Debt Diversification and Financial Flexibility

Debt Diversification and Financial Flexibility

Authors’ Information:

Nemiraja Jadiyappa

Department of Finance, IMT Nagpur, India

[email protected]

Ramesh Rao

Department of Finance, Oklahoma State University, Stillwater, OK

[email protected]

Namrata Saikia

Department of Finance and Legal Studies, Indiana University of Pennsylvania,

Indiana, PA

[email protected]

mailto:[email protected]



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Debt Diversification and Financial Flexibility

Abstract

In this study, we examine the impact of debt diversification on the financial flexibility of US firms.

Our results show that firms with diversified debt sources have a higher debt proportion, a higher

rate of change in their debt ratios and a lower cost of debt compared to that of firms with low debt

diversification. More importantly, our study shows that changes in debt ratio are accompanied

usually with the changes in diversification. No change in diversification is observed with no

change in debt proportion. Additionally, we show that diversified firms experience less financial

constraints during financial crisis period, compared to that of less diversified firms. The higher

level of debt ratios did not lead to decreased debt capacity for firms with diversified debt sources.

Overall, our results support the view that the existence of diversified debt sources is a precondition

for financial flexibility.

1. Introduction

Corporate firms, for a given level of debt, seldom use a single source of debt. About 61% of the

COMPUSTAT firm-year observations have debt from more than one source, about 38% of them

from more than two sources and about 38% of them from three or more sources. The median

number of debt sources used by US firms is about two. Therefore, debt heterogeneity, i.e., using

more than one source of debt is very common across US corporate firms. However, there is a very

limited amount of research which tries to understand this heterogeneity aspect of corporate debt,

especially its implications for financial decisions. Until now, the research in this domain has

focused on two related aspects of debt heterogeneity. The first stream is purely empirical in nature

in that it documents either the existence of or the lack of debt heterogeneity among firms (Colla

et al. 2013; Rauh and Sufi, 2010; Orlova and Harper 2016). Colla et al. (2013) shows that though

debt heterogeneity is a very common phenomenon in US, most of the firms have a dominant source

of debt. Around 85% of their sample firms have one major source of debt along with other debt

sources. Rauh and Sufi (2010) focus their attention towards the association of firm level factors

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with each debt type. Orlova and Harper (2016) identify the determinants of debt heterogeneity in

general. The most important conclusion of this stream of literature is firm size and ratings are

found to be positively associated with the level of debt heterogeneity while firms with high growth,

high cash holdings and cash flow volatility tend to be associated with a low level of heterogeneity.

The second stream of literature examines the issue of different debt securities (Carey et al., 1998;

Denis and Mihov, 2003; Diamond, 1991; Rajan, 1992; Huang and Ramirez, 2010; Hackbarth et

al., 2007). The main debt securities considered in these studies are bank debt and bond debt.

Diamond (1991) provides a theory based on the degree of information asymmetry and the

reputation of the debt subscriber to examine private vs. public debt issued by corporate firms. The

basic argument is that banks are privy to private information of corporate firms and hence face

lesser degrees of information asymmetry. The general public which subscribes to public debt does

not enjoy the same access to private information. Therefore, firms with a high degree of

information asymmetry would get debt from banks and firms with a comparatively low degree of

information asymmetry would issue public debt. Carey et al. (1998) provides empirical support

for the reputation hypothesis in explaining private vs. public debt. They argue that banks, as the

providers of debt, do not generally force firms into liquidation upon default and rather try to

renegotiate loan contracts. This is done in order to maintain their reputation in the debt market.

Blackwell and Kidwell (1988), based on the transaction cost theory, argue that corporate firms

issue the type of debt with the lowest issuing cost. Supporting this transaction cost argument,

Krishnaswami et al. (1999) find flotation cost to be an important determinant of public issue. These

studies explain why only large firms issue public debt on a larger scale. Huang and Ramirez

(2010) argue that the speed of issuance determines the kind of debt that will be issued by a firm.

The speed of issue is minimum for private lenders and relatively very high for a public issue. The

main result of this stream is that firms with good credit rating (which are assumed to have a low

degree of information asymmetry) issue public debt while firms with low credit rating issue private

debt.

This limited research on debt heterogeneity has left many questions still unanswered, especially

its impact on firm level financial policies. Does the prevalence of debt heterogeneity affect the

financing decisions of corporate firms? Our basic argument is that under the conditions of credit

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rationing and credit specialization1 of financial institutions, heterogeneous sources of debt would

decrease the supply constraints faced by the firms. Additionally, investors or suppliers ration their

credit in order to manage their credit risk exposure (Jaffee and Russell, 1976; Stiglitz and Weiss,

1981). This rationing system sets a limit for each firm with respect to the amount of debt that they

can obtain from a particular source. This leads to three empirical implications. First, firms that

wish to increase their total leverage will need to diversify their debt sources. Second, addition or

deletion of a debt source should be accompanied with an increase or decrease in debt level. Lastly,

once firms have diversified sources of debt, they will have a greater flexibility in managing their

debt. These are the three objectives that we examine in this study. We use the total number of

mutually exclusive debt sources against which a firm has an outstanding balance at the end of

financial year as a measure of debt heterogeneity.

We examine the first objective by regressing our measure of heterogeneity on the capital structure

of firms and we expect a positive association between them. Our results show the existence of such

a positive relationship in a cross-section of US non-financial firms. This leads to a difference in

leverage of about 5.2% between a firm at the 25th percentile and that at the 75th percentile.

The second objective is examined by regressing the change in heterogeneity on the change in debt

level. Our analysis shows the existence of a positive association such that an addition to debt

sources is accompanied by an increase in debt ratio of about 3%.

To examine the third objective, we make use of the standard definition of financial flexibility used

in the finance literature. Financial flexibility is defined as the ability of firms to make changes in

their debt level at the lowest possible cost (Gamba and Triantis, 2008; Denis and Sibilkov, 2011).

This definition identifies two abilities for a firm to be deemed financially flexible – the ability to

make changes in their debt level and the ability to have a lower cost of debt. Therefore, we examine

whether firms with heterogenous debt sources have a greater ability to change their debt ratio and

the overall cost of such flexibility. Debt heterogeneity should lead to a greater flexibility in debt

management for two reasons. First, having prior experience of various debt markets makes the

issue of new debt easier for the firms to further access such marketsin terms of reduction in both

1 Over a period of time financial institutions become specialized in their operations which results in their offering a

particular type of debt to corporate firms.

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time and cost of new debt issue2. Second, previous issues in a given debt market would decrease

the degree of information asymmetry and thus, the cost of debt. Therefore, firms with

heterogenous debt sources would have a greater ability to make changes in their debt level, and

the overall cost of their debt would be low compared to that of firms with lower level of debt

heterogeneity. The ability to make changes in debt level is examined by observing the annual rate

of change in debt and the frequency of such changes. Higher debt heterogeneity leads to a higher

and more frequent debt changes compared to that of lower debt heterogeneity firms. Our results

show that that the average change in annual debt increases by 0.9% for each unit of debt

heterogeneity. The impact of debt heterogeneity on the cost of debt is examined by regressing the

cost of debt3 on our measure of heterogeneity. We expect firms with heterogenous debt sources to

have a significantly lower cost of debt compared to that of firms with a low level of heterogeneity,

in a cross-sectional analysis of firms. As expected, our results show a negative impact of debt

heterogeneity on the cost of debt.

Various researchers in the finance literature argue that small firms have limited access to external

finance due to a higher degree of information asymmetry and less diversification of its income

sources (Rajan and Zingales, 1995). Because of this limited access to external finance, the financial

policies of small firms are thought to be different from that of large firms. However, there is no

clarity on whether the financial policies of small and large firms converge with each other when

small firms have equal access to external finance. The set-up of our study also provides a

framework to examine this issue. Small firms with access to heterogenous debt sources should

have a financial policy relatively similar to that of larger firms. Therefore, the relationship between

debt heterogeneity and financial flexibility should also hold true for small firms. Hence, we

conduct separate analyses for small and large firms. In all of our analyses, the impact of debt

heterogeneity is same for small and large firms.

A counter argument in the context of the above analyses is that the number of debt sources

currently accessed could prove expensive to future heterogeneity. It is based on the argument that

once firms utilize all possible sources of debt, the potential use of those sources for future fund

requirement might decline. There are three reasons which render this counter argument ineffective.

2 For example, search costs are almost zero if a firm has already accessed a particular market 3 The ratio of interest expenses to total debt

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First, the type of debt finance to be used to fund future investments greatly depends on the asset to

be created out of such financing rather than on assets already in place. Second, a firm with an

established presence in a particular debt market would find it easy to issue additional debt in that

market. Having already accessed a particular market could potentially reduce transaction costs (as

they are familiar with that market). Third, the cost of debt would be lower for firms with a prior

history of issuing debt in such markets (history of previous debt issues reduces the degree of

information asymmetry). Therefore, in the presence of heterogeneous sources of debt, its ability

to issue future debt, i.e. debt capacity is not restricted. This implies that firms with high debt

heterogeneity would finance their future projects with a higher proportion of debt. To examine

this, we regress the ratio of change in debt to change in total assets on our measure of heterogeneity

and find this ratio to be higher for heterogenous firms.

Finally, we examine the leverage behavior of firms during 2007-2010 financial crisis period. We

hypothesize that the impact of financial crisis on the changes in debt ratio should be lesser for

firms with diversified debt sources compared to that of firms with less diversified debt sources. To

examine this aspect, we interact our diversification measure with crisis dummy and regress change

in debt ratios against it. The intuition is that, if diversified firms are insulted from financial crisis,

then the average change in their debt ratio for crisis period should be greater compared to that of

firms with less diversified firms. As predicted, our results provide a strong evidence for this

hypothesis.

The rest of the paper is organised as follows. In the second section, we discuss theoretical reasons

which result in debt heterogeneity, and the implications of this heterogeneity within the framework

ofthe trade-off theory, the agency and the pecking order theories. In the third section, the pattern

of association between heterogeneity and debt level, observed from the COMPUSTAT firms is

presented. The methodology and data used in this analysis are discussed in the fourth section. In

the fifth section, the results are presented and discussed. The conclusions are drawn in the sixth

section.

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2 Theoretical Framework

2.1 Theoretical Rationale for Debt Diversification

Before getting into the empirical aspects of our study, we need to explain the reasons why firms

diversify their debt sources, i.e., the reasons behind the simultaneous use of multiple sources of

debt in a firm’s capital stucture. The literature in general explains the conditions under which a

firm issue public debt or private debt (Carey et al., 1998; Denis and Mihov, 2003; Diamond, 1991;

Rajan, 1992; Huang and Ramirez, 2010; Hackbarth et al., 2007). The essential conclusion one

could get from this literature is that firms with higher information asymmetry issue private debt

and firms with lower information asymmetry issue public debt. However, they do not discuss the

joint presence of market debt with public debt at the same time. Rauf and Sufi (2010) documents

that about 55% of the firms which had at least 10% of their debt in the form of bank debt (private

debt) had bond debt also. The theory, however, provides no possible explanations for this joint

existence of various types of debts. Though we do not intend to derive a formal model, we attempt

to offer possible explanations for this joint presence. We use various theories and concepts

developed in the finance and economics literature to explain why firms diversify their debt sources.

From the existing literature, we have identified five interrelated factors which results in debt

diversification at the firm level, three of which are demand side factors (firmlevel) and the other

two are supply side factors (financial institution level). The riskiness of projects to be undertaken,

the maturity period of projects to be undertaken and the ability to incur issue costs are the three

demand side factors. Credit rationing by the investors and financial institutions and debt

specialization of debt sources are the two supply side factors. These four factors interplay with

each other and result in firms using multiple sources of debt

Demand factors

The access to certain types of debt is restricted based on the riskiness of the project. Traditionally,

banks issue less risky debt while bond markets issue risky debt (Weinstein and Yafeh (1998)). So,

when a firm undertakes a mix of various risky projects, it tends to issue different types of debt

resulting in diversification. Further, firms generally want to match their maturity structure of their

cash inflows and cash outflows in order to avoid liquidity risk (Diamond, 1991a). A firm which

has many short-term projects would be more likely to use short-term debt sources while a firm

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which has many long-term projects would be more likely to use long-term debt sources. A firm

which concurrently undertakes long-term projects with short-term projects may use different

sources of debt matching their cash inflows with outflows resulting in debt diversification. The

third firm-level factor is the ability of a firm to incur debt issue costs. Blackwell and Kidwell

(1988) and Krishnaswami et al. (1999) argue that the ability of a firm to meet the flotation costs

of a debt issue does impact the type of debt being issued. Public debt requires higher flotation costs

compared to a private issue.

Supply factors

The first supply side factor that limits the extent of debt given to a firm is credit rationing by the

debt investors, especially financial institutions. Financial institutions ration their credit among their

borrowers in order to diversify their credit risk that arises due to information asymmetry (Jaffee

and Russell, 1976; Stiglitz and Weiss, 1981). Because of this rationing, firms would not get what

they demand from a single source forcing them to diversify the sources. The other supply side

factor is that debt sources tend to become specialized in their operations over a period of time.

Carey et al. (1998) and Weinstein and Yafeh (1998) provide empirical support for debt

specialization of various debt sources. For example: Banks would normally provide less risky debt

capital while bond market would provide risky debt, lease financing is conditional on the presence

of long-term fixed assets, commercial paper market is for short-term funds and so on. This debt

specialization of debt sources would also influence what kind of debt firms will carry in their

capital structure.

We do not treat information asymmetry as a separate factor but examine it as being related to the

risk and maturity of the projects undertaken. For some firms, there is enough information about

the risk and maturity of the projects, whereas for some others, not enough information is publicly

available. Therefore, we assume it is not information asymmetry per se but the risk and the maturity

of the project which determines what kind of debt is issued. For example: when firms with less

information asymmetry (large firms) undertake a risky project, it will issue bond debt as bond

markets are specialized in providing such capital (risk debt). When the same firm undertakes a

not-so-risky project, it might obtain the capital from banks. In the same way, the maturity period

of the project influences the type of debt being issued. This explains why large firms and better

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rated firms generally have debt from multiple sources. The absence of debt diversification in small

firms could be explained with the help of transaction costs theory.

As we discussed, both demand side factors and supply side factors interact with each other and

result in debt diversification. For example: the firms which intend to take up risky projects would

go to the bond markets as it specializes in providing the risk capital. Therefore, we are not

assuming that access to many debt sources is automatic,4 which is the standard assumption in most

of the capital structure theories. The supply of credit depends on the bankruptcy risk and maturity

patterns of the projects undertaken by the firms and the firms’ ability to meet the flotation costs.

Information asymmetry about these three factors result in credit rationing and specialization, which

then in turn lead to diversification of debt at the firm level.

2.2 Diversification and Capital Structure

Our next question should be why diversified firms tend to use an increased level of debt? We can

examine this issue from different theoretical perspectives. Again, we use the theory of credit

rationing in combination with the static trade off theory, the agency theory and the pecking order

theory to explain the relationship between diversification and capital structure.

The static trade-off theory argues that for a given level of tax rate, bankruptcy risk limits the usage

of debt (Bradley et al., 1984; Booth et al., 2001). Credit rationing by investors could lead to a

situation where a firm would have a much lower debt, from a single source, than what its

bankruptcy risk supports. In order to maximize the debt benefits (tax), a firm would raise more

debt from other sources thereby, resulting in diversification. When such firms diversify their debt

sources theyhave a higher debt proportion compared to firms which do not diversify. Previous

studies by Colla et al. (2013) and Orlova and Harper (2016) find a positive association between

firm size and the level of debt diversification in the context of US firms. Our study also finds large

firms found to have higher diversification compared to that of small firms. What follows is that

these large firms that are better able to diversify with the intent to avail of tax benefits should also

end up with increased leverage in their capital structure. This theoretical conclusion needs

empirical support and our study attempts to provide that.

4 It essentially means that supply of credit is not unlimited.

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The agency theory of Jensen and Mckling (1976) argues that the agency costs of free cash flows

determine the capital structure of a firm (Jensen, 1986). The free cash flow problem can be greatly

reduced by having more debt, and in the presence of credit rationing at debt sources, a firm can

have more debt only when they diversify their debt sources. Empirical evidence is provided by

Orlova and Harper (2016) who report a positive association between debt diversification and free

cash flows and cumulative free cash flows. Therefore, in the presence of credit rationing, the

agency theory predicts a positive association between debt diversification and the proportion of

debt in the capital structure The third main capital structure theory is the pecking order theory

proposed by Myers and Majluf (1984). Their basic argument is that investors undervalue a firm

given information asymmetry between them and the firm. The extent of undervaluation differs

across capital instruments. Debt instruments are less undervalued compared to equity,and therefore

firm prefer to issue debt compared to equity. Further, in the presence of credit rationing, firms can

maximize their debt use only when they diversify their debt sources. The empirical implication is

that firms with a higher level of uncertainty (information asymmetry) will try to optimize their

debt levels through a higher level of debt diversification compared to firms with a lower level of

information asymmetry. ,As partial support of this conclusion, Orlova and Harper (2016) report a

positive association between high R&D expenditure and debt diversification.

Therefore, all these theories predict that, in the presence of credit rationing at each source of debt,

debt diversification results in a higher debt proportion.

2.3 Literature Survey

There is a considerable amount of research that has been done with respect to debt diversification

in the finance literature. There are three main focus areas in this stream of research. The first stream

is purely empirical in nature in that it documents either the existence of or the lack of debt

diversification among firms (Colla et al. 2013; Rauh and Sufi, 2010; Orlova and Harper 2016).

Colla et al. (2013) provides evidence for debt specialization. Around 85% of their sample firms

had one major debt source. Rauh and Sufi (2010) study the association of firm level factors with

each debt type while Orlova and Harper (2016) looks into the determinants of debt diversification.

Among other studies that explore the various firm-level characteristics associated with debt

diversification, firm size and ratings are found to be associated with a higher level of

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diversification while firms with high growth, high cash holdings and high cash flow volatility tend

to be associated with a lower level of diversification.

The second stream of literature studies the rationale behind the issue of different debt securities

(Carey et al., 1998; Denis and Mihov, 2003; Diamond, 1991; Rajan, 1992; Huang and Ramirez,

2010; Hackbarth et al., 2007). The main debt securities considered in these studies are private debt

(bank debt) and public debt. Blackwell and Kidwell (1988), based on the transaction cost theory,

argue that corporate firms issue debt types with the lowest issuing cost. Supporting this

transaction cost argument, Krishnaswami et al. (1999) find flotation cost to be an important

criterion that favors a public issue Diamond (1991) builds a theory based on the degree of

information asymmetry and the reputation of the debt subscriber to understand the dynamics of

private vs. public debt issue by corporate firms. Lending banks are entitled to private information

of corporate firms and are therefore, faced with less information asymmetry. The general public

in the bond market, on the other hand, do not enjoy the privilege of access to such information and

thus, find themselves with less information compared to the banks. Apparently, firms that can

afford to furnish greater information approach banks for debt (lower costs for better information)

while those not able to do so opt for public debt. The reputation hypothesis provided in Carey et.

al. (1998) helps explain the use of private vs public debt. The argument is that banks, in their role

as debt providers, typically do not force firms into liquidation upon default and instead try

renegotiate loan contracts in order to maintain their standing in the debt market. Huang and

Ramirez (2010) put forth the speed of issuance as a determinant for the kind of debt that will be

issued by a firm. The speed of issue is relatively less for private lenders and higher for a public

issue.

The third stream of literature examines the impact of debt diversification on the leverage of a firm.

To the best of our knowledge, there has been only one study in this area. Faulkender and Petersen

(2006) examine the impact of access to bond debt market on the leverage ratio of firms and find

that firms with access to bond market have a higher debt proportion compared to firms which do

not have access to the bond market. The main limitation of this study, from the debt diversification

point of view, is that they considered only two sources of debt – public debt markets and private

debt markets (eg. banks). Analysis of empirical data shows that firms can raise debt from various

other sources too.. For example: Indian firms have raised debt from other corporate firms,

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promoters, the general public and so on. Moreover, within the bond market itself, firms can issue

various types of debt like straight debt, convertible debt, commercial papers and so forth.

3. Debt Heterogeneity and Firm Leverage

As discussed previously, we define debt heterogeneity in terms of number of sources of debt that

a firm has accessed. Table 1 we present the distribution of leverage across various heterogeneity

groups. By following Bates et al. (2008) we exclude zero debt firms, financial firms (SIC 6000 to

6999) and utility firms (SIC 4900 to 4999) from our analysis.

The first column presents the heterogeneity groups. The groups are formed based on the number

of debt sources that firms have accessed. The lowest heterogeneity group has accessed one source

of debt while the highest heterogeneity group has accessed seven mutually exclusive sources of

debt. The average debt number for the entire sample is 2.25 with a median of two sources. In the

second column, the frequency of observations within each of the heterogeneity groups is presented.

There are 107,350 firm- year observations with debt from one source. They constitute about 38.7%

of the total firm-year observations. Another 23.5% of the observations have debt from two sources.

It implies that about 38% of the observations have debt from more than two sources. The third

column gives the total average debt ratio for each heterogeneity group. The least flexible group

has an average leverage of about 26% which increases to 37% for the group with the most

heterogeneity or flexibility. Interestingly, the rate of increase decreases with each higher

heterogeneity group. In the rest of the columns, the same analysis for each of long-term and short-

term debt is presented. The impact of debt heterogeneity is seen both on long term as well as short

term leverage, though the impact is more evident on the long-term component. The same

information is plotted on a graph in Figure 1.

[Insert Table 1]

[Insert Figure 1]

The observed relationship in Table 2, between debt heterogeneity and firm leverage, in order to be

a systematic phenomenon, will have to be observed in all firms irrespective of their individual

characteristic. In Table 2, we present the same information for firms grouped based on their size.

We categorize firms into small and large firms every year based on that year’s median size value.

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Firms with below the year median value are grouped into the small firm category while those with

above the year median value are categorized as large firm. The trends presented in Table 2 for both

small and large firms exactly mirror the trends observed for the whole sample presented in Table

1. This confirms the systemic nature of the relationship that exists between debt heterogeneity and

firm leverage.

[Insert Table 2]

4. Data and Methodology

4.1 Measure of Debt Diversification

Our main independent variable is debt diversification which is the actual number of debt sources

against which a firm had an outstanding balance at the financial year-end. If a firm has an

outstanding balance against a particular debt source, then we consider that firm has accessed that

source. The mean debt number for our sample is about 2.25 over a total of 288,156 firm-year

observations [Table 3]. The average firm, therefore, appears to access to two to three types/sources

of debt.

[Insert Table 3]

4.2 Model Specification

Our basic model for estimation is presented in equation (1)

Yit = αi + β1 Firm_Sizeit + β2 ROAit + β3 Tangibilityit + β4 Growthit + β5 Diversificationit + εit

(1)

Where, Yit is the book leverage ratio of ith firm at time t. The leverage ratio is defined as the ratio

of total debt to total assets. Our main independent variable is diversification as dicussed above.

Following Rajan and Zingales (1995), we include firm size, firm performance, firm growth and

tangibility as control variables. We use log of firm sales to measure firm size, ROA to measure

performance, market to book ratio to measure growth and lastly, the ratio of net fixed assets to

total assets as a proxy of tangibility. We control for the year fixed effects by providing for year

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dummies. We use the fixed effects estimator to estimate the coefficients of our models. The year

effects are controlled for by using year dummies. Industry effects are controlled for by using

industry adjusted ratios.

5. Results and Discussion

5.1 Debt heterogeneity and leverage

To examine the impact of debt heterogeneity on the leverage ratio of a cross-section of firms, we

regress the leverage ratio on our measure of heterogeneity. The results are presented in Table 4.

The univariate analysis in column labelled Model I finds a positive and significant coefficient for

Debt_number implying a higher debt proportion for firms with a higher number of debt sources.

Debt_number alone could explain about 4.5% of the variation of leverage ratio. Even after

controlling for firm-specific factors, Debt_number continues to remain significant in Model II.

This analysis shows that for every percent increase in the number of debt sources used, the debt

ratio increases by an average of 2.6%. This value is consistent with the observed changes in the

leverage in Table 1. Titman and Wessels (1988) argue that industry-specific effects could impact

the leverage of firms. To account for industry effects, we use the industry-adjusted leverage.

Calculated as the difference between firm leverage and annual average leverage, this industry-

adjusted leverage variable is used as the dependent variable in Model III. The Debt_number

coefficient is again positive and significant providing additional evidence for the hypothesized

relationship.

[Insert Table 4]

5.2 Debt heterogeneity and leverage of across small and large sized firms

In the next table, we examine whether this positive association between number of debt sources

and leverage is seen in all firms irrespective of firm size. It is argued in the finance literature that

small firms differ from large firms in terms of their access to external finance (Rajan and Zingales,

1995). This leads to small firms having less debt in their capital structure compared to that of larger

firms. However, it is not clear from the literature how small firms would behave if their access to

external finance is eased. Theoretically, it should lead smaller firms to behave how big firms

behave with respect to their capital structure. Our insight in this study provides a framework to

examine such a hypothesis. Our measure of heterogeneity, i.e. Debt_number, actually proxies the

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access to external finance - the higher the debt number, the higher is the access to external finance.

Therefore, small firms with a higher debt number should have a debt ratio similar to that of large

firms, or in other words, the relationship between heterogeneity and leverage should be the same

for both small and large firms. Additionally, the magnitude of the relationship should not be

significantly different. To examine this aspect, we run Eq. (1) separately for large and small firms

and the results are presented in Table 5.

[Insert Table 5]

The coefficient of Debt_number in the total leverage analysis for both small firms (column two)

and large firms (column four) is positive and significant at 1%, indicating a similar positive impact

of debt heterogeneity on both groups of firms. Qualitatively, the same results have been be

observed in the industry-adjusted leverage analyses presented in columns three and five. These

results, therefore, suggest that small firms behave like large firms if their access to external finance

is eased.

5.3 Debt heterogeneity and changes in debt ratio

Next, we analyze the one of the important features of financial flexibility. (Denis and Sibilkov,

2011) argue that ability to make changes in the debt proportion is an important feature of financial

flexibility. We earlier hypothesized that firms with heterogenous debt sources have a greater ability

to make positive changes in their debt proportion compared to firms that have debt from fewer

sources. In order to examine the firm’s ability to make changes in the level of debt, we regress the

annual changes in the debt ratio on the number of debt sources that a firm has accessed. The results

in Table 6 show that with each unit increase in debt number, the change in leverage (delta leverage)

increases by 0.9%, is significant at the 1% level. To understand the economic importance of this

result, compare a firm with only one source of debt with another firm having access to seven

sources of debt - the average change in the leverage for the second firm is about 0.63% per annum,

which is seven times that of the first firm at 0.11%. This proves that firms with a higher number

of debt sources make larger changes in their debt level.

[Insert Table 6]

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5.4 Debt heterogeneity and changes in number of debt sources and debt proportion

Another important aspect of flexibility is the ability to change the number of debt sources used.

To examine this, we divide the firms into two groups - high number of debt sources and low

number of debt sources - based on the median value of the number of debt sources used. We then

calculate the average change in the leverage used by these two groups. We find that the average

change in leverage used for the low group is -0.5% and for the high group is 0.77%. which implies

that the ability of high-category firms (i.e, firms using a higher number of debt sources) to make

changes in the number of debt sources accessed is much higher than that of the low-category

firms.

[Insert Table 7]

To provide more empirical evidence on the relation between debt proportion and debt

heterogeneity, we calculate the average change in debt proportion for changes in debt number. The

results are presented in Table 7. The first column gives the information about changes in debt

number compared to the previous year. The value 0 represents no change in the number of debt

sources used from last year, value 1 represents an increase of one source and -1 represents a

decrease of one source. In the third column, corresponding changes in debt proportion are given.

Three important observations need to noted here. First, a decrease in debt sources is always

accompanied with negative changes in debt proportion (except in case of a decrease in debt sources

by six; however the number of firms in this instance is only three which is too low to draw a

significant conclusion) and an increase with positive changes in debt proportion. Second, the

relationship between changes in the number of debt sources and changes in debt proportion seems

to be almost monotonic. And third, when there is no change in debt number, the change in debt

ratio is practically zero. These observations are consistent with our credit rationing explanation for

debt heterogeneity. This table clearly establishes that unless there is a change in the number of

debt sources used, firms cannot increase their debt proportion. Therefore, firms with a higher

number of debt sources enjoy a greater flexibility in managing their leverage compared to firms

which have fewer sources of debt. In Table 8, the above analysis using regression are presented.

These results are consistent with the results presented in Table 7.

[Insert Table 8]

16

5.5 Debt heterogeneity and the cost of debt

Having discussed the impact of heterogeneity on the ability to make changes in debt level, we now

proceed to examine the impact of debt heterogeneity on the cost of debt. Denis and Sibilkov (2011)

define financial flexibility in terms of firms being able to source debt at a low cost. We argued that

firms which have access to multiple sources of debt would have a lower cost of debt on account

of lower transaction costs and a lesser degree of information asymmetry. To examine this, we

regressed the cost of debt on Debt_Number variable. The results are presented in Table 9. The

coefficient (-0.004) is negative and significant at 1% level which implies that there is a negative

association between the number of debt sources and the cost of debt. The same negative

relationship is observed for both large and small firms suggesting that cost of debt is negatively

related to our measure of heterogeneity in a cross-section of firms.

[Insert Table 9]

5.6 Debt heterogeneity and future financing flexibility

The results presented in Tables 6 through 9 provide strong evidence in support of our argument

that debt heterogeneity is associated with greater financial flexibility for firms. However, a

counter-argument in the context of the above analyses is that the number of debt sources currently

accessed could prove expensive to future heterogeneity. It is based on the assumption that once

firms utilize all the sources of debt, the potential use of those sources for future fund requirement

might decline. Further, current higher debt levels could potentially decrease their ability to issue

new debt. However, as discussed and shown previously in Table 1, this may not be the case since

additional debt is created/issued when new assets are being purchased. Current debt from various

sources, may not, therefore, decrease the firms’ ability to issue new debt while financing a new

asset in the future. Having issued debt in a particular debt market should make the new issue easier

for reasons discussed previously. Therefore, they fund their new investments using a higher

proportion of debt. Table 1 shows precisely this. To examine this aspect empirically, we calculated

the ratio of change in debt to change in total assets. This ratio gives us the information about what

proportion of debt is used to finance new addition to total asset. We regressed this debt proportion

on the lagged value of Debt_Number. This set-up examines the impact of current heterogeneity on

the ability of firms to finance their future project using debt. The results are presented in Table 10.

17

It is observed lagged Debt_Number in column two has a positive coefficient. It implies that firms

with greater heterogeneity use a larger proportion of debt to fund their future projects compared to

that of firms with lesser heterogeneity.

[Insert Table 10]

5.7 Debt heterogeneity and financial flexibility during the recent financial crisis

In this section, we analyse the impact of debt diversification on the ability of firms to make debt

changes during the subprime financial crisis period that started from the last quarter of 2007. If

access to diversified debt sources is positively associated with financial flexibility, then firms with

diversified debt sources should not suffer much during a financial crisis period compared to that

of firms with less diversified debt sources. Empirically, this could be tested by examining the

average change in debt ratios of diversified firms and non diversified firms during the crisis period.

We expect that the average change in debt ratio should be higher for firms with greater debt

diversification. Our data show that the average change in leverage for low debt-heterogeneity

firms5 during crisis period (2007-09) is -0.1%, whereas that for diversified firms is 1.4%. In Table

11, we present the regression results of this analysis6. In order to capture the differential response

to financial crisis, we interact our measure of diversification with the crisis dummy, which takes

value one for crisis period. as could be observed in the second column of Table 11, the interaction

coefficient is positive and significant, implying a greater change in the debt ratio for firms with

diversified debt sources. The results for small and large firms are presented in columns three and

four and in both analyses, the interaction coefficient is positive and significant. Overall, therefore,

these results provide additional evidence to our argument that access to diversified debt sources

increases the financial flexibility of firms.

[Insert Table 11]

5 Firms with less than the median value of number of debt sources used 6 Our financial crisis analysis covers 2003-2016 period and by following Kahle and Stulz (2013), we consider 2007

to 2009 as the crisis period. To represent this division, we first create a crisis dummy which takes the value one for

crisis period (2007 to 2009) and zero otherwise. To capture the differential response of diversified firms, we interact

this crisis dummy with our measure of diversification.

18

5.8 Robustness test using an alternative measure of debt diversification

In order to test the robustness of our findings above, we run the major regressions using an

alternative measure of debt diversification - the normalized Herfindahl-Hirschman Index (HHI).

This measure uses the same sources of debt used to compute the first measure, and additionally,

accounts for the dispersion of debt among the debt sources by assigning a higher weight to those

sources with a higher share in the overall debt. We first measure the concentration of debt, the

Herfindahl-Hirschman scores, by summing the squared ratios of individual debt to total debt.

HHit = ∑ (𝐷𝑒𝑏𝑡𝑖

𝑇𝑜𝑡𝑎𝑙 𝑑𝑒𝑏𝑡 𝑓𝑟𝑜𝑚 𝑡ℎ𝑒 𝑒𝑖𝑔ℎ𝑡 𝑠𝑜𝑢𝑟𝑐𝑒𝑠 )2

8

𝑖=1 (2)

We then normalize this value using Eq. (3) to get the measure of debt dispersion, i.e. HHI.

HHIit =HHIit−(1/8)

1−(1/8) (3)

This HHI measure is negatively correlated with debt diversification, i.e. greater HHI values

indicate lesser diversification. Since it is negatively correlated with first measure (Debt Number),

we subtract HHI from one interpretational ease.

The main findings regarding the impact of debt heterogeneity on financial flexibility using Debt

number still hold when using the HHI index as an alternative measure for debt diversification.

[Insert Table 12]

6. Conclusions

This study looks into the impact of debt diversification on the financial flexibility of US firms.

Our results show that firms with multiple debt sources have a higher debt proportion, a higher rate

of change in their debt ratios and a lower cost of debt compared to that of firms with lower debt

diversification. Results further suggest that changes in debt ratio are usually accompanied with

changes in debt diversification. No change in diversification is observed for no change in debt

proportion. We also show that diversified firms, compared to less diversified firms, experienced

less financial constraints during the financial crisis period. Overall, our results support the view

that the existence of diversified debt sources is a precondition for financial flexibility.

19

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21

Table 1: Distribution of leverage and cash ratios across various heterogeneity groups

Debt_Number is the total number of debt sources against which a firm had an outstanding balance on the balance

sheet. Total_Leverage is the ratio of the book value of total debt to total assets. LT_Leverage is the book value of long

term leverage to total assets. ST_Leverage is the book value of short term leverage to total assets.

Variables Total Leverage Short-term leverage Long-term leverage

Debt number No. of obs. Mean No. of obs. Mean No. of obs. Mean

1 107350 0.2554 108956 0.0842 111175 0.1685

2 65129 0.2977 66675 0.0945 66270 0.2116

3 53308 0.3268 54349 0.0877 54032 0.2442

4 33636 0.3643 34303 0.1059 34079 0.2638

5 13870 0.3829 14135 0.0984 14052 0.2911

6 3583 0.4035 3644 0.0927 3609 0.3151

7 548 0.4114 553 0.0803 551 0.3334

8 29 0.3689 29 0.0888 29 0.2801

Table 2: The relationship between debt heterogeneity and firm leverage across size groups

Debt_Number is the total number of debt sources against which a firm had an outstanding balance on the balance

sheet. Total_Lev is the ratio of the book value of total debt to total assets. LT_Lev is the book value of long term

leverage to total assets. ST_Lev is the book value of short term leverage to total assets. Firms are grouped into small

and large firms based on the annual median value of sales.

Average Leverage of Large Firms Average Leverage of Small Firms

Debt Number Total

Leverage

Short-term

leverage

Long-term

leverage

Total

Leverage

Short-term

leverage

Long-term

leverage

1 0.2635 0.0679 0.1912 0.2488 0.0975 0.1501

2 0.2975 0.0645 0.2362 0.2978 0.1197 0.1909

3 0.3233 0.0651 0.2598 0.3313 0.1174 0.2238

4 0.3466 0.0735 0.2749 0.3928 0.1573 0.2462

5 0.3582 0.0729 0.2870 0.4609 0.1763 0.3036

6 0.3856 0.0773 0.3090 0.5038 0.1755 0.3479

7 0.3989 0.0722 0.3279 0.5946 0.1957 0.4119

8 0.3689 0.0888 0.2801 - - -

22

Table 3: Summary statistics

Debt_Number is the total number of different debt sources against which a firm had an outstanding balance at the financial year end. Firm_size is the natural log

of firm sales, ROA is the ratio of EBIT to total assets, Tangibility is the ratio of net fixed assets to total assets, and MB is the ratio of the market value of the firms

(market value of equity plus the book value of debt) to the book value of total assets, Int_exp_Ratio is the ratio of interest expenses to total debt, Total_Lev is the

ratio of total debt to total assets, LT_Lev is the ratio of total long term debt to total assets, ST_Lev is the ratio of total short term debt to total assets.

Variable No. of Obs. Mean Std. Deviation Minimum Maximum

Debt Number 288156 2.248 1.303 1.000 8.000

Firm Size 282012 4.595 2.607 -2.442 10.492

Return on Assets 288156 -0.058 0.557 -4.245 0.336

Tangibility 282810 0.308 0.261 0.000 0.931

MB Ratio 235745 1.893 4.030 0.121 33.820

Cost of debt ratio 284326 0.093 0.096 0.000 1.000

Total Leverage (dltt + dlc) 277453 0.301 0.217 0.000 1.000

Total Leverage (sum of the eight sources of debt) 278609 0.259 0.220 0.000 1.000

Long-term Leverage 283797 0.213 0.199 0.000 1.000

Short-term Leverage 282644 0.091 0.137 0.000 1.000

(1 - Normalized HHI ) 277221 0.307 0.280 0.000 0.957

23

Table 4: Debt heterogeneity and leverage

The dependent variable is the ratio of total debt to total assets. The Ind_Adj_Lev is the difference between firm

leverage and industry average leverage. Debt_Number is the total number of different debt sources against which a

firm had an outstanding balance at the financial year end. Firm_size is the natural log of firm sales, ROA is the ratio

of EBIT to total assets, Tangibility is the ratio of net fixed assets to total assets, and MB is the ratio of the market

value of the firms (market value of equity plus the book value of debt) to the book value of total assets. The coefficients

are estimated using the fixed effects estimator. The robust (heteroscedasticity adjusted) standard errors are given in

the parenthesis. *** denotes significance at 1%, ** at 5% and * at 10%.

Model I Model II Model III

VARIABLES Leverage Leverage Industry-adjusted Leverage

Debt Number 0.030*** 0.026*** 0.023***

(46.573) (39.423) (37.238)

Firm Size

0.007*** 0.008***

(6.130) (7.567)

Return on Assets

-0.104*** -0.096***

(-28.133) (-27.699)

Tangibility

0.167*** 0.126***

(19.771) (15.985)

MB ratio

-0.004*** -0.003***

(-9.543) (-7.365)

Intercept 0.255*** 0.166*** -0.072***

(55.409) (24.063) (-11.496)

Observations 277,453 221,024 221,024

R-squared 0.045 0.092 0.069

Number of GVKEYs 26,168 22,315 22,315

Year FE Yes Yes Yes

Firm FE Yes Yes Yes

Robust t-statistics in parentheses

*** p<0.01, ** p<0.05, * p<0.1

24

Table 5: Impact of debt heterogeneity on the leverage of small and large firms

The dependent variable is the ratio of total debt to total assets. The Ind_Adj_Lev is the difference between firm

leverage and industry average leverage. Debt_Number is the total number of different debt sources against which a

firm had an outstanding balance at the financial year end. Firm_size is the natural log of firm sales, ROA is the ratio

of EBIT to total assets, Tangibility is the ratio of net fixed assets to total assets, and MB is the ratio of the market

value of the firms (market value of equity plus the book value of debt) to the book value of total assets. The coefficients

are estimated using the fixed effects estimator. The robust (heteroscedasticity adjusted) standard errors are given in

the parenthesis. *** denotes significance at 1%, ** at 5% and * at 10%.

SMALL FIRMS LARGE FIRMS

VARIABLES Leverage

Industry-adjusted

Leverage Leverage

Industry-adjusted

Leverage

Debt Number 0.036*** 0.033*** 0.020*** 0.017***

(36.470) (34.585) (23.890) (21.901)

Firm Size 0.012*** 0.012*** -0.003 0.003

(8.081) (8.516) (-1.458) (1.291)

Return on Assets -0.088*** -0.081*** -0.380*** -0.319***

(-23.740) (-23.329) (-27.624) (-25.688)

Tangibility 0.200*** 0.172*** 0.106*** 0.047***

(18.540) (16.836) (8.176) (3.910)

MB ratio -0.002*** -0.001*** 0.002 0.002**

(-5.198) (-3.684) (1.486) (2.501)

Intercept 0.114*** -0.106*** 0.244*** -0.016

(7.236) (-7.463) (22.213) (-1.582)

Observations 102,379 102,379 118,620 118,620

R-squared 0.110 0.091 0.109 0.071

Number of GVKEYs 17,008 17,008 10,006 10,006

Year FE Yes Yes Yes Yes

Firm FE Yes Yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

25

Table 6: Impact of heterogeneity on change in debt

The dependent variable is the change in debt ratio. Debt_Number is the total number of different debt sources against

which a firm had an outstanding balance at the financial year end. All control variables are in first difference form.

Firm_size in the natural log of firm sales, ROA is the ratio of EBIT to total assets, Tangibility is the ratio of net fixed

assets to total assets, and MB is the ratio of the market value of the firms (market value of equity plus the book value

of debt) to the book value of total assets. The coefficients are estimated using the fixed effects estimator. The robust

(heteroscedasticity adjusted) standard errors are given in the parenthesis. *** denotes significance at 1%, ** at 5%

and * at 10%.

Delta Leverage

VARIABLES Full Sample Small Firms Large Firms

Debt_number 0.009*** 0.017*** 0.005***

(29.728) (23.889) (18.406)

Delta Firm Size 0.010*** 0.008*** 0.026***

(7.282) (4.737) (11.446)

Delta Return on Assets -0.102*** -0.083*** -0.352***

(-22.595) (-18.133) (-23.955)

Delta Tangibility 0.180*** 0.219*** 0.087***

(20.169) (18.158) (7.879)

Delta MB ratio -0.002*** -0.001** -0.002***

(-4.038) (-2.165) (-3.184)

Intercept -0.001 -0.016 -0.006***

(-0.488) (-1.050) (-2.804)

Observations 189,670 80,176 109,474

R-squared 0.065 0.076 0.090

Number of GVKEYs 19,570 14,018 9,361

Year FE Yes Yes Yes

Firm FE Yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

26

Table 7: Changes in debt heterogeneity and changes in debt number

Delta_DN is the change in Debt_Number, which is the total number of different debt sources against which a firm had an outstanding balance at the financial year

end. Sale is the firm sales. Delata_Total_lev is the change in the total leverage, Delta_Long_term is the change in long term leverage and Delta_Short_term is the

change in short term leverage. The significance of the change is tested using the t-test. *** denotes significance at 1%, ** at 5% and * at 10%.

Delta Debt

Number

Delta Total Leverage Delta Short-term Leverage Delta Long-term Leverage

N Mean SD N Mean SD N Mean SD

-6 5 0.067 0.195 5 0.267** 0.232 5 -0.200*** 0.070

-5 20 -0.220*** 0.231 22 -0.040 0.241 23 -0.182*** 0.189

-4 167 -0.106*** 0.237 179 0.028* 0.227 187 -0.146*** 0.252

-3 1235 -0.064*** 0.206 1269 -0.009 0.197 1327 -0.067*** 0.205

-2 6106 -0.038*** 0.171 6358 -0.007*** 0.150 6375 -0.040*** 0.170

-1 23686 -0.040*** 0.149 24410 -0.023*** 0.135 24373 -0.023*** 0.151

0 175269 0.000 0.098 178671 0.002*** 0.088 179388 -0.002*** 0.095

1 23564 0.050*** 0.141 24216 0.017*** 0.123 24140 0.036*** 0.150

2 7586 0.040*** 0.144 7758 0.007*** 0.117 7746 0.034*** 0.140

3 1590 0.074*** 0.163 1624 0.023*** 0.145 1633 0.060*** 0.168

4 265 0.088*** 0.168 270 0.014* 0.121 273 0.076*** 0.168

5 57 0.069*** 0.155 60 -0.018 0.211 59 0.103*** 0.202

6 3 0.017 0.061 3 -0.018*** 0.010 4 0.162 0.259

27

Table 8: Regression analysis of the impact of changes in heterogeneity on changes in debt

ratios

The dependent variable is the ratio of change in debt to change in total assets. Delta_DN is the change in

Debt_Number, which is the total number of different debt sources against which a firm had an outstanding balance at

the financial year end. All control variables are in first difference form. Firm_size is the natural log of firm sales, ROA

is the ratio of EBIT to total assets, Tangibility is the ratio of net fixed assets to total assets, and MB is the ratio of the

market value of the firms (market value of equity plus the book value of debt) to the book value of total assets. The

coefficients are estimated using the fixed effects estimator. The robust (heteroscedasticity adjusted) standard errors

are given in the parenthesis. *** denotes significance at 1%, ** at 5% and * at 10%.

Delta Leverage


Delta Debt Number 0.024*** 0.030*** 0.017***

(49.105) (36.277) (32.076)

Delta Firm Size 0.007*** 0.005*** 0.022***

(5.222) (3.075) (9.816)


(-22.644) (-18.260) (-23.735)


(18.960) (16.972) (7.249)

Delta MB ratio -0.002*** -0.001* -0.002***

(-3.486) (-1.661) (-2.806)

Intercept 0.001 -0.003 -0.004*

(0.367) (-0.192) (-1.905)

Observations 189,670 80,176 109,474

R-squared 0.089 0.101 0.111

Number of GVKEYs 19,570 14,018 9,361

Year FE Yes Yes Yes

Firm FE Yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

28

Table 9: Impact of debt heterogeneity on the cost of debt

The dependent variable is the interest expense ratio is the ratio of total interest expense to total debt. Debt_Number is

the total number of different debt sources against which a firm had an outstanding balance at the financial year end.

Firm_size is the natural log of firm sales, ROA is the ratio of EBIT to total assets, Tangibility is the ratio of net fixed


of debt) to the book value of total assets. The coefficients are estimated using the fixed effects estimator. The robust


and * at 10%.

Cost of debt

VARIABLES Full Sample Small Firms Large firms

Debt Number -0.004*** -0.005*** -0.003***

(-15.849) (-9.157) (-12.552)

Firm Size -0.004*** -0.002*** -0.005***

(-7.725) (-2.615) (-8.160)

Return on Assets -0.012*** -0.014*** 0.020***

(-6.436) (-7.173) (4.333)

Tangibility -0.019*** -0.031*** -0.003

(-6.206) (-6.938) (-0.897)

MB ratio -0.000 -0.000 -0.000

(-0.045) (-0.614) (-0.749)

Intercept 0.069*** 0.058*** 0.068***

(30.980) (10.854) (18.273)

Observations 223,497 104,831 118,641

R-squared 0.038 0.021 0.075

Number of gvkey1 22,480 17,176 10,001

Year FE Yes Yes Yes

Firm FE Yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

29

Table 10: Impact of debt heterogeneity on the future issue of debt

The dependent variable is the ratio of change in debt to change in total assets. Debt_Number is the total number of

different debt sources against which a firm had an outstanding balance at the financial year end. Firm_size is the

natural log of firm sales, ROA is the ratio of EBIT to total assets, Tangibility is the ratio of net fixed assets to total

assets, and MB is the ratio of the market value of the firms (market value of equity plus the book value of debt) to the

book value of total assets. The coefficients are estimated using the fixed effects estimator. The robust


and * at 10%.

Debt Proportion


Debt Numbert-1 0.005*** 0.004** 0.004***

(4.965) (2.381) (2.820)

Firm Size 0.013*** 0.021*** 0.003

(8.301) (8.993) (1.011)

Return on Assets -0.003 0.019*** -0.200***

(-0.665) (3.727) (-6.342)

Tangibility 0.133*** 0.144*** 0.127***

(12.085) (9.176) (7.787)

MB ratio -0.006*** -0.001 -0.024***

(-8.499) (-1.635) (-10.302)

Intercept 0.308*** 0.294*** 0.370***

(17.152) (3.887) (16.817)

Observations 112,577 48,052 64,510

R-squared 0.013 0.013 0.025

Number of GVKEYs 18,663 13,262 8,813

Year FE Yes Yes Yes

Firm FE Yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

30

Table 11: Impact of debt diversification on the change in leverage ratio for financial crisis

period

The dependent variable is the change in debt ratios. Debt_Number is the total number of different debt sources against

which a firm had an outstanding balance at the financial year end. All control variables are in first difference form.

Firm_size is the natural log of firm sales, ROA is the ratio of EBIT to total assets, Tangibility is the ratio of net fixed


of debt) to the book value of total assets. Crisis_Dum is a dummy variable which takes value one for 2007-09 period,

otherwise zero and Crisis_Dum_DN is an interaction variable which is the product of Crisis_Dum and Debt_Number.

The coefficients are estimated using the fixed effects estimator. The robust (heteroscedasticity adjusted) standard

errors are given in the parenthesis. *** denotes significance at 1%, ** at 5% and * at 10%.

Delta Leverage

VARIABLES Full Sample Small Firms Large firms

Delta Debt Number 0.020*** 0.027*** 0.013***

(19.782) (14.111) (13.467)

Delta Firm Size 0.002 -0.000 0.014***

(0.712) (-0.080) (3.687)


(-11.564) (-9.663) (-16.395)


(6.479) (5.300) (4.524)

Delta MB ratio -0.002* -0.001 -0.007***

(-1.726) (-0.770) (-4.368)

Crisis -0.002 -0.004 -0.004

(-0.971) (-0.862) (-1.602)

Crisis*Debt number 0.002** 0.004* 0.002*

(2.542) (1.836) (1.833)

Intercept 0.004*** 0.007*** 0.001***

(11.687) (12.282) (2.682)

Observations 50,125 22,768 27,351

R-squared 0.062 0.071 0.079

Number of GVKEYs 8,248 5,201 3,769

Firm FE Yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

31

Table 12: Impact of debt heterogeneity (using HHI index) on financial flexibility

The dependent variable is total/delta/delta leverage in columns 2-4, cost of debt in column 5 and debt proportion in column 6. Total leverage is ratio of total debt

to total assets. Delta leverage is the change in debt ratio. Cost of debt is the interest expense ratio is the ratio of total interest expense to total debt. Debt Proportion

is the ratio of change in debt to change in total assets. Normalized HHI calculated using Eq. (3) and subtracted from 1. Firm_size is the natural log of firm sales,

ROA is the ratio of EBIT to total assets, Tangibility is the ratio of net fixed assets to total assets, and MB is the ratio of the market value of the firms (market value

of equity plus the book value of debt) to the book value of total assets. The coefficients are estimated using the fixed effects estimator. The robust (heteroscedasticity

adjusted) standard errors are given in the parenthesis. *** denotes significance at 1%, ** at 5% and * at 10%.

Total leverage Delta leverage Delta leverage Cost of debt Debt proportion

VARIABLES Table 4 Table 6 Table 8 Table 9 Table 10

HHI (Modified) 0.103*** 0.025***

-0.018***

(35.931) (15.921)

(-14.405)

HHIt-1 (Modified)

0.052***

(10.946)

Firm Size 0.008*** 0.009***

-0.004*** 0.012***

(6.634) (14.768)

(-7.786) (7.468)

Return on Assets -0.107*** -0.137***

-0.013*** -0.003

(-27.559) (-29.445)

(-6.825) (-0.574)

Tangibility 0.166*** 0.079***

-0.018*** 0.127***

(19.164) (18.813)

(-5.989) (11.421)

MB ratio -0.004*** -0.006***

-0.000 -0.006***

(-10.065) (-12.778)

(-0.189) (-7.983)

Delta HHI (Modified)

0.061***

(29.577)

32

Table 12: Impact of debt heterogeneity (usign HHI index) on financial flexibility (continued)

Total leverage Delta leverage Delta leverage Cost of debt Debt proportion

VARIABLES Table 4 Table 6 Table 8 Table 9 Table 10

Delta Firm Size

0.010***

(6.605)

Delta Return on Assets

-0.106***

(-21.364)

Delta Tangibility

0.175***

(19.066)

Delta MB ratio

-0.002***

(-4.470)

Constant 0.188*** -0.031*** 0.001 0.066*** 0.325***

(26.063) (-9.011) (0.400) (28.521) (17.161)

Observations 212,929 194,758 177,485 215,381 108,496

R-squared 0.084 0.060 0.073 0.040 0.015

Number of GVKEYs 22,139 20,520 19,166 22,305 18,446

Year FE Yes Yes Yes Yes Yes

Firm FE Yes Yes Yes Yes Yes


*** p<0.01, ** p<0.05, * p<0.1

33

Figure 1: Firm leverage (Y-axis) across different debt heterogeneity groups (X-axis)

0.000

0.050

0.100

0.150

0.200

0.250

0.300

0.350

0.400

0.450

1 2 3 4 5 6 7 8

Total Leverage Long-term leverage Short-term leverage

Documents

Debt Diversification and Financial Flexibility