EAST AND WEST: DIFFERENCES IN SME CAPITAL STRUCTURE BETWEEN
FORMER SOVIET-BLOC AND NON SOVIET-BLOC EUROPEAN COUNTRIES.
Graham Hall ( graham.hall@mbs.ac.uk )
Manchester Business School, Booth St West, Manchester MI5 6PB, England
Patrick Hutchinson ( phutchin@une.edu.au )
New England Business School, University of New England, Armidale, NSW 2351, Australia
Nicos Michaelas ( michaelas@demetra.com.cy )
Demetra Investment Public Ltd, P.O. Box 23584, 1684 Nicosia, Cyprus
Abstract
The break-up of the former Eastern European, Soviet-Bloc provides many opportunities to
compare the countries that emerged with other countries from the rest of Europe. Given the great
divergence between the two “systems” of communism and capitalism, differences could be
expected in many areas including the way in which small, and medium-sized, enterprises (SMEs)
were financed. In this paper data on the capital structure of SMEs from six former Soviet-Bloc
(SB) countries are compared with those from SMEs in thirteen other, non former Soviet-Bloc
(NSB) European countries. Differences are observed in capital structure (long-term and short-term
debt) between the two groups and this leads to an analysis of the determinants of capital structure.
The determinants chosen were: profitability, growth rate, future growth potential, asset structure
(collateral), company size, company age, non-debt tax shields (depreciation), stock levels and risk.
Restricted and unrestricted OLS regressions were used to test whether differences between the two
groups were due to variations in the determinants or to other, more fundamental, factors. The
results of the analyses show that whilst some of the differences in capital structure can be
explained by variations in the determinants, some cannot. This implies that other economic and
perhaps social or political, factors are at play.
1
International differences in SME capital structure and its determinants
There has been a great deal of research in the area of international accounting and finance. This
has included work on capital structure (Remmers et al., 1974; Rajan and Zingales, 1995; and
Prasad et al., 1996), and on the international differences in capital structure norms (Aggarwal,
1981), the effect of national culture on the capital structure of firms (Park, 1998), and the
relationship between capital structure and ownership and governance structures (Kester, 1986; and
Thompson and Wright, 1995). These studies have covered countries in the European Union
(Prasad et al., 1996), the USA and Japan (Kester, 1986), the "G-7" countries (Rajan and Zingales,
1995) and others (Park, 1998). The studies have derived hypotheses from various sources such as
Hofstede's classification of national culture using the dimensions of Power Distance, Uncertainty
Avoidance, Individualism-Collectivism and Masculinity-Femininity (Hofstede, 1980).
Another theory to explain differences in capital structure is premised on differences, especially in
corporate governance, between English speaking counties with a tradition of liberalism and
capitalism, and other countries that result in differences in capital structure (Thompson and
Wright, 1995). A variation of this is to expect differences between former communist and
capitalist countries with their very different legal and institutional systems
Research on capital structure has considered the relative merits of pecking order theory versus
static trade-off theory (Shyam-Sunder and Myers, 1999; Watson and Wilson, 2002), pecking order
theory and the managerial hypothesis (Griner and Gordon, 1995) and agency and tax
considerations (Walsh and Ryan, 1997). The determinants of capital structure for UK firms have
been investigated by Bennett and Donnelly (1993), Ozkan (2001) and, for UK SMEs by
2
Chittenden et al. (1996) and Jordan et al. (1998). The aim of this paper is to build on previous
research including the work done on capital structure in transitional economies in Central and
Eastern Europe (Cornelli et al, 1996; De Haas et al., 2004) by looking at differences in capital
structure between SMEs in Former Soviet Bloc and other European countries in a way that will
establish whether any differences are due to country-specific factors or to differences between
countries in firm-specific factors.
Data and samples
The study used financial data from the Amadeus database at the Manchester Business School for
93,266 SMEs from 13 non Soviet-Bloc countries and 6 Soviet-Bloc countries as shown in Table 1.
The data were from a period following the “independence” of the relevant Soviet -Bloc countries
i.e. 1995-8. As can be seen there were many fewer cases from the former Soviet-Bloc which
reflects the small number of countries involved and their stage of economic development. A
notable omission is Germany but of course, this country is unique in comprising of both former
Soviet-Bloc and non Soviet-Bloc countries.
Analysis of capital structure
Separate dependent variables for short-term debt (STD) and long-terms debt (LTD) ratios were
estimated. Following Remmers et al. (1974), Ferri and Jones (1975) and Hall et al. (2004) the two
dependent variables were calculated as STD = short-term debt to total assets, and LTD = longterm debt to total assets. Short-term debt is defined as the portion of the company's total debt
repayable within one year. This includes: bank overdraft, bank loans (current portion), and other
current liabilities. Long-term debt is the total company's debt due for repayment beyond one year.
This includes: long-term bank loans and other long-term liabilities repayable beyond one year
such as directors’ loans, hire purchase and leasing obligations. It could be expected, given the lack
3
of development of capital markets and the lack of competition in the banking system, that there
would be a shortage of both long-term and short-term debt for SMEs in the former Soviet-Bloc.
The results in Table 2 confirm this. As can be seen from Table 2, both short-term and long-term
debt averages were lower for SB SMEs. However, although this may well be due to differences
between the two sets of countries it is conceivable that it is, in fact due to differences in firmspecific attributes between the two groups. For example, the differences may be due to SB SMEs
being younger or smaller than the NSB ones. It is, therefore, necessary to consider what the
determinants of SME capital structure are and whether they vary between the two groups.
Variables and hypotheses used to test for SME capital structure determinants
The following variables and hypotheses were used to test for SME capital structure determinants
for the two groups:
PROFITABILITY = Average ratio of pre-tax profits to total assets. This return on assets measure
is similar to the one employed by Toy et al. (1974), Titman and Wessels (1988) and Chittenden et
al. (1996), amongst others. The Pecking Order Hypothesis (Myers1984) suggests that firms will
retain profits and so the higher the profit, the less the need to borrow and thus a negative
correlation between profit and borrowing (short or long-term)
GROWTH = Growth is measured as the percentage increase in total assets in the previous three
years. It was felt that growth over a period of time would give a better indication of financing
needs than just for a single year. This measure of firm growth is also used by Chittenden et al.
(1996) and Titman and Wessels (1988) amongst others. Intuitively, growth would seem to be
positively associated with borrowing and is included in this study despite their being mixed
empirical evidence for this relationship (Chittenden et al., 1996; Jordan et al., 1998). Growth is
expected to be positively related to borrowing.
4
ASSET STRUCTURE (COLLATERAL) = The asset variable is computed as the ratio of fixed
assets to total assets. This measure is employed by Chittenden et al. (1996), Van der Wijst and
Thurik (1993), Friend and Lang (1988) to mention only a few. The hypothesis for this, following
on from agency theory (Jensen and Meckling, 1976), is that because of the potential for conflict
between insiders and outsiders and the asymmetric outcomes for success and failure in lending,
lenders will require high levels of collateral from firms that are run by owner-mangers. However,
because of the tendency, in a less-than-efficient world, to match long-term debt to long-term
(fixed) assets, the correlation is likely to be strongly positive for long-term debt but not for shortterm debt.
SIZE = Following Titman and Wessels (1988) and Chittenden et al. (1996), amongst others, the
absolute value of total assets is included as a size variable in order to test for scale effects in the
relation to debt and firm size. Size is a proxy for risk (Chittenden et al., 1996), an undesirable
attribute to lenders, such that it is expected that the larger the firm the more it can borrow.
AGE = Age of the firm is the number of years since the date of incorporation (Chittenden et
al.,1996). Again the Pecking Order Hypothesis (Myers 1984) suggests that the older a firm is, the
more time it has to accumulate retained profits and therefore needs to borrow less giving a
negative correlation with debt.
NON-DEBT TAX SHIELDS (DEPRECIATION) = Following Bradley et al. (1984), depreciation
charges are used to indicate non-debt tax shields. The ratio of depreciation charges to total assets
is included in the analysis to indicate the tax advantage. This measure is also used by Titman and
Wessels (1988) amongst others. It was hypothesized that the greater the depreciation, the greater
the investment needs and the greater the need to borrow.
5
STOCK LEVEL = Following Van der Wijst and Thurik (1993), stock level is calculated as the
ratio of stock to total assets. Stock can be pledged as collateral and so can be expected to be
positively correlated with borrowing especially short-term.
FUTURE GROWTH OPPORTUNITIES = Future growth opportunities are measured as the
ratio of intangible assets to total assets. Intangible assets include: research and development
expenditure, trademarks, patents and copyrights.
Similar measures of future growth
opportunities are used by Long and Malitz (1983) and Titman and Wessels (1988). It is
hypothesised that the greater the future growth opportunities, the greater will be the
borrowing.
RISK = In addition to size as a proxy, risk can be defined as the coefficient of variation in
profitability over a period (in this case 1992-1995). As a standardised measure it is
independent of size. This measure of risk is used by Toy et al. (1974) and Titman and Wessels
(1988). As with size it is expected that it will be negatively related to debt.
NET DEBTORS = The debtors measure is computed as the ratio of debtors less creditors to
total assets. Although different variables for debtors and creditors could be included in the
regression models, the two are highly correlated, in a positive manner, indicating that small
firms tend to finance debtors by delaying payments to creditors. The net debtors variable
measures the effect of the excess of debtors over creditors (due to the inability to fully mitigate
late payments from customers by delaying payments to creditors) on gearing ratios. It is
hypothesized that the higher the net debtors, the greater the need to borrow and the higher the
debt levels, especially short-term.
6
Regression method
The statistical methods closest to those applied in this paper are cross-sectional analyses of the
determinants of debt ratios: Kester (1986), Friend and Lang (1988), Chittenden et al. (1996),
Michaelas et al. (1999) and Hall et al. (2004). These cross-sectional analyses have generally been
set up as linear regression models with a leverage measure (usually total debt to book value of
assets) as the dependent variable. Explanatory data apart from age have been drawn from income
statements and balance sheets. Regression analysis is used to test the hypotheses discussed above
by means of employing various independent variables, which are regressed against the two
measures of leverage. To determine whether there is any country effect use was made of a
restricted and an unrestricted model. In the former the dependent variables were regressed against
the ten independent variables described above. The unrestricted model included these variables
and both country constant dummies and country slope dummies for each of the explanatory
variables, an addition of one constant and ten slope dummies.
To determine whether there is any group effect an F test was applied to the difference in the
residual sum of squares (RSS) of a restricted and an unrestricted model. In the former the
dependent variables were regressed against the ten independent variables described above. The
unrestricted model included these variables and both group constant dummies and group slope
dummies for each of the explanatory variables, an addition of one constant and ten slope
dummies. Establishing for which variables their impact on long-and/or short-term debt varied
between groups was achieved through comparison of the RSS of the unrestricted model with that
of ten restricted models in which the dummies of each variable were omitted respectively. The F
test takes the form (Gujurati, 1995):
7
F
Where:
( RSS R  RSS U ) / m
( RSS U ) /( n  k )
RSS R = Residual Sum of Squares in the Restricted Models
RSS U = Residual Sum of Squares in the Unrestricted Models
m = number of linear restrictions
n = number of observations
k = number of variables in the unrestricted model
Determinants of SME capital structure
Table 3 shows the results for all countries and provides confirmation for most of the hypothesized
relationships. Profit is seen to be negatively correlated with both STD and LTD. Contrary to the
findings of some other studies on UK data (Chittenden et al., 1996 and Jordan et al., 1998) growth
is positively correlated with STD and LTD. Asset structure is positively correlated with LTD and
negatively correlated to STD. Size is positively correlated with both STD and LTD. Age is
positively correlated with LTD but, contrary to expectations, negatively with STD showing that
young firms do borrow short-term, perhaps because that is the only source available. Non-debt tax
shields are, contrary to expectations, negatively but not significantly correlated with LTD but as
hypothesized, they are positively correlated with STD. The results for stock are in the
hypothesized direction i.e. positively correlated with debt. Future growth opportunities are
positively associated with STD and LTD but not significantly for LTD. Risk is negatively and net
debtors positively, correlated with both STD and LTD as hypothesized.
8
Differences in determinants between SMEs in the former Soviet-Bloc and Non Soviet-Bloc
Tables 4 and 5 show that the values for the determinants of capital structure for the SMEs do vary
as between the two groups. The most striking differences are for profitability, asset structure, size,
age, growth opportunities, risk and net debtors with former Soviet-Bloc SMEs having less profit,
more fixed assets, smaller size, fewer years in business (age), fewer growth opportunities, lower
risk and lower net debtors. The result for profit is particularly interesting. The result is an average
of breakeven for the SB SMEs compared to an average profit of 6.5% for the NSBs. The standard
deviation for the SB SMEs’ profit is less than for the NSB SMEs. This suggests a culture of
“breakeven” in the former Soviet Bloc which is consistent with an agency view (Jensen and
Meckling, 1976) that manager-controlled firms are more likely to be subject to “shirks” and
“perks” thus reducing profit. In the former Soviet Bloc the scope for “perks” may have been
limited but shirking could well have been endemic.
Table 6 shows the unrestricted model results from which the determinants of STD and LTD for
the two groups can be summarized.
For STD for SB SMEs, the statistically significant determinants are: profitability (-ve), growth
(+ve), asset structure (-ve), age (-ve), non-debt tax shields (+ve), stock (+ve), risk (-ve) and net
debtors (+ve). For NSB SMEs, the statistically significant determinants are: profitability (-ve),
growth (+ve), asset structure (-ve), size (+ve), age (-ve), non-debt tax shields (+ve), stock (+ve),
future growth opportunities (+ve), risk (-ve) and net debtors (+ve), in other words all the
independent variables. The only differences between the groups were the lack of significance of
size and future growth opportunities for the SB SMEs.
9
For LTD for SB SMEs, the statistically significant determinants are: profitability (-ve), asset
structure (+ve), size (+ve), age (-ve), stock (-ve), future growth opportunities (+ve) and net
debtors (+). For NSB SMEs, the statistically significant determinants are: profit (-ve), growth
(+ve), asset structure (+ve), size (+ve), stock (+ve), future growth opportunities (-ve), risk (-ve)
and net debtors (+ve). Non-debt tax shields were not significant for either group. Differences
between the groups were for growth (not significant for SB), age (not significant for NSB) and
risk (not significant for SB). Of most interest are the results that are statistically significant but
have a different sign for the two groups. These are for stock which is negative for SB but positive
for NSB and for future growth opportunities which is positive for SB and negative for NSB SMEs.
Finally, whilst Table 6 shows some differences in the determinants of SME debt, it is not easy to
discern the extent to which the determinants vary between the two groups. Table 7 shows that, for
LTD, six out of the ten independent variables vary in their impact as between the two groups of
Western and Eastern European SMEs. For STD four out of the ten vary. These results are
consistent with those of Hall et al (2004) for the independent variables (profit, growth, asset
structure, size and age) that are common to the two studies. The additional five variables (nondebt tax shields, stock, future growth, risk and net debtors) have, however, proved to be more
constant across the two groups. Non-debt tax shields, risk and net debtors are constant across the
two groups for both STD and LTD. The future growth opportunities variable is constant across the
two groups for STD.
10
Conclusion
The results for the former Soviet-bloc SMEs show that they have lower levels of debt, both shortterm and long-term than the non Soviet-bloc countries. The results also show that the SB SMEs
have lower profitability, higher growth rates, are younger, have higher non-debt tax shields, higher
stocks and lower risk compared to non Soviet-bloc countries, all of which suggest higher levels of
short-term debt and at the same time they have more fixed assets, are smaller, have fewer growth
options and lower levels of net debtors which suggests lower STD. Similarly, for long-term debt,
SB SMEs have lower profit, higher growth rates, more fixed assets, more stock and lower risk
which are associated with high LTD but are smaller, younger and have lower levels of net debtors
that are associated with lower LTD.
Overall, it could be expected that SB SMEs would have debt levels, both short and long, at least as
high as those for NSB SMEs. However, this presupposes that the determinants of STD and LTD
hold equally for SB and NSB SMEs. The results of the comparison of restricted and unrestricted
models shows that, for STD, six of the ten determinants do not vary between groups and that for
LTD four of the ten variables do not vary. In conclusion, it can be expected that there was “pentup” demand for debt for the former Soviet-Bloc SMEs and an extension of this work will be to
look at the changes over the intervening years in the debt levels of the former Soviet-bloc SMEs.
To the extent to which there were differences in the determinants for former Soviet-Bloc SMEs, it
will be interesting to see if these change to be more in line with those for non Soviet-Bloc SMEs
and if not to investigate the reasons for persistent differences. Persistent differences may need to
be explained by other economic, social and cultural factors than those considered in this paper.
11
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14
Table 1: Number of Cases Across Countries
Country
Non Soviet-Bloc
Belgium
Denmark
Finland
France
Greece
Italy
Netherlands
Norway
Portugal
Spain
Sweden
Switzerland
United Kingdom
Former Soviet-Bloc
Bulgaria
Czech Republic
Estonia
Hungary
Poland
Romania
Total
Frequency
Percent
5,736
2,093
2,084
15,159
1,845
10,178
2,393
2,942
515
11,883
6,375
410
19,349
6.2%
2.2%
2.2%
16.3%
2.0%
10.9%
2.6%
3.2%
0.6%
12.7%
6.8%
0.4%
20.7%
1,187
2,426
450
617
3,852
3,772
93,266
1.3%
2.6%
0.5%
0.7%
4.1%
4.0%
100.0%
Table 2: Average Leverage Ratios Across Countries
Countries
Non Soviet-Bloc
Former Soviet Bloc
All
Short Term
0.343
0.255
0.332
Mean
Debt
Long Term Debt
0.137
0.115
0.134
Standard Deviation
Short Term Debt
Long Term Debt
0.248
0.199
0.262
0.210
0.252
0.200
15
Table 3: Completely Restricted Models
Model 1: STD Model
Std.
Variable
B
Error
t
Profitability
-8.4x10-3 0.001 -10.311
Growth
1.6 x10-3 0.000 7.410
Asset Structure
-0.171 0.004 -46.123
Size
1.6 x10-9 0.000 3.839
Age
-6.4 x10-4 0.000 -17.508
Non-Debt Tax
Shields
0.387
0.021 18.615
Stock
0.106
0.005 20.922
Future Growth
Opportunities
0.139
0.010 13.446
Risk
-9.3 x10-4 0.000 -7.334
Net Debtors
0.352
0.004 96.305
Constant
0.336
0.002 152.376
R2
0.160
Adjusted R2
0.160
F-Statistic
1642.309
Regression Sum of Squares 840.968
Residual Sum of Squares
4423.213
*Significant at 0.05level of confidence
Model 2: LTD Model
Std.
Sig. Variable
B
Error
t
0.000* Profitability
-9.1 x10-3 0.001 -13.614
0.000* Growth
7.5 x10-4 0.000
4.379
0.000* Asset Structure
0.291
0.00
97.050
0.000* Size
1.6 x10-9 0.000
4.659
0.000* Age
1.3 x10-4 0.000
4.302
Non-Debt Tax
0.000* Shields
-1.9 x10-3 0.017 -0.112
0.000* Stock
0.051
0.004 12.456
Future Growth
0.000* Opportunities
6.2 x10-3 0.008
0.736
0.000* Risk
-4.7 x10-4 0.000 -4.590
0.000* Net Debtors
0.023
0.003
7.943
0.000* Constant
0.0223
0.002 12.798
R2
0.128
Adjusted R2
0.128
0.000* F-Statistic
1264.247
Regression Sum of Squares
424.311
Residual Sum of Squares
2899.113
Sig.
0.000*
0.000*
0.000*
0.000*
0.000*
0.911
0.000*
0.462
0.000*
0.000*
0.000*
0.000*
Table 4: Determinants of Capital Structure Across Countries (Means)
Code
NSB
SB
All
Growth Asset Company Company Non-Debt
Profitability Rate Structure
Size
Age
Tax Shields
0,065
0,444
0,311
141.354 24,330
0,040
0,003
0,492
0,452
14.367
15,864
0,049
0.057
0.450
0.329
124,601
23.2
0.041
Stock
0,176
0,181
0.176
Future
Growth
Net
Opps
Risk Debtors
0,030 0,486 0,081
0,008 0,316 0,012
0.027 0.463 0.072
Table 5: Determinants of Capital Structure Across Countries (Standard Deviation)
Code
NSB
SB
All
Future
Growth Asset Company Company Non-Debt
Growth
Net
Profitability Rate Structure
Size
Age
Tax Shields Stock Opps
Risk Debtors
1,118 3,674
0,252 1.919.288
21,354
0,040 0,180 0,081 5,943 0,232
0,566 3,970
0,231
90.823
21,804
0,042 0,153 0,032 6,707 0,225
1.151
4.775
0.282
735,661
23.5
0.039
0.180 0.077 5.581 0.173
16
Table 6: Unrestricted Models Aggregated Results
Model 1: STD Model
Std.
Variable
B
Error
Profitability SB
-0.026 0.004
Profitability NSB
-0.008 0.001
Growth SB
0.002
0.001
Growth NSB
0.001
0.000
Asset Structure SB
-0.128 0.011
t
-6.774
-9.617
6.245
5.590
-11.634
Asset Structure NSB -0.155 0.004 -38.966
Size SB
-2.1x10-8 0.000 -0.898
Size NSB
1.5x10-9 0.000 3.542
Age SB
-5.3x10-4 0.000 -4.398
Age NSB
-8.3x10-4 0.000 -21.219
Non-Debt Tax
Shields SB
0.394
0.021 19.000
Non-Debt Tax
Shields NSB
0.394
0.021 19.000
Stock SB
0.194
0.017 11.391
Stock NSB
0.105
0.005 19.864
Future Growth
Opportunities SB
0.076
0.069 1.101
Future Growth
Opportunities NSB
0.104
0.011 9.818
Risk SB
-0.001 0.000 -3.121
Risk NSB
-9.3x10-4 0.000 -6.758
Net Debtors SB
0.327
0.010 31.331
Net Debtors NSB
0.352
0.004 90.380
Constant Dummy
-0.088 0.008 -11.518
Constant
0.343
0.002 148.698
R2
0.166
Adjusted R2
0.165
F-Statistic
856.902
Regression Sum of Squares
871.602
Residual Sum of Squares
4392.580
Model 2: LTD Model
Std.
Sig. Variable
B
Error
t
0.000* Profitability SB
-0.042
0.003 -13.522
0.000* Profitability NSB
-0.008
0.001 -12.374
0.000* Growth SB
6.1x10-4 0.000
1.420
0.000* Growth NSB
7.0x10-4 0.000
3.797
0.000* Asset Structure SB
0.074
0.009
8.295
Asset Structure
0.000* NSB
0.338
0.003 105.579
0.369 Size SB
7.4x10-8 0.000
3.959
0.000* Size NSB
1.3x10-9 0.000
3.676
0.000* Age SB
-8.2x10-4 0.000 -8.552
0.000* Age NSB
5.6x10-5 0.000
1.776
Non-Debt Tax
0.000* Shields SB
1.6x10-4 0.017
0.009
Non-Debt Tax
0.000* Shields NSB
1.6x10-4 0.017
0.009
0.000* Stock SB
-0.075
0.014 -5.457
0.000* Stock NSB
0.069
0.004 16.424
Future Growth
0.271 Opportunities SB
0.255
0.055
4.627
Future Growth
0.000* Opportunities NSB
-0.052
0.008 -6.133
0.002* Risk SB
-8.8x10-4 0.000
1.654
0.000* Risk NSB
-4.1x10-4 0.000 -3.675
0.000* Net Debtors SB
0.021
0.008
2.524
0.000* Net Debtors NSB
0.022
0.003
6.915
0.000* Constant Dummy
0.095
0.006 15.458
0.000* Constant
0.015
0.002
7.985
R2
0.147
Adjusted R2
0.147
0.000 F-Statistic
746.355
Regression Sum of Squares
489.738
Residual Sum of Squares
2833.686
Sig.
0.000*
0.000*
0.156
0.000*
0.000*
0.000*
0.000*
0.000*
0.000*
0.076
0.992
0.992
0.000*
0.000*
0.000*
0.000*
0.098
0.000*
0.011*
0.000*
0.000*
0.000*
0.000
Where: SB = Soviet Block Countries and NSB = Other Countries (i.e. non Soviet Block countries)
*Significant at 0.05level of confidence.
17
Table 7: F-Test: Comparing Restricted and Unrestricted Models
LSDV ‘Pure Variable Effects’
LTD Models
RSS
F
Critical F
Result
RSS restricted
unrestricted
Dropping Profitability Dummies 2837,357 2833,686
120.8*
3.84
Profitability effect varies
Dropping Growth Dummies
2833,687 2833,686
0.04
3.84
Growth effect does not vary
Dropping Asset Structure
2859,924 2833,686
863.38*
3.84
Asset structure effect varies
Dummies
Dropping Size Dummies
2834,182 2833,686
16.32*
3.84
Size effect varies
Dropping Age Dummies
2836,158 2833,686
81.34*
3.84
Age effect varies
Dropping Non-Debt Tax Shields
2833,686 2833,686
0.00
3.84
Non-Debt Tax Shields effect
Dummies
does not vary
Dropping Stock Dummies
2837,034 2833,686
110.16*
3.84
Stock effect varies
Dropping Future Growth
2834,681 2833,686
32.74*
3.84
Future Growth Opportunities
Opportunities Dummies
effect varies
Dropping Risk Dummies
2833,775 2833,686
2.92
3.84
Risk effect does not vary
Dropping Net Debtors Dummies
2833,686 2833,686
0.00
3.84
Net Debtors effect does not
vary
Dropping All Dummies (Totally 2841,586 2833,686
259.96*
3.84
Group effect varies
Restricted Model)
STD Models
F
Critical F
Result
RSS restricted RSS
unrestricted
Dropping Profitability Dummies 4393,642 4392,580
22.54*
3.84
Profitability effect varies
Dropping Growth Dummies
4393,206 4392,580
13.28*
3.84
Growth effect does not vary
Dropping Asset Structure
4392,852 4392,580
5.78*
3.84
Asset structure effect varies
Dummies
Dropping Size Dummies
4392,627 4392,580
1.00
3.84
Size effect does not vary
Dropping Age Dummies
4392,868 4392,580
6.12*
3.84
Age effect varies
Dropping Non-Debt Tax Shields
4392,580 4392,580
0.00
3.84
Non-Debt Tax Shields effect
Dummies
does not vary
Dropping Stock Dummies
4393,833 4392,580
26.60*
3.84
Stock effect varies
Dropping Future Growth
4392,588 4392,580
0.16
3.84
Future Growth Opportunities
Opportunities Dummies
effect does not vary
Dropping Risk Dummies
4392,582 4392,580
0.04
3.84
Risk effect does not vary
Dropping Net Debtors Dummies
4392,580 4392,580
0.00
3.84
Net Debtors effect does not
vary
Dropping All Dummies (Totally 4423,213 4392,580
650.28*
3.84
Group effects varies
Restricted Model)
Where m = 1; n = 93,266; k = 21.
The F-statistic follows the F distribution with m, (n-k) degrees of freedom: F(0.05)(1 and
93,266)
= 3.84
* Significant at 0.05level of confidence
18