in blockholdings) on debt choices. I also control for

the change in other control variables, along with level control variables.26

The results in Table 8

suggest that the likelihood of employing bank debt versus public debt increases with the change

in outside blockholdings, further supporting the results in the previous sections.27

In addition to examining the debt choice, I also investigate whether the debt financing costs

vary with the change in outside blockholdings. Specifically, I measure the change in debt

financing costs, ∆IntExp, as the change in interest expenses (COMPUSTAT “xint”) divided by

lagged total debt (COMPUSTAT “dlc”+”dltt”). Based on H2, if external blockholdings increase

agency costs of debt and if banks’ monitoring mechanisms attenuate this increased agency cost,

then I expect that the cost of debt increases with the change in blockholdings especially for firms

that issue public debt versus bank loans. Note that ∆IntExp is measured in the same year as the

debt issuance (year t), and therefore it is one year ahead of all test and control variables (year t-

1).28

In addition to this test variable, I also control for the change in firm characteristics. In Table

9, I present results consistent with H2 and Table 5: outside blockholdings increase debt financing

26The results continue to hold when the level control variables are not included in the model.

27 As a robustness check, I rely on stock pricing decimalization as an exogenous shock that serves as an additional

identification for the effect of the change in outside blockholdings on debt financing decisions. The NYSE switched

stock pricing from eighths to decimals starting in 2000. The NASDAQ began to switch to decimals tick sizes in

March of 2001. Finally, the SEC ordered all U.S. stock markets to convert to decimal pricing by April, 2001. I

include debt issuances in 2001 and 2002 in the sample with change in blockholdings measured in 2000 and 2001,

and I continue to find similar results that the use of bank debt increases with blockholdings using the sample of 116

observations, although the significance level drops due to this small sample (i.e., at the 10% level). 28

I do not calculate the change in interest rate as the change in spreads because this procedure would require more

firms to have issued bank or public debt covered by DealScan or FISD. However, as a robustness check, I use the

level of interest spreads as the dependent variable and the results continue to hold.

26

costs when the firm issues public debt but not when the firm borrows from banks perhaps due to

banks’ superior monitoring efficiency.

Finally, to examine whether the use of debt covenants in bank loans varies with the change

in outside blockholdings, I require the firm to have issued other bank debt in the past three years

in order to measure the change in covenant use.29

While the number of observations and the

power of the test drop significantly due to this procedure, I find that the change in the use of

collateral requirements and capital financial covenants increases with the change in

blockholdings (untabulated).

5.2 Propensity Score Matching

In this section, I employ the propensity score matching method to address the endogeneity

concern. Specifically, I estimate Equation (2) with an adjustment of using high versus low

blockholdings compared to the sample median as the dependent variable (i.e., HighB=1 [0] for

firm-years with blockholdings higher [lower] than the sample median) to generate the likelihood

of having high outside blockholdings. For each high blockholding firm-year (i.e., HighB=1), I

find matched samples with estimated likelihood within 1% of the high blockholding firm from

the sample where HighB=0, with replacements. This procedure generates 405 observations, with

212 from the high blockholding sample. Results in Table 10 are consistent with H1 and Table 3:

the likelihood of bank debt versus public debt increases with the outside blockholdings,

suggestive of firms’ reliance on banks’ superior monitoring efficiency in mitigating agency risk

of debt associated with outside blockholdings.

5.3 Other Corporate Governance Mechanisms

29In the sample selection process, if Dealscan indicates that the firm has the same type of loan syndicated by the

same lead arranger then the loan of interest is removed from the sample. Therefore, in this change analysis, only

loans syndicated by other lead banks or other types of debt issued in the previous three years will be included.

27

In this section, I provide a counterfactual analysis by investigating the relation between

corporate governance that reduces agency risk of debt and debt financing. Specifically, based on

Ashbaugh-Skaife et al.’s (2006) findings that board structure aligning management’s interests

reduces rather than increases agency costs of debt, firms with these governance mechanisms may

not need to rely on banks’ monitoring, and therefore do not choose bank debt over public debt.

To test this prediction, I collect board structure data from IRRC and then merge it with the main

sample of this study. Based on Ashbaugh-Skaife et al. (2006) and Klein (2002), I calculate the

first principal component of three board structure variables that align management-shareholder

interest as the test variable (BOARD): percentage of independent directors on board, share

ownership of independent directors and whether more than 50% of audit committee members are

independent. I do not have a signed prediction on this test variable.

I report the results of this analysis in Table 11. I find that while the likelihood of using bank

debt versus public debt increases with outside blockholdings, it does not increase with board

structure designed to reduce the management-shareholder conflicts. This finding supports the

monitoring role of banks in mitigating agency problems of debt associated with blockholdings

and the notion that firms’ debt financing decisions depend on the type of governance

mechanisms.

I also investigate the relation between debt financing decisions and insider blockholdings.

Firms with inside blockholdings share many firm characteristics with those with outside

blockholders.30

Because the effect of inside blockholdings on reducing management

30 For example, Mikkelson and Parch (1989) find that inside blockholdings decrease with firm size and argue that

firm size is the most important determinant. In addition, based on the data in my sample, inside and outside

blockholdings are both positively correlated with leverage and negatively correlated with the market-to-book ratio

and default risk.

28

entrenchment is different from outside blockholdings despite their similarity in determinants, I

can take advantage of this difference to provide a further identification. Unlike monitoring

provided by outside blockholders, when management’s ownership exceeds 5%, management-

shareholder alignment declines (Morck et al, 1988).31

Therefore, the agency conflicts with

debtholders caused by inside blockholdings should be weaker than outside blockholdings. As a

result, if a positive relation between inside blockholdings and debt financing is not observed,

then omitted variables are less likely to explain my findings above. On the other hand, if omitted

variables drive my results, then to the extent that these omitted variables also affect inside

blockholdings, inside blockholdings should increase the use of bank debt. In untabulated results,

I find that the association between inside blockholdings and bank debt financing is not

significant, suggesting that the relation between bank debt financing and outside blockholdings is

less likely to be caused by omitted variables.

5.4 Other Additional Analyses and Robustness Checks

To further understand the differences in mechanisms in mitigating agency risk of debt

between bank loans and public debt, I examine the effect of debt covenants in reducing the

increased interest rates caused by external blockholdings. Specifically, I interact debt covenants

and external blockholdings in the interest rate models to investigate the effectiveness of various

covenants in reducing agency conflicts associated with blockholders. In untabulated results, I

find that capital financial covenants are most effective in reducing interest rates associated with

31Morck et al. (1988) argue that management-shareholder alignment increases with management ownership up to

5%. Related to this finding, Begley and Feltham (1999) find that agency costs of debt increase with management

ownership. However, when management ownership exceeds 5%, management-shareholder alignment declines.

Begley and Feltham (1999) do not examine this non-linearity effect on agency costs of debt.

29

blockholdings for the bank loan sample. On the other hand, I do not find similar results for the

public debt sample.

Following Ashbaugh-Skaife et al. (2006), I control for the firm’s past performance (i.e.,

lagged stock returns) to further address the endogeneity concern. All results continue to hold. In

addition, to ensure that I am not simply capturing the size effect, I conduct the same analysis by

size partitions. I find that the coefficients on blockholdings do not differ significantly between

the two partitions, suggesting that my results are not driven by the size effect. Further, although I

run the analyses using quintile rankings of outside blockholdings to mitigate the effect of

measurement error, the results continue to hold when I use the original level of block ownership

as the test variable (significant at 10%). Finally, I substitute the loan issue ratio (i.e., the ratio of

total bank debt issue over total debt issued in a year) for the loan versus bond dummy as the

dependent variable in the firms’ debt choice model, and the OLS estimation results are consistent

with the Probit model, significant at the 5% level. This finding suggests that the results are not

driven by the incremental approach I use.

6. Conclusions

This study extends the growing literature that argues that some governance mechanisms

align management’s and shareholders’ interests to the detriment of debtholders. Using outside

blockholdings as a proxy for such mechanisms, I examine how lenders’ different monitoring

mechanisms reducing this agency conflict of debt interact with blockholders’ monitoring in the

overall corporate governance system. Specifically, I investigate how the inter-relation between

monitoring provided by lenders and outside blockholders affects a firm’s debt financing choices.

I hypothesize and find that firms with higher external blockholdings rely on bank debt financing

more than public debt. I find that the adverse effect (i.e., increased interest rates) of outside

30

blockholdings documented in the prior studies is lower in bank loans than in public debt,

suggesting that banks demand lower price protection than bondholders. I also find corroborating

evidence that among firms that issue bank debt, those with a higher level of outside

blockholdings are more likely to include various covenants. I do not find similar results for the

public debt sample, supportive of banks’ superior monitoring mechanisms in countering agency

risk of debt associated with blockholders.

This study also contributes to both the debt and corporate governance literatures. In

addition to extending the prior studies that examine the relation between corporate governance

and the cost of debt, this study suggests that different lenders’ monitoring interacts with

corporate governance mechanisms in shaping firms’ debt financing decisions. Prior studies do

not consider the differential monitoring provided by banks versus public debtholders. It also

responds to Weber’s (2006) and Armstrong et al.’s (2010) call for research that incorporates

lenders’ monitoring in the overall governance system. My findings further suggest that

monitoring by banks and outside blockholders are complements rather than substitutes. This

study also documents that the effect of differential monitoring by lenders on debt financing

depends on the types of corporate governance mechanisms. I document that firms with

governance that reduces the costs of debt do not prefer bank loans in the debt markets. Finally

this study finds that the debt covenant choice is endogenous to other corporate governance

mechanisms.

31

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34

Appendix: Definition of Variables

Variable Definition

Firm Characteristics

%OBLK Proportion of outstanding shares owned by outside blockholders, which

is collected from the database compiled by Dlugosz, Fahlenbrach,

Gompers and Metrick (2006).

OBLK Quintile ranking of %OBLK.

Size The natural log of market value of equity (COMPUSTAT “csho”*

“prcc_f”).

MTB Market value of total assets (COMPUSTAT “at” – COMPUSTAT “ceq”

+ COMPUSTAT “csho”* “prcc_f”) divided by book value of total

assets (COMPUSTAT “at”).

CFO Cash flow from operating activities (COMPUSTAT “oancf”) scaled by

lagged total assets (COMPUSTAT “at”).

Payout Dividend (COMPUSTAT “dvt”) plus repurchase (COMPUSTAT

“prstkc”) divided by lagged total assets (COMPUSTAT “at”).

Leverage Long-term debt (COMPUSTAT “dltt”) divided by total assets

(COMPUSTAT “at”).

Tangibility Net property, plant and equipment (COMPUSTAT “ppent”) scaled by

total assets (COMPUSTAT “at”).

Zscore 1.2* (COMPUSTAT “act”-“lct”/ “at”) +1.4* (“re”/”at”) + 3.3* (“ebit”/

“at”) + 0.6* ( “csho”* “prcc_f” / “at”) + “revt”/”at”.

Current Ratio Current assets (COMPUSTAT “act”) divided by current liabilities

(COMPUSTAT “lct”).

Debt Characteristics

LIBOR

Spreads

All-in-Spreads Drawn of loans charged by the bank over LIBOR

collected from the LPC Dealscan database.

Treasury

Spreads

The interest spread on corporate bonds over the interest rate on a

treasury note of similar maturity, collected from the FISD database.

Security An indicator variable equal to one if the debt requires collateral and zero

otherwise.

Debt Size The ratio of debt size from the Dealscan or FISD databases to the total

assets.

Maturity The natural log of number of months between the start and stated

termination dates of the debt from the Dealscan or FISD databases.

Restriction An indicator variable equal to one if the debt includes dividend payment

restrictions and zero otherwise.

Fin Cov An indicator variable equal to one if the debt includes any financial

covenant and zero otherwise.

CCov An indicator variable equal to one if the bank loan uses quick ratio,

quick ratio, debt-to-equity ratio, debt to tangible net worth ratio,

leverage ratio, senior leverage ratio, net worth or tangible net worth ratio

covenants, zero otherwise.

35

PCov An indicator variable equal to one if the bank loan includes either cash

interest coverage ratio, debt service coverage ratio, EBITDA, senior debt

to EBITDA, fixed-charge coverage ratio, interest coverage ratio, or debt

to EBITDA covenants, zero otherwise.

Cross-

Acceleration

An indicator variable equal to one if the bond includes cross-

acceleration provisions, zero otherwise.

Cremers Cov An indicator variable equal to one if the bond includes either poison put,

leverage restriction and net worth covenants, and zero otherwise.

36

Table 1: Firm and Debt Characteristics Partitioned by Outside Blockholder Ownership

High Outside Blockholder

Ownership

Low Outside Blockholder

Ownership

Mean Median Mean

(t-stats for the

difference

between High

and Low

ownership)

Median

(Wilcoxon z-

stats for the

difference

between High

and Low

ownership)

Firm Characteristics

% OBLK 26.571 24.450 4.144

(32.29)***

5.000

(19.97)***

Size 7.763 7.853 8.782

(-8.09)***

8.864

(-7.41)***

MTB 1.681 1.437 2.130

(-4.54)***

1.681

(-4.83)***

CFO 0.100 0.094 0.120

(-3.04)***

0.116

(-3.41)***

Payout 0.039 0.021 0.051

(-2.94)***

0.037

(-3.95)***

Leverage 0.312 0.296 0.252

(4.34)***

0.223

(4.77)***

Tangibility 0.438 0.424 0.416

(1.14)

0.394

(0.84)

Zscore 2.783 2.457 3.524

(-4.16)***

3.116

(-3.97)***

Current Ratio 1.473 1.300 1.326

(2.55)**

1.206

(2.50)**

N 262 263

Loan Characteristics

LIBOR Spreads 116.1 87.500 85.829

(2.50)***

50.000

(4.14)***

Debt Size 0.314 0.227 0.234

(2.19)**

0.146

(2.87)***

Maturity 3.387 3.584 3.446

(-0.69)

3.584

(-0.70)

Security 0.454 0.000

0.380

(1.72)*

0.000

(1.71)*

Capital Fin Cov 0.610 1.000 0.500

(1.70)*

0.500

(1.69)*

Performance Fin

Cov

0.695 1.000 0.480

(3.43)***

0.000

(3.35)***

37

Dividend

Restriction

0.532 1.000 0.390

(2.19)**

0.000

(2.17)**

N 141 100

Bond Characteristics

Treasury

Spreads

215.3 185.000 136.1

(5.50)***

106.000

(6.45)***

Debt Size 0.122 0.090 0.115

(0.37)

0.064

(3.75)***

Maturity 4.812 4.802 4.772

(0.58)

4.802

(0.19)

Fin Cov 0.041 0.000 0.043

(0.07)

0.000

(0.06)

Dividend

Restriction

0.017 0.000 0.031

(-0.76)

0.000

(-0.75)

Cross-

acceleration

0.372 0.000 0.393

(-0.35)

0.000

(-0.62)

Cremers Cov 0.066 0.000 0.049

(0.61)

0.000

(-0.35)

N 121 163

Note: ***, ** and * represent 1%, 5% and 10% significance levels, respectively. See Appendix

for definition of variables.

38

Table 2: Firm Characteristics Partitioned by the Use of Bank Loan vs. Bond

Loan Sample Bond Sample

Mean Median Mean

(t-stats for the

difference

between loan

and bond

samples)

Median

(Wilcoxon z-

stats for the

difference

between loan

and bond

samples)

OBLK 2.237 2.000 1.715

(4.14)***

2.000

(4.07)***

% OBLK 17.769 16.000 13.281

(3.77)***

9.835

(3.86)***

Loan Proportion 0.921 1.000 0.074

(65.43)***

0.000

(64.00)***

Size 7.890 7.879 8.560

(-5.43)***

8.615

(-5.20)***

MTB 1.693 1.479 2.087

(-3.95)***

1.608

(-3.45)***

CFO 0.098 0.092 0.121

(-3.72)***

0.115

(-3.54)***

Payout 0.040 0.023 0.050

(-2.25)**

0.036

(-3.53)***

Leverage 0.306 0.276 0.262

(3.17)***

0.239

(2.96)***

Tangibility 0.413 0.374 0.439

(-1.32)

0.427

(-1.53)

Zscore 2.825 2.496 3.434

(-3.38)***

2.979

(-3.21)***

Current Ratio 1.454 1.315 1.353

(1.75)*

1.213

(1.82)*

N 241 284

Note: ***, ** and * represent 1%, 5% and 10% significance levels, respectively. See Appendix

for definition of variables. Loan Proportion is measured as the ratio total bank debt issued in a

firm-year to total debt issued.

39

Table 3: Coefficients (and z-stats) of Probit Estimation of the Determinants of Choice of

Bank Debt versus Corporate Bond (Dependent Variable Equals One for Bank Loans and

Zero for Bonds)

Choice of Bank Loan vs. Public Debt = β0 + β1 OBLK + β2Size + β3MTB + β4CFO + β5Payout

+β6Leverage + β7Tangibility + β8Zscore+ β9Current Ratio +ε

Model 1

(Probit)

Model 2

(IV Probit)

Variable Predictions Coefficient

(z-stat)

Coefficient

(z-stat)

Intercept +/- 1.273

(1.87)*

0.465

(0.54)

OBLK + 0.078

(1.85)**

P(OBLK) + 0.102

(2.10)**

Size - -0.089

(-1.47)*

-0.019

(-0.24)

MTB + -0.128

(-1.05)

0.182

(1.44)*

CFO - -0.841

(-0.88)

0.949

(0.98)

Payout +/- 0.473

(0.29)

0.805

(0.48)

Leverage + 0.488

(0.83)

0.630

(1.10)

Tangibility - -0.628

(-1.95)**

-0.584

(-1.81)**

Zscore - 0.032

(0.43)

0.067

(0.90)

Current Ratio +/- -0.048

(-0.36)

-0.083

(0.63)

Pseudo-R Squared 0.0604 0.0649

N 525 525

Note: Z-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance, respectively. The dependent variable

equals one for bank loans and zero for bonds. See Appendix for definition of other variables.

40

Table 4: Coefficients (and z-stats) of ordered Probit of the Determinants of Outside

Blockholders’ Ownership

OBLK = β0 + β1ILLIQ + β2 KW+ β3Size + β4MTB + β5CFO+ β6Payout +β7Leverage +

β8Tangibility + β9Zscore + β10Current Ratio +ε

Variables Predictions Coefficients

Clustered z-stats

ILLIQ - -0.735 -2.15**

KW + 0.483 2.82***

Size - -0.372 -6.28***

MTB +/- 0.192 1.67*

CFO +/- 0.324 0.33

Payout +/- -0.740 -0.45

Leverage +/- -0.359 -0.68

Tangibility +/- -0.226 -0.74

Zscore +/- -0.132 -1.82*

Current Ratio +/- 0.144 1.32

N 525

R-Squared 0.0834

Test of Coefficients on ILLIQ = KW= 0

Partial F-stat =

13.78

Note: Z-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance levels, respectively. ILLIQ is defined as

three-year average of 1000* VolumeTradingDollarreturndaily __/|_| (using daily data)

measured in the year before blockholding is measured. KW is an indicator variable that equals

one if the number of analysts covering the firms exceeds the sample median and if the firm’s

ROE has been in the bottom tercile of the sample for two consecutive years; zero otherwise. This

variable is also measured the year before blockholding is measured. See Appendix for definition

of other variables.

41

Table 5: Coefficients (and t-stats) of OLS Estimation of Interest Spreads on Bank Loans

and Public Debt LIBOR Spreads (or Treasury Spreads) = β0 + β1 OBLK + β2Size + β3MTB + β4CFO + β5Payout

+β6Leverage + β7Tangibility + β8Zscore+ β9Current Ratio + β10Debt Size+ β11Maturity + β12

Security + β13 Fin Cov+ β14 Restriction + Σ βOther Covenants+ ε

Bank Loans

(OLS)

Public Debt

(OLS)

Bank Loans

(IV)

Public Debt

(IV)

Variable Pred Coeff

(t-stat)

Coeff

(t-stat)

Coeff

(t-stat)

Coeff

(t-stat)

Intercept +/- 268.210

(6.04)**

638.586

(7.59)***

129.540

(2.20)**

482.736

(5.89)***

OBLK + 1.074

(0.45)

12.705

(2.63)***

P(OBLK) + 2.724

(0.74)

17.549

(2.80)***

Size - -4.823

(-1.14)

-37.956

(-6.29)***

-6.709

(-1.18)

-27.091

(-3.94)***

MTB + 5.554

(0.56)

4.0475

(0.50)

10.562

(1.03)

-1.924

(-0.17)

CFO - -102.853

(-1.44)*

71.609

(0.68)

-129.882

(-1.68)**

35.289

(0.35)

Payout +/- 94.828

(1.05)

-312.898

(-2.40)**

5.500

(0.06)

-306.362

(-2.42)**

Leverage + 64.182

(1.16)

174.752

(3.65)***

130.768

(2.46)***

172.785

(3.62)***

Tangibility - -20.200

(-1.07)

-53.994

(-1.68)**

-21.993

(-1.03)

-44.109

(-1.40)*

Zscore - -10.622

(-1.70)**

-12.680

(-1.62)*

-6.539

(-0.99)

1.320

(0.18)

Current Ratio - -37.699

(-5.47)***

-16.750

(-1.74)**

-8.254

(-0.97)

-16.318

(-1.69)**

Debt Size +/- 24.818

(1.32)

21.969

(0.93)

5.998

(0.31)

10.548

(0.39)

Maturity +/- -10.339

(-2.06)**

-30.425

(-3.73)***

-6.8221

(-1.22)

-29.437

(-3.71)***

Security +/- 84.670

(5.88)***

146.892

(3.77)***

98.514

(6.28)***

122.570

(3.62)***

Fin Cov +/- -10.051

(-1.36)

45.115

(0.84)

-9.842

(-1.19)

46.782

(0.82)

Dividend

Restriction

+/- 17.675

(2.42)**

96.03

(1.41)

24.662

(3.07)***

72.305

(1.01)

Cross-

Acceleration

+/- N/A -56.949

(-5.16)***

N/A -56.557

(-5.15)***

42

Cremers Cov +/- N/A 30.966

(0.57)

N/A 29.284

(0.51)

R Squared 0.7112 0.5546 0.6690 0.5351

N 241 284 241 284

Test of the Equality of

Coefficients on OBLK or

P(OBLK) across the Two

Samples

χ2(1) = 3.20,

p-value = 0.037

χ2(1) = 4.04,

p-value = 0.023

Note: T-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance, respectively. P-values of the equality of

coefficients on OBLK and P(OBLLK) are based on one-tailed tests. See Appendix for definition

of other variables.

43

Table 6: Coefficients (and z-stats) of Probit Estimation of the Determinants of Various

Debt Covenants in Bank Loans

Covenant = β0 + β1 OBLK + β2Size + β3MTB + β4CFO + β5Payout +β6Leverage + β7Tangibility +

β8Zscore+ β9Current Ratio + β10Debt Size+ β11Maturity + ε

Security

Requirement

(Security)

Capital

Financial

Covenant

(CCov)

Performance

Finance

Covenant

(PCov)

Dividend

Restriction

(Restriction)

Variable Pred Coeff

(z-stat)

Coeff

(z-stat)

Coeff

(z-stat)

Coeff

(z-stat)

Intercept +/- 0.907

(0.74)

0.118

(0.13)

2.416

(2.54)**

-0.403

(-0.17)

OBLK + 0.058

(0.75)

0.103

(1.69)**

0.166

(2.45)***

0.102

(1.54)*

Size - -0.325

(-2.55)***

-0.062

(-0.73)

-0.358

(-3.97)***

-0.256

(-2.66)***

MTB + -0.144

(-0.50)

0.092

(0.41)

0.114

(0.34)

0.216

(0.71)

CFO - -2.438

(-1.32)*

-0.004

(-0.00)

1.117

(0.44)

-1.150

(-0.42)

Payout +/- -3.435

(-1.14)

-2.38

(-1.01)

-0.672

(-0.11)

-3.801

(-1.78)*

Leverage +/- 2.147

(2.38)**

-1.673

(-1.98)**

1.952

(2.39)**

1.404

(1.87)*

Tangibility +/- -0.303

(-0.51)

1.183

(2.32)**

-1.360

(-2.84)***

0.844

(1.67)*

Zscore +/- -0.101

(-0.94)

-0.016

(0.02)

0.094

(0.85)

0.046

(0.23)

Current Ratio +/- 0.058

(0.30)

0.407

(2.34)**

-0.227

(-1.25)

0.176

(1.11)

Debt Size +/- 0.939

(1.66)*

-0.462

(-1.14)

0.763

(1.43)

0.769

(1.97)**

Maturity +/- 0.171

(1.04)

-0.005

(-0.20)

-0.001

(-0.00)

0.202

(1.63)

R Squared 0.2862 0.0683 0.1791 0.1792

N 241 241 241 241

Note: Z-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance, respectively. See Appendix for definition

of other variables.

44

Table 7: Coefficients (and z-stats) of Probit Estimation of the Determinants of Various

Debt Covenants in Public Debt

Covenant = β0 + β1 OBLK + β2Size + β3MTB + β4CFO + β5Payout +β6Leverage + β7Tangibility +

β8Zscore+ β9Current Ratio + β10Debt Size+ β11Maturity + ε

Financial

Covenant

(Fin Cov)

Dividend

Restriction

(Restriction)

Cross-

acceleration

Cremers

Covenant

(Cremers

Cov)

Variable Pred Coeff

(z-stat)

Coeff

(z-stat)

Coeff

(z-stat)

Coeff

(z-stat)

Intercept +/- -0.037

(-0.00)

5.056

(1.97)**

0.147

(0.02)

-0.216

(-0.10)

OBLK + -0.181

(-1.49)

-0.787

(-1.55)

-0.039

(-0.35)

-0.068

(-0.58)

Size - -0.189

(-0.85)

-0.017

(-0.01)

-0.062

(0.43)

-0.222

(-1.32)*

MTB + 0.128

(0.26)

-2.307

(-1.70)*

-0.330

(-1.75)*

0.021

(0.01)

CFO - 0.32

(0.18)

-11.820

(-1.99)**

1.245

(0.77)

1.600

(1.16)

Payout +/- -8.011

(-1.39)

-67.341

(-2.57)**

-4.071

(-1.66)*

-7.221

(-1.14)

Leverage +/- -0.775

(-0.93)

-2.885

(-1.81)*

-0.669

(-0.71)

0.126

(0.14)

Tangibility +/- -0.224

(-0.08)

-1.185

(-0.65)

0.181

(0.12)

0.126

(0.15)

Zscore +/- -0.024

(-0.90)

-1.181

(-2.62)***

0.134

(1.15)

0.014

(0.08)

Current Ratio +/- 0.382

(1.76)*

1.223

(2.84)***

-0.183

(-0.80)

0.230

(1.03)

Debt Size +/- 0.516

(0.64)

7.817

(4.11)***

-0.073

(-0.03)

-0.379

(-0.17)

Maturity +/- -0.024

(-0.26)

-0.055

(0.10)

0.158

(1.03)

0.040

(0.18)

R Squared 0.1212 0.6000 0.0582 0.1057

N 284 284 284 284

Note: Z-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance, respectively. See Appendix for definition

of other variables.

45

Table 8: The Effect of Change in Outside Blockholdings on the Use of Bank Loan vs. Public

Debt

Choice of Bank Loan vs. Public Debt = β0 + β1ΔOBLK + β2ΔSize + β3ΔMTB + β4ΔCFO +

β5ΔPayout +β6ΔLeverage + β7ΔTangibility + β8ΔZscore +β9ΔCurrent Ratio +Σ αLevel Variables +

ε

Variables Prediction Coefficients clustered z-stats

Intercept +/- 1.010 1.45

ΔOBLK + 0.089 1.86**

ΔSize - -0.109 -0.41

ΔMTB + 0.384 1.46*

ΔCFO - -0.046 -0.01

ΔPayout +/- -1.542 -0.78

ΔLeverage + 1.059 1.33*

ΔTangibility - 0.324 0.21

ΔZscore - -0.249 -1.62*

ΔCurrent Ratio +/- 0.259 1.18

Control for Level

Variables YES

N 406

Pseudo R-squared 0.084

Note: Z-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance levels, respectively. The dependent variable

equals one for bank loans and zero for bonds. See Appendix for definition of other variables.

46

Table 9: The Effect of Change in Outside Blockholdings on Interest Rates for Bank Loan

vs. Public Debt

ΔIntExp= β0 + β1ΔOBLK + β2ΔSize + β3ΔMTB + β4ΔCFO + β5ΔPayout +β6ΔLeverage +

β7ΔTangibility + β8ΔZscore +β9ΔCurrent Ratio + ε

Bank Loan Public Debt

Variables Prediction Coefficients

(T-stats)

Coefficients

(T-stats)

Intercept +/- -0.024

(-1.49)

0.004

(0.99)

ΔOBLK +

-0.001

(-0.34)

0.003

(1.74)**

ΔSize - -0.005

(-0.58)

-0.022

(-2.55)**

ΔMTB + 0.030

(2.79)***

-0.008

(-0.09)

ΔCFO - -0.082

(-1.83)**

0.032

(0.88)

ΔPayout +/- -0.049

(-0.62)

-0.010

(-0.16)

ΔLeverage + -0.188

(-4.75)***

-0.164

(-3.52)***

ΔTangibility - 0.157

(2.76)***

0.109

(2.53)***

ΔZscore - -0.003

(-0.57)

0.024

(4.29)***

ΔCurrent Ratio +/- -0.003

(-0.57)

0.012

(1.60)

N 176 224

R-squared 0.3371 0.4757

Test of Equality of Coefficients on ΔOBLK

between Bank Loan and Public Debt

Samples

χ2(1) = 2.89, p-value = 0.0446

Note: T-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance levels, respectively. Dependent variable is

the change in interest expenses (COMPUSTAT “xint”) divided by lagged total debt

(COMPUSTAT “dlc” + “dltt”). See Appendix for definition of other variables.

47

Table 10: The Effect of Outside Blockholdings on the Use of Bank Loan vs. Public Debt

Based on Propensity Score Matched Samples

Choice of Bank Loan vs. Public Debt = β0 + β1OBLK + β2Size + β3MTB + β4CFO + β5Payout

+β6Leverage + β7Tangibility + β8Zscore +β9Current Ratio + ε

Variables Prediction Coefficients clustered z-stats

Intercept +/- 0.8462 1.29

OBLK + 0.0920 1.95**

Size - -0.100 -1.54*

MTB + -0.082 -0.61

CFO - -0.568 -0.38

Payout +/- 0.146 0.11

Leverage + 0.638 1.45

Tangibility - -0.660 -1.91**

Zscore - 0.007 0.09

Current Ratio +/- -0.011 -0.08

N 405

Pseudo R-squared 0.0406

Note: Z-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance, respectively. The dependent variable

equals one for bank loans and zero for bonds. See Appendix for definition of other variables.

48

Table 11: The Effect of Outside Blockholdings and Other Corporate Governance on the

Use of Bank Loan vs. Public Debt Based

Choice of Bank Loan vs. Public Debt = β0 + β1OBLK + β2BOARD +β3Size + β4MTB + β5CFO +

β6Payout +β7Leverage + β8Tangibility + β9Zscore +β10Current Ratio + ε

Variables Prediction Coefficients clustered z-stats

Intercept +/- 0.939 1.54

OBLK + 0.091 2.01**

BOARD +/- -0.095 -1.56

Size - -0.108 -1.92**

MTB + -0.082 0.70

CFO - -0.913 -0.94

Payout +/- 0.122 0.08

Leverage + 0.592 1.11

Tangibility - -0.598 -1.87**

Zscore - 0.001 0.00

Current Ratio +/- 0.008 0.09

N 522

Pseudo R-squared 0.0653

Note: Z-stats are based on clustering at the firm level. ***, ** and * represent one-tailed or two-

tailed (as appropriate) 1%, 5% and 10% significance, respectively. The dependent variable

equals one for bank loans and zero for bonds. BOARD is measured as the first principal

component of three board structure variables: the percentage of independent directors on board,

the ownership of independent directors and whether the audit committee is comprised of more

than 50% independent directors. See Appendix for definition of other variables.