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1 EAST AND WEST: DIFFERENCES IN SME CAPITAL ... - gregor

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 = long-

term 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 firm-

specific 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 short-

term 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.

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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.

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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):

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( RSS R ! RSSU

) / m

F =

( RSS ) /( n ! k)

Where: RSS = Residual Sum of Squares in the Restricted Models

R

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

U

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.

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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.

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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 (non-

debt 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.

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Conclusion

The results for the former Soviet-bloc SMEs show that they have lower levels of debt, both short-

term 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 “pent-

up” 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.

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Table 1: Number of Cases Across Countries

Country Frequency Percent

Non Soviet-Bloc

Belgium 5,736 6.2%

Denmark 2,093 2.2%

Finland 2,084 2.2%

France 15,159 16.3%

Greece 1,845 2.0%

Italy 10,178 10.9%

Netherlands 2,393 2.6%

Norway 2,942 3.2%

Portugal 515 0.6%

Spain 11,883 12.7%

Sweden 6,375 6.8%

Switzerland 410 0.4%

United Kingdom 19,349 20.7%

Former Soviet-Bloc

Bulgaria 1,187 1.3%

Czech Republic 2,426 2.6%

Estonia 450 0.5%

Hungary 617 0.7%

Poland 3,852 4.1%

Romania 3,772 4.0%

Total 93,266 100.0%

Table 2: Average Leverage Ratios Across Countries

Mean Standard Deviation

Countries Short Term Debt Long Term Debt Short Term Debt Long Term Debt

Non Soviet-Bloc 0.343 0.137 0.248 0.199

Former Soviet Bloc 0.255 0.115 0.262 0.210

All 0.332 0.134 0.252 0.200

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Table 3: Completely Restricted Models

Model 1: STD Model Model 2: LTD Model

Std.

Std.

Variable B Error t Sig. Variable B Error t Sig.

Profitability -8.4x10 -3 0.001 -10.311 0.000* Profitability -9.1 x10 -3 0.001 -13.614 0.000*

Growth 1.6 x10 -3 0.000 7.410 0.000* Growth 7.5 x10 -4 0.000 4.379 0.000*

Asset Structure -0.171 0.004 -46.123 0.000* Asset Structure 0.291 0.00 97.050 0.000*

Size 1.6 x10 -9 0.000 3.839 0.000* Size 1.6 x10 -9 0.000 4.659 0.000*

Age -6.4 x10 -4 0.000 -17.508 0.000* Age 1.3 x10 -4 0.000 4.302 0.000*

Non-Debt Tax

Non-Debt Tax

Shields 0.387 0.021 18.615 0.000* Shields -1.9 x10 -3 0.017 -0.112 0.911

Stock 0.106 0.005 20.922 0.000* Stock 0.051 0.004 12.456 0.000*

Future Growth

Future Growth

Opportunities 0.139 0.010 13.446 0.000* Opportunities 6.2 x10 -3 0.008 0.736 0.462

Risk -9.3 x10 -4 0.000 -7.334 0.000* Risk -4.7 x10 -4 0.000 -4.590 0.000*

Net Debtors 0.352 0.004 96.305 0.000* Net Debtors 0.023 0.003 7.943 0.000*

Constant 0.336 0.002 152.376 0.000* Constant 0.0223 0.002 12.798 0.000*

R 2

0.160 R 2

0.128

Adjusted R 2 0.160 Adjusted R 2 0.128

F-Statistic 1642.309 0.000* F-Statistic 1264.247 0.000*

Regression Sum of Squares 840.968 Regression Sum of Squares 424.311

Residual Sum of Squares 4423.213 Residual Sum of Squares 2899.113

*Significant at 0.05level of confidence

Code Profitability

Table 4: Determinants of Capital Structure Across Countries (Means)

Growth

Rate

Asset

Structure

Company

Size

Company

Age

Non-Debt

Tax Shields Stock

Future

Growth

Opps Risk

Net

Debtors

NSB 0,065 0,444 0,311 141.354 24,330 0,040 0,176 0,030 0,486 0,081

SB 0,003 0,492 0,452 14.367 15,864 0,049 0,181 0,008 0,316 0,012

All 0.057 0.450 0.329 124,601 23.2 0.041 0.176 0.027 0.463 0.072

Table 5: Determinants of Capital Structure Across Countries (Standard Deviation)

Code Profitability

Growth

Rate

Asset

Structure

Company

Size

Company

Age

Non-Debt

Tax Shields Stock

Future

Growth

Opps Risk

Net

Debtors

NSB 1,118 3,674 0,252 1.919.288 21,354 0,040 0,180 0,081 5,943 0,232

SB 0,566 3,970 0,231 90.823 21,804 0,042 0,153 0,032 6,707 0,225

All 1.151 4.775 0.282 735,661 23.5 0.039 0.180 0.077 5.581 0.173

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Table 6: Unrestricted Models Aggregated Results

Model 1: STD Model Model 2: LTD Model

Std.

Std.

Variable B Error t Sig. Variable B Error t Sig.

Profitability SB -0.026 0.004 -6.774 0.000* Profitability SB -0.042 0.003 -13.522 0.000*

Profitability NSB -0.008 0.001 -9.617 0.000* Profitability NSB -0.008 0.001 -12.374 0.000*

Growth SB 0.002 0.001 6.245 0.000* Growth SB 6.1x10 -4

0.000 1.420 0.156

Growth NSB 0.001 0.000 5.590 0.000* Growth NSB 7.0x10 -4

0.000 3.797 0.000*

Asset Structure SB -0.128 0.011 -11.634 0.000* Asset Structure SB

Asset Structure

0.074 0.009 8.295 0.000*

Asset Structure NSB -0.155 0.004 -38.966 0.000* NSB 0.338 0.003 105.579 0.000*

Size SB -2.1x10 -8 0.000 -0.898 0.369 Size SB 7.4x10 -8

0.000 3.959 0.000*

Size NSB 1.5x10 -9 0.000 3.542 0.000* Size NSB 1.3x10 -9

0.000 3.676 0.000*

Age SB -5.3x10 -4 0.000 -4.398 0.000* Age SB -8.2x10 -4

0.000 -8.552 0.000*

Age NSB -8.3x10 -4 0.000 -21.219 0.000* Age NSB 5.6x10 -5

0.000 1.776 0.076

Non-Debt Tax

Non-Debt Tax

Shields SB 0.394 0.021 19.000 0.000* Shields SB 1.6x10 -4

0.017 0.009 0.992

Non-Debt Tax

Non-Debt Tax

Shields NSB 0.394 0.021 19.000 0.000* Shields NSB 1.6x10 -4

0.017 0.009 0.992

Stock SB 0.194 0.017 11.391 0.000* Stock SB -0.075 0.014 -5.457 0.000*

Stock NSB 0.105 0.005 19.864 0.000* Stock NSB 0.069 0.004 16.424 0.000*

Future Growth

Future Growth

Opportunities SB 0.076 0.069 1.101 0.271 Opportunities SB 0.255 0.055 4.627 0.000*

Future Growth

Future Growth

Opportunities NSB 0.104 0.011 9.818 0.000* Opportunities NSB -0.052 0.008 -6.133 0.000*

Risk SB -0.001 0.000 -3.121 0.002* Risk SB -8.8x10 -4

0.000 1.654 0.098

Risk NSB -9.3x10 -4 0.000 -6.758 0.000* Risk NSB -4.1x10 -4

0.000 -3.675 0.000*

Net Debtors SB 0.327 0.010 31.331 0.000* Net Debtors SB 0.021 0.008 2.524 0.011*

Net Debtors NSB 0.352 0.004 90.380 0.000* Net Debtors NSB 0.022 0.003 6.915 0.000*

Constant Dummy -0.088 0.008 -11.518 0.000* Constant Dummy 0.095 0.006 15.458 0.000*

Constant 0.343 0.002 148.698 0.000* Constant 0.015 0.002 7.985 0.000*

R 2

0.166 R 2

0.147

Adjusted R 2 0.165 Adjusted R 2 0.147

F-Statistic 856.902 0.000 F-Statistic 746.355 0.000

Regression Sum of Squares 871.602 Regression Sum of Squares 489.738

Residual Sum of Squares 4392.580 Residual Sum of Squares 2833.686

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 restricted RSS

unrestricted

F Critical F Result

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

Dummies

2859,924 2833,686 863.38* 3.84 Asset structure effect varies

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

Restricted Model)

2833,686 259.96* 3.84 Group effect varies

STD Models RSS restricted RSS

unrestricted

F Critical F Result

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

Dummies

4392,852 4392,580 5.78* 3.84 Asset structure effect varies

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

Dummies

4392,580 4392,580 0.00 3.84 Non-Debt Tax Shields effect

does not vary

Dropping Stock Dummies 4393,833 4392,580 26.60* 3.84 Stock effect varies

Dropping Future Growth

Opportunities Dummies

4392,588 4392,580 0.16 3.84 Future Growth Opportunities

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

Restricted Model)

4392,580 650.28* 3.84 Group effects varies

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

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