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How does capital structure affect firm performance?

Recent evidence from Europe countries

Name: Guangchen Shen

Supervisor: Prof. Marco Da Rin

ANR number: 304011

Graduate Time: 1/09/2012

Department: Tilburg School of Economics and Management

July, 2012

Abstract: The relationship between capital structure and firm

performance has long been a topic in modern capital structure

literature. This present paper empirically exams the effect of capital

structure on firm’s performance based on 2007 data from 4 big

economics in Europe: Germany, France, Italy, and UK. The paper finds

a negative relationship between firm’s leverage and firm’s

performance and finds the relationship between capital structure and

firm performance may be not linear in case of Germany and France.

Key words: Europe, Capital Structure, Performance.

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How does capital structure affect firm performance?

Recent evidence from Europe countries

1. Introduction

1.1 Background

Now it is already 55 years since Modigliani and Miller made their first paper of

irrelevance theory regarding capital structure and firm performance. Lots of

literatures have been made on this topic and lots of departures of the irrelevance

theory have been found. For example, agency cost and information asymmetric

problem will affect the way a firm chooses its capital structure, thus affecting the

firm’s performance.

Being advantageous of nourish data of listed and unlisted companies in Europe, the

purpose of this study is to take a close look at the theories of capital structure and

firm performance. The paper focus on four largest economics in Europe and take

Germany, France, Italy, UK as sample, and then test them empirically on a

cross-sectional base.

1.2 Previous literature of capital structure and firm performance

The Modigliani and Miller (1958), in their known capital structure irrelevance theory,

claims that in an efficient market which has no tax, no transaction cost, no

information asymmetry , the value of a firm is unaffected by how that firm is

financed. MM theory predicts that there is no relationship between a firm’s capital

structure and its performance. The MM theory makes the core stone of the modern

corporate finance.

After the original paper in 1958, Modigliani and Miller (1963) states that, considering

the effect of corporate tax and tax deduction, the firm’s value will increase when firm

takes on more debt and this increasing amount will be the value of tax shield. This

means that firms will benefit from taking more leverage.

However, the Modigliani-Miller theorem will lose its explaining power when the

market is not efficient. The inefficient market concept is closer to reality, which has

taken taxes, information asymmetry, transaction costs, bankruptcy costs, agency

conflicts and other “imperfect” elements into considerations.

Since then, the following literature is awash by various extensions of the

Modigliani-Miller theory. Usually one of the “imperfect” elements mentioned above

is chosen, and the author will test how this introduction of imperfect elements will

affect the MM theory which is made on an efficient market assumption. And then a

lot of departures from irrelevance theory are found and the modern capital structure

theory is developing at the mean time.

When considering the corporate income tax, there is a tax shield benefit, so

according to Modigliani and Miller (1963), the firms should use as many debts as

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possible. But more debt than necessary in a firm’s capital structure is found in reality,

and it is obvious that excessive debt will introduce risk into firms. Then the concept

of bankruptcy cost is introduced as an offset effect to the benefit of using debt as tax

shield. Kraus and Litzenberger (1973) considered a balance between the benefit of

tax shield and the risk added from bankruptcy cost, so there will be an optimal

capital structure, any departure from the optimal capital structure cannot maximum

the value of the firm. This is the trade-off theory.

Myers (1984) identifies the pecking order theory. Because of information asymmetry,

managers will first use internal funds, then debt, then equity as their source of

finance when making financing decisions.

Jensen and Meckling (1976) identify agency cost. The agency cost theory suggests

that due to the effect of the separation of control and ownership, the agency of a

firm will not always work on the behalf of the shareholders. When firm raise debts,

there will also be conflicts between shareholders and bondholders. The conflicts

between shareholders and managers, as well as the conflicts between shareholders

and bondholders will all raise cost in the firms’ operation, investing and financing

activities.

Agency cost theory plays an important role, according to theory, in how we

understand the effect that leverage exerts on firm performance. The separation of

control and ownership in a firm might make the manager to max his personal utility

rather than that of shareholders. And this conflict between shareholders and

managers cause agency cost.

Under agency cost theory, when firms take a higher leverage, due to the rising

potential bankruptcy probabilities, the managers are also faced with the risk of losing

their positions. The managers then will act on the best interest of shareholders and

managers will have less free cash flow to use for their individual interest. Less

empire-building activities, less under-investment problems, better investment

decisions are also expected. So it should be expected to observe a positive

relationship between firm performance and firm leverage.

However, when the rise of debt decreases the amount of money that mangers can

use for their personal purposes, it also decreases the money that company can use

for investment and raises the cost of outside financing. If the proportion of debt in

capital structure increases above a certain level, the adding cost of debt includes a

higher bankruptcy cost, higher financial distress problem and more conflict between

shareholders and debt holders, thus damaging the firm performance.

What’s more, according to the pecking order theory, firms tend to use internal funds

rather than debt in financing activities because the information asymmetry problems.

So more profitable firms should choose less debt, a negative relationship between

leverage and firm performance is expected under the pecking order theory.

The empirical test result of the relation between firm’s leverage and firm

performance is mixed.

Abor (2005) compares the capital structures of publicly quoted firms, large unquoted

firms, and small and medium enterprises in Ghana and made research on the

influence of capital structure on profitability of listed companies on the Ghana Stock

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Exchange. The result suggests a positive relationship between firm’s leverage and

performance. Capon et al. (1990) conduct a meta-analysis of results from 320

published studies related to financial performance, and find a positive relationship

between usage of leverage and the financial performance. Ari (2011) use eastern

Asian companies as a sample and find a positive between firm’s performance and the

leverage.

Zeitun and Tian (2007) use a sample consist of 167 Jordanian companies and the

research shows a significantly negative relationship. Rajan and Zingales (1995), use

data from G7 countries and find a negative relationship between firm leverage and

firm performance. Onaolapo (2010) use data from Nigeria and find a significantly

negative relationship between firm’s debt ratio and a firm’s ROE or ROA. Majumdar

and Chhibber (1997), Fama and French (2002), Booth (2001) also claim negative

relationship between financial leverage and performance.

1.3 Theories of Reverse Causality from Performance to Capital Structure

This paper is about how capital structure affects firm performance, but there is also

possibility that the firm’s performance will affect the way that managers choose the

capital structure of the firm. If performance can affect capital structure as well, then

there will be a reverse causality problem.

Berger and di Patti (2002), using data from the US banking industry, use a

simultaneous-equation model which shows how performance can affect capital

structure. They give two hypotheses regarding how performance can affect capital

structure: the efficiency-risk hypothesis and the franchise-value hypothesis.

The efficiency-risk hypothesis claims that higher profitability often reduces the

bankruptcy cost of a firm. Because when a firm is performing well, the firm will

usually have a high expected return. A high expected return can be seen as a

substitute of equity, because they can both be used for deduction of potential

portfolio risk of the firm. So according to the positive relationship between

performance and expected return, and the substitute relationship between expected

return and equity, a firm with better performance will tend to use less equity in its

capital structure. This hypothesis suggests a positive relationship between a firm’s

leverage and its performance.

The franchise-value hypothesis, on the other hand, takes a new look from the aspect

of economic rent. According to the franchise-value theory, a better performance

might produce economic rents for firms in future, and firms are willing to take a

lower leverage to protect this franchise value. So when firms have better

performance, they tend to maintain a lot of equity in their capital structure. Contrast

to the efficiency-risk theory, franchise-value hypothesis indicates s a negative

relationship between a firm’s leverage and its performance.

According to these two hypotheses, firm performance can affect its capital structure

in two ways, and the two effects are opposite to each other. Berger and di Patti (2002)

do not actually solve the reverse causality problem, however, they give a new theory

about how firm performance can affect firm capital structure.

Some previous literatures develop some innovative approach to solve the problem by

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taking an exogenous shock which only affect the firm’s leverage or the company’s

competing environment. For example, Zingales (1998) study the truck industry using

deregulation as an outside shock. However, due to the limitation character of data, it

is not possible to find an instrument variable in regression to solve the reverse

causality. The paper stills needs further development in case of endogeneity

problem.

The paper is developed as follow. The second section will discuss the data, the third

section discuss expectations and method. The fourth section will build up the

methodology and analysis the result. The fifth section concludes.

2. Data and preliminary observations

The paper tests the relationship between capital structure and firm performance

using specific 2007 accounting data of companies in Germany, France, Italy and UK.

The data is collected from Amadeus Dataset (Analyses Major Databases from

European Sources). Amadeus Dataset has financial statement information of over 7

million European firms and includes listed and unlisted companies. The original data

of this paper is composed of 942,337 German companies, 794,486 French companies,

934,832 Italian companies and 2,385,106 British companies. All subsamples include

all listed and part of unlisted companies in each country.

2.1 Measurement

According to previous literature, there are numerous ways to measure the

performance of firms.

1) Use the information from financial statement (e.g. calculate the value of target

firm’s ROE).

2) Use book value with market information, Tobin’s q is a frequently used method.

3) Stock market return.

4) Modeled method like Z-score method.

Due to the lack of access to market information of the data from Amadeus Dataset,

this paper will use the first method, ROE as a proxy for firm’s performance

measurement. In preview literature, Zeitun and Tian (2007), Lemmon and Roberts

and Zender (2008) use ROE or ROA as a proxy for firm’s performance measure.

The paper uses debt to equity ratio as the proxy for leverage, log (Asset) as proxy for

firm size and the previous year sales growth rate as a proxy for firm’s growth.

Tangible asset is measured by using total tangible fixed asset divided by total asset

and the firm’s tax burden is measured by calculating the ratio of tax of net profit. The

Amadeus uses a 4-digit identifies to distinguish different industries, for simply, this

paper takes only the first 2 digit as identification for a firm.

All the definitions and construction of variables in regressions are listed in table 1

below:

Table 1 Definition of variables

The table illustrates the constructions of variables for analysis. All variables are calculated by the

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2007 data from Amadeus Dataset and are calculated on an annual base.

Variable Definitions Construction

ROE Firm's performance Gross profit / Total equity

LEV Leverage (Current liabilities + Non-current liabilities)/ Total equity

LEVsquare Square of Leverage Square of LEV

ASSET Total Asset Fixed Asset + Current Asset

SIZE Size of firm Log (Asset)

AGE Age in operation 2007 – Birthday + 1

GROWTH Sales growth in previous year Sales of 2007/ Sales 2006 - 1

TAN Tangible asset ratio Tangible Fixed Asset/ Total Asset

TAX Tax burden Tax / Gross profit

INDdummy Industry identification The first two digit of nace_rev11 as industry identification.

2.2 Summary

The summary of all the variables are shown in Table 2 below:

Table 2 Summary of variables

The sample is all firms in the merged Amadeus Data in 2007. The table gives information about

mean, median and standard deviance (SD) of variables, as well as the summary statistics in

subsamples: France, Germany, Italy and UK. The definitions of variables are provided in Table 1.

Country ROE LEV SIZE AGE GROWTH TAN TAX

FRANCE

Mean 0.27 3.20 5.30 10.82 0.01 0.14 0.14

Median 0.20 1.38 5.28 7.00 0.00 0.07 0.11

SD 6.86 71.85 1.53 11.06 348.56 0.20 0.73

GERMANY

Mean 0.50 8.72 5.55 15.65 0.23 0.18 0.79

Median 0.12 0.71 5.42 10.00 0.25 0.06 0.25

SD 36.91 411.81 2.04 20.67 553.39 0.26 21.29

ITALY

Mean 0.04 12.60 6.05 10.79 0.06 0.20 1.09

Median 0.05 3.32 6.09 7.00 0.00 0.07 0.28

SD 14.69 299.74 1.91 11.81 202.48 0.27 68.92

UK

Mean 3.73 12.05 4.55 9.03 -0,06 0.25 0.35

Median 0.24 0.63 4.39 5.00 0.00 0.11 0.24

SD 486.26 1684.12 2.47 11.73 1264.84 0.30 52.90

Total

Mean 0.76 9.74 5.25 10.87 0,03 0.20 0.63

Median 0.13 1.16 5.26 6.00 0.00 0.08 0.17

SD 200.09 1024.73 2.18 13.98 709.25 0.27 50.44

The mean ROE of the full sample is 0.76, which means that out of every 100 equity,

76 net profits is earned. Germany and France have a ROE of 0.5 and 0.27,

respectively. Italy has a low ROE ratio, while the UK sample has a quite high mean

ROE of 3.7. So in terms of ROE, the UK firms seem to be more efficient than firms

from other countries.

The four Europe country firms have an average debt to equity ratio of 9.7 and the

specific number fluctuates within a small range among each country. Italy has the

highest leverage ratio of 12.6 while France takes a small leverage on average of 3.2.

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What’s more, the average size of firms of each country does not differ from each

other a lot. The Germany firm has an average age of 15 years, more than the other 3

countries that have an average age of around 10 years operating history. All countries

have an average sale growth 3% in the year of 2007, but German firms have a quicker

sale growth of 23%, higher than France’s 1% and Italy’s 6%, while UK firms have a

negative sale growth of -6%. The average tangibility asset ratio is 20%, UK and Italy

firms have ratios more than 20%, Germany has a ratio of 18%, and France has a ratio

of 14%. Considering the tax burden variable, it is easy to find that Italian firms are

suffering from a much heavier tax burden than firms in other countries. On average

the tax amount is even more than the amount of net profit.

At first sight, there is a positive relationship between leverage and firm’s ROE, but

this is just obvious among the German, France and UK sample. The Italian firms take

a rather high leverage, but the ROE is the lowest among the four countries. As for

other variables, size seems to exert a negative effect on firm’s performance, Growth

has a positive relationship with ROE and the other relationships are not easy to tell

straight from a simple descriptive statistics.

In case of potential correlation problem among dependent variables, a correlation

matrix is made and correlation between variables is shown in table 3.

Table 3 Correlation matrix

The sample is all firms in the merged Amadeus Data in 2007. The table presents correlation

between each variable and the p-value of the correlation. For example, the correlation between

ROE and LEV is -0.02, the p value equals 0.00. The definitions of variables are provided in Table 1.

ROE LEV SIZE AGE GROWTH TAN TAX

ROE 1.00

LEV -0.02 1.00

(0.00)

SIZE -0.00 0.02 1.00

(0.01) (0.00)

AGE -0.00 -0.00 0.26 1.00

(0.08) (0.00) (0.00)

GROWTH 0.45 0.00 0.01 -0.00 1.00

(0.00) (0.26) (0.00) (0.02)

TAN -0.00 -0.00 0.10 0.04 -0.00 1.00

(0.10) (0.04) (0.00) (0.00) (0.01)

TAX -0.00 -0.00 0.01 0.00 -0.00 -0.00 1.00

(0.95) (0.86) (0.00) (0.15) (0.68) (0.45)

(Coefficients in first line, p-value in second line)

In the correlation matrix, we can observe a negative relationship between ROE and

leverage, and it is significant. What’s more, size has a negative correlation with ROE

as well, but the correlation is quite small. There is no obvious correlation between

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ROE and AGE, GROWTH, TAN and TAX because all these correlations are not

significant. There is also small but significant positive correlation between leverage

and AGE, SIZE, which means that the age and size of a company might have effect on

the firm’s leverage decision making.

3. Expectations and methodology

3.1 Expectations and hypotheses

LEV: The agency theory predicts that, when firm uses more debt, the manager will

face more risk of bankruptcy and then be more efficient, agency cost decreases and a

better performance of company is expected. So under the agency theory, there

should be a positive relationship between leverage and the firm’s performance.

When a firm is operating well, the potential bankruptcy risk is low and the firm can

be able to use a heavier leverage. However, under pecking order theory, firms with

better profitability will tend to use less debt. As the franchise-value hypothesis in

Berger and di Patti (2002) goes, the firms may use equity to protect the rents or

franchise value, they will still maintain equity when they are performing well. And

when a firm is over-leveraged, additional cost of debt will damage the performance

of company. So the relationship between leverage and firm performance is mixed.

LEV square: Square of leverage is used to test if there is a non-linear relationship

between firm’s leverage and firm performance.

SIZE: Size is an important determinant of a firm’s performance. Larger firms are

usually more diversified, better-managed and have a larger risk tolerance. Small firms,

on the other hand, may find it more difficult to solve the information asymmetry

problem and thus may appear to perform worse than big companies. Penrose (1959)

argues that bigger company is easier to achieve economic of scale and then results in

a better performance. The paper expects a positive relationship between firm size

and firm performance. The following hypotheses will be tested:

Hypotheses: There is a positive relationship between size and firm performance.

AGE: When firms grow older, they are usually more experienced. During the growth,

firms invest in research and development, store their human capital resource, and

gradually discover what they are good at. Hopenhayn (1992) shows that older firms

are expected to enjoy better performance. The paper expects a positive relationship

between firm age and firm performance. The following hypotheses will be tested:

Hypotheses: There is a positive relationship between age and firm performance.

GROWTH: It is obvious that growth opportunity is important to a firm’s performance.

The paper uses sales growth as proxy. Brush (1999) argues that sales growth

influence the firm’s ability to catch opportunities of investment, the use of new

technology and provides opportunities for economic of scale, thus benefiting the

health of the whole company. The paper expects a positive relationship between firm

sales growth rate and firm performance. The following hypotheses will be tested:

Hypotheses: There is a positive relationship between sales growth rate and firm

performance.

TAN: Tangibility is also a consideration related to firm performance, when firm has

more tangible asset, it is faced with less bankruptcy risk and is considered to have

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more flexibility when making financing decisions. Murillo (2007) has a result of

positive relation between firm tangibility asset ratio and firm performance. The

paper expects a positive relationship between firm tangibility asset ratio and firm

performance. The following hypotheses will be tested:

Hypotheses: There is a positive relationship between tangibility asset ratio and firm

performance.

TAX: Tax levels and tax structure will all influence the profitability and performance of

company. When a firm is performing well, it receives a better profit, which means the

firm will have more profit before tax, so it will tend to pay more tax. So this paper

expects a positive relationship between firm tax burden and firm performance. The

following hypotheses will be tested:

Hypotheses: There is a positive relationship between firm tax burden and firm

performance.

3.2 Methodology

This paper will use two OLS regressions to study the relationship between capital

structure and firm performance. The first regression is set without square of leverage;

the second regression is set with square of leverage to exam if there is a non-linear

relationship between the dependent variable and the independent variable. All

variables other than leverage are control variables which control for the characters of

firms that may affect firm performance. Robustness check is added to both

regressions in case of heterogeneity problem. All data and regressions are run by

STATA. The two regression formulas are shown below:

ROE = α + β1 Lev + β2SIZE + β3AGE + β4GROWTH + β5INDdummy + β6TAN

+ β7 TAX + β8DIV + e

ROE = α + β1 Lev + β2 Lev2 + β3SIZE + β4AGE + β5GROWTH + β6INDdummy

+ β7TAN + β8 TAX + β9DIV + e

4. Result and Discussions

Table 4 and table 5 below show the regression result.

Table 4 Regression without square of leverage

The sample is all firms in the merged Amadeus Data in 2007. The table presents the results of

OLS regressions for the full sample and four subsamples: Germany, France, Italy and UK. The

dependent variable is ROE and independent variables are LEV, SIZE, AGE, GROWTH, TAN, TAX and

INDdummy. The results are composed of coefficients of regression, the t statistic and the product

of coefficient and SD as a check for economic significance, the coefficients of industry dummies

do not show in the table. For example, in full sample, the coefficients of LEV is -0.01, the t

statistic of this coefficient is -1.16, a one standard deviation change in LEV is associated with a

-7.26 change in ROE. Robustness check is added to the regression. The definitions of variables are

provided in Table 1.

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Hypothesis

Full

Sample Germany France Italy UK

LEV - -0.01 -0.01 -0.11*** -0.02* -0.00

(-1.16) (-0.67) (-3.60) (-2.15) (-0.68)

[-7,26] [-5,11] [-8,25] [-7,04] [-1,87]

SIZE + -0.48* 0.16 0.18** 0.07 -1.02***

(-2.14) (0.71) (3.21) (1.44) (-4.58)

[-1,04] [0,32] [0,27] [0,13] [-2,51]

AGE + 0.00 -0.02 -0.01*** -0.00 0.01

(0.54) (-1.63) (-4.23) (-0.69) (1.15)

[0,06] [-0,41] [-0,14] [-0,05] [0,15]

GROWTH + 0.08 0.00 -0.00 -0.00 0.13***

(1.87) (0.68) (-1.64) (-0.02) (5.14)

[0,06] [0] [0] [0] [0,09]

TAN + 0.64 -1.01 -0.03 -0.19 0.22

(1.27) (-0.88) (-0.17) (-1.21) (0.19)

[0,17] [-0,27] [-0,01] [-0,05] [0,07]

TAX + 0.00 -0.01 0.06 0.00 0.00

(0.23) (-0.59) (1.17) (1.94) (0.44)

[0,04] [-0,24] [0,04] [0,14] [0,13]

_cons

3.49* 0.86 -0.50 -0.46 8.83***

(2.48) (0.62) (-1.96) (-1.59) (4.71)

N 385248 37737 140459 140194 66858

R2

0.425 0.049 0.658 0.123 0.759

adj. R2 0.425 0.048 0.658 0.123 0.759

(Coefficients in first line, the product of coefficient and SD in brackets, t statistics in parentheses, * p < 0.05, ** p < 0.01, *** p <

0.001)

Table 4 shows the regression result without square of leverage. It is apparent that in

all full-sample and sub-samples, leverage shows a negative relationship with ROE.

But this negative relationship is only significant in the case of France and Italy, while

in the full sample case, it is only significant at a 12.3% level. There are three different

explanations to the negative sign. The first one is pecking order theory, firms tend to

use internal funds rather than debt in financing activities, so more profitable firms

should choose less debt. The second explanation is that, the firm is over leveraged by

the manager, thus hurting the company’s performance. The third explanation is the

franchise-value suggested by Berger and di Patti (2002): franchise value is associated

with high efficiency from the possibility of liquidation, which means that equity

serves as a substitute of firm performance. The negative relationship between

leverage and ROE is consistent with the result of previous empirical literature of

Rajan and Zingales (1995), Rajan and Zingales (1995) use the financial data of G7

countries of 1990s. As their cross-sectional regression shows, the leverage of

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Germany, France, Italy and UK all has a negative relationship with profitability, but

only the UK sample is significant at 10%. Compared to the result of Rajan and

Zingales (1995), this paper finds a more significant relationship between France and

Italy while UK is becoming less significant. However this may due to the fact that

Rajan and Zingales (1995) use the data in 1990s and these two papers are using

different proxies for profitability and leverage. Zeitun and Tian (2007), Majumdar and

Chhibber (1997) and Booth (2001) found similar relationship between capital

structure and firm performance in their findings.

For size, the sign is positive in case of Germany, France and Italy but not significant. A

positive sign is consistent the previous expectations, the bigger firms are expected to

achieve better performance. However, in case of UK, size has a negative and

significant effect on ROE of UK companies, small companies sometimes suffer less

from agency problem and may have a more flexible structure to fit the change (Big

companies are too big to change), the similar negative relationships are found by

Yang and Chen (2009).

Age works as a negative and significant determinant for ROE of Germany and France.

This is in contract with the original expectations. It seems that older companies are

not performing better than their young competitors. When firms grow older, they

may become more inert and inflexibility. Barron et al. (1994) gives two explanations,

the first one is because older firms do not fit well to changing environment, and

second one is that older companies become ossified by accumulated routines and

old structures.

GROWTH has a positive effect on firm performance, extremely significant in case of

UK. This finding indicates that previous sales growth serves as a quite good predictor

of firm performance and work very well in case of UK. And this is basically in

consistent with our previous expectations.

Tangible asset ratio has a general positive but insignificant effect on ROE. Our original

expectation is that companies with more tangible asset have more flexibility in

investing, a better access to financing. This result confirms our previous expectations.

The relationship of TAX and ROE is positive, only significant in case of companies in

Italy. This is also in consistent with our previous expectations.

What’s more, this paper adds the product of coefficient and SD of variable to

illustrate the economic significance of independent variable. The paper times the

standard deviation of the independent variable with the coefficient of independent

variable, this result shows the unit change of dependent variable due to one

standard deviation change of independent variable. Take the independent variable of

Germany as example, one standard deviation change in Germany firm’s age will

cause a -0.41 change in the ROE of Germany firms. The mean of German firms’ ROE

is 0.50, so the -0.41 change is quite a lot compared to the mean and is obvious of

economic significance. The papers find that, except for the cases of growth, all

independent variables are economic significant.

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Table 5 Regression with square of leverage

The sample is all firms in the merged Amadeus Data in 2007. The table presents the results of

OLS regressions for the full sample and four subsamples: Germany, France, Italy and UK. The

dependent variable is ROE and independent variables are LEV, SIZE, AGE, GROWTH, TAN TAX and

INDdummy. The results are composed of coefficients of regression, the t statistic and the product

of coefficient and SD as a check for economic significance, the coefficients of industry dummies

do not show in the table. For example, in full sample, the coefficients of LEV is -0.01, the t

statistic of this coefficient is -1.22, a one standard deviation change in LEV is associated with a

-6.26 change in ROE. Robustness check is added to the regression. The definitions of variables are

provided in Table 1.

Hypothesis

Full

Sample Germany France Italy UK

LEV - -0.01 0.03 0.00 -0.02* -0.00

(-1.22) (1.36) (0.22) (-2.05) (-0.64)

[-6,26] [12,81] [0,14] [-7,04] [-1,3]

LEVsquare - -0.00 -0.00 0.00*** -0.00 -0.00

(-0.67) (-1.39) (18.04) (-0.00) (-1.45)

[-31,29] [-29,9] [6,79] [-0,03] [-32,16]

SIZE + -0.47* 0.11 -0.01 0.07 -1.00***

(-2.11) (0.59) (-0.72) (1.55) (-4.50)

[-1,02] [0,22] [-0,02] [0,13] [-2,47]

AGE + 0.00 -0.01 -0.00* -0.00 0.01

(0.51) (-1.62) (-2.01) (-0.70) (1.07)

[0,06] [-0,31] [-0,02] [-0,05] [0,14]

GROWTH + 0.08 -0.00 0.00 -0.00 0.13***

(1.87) (-1.45) (1.16) (-0.03) (5.14)

[0,06] [0] [0] [0] [0,09]

TAN + 0.62 -2.21* -0.08 -0.19 0.18

(1.24) (-2.47) (-1.05) (-1.29) (0.16)

[0,17] [-0,58] [-0,02] [-0,05] [0,06]

TAX + 0.00 0.01 0.01* 0.00 0.00

(0.21) (0.58) (1.97) (1.95) (0.43)

[0,03] [0,22] [0,01] [0,14] [0,12]

_cons

3.43* 1.09 0.24** -0.46 8.71***

(2.45) (1.00) (2.66) (-1.90) (4.65)

N 385248 37737 140459 140194 66858

R2

0.425 0.158 0.909 0.123 0.759

adj. R2 0.425 0.157 0.909 0.123 0.759

(Coefficients in first line, the product of coefficient and SD in brackets, t statistics in parentheses, * p < 0.05, ** p < 0.01, *** p <

0.001)

13

Table 5 shows regression with square of leverage. After adding the square of leverage,

the R square of regression of Italy and UK remain basically the same, but the

explaining power of regression of Germany and France increase dramatically, R

square of regression of Germany increase from 4.9% to 15.8%, France increases from

65.8% to 90.88%, and the sign of coefficient of leverage and ROE change. Although

the results are not significant at high levels, there is some evidence that in case of

Germany and France, the relationship between leverage and firm performance is not

simply linear.

5. Conclusion

This present paper empirically exams the effect of capital structure on firm’s

performance based on 2007 data from 4 big economics in Europe: Germany, France,

Italy, and UK. Being advantageous in nourish data of nearly 400 thousand companies

in these four economics with a lot of observations of unlisted companies, the

cross-sectional regression finds a negative relationship between firm’s leverage and

firm’s performance. This finding is quite similar compared to the findings of Rajan

and Zingales (1995), who use the data of G7 countries in 1990s. The similarity may

indicate that the relationship between capital structure and firm profitability in

Europe developed countries does not change during the past decades. The negative

relationship does not support the positive expectation made under agency cost

theory, however, the finding is in line with the predication of pecking order theory

and the franchise-value hypothesis.

In the second regression with a new variable square of leverage, the regression result

of Germany and France changes a lot. The explanation power of the second

regression has a dramatic rise compared to previous regressions without square of

leverage, and the sign of coefficients of leverage change. This indicates that in case of

Germany and France, the relationship between leverage and firm performance might

not be linear.

The paper confirms the positive effect of potential growth ability and tangibility ratio

on a firm’s performance, which is in consistent with the findings with previous

literatures. The age of a firm is found to be a negative determinant of firm

performance, which is not in consistent with many findings of previous literature.

The possible explanation could be older firms have slow reactions to the fast

changing business environment, or with the passage of time, firms tend to get old

routines and management structures which are not fit the nowadays business

environment.

This paper tries to make contributions in empirical test on relationship between

capital structure and firms’ performance based on recent data in Europe countries.

But due to limitations of data, the paper can only use basic accounting information of

a company, so there is no market information used in the regressions. Regression

result will be more solid if variable related to market information is introduced, for

example Tobin’s q as a proxy for firm performance.

14

What’s more, reverse causality is only discussed by not solved in this paper. It could

be capital structure that affects the performance, but there is also possibility that the

performance of a firm can affect the decisions made by managers about how to

construct a firm’s leverage. Further development can be made through introducing

instrument variable to rule out the possibility of such endogeneity problem.

15

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