Abstract - Aidea 2013 · Web viewMarket Quotation and Debt Maturity Structure Abstract We analyse debt maturity structure of 16,720 firms in 24 OECD countries between 1990 and 2011.

Market Quotation and Debt Maturity Structure

Abstract

We analyse debt maturity structure of 16,720 firms in 24 OECD countries between

1990 and 2011. We find that although the size of the banking sector is significantly correlated

with debt maturity, its impact depends on the country’s governance index. In strong investor

protection countries, firms have more long-term debt when banking sector is bigger and the

variation in the size of the insurance sector is uncorrelated with debt maturity. In contrast, in

weak protection countries with a large insurance sector, firms use more short-term debt.

Moreover, we find that, unlike previous studies, firms in strong investor protection countries

with developed bond and stock markets tend to use longer debt maturity, but the access to

international and non-resident bank debt increases the proportion of long-term debt only in

weak protection counties. The results are strong after controlling for signalling, agency cost,

asset maturity, and tax effects.

JEL classification: G32

Keywords: Debt maturity; Financial Institutions; Agency Costs; Signalling; Tax

1. Introduction

Previous studies identified four main theories to explain how firms choose between

short- and long-term debt in imperfect capital markets: agency costs, signalling, tax, and

matching hypotheses. Within the agency costs theory, firms are expected to use more short-

term debt to mitigate their underinvestment problem (Myers, 1977) and the asset substitution

problem as short-term debt is less sensitive to shifts in the risk of the firm’s underlying assets

(Barnea et al., 1980). Similarly, the signalling theory suggests that firms should rely on short-

term debt to signal their quality in the presence of transaction costs (Flannery, 1986). In

contrast, under the tax hypothesis, firms should prefer to use long-term debt in the presence

of a non-monotonic structure of interest rates, when the term structure of interest rates is

upward sloping (Brick and Ravid, 1985), Finally, the matching principle argues that debt

maturity should be matched with the life maturity of the assets, as when debt has a longer

maturity, the firm’s assets should generate enough future cash flow to cover debt obligations.

The empirical evidence provided to-date on these factors in single country setting is mixed.1

More recent evidence that use richer international data to assess the impact of cross-country

institutional differences, such as financial and governance systems, is also mixed. For

example, while Demirgüç-Kunt and Maksimovic (1999) show that the banking sector is

uncorrelated with debt maturity structure of large firms, Fan et al. (2012) find that firms in

countries with larger banking sectors use short-term debt, but there is little evidence on the

relationship between insurance sectors and corporate financing choices.2

1 For example, Barclay and Smith (1995) find a positive relationship between debt maturity and size as a proxy for the agency hypothesis, in contrast with Guedes and Opler (1996). Similarly, Antoniou et al. (2006) find positive and significant effects of term structure of interest rates on debt maturity in the UK, in line with the tax predictions, but inconsistent with Barclay and Smith (1995), Stohs and Mauer (1996), Guedes and Opler (1996), Scherr and Hulburt (2001), and Ozkan (2002).2 Similar mixed results are shown by studies that focus on leverage. For example, While De Jong et al. (2008) find that that firms in countries with developed bond markets and higher GDP rates tend to use more debt than equity, Song and Philippatos (2004) find no evidence to support the importance of legal institutional difference on firms’ capital structure.

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We contribute to the literature by assessing debt maturity structure in a multi-country

framework. We focus on the impact of the bond markets, and international and non-resident

bank debt, in addition to previously documented factors, such as stock market development,

insurance and banking sectors, and economic conditions (e.g., Demirgüç-Kunt and

Maksimovic, 1999; Sorge and Zhang, 2009; Fan et al., 2012), on debt maturity structures,

across 24 OECD countries from 1990 to 2011, resulting in 204,082 firm-year observations.

We expect firms in strong investor countries to have longer maturities, as, following La Porta

et al. (2000), the corporate governance that accompanies broad financial markets is more

effective, the supply of capital is more efficient, and the credit markets is larger than in weak

investor protection countries.

We find strong evidence that firms in strong investor countries exhibit significantly

higher debt maturities. Our results hold even if we account for all firm and country

characteristics. We also show that the US exhibits the highest maturity structure, but, even

when the US is excluded, firms in strong governance countries in the rest of the world

(ROW) have statistically higher maturities than firms in weak investor protection systems.

We also report that within strong protection countries, the banking sector has a positive

impact of debt maturity, while within weak protection countries; this association is negative,

showing that firms use more short-term debt. These results suggest that banks in strong

protection countries are more likely to offer long-term debt consistent with Diamond’s (1984)

argument that intermediaries take benefit from economies of scale. While banks in weak

protection countries tend to hold more short-term liabilities, and hence offer short-term loans,

in line with Fan et al. (2012). Interestingly, while in strong investor protection countries the

insurance market is not significant, within the weak investor protection system, firms in

countries with bigger insurance sectors tend to use more short-term debt.

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Further analysis reveals that in strong protection countries including the US, the bond

market development has a positive effect on debt maturity, whereas its effect is insignificant

in weak protection countries, although international debt market and non-resident bank loans

are positively associated with long-term debt. We find strong evidence to support the impact

of developed stock markets only in countries with strong investor protections, suggesting that

active stock markets in those countries increase the ability of firms to obtain long-term credit.

We also find that firms in weak protection countries tend to use shorter debt maturity when

the inflation rate and domestic savings are higher and GDP growth is lower. In contrast, firms

in strong protection countries use shorter debt maturity when the inflation rate is lower and

GDP growth rate is higher, but the impact of domestic saving on debt maturity is weak.

Considering firm-specific variables, we find strong evidence that debt maturity is

longer when firms have higher leverage. Consistent with the agency theory, the market-to-

book ratio has a considerable negative effect on debt maturity structure across countries with

different governance index. Myers (1977) argues that firms with greater growth opportunities

use shorter maturity of debt in order to mitigate the underinvestment problem. We also show

that bigger firms with higher profitability tend to use longer debt maturity. Our findings

provide strong support for the signalling hypothesis for US firms, in contrast to firms in

strong protection countries where the negative effects of abnormal earnings are not

significant, and to weak protection counties where the impact of abnormal earnings on debt

maturity is positive. The results of the US are in line with those of Barclay and Smith (1995)

and Stohs and Mauer (1996), but inconsistent with Ozkan (2000) and Antoniou (2006). We

do support the matching principle, which emphasises on matching the debt maturity and asset

maturity. In addition, consistent with the tax hypothesis, the term structure of interest rate

has a positive and significant effect in strong investor protection countries including the US,

suggesting that companies use longer maturity of debt when the term structure of interest rate

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is upward sloping. But we find no evidence to support the impact of the term structure on

interest rate within weak protection countries.

The rest of the paper is organised as follows. Section 2 provides the review of the

literature and the hypotheses tested. Section 3 discusses the data and the methodology used.

Section 4 presents the empirical results and the conclusions are in Section 5.

2. Literature Review

2.1 Institutional Characteristics

There is a growing literature that considers the impact of institutional differences on

corporate financing choices. The specific characteristics of countries are likely to highlight a

large number of interesting issues relating to the way companies make debt maturity decision.

Miller (1977) shows that investors’ preferences for holding debt versus equity affect ta firm’s

debt ratio. Consistently, Fan et al. (2012) argue that firms in countries with developed

banking system tend to use more short-term debt as banks hold more short-term liabilities.

While insurance companies prefer to have long-term assets and thus firms in countries with a

larger insurance sector are more likely to use long-term debt. Overall, they consider the

preferences of capital suppliers on the structure of debt maturity. Their results support the

negative impact of the banking sector on debt maturity, but their results are not consistent

with Demirgüç-Kunt and Maksimovic (1999) who find insignificant effects of banking sector

on debt maturity.

To proxy for the preferences of the suppliers of the capital, we use banks’ deposits

over gross domestic product (GDP) to measure the available funds for the banking sector. We

expect that firms in countries with a bigger banking sector tend to use short-term debt.

However, banks’ risk will influence the lending and maturity choices of banks. Banks’

capital, measured by banks’ capital over GDP, moderates the risk banks run, and hence

reducing banks’ need to seek more liquid short-term debt. Therefore, firms in countries with

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more bank capital are more likely to use long-term debt. In addition, we use banks’ credit to

banks’ deposits to measure their risk. High-credit banks have a greater ability to pay their

debt when its due, thereby reducing the risk of banks run. We expect that firms in countries

with low-risk banks, measure by banks’ capital and their credit, are more likely to use long-

term debt. To proxy for the insurance sector, we use total insurance premium (life and non-

life) over GDP. We expect that firms in countries with a bigger insurance sector tend to use

long-term debt. To measure the amount of funds available for all financial intermediaries, we

use gross domestic saving over GDP, expecting that firms in countries with greater supplier

of capital use more long-term debt.

Grossman (1976) argues that prices of listed companies transfer information that can

be useful for creditors, and hence lending to quoted firms is less risky due to their

transparency in the stock market. Therefore, it is expected that firms in countries with

developed stock markets are more able to obtain long-term credit, using more long-term debt.

Demirgüç-Kunt and Maksimovic (1996, 1999) show that leverage and debt maturity increase

with the size of stock markets. In addition, higher bond market development provides a better

protection for borrowers and hence we expect that firms in countries with better and

diversified bond markets, measured by bond market capitalisations over GDP, international

debt issued over GDP, and loan from non-resident banks over GDP, use more long-term debt.

Finally, we control for the economic condition using the inflation and GDP growth

rates. Inflation makes it costly for firms and investors to contract (Demirgüç-Kunt and

Maksimovic 1999) and we expect that firms use more short-term debt when the inflation rate

is high while they use long-term debt when the GDP growth is high. The inflation rate is

measured by annual rate of change on consumer price index.

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2.2 Firms’ Characteristics

A large number of empirical studies have investigated the impact of firms’

characteristics on debt maturity based on four main theories; signalling, tax, agency costs,

and matching principles.

Myers (1977) refers to the conflict between debt-holders and shareholders, which

result in the underinvestment problem. This problem arises when debt-holders desire to invest

in safe projects that may not create any benefits for shareholders. Accordingly, shareholders

may reject positive NPV projects. Conversely, shareholders get the benefits of investing in a

negative NPV project at the expense of debt-holders. In this situation, debt-holders will lose

if the project is unsuccessful while equity-holders would not be affected. He suggests that the

underinvestment problem can be mitigated by using short-term debt because it matures before

the growth opportunities will be exercised. Following empirical studies (Titman and Wessles,

1988; Rajan and Zingales, 1995; Guedes and Opler, 1996), we use market-to-book ratios to

measure growth opportunities to control for agency conflicts.

Brick and Ravid (1985) provide a model for debt maturity structure based on tax

effects. They show that when the term structure of interest rate is upward sloping, the value

of firm is increasing function of long-term debt. The reason is that tax shields of interest

payments would be accelerated by using long-term debt. Their model is characterised

conditions under which firms consider first their capital structure and then their structure of

debt maturity. By contrast, when leverage and debt maturity are considered simultaneously,

Lewis (1990) shows that the tax does not have any effect on the structure of debt maturity. He

assumes that there is no difference in tax expenses between short-term and long-term debt.

The literature investigates the effect of tax on debt maturity, but this literature

provides mixed evidence for tax effects. Using small and medium sized companies, Garcia-

Teruel and Martinez-Solano (2007) find a positive relationship between the term structure of

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interest rate and the maturity structure of debt. Their results are consistent with the model

provided by Brick and Ravid (1985). However, Scherr and Hulburt (2001) provide limited

evidence for the impact of tax. Barclay and Smith (1995) and Guedes and Oplimer (1996)

studying US large companies, and Ozkan (2000) studying UK large companies, do not

support the tax effect. Following Brick and Ravid (1985), we use term structure of interest

rate to test the tax hypothesis and its subsequent effects on debt maturity structure. We expect

that companies use long-term debt when the term structure of interest rate is upward sloping.

Falnnery (1986) develops a model to show that a firm’s debt maturity structure can

signal information about its quality. In that model, under asymmetric information, high

quality firms use short-term debt to signal the markets that they can afford to repay the short-

term principal when it is due. He argues that, under asymmetric information, both long-term

debt and short-term debt are mispriced in the market. However, long-term debt is more

sensitive to asymmetric information. Therefore, when the capital market cannot distinguish

between low quality and high quality firms, high quality ones suppose that long-term debt is

relatively overpriced and prefer to issue short-term debt while low quality firms decide to

issue overpriced debt (long-term debt). Hence, in the presence of transaction costs, low

quality firms cannot imitate high quality ones. High quality firms use short-term debt to

signal markets that they can afford to repay the short-term covenant when it is due, while low

quality firms cannot afford to roll over short-term debt, and hence prefer to issue long-term

debt (Falnnery, 1986). To date, empirical studies use abnormal earnings as proxies for firms’

quality (e.g., Barclay and Smith, 1995; Stohs and Mauer, 1996; and Ozkan, 2002).3 Studying

large companies, Stohs and Mauer (1996) report a negative relationship between firms’

quality and the maturity structure of debt. Their results are in line with those of Barclay and

Smith (1995), who find a negative relationship between long-term debt and abnormal

3 Abnormal earnings are calculated as earnings per share in year t+1 minus earnings per share in year t, all divided by share price in year t.

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earnings as a proxy for firms’ quality, supporting the signalling hypothesis. But their results

are inconsistent with the findings of Ozkan (2000) and Antoniou et al. (2006), who do not

provide any evidence to support the signalling hypothesis.

Morris (1976) theoretically shows that firms can choose the debt maturity along with

their assets life to mitigate the risk when their cash flows are not sufficient to cover their

commitments. Therefore, it is expected that cash flows generated by assets will be sufficient

to pay their commitments. Debt with maturity longer than the maturity of assets is risky

because the assets may not be enough to repay the debt covenants. Consequently, maturity

matching could mitigate the expected costs of financial risk. Based on this notion, firms with

more long-term assets use longer maturity debt, and thus a positive association would be

expected.

3. Data and Methodology

We first collect all firms registered in OECD countries from DataStream. We exclude

Korea, Czech Republic, Solvak Republic, Iceland, and Greece for lack or unreliable data,

leaving us 24 OECD countries. We exclude financial firms and those non-financial firms with

negative book equity. Our sample includes 16,720 firms from 1990 to 2012, resulting in

204,082 firm-year observations. Data for firm-specific variables are collected form

DataStream. Data on country-specific variables are collected from several sources which are

specified in Table 1.

[Insert Table 1 here]

To test our hypotheses, we use the fixed-effects model, Equation (1):

LTDRi , t=β X i ,t +γ Y j ,t+ (α i+αt )+εi , t Equation (1)

Where LTDRi,t is the dependent variables which is long-term debt divided by total

debt (Fan et al., 2012; Antoniou et al., 2006; and Barclay and Smith, 1995). Xi,t is the vector

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of firms’ characteristics defined in Table 1. Yj,t is the vector of country-level data defined in

Table 1. αi is firm-specific effects. αt is time-specific effect and εi,t is the error term.

Furthermore, for robustness check, we use the partial adjustment model, indicating

whether firms adjust their debt maturity ratio towards their target level within each time

periods. There is a considerable number of studies presenting a dynamic model of capital

structure (e.g., Falnnery and Rangan, 2006; Maghyereh, 2005; and Drobetz and Fix, 2005).

Despite the substantial literature in the dynamic framework of capital structure, little attention

has been paid to the dynamic model of debt maturity structure. Following work by Ozkan

(2000) and Antoniou et al. (2006), a dynamic debt maturity structure is given in Equation (2):

LTDRi , t−LTDR i, t−1=δ ( LTDR¿i ,t−LTDRi ,t−1)+εi , t Equation (2)

Where LTDRi,t is long-term debt divided by total debt. LTDRi,t-1 is lagged long-term

debt divided by total debt. LTDR*i,t- is target ratio of long-term debt divided by total debt. δ is

the speed of adjustment and εi,t is the error term.

The target ratio is a function of firm- and country-level explanatory variables as given

in Equation (3):

LTDR¿i ,t=β X i , t+γ Y j , t Equation (3)

Substituting Equation (3) into Equation (2), result in the partial adjustment model,

Equation (4):

LTDRi , t=¿ Equation (4)

Where LTDRi,t is long-term debt divided by total debt. LTDRi,t-1 is lagged long-term

debt divided by total debt. LTDR*i,t- is target ratio of long-term debt divided by total debt. δ is

the speed of adjustment. Xi,t is the vector of firms’ variables and Yj,t is the vector of country-

level data which are both defined in Table 1. εi,t is the error term.

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4. Results and Discussions

4.1. Descriptive Statistics

Table 2, Panel A, summarises the descriptive statistics of the variables used in our

sample. We also report descriptive statistics ranked by governance index, strong and weak

protection countries in Panel A.4 We follow Alzahrani and Lasfer (2012) and Djankov et al.

(2008) and use anti-self dealing index consistent to rank the governance index. Strong

protection countries include those firms in countries with above average ant-self dealing

index while weak protection countries include the remaining firms. We compute the Pearson

correlation coefficients for all variables in Panel B. Panel C reports the country-by-country

mean (median) values of explanatory variable, including both the country- and firm-level

variables.

Panel A in Table 2 shows that the average long-term debt is 56%. However, the

average long-term debt is higher in strong protection countries (65%) than weak protection

countries (49%). The reported results in Panel C show that Norway has the highest long-term

debt ratio (71%) while Turkey has the lowest long-term debt ratio (33%) across countries.

Panel A also shows that both firm- and country-level variables are statically different

between strong and weak protection countries. Overall, our results show that firms is strong

protection countries have longer debt maturity, higher growth opportunities, tangible assets

while they have lower leverage than weak protection countries. In weak protection countries,

baking sectors and bond markets are bigger, while in strong protection countries, stock

markets are bigger and non-resident bank loans and international debt are greater.

Panel B suggests that the institutional differences in countries influence debt maturity

structure. In particular, firms in countries with high-credit banks and more bank capital tend

to have more long-term debt. While firms in countries with a bigger insurance sector,

4 More statistics including min, maximum, and standard deviation across strong and weak protection countries are provided in Table A-1 in Appendix A.

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developed bond markets, and bank deposits use more short-term debt. Using international

debt and non-resident bank loans, inflation rate, stock market activity, GDP growth, and

domestic savings are associated with more long-term debt. The overall results for firms’

characteristics show that debt maturity increases with leverage, size, return on assets, asset

maturity, and term structure of interest rate while it decreases with the market-to-book ratio

and abnormal earnings.

[Insert Table 2]

4.2. Regressions

4.2.1. Determinants of Debt Maturity Structure

In this section, we consider the joint effects of both firm and country variables using

Equation (1) based on the fixed effects model. Table 4 reports the empirical results of the

debt maturity structure for the whole sample, companies in strong protection countries, and

those in weak protection countries. Strong protection countries include those firms in

countries with above average ant-self dealing index while weak protection countries include

the remaining firms (Alzahrani and Lasfer, 2012; Djankov et al., 2008).

The results for the effect of banking system on debt maturity are mixed across strong

and weak protection countries. Within strong protection countries including the US, bank

deposit and credit increase long-term debt, supporting Demirgüç-Kunt and Maksimovic’s

(1999) argument that strong rights promote access to long-term credit. Therefore, we expect

that firms in strong protection countries use more long-term debt. While within weak

protection countries, the results show that bank deposits and capital negatively related to

long-term debt, suggesting that those firms use more short-term debt when the banking

system is developed. These results are supported by Fan et al. (2012) who argue that banks

tend to have more short-term debt as they hold more short-term liabilities, and hence firms in

countries with a larger banking sector are more likely to use short-term debt.

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Moreover, we find that, apart from the US, debt maturity is longer in countries with

high-credit banks, reflecting the willingness of banks to lend debt with longer maturity. In

weak protection countries, consistent with the preference of capital suppliers, we find that,

firms in countries with a bigger insurance sector tend to use long-term debt. However, we

find no relationship between the insurance sector, measured by total insurance premium (life

and non-life) over GDP, and debt maturity within strong protection countries. This result is

consistent with Fan et al. (2012), who investigate the relationship between insurance

penetration and debt maturity in developed and developing countries. We also measure the

amount of funds available for all financial intermediaries by gross domestic saving over GDP

and find that, in the US, firms with greater level of domestic savings have more long-term

debt, while in weak protection countries, to use more short-term debt.

In contrast to weak protection countries, the results show that within strong protection

countries including the US, the size of bond market, measured by the ratio of bond market

capitalisations to gross domestic product (GDP), increases long-term debt, suggesting that

strong rights uphold long-term credit, thereby using more long-term debt. However, in weak

protection countries, firms have longer debt maturity when they have access to international

debt and non-resident banks. Similar to the results of bond markets, we find that firms in

strong protection countries with active stock markets, measured by stock traded over GDP,

use more long-term debt. There is less evidence that the level of market activity is related to

debt maturity for firms in weak protection countries which are consistent with those of

Demirgüç-Kunt and Maksimovic (1999), who find that stock market activity is significant for

large firms.

Consistent with Fan et al. (2012), the inflation rate is positively associated with long-

term debt in strong protection countries, but negatively related to debt maturity in weak

protection countries which in line with those of Demirgüç-Kunt and Maksimovic (1999).

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Finally, in contrast to strong protection countries, within weak protection countries, firms use

more long-term debt when the GDP growth is higher.

For firms-level data, Table 4 shows that increase in leverage is associated with

significantly higher long-term debt. The positive relationship between leverage and the

structure of debt maturity across countries supports Morris (1992), who argues that firms with

higher leverage use long-term debt to postpone their probability of bankruptcy. But the

results are inconsistent with those of Dennis et al. (2000), who show that leverage is inversely

related to debt maturity. They suggest that the underinvestment problem could result in the

use of short-term debt.

Consistent with the agency hypothesis, firms with higher growth opportunities, as

measured by the market-to-book ratio use shorter maturity of debt. The negative and

significant effect of growth opportunities on the long-term debt ratio is consistent across the

whole sample as well as across the two sets of markets. These results support the argument of

Myers (1977) that firms with higher growth opportunities use short maturity of debt to

mitigate the underinvestment problem and are in line with those of Barclay and Smith (1995).

Our findings are consistent with those of Barclay and Smith (1995) and Gueded and Opler

(1996) but different from those of Stohs and Mauer (1996) and Antoniou et al. (2006), who

report mixed evidence.

The table shows that, as predicted, the coefficient of abnormal earnings as a proxy for

firm’s quality is negative and significant for firms in strong protection countries including the

US, but this coefficient is positive and significant for firms in weak protection countries.

Therefore, the results are mixed, as strong protection countries support the signalling

hypothesis that high quality firm use long-term debt while they use more short-term debt in

weak protection countries. These results for weak protection countries suggest that they do

not use the maturity structure of debt as an instrument with which to signal their quality to the

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market. Previous empirical studies provide mixed findings. Ozkan (2000) presents little

evidence for the signalling hypothesis in contrast with the study of Stohs and Mauer (1996),

who find that firms with larger abnormal earnings tend to use short-term debt

The results also support the matching hypothesis. Morris (1976) argues that firms can

choose their debt maturity along with their assets life to mitigate the risk. The table reveals

that the coefficient of asset tangibility is positive and highly significant, which indicates that

firms with greater tangible fixed assets use long maturity of debt. Consequently, the results

are consistent with the prediction of the matching principle.

With respect to the tax hypothesis, we do find a significant and positive relationship

between the term structure of interest rate and long-term debt ratio for the whole sample and

firms in strong protection countries including the US. The results suggest that more long-term

debt is used when the term structure of interest rate is upward sloping, supporting the tax

hypothesis discussed by Brick and Ravid (1985). In contrast, firms in countries with weak

protections do not support the tax hypothesis as the coefficient of the structure of interest rate

is not statistically significant. Finally, we control for size and return on assets. The results

show that bigger companies and those with higher profitability, measured by return on assets,

use more long-term debt.

[Insert Table 4 here]

4.2.2 Robustness Check

In this section, we conduct several robustness tests for our empirical findings by

pooling all variables and running the dynamic model of debt maturity.

In the empirical investigations above, we find institutional differences between strong,

in particular the US, and weak investor protection countries. Therefore, we pool all variables

to control for the US and governance indices. We obtain similar results in Table 5, suggesting

that firms in strong investor protections, in particular the US, have longer maturity of debt

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when bond and stock market are bigger and banking sector is smaller. Moreover, variation in

the size of insurance sector is uncorrelated with debt maturity in strong protection countries.

In contrast, in weak protection countries with a large insurance sector, firms use more short-

term debt. The access to international and non-resident bank debt increases the proportion of

long-term debt only in weak protection counties. These results are strong after controlling for

signalling, agency cost, asset maturity, and tax effects.

[Insert Table 5 here]

In addition, we study the partial adjustment model of debt maturity structure in order

to check the robustness of our results as well as to find the adjustment speed using Equation

4. We also attempt to find out whether the country-level variables might result in different

speeds of adjustment towards the target level of debt maturity.

For the purposes of this section, we apply a dynamic GMM method. Although,

previous literature uses the GMM method of the first differences (GMM-DIF), recent studies

argue that the GMM-DIF estimator has a problem with weak instruments (e.g. Antoniou et

al., 2008). The GMM-system method considers lagged regressors in both levels and first

differences to reduce the finite sample bias substantially by exploiting the additional moment

conditions (Blundell and Bond, 1998). Therefore, we use the two-step GMM system, which

considers both level and first differenced lagged regressors as instruments. Table 6 reports the

results of the partial adjustment model.

Table 6 also presents the Sargan statistic (value of the GMM objective function at

estimated parameters) that tests the null hypothesis of over-identifying restrictions. Actually,

the tested hypothesis concerns whether the instrumental variables are uncorrelated to the set

of residuals. The p-values show that the tests of over-identifying restrictions are not rejected,

and therefore the instruments are valid by this criterion, suggesting that the GMM estimation

can be applied to these data.

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The overall results in Table 6 support the dynamic model of debt maturity structure

across countries, showing that companies in weak protection countries eliminate their

deviation more slowly than companies in strong protection countries, after controlling for

other variables. The adjustment speed in weak protection countries is λ=1−0.048=0.592,

compared to strong protection countries: λ=1−0.341=0.659. However, the results for

adjustment speeds show that adjustment speeds are similar after controlling for country-

specific variables.

[Insert Table 6 here]

5. Conclusions

We examine the determinants of maturity structure of debt across 24 OECD countries.

The sample includes 204,082 firm-year observations from 1990 to 2011. This paper

investigates the impact of institutional differences across countries on debt maturity in

addition to the theories discussed in the literature of debt maturity structure, including the

agency, signalling, matching, and tax hypotheses. As far as we are aware, this analysis is

distinctive by testing a set of variables related to debt markets across countries with strong

and weak investor protections. We find that firm-specific variables that explain the variation

in the use of long-term debt are relatively similar across countries, whereas institutional

differences across countries and within strong and weak protection countries explain a large

proportion of the variation in the maturity structure of debt.

Inconsistent with Demirgüç-Kunt and Maksimovic (1999) who find weak evidence to

support the impact of banking sectors on debt maturity, our results show that although the

size of banking sector is significantly correlated with debt maturity, its impact depends on a

country’s governance index. In strong investor protection countries, firms have more long-

term debt than in weak protection countries, when banking sector is bigger. We find that

whereas the variation in the size on insurance sector is uncorrelated with debt maturity (in

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line with those of Fan et al., 2012) in strong protection countries, in weak protection countries

with a large insurance sector, firms use more short-term debt.

We find strong evidence that, for strong protection countries, firms in developed stock

and bond markets use longer maturity of debt, but the relationship between debt maturity and

international and non-resident bank debt is weak. In contrast, for weak protection countries,

we find a positive impact of international and non-resident bank debt on debt maturity, but

we do not find evidence of longer debt maturity in countries with developed bond and stock

markets.

We also control for macroeconomic factors (such as GDP growth, inflation, and

domestic savings). Although we find that GDP growth, inflation, and domestic savings are

strongly related to debt maturity, we acknowledge that their signs and significance levels

depend on countries’ governance index. The results show that, in countries with strong

investor protections, regardless of domestic savings which have insignificant impact on debt

maturity, firms use longer maturity of debt when the inflation rate is higher and GDP growth

is lower. In contrast, in weak protection counties, firms use longer maturity of debt when the

inflation domestic savings rates are lower and GDP growth is higher.

The results for firms’ specific variables show that debt maturity for bigger firms with

higher leverage and profitability. The results also significantly support the agency hypothesis

discussed by Myers (1977). We show that debt maturity is inversely related to the market-to-

book ratio as a proxy for growth opportunities. In line with the empirical studies of Barclay

and Smith (1995) and Ozkan (2000), we find that firms with greater growth opportunities use

shorter maturity of debt to control for the conflicts between shareholders and debt-holders.

Some empirical studies report mixed evidence for the effect of growth opportunities on the

structure of debt maturity (e.g., Stohs and Mauer, 1996 and Antoniou et al., 2006)

18

We find strong support for the matching hypothesis, which predicts that firms will

match their maturity of debt with their assets’ structure. The coefficient of asset maturity is

significant and positive across countries. Accordingly, the evidence of this study is consistent

with the argument of Morris (1976) that debt with maturity longer than the maturity of assets

is risky because the assets may not be sufficient to repay the debt covenants. Therefore, firms

with more long-term assets use longer maturity of debt.

In keeping with the tax hypothesis, the results show that firms use long-term debt

when the term structure of interest rate is upward sloping in strong protection counties.

However, in weak protection countries, we do no find evidence that firms use longer debt

maturity when the term structure of interest rate is upward sloping.

As a robustness check, we use the partial adjustment model which also ascertains the

adjustment speed, i.e. how fast companies eliminate their deviation from the optimal ratio

across countries. The results strongly support the dynamic framework of debt maturity

structure, suggesting that firms have long-term debt ratios and adjust towards their target

ratio. However, companies have different speeds of adjustment across countries. In strong

protection countries, we find that companies adjust to their target ratio faster than those in

weak protection countries. The results suggest that companies in strong protection countries

rely more on public long-term debt, and hence the costs of deviation from the target are

significant for those companies, so that they adjust faster.

Overall, the evidence provided here suggests that country-specific variables determine

the choice of debt maturity across countries.

19

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21

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22

Table 1: Definitions of Variables

Variable Description SourcePanel A: Firm-level variablesLev TD/TA DataStreamMB Market to book ratio DataStreamSize LnMK DataStreamAB (EPSt+1 - EPSt)/ SPt DataStreamROA EBIT/Total Assets DataStreamAM PPE/ Dep DataStreamTS BY10y – BY3m DataStreamPanel B: Country-level variablesBank Capital Bank capital over total assets Economic and Social Data Service,

International Financial Statistics, World Bank

Bank Dep. Bank deposits to GDP World Bank, Financial Structure Database).

Bank Credit Bank credit to bank deposits World Bank, Financial Structure Database).

Ins. Prem. Life and non-life insurance premium volume to GDP

World Bank, Financial Structure Database).

Bond Cap. Public and private bond market capitalisation to GDP

World Bank, Financial Structure Database).

Inter. Debt International debt issues to GDP World Bank, Financial Structure Database).

Loans Loans from non-resident banks to GDP World Bank, Financial Structure Database).

Stock Traded Total value of stock traded to GDP Economic and Social Data Service, International Financial Statistics

Inflation Annual rate of change on consumer price index Economic and Social Data Service, International Financial Statistics

GDP Growth Annual rate of change on GDP Economic and Social Data Service, International Financial Statistics

Domestic Savings Gross domestic saving to GDP Economic and Social Data Service, International Financial Statistics

This table shows the empirical predictions of proxy variables using both firm and country data. Panel A show the firm-level data. Lev is leverage measured as total debt over total assets. MB is market to book ratio calculated as a firm’s market value of assets to book value of assets. Size is natural logarithm of market value of firms. AB is abnormal earnings calculated as EPSt+1-EPSt/ SPt which is earnings per share in year t+1 minus earnings per share in year t, divided by share price in year t. ROA is return on assets computed as earnings before interest and tax over total assets. AM is asset maturity which is the ratio of net property, plant and equipment to depreciation. TS is term structure calculated as the differences between the month-end yields on 10-year government bond and three-month treasury bills (BY10y-BY3m) or interbank rate if the data is not available). Panel B presents the country-level data. Bank capital is bank capital of the country over total assets. Bank Dep. is the ration between bank deposits and GDP. Band Credit is bank credit over bank deposits. Ins. Prem. is total life and non-life insurance premium over GDP. Bond Cap is the country’s public and private bond market capitalisation over GDP. Inter. Debt is the country’s international debt issues over GDP. Loans are the country’s loans from non-resident banks to GDP. Stock traded is the country’s total value of stock traded over GDP. Inflation is the annual rate of change on consumer price index. GDP growth if the country’s annual rate of change on GDP. Domestic saving is the country’s gross domestic saving over GDP. All variables are measured in US dollars.

23

Table 2: Descriptive Statistics

Panel A Full Sample Strong Protection Countries

Weak Protection Countries

t-statistics for differences in means

N Mean SD Median Min Max Mean MeanLTDR 166,562 0.56 0.34 0.61 0.00 1.00 0.65 0.49 96.91***Lev 203,985 0.19 0.18 0.16 0.00 0.55 0.16 0.22 -81.14***MB 182,408 2.38 2.20 1.61 0.41 9.04 2.83 1.92 90.80***Size 189,839 11.84 2.07 11.73 8.36 15.77 11.71 11.99 -29.50***AB 175,042 0.00 0.03 0.00 -0.07 0.08 0.02 0.00 3.35***ROA 197,867 0.00 0.18 0.05 -0.54 0.22 -0.04 0.05 -104.12***AM 202,956 0.30 0.23 0.25 0.01 0.80 0.30 0.29 17.11***TS 201,068 0.52 1.12 0.74 -1.63 2.37 0.04 1.02 -218.92***Bank Capital 149,883 6.14 2.20 5.30 3.70 11.10 6.90 5.25 16.00***Bank Dep. 202,680 96.20 68.39 74.24 0.00 394.60 61.78 131.43 -266.35***Bank Credit 202,680 86.51 57.06 83.54 0.00 1574.00 87.24 85.76 5.84***Ins. Prem. 202,680 6.32 3.08 6.83 0.00 18.19 6.73 5.90 61.73***Bond Cap. 202,680 129.38 1121.9

889.58 2.14 82559.61 94.79 164.80 -14.05***

Inter. Debt

202,680

26.18 26.25 18.92 0.00 265.89 34.56 17.60 15.00***

Loans 202,680 24.12 51.06 14.75 0.00 1366.39 28.14 20.01 35.95***Stock Traded 193,353 98.08 71.26 82.63 15.0

5283.77 127.91 67.58 2.10**

Inflation 193,205 1.88 1.50 2.05 -0.72 4.48 2.63 1.11 2.60***GDP Growth 193,653 2.42 2.81 2.38 -3.37 8.44 2.62 2.22 30.84***Domestic Savings 204,082 22.20 8.25 22.90 0.00 38.80 0.22 0.22 4.85***

24

Panel B (1 (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) 16) (17) (18) (19)(1) LTDR 1.00(2) Lev 0.22 1.00(3) MB -

0.04-

0.041.00

(4) Size 0.30 0.17 0.11 1.00(5) AB -

0.02-

0.01-

0.010.03 1.00

(6) ROA 0.13 0.14 -0.19

0.43 0.11 1.00

(7) AM 0.20 0.27 -0.15

0.07 -0.01

0.07 1.00

(8) TS 0.06 0.12 -0.15

0.07 0.02 0.13 0.05 1.00

(9) Bank Capital 0.07 -0.06

0.17 0.00 0.00 -0.08

-0.10

-0.35

1.00

(10) Bank Dep. -0.19

0.12 -0.17

0.03 -0.02

0.08 0.02 0.27 -0.42

1.00

(11) Bank Credit 0.10 0.03 0.09 0.02 0.01 -0.02

0.01 0.05 -0.05

0.05 1.00

(12) Ins. Prem. -0.02

-0.02

0.05 -0.02

0.01 -0.03

-0.08

-0.03

0.00 0.06 -0.04

1.00

(13) Bond Cap. -0.01

0.00 -0.01

0.00 0.00 0.01 0.00 0.03 -0.01

0.08 -0.02

0.01 1.00

(14) Inter. Debt 0.11 -0.10

0.10 -0.06

0.01 -0.14

-0.06

-0.20

0.05 -0.28

0.24 0.22 0.08 1.00

(15) Loans 0.02 -0.04

0.03 -0.01

0.01 -0.01

-0.06

-0.09

-0.02

-0.08

-0.11

0.30 0.36 0.40 1.00

(16) Stock Traded 0.05 -0.15

0.17 -0.08

-0.01

-0.19

-0.15

-0.45

0.54 -0.20

-0.16

0.35 -0.01

0.25 0.18 1.00

(17) Inflation 0.16 -0.10

0.21 -0.04

-0.02

-0.10

0.02 -0.41

0.40 -0.63

0.22 -0.24

-0.02

0.27 0.06 0.12 1.00

(18) GDP Growth 0.03 -0.03

0.04 0.02 0.04 0.00 0.01 -0.09

0.04 -0.12

-0.02

-0.01

0.00 -0.03

-0.04

0.01 0.09 1.00

25

(19) Domestic Savings

0.01 0.03 0.02 -0.02

-0.01

0.01 0.00 0.09 0.01 0.04 0.18 0.21 0.00 0.02 0.02 -0.01

0.07 0.04 1.00

Panel C LTDR Lev MB Size AB ROA AM TS Bank Capital

Bank Dep.

Bank credit

Ins. Prem.

Bond Cap

Inter.Debt

Loans Stock Traded

Inflation GDP Growth

Domestic Savings

Australia 0.69 0.16 2.74 11.76 0.00 -0.05 0.35 0.22 5.55 70.39 121.52 5.84 69.80 35.85 13.80 83.39 2.87 2.78 0.22

[46,673] (0.84) (0.11)

(1.90)

(11.59) (0.00)

(0.04) (0.29) (0.49) (5.40) (69.89) (127.46)

(5.94) (70.05) (40.01) (16.34) (89.00) (2.85) (2.52) (0.23)

Austria 0.53 0.24 1.91 11.75 0.00 0.06 0.32 1.13 5.93 75.83 113.21 4.42 72.69 29.09 23.22 17.30 2.09 2.61 0.24

[1,488] (0.55) (0.23)

(1.53)

(11.44) (0.00)

(0.07) (0.33) (1.57) (5.20) (82.81) (124.11)

(4.91) (71.62) (25.18) (26.85) (15.05) (2.06) (2.24) (0.23)

Belgium 0.57 0.25 2.22 12.20 0.00 0.05 0.29 1.49 4.02 77.94 81.73 5.39 119.26 51.47 19.08 25.23 2.15 2.13 0.23

[1,345] (0.61) (0.25)

(1.61)

(11.99) (0.00)

(0.06) (0.26) (1.77) (3.70) (89.05) (88.22) (7.59) (118.66) (49.90) (5.31) (21.66) (2.09) (1.87) (0.22)

Canada 0.67 0.16 2.33 11.99 0.00 -0.02 0.42 1.08 4.53 81.75 65.21 4.81 87.56 29.07 15.11 79.63 2.04 2.44 0.21

[9,469] (0.81) (0.12)

(1.65)

(11.91) (0.00)

(0.04) (0.42) (1.06) (4.60) (111.74)

(82.81) (5.42) (89.63) (29.84) (16.21) (86.60) (2.14) (1.69) (0.21)

Denmark 0.58 0.23 2.18 11.51 0.00 0.04 0.32 0.75 5.45 53.27 162.48 7.15 179.22 23.81 18.59 42.29 2.16 2.14 0.23

[1,730] (0.63) (0.21)

(1.47)

(11.32) (0.00)

(0.07) (0.29) (0.88) (5.70) (54.01) (77.38) (7.98) (187.39) (17.79) (5.31) (43.98) (2.11) (2.67) (0.22)

Finland 0.65 0.25 2.37 12.08 0.00 0.07 0.28 1.28 7.12 49.52 132.06 3.46 47.28 36.67 19.06 94.33 1.82 2.40 0.23

1,846] (0.71) (0.26)

(1.71)

(12.01) (0.00)

(0.08) (0.25) (1.50) (6.30) (48.58) (140.06)

(3.62) (44.07) (41.05) (18.82) (99.52) (1.40) (2.22) (0.24)

France 0.55 0.20 2.41 11.86 0.00 0.05 0.18 1.37 4.87 60.36 130.21 8.03 92.27 39.92 26.62 65.80 1.69 2.12 0.24

[8,374] (0.60) (0.19)

(1.74)

(11.55) (0.00)

(0.06) (0.13) (1.57) (4.80) (66.86) (135.87)

(8.64) (89.93) (44.34) (31.82) (61.58) (1.70) (1.45) (0.23)

Germany 0.55 0.19 2.49 11.66 0.00 0.03 0.24 0.89 4.34 87.12 119.83 5.12 80.42 27.58 26.32 53.65 1.67 1.25 0.23

[8,877] (0.61) (0.17)

(1.81)

(11.33) (0.00)

(0.06) (0.20) (1.16) (4.30) (95.97) (114.87)

(5.22) (79.44) (8.51) (30.81) (48.83) (1.58) (1.63) (0.22)

Ireland 0.69 0.21 2.52 12.38 0.00 0.04 0.28 1.02 5.49 75.87 151.26 7.06 79.08 81.28 165.02 26.10 2.64 2.24 0.23

[666] (0.80) (0.22)

(1.81)

(12.68) (0.00)

(0.07) (0.24) (1.28) (5.50) (74.99) (144.54)

(8.11) (84.67) (40.80) (184.14) (21.32) (2.58) (1.78) (0.23)

Italy 0.50 0.27 2.02 12.50 0.00 0.04 0.25 1.38 7.64 58.09 140.46 5.72 116.96 36.84 20.06 44.31 2.45 1.94 0.22

[2,823] (0.51) (0.28)

(1.50)

(12.31) (0.00)

(0.06) (0.21) (1.75) (7.60) (53.98) (147.72)

(6.41) (115.18) (41.13) (20.56) (43.79) (2.22) (1.93) (0.21)

Japan 0.44 0.23 1.60 12.03 0.00 0.04 0.30 1.01 4.48 187.49 61.16 6.32 159.89 6.06 10.74 69.17 0.18 2.15 0.22

[54,097] (0.45) (0.21)

(1.12)

(11.87) (0.00)

(0.04) (0.28) (0.95) (4.60) (191.40)

(52.49) (7.06) (170.91) (6.76) (11.86) (56.94) -(0.12) (2.47) (0.21)

Luxembourg

0.67 0.22 1.93 12.55 0.01 0.08 0.35 2.07 5.10 286.17 38.70 5.20 11626.42 69.21 861.50 15.05 2.31 2.07 0.21

[300] (0.75) (0.18)

(1.23)

(12.39) (0.00)

(0.09) (0.30) (2.37) (5.00) (324.94)

(39.75) (4.53) (103.58) (42.30) (1101.11) (15.05) (2.30) (2.31) (0.23)

26

Mexico 0.64 0.23 1.61 13.15 0.01 0.09 0.46 1.63 10.11 22.13 73.21 1.40 30.35 10.52 5.99 15.19 4.28 2.14 0.21

[1,440] (0.74) (0.22)

(1.22)

(13.34) (0.00)

(0.09) (0.50) (2.09) (9.90) (23.34) (72.95) (1.46) (26.78) (10.31) (5.31) (15.05) (4.48) (2.71) (0.22)

Netherlands 0.65 0.20 3.12 13.47 0.00 0.08 0.25 1.06 4.42 86.42 201.66 6.09 95.58 77.39 25.37 108.79 2.16 3.30 0.22

[1,205] (0.71) (0.19)

(2.32)

(13.51) (0.00)

(0.10) (0.23) (1.32) (4.30) (94.17) (145.27)

(6.58) (103.58) (77.86) (5.31) (104.36)

(2.11) (3.69) (0.23)

New Zealand

0.66 0.22 2.31 11.37 0.00 0.05 0.40 0.41 6.14 68.25 131.68 2.68 35.80 8.61 18.81 16.73 2.62 3.27 0.22

[1,105] (0.84) (0.22)

(1.61)

(11.29) (0.00)

(0.09) (0.37) (0.72) (5.90) (76.47) (149.42)

(2.52) (26.47) (8.51) (20.61) (15.34) (2.38) (3.67) (0.23)

Norway 0.74 0.29 2.23 12.02 0.00 0.00 0.36 0.16 6.47 27.03 76.31 4.47 44.47 4.54 0.00 55.47 2.09 3.83 0.22

[1,896] (0.84) (0.30)

(1.59)

(11.92) (0.00)

(0.05) (0.30) (0.34) (6.40) (46.62) (107.30)

(4.67) (41.07) (0.00) (0.00) (51.98) (2.27) (3.81) (0.23)

Poland 0.45 0.16 2.04 10.86 0.00 0.06 0.33 0.33 7.89 39.64 51.86 3.08 37.01 9.82 11.25 16.32 3.08 3.42 0.22

[2,836] (0.44) (0.14)

(1.41)

(10.66) (0.00)

(0.07) (0.31) (0.50) (7.90) (38.77) (75.49) (3.27) (35.77) (10.47) (13.96) (15.05) (3.58) (4.07) (0.23)

Portugal 0.56 0.35 1.99 12.17 0.00 0.05 0.35 1.25 6.02 90.45 133.99 5.81 66.10 44.76 9.78 25.28 2.79 2.60 0.23

[634] (0.61) (0.36)

(1.37)

(11.95) (0.00)

(0.05) (0.35) (1.46) (5.80) (86.47) (150.71)

(5.93) (60.67) (33.49) (5.31) (20.32) (2.74) (2.68) (0.23)

Spain 0.53 0.27 2.41 12.93 0.00 0.07 0.35 1.28 6.69 94.37 127.07 4.33 68.93 50.51 24.04 109.04 3.03 2.77 0.23

[1,654] (0.57) (0.27)

(1.72)

(12.89) (0.00)

(0.07) (0.32) (1.57) (6.40) (79.46) (133.67)

(4.71) (66.46) (38.61) (27.27) (114.36)

(3.20) (2.42) (0.24)

Sweden 0.65 0.17 2.93 11.33 0.00 -0.02 0.19 1.12 5.00 35.71 115.23 6.37 85.04 40.11 29.77 113.46 1.59 3.15 0.23

[3,947] (0.76) (0.13)

(2.11)

(11.09) (0.00)

(0.05) (0.11) (1.20) (4.90) (45.72) (0.00) (6.61) (83.27) (46.99) (37.93) (96.19) (1.36) (3.09) (0.25)

Switzerland 0.63 0.22 2.37 12.60 0.00 0.06 0.32 0.94 5.27 120.88 122.90 7.35 62.22 26.85 88.51 183.71 1.17 2.32 0.23

[2,782] (0.70) (0.20)

(1.70)

(12.49) (0.00)

(0.07) (0.29) (1.09) (5.30) (126.32)

(122.09)

(7.86) (62.34) (12.23) (111.50) (181.77)

(0.82) (2.62) (0.24)

Turkey 0.33 0.20 2.06 11.65 0.01 0.09 0.34 0.13 10.68 34.97 63.48 0.96 302.82 6.76 14.79 40.45 4.48 2.46 0.23

[2,880] (0.28) (0.16)

(1.41)

(11.50) (0.00)

(0.10) (0.33) (-0.89) (11.10) (33.79) (66.29) (1.03) (30.71) (7.33) (16.30) (39.63) (4.48) (3.58) (0.24)

United Kingdom

0.55 0.15 2.70 11.62 0.00 0.01 0.26 -0.54 6.10 0.00 0.00 11.67 53.51 52.12 101.59 139.87 2.44 2.36 0.22

[13,544] (0.64) (0.12)

(1.89)

(11.36) (0.00)

(0.06) (0.20) (-0.92) (5.50) (0.00) (0.00) (12.97) (47.24) (37.40) (120.63) (126.78)

(2.32) (1.93) (0.23)

United States

0.63 0.15 3.19 11.59 0.00 -0.05 0.22 -0.31 10.09 69.14 77.54 6.64 154.97 26.56 19.02 204.47 2.54 2.54 0.23

[32,471] (0.76) (0.08)

(2.20)

(11.57) (0.00)

(0.04) (0.14) (-0.15) (10.30) (67.25) (77.54) (7.35) (152.46) (27.53) (17.97) (211.21)

(2.83) (1.45) (0.24)

Total 0.56 0.19 2.38 11.84 0.00 0.00 0.30 0.52 6.14 96.20 86.51 6.32 129.38 26.18 24.12 98.08 1.88 2.42 0.22

[204,082] (0.61) (0.16)

(1.61)

(11.73) (0.00)

(0.05) (0.25) (0.74) (5.30) (74.24) (83.54) (6.83) (89.58) (18.92) (14.75) (82.63) (2.05) (2.38) (0.23)

The sample includes 204,082 firm/year observations from 24 OECD countries. Panel A reports summary statistics of the variables defined in Table 1 as well the mean of variables for strong and weak protection countries. t-statistics are also reported to test the differences in means between strong and weak protection countries. N is for number

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of observations (N), SD is standard deviation. Panel B presents the Pearson correlation coefficients across our variables (significant at 1% level are in bold. Panel C reports the means (medians) of our variables per country. The data is winsorized at 95% level.

Table 4: Determinants of Debt Maturity Structure

Dependent Variable: Long-Term Debt/Total DebtFull Sample US Strong Protection ROW Weak Protection ROW

Lev 0.428*** 0.426*** 0.427*** 0.531*** 0.583*** 0.483*** 0.544*** 0.544*** 0.344*** 0.278*** 0.278***(68.62) (53.96) (53.98) (29.42) (29.75) (37.61) (47.21) (47.22) (36.79) (25.63) (25.62)

MB -0.007***

-0.008*** -0.008*** -0.005*** -0.009*** -0.008*** -0.010***

-0.010*** -0.008*** -0.007*** -0.007***

(-16.11) (-15.07) (-15.09) (-4.40) (-7.55) (-9.93) (-12.86) (-12.88) (-11.71) (-8.92) (-8.76)Size 0.043*** 0.045*** 0.045*** 0.043*** 0.062*** 0.039*** 0.054*** 0.054*** 0.037*** 0.032*** 0.032***

(57.56) (49.93) (49.95) (17.33) (26.57) (23.11) (44.01) (44.02) (25.66) (25.70) (25.71)AB 0.038* 0.04* 0.04* -0.131** -0.189* -0.083** -0.039 -0.039 0.03 0.04* 0.05**

(2.07) (1.89) (1.90) (-1.98) (-2.44) (-2.21) (-1.01) (-1.01) (1.37) (1.84) (1.95)ROA 0.078*** 0.076*** 0.076*** 0.069*** 0.083*** 0.086*** 0.108*** 0.108*** 0.101*** 0.043*** 0.043***

(11.66) (9.85) (9.90) (4.30) (5.16) (7.02) (10.81) (10.81) (8.07) (3.28) (3.30)AM 0.119*** 0.142*** 0.142*** 0.148*** 0.201*** 0.063*** 0.108*** 0.108*** 0.117*** 0.180*** 0.180***

(19.64) (19.46) (19.40) (6.37) (10.18) (5.03) (11.01) (11.03) (10.43) (16.37) (16.41)TS 0.004*** 0.001 0.001 0.007*** 0.063** 0.007*** 0.003** 0.003** -0.001 0.003 0.003*

(6.65) (0.70) (0.65) (3.50) (2.20) (5.62) (2.18) (2.19) (-0.72) (1.63) (1.85)Bank Capital -0.001 -0.001 -0.145*** -

0.006***-0.006*** -0.011*** -0.010***

(-1.27) (-1.55) (-2.64) (-3.85) (-3.87) (-7.66) (-7.55)Bank Dep. -0.001*** -0.001*** 0.078*** 0.000*** 0.000*** -0.001*** -0.001***

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(-18.28) (-18.32) (2.73) (4.82) (4.82) (-17.25) (-16.60)Bank Credit 0.000*** 0.000*** -0.001 0.000*** 0.000*** 0.000*** 0.000***

(6.62) (6.45) (-0.067) (5.01) (5.10) (3.54) (3.68)Ins. Prem. -0.002*** -0.002*** 0.107 -0.001 -0.001 -0.004*** -0.004***

(-4.64) (-4.43) (0.97) (-1.07) (-1.09) (-6.21) (-6.21)Bond Cap. -0.002* -0.002 0.015* 0.000*** 0.000*** -0.000** -0.001

(-1.89) (-1.46) (1.92) (3.61) (3.64) (-2.28) (-0.80)Inter. Debt 0.000*** 0.000*** 0.028** 0.002 0.002 0.000*** 0.000***

(10.52) (11.38) (2.46) (0.51) (0.69) (6.27) (6.56)Loans 0.000** 0.010 0.000***

(2.53) (0.10) (6.55)Stock Traded 0.000*** 0.000*** 0.002** 0.000** 0.000** 0.020 (0.00)

(6.87) (7.20) (2.34) (2.05) (2.19) (0.20) (0.74)Inflation 0.003*** 0.003*** -0.042** 0.004*** 0.004*** -0.008*** -0.007***

(2.74) (2.95) (-2.16) (3.15) (3.27) (-5.79) (-5.21)GDP Growth 0.001*** 0.001*** -0.003 -

0.001***-0.001*** 0.003*** 0.003***

(4.28) (4.08) (-0.38) (-3.34) (-3.38) (8.82) (8.63)Domestic Savings

0.045*** 0.045*** 0.289** 0.029 0.028 -0.051*** -0.043***

(3.92) (3.88) (2.43) (1.60) (1.603) (-3.11) (-2.62)Constant -

0.072***-0.078*** -0.076*** 0.002 2.260** 0.079*** -

0.179***-0.179*** -0.063*** 0.181*** 0.175***

(-7.80) (-5.28) (-5.17) (0.07) (2.09) (3.59) (-7.85) (-7.86) (-3.55) (8.54) (8.22)N 139,000 90,306 903,06 18,773 13,317 41,858 41,252 41,252 78,342 49,054 49,054R2 0.22 0.20 0.28 0.20 0.33 0.26 0.28 0.36 0.10 0.20 0.20

Table 4 presents the regressions of debt maturity on both firm and country variables which are defined in Table 1. ROW is for Rest of the World (excluding the US). All regressions control for industry effects. The sample of 24 OECD countries is split into three subsamples, the US, strong, and weak protection countries. For the US, loans are excluded because of the collinearity problem. This table also reports the number of firm-year observations (N) and adjusted R 2. t-statistics are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.

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30

Table 5: Robustness Check

Dependent Variable: Long-Term Debt/Total DebtFull sample

Control for the US Control for strong/weak protection countries

Lev 0.431*** 0.393*** 0.439*** 0.432***(69.08) (45.50) (70.70) (54.81)

MB -0.007***

-0.009*** -0.008*** -0.008***

(-17.30) (-14.26) (-18.69) (-15.69)Size 0.044*** 0.043*** 0.043*** 0.044***

(58.50) (43.86) (58.75) (49.99)AB -0.033* -0.037* -0.024 -0.02

(-1.80) (-1.714) (-1.31) (-0.93)ROA 0.087*** 0.099*** 0.102*** 0.096***

(13.03) (11.13) (15.26) (12.46)AM 0.126*** 0.118*** 0.107*** 0.127***

(20.67) (15.03) (17.79) (17.41)TS -

0.003***0.003* -0.001* 0.004***

(-4.78) (2.45) (-1.76) (4.00)Bank Capital -0.008*** -0.004***

(-7.57) (-5.48)Bank Dep. -0.000*** -0.000***

(-12.94) (-11.63)Bank Credit 0.006*** 0.002***

(6.66) (6.21)Ins. Prem. -0.002*** -0.003***

(-5.06) (-5.97)Bond Cap. -0.001 -0.008

(-1.62) (-1.43)Inter. Debt 0.000*** 0.000***

(5.04) (5.76)Loans 0.000*** 0.000***

(3.89) (2.73)Stock Traded -0.001 0.000***

(-1.42) (3.19)Inflation -0.001 0.000

(-0.96) (-0.26)GDP Growth 0.001*** 0.001***

(3.99) (3.44)Domestic Savings 0.006 0.019

(0.49) (1.64)US Dummy 0.093*** 0.110***

(16.01) (19.79)Strong/weak Dummy 0.105*** 0.148***

(31.82) (22.64)

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Constant -0.098***

-0.033* -0.144*** -0.103***

(-10.46) (-2.06) (-15.34) (-7.00)N 139,000 76,989 139,000 76,989R2 0.22 0.24 0.20 0.21

Table 5 presents the robustness check for the regressions of debt maturity on both firm and country variables which are defined in Table 1. The first two columns include the dummy variable for US companies. The last two columns include the dummy variable for strong/ weak protection countries. It is equal to one if companies are in strong protection countries and 0 for the remaining OECD countries which have weak protections. All regressions control for industry effects. This table also reports the number of firm-year observations (N) and adjusted R2. t-statistics are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.

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Table 6: Determinants of Dynamic Debt Maturity

Full Sample US Strong Protection ROW Weak Protection ROWL.Debt Maturity 0.371*** 0.302**

*0.364*** 0.374*** 0.341*** 0.336*** 0.408*** 0.409***

(47.06) (37.15) (19.60) (14.80) (29.24) (22.42) (41.04) (29.72)Lev 0.244*** 0.220**

*0.121 0.179 0.117** 0.138* 0.371*** 0.136*

(6.48) (4.58) (1.60) (1.61) (2.28) (1.94) (7.94) (1.92)MB 0.005*** 0.010**

*0.004 0.002 0.005* 0.001 0.006** 0.002

(2.69) (1.99) (0.95) (0.06) (1.84) (0.33) (2.42) (0.43)Size -0.003 -0.002 -0.006 0.058** -0.002 0.017* 0.002 0.016**

(-0.66) (-0.42) (-0.72) (1.95) (-0.25) (1.74) (0.38) (2.06)AB 0.359*** 0.250**

*-0.005 0.177 0.474*** 0.585* 0.288*** 0.388***

(4.32) (3.22) (-0.02) (0.38) (3.01) (2.44) (3.87) (3.49)ROA 0.165*** 0.178**

*0.187*** 0.01 0.071 -0.044 0.216*** 0.046

(3.81) (2.51) (2.69) (0.10) (1.36) (-0.61) (3.70) (0.58)AM 0.136** 0.131** 0.11 -0.455** 0.241*** 0.024 -0.009 0.302***

(2.34) (2.54) (0.88) (-2.05) (3.09) (0.18) (-0.14) (2.95)TS 0.005*** 0.002**

*0.007*** 0.014 0.004*** 0.004* 0.004*** 0.003

(5.30) (4.58) (3.80) (0.48) (2.98) (1.78) (3.23) (1.25)Bank Capital -0.005* -0.151** -0.008*** -0.001

(1.78) (-2.00) (-3.23) (-0.40)Bank Dep. -0.020 0.032** 0.001*** -0.012**

(-1.58) (2.31) (3.27) (-2.25)Bank Credit 0.001* 0.013 0.001** 0.010***

(1.85) (1.01) (2.44) (3.11)Ins. Prem. -0.004 -0.099 0.001 -0.001*

(-0.28) (-0.68) (0.49) (-1.77)

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Bond Cap. -0.010** 0.001** 0.001*** -0.008***(-2.05) (2.14) (3.81) (-3.17)

Inter. Debt 0.000** 0.025 0.004 0.002*(2.12) (1.62) (0.71) (1.76)

Loans 0.024***

-0.016* 0.080 0.025**

(3.05) (-1.82) (0.03) (2.27)Stock Traded 0.004** 0.002*** 0.010*** 0.012

(1.99) (2.66) (2.86) (0.21)Inflation 0.021**

*-0.045** -0.002 -0.004**

(2.58) (-2.10) (-1.01) (-2.07)GDP Growth 0.010* 0.013 -0.001** 0.001***

(1.78) (1.23) (-2.05) (3.55)Domestic Savings 0.058**

*0.245** 0.040** -0.036*

(3.25) (1.99) (2.04) (-1.78)N 114,000 90,306 13,703 9,523 46,668 30,367 67,397 38,094Sargan test: p-value

0.524 0.532 0.665 0.647 0.190 0.235 0.476 0.470

We adopt the two-step GMM estimation method in Table 6 to show the robustness check for the regressions of debt maturity on both firm and country using Equation 4. ROW is for Rest of the World (excluding the US). The first two columns show the results for full sample. The second two columns show the results for the US. The last four columns show the results for strong and weak protection countries. L.Debt Maturity is the lag of dependent variable which is debt maturity calculated as long-term debt over total debt. The remaining variables are defined in Table 1. All regressions control for industry effects. This table also reports the number of firm-year observations (N) and adjusted the p-values of Sargan test. t-statistics are in parentheses. *, **, and *** indicate significance at 10%, 5%, and 1% levels, respectively.

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35

Appendix ATable A-1: Descriptive Statistics between Strong and Weak Protection Countries

Strong Protection Countries Weak Protection CountriesN Mean SD Median Min Max N Mean SD Median Min Max

LTDR 74,997 0.65 0.36 0.79 0.00 1.00 91,565 0.49 0.30 0.51 0.00 1.00Lev 103,831 0.16 0.17 0.10 0.00 0.55 100,154 0.22 0.17 0.20 0.00 0.55MB 92,508 2.83 2.44 1.95 0.41 9.04 89,900 1.92 1.79 1.33 0.41 9.04Size 96,639 11.71 2.26 11.59 8.36 15.77 93,200 11.99 1.84 11.82 8.36 15.77AB 85,538 0.02 0.03 0.00 -0.07 0.08 89,504 0.00 0.03 0.00 -0.07 0.08ROA 99,837 -0.04 0.22 0.04 -0.54 0.22 98,030 0.05 0.11 0.05 -0.54 0.22AM 102,937 0.30 0.26 0.23 0.01 0.80 100,019 0.29 0.20 0.26 0.01 0.80TS 103,928 0.04 1.11 -0.05 -1.63 2.37 97,140 1.02 0.88 1.06 -1.63 2.37Bank Capital 81,657 6.90 2.28 5.70 3.70 11.10 68,226 5.25 1.70 4.80 3.70 11.10Bank Dep. 102,526 61.78 35.44 67.25 0.00 151.93 100,154 131.43 75.67 173.78 0.00 394.60Bank Credit 102,526 87.24 50.15 83.22 0.00 1148.00 100,154 85.76 63.34 84.96 0.00 1574.00Ins. Prem. 102,526 6.73 3.39 6.73 0.00 18.19 100,154 5.90 2.65 6.93 0.00 11.13Bond Cap. 102,526 94.79 43.65 79.07 15.76 184.58 100,154 164.80 1594.70 103.58 2.14 82559.61Inter. Debt 102,526 34.56 25.69 31.28 0.00 265.89 100,154 17.60 23.93 7.75 0.00 186.83Loans 102,526 28.14 42.12 16.48 0.00 363.52 100154 20.01 58.54 12.27 0.00 1366.39Stock Traded 97,743 127.91 77.82 107.26 15.05 283.77 95,610 67.58 47.31 53.38 15.05 283.77Inflation 97,811 2.63 1.05 2.68 -0.72 4.48 95,394 1.11 1.50 0.94 -0.72 4.48GDP Growth 98,143 2.62 2.99 2.37 -3.37 8.44 95,510 2.22 2.59 2.47 -3.37 8.44Domestic Savings 103,928 0.22 0.09 0.23 0.00 0.39 100,154 0.22 0.08 0.22 0.00 0.39

36