Results and discussions Corporate Governance
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Variable definition: INTCOST = Total internal audit cost in RM; EXTCOST = Total external audit costs in RM;
NEDREMM = Total NED remunerations in RM; MONITOR = Total monitoring costs in RM; MGROWN = Ex
ecutive directors‘ shareholdings ;DEBTSTRC = δong term debt to market value of the firm; RECINV = Ratio of inventories and receivables to total assets; COMPLEX =
number of subsidiariesincluding the head office; SIZE = Total assets in RM; ROA = ROA; GR
OWTH = Tobin‘s Q; RISK = Current year lossDummy; δISTSTAT = Board listing Dummy; CONSTRASE = Companies in consumer, trading and service sectors Dummy; INDPROP =
Companies in industrial, constructions and property sectors Dummy.
Panel A shows that non- executive directors‘ remunerations constitute the largest
component of monitoring costs, followed by internal audit costs and external audit costs ranking second and third respectively. The mean percentage of shareholdings by the
managers is about 27, which is approximate the 34 average of Haniffa Hudaib 2006 findings using Malaysian data. The ratio of long term debt to the market value
ranges from 0 to 93 with the average close to 15. The descriptive statistics also show that the sample companies cover a wide range of companies, some moderately
small and some relatively large, range from those with RM18 millions to RM65,092 millions of total assets. The complexity of the companies in terms of their operations
range from simple, where there are companies with only their head office with no subsidiary, to more complex. The complexity of their assets‘ compositions also reflect
the same pattern, the ratio of inventories and receivables to total assets range from 0.19 to 80 and the average is about 31. On average, the respondent companies
have the total assets of RM1,564 millions and β0 subsidiaries, while the average Tobins‘
Q is approximately 1.05. Panel B reports that about 75 of the companies are listed in the main board of the Bursa Malaysia, and the balance in the second board. Only 20
of the companies suffer a loss in the current year.
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Table 2 : Normality test statistics of sample companies Variable
Mean Minimum
Maximum Std Dev
Skew ness
Kur tosis
MONITOR
12.9841 10.949 16.861
1.0005 0.864
0.922
MGROWN
0.2727 0.0000
0.8637 0.2324
0.210 -1.230
REVINV
0.3088 0.0019
0.8046 0.1945
0.329 -0.888
COMPLEX
2.4998 1.0000
445.00 0.9091
0.232 1.430
RISK
0.2000 1
0.3980 1.544
0.386
SIZE
19.744 16.720
24.899 1.4171
0.911 0.887
DEBTSTRC
0.1468 0.0000
0.9328 0.1584
1.860 4.366
LISTSTAT
0.7400 1
0.4370 -1.130
-0.731
CONSTRASE
0.3300 1
0.4720 0.718
-1.497
INDPROP
0.5400 1
0.5000 -0.146
-1.996
ROA
0.0101 -3.0172
0.2037 0.2259
-10.814 140.20
GROWTH
1.0515 0.3081
7.9680 0.7092
5.424 42.856
Note: Figure in the parenthesis is the P value Variable definition:
εONIITOR = Total monitoring costsln; εGROWN = Executive directors‘ shareholdings ; DEBTSTRC = Long term debt to market value of the firm; SIZE = Total assetsln; COMPLEX =
number of subsidiariesln; RECINV = Ratio of inventories and receivables to total assets; ROA = ROA; RISK = Current year lossDummy; GROWTH = Tobin‘s Q; δISTSTAT = Board listing
Dummy; CONSTRASE = Companies in consumer, trading and service sectors; INDPROP = Companies in industrial, constructions and property sectors.
The results of standard tests on skewness and kurtosis in Table 2 indicate that there is no problem with normality assumption
47
. A visual check for normality using histogram and normal probability plots is also carried out. All the histograms appear to be
reasonably normally distributed and the normal distribution of the probability plot forms a straight line and the values appeared to fall approximately on this normality line. Thus,
these variables can reasonably be considered as normally distributed. In summary, the model does not violate the basic OLS assumptions and could be used to test the
expected hypotheses.
47
The data is said to be normal if the standard skewness is within ±1.96 and standard kurtosis is between ±γ.0 εat Nor Sulong, β007; Abdul Rahman εohamed Ali, β006; Haniffa
Hudaib, 2006.
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Table 3 presents the correlation matrix for the dependent and independent variables. The result indicates that there is no multicollinearity problem, as the correlations are
below the threshold value of 0.8 Gujarati, 2003, p. 359.
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Table 3 : Correlation matrix
Variable M
O N
ITOR M
G R
O WN
D E
B TS
TR R
E C
IN V
R IS
K S
IZE C
O M
P LE
X
R O
A G
R O
WTH LIST
S TA
T
C O
N S
TR A
S E
IN D
P R
O P
MONITOR
1.00
MGROWN
-0.26 1.00
DEBTSTRC 0.24
-0.01 1.00
RECINV -0.21
0.19 -0.37
1.00
RISK -0.25
-0.03 0.07
0.00 1.00
SIZE
0.82 -0.21
0.42 -0.40
-0.23 1.00
COMPLEX
0.61 -0.10
0.22 -0.14
-0.04 0.52
1.00
ROA
0.15 0.07
0.02 0.05
-0.43 0.20
-0.05 1.00
GROWTH
0.09 -0.13
-0.16 0.00
0.01 0.05
-0.04 -0.50
1.00
LISTSTAT 0.32
-0.13 0.06
-0.23 -0.28
0.47 0.21
0.18 0.06
1.00
CONSTRASE 0.11
-0.11 -0.02
0.09 -0.10
0.02 0.09
0.07 0.04
0.00 1.00
INDPROP -0.15
0.10 0.01
0.09 0.09
-0.09 -0.07
-0.08 -0.08
-0.09 -0.76
1.00 Notes: significant at 1 level
significant at 5 level significant at 10 level
See variable definition in Table 2
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i Main results
Column two of Table 4 presents the multiple regression analysis used to test the main model. The adjusted R squared for the model is 0.753 and the F-value of 66.022 is
significant p 0.000. The value of the adjusted R square is very high, as well as statistically significant, which suggests that it is a good predictive model of monitoring
costs for Malaysian data. It means more than 75 of the variation in the monitoring costs can be explained by the model. This adjusted R squared is also very much higher
compared to a similar study by Anderson et al. 1993 on monitoring cost, which use Australian data, but with only one independent variable assets in place, where its
adjusted R-squared is 0.423.
The independent variable, managerial ownership appears to have significantly negative relationship with monitoring costs as predicted by agency theory. This result implies that
the greater the managerial ownership in an organisation the lower is its total monitoring costs. This finding is consistent with earlier studies in western countries by Jensen
Meckling 1976, Fleming et al. 2005, Ang et al. 2000, Jensen 1993, Nimie 2005 and Friend Lang 1986.
This result is also consistent with the convergence of interest model which claim that an increase in the proportion of firm‘s equity owned by insiders is expected to increase firm
value as the interest of inside and external shareholders are realigned, thus result in less conflict among the shareholders. Furthermore there will be less information asymmetry
and less hierarchical organisational structure as the managers are now the owners, and are actively engaged in day to day activities of the organisations Nimie. 2005. This is
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agreed by Ang et al. 2000 and Fleming et al. 2005 who claim that the managers‘
incentive to consume perquisites declines as their ownership share rises because his share of t
he firm‘s profits rises with ownership while his benefits from perquisite consumption are constant. A local study by Mat Nor Sulong 2007 also argues along
the same line by claiming that when managers own a smaller portion of the organisation‘s share, they have greater incentive to pursue personal benefits and less
incentive to maximise firm values. In addition, holding common stocks also motivate the managers for its underlying voting rights, such as increase their influence on board of
directors and he nce on the firm‘s general policy DeAngelo DeAngelo, 1985.
Furthermore, this result may also be more pronounced in Malaysian concentrated business environment, where owner-managed companies are common among listed
companies in Malaysia Mat Nor Sulong, 2007, especially with family businesses as claimed by Haniffa Hudaib, 2006. This concentrated agency setting is expected to
have low conflict among the contracting parties Fleming et al., 2005; Fama Jensen, 1983a, thus lead to low risk Francis Wilson, 1988 and low monitoring costs. They
tend to run the businesses themselves or appoint family members, and they are concerned with the survival of the organisations, not only over their lifetime, but also with
the well-being of the next generations Bhattacharya Ravikumar, 2001. Thus, they will really consider the monitoring costs incurred by the companies and the allocation of the
resources in order to ensure the future survival of the organisations.
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Table 4: Cross sectional OLS regression of monitoring costs on managerial ownership and control variables
VARIABLES
Total ED shareholding
is considered
Only direct shareholding
is considered Only indirect
shareholding is considered
Total BOD shareholding
is considered Segmented the
companies to those with high and low
managerial shareholding
Segmented the companies to those
with and without managerial
shareholding
INTERCEPT
1.833 2.786
1.741 2.621
1.525 2.343
1.844 2.801
1.760 2.678
1.977 2.928
MGROWN
-0.400 -2.708
-0.506 -1.978
-0.230 -1.535
-0.395 -2.729
-0.166 -2.426
-0.257 -2.597
DEBTSTRC
-0.502 -2.028
-0.518 -2.072
-0.562 -2.259
-0.516 -2.087
-0.538 -2.174
-0.482 -1.933
RECINV
0.510 2.536
0.473 2.350
0.458 2.270
0.500 2.494
0.481 2.399
0.513 2.544
RISK
-0.180 -1.864
-0.157 -1.619
-0.171 -1.747
-0.173 -1.790
-0.172 -1.775
-0.204 -2.079
SIZE
0.544 14.950
0.542 14.546
0.558 15.337
0.545 15.037
0.547 15.037
0.541 14.790
COMPLEX
0.268 6.058
0.273 6.107
0.264 5.896
0.266 6.017
0.269 6.052
0.283 6.321
ROA
0.084 0.409
0.118 0.569
0.057 0.273
0.074 0.360
0.096 0.465
0.033 0.160
GROWTH
0.078 1.315
0.094 1.582
0.080 1.332
0.070 1.189
0.075 1.266
0.067 1.131
LISTSTAT
-0.248 -2.838
-0.228 -2.586
-0.251 -2.830
-0.250 -2.856
-0.251 -2.854
-0.255 -2.900
CONSTRASE
-0.042 -0.375
0.009 0.079
-0.034 -0.306
-0.042 -0.383
-0.035 -0.313
-0.062 -0.555
INDPROP
-0.147 -1.405
-0.115 -1.092
-0.151 -1.422
-0.158 -1.509
-0.149 -1.423
-0.153 -1.461
R-squared Adj R-squared
F-Statistics P-value
0.765 0.753
66.022 0.000000
0.762 0.750
64.741 0.000000
0.760 0.748
64.157 0.000000
0.765 0.754
66.064 0.000000
0.764 0.752
65.479 0.000000
0.764 0.753
65.802 0.000000
See variable definition in Table 2
The finding of this study is also consistent with a study by Nikkinen Sahlstrom 2004 who conduct an analysis of audit pricing one of the monitoring costs in this study and
its relationship with agency theory by using data from seven countries including Malaysia. Consistent with the theory, they find a significant negative relationship of
managerial ownership with audit fees at 5 level of confidence for Malaysian data.
ii Further tests
In order to get a clear picture of the ownership characteristics of Malaysian companies, sensitivity analysis are also carried out. The proxy for managerial ownership
εGROWN is defined as the percentage of executive directors‘ total shareholdings. As a test of sensitivity, the main model is re-estimated with the independent variable
εGROWN redefined as the percentage of executive directors‘ direct shareholdings only. The result for the model is not affected by this alternative. As expected in agency
theory, the result in column three of Table 4 appears to suggest that the greater the direct managerial control in the organisation, the lower is the relative expenditure in
total monitoring.
Another test of sensitivity is conducted where MGROWN is redefined as the percentage of executive directors‟ indirect shareholdings only. Again, the result
for the model is not affected by this alternative. As expected in agency theory, the result indicates that indirect managerial control in the organization has an
inverse relationship with total monitoring costs refer column four of Table 4.
Alternatively, the proxy for managerial ownership MGROWN is redefined as the percentage of board of directors‟ total shareholdings which includes both
executive and non- executive directors‟ shareholdings. The result for the model
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in column five of Table 4 is not affected by this alternative. The result suggests that the greater the managerial control by both executive and non-executive
directors in the organization, the lower is the relative expenditure in total monitoring costs in directorship and auditing. The direction of the relationship is
as predicted in agency theory.
Further tests in column six and seven of Table 4 are carried out by segmenting the sample companies into a companies with high and low managerial shareholdings by
using the average managerial shareholdings in Table 1 as a cut-off point; and b those companies which have managerial shareholdings and those with no managerial
shareholding. The main model is re-estimated using these alternatives. The re- estimated results for both alternatives indicate that managerial ownership has
negatively significant relationship with monitoring costs at p 0.01, while other variables remain the same.
Independent t-tests are also carried out using the same segmented data in a and b. Both test results show significant results. The result of the test reveals that the
monitoring costs of companies which have high managerial shareholdings are significantly different from those with low shareholdings at p-value 0.00. The
average monitoring costs for those with high and low shareholdings are RM533, 436 and RM1,196,508 respectively. The t-test result for those companies with and without
managerial shareholdings is also significant and shows the same pattern of result. The average monitoring costs of companies which have managerial shareholdings is
RM656, 491, which is less than RM1,897,687, for those companies without such
shareholdings.
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