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.
2
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.
3
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
4
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
5
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
7
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.
8
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
9
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.
10
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.
11
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.
12
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).
13
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
14
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
15
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
17
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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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
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