the effect of notional interest deduction on the financing

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2008 – 2009

The effect of notional interest deduction on the financing policy of SMEs

Masterproef voorgedragen tot het bekomen van de graad van

Master in de Toegepaste Economische Wetenschappen

Sofie Cornelis

Steffie Merckx

onder leiding van

Prof. Dr. Philippe Van Cauwenberge

PERMISSION

Ondergetekenden verklaren dat de inhoud van deze masterproef mag geraadpleegd en/of

gereproduceerd worden, mits bronvermelding.

Sofie Cornelis

Steffie Merckx

The effect of notional interest deduction on the financing policy of SMEs I

PREFACE

Writing this master thesis after a four-year education in applied business economics feels like

fitting the final piece of a giant puzzle. A lot of effort was necessary. However, looking back

to the result gives much satisfaction in return.

There are some people we would like to thank for their support. First, we owe many thanks to

our promoter Prof. Dr. Philippe Van Cauwenberge for his time, guidance and critical

reflections. Next, we are grateful to Katrien Kestens and Kelly De Brabanter for their

assistance and useful advice.

Furthermore, we thank our life partner and our family for giving us their moral support.

May 2009,

Sofie Cornelis

Steffie Merckx

The effect of notional interest deduction on the financing policy of SMEs II

TABLE OF CONTENTS

PREFACE ....................................................................................................................................... I

TABLE OF CONTENTS .................................................................................................................. II

LIST OF ABBREVIATIONS ............................................................................................................ III

LIST OF TABLES ......................................................................................................................... IV

LIST OF FIGURES ........................................................................................................................ IV

ABSTRACT ................................................................................................................................... 1

1. Introduction ........................................................................................................................ 2

2. The notional interest deduction .......................................................................................... 4

3. Literature overview ............................................................................................................ 6

3.1. Allowances for corporate equity in practice ............................................................... 6

3.2. Effects of notional interest deduction on the marginal tax rate .................................. 6

3.3. Simulation of the marginal tax rate .......................................................................... 13

3.4. Effects of the marginal tax rate on debt policies ...................................................... 15

3.5. Capital structure theories .......................................................................................... 16

3.5.1. The irrelevance theorem ................................................................................... 16

3.5.2. Trade-off theory ................................................................................................ 17

3.5.3. Pecking order theory ......................................................................................... 20

4. Empirical analysis ............................................................................................................ 22

4.1. Sample ...................................................................................................................... 22

4.2. Descriptive statistics ................................................................................................. 23

4.3. Multivariate analysis................................................................................................. 28

4.3.1. General model ................................................................................................... 30

4.3.2. Extension .......................................................................................................... 37

5. Conclusions ...................................................................................................................... 41

REFERENCES .............................................................................................................................. V

APPENDIX .................................................................................................................................. IX

The effect of notional interest deduction on the financing policy of SMEs III

LIST OF ABBREVIATIONS

ACE Allowances for Corporate Equity

Adj. Adjusted

EBIT Earnings Before Interests and Taxes

Excl. Exclusive

GDP Gross Domestic Product

Incl. Inclusive

MTR Marginal Tax Rate

NACE-BEL Nomenclature générale des Activités économiques dans les

Communautés Européennes - Belgique

NID Notional Interest Deduction

OLO Obligation Linéaire – Lineaire Obligatie

OLS Ordinary Least Squares

POT Pecking Order Theory

PV Present Value

SME Small and Medium sized Enterprise

Th. Thousands

TLCF Tax-Loss Carryforward

TOT Trade-Off Theory

VAT Value Added Tax

VIF Variance Inflation Factor

The effect of notional interest deduction on the financing policy of SMEs IV

LIST OF TABLES

Table 1 : Example (1) of Graham‟s method without NID..........................................................8

Table 2 : Example (1) of Graham‟s method without NID (continued).......................................8

Table 3 : Example (1) of Graham‟s method with NID...............................................................9

Table 4 : Example (1) of Graham‟s method with NID (continued)..........................................10

Table 5 : Example (2) of Graham‟s method without NID........................................................11

Table 6 : Example (2) of Graham‟s method without NID (continued).....................................11

Table 7 : Example (2) of Graham‟s method with NID.............................................................12

Table 8 : Example (2) of Graham‟s method with NID (continued)..........................................12

Table 9 : Descriptive statistics..................................................................................................24

Table 10 : Wilcoxon Signed Ranks Test (N=763)....................................................................27

Table 11 : Pearson correlation coefficients (N = 765)..............................................................28

Table 12 : Regression results: General model..........................................................................32

Table 13 : Regression results: General model (continued).......................................................36

Table 14 : Regression results: Extension..................................................................................39

Appendix A : Definition of variables.......................................................................................IX

Appendix B : Empirical predictions by the main theories and their outcomes

concerning the determinants of capital structure in SME financing..........................................X

LIST OF FIGURES

Figure 1 : Firm distribution by industries (N=1127).................................................................26

Figure 2 : Evolution of the average proportion of capital structure components (N=763).......26

The effect of notional interest deduction on the financing policy of SMEs 1

ABSTRACT

The principal aim of this paper is to test how the notional interest deduction affects the capital

structure of Small and Medium sized Enterprises (SMEs). To assess any impact of notional

interest deduction on the leverage of firms, we make use of simulated corporate marginal

income tax rates. We carry out an empirical analysis of 1127 Belgian SMEs over the period

2005-2007, modelling the leverage ratio as a function of firm specific attributes hypothesized

by capital structure theories. The results suggest that the marginal tax rate is positively related

to leverage. The general conclusion in this study is that the notional interest deduction has a

negative impact on the optimal level of debt in SMEs.

Key words: Capital Structure, Financing, Small and Medium sized Enterprises, Notional

Interest Deduction, Marginal Tax Rate.


1. Introduction

Since January 1st 2006, the Belgian tax regime incorporates notional interest deduction to

eliminate the fiscal discrimination between debt versus equity financing. Previously, the

corporate tax law allowed the deduction of interests from a companies taxable income, while

this was not permitted for dividends. Although the Belgian notional interest deduction is

merely an application of the more encompassing ACE-system, where ACE is known as

„Allowances for Corporate Equity‟, the existence of many variants together with the fact that

few countries are willing to adopt the ACE approach makes the Belgian tax system unique in

the world (Klemm, 2007).

So far, no other academic studies have empirically examined the notional interest deduction.

This paper is the first to investigate the impact of notional interest deduction on the capital

structure of small and medium sized enterprises (SMEs). Whereas a lot of existing literature

has examined the impact of tax incentives in a static legislative environment, we now have the

opportunity to conduct research in a time period where tax legislation changes. Our study

therefore complements the existing literature.

To assess the existence and size of any impact of notional interest deduction on the leverage

of firms, we make use of corporate marginal income tax rates. The simulation of these

marginal tax rates is based on the approach developed by Shevlin (1990) and Graham (1996).

We define the corporate marginal tax rate as the change in the present value of expected

current and future tax liabilities caused by a one-time additional euro of interest cost today.

As the deduction of a fictitious notional interest will decrease taxable income, the companies

marginal tax rate will decrease. This means companies will experience less incentives to

increase debt in order to shield profits from taxes. In this light, we expect the notional interest

deduction to have a negative impact on leverage.

We focus on SME financing because of two reasons. First, this type of company represents a

large portion of all companies in Belgium. On January 1st 2007, about 99,4%

1 of total

companies were classified as SME. In addition, the percentage used to calculate the notional

1 Source: http://www.unizo.be/statistieken/images/res347073_6.doc

http://www.unizo.be/statistieken/images/res347073_6.doc


interest is always 0,5 percentage points higher for companies defined as SME. Compared to

large firms, this special treatment should make it even more favourable for smaller firms to

finance with equity.

The analyses in this paper are based on data from the Belfirst database provided by Bureau

Van Dijk. We gather a sample of 1127 SMEs. The question that arises is whether or not the

decrease in the marginal tax rate due to notional interest deduction is significant enough to

find an appreciable impact on their optimal capital structure. We examine this relationship by

means of an OLS regression. To isolate the individual effect of notional interest deduction, we

control for profitability, investments, growth opportunities, tangibility, size and dividend

payout. The time period covered in our study is 2005 until 2007.

The remainder of this paper is organized as follows. Section 2 describes the features of

notional interest deduction into more detail. Section 3 continues with an overview of

literature. First, section 3.1 covers the more general tax system known as Allowances for

Corporate Equity (ACE). Next, section 3.2 describes the effect of notional interest deduction

on the marginal tax rate of a firm through some illustrative examples. Section 3.3

subsequently discusses the simulation of marginal tax rates as developed by Graham. We will

further extend this model to include notional interest deduction into the calculation of taxable

income. This is followed by the formulation of our hypothesis concerning the effect of the

notional interest deduction on debt policies in section 3.4. In section 3.5 we briefly discuss the

existing theories relating to capital structure together with previous findings in literature for

small firms. Section 4 describes the sample selection, contains some descriptive statistics,

explains the econometric methodology applied and presents the regression results. Finally, we

conclude in section 5. The definition of variables together with an overview of the empirical

predictions by the main theories and their outcomes concerning the explanatory variables in

SME financing, are provided in the Appendix.


2. The notional interest deduction

In accordance with article 205bis of the Income Tax Code, introduced by the law of June 22nd

2005, companies submitted to the Belgian corporation tax can use a deduction for risk capital,

also known as “notional interest deduction”.

The goal of the notional interest deduction system is to encourage the strengthening of a

companies‟ equity by narrowing the fiscal discrimination between funding with equity and

funding with debt, as currently, interests are tax deductible while dividends are not.

There are two main advantages of notional interest deduction. First, when companies exploit

the opportunity of free tax deduction by strengthening their equity, they will be better

protected against failure. Second, the licences of coordination centres to enjoy substantial tax

benefits would terminate since December 31st 2005

2. The introduction of notional interest

deduction was the only valuable alternative for the Belgian government to keep Belgium an

attractive country for domestic and foreign investors.

In addition to these main advantages, the notional interest deduction will be especially

important for SMEs in particular. As these smaller firms are yearly responsible for more than

70%3 of the Belgian GDP, it is quite important for the Belgian economy that these firms are in

good health. The notional interest deduction system gives them the opportunity to substitute

equity for debt in financing decisions.

The law of June 22nd

2005 came into operation on January 1st 2006. It offers companies the

opportunity to reduce their taxable basis with a deduction for risk capital. The amount of this

deduction for a given year is calculated as a percentage of their adjusted equity at the end of

the previous taxable period. Some corrections of the equity are needed to eliminate double

counting and to avoid abuse of the system by artificially raising the equity.

If the company‟s taxable income is not sufficient to completely utilize the notional interest

deduction, the difference can be transferred to the next taxable period. However, in contrast to

2 Source: http://www.senate.be/www/?MIval=/publications/viewPubDoc&TID=50350417&LANG=nl

3 Source: http://www.diplomatie.be/nl/belgium/belgiumdetail.asp?textID=49043

http://www.senate.be/www/?MIval=/publications/viewPubDoc&TID=50350417&LANG=nl

http://www.diplomatie.be/nl/belgium/belgiumdetail.asp?textID=49043


normal losses that can be transferred without limitation, the unused amount of notional

interest can be transferred for only seven years following the year of the initial deduction.

For tax year 2008, the percentage used to calculate the notional interest deduction amounts to

3,78%4. This figure changes every year. The interest rate is based on the average of monthly

reference indices of Belgian OLO bonds on 10 years publicized during the year prior to the

financial year to which the relevant tax year refers. For companies defined as SMEs according

to article 15, §1 of the Code of Companies, the rate increases with 0,50%. In other words,

SMEs can deduct 4,28% of their corrected equity at December 31, 2006 from their taxable

income of 2007. This makes it even more favourable for smaller firms to finance with equity.

In reality, there prevails however big confusion about the precise definition of SME that is

applicable. While article 205quater of the Income Tax Code only refers to the quantitative

criteria of article 15, §1 of the Code of Companies, there is no consistency in the law of June

22nd

2005 about whether or not to use the time related criterion. Moreover, the Minister of

Finance takes up another position. Going counter to article 205quater of the Income Tax

Code, he explains that a company which is defined as an SME according to the quantitative

criteria as well as the time related criterion of article 15, §1 of the Code of Companies, can

take advantage of the higher percentage for notional interest deduction. This means when a

company exceeds no more than one of the following quantitative criteria:

1) workforce of 50 employees on average,

2) turnover of € 7,3 million (VAT excluded),

3) balance sheet total of € 3,65 million,

and when the workforce includes no more than 100 employees. These quantitative criteria

have to be satisfied for two successive years prior to the financial year in which a company

could be classified as an SME (time related criterion). The fiscal administration seems to

follow the Minister of Finance‟s approach5.

4 Source: http://www.atpartners.be/documents/news-items/notionele_intrestaftrek_update.xml?lang=nl

5 Source: http://www.iec-iab.be/nl/leden/Publicaties/mededelingen-van-het-Instituut/Pages/20080828-Artikelen-

205quater-%C2%A7-6-en.aspx

http://www.atpartners.be/documents/news-items/notionele_intrestaftrek_update.xml?lang=nl

http://www.iec-iab.be/nl/leden/Publicaties/mededelingen-van-het-Instituut/Pages/20080828-Artikelen-205quater-%C2%A7-6-en.aspx



3. Literature overview

3.1. Allowances for corporate equity in practice

The Belgian notional interest deduction can be considered as an example of an ACE tax

system, where ACE stands for “allowances for corporate equity”. In most corporate income

tax systems, interest costs incurred due to the use of debt capital are deductible from the

companies taxable income, while dividends are not. This results in an unequal tax treatment

of the returns on debt and equity. The aim of an ACE is to tackle the discrimination against

equity capital by allowing firms to deduct a notional return from their taxable income in

addition to any interest expense from debt capital. This notional return is calculated as the

firm‟s equity at the end of the previous year multiplied by a risk free nominal interest rate.

However, ACE tax systems also have some drawbacks. As the allowance for corporate equity

reduces a firm‟s taxable base, the government‟s revenues will decrease significantly in

aggregate. Most likely, a higher tax rate will need to be imposed to finance this gap. In times

of strong globalization, such a decision can cause the tax system to lose competitiveness. In

addition, when dividends and interests are not equally taxed at the individual level, the so-

called neutrality between equity and debt as a financing choice would not occur.

In the past, other countries like Croatia in 1994, Italy in 1997 and Austria in 2000 have

introduced ACE systems. By the year of 2004, they nevertheless withdrawed from using the

system. Outside Europe, Brazil uses another variant of an ACE since 1996. Its corporate tax

law only allows notional interest deduction to the extent that it is paid out to the shareholders.

The existence of variants to the ACE tax system as described in literature combined with the

fact that few countries are willing to adopt the ACE system makes the application of notional

interest deduction in Belgium unique in the world. (Klemm, 2007)

3.2. Effects of notional interest deduction on the marginal tax rate

It is widely recognized that tax implications are important for understanding a variety of

corporate decisions, including the determination of capital structure. For example, firms with

a high marginal tax rate are hypothesized to use more debt.


A corporation‟s marginal tax rate (MTR), as defined in Graham (1996a) is the present value

of expected current and future taxes paid on an additional dollar of income earned today. In a

later paper, Graham (2001) demonstrates that the present value of those extra taxes paid is

equal to the present value of the tax benefit of an incremental dollar of interest deducted from

this additional dollar of income.

The approach to estimating corporate MTRs is originally developed by Shevlin (1990) and

subsequently elaborated by Graham (1996a,b). A key insight is that the MTR depends

essentially upon expectations of future taxable income. This means that the companies

management is required to make forecasts of income before interest and taxes (EBIT) for

future periods. Before we explain the simulation of these forecasts in section 3.3, we will first

give two examples to demonstrate how to interpret the MTR in accordance with the second

definition of Graham. After each example, we will describe how the MTR will change if we

include notional interest deduction.

We refer to today‟s time period as period 0, next year‟s period as period 1, etc. A practical

issue is the determination of the length of the forecasting interval. On the one hand, the carry-

forward of tax losses is unlimited in time in Belgium. On the other hand, the carry-forward of

an unused amount of notional interest from a given year is only permitted up to seven years.

Therefore, we consider seven years after the year of consideration as a cut-off point. In the

following two examples we assume a statutory tax rate of 33,99% and a company discount

rate rd equal to 10%. The percentage rNID for the calculation of notional interest is 4,281% for

SMEs in the financial year 2007.

Suppose that the firm has an opportunity to undertake a valuable project. The first line of

tables 1 to 8 shows us the predicted income for period 0 to 7 that the firm would enjoy if it

invests in the project. In the first period presented in table 1, the firm will earn €100 of

income, have €50 of interest deductions and a tax loss carry-forward (TLCF) of €10. This

results in a taxable income of €40. Therefore, the firm will pay €13,60 of taxes (= 33,99% *

40) in period 0. In period 1, the firm earns an EBIT of €120, still deducts €50 of interests from

debt but has no longer tax loss carry-forwards outstanding. The taxable income is 70, which

results in a tax liability of €23,79. The same process continues for period 2 to 7. The present

value of the tax liability in each period is calculated at a 10% discount rate rd. The sum of

these expected current and discounted future tax liabilities from period 0 until period 7 is

equal to €112,08.


Table 1 : Example (1) of Graham‟s method without NID

period 0 period 1 period 2 period 3 period 4 period 5 period 6 period 7

EBIT 100 120 130 95 110 105 95 100

Less: interest 50 50 50 50 50 50 50 50

Less: TLCFs from previous years 10 0 0 0 0 0 0 0

Taxable income 40 70 80 45 60 55 45 50

Taxes (@ 33,99%) 13,60 23,79 27,19 15,30 20,39 18,69 15,30 17,00

Earnings after taxes 26,40 46,21 52,81 29,70 39,61 36,31 29,70 33,01

PV tax liability 13,60 21,63 22,47 11,49 13,93 11,61 8,63 8,72

Sum of PV tax liabilities 112,08

The firm decides to finance the project with debt so that the interest cost increases once with

€1 in period 0 (Table 2). As of period 1, the increase in debt will be undone. Consequently,

the interest cost turns back to its former level of €50. The computations proceed just as before

by determining the tax liability for each year. The present values are again calculated at a 10%

discount rate. Only by paying one extra euro of interest, the present value of the expected

current and future tax liabilities decreases to €111,74.

To determine the MTR(excl. NID), we calculate the change in the sum of the present values of

the tax liabilities from the incremental euro of interest deduction in period 0. By making the

decision to finance with debt, the firm saves €0,3399 of tax payments (= 112,08 – 111,74),

which is equal to the statutory tax rate t. Consequently, it increases the firm value by (t * €1).

Table 2 : Example (1) of Graham‟s method without NID (continued)


EBIT 100 120 130 95 110 105 95 100

Less: interest 51 50 50 50 50 50 50 50


Taxable income 39 70 80 45 60 55 45 50

Taxes (@ 33,99%) 13,26 23,79 27,19 15,30 20,39 18,69 15,30 17,00


PV tax liability 13,26 21,63 22,47 11,49 13,93 11,61 8,63 8,72


The next step in our analysis is to illustrate the effect of notional interest deduction (NID) on

the MTR of a company. We apply the same story. Consider table 3, which includes NID in

the calculation of earnings after taxes. This time we make the assumption that earnings after

taxes are completely distributed as dividends and that the firm will not issue shares in the


eight year period. Accordingly, equity and the amount of NID will remain constant over the

whole period. To determine the amount of notional interest, we assume an adjusted equity of

€800 at the end of period -1. This figure multiplied by rNID (4,281%) gives us a notional

interest of €34,25. Because of the notional interest deduction, the tax liability (€1,95) in

period 0 is lower than in the situation without NID in table 1. The same conclusion applies for

period 1 to 7. The net present value of the expected current and future tax liabilities is now

equal to €43,77.

Table 3 : Example (1) of Graham‟s method with NID


EBIT 100 120 130 95 110 105 95 100

Less: interest 50 50 50 50 50 50 50 50

Less: notional interest deduction 34,25 34,25 34,25 34,25 34,25 34,25 34,25 34,25


Taxable income 5,75 35,75 45,75 10,75 25,75 20,75 10,75 15,75

Taxes (@33,99%) 1,95 12,15 15,55 3,65 8,75 7,05 3,65 5,35


PV tax liability 1,95 11,05 12,85 2,75 5,98 4,38 2,06 2,75


To assess the MTR(incl. NID), we assume that the firm decides again – as in table 2 – to

finance the valuable project with debt so that the interest cost increases with €1 in period 0

(Table 4).

However, in this case, we need to give some further explanations about the increase in debt

that corresponds to the €1 increase in interest cost. For a constant balance sheet total, an

increase in debt corresponds to a decrease in the level of equity and hence a proportional

decrease in NID. If we assume that the interest cost on debt capital is equal to 4,85%, an

increase of €1 of interest corresponds to an increase in debt and an identical decrease in equity

of €20,62 (= 1/0,0485). This change will in turn lead to a decrease in NID of €0,88 (= 0,04281

* 20,62). The amount of NID becomes €33,37.

For the periods 1 to 7, the interest and NID revert to former levels. The sum of the present

values of the expected current and future tax liabilities is equal to €43,72. The net effect is a

reduction from €43,77 to €43,73 caused by an extra euro of interest. The firm has a marginal

tax benefit of €0,0408. In other words, MTR(incl. NID) is 4,08%.


The difference between MTR(excl. NID) and MTR(incl. NID) is equal to 29,91 percentage

points (=33,99% - 4,08%). This means that, everything else remaining equal, the MTR of a

firm decreases due to the incorporation of NID in the corporate tax system. More specifically,

there are two partial effects of NID on the MTR. First, the deduction will decrease the taxable

income. Second, an increase of €1 in interest costs corresponds to a decrease in equity which

will decrease the amount of NID and thus increase the taxable income. The latter is only true

under the assumption of a constant balance sheet total. Without this prudence measure, equity

would not necessarily decrease after an increase in debt. Consequently, the NID would not

automatically decrease and taxable income and tax liabilities would not increase

subsequently. When the assumption of a constant balance sheet total does not go up, the effect

of NID will be even bigger.

Table 4 : Example (1) of Graham‟s method with NID (continued)


EBIT 100 120 130 95 110 105 95 100

Less: interest 51 50 50 50 50 50 50 50


Less: TLCFs from previous years 10,00 0,00 0,00 0,00 0,00 0,00 0,00 0,00

Taxable income 5,63 35,75 45,75 10,75 25,75 20,75 10,75 15,75

Taxes (@ 33,99%) 1,91 12,15 15,55 3,65 8,75 7,05 3,65 5,35


PV tax liability 1,91 11,05 12,85 2,75 5,98 4,38 2,06 2,75


In the previous example, EBIT was always large enough to carry the amount of interest,

notional interest and TLCFs from previous years. However, when this is not the case, it is

necessary to take into account the carryforward tax treatment of notional interest and the

carryback and carryforward tax treatment of net operating losses in the calculation of the

MTR. In Graham‟s papers, a firm that experiences a net operating loss is allowed to

„carryback‟ the loss and to receive a tax refund for taxes paid in the previous two years.

Losses not carried back can be carried forward and are used to offset taxable income up to 20

years in the future. This regulation differs from the Belgian tax system, which does not apply

the carryback of tax losses, but allows carryforwards for an unlimited time period. Tables 5 to

8 provide an illustration of how debt dynamics can complicate the calculation of marginal tax

rates. The only adjustment that is made to the previous example in tables 1 to 4 is that EBIT


in period 0 is forecasted to be €60 instead of €100. When no notional interest is deducted

(Tables 5 and 6), the change in the sum of the present values of the tax liabilities caused by an

incremental euro of interest deduction in period 0 is equal to €0,3090, which is the MTR(excl.

NID).

Table 5 : Example (2) of Graham‟s method without NID


EBIT 60 120 130 95 110 105 95 100

Less: interest 50 50 50 50 50 50 50 50


Taxable income 0 70 80 45 60 55 45 50

Taxes (@ 33,99%) 0,00 23,79 27,19 15,30 20,39 18,69 15,30 17,00


PV tax liability 0,00 21,63 22,47 11,49 13,93 11,61 8,63 8,72


Table 6 : Example (2) of Graham‟s method without NID (continued)


EBIT 60 120 130 95 110 105 95 100

Less: interest 51 50 50 50 50 50 50 50


Taxable income 0 69 80 45 60 55 45 50

Taxes (@ 33,99%) 0,00 23,45 27,19 15,30 20,39 18,69 15,30 17,00


PV tax liability 0,00 21,32 22,47 11,49 13,93 11,61 8,63 8,72


However, when NID is applied (Tables 7 and 8), the firm has not enough EBIT to fully deduct

the amount of notional interest which is equal to €34,25 (= 0,04281 * 800). After deducting

the interest costs of €50 the firm has only €10 of earnings left in period 0. The firm can only

subtract €10 of notional interest and carryforwards the remainder of €24,25 to period 1 in

order to reduce taxable income in that period. The tax-loss carryforward of €10 is also

transferred to future periods. Because the firm has no taxable income in period 0, the tax

liability is zero. In period 1, the notional interest of period 1 as well as the carryforward of

notional interest from period 0 can be absorbed completely (34,25 + 24,25 = 58,50). Even the

tax losses from previous periods can be fully deducted. For periods 2 to 7, there is no change

in the calculations. The sum of the present values of the expected current and future tax


liabilities is equal to €31,23. To calculate MTR(incl. NID), consider again in period 0 the one-

time increase of €1 in interest costs and a one-time decrease of notional interest to €33,37.

Further reasoning remains the same.

As in the first example (tables 1-4), MTR(incl. NID) is again lower than the statutory tax rate.

The marginal tax benefit of deducting an extra euro of interest in period 0 is €0,0371, when

we apply NID, instead of €0,3090 in the situation without NID. In addition, MTR(incl. NID)

and MTR(excl. NID) are both lower than in the first example. Next to the decrease in EBIT, the

reason for this decrease in tax benefit is having not sufficient profit in period 0 to cover the

amount of interests, notional interest and TLCFs completely in period 0. Consequently, tax

deductions are realized in a later period. Considering the time value of money, no full tax

benefits will be realized.

Table 7 : Example (2) of Graham‟s method with NID


EBIT 60 120 130 95 110 105 95 100

Less: interest 50 50 50 50 50 50 50 50



Taxable income 0,00 1,50 45,75 10,75 25,75 20,75 10,75 15,75

Taxes 0,00 0,51 15,55 3,65 8,75 7,05 3,65 5,35


PV tax liability 0,00 0,46 12,85 2,75 5,98 4,38 2,06 2,75


Table 8 : Example (2) of Graham‟s method with NID (continued)


EBIT 60 120 130 95 110 105 95 100

Less: interest 51 50 50 50 50 50 50 50



Taxable income 0,00 1,38 45,75 10,75 25,75 20,75 10,75 15,75

Taxes 0,00 0,47 15,55 3,65 8,75 7,05 3,65 5,35


PV tax liability 0,00 0,43 12,85 2,75 5,98 4,38 2,06 2,75



3.3. Simulation of the marginal tax rate

In this section, we describe the procedure we will use later on to simulate dynamic marginal

tax rates for the companies in our sample. To account for the dynamic nature of the tax code

in our simulation, we follow the approach originally developed by Shevlin (1990) and

subsequently elaborated by Graham (1996a,b).

We define the corporate marginal income tax rate as the change in the present value of current

and expected future tax liabilities caused by a one-time additional euro of interest cost today.

MTR2004(i) is the MTR of firm i in the year 2005, determined with data available at the end of

the year 2004. 2005 is the last year for which there was no NID. Similarly, MTR2006(i) is the

MTR of firm i in the year 2007, based on data at the end of 2006. Since 2006, NID is already

applied.

It is desirable to estimate the MTR by supplementing historical data with a stream of

forecasted future taxable incomes and then to calculate the tax bill over the entire time

horizon in a manner that is consistent with the tax rules. Tax-loss carrybacks allow the firm to

carry losses retroactively back to receive a refund for taxes paid in the previous years. Tax-

loss carryforwards (TLCFs) indicate that a firm can carry forward any tax losses not carried

back and use them to shield future profits from taxes. In Graham‟s approach, TLCFs are

limited up to 20 years and tax-loss carrybacks are possible for a maximum of 2 years

(Graham, 2001). However, the Belgian tax law does not allow to carryback any tax losses

while TLCF are permitted unlimited in time. We limit the length of the forecasting period to

eight years as notional interest may only be carried forward up to seven years.

To forecast the stream of future taxable incomes at the end of year t-1, Graham‟s main model

states that a firm i’s earnings follow a pseudo-random walk with drift:

∆EBITit = μi + εit,

where ∆EBITit is the first difference in earnings, μi is the maximum of the mean of ∆EBITi

and zero, and εit is the deviation of ∆EBITit from its mean. εit is normally distributed with

mean zero and variance equal to that of ∆EBITi.

The first step in simulating the MTR consequently consists of calculating the mean and

variance of the change in „earnings before interest, TLCFs, and taxes‟ (EBIT) at time t-1.


Unlike literature (Graham 1996a, 1996b, 2008), where the calculation of mean and variance

of the change in EBIT for a given firm is based on historical data until year t, we use data

from the firm‟s industry in year t-1.

The second step forecasts EBIT for year t and beyond. Using the mean and variance

calculated in step one, we forecast the change in earnings for each firm 8 years into the future.

Adding these changes to EBITt-1 will result in a time series of forecasted EBITs (from year t

to year t+7).

After the deduction of interests and any TLCFs, the third step then calculates the current and

future tax liabilities on the taxable income of each time period. The computation of these tax

liabilities are based on a progressive tax rate. Next, we calculate the present value of the tax

liabilities by discounting to year t, using the average interest rate of year t-1 (for example:

4,73% in 2006).

The fourth step adds a one-time additional euro of interest cost today and recalculates the

present value of expected current and future tax liabilities. The decline in tax liability, in

comparison with the third step, is the present value tax advantage from having an extra euro

interest cost today. Such a simulation provides one estimate of the MTR, for a single firm-

year.

The procedure just described is repeated 50 times to obtain 50 estimates of the MTR in year t,

with each simulation based on a new forecast of 8 years of EBIT. The mean of the 50

estimates of the marginal tax rate is the expected MTR of firm i in year t, a single expected

marginal tax rate for a single firm-year.

We replicate the steps for each firm and each year that is included in our empirical analysis.

The marginal tax rates vary across firms and can also vary through time for a given firm.

This method can be applied to calculate MTR2004(i), which is the marginal tax rate for the last

year in which there was no NID. However, to calculate MTR2006(i), we have to extend

Grahams method to a model that takes the notional interest deduction into account. First, we

assume that realized profits after taxes are held within the company. In other words, we

assume that firms do not distribute profit after taxes as dividends. This is a reasonable

assumption as at least 80% of the SMEs in our sample (infra, p. 25) does not pay dividends.

The consequence is that equity increases when a firm is profitable. The extended model

should therefore deal with the increase in equity to continue its calculations of the NID.


The first and second step of Grahams method remain the same. The third step differs when

NID is taken into account. To calculate taxable income, en thus the tax liability, we first have

to determine the amount of notional interest that will be deducted from EBIT after interest

expenses but before TLCFs. In each forecasted period, this amount will be a percentage on the

amount of equity at the end of the previous period, while equity increases each period with the

after tax profit. The fourth step calculates the MTR by adding €1 extra interest cost in the first

period, next to accounting for the NID. However, an increase in interest costs implies a

change in the level of debt. For a given balance sheet total, the increase in debt corresponds in

a decrease in the proportion of equity. This lower equity base leads to a decrease in NID and a

higher tax liability than in the third step of this model. Therefore, the model has to take into

account these considerations when NID is applied. Next, the MTR is calculated by taking the

difference of the sum of the present values of the tax liabilities calculated in the third and

fourth step. This is one single estimate of MTRit. A total of 50 simulations are performed for

each firm in the sample for each year in the time series. We then take the average of the 50

simulated MTRs, which will represent the expected marginal tax rate of firm i in year t.

3.4. Effects of the marginal tax rate on debt policies

Financial theory is clear that the marginal tax rate is relevant when analyzing optimal debt

levels and incremental financing choices. Literature (MacKie-Mason, 1990; Graham, 1996a)

documents a positive relationship between tax rates and the use of debt as a financing

instrument. In other words, firms with a high marginal tax rate have a greater incentive to

issue debt compared to a low-MTR firm, because of the higher tax advantage from interest

deductibility. This implies a positive relationship between a firm‟s MTR and its ultimate debt

level.

In this paper, our primary focus is the effect of the NID on the optimal level of debt. Since the

notional interest is an extra (fictitious) deduction on EBIT, it reduces a firm‟s taxable income.

Therefore, after the introduction of notional interest, the MTR will be lower. This effect is

illustrated in the examples discussed in section 3.2. Consequently, because of the positive

relationship between the MTR and debt, firms will aim at a lower proportion of debt in their

capital structure when they apply NID.


We translate this in the following hypothesis:

Notional interest deduction has a negative influence on leverage (H1).

While the focus of this paper is to investigate whether leverage decisions are affected by the

change in MTR (= MTRNID - MTRNO NID), it is important to control for competing

explanations of the debt policy. These control variables are discussed in the next section.

3.5. Capital structure theories

A lot of theory has been written in literature about the existence of an optimal capital structure

that maximizes the value of a company.

3.5.1. The irrelevance theorem

The debate starts with the irrelevance theorem of Modigliani and Miller (1958). These two

economists state that in a so-called “perfect” world debt-equity choices and debt maturity

choices are irrelevant. In other words, capital structure decisions cannot affect the value of the

firm. There would not be an optimal capital structure. The value of a firm is only equal to the

present value of all future operating cash flows which depend on the profitability of its assets

and future investment opportunities. Capital structure decisions would only change the

distribution of cash flows to the providers of funds but do not affect their availability.

Modigliani and Miller (M&M) argued that investors can finance a part of their portfolio of

shares with debt equally well when an organization is not willing to do so. The capital

structure of a company is therefore not of value to investors. Moreover, when two companies

with the same operational performance are traded at different prices because of a different

capital structure, arbitrage will dissolve the price difference.

The theorem of M&M is only true under a lot of explicit and implicit assumptions, mainly:

1) no corporate nor personal taxation, 2) perfect capital markets, containing no information

asymmetry, no transaction costs and no institutional restrictions, 5) no bankruptcy costs,

6) homogeneous expectations about the total return of a company and 7) firms can be

assigned to a risk class which includes firms with the same operational risk.


However, none of the assumptions really applies in reality. In an attempt to explain why the

way of financing a company does seem to matter, a large amount of research has relaxed quite

a number of these assumptions.

3.5.2. Trade-off theory

According to the static trade-off theory (TOT) the capital structure of a firm moves towards

an optimal debt ratio as companies weigh up the corporate tax savings advantage of debt and

the costs of financial distress.

The corporate tax law allows interest payments to be deducted from the taxable income of the

firm while deduction of dividend payments is not permitted. The trade-off theory states that

firms will prefer debt to other financing resources due to this fiscal approach. The tax

advantage of debt will increase the value of the firm as it increases its after-tax cash flow. “If

there were only a corporate profits tax and no individual taxes on the returns from corporate

securities, the value of a debt-financed company would equal that of an identical all-equity

firm plus the present value of its interest tax shields” (Barclay & Smith, 2005, p.9) or

V with debt = V without debt + PV interest tax shields

with: PV interest tax shields = (MTR x ID x D) / ID

= MTR x D

where V stands for the market value of the company, PV interest tax shields for the present value of

the interest tax shields, MTR means marginal tax rate, ID represents the cost of debt capital

and D the principal amount of outstanding debt. The formulation assumes that the firm will

maintain its current debt level in the future and that there will always be enough earnings to

deduct the interests from.

The trade-off theory predicts that firms with high profitability should have higher debt ratios,

because they use debt as a means of reducing their tax payments. Higher profits allow the firm

more corporate tax deductions. Some authors like López-Gracia and Sogorb-Mira (2008) have

pointed out that this fiscal approach cannot be applied in the small firm context. SMEs are

less likely to be profitable and are therefore less likely to use debt in order to get tax shields


because they will not need them. Following this line of reasoning there should not exist a

relationship between debt and taxes in SMEs.

A second element the theory takes into account to determine the optimal capital structure of a

firm is the cost of financial distress. Financial distress arises when the perceived probability

that the firm will default is greater than zero. The stakeholders of the company are uncertain

about the expected future cash flows and each will start to bring its own interests to safety,

based on specific and incomplete information. Consequently, the threat of bankruptcy is

already sufficient to cause direct and indirect costs. Direct costs of bankruptcy are deadweight

costs peculiar to the bankruptcy process while indirect costs of bankruptcy are related to lost

opportunities. Since debt implies fixed interest and capital repayments, a rising debt ratio

increases the risk of facing liquidity problems. The trade-off theory argues when the costs of

financial distress are relatively high the optimal debt ratio will be relatively low.

From the same point of view, we can accept there is a positive relationship between firm size

and debt. Size is often used in research as an inverse proxy for the probability of default.

Larger firms are likely to be more diversified which reduces their earnings volatility and thus

their operating risk. Besides they mostly have a better reputation on the capital markets as a

result of which they can ask for a higher amount of debt financing. All this reduces financial

distress and will allow a higher debt ratio. Empirical research by López-Gracia and Sogorb-

Mira (2008), Sogorb-Mira (2005), Cassar and Holmes (2003), Michaelas, Chittenden and

Poutziouris (1999), Chittenden, Hall and Hutchinson (1996a) and Van der Wijst and Thurik

(1993) confirms this prediction.

Besides the costs of financial distress, agency costs are often included in the balancing

process. The agency theory investigates the conflict of interests between various financial

stakeholders of the firm, which occur in SMEs especially between shareholders and

debtholders. We could expect these agency problems to be higher in smaller firms as a small

business owner/manager is more likely to put his own interest first. The occurrence of

information asymmetries adds to these problems. Furthermore, the alternatives to solve

agency problems are relatively more expensive for small businesses. Monitoring will be

difficult and expensive because they are allowed to disclose less information than big firms.

Incentive systems and debt covenants will also be difficult to implement. These costs can be

avoided by offering a collateral to banks (Stiglitz & Weiss, 1981). It is assumed that firms,


which possess tangible assets with a high collateral value, will have easier access to external

funds and therefore have a high level of debt in their capital structure. The existing research

does not find consisting evidence though. Some authors found positive relationships with debt

as well as with long-term debt and short-term debt (Michaelas et al., 1999), some authors

acquired positive relationships with debt and long-term debt while a negative relationship

with short-term debt (Sogorb-Mira, 2005; Chittenden et al., 1996a; Van de Wijst & Thurik,

1993), and others obtained a negative relationship with debt because the negative relationship

with short-term debt dominated the positive relationship with long-term debt (Cassar &

Holmes, 2003).

In accordance with Myers (1977) the underinvestment problem - forgo investment projects

with a positive net present value - caused by potential costs of financial distress will become

more intense in companies with high growth opportunities. Creditors will reduce their supply

of funds to this type of firms as they fear bigger information asymmetries. Myers states one of

the possible solutions could be the issuance of short-term debt instead of long-term debt. In

line with this proposition, long-term debt should have a negative relationship with growth

opportunities while short-term debt should be positively affected by growth opportunities. As

SMEs mainly rely on short-term debt financing, authors like Sogorb-Mira (2005), Michaelas

et al. (1999) and Chittenden et al. (1996a) have found a positive relationship between debt and

growth opportunities, which is in contrast to Myers‟ first assertion.

On the other hand, companies having extensive free cash flow – operating cash flow that

cannot be reinvested profitably – can face to an overinvestment problem when they use it to

sustain growth. After all, investing in projects with a negative net present value will

undermine profitability. In order to maximize its value, such a company should distribute its

free cash flow. Accordingly the agency theory predicts a negative relationship between a

companies leverage and dividend payout, as they are seen as two main and substitutable

alternatives for cash flow distribution. In addition, when comparing companies that generate

similar amounts of cash flow, it is obvious that companies with more profitable investments

will be less levered. Therefore, the agency theory also predicts a negative relationship

between the level of investments and leverage (Fama & French, 2002). Yet, there is little

evidence for these predictions in the literature.


3.5.3. Pecking order theory

In contrast with the static trade-off theory, the pecking order theory (POT) denies the

existence of an optimal capital structure. According to Myers and Majluf (1984), the actual

capital structure of a company is rather achieved by accident. It is the result of some pecking

order behaviour.

In their attempt to maximize value, companies will always choose the cheapest way of

financing. For that reason, managers will prefer internally generated funds as a primary

source. Only when internal financing isn‟t sufficient, they will resort to external sources. In

that case, managers will again choose the cheapest financing method. They will prefer debt to

equity and short-term debt to long-term debt.

This pecking order behaviour would be caused by the presence of information asymmetries

between the firm and potential external financiers. In other words, it is believed that insiders

have more private information about the firm than outside investors. Consequently, to protect

themselves against the risk of overestimating of the value of issued debt or equity, new funds

providers will demand a higher rate of return on their invested capital. This rate of return,

which is a financing cost for the company, will relatively vary between different financing

choices. Equity holders will expect a higher risk premium than debt holders, because they are

the residual owners of the company and therefore experience a greater risk than the latter.

After our literature research, we found several reasons to believe that information

asymmetries are much bigger for SMEs than for large firms. First, the quality of the reported

financial statements of Belgian SMEs turns out to vary a lot (Pettit & Singer, 1985). Some

SMEs report according to the complete format, while a lot of other SMEs report according to

the abbreviated format in which they are allowed to disclose less information. In addition, the

costs to take care of audited financial statements seem to be too high for SMEs. Second, SME

owners are usually also manager of the firm. Consequently, they will not make financing

choices in which they lose property and control over their company (Hamilton & Fox, 1998;

Holmes & Kent, 1991). After the use of retained earnings, they would first appeal to funds

supplied by current shareholders of the firm before going to external sources of finance (Ang,

1991). Moreover, owner-managers of small firms are more likely to take risks than managers

in large firms (Pettit & Singer, 1985). Finally, most SME securities are not publicly traded.


This makes it difficult to determine the true value of shares and bonds, which drives potential

investors more insecure. Concluding from the above statements and from existing research,

we can believe that SMEs exhibit a strong pecking order behaviour.

From this pecking order perspective, we deduct the assumption that more profitable firms will

have lower levels of debt than less profitable firms, as companies will tend to finance as much

investments as possible with internally generated funds. The finance literature examining

SMEs holds a lot of confirming results (Heyman, Deloof & Ooghe, 2008; Sogorb-Mira, 2005;

Cassar & Holmes, 2003; Michaelas et al., 1999; Chittenden et al., 1996a; Van der Wijst &

Thurik, 1993)

Next, growing firms have a higher need for internally generated funds. Consequently, they

will attract external financing when the investment needs exceed the internal resources

available (Shyam-Sunder & Myers, 1999). We can therefore assume that investments are

positively related to the level of debt. López-Gracia and Sogorb-Mira (2008), Cassar and

Holmes (2003), Michaelas et al. (1999) and Chittenden et al. (1996a) invested this

relationship with SMEs and found a statistically significant and positive result.


4. Empirical analysis

4.1. Sample

Our population consists of all small and medium sized enterprises in 2007 that are submitted

to the Belgian corporation tax. Data about a sample of these companies is gathered from the

Belfirst database. This database, administered by Bureau Van Dijk, contains financial

information about Belgian companies that filed or should have filed their annual financial

statements at least once in the last ten years.

Since the Ministry of Finance follows the full definition of article 15, §1 of the Code of

Companies rather than only its quantitative criteria referred to in article 205quater of the

Belgian Income Tax Code (supra, p. 5), we also apply article 15, §1 of the Code of

Companies to classify companies as an SME. This is when a company exceeds no more than

one of the following criteria:

1) workforce of 50 employees on average,

2) turnover of € 7,3 million (VAT excluded),

3) balance sheet total of € 3,65 million,

and when the workforce includes no more than 100 employees. The criteria have to be

satisfied for two successive years prior to the financial year in which a company could be

classified as an SME. Subsequently, we select 106745 SMEs out of Belfirst.

Unlike big companies, SME‟s have the choice to draw up their annual financial statements in

an abbreviated format or a complete format. We only select SME‟s that use a complete format

in order to have the most detailed information available. This requirement considerably

reduces our sample to 4387 SMEs, which shows that most SMEs do not freely opt for a

complete format.

Next, we require the companies to be independent. This decision is based on the issue that the

financial structure of subsidiaries might be influenced by the financial strategy of the

corporate parent. This might cause different behaviour of subsidiaries compared to

independent companies in relation to tax incentives like the notional interest deduction

(Rommens, Cuyvers & Deloof, 2008). In Belfirst, a company is labelled as independent when


there are no shareholders known to have a direct or total ownership of more than 25% of the

shares. After applying this condition, 1580 SMEs remained in our sample.

Because of their different financial behaviour and the specific legislation that is related to

them, we exclude financial and insurance companies (NACE-BEL codes 64, 65 & 66). Our

sample subsequently decreases with 260 sample elements.

Listed companies are also excluded because the rigid conditions they have to fulfil might

restrict their financial flexibility. Only 2 firms have to be deleted.

Additionally, the companies are required not to be engaged in a bankruptcy process or any

other specific legal status like a merger, acquisition, company closure or composition among

other. This condition further reduces our sample with 32 firms.

Furthermore, we need the companies to be at least five years old to have the financial data for

all the required years at our disposal when calculating the variables.

The combination of these criteria results in a sample consisting of 1212 SMEs. However, due

to econometrical issues (infra, p. 29) we need to remove the companies which have a negative

equity in 2007. After deleting these observations, there eventually remains a sample of 1127

SMEs.

In our further empirical analysis, it is our aim to investigate the effect of the notional interest

introduction on the capital structure of Belgian SMEs. We will make use of data from the

unconsolidated accounts. The time frame used for the calculation of the variables is January

1st 2003 until December 31

st 2007.

4.2. Descriptive statistics

A preliminary study provides us with a succinct description of the companies in our sample.

Table 9 presents an overview of some few capital structure variables, their determinants and

other characteristic figures. The definition of all variables used can be found in Appendix A.

In the first place, the mean (median) leverage of the sample firms is about 56% (59%) of total

assets. Additionally, the mean (median) firm in the sample has a debt to equity ratio (cfr.

Gearing) of 4,5802 (1,4754). However, it is important to recognize that these central values

relate to a sample which excludes firms with a negative equity in 2007. Including firms with a


Table 9 : Descriptive statistics

N Mean Std. Deviation Minimum 25% Median 75% Maximum

Dependent Variables

Leveraget 816 0,56195 0,25901 0,00331 0,34825 0,59604 0,77678 0,99631

Long-term Debt Ratiot 820 0,22092 0,30647 0 0 0,05242 0,33607 0,97415

Financial Debt Ratiot 820 0,21411 0,30676 0 0 0,01252 0,36335 0,99784

Gearingt 816 4,58024 16,35929 0,00332 0,53433 1,47549 3,47983 269,92448

Long-term to Short-term Debt Ratiot 820 1,84809 5,20705 0 0 0,05532 0,50617 37,68835

Financial to Non Financial Debt Ratiot 820 1,84084 16,39277 0 0 0,01268 0,57071 461,49304

Independent Variables

MTRt-1 (incl.NID) 988 0,02301 0,02855 0 0,01240 0,02521 0,02751 0,33298

MTRt-1 (excl.NID) 988 0,23597 0,11788 0 0,17317 0,29800 0,31991 0,34874

MTRt-1 (incl.- excl.) 988 -0,21296 0,11141 -0,32558 -0,29328 -0,27340 -0,12463 0

Control Variables

Profitabilityt-1 964 0,07071 0,12637 -0,47734 0,01099 0,04528 0,11955 0,64518

Investmentst-1 945 0,05765 0,23891 -2,76830 -0,00981 0,07014 0,16903 0,68247

Growth Opportunitiest-1 974 0,00748 0,04067 0 0 0 0,00023 0,76769

Total Assetst-1 (th€) 974 17075,74 217082,04 47,82 1467,75 2757,83 5730,36 6599839,00

Tangibilityt-1 974 0,20309 0,27262 0 0,01612 0,07531 0,27278 0,99483

Dividend Payoutt-1 969 0,02032 0,06316 0 0 0 0 0,50411

Capital Structure Components

Equityt (th€) 820 11314,57 192625,55 7,96 411,19 1064,97 3029,62 5497498,00

Debtt (th€) 820 11143,26 132213,31 9,10 597,28 1534,62 3476,86 3621783,00

Long-term Debtt (th€) 820 3551,71 11393,53 0 0 62,98 695,85 137447,00

Long-term Financial Debtt (th€) 820 3250,82 10970,63 0 0 0 378,35 137447,00

Short-term Financial Debtt (th€) 820 4574,86 121437,23 0 0 0 4,07 3476505,00

Total Assetst (th€) 820 22457,84 321296,63 67,32 1561,46 3068,41 6424,90 9119281,00

Other Company Characteristics

Aget-1 1127 25,25 19,87 5 12 18 33 105

Average Workforcet-1 1112 14,71 13,51 1 4 11 21 99

Turnovert-1 (th€) 964 3746,21 3690,55 3,65 1328,46 3217,50 5062,04 54521,55

MTRt-3 988 0,22977 0,12053 0 0,12428 0,28730 0,32217 0,34470

Notes: t = 2007. N is the number of observations. Variable definitions are given in Appendix A.


negative equity would increase the magnitude of leverage and gearing. The mean long-term

debt ratio represents around 22% of total debt of SMEs in 2007, which in turn means that

short-term debt represents around 78% of total debt. This highlights the importance of short-

term debt over long-term debt in SME financing. 32,6% of the firms does not even engage in

long-term debt financing (not reported).

Among the tax variables, the average marginal tax rate inclusive of notional interest deduction

and the average marginal tax rate without notional interest deduction have means of 0,023 and

0,2359 respectively, indicating that the marginal tax rate has decreased substantially after the

introduction of the notional interest deduction.

Table 9 also provides information about firm characteristics which we will use later on as

control variables in the multivariate analysis. With respect to asset structure, we find that

intangible assets (cfr. Growth Opportunities ratio) represent only 0,7% of total assets, whereas

fixed assets (cfr. Tangibility ratio) represent about 20% of total assets. This difference will be

mainly due to the fact that few companies invest in intangible assets. However, the average

company which does invest in intangibles still has only 1,8% on its balance sheet. The

average size of the SMEs was approximately € 17,1 million in terms of assets in 2006, but the

median is much smaller at € 2,7 million. This suggests that our sample includes some large

firms. In comparison with 2006, the asset base of our sample firms has increased in 2007. As

far as profitability is concerned, the average return on assets mounts up to 7,1% in 2006. With

a minimum value of -47,7% and a maximum value of 64,5%, we find a disparity of 112,2

percentage points in the profitability of the two most extreme firms. Furthermore, it is

remarkable that the median firm in our sample does not pay dividends. Barely 20% – to be

more specific: 190 out of 969 companies for which the values were given – of the sample

firms pays dividends to their shareholders (not reported). Moreover, their payout is restricted

to an average of 10,36% of the asset base (not reported).

At last, the average SME is fairly small – as expected – with an average number of employees

of less than 15 units.

Figure 1 shows the industry classification and the percentage represented by each sector

within our sample. As can be observed, both wholesale, retail & maintenance and real estate,

renting & company services prevail over other industries.


Figure 1 : Firm distribution by industries (N=1127)

15%

7%

34%10%

1%

1%

29%

3%

Manufacturing

Construction

Wholesale, Retail & Maintenance

Transportation & Storage

Hotels & Restaurants

Communication

Real Estate, Renting & Company Services

Community, Socio-cultural and PersonalServices

Notes: N is the number of observations.

Figure 2 presents summary statistics about the evolution of the average proportion of equity,

long-term debt and short-term debt over a two-year period. We notice that the proportion of

equity has increased since 2005, while that of long-term and short-term debt has decreased.

This observation is in line with the goal of the notional interest deduction system, which is to

encourage the strengthening of the companies‟ equity.

Figure 2 : Evolution of the average proportion of capital structure components (N=763)

39,49% 42,26% 43,67%

15,56%14,40% 13,98%

44,95% 43,34% 42,34%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2005 2006 2007

Equity ratio Long Term Debt ratio Short Term Debt ratio

Notes: N is the number of observations. Variable definitions are given in Appendix A.


Table 10 presents bivariate results for the differences in the proportion of several capital

structure components between 2005 and 2007. Some of these components are also shown in

Figure 2. We use a Wilcoxon paired samples test to verify whether the means of the two

paired subgroups – capital structure ratio‟s of 2005 versus 2007 – are significantly different

from each other. The Wilcoxon test is a good alternative for a t-test when the assumption of

normality is rejected. Panel A shows that the proportions of all components except short-term

financial debt are significantly different from each other over time. From Panel B, we can

now conclude that there is a highly significant increase in the equity ratio and a highly

significant decrease in almost all debt component ratio‟s from 2005 until 2007. This outcome

confirms that the trends in Figure 1 are significant at the 1% level.

Table 10 : Wilcoxon Signed Ranks Test (N=763)

Panel A: Test Statistics

Variables Z-statistic Significance level

Equityt - Equityt-2 -6,119 9,399E-10 ***

Debtt - Debtt-2 -6,119 9,409E-10 ***

Long-term debtt - Long-term debtt-2 -6,482 9,022E-11 ***

Short-term debtt - Short-term debtt-2 -3,027 2,469E-03 ***

Long-term financial debtt - Long-term financial debtt-2 -6,705 2,017E-11 ***

Short-term financial debtt - Short-term financial debtt-2 -0,936 3,490E-01

Panel B: Descriptive Statistics

Variables Mean Std. Deviation

Equityt 0,437 0,261

Equityt-2 0,395 0,319

Debtt 0,563 0,261

Debtt-2 0,605 0,319

Long-term debtt 0,140 0,221

Long-term debtt-2 0,156 0,244

Short-term debtt 0,423 0,268

Short-term debtt-2 0,450 0,325

Long-term financial debtt 0,108 0,209

Long-term financial debtt-2 0,121 0,226

Short-term financial debtt 0,031 0,088

Short-term financial debtt-2 0,038 0,185

Notes: t = 2007. N is the number of observations. ***: denotes statistical significance at the 1% level respectively (two tailed). Each variable has been deflated by the total of assets of the corresponding year. Variable definitions are given in Appendix A.


Ultimately, we assess in Table 11 the Pearson correlations among leverage and other firm

characteristics we will use later on as respectively the dependent and explanatory variables in

our multivariate analysis. Leverage is positively correlated with MTRt-1 (incl. NID) and

tangibility, while negatively correlated with profitability. However, there does not seem to be

a significant correlation between leverage on the one hand and investments, growth

opportunities, size and dividend payout on the other hand. The positive correlation between

leverage and MTRt-1 (incl. NID) points out that firms with higher tax incentives are more

levered or vice versa. Finally, the correlations between size and other explanatory variables

indicate that larger firms have a lower MTR, lower profitability, a higher proportion of

tangible assets and pay less dividends.

Table 11 : Pearson correlation coefficients (N = 765)

(1) (2) (3) (4) (5) (6) (7) (8)

(1) Leveraget 1***

(2) MTRt-1 (incl.NID) 0,107*** 1***

(3) Profitabilityt-1 -0,109*** 0,410*** 1***

(4) Investmentst-1 0,043*** 0,076*** 0,062*** 1***

(5) GrowthOppt-1 0,051*** -0,079*** -0,058*** 0,026*** 1***

(6) Sizet-1 0,043*** -0,084*** -0,248*** 0,048*** -0,028*** 1***

(7) Tangibilityt-1 0,202*** 0,036*** -0,186*** 0,028*** -0,021*** 0,482*** 1***

(8) Dividend Payoutt-1 0,030*** 0,100*** 0,389*** -0,064*** -0,028*** -0,103*** -0,105*** 1***

Notes: t = 2007. N is the number of observations. ***, **, * : denotes statistical significance of the correlation at the 1%, 5%, 10% level respectively (2-tailed).

4.3. Multivariate analysis

In this section, it is our aim to empirically investigate the significance, nature and extent to

which notional interest deduction has an influence on capital structure while taking into

account other determinants known in finance and accounting literature. Focusing on the year

2007, we use cross-sectional data to run OLS regressions of 4 dependent variables namely

leverage, the change in leverage, the long-term debt ratio and the financial debt ratio.

However, before we introduce any regression equation, it is necessary to first consider an

appropriate functional form. Empirical studies of capital structure mostly use either leverage

or gearing as a measure for the level of debt. Given their formula, both measures are

constrained within a certain range of values. For companies with non-negative equity,


leverage (i.e. debt/(debt + equity) ) is limited between zero and one while gearing (i.e.

debt/equity) only delivers values from zero to infinity. Unfortunately, the use of these

bounded variables as a dependent variable in a linear regression model will not guarantee

estimates of the regressand that meet those restrictions. An econometrical problem occurs

since estimates beyond the limits of leverage resp. gearing are useless. In addition, the

goodness of fit of the regression model (R²) will not be as high either. Nevertheless, the issue

can be dealt with by using a logistical transformation of leverage (Jordan, Lowe & Taylor,

1998).

As estimates of leverage can only exist within the range of zero to one, the relationship

between leverage and its set of linearly related explanatory variables is rather described by the

cumulative logistic distribution function:

Leverage 1

1 + e-(0 + 1 X1 + … + )

The logistical transformation of leverage is then a linear function of the set of explanatory

variables:

Ln Leverage

= 0 + 1 X1 + … + 1 - Leverage

This equation can be rewritten as:

Ln Debt

= 0 + 1 X1 + … + Equity

where ln(debt/equity) denotes a logarithmic transformation of gearing.

The logistical transformation of leverage turns out to be equal to a logarithmic transformation

of gearing which is an unbounded measure. When leverage moves from zero to one and

gearing varies from zero to +, the natural logarithm of gearing reaches from - to +.

The use of the natural logarithm of gearing as a dependent variable consequently solves the

econometrical issue concerning bounded variables.

As our other dependent variables also have two boundaries, we will subject them to the same

logistical transformation which results in the following formulation: the change in the natural

logarithm of gearing, the natural logarithm of the long-term to short-term debt ratio and the

natural logarithm of the financial to non financial debt ratio.


Finally, we remark that the logistical transformation of the dependent variables does not have

any impact on the interpretation of our results. We merely apply the transformation to solve

an econometrical issue. A significant positive resp. negative impact of any explanatory

variable on, for instance, the natural logarithm of gearing can still be interpreted as a positive

resp. negative impact on leverage, given that gearing is positively related to leverage. Our

hypothesis thus remains:


4.3.1. General model

Although a lot of previous research has been done about the determinants of capital structure,

this is the first study which examines the effect of notional interest deduction. As indicated in

section 3.4, we expect notional interest deduction to have a negative influence on leverage.

We include the marginal tax rate in a multivariate regression to assess the effect of notional

interest deduction on a companies tax incentives while ln(gearing) will serve as a measure for

leverage. To control for other factors which could simultaneously influence leverage, we will

also take into account the most important determinants already found in literature.

First, the pecking order theory predicts more profitable firms to be less levered than less

profitable firms because the more profitable firms can rely on a higher level of internal

resources to fund investments. Additionally, the same theory asserts high growth firms to be

more levered since they need higher levels of funding compared to other firms which grow

slower. As existing research contains evidence that SMEs exhibit strong pecking order

behaviour, we expect profitability and investments to be negatively resp. positively related to

leverage. Whereas theory clearly indicates the determinants, there is however a lot of

controversy in the literature about how to measure these concepts. We chose EBIT to total

assets as a measure for profitability in order to capture the operational result which gives a

more robust indication for the profitability of a companies business. To measure the level of

investments it is necessary to correct the value of total assets in a given year for all

depreciations, impairment losses and revaluations on new and existing assets to make the

amount comparable to the value of total assets in the previous year. We consider an increase

in total assets as an investment of money, while a decrease represents the opposite.


Consistent with the trade-off theory, we include size as another control variable. Size is often

used as an inverse proxy for default because smaller firms tend to be less diversified and

therefore carry a higher operating risk. We will measure size as the natural logarithm of total

assets.

According to the underinvestment problem of the agency theory, firms with high growth

opportunities will be less levered than other firms. We measure growth opportunities as the

ratio of intangible assets to total assets of the firm. The agency theory also indicates that firms

with relatively more tangible assets will be more levered as they have easier access to finance.

The higher liquidation value of this type of firms and the possibility for creditors to demand a

collateral will reduce adverse selection and moral hazard. We will use the ratio of fixed

tangible assets to total assets as a proxy for tangibility. The theory further predicts that the

amount of dividends paid will be negatively related to leverage, as dividends and leverage are

used as substitutes to control free cash flow problems. We define dividend payout as the ratio

of dividends to total assets. Finally, we include seven dummies in each regression model to

control for industry effects. Appendix A summarizes the definition of the variables we use.

Appendix B gives an overview of the empirical predictions by the main theories and their

outcomes concerning the explanatory variables.

Before we proceed to the discussion of our general regression model, we first investigate the

effect of the several control variables on leverage. The OLS regression equation of the natural

logarithm of gearing is as follows:

Ln(Gearing)t = 0 + 1 Profitabilityt-1 + 2 Investmentst-1 + 3 GrowthOppt-1 +

4 Sizet-1 + 5 Tangibilityt-1 + 6 Dividend Payoutt-1 +

7 Industry + (1)

The explanatory variables in each regression will be one year lagged as they serve as a proxy

for the next year. The idea is that they will create expectations for year t that are more or less

consistent with year t-1. In this paper, 2007 (= t) is the year of focus.

The regression results are shown as model (1) in table 12. With an F-statistic of 4,747, the

model is highly significant, which means that we can draw conclusions from the coefficients

of each individual variable.

Profitability has a negative and statistically significant impact on leverage. This provides

some evidence for Myers‟ pecking order theory, where more profitable companies tend to use


Table 12 : Regression results: General model

This table reports the results for the OLS regressions of gearing on the marginal tax rate (MTR) and six

other determinants of capital structure. The relationship for model 2 is estimated as follows:

Ln(Gearing)t = 0 + 1 MTRt-1 (incl.NID) + 2 Profitabilityt-1 + 3 Investmentst-1 + 4 GrowthOppt-1 +

5 Sizet-1 + 6 Tangibilityt-1 + 7 Dividend Payoutt-1 + 8 Industry +

Model: (1) (2) (3) Dependent variable: Ln(Gearing)t Ln(Gearing)t Ln(Gearing)t

Constant 1,119 0,733 0,868

(3,045) (1,645) (1,874) 0,002*** 0,100* 0,061*

MTRt-1 (incl.NID) 17,725

(3,94) 0,000***

MTRt-1 (excl.NID) 30,157

(5,941) 0,000***

MTRt-1 (incl.- excl.) 33,569

(6,169) 0,000***

Profitabilityt-1 -1,686 -2,522 -1,298

(-3,444) (-4,765) (-2,257) 0,001*** 0,000*** 0,024**

Investmentst-1 0,376 0,326 0,366

(1,416) (1,240) (1,414) 0,157 0,215 0,158

GrowthOppt-1 5,619 6,240 5,326

(1,953) (2,187) (1,893) 0,051* 0,029** 0,059*

Sizet-1 -0,133 -0,124 -0,078

(-2,829) (-2,672) (-1,669) 0,005*** 0,008*** 0,096*

Tangibilityt-1 1,073 0,966 0,835

(5,053) (4,559) (3,97) 0,000*** 0,000*** 0,000***

Dividend Payoutt-1 2,373 2,609 2,423

(2,603) (2,883) (2,717)

0,009*** 0,004*** 0,007***

N° observations 765 765 765

R² 0,076 0,095 0,124

Adj. R² 0,060 0,078 0,106

F-statistics 4,747 5,602 7,070 0,000*** 0,000*** 0,000***

Notes: t = 2007. Model 3 differs from model 2 only by the substitution of MTRt-1 (incl. NID) by MTRt-1 (excl. NID) and MTRt-1 (incl.- excl.). In this way, model 3 makes the effect of notional interest deduction explicit. T-statistics are displayed in parentheses below the Bèta-coefficients. P-values are given in italics. ***, **, * : denotes statistical significance at the 1%, 5%, 10% level respectively. All OLS regressions include seven industry dummies. The coefficients on these dummies are not reported. Variable definitions are given in Appendix A.


less debt when financing their activities. As expected, investments and leverage are found to

be positively connected although not significantly, whereas growth opportunities is positively

related to leverage but only significant at the 10% level. This latter result does not support the

prediction of the agency theory but is consistent with other research. As SMEs mainly use

short-term debt financing, the negative effect of growth opportunities on long-term lending

will be dominated by an increase in the use of short-term debt. Size is negatively related to

leverage, contrary to the expected positive relationship by the trade-off theory. As access to

long-term debt is often restricted by lenders to small businesses, it is especially long-term

debt that is positively related to firm size (Sogorb-Mira, 2005). Small companies will

compensate this restriction by using relatively more short-term debt. In that case, the negative

effect of size on short-term debt can outweigh the positive effect of size on long-term debt.

Consistent with the prediction of the agency theory, we find that tangibility is positively

related to leverage. We can accept there is evidence that assets with a high collateral value

help to reduce problems of adverse selection and moral hazard. Finally, the results show that

dividend payout has a positive impact on leverage, which is in contrast with our expectations.

This outcome could be explained by Myers (1984) who states that dividends are for unknown

reasons sticky in the short term. Since financing costs are higher for equity than for debt, the

pecking order theory expects leverage to respond strongly to short-term variation in earnings

and investment. Fama and French (2002) provide confirming evidence.

Next, we include the marginal tax rate in our regression analysis. By means of the following

equation we investigate whether the marginal tax rate which includes the effect of notional

interest deduction has a significant influence on leverage:

Ln(Gearing)t = 0 + 1 MTRt-1 (incl.NID) + 2 Profitabilityt-1 +

3 Investmentst-1 + 4 GrowthOppt-1 + 5 Sizet-1 +

6 Tangibilityt-1 + 7 Dividend Payoutt-1 + 8 Industry + (2)

Model (2) in table 12 reports the results of our general regression model. It is apparent that the

goodness of fit is higher than that of model (1) which does not include the marginal tax rate.

The adjusted R² increases from 6% to 7,8%. Moreover, the marginal tax rate has a strongly

significant positive relationship with ln(gearing). This means that, after considering notional

interest deduction, companies with perspectives of a relatively lower tax advantage on


incremental interest deductions engage in lower debt levels compared to companies with

perspectives of a higher potential tax advantage.

We have reason to believe that due to enormous media attention concerning notional interest

deduction, company owners have been focussing special attention on the opportunities of this

tax deduction. Therefore, we will separate the effect of notional interest deduction on leverage

from the effect of other components in the marginal tax rate:

Ln(Gearing)t = 0 + 1 MTRt-1 (excl.NID) + 2 MTRt-1 (incl.- excl.) +

3 Profitabilityt-1 + 4 Investmentst-1 + 5 GrowthOppt-1 +

6 Sizet-1 + 7 Tangibilityt-1 + 8 Dividend Payoutt-1 +

9 Industry + (3)

The marginal tax rate is represented by two components. MTR t-1 (excl.NID) denotes the

marginal tax rate to be considered when the notional interest deduction would not be applied,

while MTRt-1 (incl.- excl.) embodies the effect of notional interest deduction on the marginal

tax rate. The regression results are shown in model (3) of table 12. It is noticeable that the

adjusted R² has increased again. Next, the coefficients of the marginal tax rate variables are

again highly significant which is not the case anymore for those of profitability, investments,

growth opportunities and size. We can conclude that companies take into account the change

in their marginal tax rate while determining their capital structure. Controlling for other

determinants, the bigger the companies decrease in marginal tax rate caused by the notional

interest deduction, the smaller the companies proportion of debt in the subsequent reporting

period. Moreover, it is remarkable that the coefficient of MTRt-1 (incl.- excl.) is bigger than

that of MTRt-1 (excl.NID). This indicates that notional interest deduction causes a decrease of

future tax advantages from additional interest deduction which is of greater importance for the

determination of leverage than the original level of those future tax advantages. A decrease in

the marginal tax rate caused by notional interest deduction will have a greater impact on

leverage than a decrease in the marginal tax rate caused by other factors.

While making these interpretations, we need to consider simultaneously the potential issues of

multicollinearity. Multicollinearity exists when there is a strong linear relationship among

some or all explanatory variables of a regression model. Depending on the severity of the

collinearity problem, the effects on the interpretation of results can be huge. If


multicollinearity is high, the regression coefficients will possess large standard errors which

means the coefficients cannot be estimated with great precision. The slightest change in the

data will produce different beta-coefficients. Additionally, despite a high R², multicollinearity

may make it impossible to isolate the marginal effect of a highly correlating explanatory

variable.

Multicollinearity can be traced by means of the “variance inflation factor” (VIF). When there

is no correlation between the explanatory variables, the value of this factor is one. As

collinearity increases, the VIF will increase toward infinity. Values of 10 are still more or less

acceptable, while higher values already raise some concern. In this subsection, most

explanatory variables had VIF values between 1 and 2 (not reported). We only found

multicollinearity among the marginal tax rate variables of model (3). MTRt-1 (excl.NID) and

MTRt-1 (incl.- excl.) both have a huge VIF value of 129 and 128 respectively (not reported).

This outcome is not surprising as both variables are driven by the same factors in our tax

simulation. Unfortunately, there is not much to do about this issue. Techniques to mitigate

problems associated with multi-collinearity can create problems that are worse than those they

solve.

Before deciding about our hypothesis, we investigate whether the notional interest deduction

has also contributed to a subsequent decrease in leverage. If this would not be the case, the

positive relationship between leverage and MTRt-1 (incl.- excl.) discovered in model (3) could

be owing to the negative correlation of MTRt-1 (incl.- excl.) with the level of equity in t-1. The

higher the amount of equity in year t-1, the bigger the decrease in the marginal tax rate will be

due to notional interest deduction and the lower leverage will automatically be in year t, even

without a change in leverage. We therefore run a regression in differences:

Ln(Gearing)[t-(t-2)] = 0 + 1 MTR[(t-1)-(t-3)] (incl.NID) + 2 Profitability[(t-1)-(t-3)] +

3 Investments[(t-1)-(t-3)] + 4 GrowthOpp[(t-1)-(t-3)] +

5 Size[(t-1)-(t-3)] + 6 Tangibility[(t-1)-(t-3)] +

7 Dividend Payout[(t-1)-(t-3)] + 8 Industry + (4)

We consider a two-year period – 2006 and 2007 – to measure the effect of notional interest

deduction on the change in leverage. As it is necessary to take into account influences from

other determinants, we consequently include the changes in our control variables as well.

Model (4) in table 13 illustrates the outcomes. Although the model is highly significant, no


Table 13 : Regression results: General model (continued)

This table reports the results for the OLS regressions of the change in gearing on the marginal tax rate

(MTR) and six other determinants of capital structure. The relationship for model 4 is estimated as follows:

Ln(Gearing)[t-(t-2)] = 0 + 1 MTR[(t-1)-(t-3)] (incl.NID) + 2 Profitability[(t-1)-(t-3)] + 3 Investments[(t-1)-(t-3)]

+ 4 GrowthOpp[(t-1)-(t-3)] + 5 Size[(t-1)-(t-3)] + 6 Tangibility[(t-1)-(t-3)] + 7 Dividend Payout[(t-1)-(t-3)]

+ 8 Industry +

Model: (4) (5)

Dependent variable: Ln(Gearing)[t-(t-2)] Ln(Gearing)[t-(t-2)]

Constant -0,098 -0,009

(-0,659) (-0,059)

0,510 0,953

MTR[(t-1)-(t-3)] (incl.NID) 0,414

(1,489)

0,137

MTR[(t-1)-(t-3)] (excl.NID) 0,022

(0,065)

0,948

MTRt-1 (incl.- excl.) 0,772

(2,385)

0,017**

Profitability[(t-1)-(t-3)] -1,503 -1,254

(-5,599) (-4,296)

0,000*** 0,000***

Investments[(t-1)-(t-3)] 0,412 0,400

(4,006) (3,897)

0,000*** 0,000***

GrowthOpp[(t-1)-(t-3)] 1,826 2,059

(0,691) (0,781)

0,490 0,435

Size[(t-1)-(t-3)] 0,034 0,069

(0,345) (0,688)

0,730 0,492

Tangibility[(t-1)-(t-3)] 0,694 0,671

(1,701) (1,648)

0,089* 0,100*

Dividend Payout[(t-1)-(t-3)] 1,796 1,767

(3,499) (3,452)

0,000*** 0,001***

N° observations 686 686

R² 0,102 0,108

Adj. R² 0,083 0,088

F-statistics 5,446 5,417

0,000*** 0,000***

Notes: t = 2007. Model 5 differs from model 4 only by the substitution of MTRt-1 (incl. NID) by MTRt-1 (excl. NID) and MTRt-1 (incl.- excl.). In this way, model 5 makes the effect of notional interest deduction explicit. T-statistics are displayed in parentheses the below Bèta-coefficients. P-values are given in italics. ***, **, * : denotes statistical significance at the 1%, 5%, 10% level respectively. All OLS regressions include seven industry dummies. The coefficients on these dummies are not reported. Variable definitions are given in Appendix A.


significant influence from the change in marginal tax rate is found on the change in leverage

since the introduction of the notional interest tax deductibility. This while the change in

profitability, investments and dividend payout do have a significant influence.

On the other hand, as we assumed special media attention to the deductibility of notional

interest, we run the same regression in differences as in model (4) but with the effect of

notional interest on the marginal tax rate made explicit in model (5):

Ln(Gearing)[t-(t-2)] = 0 + 1 MTR[(t-1)-(t-3)] (excl.NID) + 2 MTRt-1 (incl.- excl.) +

3 Profitability[(t-1)-(t-3)] + 4 Investments[(t-1)-(t-3)] +

5 GrowthOpp[(t-1)-(t-3)] + 6 Size[(t-1)-(t-3)] +

7 Tangibility[(t-1)-(t-3)] + 8 Dividend Payout[(t-1)-(t-3)] +

9 Industry + (5)

In contrast to model (4), we do find a significant and positive contribution of MTRt-1 (incl.-

excl.) to the change in leverage over the past two years. Although the significance only counts

at the 5% level, this result is very satisfying as we know that regressions in differences are

more severe than regressions in levels. Ultimately, we can conclude that there is evidence

consistent with our hypothesis:


4.3.2. Extension

In the general analysis, the results confirm our assumption that notional interest deduction

would have a negative impact on leverage if companies conduct as predicted by the tax

considerations of the static trade-off theory. However, we are still unsure about the impact on

the structure of debt itself. Therefore we would like to do some additional and more profound

research about the effect of notional interest deduction on two main components of debt,

namely long-term debt and financial debt.

Long-term debt is considered to be the counterpart of equity, as both are sources for funding

on the long-term. We expect to find the negative influence of notional interest deduction on

leverage largely back in this component of debt. By means of the following regression


function we will analyse the existence and direction of a significant impact on the proportion

of long-term debt to total debt:

Ln(Long-term debt / Short-term debt)t = 0 + 1 MTRt-1 (incl.NID) +

2 Profitabilityt-1 + 3 Investmentst-1 +

4 GrowthOppt-1 + 5 Sizet-1 +

6 Tangibilityt-1 + 7 Dividend Payoutt-1 +

8 Industry + (6)

Remember the need for a logistical transformation of the regressand when its values are

limited between 0 and 1. For the same reason, it was required to transform the long-term debt

ratio into the natural logarithm of the long-term to short-term debt ratio.

The results are presented in Table 14 as model (6). With an F ratio of 34,088, the model is

highly significant at the 1% level. The adjusted R² statistic is 44,7%, which is far higher than

for any of the equations based on leverage as dependent variable. Moreover, the marginal tax

rate has a strongly significant and positive relationship with the long-term debt ratio, which

indicates that the small business owner has less long-term debt outstanding when his

perspectives of future tax advantages on additional debt are relatively lower. Most of the

control variables are highly significant except growth opportunities does not seem to have an

impact.

In model (7), we run the same regression as in model (6) but with the marginal tax rate

represented by two components to make the effect of notional interest on the marginal tax rate

explicit:

Ln(Long-term debt / Short-term debt)t = 0 + 1 MTRt-1 (excl.NID) +

2 MTRt-1 (incl.- excl.) +

3 Profitabilityt-1 + 4 Investmentst-1 +

5 GrowthOppt-1 + 6 Sizet-1 +

7 Tangibilityt-1 + 8 Dividend Payoutt-1 +

9 Industry + (7)

The results are shown in Table 14. Most of the estimated coefficients and their respective

probability levels are much the same as in model (6). Also the direction of the relationships


Table 14 : Regression results: Extension

This table reports the results for the OLS regressions of long-term to short-term debt ratio resp. financial to

non financial debt ratio on the marginal tax rate (MTR) and six other determinants of capital structure.

The relationship for model 6 is estimated as follows:

Ln(Long-term debt / Short-term debt)t = 0 + 1 MTRt-1 (incl.NID) + 2 Profitabilityt-1 + 3 Investmentst-1

+ 4 GrowthOppt-1 + 5 Sizet-1 + 6 Tangibilityt-1 + 7 Dividend Payoutt-1 + 8 Industry +

The relationship for model 8 is estimated as:

Ln(Financial debt / Non financial debt)t = 0 + 1 MTRt-1 (incl.NID) + 2 Profitabilityt-1 + 3 Investmentst-1

+ 4 GrowthOppt-1 + 5 Sizet-1 + 6 Tangibilityt-1 + 7 Dividend Payoutt-1 + 8 Industry +

Model: (6) (7) (8) (9)

Dependent variable: Ln (Long-term debt / Short-term Debt)t

Ln (Long-term debt / Short-term Debt)t

Ln (Financial debt / Non financial debt)t

Ln (Financial debt / Non financial debt)t

Constant -6,475 -6,454 -5,745 -5,677

(-8,764) (-8,701) (6,551) (-6,486)

0,000*** 0,000*** 0,000*** 0,000***

MTRt-1 (incl.NID) 7,970 3,031

(3,476) (1,198)

0,001*** 0,232

MTRt-1 (excl.NID) 7,821 1,712

(3,35) (0,654)

0,001*** 0,513

MTRt-1 (incl.- excl.) 8,104 3,571

(3,482) (1,406)

0,001*** 0,160

Profitabilityt-1 -1,648 -1,478 -1,147 0,074

(-2,223) (-1,662) (-1,112) (0,061)

0,027** 0,097* 0,267 0,951

Investmentst-1 -0,938 -0,932 -1,012 -0,982

(-2,268) (-2,248) (-2,230) (-2,169)

0,024** 0,025** 0,026** 0,031**

GrowthOppt-1 -0,705 -0,795 10,241 9,323

(-0,219) (-0,246) (2,154) (1,957)

0,827 0,806 0,032** 0,051*

Sizet-1 0,450 0,454 0,451 0,480

(6,157) (6,129) (5,277) (5,544)

0,000*** 0,000*** 0,000*** 0,000***

Tangibilityt-1 3,133 3,126 2,889 2,869

(10,356) (10,298) (8,166) (8,127)

0,000*** 0,000*** 0,000*** 0,000***

Dividend Payoutt-1 -2,521 -2,543 -5,706 -5,743

(-1,702) (-1,714) (-2,411) (-2,432)

0,089* 0,087* 0,016** 0,015**

N° observations 573 573 484 484

R² 0,461 0,461 0,399 0,404

Adj. R² 0,447 0,447 0,381 0,384

F-statistics 34,088 31,774 22,237 21,106 0,000*** 0,000*** 0,000*** 0,000***

Notes: t = 2007. Models 7 and 9 differ from models 6 and 8 only by the substitution of MTRt-1 (incl. NID) by MTRt-1 (excl. NID) and MTRt-1

(incl.- excl.). In this way, the models 7 and 9 make the effect of notional interest deduction explicit. T-statistics are displayed in parentheses below the Bèta-coefficients. P-values are given in italics. ***, **, * : denotes statistical significance at the 1%, 5%, 10% level respectively. All OLS regressions include seven industry dummies. The coefficients on these dummies are not reported. Variable definitions are given in Appendix A.


are the same. We conclude that the notional interest deduction, embodied by the change in the

marginal tax rate, negatively influences the proportion of long-term debt to total debt. Again,

the change in future tax advantages caused by notional interest deduction seems to be of

greater importance for leverage than a change that would be caused by other determinants of

the marginal tax rate. Regarding the control variables, profitability, investments and dividend

payout carry a negative sign while size and tangibility carry a positive sign. Once again,

growth opportunities does not have a statistically significant impact on the long-term debt

ratio.

Next, we investigate the existence and direction of a significant impact of notional interest

deduction on the proportion of financial debt. As we regard financial debt as being more

adaptable when converting to a desired capital structure, we expect to find a negative impact

of notional interest deduction, which is measured by use of the marginal tax rate.

Nevertheless, no significant results are found in both model (8) and model (9) (cfr. Table 14).

Finally, we recognize that also in this subsection some multicollinearity arises between the

marginal tax rate variables in models (7) and (9). Although the VIF values are far lower than

in model (3), MTRt-1 (excl.NID) and MTRt-1 (incl.- excl.) show values of 13 and 12

respectively (not reported) for the regression of long-term debt, and values of 11 and 10

respectively (not reported) for the regression of financial debt.


5. Conclusions

This paper investigates the effect of the notional interest deduction on the capital structure of

small and medium sized enterprises. The notional interest deduction is an extra fictitious

deduction on the taxable income, which should partially compensate the firm‟s fiscal

advantage on debt financing. We illustrated through examples that, when everything else

remains equal, the marginal tax rate will be lower after the introduction of notional interest

deduction. Thus, since the marginal tax rate serves as a proxy for the tax incentives that firms

experience under the trade-off theory, we assess whether leverage decisions are affected by

the change in marginal tax rates. In order to include these marginal tax rates in a regression

model, we first simulate them by making use of Graham‟s method. To isolate the individual

effect of tax incentives, we control for the most important determinants found in literature,

which are profitability, investments, growth opportunities, size, tangibility and dividend

payout.

We can present two major findings. First, it appears that the capital structure of SMEs is also

determined by the notional interest deduction. The bigger the companies decrease in marginal

tax rate caused by notional interest deduction, the smaller the companies proportion of debt in

the subsequent reporting period. We also find evidence that a decrease in the marginal tax rate

caused by notional interest deduction will have a greater impact on leverage than a decrease in

the marginal tax rate caused by other factors. Second, we observe a significant and positive

contribution of the change in a companies marginal tax rate caused by notional interest

deduction to the change in leverage over the past two years.

These findings support our hypothesis that the notional interest deduction has a negative

influence on leverage. In addition, we can conclude that the notional interest deduction,

embodied by the marginal tax rate, negatively influences the proportion of long-term debt to

total debt.

In summary, the findings of the research in our paper confirm the prediction that the

introduction of notional interest has caused a decrease in marginal tax rates, and consequently

an increase in the proportion of a companies equity. Additionally, the results generally

support static trade-off and pecking order arguments concerning the control variables.


In order to have the most detailed information available, we did not include in our sample the

firms that report their financial statements through an abbreviated format. When these firms

are significantly smaller than the firms which report through a complete format, our findings

will be only generalizable for bigger SMEs.

Our regression model assumes that a companies capital structure is situated at an optimal

level every year. Literature (Flannery & Rangan, 2006) has already indicated that this is not the

case in reality. Companies do not convert immediately and completely toward a new target

structure when there is a significant change in its determinants. On the contrary, they only

partially change their level of debt due to transaction costs. In other words, the capital

structure will be also determined by historic debt levels. We therefore recognize the lack of

dynamics in our approach as a limitation to this research.

As this research only uses secondary information from financial statements, we do not know

for sure whether the companies in our sample actually deduct a notional interest from their

taxable income. Companies that satisfy the definition of SME according to article 15, §1 of

the Code of Companies have to chose between either a deduction of notional interest or a

deduction for newly invested assets. A lot of factors will influence this decision. For example,

companies with a relatively low equity will rather chose a deduction for investments. The

leverage of these companies will therefore not react to the introduction of notional interest as

they do not make use of it. This limitation creates opportunities for further research.

Furthermore, within the scope of a partial adjustment of capital structures, it will be

interesting to investigate the impact of notional interest deduction in the coming years. Any

contribution of the subprime crisis can then be studied as well.

The effect of notional interest deduction on the financing policy of SMEs V

REFERENCES

Articles

Ang, J.S., 1991, Small Business Uniqueness and the Theory of Financial Management, The Journal of

Small Business Finance, Vol. 1 (1), pp. 1-13.

Barclay, M.J. & C.W. Smith, 2005, The Capital Structure Puzzle: The Evidence Revised, Journal of

Applied Corporate Finance, Vol. 17 (1), pp. 8-17.

Blouin, J., J.E. Core & W. Guay, 2008, Improved Estimates of Marginal Tax Rates: Implications for

the Under-leverage Puzzle, Working Paper (University of Pennsylvania).

Cassar G., & S. Holmes, 2003, Capital Structure and Financing SMEs: Australian Evidence,

Accounting and Finance, Vol. 43, pp. 123-147.

Chittenden, F., H. Hall & P. Hutchinson, 1996, Small Firm Growth, Access to Capital Markets and

Financial Structure: Review of issues and Empirical Investigation, Small Business Economics, Vol. 8,

pp. 59-67.

Fama, E.F. & K.R. French, 2002, Testing Trade-off and Pecking Order Predictions About Dividends

and Debt, The Review of Financial Studies, Vol. 15 (1), pp. 1-33.

Flannery, M.J. & K.P. Rangan, 2006, Partial Adjustment toward Target Capital Structures, Journal of

Financial Economics, Vol. 79, pp. 469-506.

Graham, J.R., 1996a, Debt and the marginal tax rate, Journal of Financial Economics, Vol. 41 (1), pp.

41-73.

Graham, J.R., 1996b, Proxies for the Corporate marginal Tax Rate, Journal of Financial Economics,

Vol. 42, pp. 187-221.

Graham, J.R., 2000, How Big are Tax Benefits of Debt?, The Journal of Finance, Vol. 55 (5), pp.

1901-1941.

The effect of notional interest deduction on the financing policy of SMEs VI

Graham, J.R., 2001, Estimating the Tax Benefits of Debt, Journal of Applied Corporate Finance, Vol.

14 (1), pp. 42-54.

Graham, J.R., 2003, Taxes and corporate finance: A review, Review of Financial Studies, Vol. 16 (4),

pp. 1075-1129.

Graham, J.R. & L.F. Mills, 2008, Using tax return data to simulate corporate marginal tax rates,

Journal of Accounting and Economics, Vol. 46 (2-3), pp. 366-388.

Hamilton, R.T. & M.A. Fox, 1998, The Financing Preferences of Small Firm Owners, International

Journal of Entrepreneurial Behaviour and Research, Vol. 4 (3), pp. 239-248.

Heyman D., M. Deloof & H. Ooghe, 2008, The Financial Structure of Private Held Belgian Firms,

Small Business Economics, Vol. 30, pp. 301-313.

Holmes, S. & P. Kent, 1991, An Empirical Analysis of the Financial Structure of Small and Large

Australian Manufacturing Enterprises, The Journal of Small Business Finance, Vol. 1 (2), pp. 141-

154.

Jordan J., J. Lowe & P. Taylor, 1998, Strategy and Financial Policy in UK Small Firms, Journal of

Business and Accounting, Vol. 25 (1) & (2), pp. 1-27.

Klemm, A., 2007, Allowances for Corporate Equity in Practice, CESifo Economic Studies, Vol. 53 (2),

pp. 229-262.

Lopez-Gracia,J. & F. Sogorb-Mira, 2008, Testing Trade-off and Pecking Order Theories financing

SMEs, Small Business Economics, Vol. 31, pp. 117-136.

MacKie-Mason, J.K., 1990, Do Taxes Affect Corporate Financing Decisions?, The Journal of

Finance, Vol. 45 (5), pp. 1471-1493.

Michaelas, N., F. Chittenden & P. Poutziouris, 1999, Financial Policy and Capital Structure Choice in

U.K. SMEs: Empirical Evidence from Company Panel Data, Small Business Economics, Vol. 12, pp.

113-130.

The effect of notional interest deduction on the financing policy of SMEs VII

Modigliani, F. & M. Miller, 1958, The Cost of Capital, Corporation Finance, and the Theory of

Investment, American Economic Review, Vol. 48, pp. 261-297.

Myers, S.C., 1977, Determinants of Corporate Borrowing, Journal of Financial Economics, Vol. 5,

pp. 147-175.

Myers, S.C. & N.S. Majluf, 1984, Corporate Financing and Investment Decisions when Firms have

Information that Investors do not have, Journal of Financial Economics, Vol. 13, pp. 187-221.

Pettit, R. & R. Singer, 1985, Small Business Finance: A Research Agenda, Financial Management,

Vol. 14 (3), pp. 47-60.

Rommens, A., L. Cuyvers & M. Deloof, 2008, The Dividend Policy of Privately Held Companies:

Stand-alone Companies and Group Companies in Belgium, Working paper (University of Antwerp).

Shackelford, D.A. & T. Shevlin, 2001, Empirical Tax Research in Accounting, Journal of Accounting

and Economics, Vol. 31, pp. 321-387.

Shevlin, T., 1990, Estimating Corporate Marginal Tax Rates With Asymmetric Tax Treatment of

Gains and Losses, Journal of the American Taxation Association, Vol. 11 (1), pp. 51-67.

Shyam-Sunder, L. & S.C. Myers, 1999, Testing Static Tradeoff Against Pecking Order Models of

Capital Structure, Journal of Financial Economics, Vol. 51, pp. 219-244.

Sogorb-Mira, F., 2005, How SME Uniqueness Affects Capital Structure: Evidence From a 1994-1998

Spanish Data Panel, Small Business Economics, Vol. 25, pp. 447-457.

Stiglitz, J.E. & A. Weiss, 1981, Credit Rationing in Markets With Imperfect Information, American

Economic Review, Vol. 71 (3), pp. 393-410.

Van der Wijst, N. & R. Thurik, 1993, Determinants of Small Firms Debt Ratios: An Analysis of Retail

Panel Data, Small Business Economics, Vol. 5, pp. 55-65.

The effect of notional interest deduction on the financing policy of SMEs VIII

Books

Beghin, P., K. Flamant & I. Van De Woesteyne, 2007, Handboek Vennootschapsbelasting,

Antwerpen-Oxford: Intersentia.

Gujarati, D.N., 1995, Basic Econometrics, New York: McGraw-Hill Book Company.

Ooghe, H., M. Deloof & S. Manigart, 2003, Handboek Bedrijfsfinanciering, Antwerpen-New York-

Oxford: Intersentia.

Ooghe, H. & C. Van Wymeersch, 2006, Handboek Financiële Analyse van de Onderneming,

Antwerpen-Oxford: Intersentia.

Internet Websites

A&T Partners, Forceville M., 2008, De notionele intrestaftrek - een stand van zaken, URL:

< http://www.atpartners.be/documents/news-items/notionele_intrestaftrek_update.xml?lang=nl >

(03/12/2008).

Belgische Senaat, Mw. Anseeuw, 2006, Notionele intrestaftrek – Gevolgen voor de coördinatiecentra,

URL: <http://www.senate.be/www/?MIval=/publications/viewPubDoc&TID=50350417&LANG=nl>

(03/12/2008).

Federale Overheidsdienst: Buitenlandse zaken, Buitenlandse handel en Ontwikkelings-samenwerking,

Belgische economie, URL:

< http://www.diplomatie.be/nl/belgium/belgiumdetail.asp?textID=49043 > (03/12/2008).

Instituut van de Accountants en de Belastingsconsulenten (IAB), Hürner S.G., 2008, Artikelen

205quater, § 6 en 196, § 2 WIB 1992 en het begrip kmo, URL:

< http://www.iec-iab.be/nl/leden/Publicaties/mededelingen-van-het-Instituut/Pages/20080828-

Artikelen-205quater-%C2%A7-6-en.aspx > (21/02/2009).

Unie van Zelfstandige Ondernemers (UNIZO), 2007, Aantal KMO’s in Vlaanderen, Brussel en

Wallonië, pp. 1-3, URL: < http://www.unizo.be/statistieken/images/res347073_6.doc >

(06/05/2009).

Unie van Zelfstandige Ondernemers (UNIZO), 2008, De notionele intrestaftrek: voldoende

KMO-gericht?, pp. 1-19, URL: < http://www.unizo.be/images/res335062_1.doc > (30/10/2008).

http://www.atpartners.be/documents/news-items/notionele_intrestaftrek_update.xml?lang=nl

http://www.senate.be/www/?MIval=/publications/viewPubDoc&TID=50350417&LANG=nl

http://www.diplomatie.be/nl/belgium/belgiumdetail.asp?textID=49043



http://www.unizo.be/statistieken/images/res347073_6.doc

http://www.unizo.be/images/res335062_1.doc

The effect of notional interest deduction on the financing policy of SMEs IX

Appendix A: Definition of variables

Variables Definition Codes Financial Statement (complete format)

Leveraget (Total Liabilities + Provisions & Deferred Taxes)t / Total Assetst (|16|+|17/49|) / (|10/49|)

Long-term Debt Ratiot (Long-term Liabilities + Provisions & Deferred Taxes)t / (Total Liabilities + Provisions & Deferred

Taxes)t

(|16|+|17|) / (|16|+|17/49|)

Financial Debt Ratiot (Long-term Financial Liabilities + Short-term Financial Liabilities)t / (Total Liabilities + Provisions &

Deferred Taxes)t

(|170/4|+|43|) / (|16|+|17/49|)

Ln(Gearing)t Ln[(Total Liabilities + Provisions & Deferred Taxes)t / Equityt] = Logistical transformation of Leverage (|16|+|17/49|) / <10/15>

Ln(Long-term to Short-term Debt

Ratio)t

Ln[(Long-term Liabilities + Provisions & Deferred Taxes)t / (Short-term Liabilities + Accruals)t] =

Logistical transformation of Long-term debt ratio

(|16|+|17|) / (|42/48|+|492/3|)

Ln(Financial to Non Financial Debt

Ratio)t

Ln[(Long-term Financial Liabilities + Short-term Financial Liabilities)t / (Provisions & Deferred Taxes +

Long-term Non Financial Liabilities + Short-term Non Financial Liabilities + Accruals)t] = Logistical

transformation of Financial debt ratio

(|170/4|+|43|) / (|16|+[|17|-

|170/4|]+[|42/48|-|43|]+|492/3|)

MTRt-1 (incl. NID) Current and expected future taxes paid on an additional euro of income earned today, taking into

account NID.

MTRt-1 (excl. NID) Current and expected future taxes paid on an additional euro of income earned today, NOT taking into

account NID.

∆MTRt-1 (incl.-excl.) The difference between current and expected future taxes paid on an additional euro of income earned

today, when we apply NID and when we do not apply NID.

Profitabilityt-1 EBITt-1 / Total Assetst-1, where EBIT denotes Earnings Before Interest and Taxes <70/64> / |10/49|

Investmentst-1 [(Total Assetst-1 + Depreciationst-1 + Impairment Lossest-1 - Revaluationst-1) - Total Assetst-2] / (Total

Assetst-1 + Depreciationst-1 + Impairment Lossest-1 - Revaluationst-1)

[(|20/58|+|630|+<631/4>+<651>-

|760/1|+|660|+<661>)-|20/58|] /

(|20/58|+|630|+<631/4>+<651>-

|760/1|+|660|+<661>)

Growth Opportunitiest-1 Intangible Assetst-1 / Total Assetst-1 |21| / |20/58|

Sizet-1 Ln (Total Assetst-1 / 1000) |20/58|

Tangibilityt-1 Fixed Tangible Assetst-1 / Total Assetst-1 |22/27| / |20/58|

Dividend Payoutt-1 Dividendst-1 / Total Assetst-1 |694| / |10/49|

Aget-1 Age of the firm at the time since date of incorporation

Average Workforcet-1 Average number of people who are employed within the company

Turnovert-1 Ratio of annual sales to inventory |70|

The effect of notional interest deduction on the financing policy of SMEs X

Appendix B: Empirical predictions by the main theories and their outcomes concerning the determinants of capital structure in SME financing

Variable Theory Effects on Capital Structure

Expected

Relation

Relation in

previous

research

Actual

Relation

Marginal tax rate Trade-off theory Firms which pay more taxes will have higher optimal debt levels + + +

Profitability Pecking order theory More profitable firms will need less debt - - -

Trade-off theory More profitable firms will have higher optimal debt levels +

Investments Pecking order theory Firms with more investments will need more debt + + +

Agency theory Firms with more profitable investments will have a lower level of debt -

Growth opportunities Agency theory Firms with greater growth opportunities will be less levered - + +

Agency theory Firms with greater growth opportunities will have less long-term debt - + -

Size Trade-off theory Smaller firms will have lower optimal debt levels + + -

Tangibility Agency theory Firms with a high proportion of tangible assets will be more levered + (no consensus) +

Dividend Payout Agency theory Firms with a high payout ratio will be less levered - - (not significant) +

Notes: The expected relation refers to the relevant theory and shows the expected sign of the coefficient in a regression. The relation in previous research indicates the sign that other researchers

have found. The actual relation denotes the sign of the coefficient in the outcomes of our study.

the effect of notional interest deduction on the financing

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