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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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