154 Friend and Lang 1988 use a
dummy variable to represent the existence of external block shareholders with a
holding of 10 or more of a firm‘s outstanding stock. This study extends
Friend and Lang 1988 by using the proportion of outstanding shares owned by
external block holders.
The two variables used to control for risk are:
SIZE = natural log total assets
. Many studies suggest that firm
size is one important factor, which affects a firm‘s debt policy and therefore risk
Scott and Martin 1975, Ferri and Jones 1979, Agrawal and Nagarajan 1990.
IND = zero-one dummy variable for industry classification, where IND = 1 if
industrial company and IND = 0 if natural resources company
. A
more detailed
industrial classification is not used because the
increase in the number of dummy variables imposes too severe restriction on
the degrees of freedom in the regression. It is argued that industry class is a potential
determinant of capital structure because firms, in the same industry, face similar
demand and supply conditions, and thus have similar risk characteristics Scott and
Martin 1975, Ferri and Jones 1979.
The three variables used to control for agency costs are:
GROWTH = the annual percentage change in total assets
. Titman and Wessels 1988 suggest
that a firm‘s growth opportunities are a good proxy for the agency costs of debt.
They suggest that the tendency to invest sub-optimally to expropriate wealth from a
firm‘s debt holders is likely to be higher for firms in growing industries. On the other
hand, growth may also be an indicator of profitability and success of the firm. If this
is the case, GROWTH will be a proxy for available internal funds.
FCF is a direct measure of Jensen’s 1986 free cash flow hypothesis
. The free cash flow hypothesis
states that managers endowed with excessive free cash flows will invest sub-
optimally rather than paying the free cash flow out to shareholders. Jensen 1986
predicts that firms with excessive free cash flow are likely to have higher leverage.
FCF is defined in a similar manner to Lehn and Poulsen 1989.
PROF = operating income before interest and taxes scaled by total assets
. Indicators of a firm‘s profitability
include ratios of operating income over sales and operating income over total assets
Titman and Wessels 1988 and ratios of average earnings before interest and taxes
over total assets Wald 1995.
The variable used to control for asset specificity is:
INTA = Total Intangibles ÷ Total Assets .
Balakrishnan and Fox 1993 argue that asset specificity creates problems for
debt financing due to the non-redeploy ability characteristics of specific assets.
More specifically,
asset specificity
adversely affects a firm‘s ability to borrow. Balakrishnan and Fox 1993 suggest that
examples of firm-specific assets are intangible assets such as brand names,
research and development expenditure and other reputational investments.
The last explanatory variable is used to control for the effect of taxes:
NDTS = Annual Depreciation Expense ÷ Total Assets
. NDTS variable is used to capture the non-debt tax shields argument
put forward by DeAngelo and Masulis 1980. They argue that the greater the level
of non-debt tax shields, the lower is the benefit of additional debt. Thus, all else
equal, firms with higher non-debt tax shields are expected to receive lower tax
benefits from issuing debt and therefore will utilize less debt.
2. Model II
To test for the hypothesized curvilinear relationship between managerial
share ownership and capital structure as proposed by Brailsford et al. 2002, the
managerial share ownership variable and the square of managerial share ownership
variable is augmented to the regression model:
Ln DE
it
= α +
MSO
it
+
1
MSO
2 it
+
2
SIZE
it
+
3
IND
it
+
4
GROWTH
it
+
5
PROF
it
+
6
FCF
it
+
7
INTA +
8
NDTS + e
it
2
155 where:
MSO = = percentage of ordinary shares owned by all executive and
non-executive directors. Share ownership
of corporate
directors are used by Morck et a. 1988 and Keasey et a.
1994, amongst others to proxy for managerial share
ownership.
Other variables are as previously described in Model I.
3. Model III
Finally, a joint test is used to investigate the third and fourth hypotheses,
which explore the relationship between external block ownership and leverage at
different levels of managerial share ownership.
Specifically, the
third hypothesis predicts that external block
ownership and debt are positively related when the level of managerial share
ownership is low. The fourth hypothesis predicts that at high levels of managerial
share ownership, the association between
external block ownership and the firm‘s debt ratio is less significant than at low
levels of managerial share ownership, since the positive monitoring effect of external
block ownership is offset by the negative entrenchment
effect associated
with managerial share ownership.
To test these two hypotheses, a dummy variable D, denoting different
levels of managerial share ownership is employed. D takes the value of 0 if the
level of managerial share ownership is less than
20. When
managerial share
ownership is 20 or more D takes the value of 1. While there is generally little
theoretical justification for the particular cut-off, the 20 level has been used in
several previous studies Brailsford et al., 2002.
For example,
Hermalin and
Weisbach 1991 find that the entrenchment effect of managerial share ownership sets in
after 20 of managerial share ownership. The natural log of DE is then
regressed against MSO, MSO
2
, EBO, DEBO and the control variables:
Ln DE
it
= α +
MSO
it
+
1
MSO
2 it
+
2
EBO
it
+
3
DEBO
it
+
4
SIZE
it
+
5
IND
it
+
6
GROWTH
it
+
7
PROF
it
+
8
FCF
it
+
9
INTA +
10
NDTS + e
it
3
The coefficient on the EBO variable reflects the relation between
external block ownership and the debt level when the level of managerial share
ownership is low.
The coefficient on the DEBO
it
variable then reflects the difference in the external block ownership and leverage
relationship between high and low levels of managerial share ownership.
In order to infer the relationship between external block ownership and
leverage at high levels of managerial share ownership, the coefficient β
3
is added to the coefficient β
2
. In other words, the sum of β
2
and β
3
gives the slope for the relationship between external block ownership and
leverage when the level of managerial share ownership is high.
RESULT AND ANALYSIS 1. The effect of external block ownership
on capital structure
In general, all control variables, except External Block Ownership EBO,
Size, and Industry, are positively skewed. Correlation analysis Table 5 shows that
some explanatory variables are significantly correlated.
First, there is significant negative correlation between the SIZE and FCF
variables -0.195, even though the correlation is quite small. This may be quite
surprising since larger firms normally are expected to have higher free cash flow.
Free cash flows have been identified in the literature as being in evidence in large,
diversified firms Berger and Ofek 1995, Smith and Kim 1994. However, by
considering the effect of financial crisis on
East Asia firms‘ asset and the ability to generate free cash flow, this phenomena at
least can explain this effect.
156
Table 5 : Coefficient Correlation
Independent Variable
MSO MSO2 EBO DEBO SIZE Industry Growth PROF FCF INTA NDTS MSO
1.000 .920
-.479 .846
-.158 .037
.045 .042 .045 -.082 .001
MSO2 .920 1.000
-.371 .755
-.101 .016
.023 .041 .035 -.055 .028
EBO -.479 -.371 1.000
-.355 .055
-.053 -.161
-.112 .017 .065 -.026 DEBO
.846 .755 -.355 1.000 -.157
.001 -.012
-.015 .032 -.077 .000 SIZE
-.158 -.101 .055
-.157 1.000 -.045
-.047 -.006 -.195 .033 .005
Industry
.037 .016
-.053 .001
-.045 1.000
.065 -.071 .028 .066 -.141
Growth
.045 .023 -.161 -.012
-.047 .065
1.000 .089 -.037 .014 -.030
PROF
.042 .041
-.112 -.015
-.006 -.071
.089 1.000 -.023 .003 .135
FCF
.045 .035
.017 .032
-.195 .028
-.037 -.023 1.000 .026 -.087
INTA
-.082 -.055
.065 -.077
.033 .066
.014 .003 .026 1.000 .049
NDTS
.001 .028
-.026 .000
.005 -.141
-.030 .135 -.087 .049 1.000 Significant at the 0.01 level 2-tailed
Significant at the 0.05 level 2-tailed
The SIZE and MSO variables are also negatively correlated significantly -
0.158. This statistic confirms the wealth constraint argument, which suggests that
the personal wealth constraint of corporate insiders is one important barrier to
managerial share ownership. Specifically, as the size of the firm increases, it becomes
more costly for managers to purchase a larger percentage of shares. A significant
negative correlation between GROWTH and EBO - 0.161 is also found. This
implies that the existence of external block
holders reduces the firms‘ opportunity to use profit or earnings as retained earnings
or expanding funds. A significantly negative correlation between INDUSTRY
and NDTS -0.141 suggests that certain industries do not use annual depreciation
expense as a mode to receive lower tax benefits and it could also explain the fiscal
policy in East Asian countries on non-debt tax shield policy. Finally, PROF and NDTS
also have a significant positive correlation 0.135 which implies that profitable firms
are more likely to allocate its earnings to get free cash flow in form of annual
depreciation expense.
In order to test whether the external block ownership affect companies‘ capital
structure, the study run a multivariate regression model that puts
firm‘s debt to equity ratio as dependent variable.
Table 6 presents the regression results for the external block ownership test
that is, Model I. As can be seen from this table, there is supportive evidence of a
positive relation between external block ownership and leverage. The coefficient on
the EBO variable is positive and statistically significant t = 2.799.
This positive relation is consistent with the active monitoring hypothesis that
suggests that large shareholders have greater incentives to monitor management
due to their significant investment in the firm. The increased monitoring by external
block
holders decreases
managerial opportunism, leading to lower agency
conflicts. The results obtained are also consistent with those of Friend and Lang
1988 who obtain evidence that firms with large
non-managerial investors
have significantly higher average debt ratios than
those without external block holders. It does not support the passive voting
hypothesis Pound, 1988 which suggests that
large shareholders
vote with
management without due regard to the interests of dispersed shareholders.
The overall regression explains approximately 20 of the variation in the
dependent variable. Consistent with the size argument, the SIZE variable in the
regression has a significant positive coefficient t-statistic = 6.358, suggesting
that larger firms have higher leverage. This is consistent with Scott and Martin 1975
and Ferri and Jones 1979 and empirical evidence
obtained by
Agrawal and
Nagarajan 1990.
157
Table 6: The Effect Of External Block Ownership On Capital Structure
Model I
Variable Coefficient T-Statistic Significance
Constant -2.277
-8.970 .000
EBO .609
2.799 .005
SIZE .233
6.358 .000
Industry -.266
-2.412 .016
GrowthTA -.002
-1.925 .055
PROF -2.801
-6.333 .000
FCF 4.69E-005
.250 .802
INTA .240
.332 .740
NDTS -2.434
-1.281 .201
MODEL F value = 14.71 4.88
Adjusted R
2
= 0.200
= significant at α level = 1 t2.575 or t-2.575 = significant at α level = 5 t1.959 or t-
1.959 = significant at α level = 10 t1.644 or t-
1.644
The significant negative coefficient on PROF t-statistic = -6.333 is consistent
with the pecking order hypothesis of Myers 1977 and Myers and Majluf 1984, and
the empirical results of Titman and Wessels 1988, Friend and Lang 1988, Chiarella
et al 1992, Allen 1993 and Wald 1995.
The ―pecking order‖ hypothesis suggests that profitable firms will demand less debt
because internal funds are available for financing projects.
The IND variable which proxies for firm risk has a positive and significant
coefficient t-statistic = -2.412. This is consistent with Scott and Martin 1975 and
Bradley et al. 1984. It also suggests that resource companies have higher leverage
than industrial companies in term of East
Asian companies‘ characteristic that mainly operate in exploiting natural resources.
The coefficient on the GROWTH variable is negative and marginally
significant t-statistic = -1.925. This is consistent with Bradley et al. 1984 and
Titman and Wessels 1988 who obtain a significant negative relationship between
growth opportunities and firm‘s leverage. The unanticipated negative but
insignificant coefficient on FCF may be due to the fact that the independent variables
included in the models overlap and capture more than one effect. In this case, a few
variables may proxy for the effect of free cash flow simultaneously. In particular,
besides capturing the profitability effect, PROF variable may also proxy for the
effect of free cash flow. To the extent that some industries have significant potential
for free cash flow abuse, the IND variable may also capture some of the free cash flow
effects. As a result, the FCF variable may only reflect the residual effects of free cash
flow, as Brailsford et al. 2002 argued it.
The coefficient on the NDTS variable is not significant. This supports the
tax neutrality of capital structure under an imputation tax system.
2. The effect of managerial share ownership on financing decision
