Empirical investigation of debt-maturity structure: evidence from Pakistan.
Shah, Attaullah ; Khan, Shahid Ali
We examine the empirical determinants of debt-maturity structure of
266 firms listed on the KSE over the period 2000 to 2004 using several
variants of dynamic panel data models. We find mixed support for the
agency cost hypothesis as our results show that debt-maturity increases
with the size of the firm; however, growth options do not have any
significant influence on debt-maturity structure. Our results lend
unambiguous support to the maturity-matching hypothesis as debt-maturity
varies inversely with operating activities and directly with the
maturity of long-lived assets. Finally, we find evidence that supports
the tax-based hypothesis but no evidence to support the signaling
hypothesis. Moreover, the results demonstrate that there is a
significant dynamic component in the determination of optimal
debt-maturity structure of the sampled firms.
JEL classification: G32
Keywords: Debt Maturity, Capital Structure, Panel Data, GMM,
Pakistan.
1. INTRODUCTION
Capital structure theories suggest many ways in which firms can
adjust overtime to the target debt ratio in order to optimise the cost
of capital and maximise the wealth of shareholders. In doing so, a firm
can use different mixes of equity, debt, and hybrid securities. These
areas of capital structure have already been extensively
researched--both theoretically and empirically [e.g., Hatfield, et al.
(1994); Haris and Raviv (1991); Lewis and Sappington (1995); Miao
(2005)]. Recent developments in corporate finance research show that the
optimal capital structure decision is not limited only to choosing what
percentage of debt or equity should be used, but the decision also has
to involve the choice of short-term or long-term debt [Leland and Toft
(1996); Myers (1977); Yi (2005)].
In developed markets, firms can easily choose between short or
long-term debts as per their requirements of optimal debt maturity
structure. They are not constrained by the availability of either type
of debt as the banking industry and capital markets are both developed
and competitive. Unfortunately, firms operating in developing countries
are not that lucky. Because of less developed capital markets and
instable interest rates, firms in developing countries usually find it
difficult to use long-term debt. Besides these obvious reasons, we need
to know empirically what factors influence the debt maturity choice in
developing countries like Pakistan.
As far as we know, there is no formal study to empirically examine
the determinants of debt-maturity structure of Pakistani firms. In a
study on determinants of capital structure of Pakistani listed firms,
Shah and Hijazi (2004) report greater percentage of short-term debt in
the total debt of the listed firms. Similarly, Booth, et al. (2001) and
Demiriguc-Kunt and Maksimovic (1999) document higher percentage of
short-term debt in developing countries. How higher is the percentage of
the short-term debt in Pakistani listed firms and what are the
determinants of debt maturity structure in Pakistan? This study aims to
answer these questions.
This study contributes to the empirical literature by presenting
evidence for the first time on how listed firms in Pakistan make their
choices between long-term and short-term debt. The empirical literature
is rich on capital structure decisions but not on debt-maturity
decisions. This study contributes to the empirical literature by
presenting evidence for the first time on how listed firms in Pakistan
make their choices between long-term and short-term debt. The empirical
literature is rich on capital structure decisions but not on
debt-maturity decisions. There is a need to add empirical evidence to
the literature on the debt-maturity choices not only from the
methodological standpoint but also from the view of including detailed
analysis of large data sets of individual countries, especially from
developing ones. In this regard, the study contributes to empirical
literature by using all relevant models of dynamic panel data. Tools
like Generalised Methods of Moments (GMM) rarely have been used in
debt-maturity research. Ozkan (2000) is one notable study that used GMM
for the first time in debt-maturity research. The assumption that firms
swiftly change the maturity structures of their debts may not hold true
in situations where costs of adjustments are higher. If this assumption
does not hold true, then the use of a static model will not be
appropriate. Our results justify the use of dynamic models in the
debt-maturity research because firms included in the sample find it
costly to adjust instantly to their target debt-maturity structure,
which causes delays in the adjustment process.
The paper is organised as follows. Section one introduces the
paper. Section 2 presents a summary of literature related to debt
maturity-structure. Section 3 describes the data and discusses the
dependent and explanatory variables. Section 4 presents various
specification choices of the potential model for our analysis. Results
from alternative specifications are presented in Section 5. Section 6
concludes the paper.
2. RELATED LITERATURE
The basic objective behind any capital structure decision is to
optimise the cost of capital. Corporate finance literature suggests that
maturity of debt can play a significant role in lowering the cost
associated with debt financing. Four underlying theories explain why a
firm will have a specific debt-maturity structure. These theories are
the agency cost theory, the signaling and liquidity risk theory, the
maturity matching and the tax-based model.
Myers (1977) says that a firm may pass up some profitable
investment opportunities in the presence of risky debt. This is known as
an under- investment problem. But if the maturity of debt is short, such
problems will not arise as the firm will pay the debt before the growth
option expires. This suggests that if a firm has more growth
opportunities, it will have more short-term debt. Consistent with the
above, Barclay, et al. (2003), Guedes and Opler (1996) and Varouj, et
al. (2005) all find an inverse relationship between the proxy for growth
opportunities and corporate debtmaturity.
Myers (1977) suggests another solutions to the under-investment
problem. He proposes to match the maturity of a firm's debts to
that of its assets. The maturity matching ensures that debt payments
correspond to the fall in the value of existing assets. It means that
maturity of assets should be matched with the maturity of debt. With a
different argument, Stohs and Mauer (1996) also recommend maturity
matching. They say that when a firm has longer maturity of assets as
compared to the maturity of its debts, the cash flow from its assets
will not be sufficient to meet the debt obligation. Demirguc- Kunt and
Maksimovic (1999) add another aspect of asset maturity in relation to
debt maturity. They suggest that fixed assets facilitate borrowing by
serving as collateral.
The agency model suggests that smaller firms have higher agency
costs because the potential conflict of risk shifting and claim dilution
between shareholder and bondholders is more severe in these firms [Smith
and Warner (1979)]. This agency cost can be controlled with short-term
debt Barnea, et al. (1980). Moreover, the information asymmetry problem
is severe with small firms, as they find it costly to produce and
distribute information about themselves [Pettit and Singer (1985)].
Because of information asymmetry, their access to capital market for
long-term debt remains limited. The large fixed cost of flotation of
fixed securities relative to the small size of the firm is another
impediment that stops small firms approaching the capital market
[Easterwood and Kadapakkam (1994)]. Examining the maturity of
firm's liabilities in thirty developed and developing countries
during 1980-1991, Demirguc-Kunt and Maksimovic (1999) find that large
firms have higher long-term debt ratios as compared to that of small
firms.
The signalling model suggests that firms generate signals to the
outside world about their credit quality of their cash flows when they
use a specific type of financing option. [Flannery (1986)] says debt
maturity can reduce the costs of information asymmetry between firm
managers and investors. He theoretically proves that if bond market
investors cannot isolate good firms from bad ones, good firms will
consider their long-term debt to be under-priced and will, therefore,
issue short-term debt. Conversely in the same circumstances, bad firms
will sell over-priced bonds. Flannery (1986) further argues that debt
maturity serves as a signalling device. Short-term financing subjects a
firm to more frequent monitoring; hence only good-quality firms will be
more willing than bad-quality firms to use short-term debt. Highlighting
the relevance of transaction costs of debt, Mitchell (1991) argues that
low quality firms have no option but to use long-term debt because they
will find it difficult to roll over short-term debt as it would subject
them to transaction costs which may not the case for high-quality firm.
Furthermore, financially strong firms can use more of short-term debt as
they are better equipped to face refinancing risk and the interest risk
of short-term debt [Jun and Jen (2003)]. Guedes and Opler (1996) find
empirical support for the above argument and report that financially
sound firms use more short-term nonconvertible debt as compared to firms
that have low credit ratings. Goswami, et al. (1995) adds another aspect
of temporal distribution of information asymmetry. They say that a firm
issues long-term debt when information asymmetry is related to
uncertainty of long-term cash flows. However, firm will issue short-term
debt when informational asymmetry is randomly distributed across short
and long-term debt.
Tax-based model, suggested by Brick and Ravid (1985), states that
after adjusting for the default risk, a firm will preferably make use of
long-term debt when the interest rate is expected to slope upward,
because long-term debt will reduce the estimated tax expenses. The basic
assumption of their model was that the leverage decision is made before
the debt maturity decision. Lewis (1990) says that taxes will not impact
a firm's value when optimal capital structure and debt maturity
structure are determined at the same time. Kane, et al. (1985), using
dynamic model, predict that optimal debt maturity will increase when
contracting costs increase, the benefits of tax-shields decreases, and
the volatility of firm worth decreases.
3. DATA AND METHODOLOGY
Data
Data for the study has been taken from "Balance Sheet Analysis
of Joint Stock Companies Listed on the Karachi Stock Exchange
(1999-2004)", a publication of Statistics Department of State Bank
of Pakistan. The book contains six years data of balance sheets and
income statements of non-financial firms.
For our analysis, we first selected firms for which data was
available in all six years. Second, care was taken not to include public
utility firms, because they are regulated differently. There were many
firms with negative equity. All such firms were excluded from the
analysis, as capital structure and debt-maturity structure decisions in
these firms are influenced by the financial constraints they face.
Similarly, firms that had accumulated-losses in all six years were
excluded. Al1 outliers with 3 standard deviations from the mean value
were removed. Initially, we included all six years in our analysis.
However, the construction of some variables required calculation of
yearly change, and because of this, the year 1999 was dropped.
Resultantly, we were left with a panel of 266 firms and five years.
Dependent and Independent Variables
Dependent Variable
Empirically, several proxies have been used for debt-maturity. For
example, some studies have used the ratio of liabilities maturing in (i)
5 years (ii) 1 year to total liabilities [(Ozkan (2000)]. Others have
used the ratio of debt maturing in more than 3 years to total debt
[Barclay and Smith (1995); Varouj, et al. (2005). Our data source i.e.,
the Balance Sheet Analysis book published by the State Bank of Pakistan
does not provide data on different maturities of debt. Given this
limitation, our measure of debtmaturity, denoted by DEMA, is as follows:
DEMA = Debt Maturing in more than one year / Total debt
Independent Variables
Growth
Following the under-investment hypothesis, we expect a negative
relation between growth and debt-maturity. To measure growth, either
market-value of book-value based approach can be used. Though many
research studies on debt maturity structure use market-to-book ratio, we
use the proxy of annual percentage increase in total assets for growth.
The reason for this is that our data comes from the years 1999 to 2004.
The Karachi Stock Exchange experienced a boom in 2002 and onward where
share prices for a majority of companies increased dramatically. If we
use market-value based proxy it will unnecessarily indicate that the
listed companies experienced abnormal growth in 2002 and onward. In
comparison, the book-value approach provides a consistent measure of
growth.
Size
Agency theory suggests that agency costs are higher for small
firms. These costs can be controlled with the help of short-term
financing. This suggests positive relationship between size firm and
maturity structure of debt. The same positive relationship is suggested
by information asymmetry hypothesis. Furthermore, fixed flotation costs
of long-term securities make access to capital market difficult for
small firms that again suggest a positive relationship between maturity
of debt and size of the firm. Our proxy for the size of firm is the
natural log of total asset.
Asset Maturity
Stohs and Mauer (1996) say when a firm has longer maturity of
assets than the maturity of its debt, the cash flow from its assets will
not be sufficient to meet the debt obligation. On the other hand, if a
firm finances its short-term assets with longer maturity debt, then the
funds will remain useless in periods of low activity. This suggests that
asset maturity has a positive relationship with debt maturity. We use
two proxies for assets' maturity; (a) Assmat, which is obtained by
dividing net fixed assets on annual depreciation charge and (b)
Oppcycle, which is a ratio of net sales to net fixed assets. The first
proxy will capture the maturity of fixed assets, and the second proxy,
as argued by Demirguc-Kunt and Maksimovic (1999) is a descriptor of the
firm's operating cycle. It captures the yearly fluctuations in
operational activities. A high ratio of Oppcycle will show that the firm
may need short-term financing to support sales.
Firm Quality
Information asymmetry that may exist between managers and investors
usually results in under pricing of long-term securities. In order to
reduce this cost of information asymmetry, Flannery (1986) argue that
good firms will prefer to issue short-term debt. Furthermore, only good
quality firms will be willing to subject themselves to frequent
monitoring that comes after short-term financing. This suggests an
inverse relationship between debt maturity and firm quality. Following
Barclay and Smith (1995), we use abnormal future earning as a proxy of a
firm's quality. It is assumed that a higher-quality firm will have
positive future abnormal profit. Abnormal profit can be defined as
follows:
Quality = [Earning.sub.t+1] - [Earning.sub.t]/[Earning.sub.t]
Tax Rate
In model developed by Kane, et al. (1985), an optimal mix of
long-term and short-term debt is determined by a trade-off that exists
between three factors. These factors are bankruptcy costs, floatation
costs of debt, and the benefits of tax-shields. They argue that
debt-maturity increases with floatation costs and decreases with
tax-shield benefits of debt. Our measure of tax rate is as follows:
Tax Rate = Annual Tax Expense/Taxable Income
Table 1 summarises the independent variables, their measures, and
expected relationship with the dependent variable DEMA. Table 2 shows
descriptive statistics for variables which are included in our analysis.
Further, Table 3 shows means of independent variable grouped by
industries.
4. MODEL SPECIFICATION
Studying phenomenon like capital structure or debt maturity
structure where a choice has to be made between two options, one has to
make certain assumptions about the way the choice is made. In case of
debt maturity structure, available options are whether (i) to use debt
of short maturity (ii) or to use debt of long maturity. If firms can
instantly switch between these options and there are no costs of
switching over or adjustments to reach the target debt maturity
structure, we can adopt static model for analysis. However, if firms
experience delays in the process of adjustments then the use of static
model will be inappropriate. As reported by Antoniou, et al. (2006) and
Ozkan (2000), firms do experience delays in the process of adjustment
which implies that their actual debt maturity structure may not be the
desired debt maturity structure. This is why we prefer to use partial
adjustment model:
[DEMA.sub.it] = [alpha][DEMA.sub.i,t-1] + [k.summation over (k=1)]
[[beta].sub.k] [X.sub.it] + [[lambda].sub.i] + [[lambda].sub.t] +
[e.sub.it] ... ... ... (1)
[DEMA.sub.it] is debt maturity ratio of firm i in time t.
[X.sub.it] represents various independent variables as discussed in the
previous section. [[lambda].sub.i] is a dummy variable that capture firm
specific effects that do not change over time. [[lambda].sub.i] is dummy
variable for year specific effects that do not change across firms like
macroeconomic factors, [e.sub.it] is the normal error term that is
assumed to be serially uncorrelated with zero mean.
As shown in Bond (2002), the individual effects [[lambda].sub.i])
are assumed to be stochastic. If so, these effects will be correlated
with the lagged dependent variable [DEMA.sub.i,t-1]. In such a case the
OLS estimator of [alpha] and [[beta].sub.k] are inconsistent and the
estimator of [alpha] are biased upward because the lagged variable
[DEMA.sub.i,t-1] is positively correlated with the error term defined as
[[lambda].sub.i] + [e.sub.it]). Within Group estimator can remove this
inconsistency by transforming the variables such that observations are
expressed as deviations from group means. The transformation removes the
individual effects [[lambda].sub.i]. However, such transformation
invites a correlation between the error term -[ei.sub.t-1]/T - 1] and
the lagged dependent variable -[DEMA.sub.it-1]/T - 1] and the resultant
estimate of [alpha] is heavily biased downward. [Bond (2002)] says that
OLS and the Within Group estimates are biased in opposite directions and
help in evaluating a candidate consistent estimate that will lie between
the two. Instead of using the Within Group estimate, the firm specific
effects can be removed with taking the first difference of the Equation
(1).
[DELTA][DEMA.sub.it] = [alpha][DELTA][DEMA.sub.i,t-1] +
[k.summation over (k=1)] [[beta].sub.k] [DELTA][X.sub.it] +
[DELTA][[lambda].sub.i] + [DELTA][e.sub.it] ... ... (1)
However, in the above model too, the error terms [DELTA][e.sub.it]
are correlated through terms [DEMA.sub.i,t-1] and [e.sub.i,t-1]. To
overcome this weakness, Anderson and Hsiao (1982) developed a model (AH
2SLS) where [DELTA][DEMA.sub.i,t-2] or [DEMA.sub.i,t-2] are used as
instruments for the first difference of the lagged dependent variable.
The instruments are correlated with [DEMA.sub.i,t-1] but uncorrelated
with [DELTA][e.sub.it]. However, AH 2SLS method does not use all
possible moment conditions. Further precision in the estimates can be
obtained through a method of Generalised Methods of Moments (GMM), a
technique suggested by Arrelano and Bond (1991). Under this method, all
available moments can be used by using the orthogonality conditions
which are present between the lagged values of dependent variable and
error terms. Arrelano and Bond (1991) GMM is also called difference GMM.
However, Blundell and Bond (2000) demonstrate that the difference GMM
value is biased downward in the presence of finite sample bias which is
expected when the series is highly persistent. They suggest that one
should examine the time series properties of each series when using GMM
estimator for dynamic panel data models. After investigation if the
individual series turn out to be persistent, then instruments available
in first differences tend to be less powerful. In contrast, system GMM
which was introduced by Arellano and Bover (1995) and late on extended
by Blundell and Bond (1998) significantly smaller finite sample bias and
works with a good deal of precision when estimating autoregressive
parameters in persistent series. In our case, after investigation we
found that the variables Size, Assmat, and Oppcycle showed persistence.
(1) Therefore, we report the results of system GMM technique alongside
the results of OLS, Within Group regression, Anderson Hsiao 2SLS, and
difference GMM.
5. RESULTS
Table 4 presents five alternative estimation procedures starting
from a basic OLS in levels, the Within Group (WG), Anderson - Hsiao 2SLS
regression, difference GMM and finally system GMM. The first two columns
of the table show names of the selected variables and hypothesized
signs. In rest of the columns, coefficients and p- values are reported
under each specification method. The heteroskedasticity robust standard
errors ate reported in parenthesis below each coefficient value. In all
models there are 266 1 Regressing each variable on its one period lagged
values yielded the coefficient values of 0.8099 for size. 0.6547 for
Assmat, 0.7693 for Opcycle, .0150 for Quality, .0396 for Tax, and 0.0835
for Growth. firms and 1330 observations, however, usable observations
vary according to estimation method. We use Sargan test of
overidentifying restrictions to check the validity of instruments set.
The null hypothesis of the test is that there is no correlation between
instruments and the error term. AR(1) and AR(2) test whether first and
second order serial correlation in the residuals exist of not. All
models have time dummies to capture the effect of macro-economic shocks.
The joint significance of these dummies is tested by Wald test. All GMM
models were estimated using xtabond2 command written by Roodman (2006)
for Stata.
In OLS estimation, the lagged dependent variable [DEMA.sub.i,t-1]
is treated as exogenous and firm fixed effects are not captured. As
discussed in the previous section, the dependent variable which is
lagged one period is correlated with the error term and the resultant
coefficient is biased upward. The WG estimator purges the fixed effects
by transforming the observations as deviation from group means. However,
this estimator too is biased but now in opposite direction of OLS. The
OLS and the WG coefficients of the dependent variable [DEMA.sub.i,t-1]
are 0.7010 and 0.1076 respectively. The AH difference regression gives
[alpha] value 0.6847 that is in between the OLS and WG.
Estimating the regression with GMM technique, we first need to
account for the problem of endogeneity and exogeneity of the explanatory
variables because the valid set of instruments depends upon the
relationship between the transformed error term [e.sub.it] and
explanatory variables [X.sub.it]. Following the approach of Blundell, et
al. (1992), we examine the possibility whether the [X.sub.it] variables
are predetermined with respect to [e.sub.it]. If a specific explanatory
variable [x.sub.it] is correlated with [[DELTA].sub.et] and the
[e.sub.it] is serially uncorrelated, then adding instrument dated t-1
will cause the estimate of the coefficient of x variable to fall.
Similarly, the possibility of strict exogeneity of [X.sub.it] variables
with respect to [e.sub.is] can be examined by including present as well
as lagged values dated t-1, t-2, and earlier of [X.sub.it] variable in
the instruments set. Again if the coefficient estimates of the X
variables fall, then the variable cannot be considered as exogenous. As
a result of this procedure, we found that the variables Opcycle and Tax
were exogenous and including present as well as lagged values of them to
the instrument set gave desirable results. For other explanatory
variables, instrument set dated t-1 was found to give better coefficient
estimates and efficient standard errors, suggesting no measurement
errors in them. However, they were not strictly exogenous. For the
lagged dependent variable [DEMA.sub.i, t-1], instruments dated t-2 and
earlier were found valid and efficient. The Sargan test of
overidentifying restriction clearly accepts the validity of the
instruments in both of the GMM models. The AR(I) test indicates that
there is first order serial correlation in the residuals, however, AR(2)
test provides evidence that second order serial correlation is absent.
The difference Sargan test accepts the validity of the additional level
instruments at any conventional level in the system GMM estimation.
Further precision is obtained with the GMM technique which gives
the [alpha] value below the value under OLS and AH 2SLS estimation, but
well above the Within Group estimator. The difference GMM gives [alpha]
value of 0.4739; however, system GMM produces a higher value of 0.5871
for [alpha]. The difference GMM estimates of coefficients for other
variables too are barely higher than the Within Group estimates. This
observation provides some evidence of finite sample bias associated with
weak instruments in the presence of persistent series. For this reason,
the system GMM results ate our preferred results.
In all of the above models, the [alpha] value is positive and
significant. The adjustment coefficient, [gamma] = (1-[alpha]), is close
to 0.5. It means that there is adjustment process and firms face
difficulty in instantly adjusting toward their target debt maturity
structure. Because of the problems associated with OLS, WG, and AH
regressions as discussed previously, we mainly focus on GMM models for
our analysis. Al1 the explanatory variables have the predicted signs in
all models except the Growth and Quality variables; however, they are
insignificant in almost all models.
The variable Growth is insignificant at any conventional level in
all models except in OLS. The finding suggests that growth (measured by
annual percentage increase in total assets) does not have any impact on
the debt maturity decision. Our results do not conform to the
under-investment hypothesis of Myers (1977) that growing firms will
shorten the maturity of debt so as to avoid an under-investment problem.
Our finding is also in contrast to the finding of Barclay and Smith
(1995); Varouj, et al. (2005) Majority of the previous research studies
on debt maturity structure have used market-to- book value of equity as
a proxy for growth; however, we use the proxy of annual percentage
increase in total assets. One may suspect that our proxy for Growth does
not effectively represent growth opportunities; be that as it may, a
similar insignificant relationship is reported by Stohs and Mauer
(1996), though they use the proxy of market-to-book ratio (MV/BV) for
growth.
Though the Growth variable does not support the prediction of
agency cost hypothesis, our Size variable does support the agency cost
hypothesis, given by Barnea, et al. (1980), that small firms have more
agency problems and will use more short- term debt to lower the costs of
these problems. The Size variable is positively related to maturity
structure and is significant in all models except in AH 2SLS and
difference GMM. The level of significance is 1 percent in OLS and Within
Group regressions and 5 percent in system GMM. The coefficient value of
0.0304 suggests that Size is the most significant determinant of debt
maturity structure in Pakistan. As the size of a firm increases, the
percentage of long-term debt to total debt also increases. Besides the
agency cost hypothesis, our results confirm to the argument by Pettit
and Singer (1985) that information asymmetry problem is severe with
small firms as they find it costly to produce and distribute information
about themselves. Information asymmetry makes their access to capital
market difficult for long-term debt. The large fixed flotation cost of
long-term securities is another impediment that stops small firms
approaching capital market.
The variable Assmat has the predicted sign and is significant in
all models at 1 percent level with the exception of difference GMM where
it is significant at 5 percent level. In term of importance, Assmat has
the second largest coefficient of 0.006 after the Size variable. On
other hand, the other proxy for maturity matching (Opcycle) has also the
expected negative sign and is significant at 5 percent level in system
GMM and at 1 percent level in OLS and WG regressions. Both of the
proxies for maturity matching show that the maturity of debt varies with
the maturity of firms' assets. A firm uses more short-term debt
when sales and production activities pace up. However, the proportion of
long-term debt increases when the percentage of assets with longer lives
increases. The significance of Assmat and Opcycle lend unambiguous
support to maturity matching hypothesis.
The Quality variable as measured by the proxy of abnormal profit
has neither the expected sign nor statistically significant coefficient
in any model. This finding is strictly in contrast to signalling
hypothesis presented by Flannery (1986) that short- term debt serves as
a signalling device when information asymmetry between firm's
managers and investors with respect to quality of the firm is higher.
Finally, Consistent with the tax-based hypothesis, the coefficient
estimate on the variable Tax is negative and significant in all models
except in Within Group. The level of significance is 1 percent in all
models. This shows that corporate tax rate does have an influence the
maturity structure of debt.
Robustness of the Results
In order to test the robustness of the results, we estimate the
relationship between the dependent variable DEMA and the six explanatory
variables with static panel data models. Specifically, we apply pooled
regression model, fixed-effects model and cross- sectional model. Table
5 summarises the regressions' output for these models. The first
two columns show the names of variables and their hypothesised signs
respectively. The last three columns report the results for pooled,
fixed-effects and cross-sectional regressions respectively. Standard
errors are reported in the parentheses below the coefficient values. The
alternative estimations under static panel data models substantiate the
main findings of our prior estimations under dynamic panel data models.
The Growth variable is still statistically insignificant in all of the
three models whereas the Quality variable has the expected negative sign
but is insignificant in the first two models. Assmat, Size and Opcycle
are highly significant and have the expected signs in all of the three
models. The Tax variable shows inconsistency in the static models.
Though it has the expected sign, it is insignificant in the
fixed-effects model and cross-sectional model. Overall, the static
models are in agreement with the results of our prior estimations under
dynamic panel data models.
6. CONCLUSION
In this study we examine the empirical determinants of debt
maturity structure for a sample of 266 firms in non-financial sector
over the period 2000 to 2004 by using several variants of dynamic panel
data models. Our study on debt maturity structure is a first one in
Pakistan and hence contributes to literature by providing evidence from
a developing country. To examine the dynamic nature of debt maturity
structure, we start our analysis with a partial adjustment model using
OLS estimation ignoring the individual effects. Going a step forward
with the model, individual effects are purged out with Within Group (WG)
estimation. To account for the endogeneity problem, GMM estimation is
used next and precision in the estimates is obtained with the proper set
of instruments.
To test the relevant theories of debt maturity structure suggested
in the literature, we examine the effect of six explanatory variables on
long-term debt ratio which is calculated as a ratio of debt maturing in
more than year divided by total debt. These theories include agency cost
theory, signalling and liquidity risk theory, the maturity matching
hypothesis, information asymmetry hypothesis, and tax hypothesis. We
find mixed support for the agency cost hypothesis. Our results show that
smaller firms use more short-term debt; however, there is no evidence
that growing firms use more of short-term debt as predicted by Myers
(1977) that debt maturity is inversely related to proxies for growth
options in firms' investment opportunity sets. The significance of
Size variable also substantiates the information asymmetry hypothesis
that information asymmetry is greater with small firms and hence they
find it costly to approach capital market for long-term debt. We find
unambiguous support for maturity matching hypothesis. Our results show
that the long-lived assets are positively correlated with debt maturity
structure. On the other hand, the yearly ups and downs in operating
activities cause the short term financing to rise and fall accordingly.
The signalling hypothesis suggested by Flannery (1986) is not supported
by our results. Flannery (1986) had argued that good quality firms will
use more short-term debt in order to generate positive signals to the
outside world. Our proxy for firm quality is insignificant in any model.
Finally we find support for the tax-based hypothesis. The coefficient of
the Tax variable is negative and significant in almost all models.
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<INSERT FOOTNOTE>
Attaullah Shah <attaullah.shah@inasciences.edu.pk> is
Assistant Professor, Institute of Management Sciences, Peshawar. Shahid
Ali Khan, Faculty Member, Institute of Management Sciences, Peshawar.
Table 1
Independent Variables and Their Relationship with Dependent
Variable
Variables Measure Expected Sign
Growth %age change in assets Negative
Size Log of assets Positive
Assmat Fixed assets/depreciation Positive
Opcycle Sales/fixed assets Negative
Firm Quality Earnings in [(t +1)--t]/ t Negative
Tax Tax charge/taxable income Negative
Table 2
Descriptive Statistics of Selected Variables
Variables Observations Mean Std. Dev. Min Max
DEMA 1330 0.21 0.21 0.00 0.91
Growth 1330 0.12 0.25 -0.70 2.32
Assmat 1330 12.72 7.88 0.00 53.49
Size 1330 6.76 1.46 1.63 11.08
Quality 1330 0.31 3.00 -30.00 26.33
Opcycle 1330 3.97 4.72 0.01 36.70
Tax 1330 0.27 1.19 0.00 29.00
Table 3
Means of Selected Variables by Industries
Textile Chemical Engineering Sugar Paper Cement
DEMA 0.25 0.15 0.11 0.14 0.15 0.40
Growth 0.14 0.10 0.14 0.13 0.10 0.06
Assmat 12.86 11.33 11.05 14.83 9.42 17.24
Size 6.76 6.78 6.43 6.51 5.83 7.62
Quality 0.46 0.23 0.24 -0.41 -0.06 0.98
Opcycle 2.55 5.74 7.09 2.56 6.57 1.38
Tax 0.19 0.25 0.40 0.69 0.23 0.16
Power Misc.
DEMA 0.23 0.16
Growth 0.06 0.08
Assmat 11.09 14.43
Size 8.08 5.99
Quality 0.03 0.53
Opcycle 6.24 6.62
Tax 0.17 0.18
Table 4
OLS in Level, Within Group, AH 2SLS, GMM Difference and System GMM
OLS Within Group
Predic-
Variables ted Sign Coeff: p-values Coeff: p-values
[DEMA.sub.t-1] + 0.7010 0.00 0.1076 0.00
(0.029) (0.037)
Growth - 0.0528 0.01 0.0019 0.92
(0.02) (0.018)
Assmat + 0.0025 0.00 0.0043 0.00
(0.001) (0.001)
Size + 0.0088 0.00 0.0761 0.00
(0.003) (0.023)
Quality - -0.0015 0.28 0.0011 0.47
(.001) (0.001)
Opcycle - -0.0034 0.00 -0.0042 0.00
(0.001) (0.002)
Tax - -0.0040 0.01 0.001 0.71
(0.002) (0.002)
No. of Firms 266 266
No. of Obs 1,330 1,330
Wald (joint) 2175(7) 0.00 32.12(7) 0.00
Wald (time) 10.97(3) 0.01 5.857 0.11
Sargan Test -- --
Difference Sargan -- --
AR(l) -0.27 0.79 (3.84) 0.00
AR(2) 0.33 0.74 -6.6800 0.00
AH 2SLS GMM Difference
Variables Coeff: p-values Coeff: p-values
[DEMA.sub.t-1] 0.6847 0.00 0.4739 0.000
(0.129) (0.077)
Growth 0.0301 0.31 0.0213 0.350
(0.03) (0.023)
Assmat 0.0051 0.00 0.0063 0.032
(0.002) (0.003)
Size -0.0088 0.79 0.1184 0.129
(0.033) (0.078)
Quality 0.0017 0.38 0.0026 0.254
(0.002) (0.002)
Opcycle -0.0013 0.63 0.0002 0.939
(0.003) (0.003)
Tax -0.0106 0.00 -0.0092 0.000
(0.002) (0.002)
No. of Firms 266 266
No. of Obs 1,330 1,330
Wald (joint) 5.25(9) 0.00 60.98(7) 0.00
Wald (time) 4.96(2) 0.01 9.94(3) 0.02
Sargan Test -- 31.59(37) 0.72
Difference -- --
AR(l) -4.76 0.00 -4.7600 0.00
AR(2) 1.080 0.28 0.3600 0.72
GMM System
Variables Coeff: p-values
[DEMA.sub.t-1] 0.5871 0.00
(0.076)
Growth 0.0202 0.35
(0.021)
Assmat 0.0060 0.00
(0.002)
Size 0.0304 0.04
(0.015)
Quality 0.0017 0.47
(0.002)
Opcycle -0.0035 0.03
(0.002)
Tax -0.0074 0.00
(0.001)
No. of Firms 266
No. of Obs 1,330
Wald (joint) 478(10) 0.00
Wald (time) 11.99(3) 0.01
Sargan Test 54.94(56) 0.52
Difference 0.22
AR(1) -6.1600 0.00
AR(2) 0.9100 0.36
Table 5
Regression Output of Pooled, Fixed-Effect and Cross
Sectional Models
Pooled Fixed-effects
Predicted
Variables Sign Coeff: p-value Coeff: p-value
Growth - 0.0014 0.945 0.0005 0.974
(0.0203) (0.0174)
Assmat + 0.0074 0.000 0.0039 0.000
(0.0008) (0.0009)
Size + 0.0222 0.000 0.0953 0.000
(0.0034) (0.0194)
Quality - -0.0019 0.225 -0.0013 0.244
(0.0015) (0.0011)
Opcycle - -0.0135 0.000 -0.0073 0.002
(0.0009) (0.002)
Tax - -0.0078 0.044 -0.0007 0.818
(0.0038) (0.0029)
Cross Section
Variables Coeff: p-value
Growth 0.0175 0.747
(0.0539)
Assmat 0.0075 0.000
(0.0017)
Size 0.0150 0.084
(0.0087)
Quality -0.0059 0.039
(0.0029)
Opcycle -0.0182 0.000
(0.0029)
Tax -0.0109 0.248
(0.0094)
