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All content in this area was uploaded by Teshome Mengstu Mekonen on Aug 08, 2023
Content may be subject to copyright.
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ASSESSMENT OF FACTORS THAT AFFECTS
CAPITAL STRUCTURE OF MEDIUM AND
LARGE - SIZED ENTERPRISES IN EASTERN
ZONE OF TIGRAY REGION
TESHOME MENGSTU,
Assistant Professor, Department of Accounting and Finance
Dr. V. PRABAKARAN
Assistant Professor, Department of Accounting and Finance
NEGASI MEKONEN
Lecturer, Department of Accounting and Finance,
College of Business and Economics, Adigrat University, Ethiopia.
ABSTRACT
The purpose of the study is to assess the factors affects capital structure in medium and large- size business enterprises
in eastern zone Tigray Region. Secondary source of data audited financial statements from 2014 to 2018 G.C was
used and multiple regression analysis was used. The fining indicated that age and size were positively and significant
determinant factors for capital structure. Tangibility, ROA and Growth were positively related to debt to equity ratio.
And earning volatility variables were found negatively and insignificantly related to debt to equity ratio.
Keywords: Leverage, ROA, earning volatility, and Tangibility.
I. INTRODUCTION
Capital structure refers to ‘the mix of debt and equity maintained by the firm’ (Margaretha, F. 2014). Nawi, H. M.
(2015) categories capital structure into four main parts: capital and retained profits, family loans, debt, and equity.
Alternatively, Freeman. (2013) suggests five types of source of finance, namely owner equity, related person debt,
trade credit, bank loan, and other debt or equity such as credit cards, venture capital, and government loans. On the
other hand, Joseph, E., Et al. (2018) classifies sources of finance into two categories: long-term finance such as equity
from private investment and other people’s money, bank loans, leasing, and hire purchase, and short-term finance,
for instance, bank overdrafts, short-term loans, and factoring. Nawi, H. M. (2015) categories it into three types: private
investment (e.g. Personal monies and funds from friends and families), public investment (e.g. Government loans,
grants, and public equity finance) and private external finance (e.g. bank loans and overdrafts, asset finance and asset-
based finance). Different study confirms a significant association between the availability of finance and medium and
large-size firm’s growth (Zeneli, F and Zaho, L. 2014). Leading to the notion of a financial gap. The finance gap
refers to ‘a situation where a firm has profitable opportunities, but there are no, or insufficient, funds (either from
internal or external sources) to exploit those opportunities’ (Daskalakis, N. 2012).
According to Abdullah and Manan (2010), accessibility and sufficiency of funds are the major barrier to the growth
of medium and large size enterprises. Kira, A. R. (2013) suggests that the financial accessibility of medium and large
size enterprises could be achieved through improving understanding of their financial practices. Hence, it is important
to investigate the determinants of capital structure of medium and large size enterprises to understand their financial
practices further.
Most capital structure studies to date are based on data from developed countries’ firms and very few studies provide
evidence from developing countries.
The capital structure of medium and large size enterprises has not also been investigated; there is no clear
understanding on how banks construct their capital structure and what internal (firm-specific) factors influence their
corporate financing decision. Therefore, given the unique financial features of medium and large enterprise and the
environment in which they operate, there is a strong ground to conduct a separate study on capital structure
determinants in banks. Ethiopia differs from other developing countries previously studied in such a way it has no
secondary capital market, which makes things easier for firms to raise funds and choose the best mix of debt and
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equity sources (Kibrom, M. (2010). This study, therefore, tried to examine determinants of capital structure of the
medium and large sized firms by using its firm-specific determinant factors.
II. STATEMENT OF THE PROBLEM
Any kind of business activity depends on finance to meet its plant assets and working capital requirements, as well
as finance is accelerating engine of business activities. Whether the businesses are big or medium, they need funds
to fulfill their business activities. Accordingly, the capital structure decision of a company is at the heart of other
decisions in the area of corporate finance. The Capital structure is one of the most intriguing fields in financial
management (Muhammed, A. 2014).
A number of factors have been identified to have an influence on a firm’s capital structure of the medium and large
size enterprises’ in different countries according their economic developments. Existing theoretical frameworks from
finance and strategic management set out to explain the determinants of the capital structure of medium and large
size enterprises’. These include pecking order theory (Donaldson, 1961 and Myers, S. C. 1984), trade-off theory
(Jensen, M.C. et.al 1984), agency theory ((Jensen, M.C et.al 1984), and financial growth cycle theory (Berger and
Udell, 1998) from the finance paradigm, and theoretical frameworks developed by several authors in the strategic
management paradigm. Although numerous empirical studies have been undertaken to examine the determinants of
capital structure on the basis of these theories, there is still no agreement among scholars and economists as to which
of the existing theories present the best description of the actual behavior of firms.
In addition, while there is a broad and growing body of empirical studies investigating the influence of these factors
on firms’ capital structure, the findings are not always consistent in terms of direction of the association between
capital structure and its determinants. Graham, J. R., & Leary, M. T. (2012) established that, although a lot of studies
had been done in investigating the capital structure of the firms, the results obtained are still unclear. They asserted
that it might be due to wrong measurement of key variables, investigation on the wrong models or issues,
misspecification of managerial decision process, or unresponsive of owner-managers. Most studies on capital
structure theories in different countries are conducted using the data set of large firms. These studies have contributed
a lot to these theories, i.e., Evidence based upon these firms tends to support these theories. Little evidence obtained
through empirical investigation of Medium and large Enterprises firms in developed nations. Here in our country
Ethiopia there is no any study conducted on determinants factors that effect of capital structure in Medium and large
Enterprises specifically in the Eastern Zone of Tigray Region. This fact reveals a great need for study to update the
existing evidence. And attempts to test the validity of these theories to Medium and large Enterprises Eastern Zone
of Tigray Region with available data set.
III. O
BJECTIVE
S
OF THE STUDY
a) General objective of the study
The main objective of the study was to find out the factors that affect the capital structure in medium and large -sized
enterprises in the Eastern Zone of Tigray Region.
b) Specific objective of the study
1. To find out the variability of capital structure with the size of the firms.
2. To investigate the influence of tangibility of firms’ assets on capital structure.
3. To find out the extent to which profitability influences the capital structure of the firms.
4. To examine the influence of earnings volatility on firms’ capital structure.
5. To investigate the variability of capital structure with the growth of the firms; and
6. To find out the influence of firms’ age of its capital structure.
IV. REVIEW OF LITERATURE
According to Harris and Raviv (1991), theories of capital structure have identified a large number of potential
determinants that might have an impact on debt levels. Among these factors, which have been found by a large
number of studies to influence the firms’ capital structure are size, tangibility, profitability, risk, non-debt tax shield,
growth, uniqueness, dividends, free cash flow, liquidity, age, and regulation.
4.1 Size
Kashefi-Pour, E., & Lasfer, M. (2012). Basing on the panel data they found size being positively related to leverage
across firms’ sizes (small, medium, and large firms). Thus, they concluded that large firms use more debt in their
capital structure compared to small companies. Bas et al. (2009) also by examining the differences in the determinants
of capital structure decisions of private and listed firms, and small and large firms in developing countries, found size
being positively related to leverage. Thus, they concluded that as firms get larger, their debt increases. This conclusion
indicates that large firms have positive associations with leverage, while small firms have a negative association.
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Daskalakis, N., & Thanou, E. (2012) by examining a number of hypotheses relating to the capital structure decision
in relation to the firms’ size, i.e., Distinguishing among micro, small and medium firms they found the size of the
firm being positively related to leverage. And, concluded that larger firms are associated with higher debt, as found
by other studies and supported by theoretical considerations.
4.2 Tangibility
Agency theory suggests that equity-holders of leveraged firms have an incentive to invest in risky investment to
expropriate wealth from the firm's debt-holders. If debt can be collateralized, the borrowers are restricted to use the
funds for a specified project. Since no such guarantee can be used for projects that cannot be collateralized, creditors
may require more favorable terms. This reveals a positive relation between debt ratios and the capacity of firms to
collateralize their debt (Jensen, M.C. and Meckling, W.H. 1976)
According to Bulletin, A. E. (2015) firms with high collateralizable tangible assets tend to have easier access to debt
by pledging those assets as collateral. Anwar, W. (2012) suggest that firms with large amount of fixed assets tend to
incur debt at relatively lower rate of interest by providing these assets to creditors as an assurance. Thus, firms with
a higher percentage of fixed assets tend to borrow more as compared to firms whose cost of borrowing is higher
because of having less fixed assets.
In contrary, Buferna, F., Et. al. (2005) suggest a negative relationship between tangibility and leverage, i.e., Support
the pecking order theory. Daskalakis, N., & Thanou, E. (2012) in examining a number of hypotheses relating to the
capital structure decision in relation to the firms’ size, i.e., Distinguishing among micro, small and medium firms
they found tangibility being negatively correlated with leverage. This result leads them to the conclusion that firms
view tangible assets as a stable source of return which provides more internally generated funds and leads firms to
use less debt, following the pecking order theory.
4.3 Profitability
The pecking order theory explains the relationship between firm profitability and capital structure. It suggests that
firms prefer internal finance first, and then, if external financing is required, they issue the safest security first. That
is, they start with debt, then possible hybrid securities such as convertible bonds, then perhaps equity as a last resort
(MYERS, S. C. 1984). According to this theory, firms that are profitable and therefore generate high earnings use
less debt capital than those generate low earnings, thus it suggests an inverse relationship between profitability and
leverage. Consistent to this theory, Titman, S. and Wessels, R. (1988) suggest that firms with high profit tend to
maintain relatively lower debt ratios since they generate funds from internal sources (retained earnings).
In contrast, trade-off theory predicts that profitable firms have more debt since bankruptcy costs are lower and interest
tax shields are more valuable for profitable firms (Murray, Z., & Vidhan, K. 2010). Profitable firms are more attractive
to financial institutions as lending prospects; therefore, they can always take on more debt capital (Chiang, Y., Et al.
2010) This theory suggests a positive relationship between profitability and debt.
Daskalakis, N., & Thanou, E. (2012) by examining a number of hypotheses relating to the capital structure decision
in relation to the firms’ size, i.e., by distinguishing among micro, small and medium firms they found profitability
being negatively related to leverage. Having this result, they concluded that firms that generate relatively high internal
funds tend to avoid debt financing.
4.4 Earnings Volatility
Trade-off theory suggests that earnings volatility is a proxy for the probability of financial distress and firms are
expected to pay a risk premium to outside fund providers. Thus, it predicts an inverse relationship between earnings
volatility and leverage. The pecking order theory also predicts the same and it suggests that to reduce costs of capital
caused by earnings volatility, firms tend to use internally generated funds first and then outside funds.
According to Cassar, G. and Holmes, S. (2003) if firms are likely to be exposed to agency and bankruptcy costs, they
tend to reduce the level of debt within their capital structure. One factor that impacts such exposure is firms operating
risk, i.e., the more volatile firm’s earnings streams, the greater the chance of the firms defaulting and being exposed
to such costs. Consequently, these firms with relatively higher operating risk will have incentives to have lower
leverage than firms with more stable earnings. Anwar, W. (2012) also suggest that the magnitude of earnings volatility
is a sign of expected bankruptcy. Firms with higher volatility are considered risky because they can go bankrupt. The
cost of debt for such firms should be more and thus these firms tend to employ a low level of debt within their capital
structure.
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4.5 Growth
The pecking order theory suggests that firms with higher growth opportunities need external finance to cover their
investments when they do not generate enough internal funds (MYERS, S. C. (1984). Moreover, this theory predicts
that firms with more investments should accumulate more debt over time (Frank and Goyal, 2008). Thus, according
to this theory, growth and leverage are expected to be positively related.
In contrast, both the trade-off and agency theories predict a negative relation between leverage and growth. The
former suggests growth firms lose more of their value when they go into distress, and the latter suggests as growth
options increase, asset substitution problems also become more severe. In high growth firms, it is easier for equity-
holders to increase project risk and it is harder for debt-holders to detect such changes. Thus, debt is costlier for firms
with high growth opportunities.
Myers, S. C. (1984) referring to agency theory, holds the view that firms with growth opportunities will have a smaller
proportion of debt in their capital structure. This is because conflicts of interest between debt and equity holders are
especially serious for assets that give the firm the option to undertake such growth opportunities in the future.
Daskalakis, N., & Thanou, E. (2012) in examining a number of hypotheses relating to the capital structure decision
in relation to the firms’ size, i.e., Distinguishing among micro, small and medium firms they found growth being
positively related to debt for all groups of firms. The result leads them to the conclusion that high-growth firms are
most likely to exhaust internal funds and use debt as a good alternative in their search for additional capital, as raising
equity may be difficult and time-consuming for smaller firms.
4.6 Age
It is believed that as firms continue longer in business, they establish themselves as an ongoing business, thus increase
their capacity to access more debt. Esperanca et al. (2003), referring to agency theory suggests that financiers use the
reputation of the firms as a measure of their creditworthiness. Reputation refers to the good name firms have built up
over the years (historical) and which is understood by the market, which has observed their ability to meet their
obligations in a timely manner. Managers concerned with a firm reputation tend to avoid riskier investments in favor
of safer investments, even when equity-holders do not approve the safer the investment, thus reducing debt agency
cost.
Johnson, S. (1997) also argues that the reputational capital of older firms is sufficient to ensure they will avoid actions
harmful to lenders even though they are unmonitored, and thus can borrow in public debt markets. These arguments
indicate a positive association between age of the firm and leverage.
In contrast, referring to pecking order theory, Macan Bhaird, C., & Lucey, B. (2010) suggests that older firms are
able to accumulate funds and needless to borrow either long-term or short-term. In other words, a new firm will not
have had time to retain funds and may be forced to borrow. Esperança et al. (2003), however, found age being
negatively related to both long-term and short-term debt. Hutchinson (2003) also found age being negatively related
to short-term debt, but insignificant.
V. RESEARCH METHODOLOGY
5.1 Research Design
The researchers used a mixed research approach which is quantitative and qualitative research approach. But the
tendency this research was on quantitative research approach beside to the qualitative research approach. And the
study was employed a survey design administered through structured record review.
5.2 Sampling Design
In this study, a list of Medium and large business Enterprises in Eastern Zone of Tigray Region which were Adigrat,
Wukro, Freweyni (including Endaga hamus), and Hawzeni towns from 1, 204 total population 175 were taken as a
population of the study those who fulfil the five year /2014-2018 G.C/ financial statement data and based on the
following sample size determination, but the selection of the areas/towns/ from Eastern Zone of Tigray were be based
on purposive sampling because the researchers believe that those who have a large number of medium and large size
enterprises’ can provide a good information and degree of collaboration.
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TABLE 1
Sample size for ±3%, ±5%, ±7% and ±10%
Precision Levels Where Confidence Level is 95% and P=0.5
S. No
Size of Population
500
Sample Size (n) for Precision (e) of:
±3%
±5%
±7%
±10%
1
600
A
240
152
86
2
700
A
255
158
88
3
800
A
267
163
89
4
900
A
277
166
90
5
1000
A
286
169*
91
6
2000
714
333
185*
95
7
3000
811
353
191
97
8
4000
870
364
194
98
9
5000
909
370
196
98
10
6000
938
375
197
98
11
7000
959
378
198
99
12
8000
976
381
199
99
13
9000
989
383
200
99
14
10,000
1000
385
200
99
15
15,000
1034
390
201
99
16
20,000
1053
392
204
100
17
25,000
1064
394
204
100
18
50,000
1087
397
204
100
19
100,000
1099
398
204
100
20
>100,000
1111
400
204
100
a = Assumption of normal population is poor (Yamane, 1967). The entire population should
be sampled
*= the researcher was taken a sample in between 169* and 185*
Source: University of Florida sample size determination
5.3 Method of Data Analysis
Multiple regression analysis was used to determine whether there exists a relationship between the multiple
independent variables (Determinants = Profitability, Tangibility, Size, Growth, Age, Earning) and the dependent
variable (Leverage = Debt to Equity Ratio). One regression equation was used to test the hypotheses constructed in
relation to firm-specific determinants (Profitability, Tangibility, Size, Growth, Age, and Earning) and the leverage
(Debt-Equity Ratio).
Data were regressed using the STATA application software and the resulted (or obtained) regression outputs are
analyzed. On top of this, MS Excel 2016 was also used to compute and feed convenient data into the STATA
employed.
The data were used and hypotheses were tested and analysis of the result is made based on the multiple regression
output. First, data is tested to ensure the validity of the classical linear regression model (CLRM) assumptions.
Second, test of the hypotheses was developed here and made based on the general estimated model which was
examined the relationship between the leverage ratio and its determinants for the medium and large enterprise firms.
LEV = α + β1 (SZ) + β2 (TG) – β3 (PR) – β4 (EV) – β5 (GR) + β6 (AG) + u
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Where,
α = constant term
βs = regression parameters
u = an error term
SZ = Size of the firm.
TG = Tangibility of the firm (asset)
PR = Profitability of the firm
EV = Earnings volatility of the firm
GR = Growth of the firm
AG = Age of the firm
With the above multiple regression equation, since it is panel data, the impact of each explanatory variable on leverage
was assessed in terms of the statistical significance of the coefficients ‘βs’. Using a 1%, 5%, and 10% level of
significance, an estimated coefficient was also statistically significant tested: at 1%, if p-value ≤ 0.01, at 5%, if p-
value ≤ 0.05 and at 10%, if p-value ≤ 0.1. It is conventional to use a 5% significance level, but 10% and 1% are also
commonly used (Brooks 2008). The signs in the model reveal the expected relationship between the dependent
variable, and independent variables.
5.4 H
ypothe
s
i
s
of the Study
Based on these theories and studies, the researchers hypothesize that:
Hypothesis 1: There is a positive relationship between the size of the firm and debt ratio.
Hypothesis 2: There is a positive relationship between tangibility of the firm’s assets and debt ratio.
Hypothesis 3: There is a negative relationship between the profitability of the firm and debt ratio.
Hypothesis 4: There is a negative relationship between earnings volatility of the firm and debt ratio.
Hypothesis 5: There is a negative relationship between the growth of the firm and debt ratio.
Hypothesis 6: There is a positive relationship between age of the firm and debt ratio.
FIGURE 1
Conceptual Framework
Size
Tangibility
Growth
Profitability
Earning
volatility
Age
Leverage
Sources: Researcher own drawing from Literature 2018/19
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TABLE 2
Summary of Variables and their Definition/Measurements
Variables
Definition/ measurements
Relationship with
leverage which expected
Dependent variable
Leverage = total debt / Total assets
Explanatory variables
Size
Total asset
+
Significant
Tangibility
Fixed asset/total asset
+
Significant
Profitability
Earning before tax/ total asset
-
Significant
Growth
% change in total asset= Total Assets t−Total Assets t−1
Total Assets t−1
-
Significant
Age
Subtracting the business year of establishment from
each year
+
Significant
Earnings volatility
Standard deviation of earnings before tax (EBT)
-
Significant
Sources: Organized from different literatures 2018/19
VI. RESULTS AND DISCUSSION
6.1 Descriptive Statistics
The average (mean) leverage of firms was 0.4126977 and this indicates that the firms financed (leveraged) with debt
at approximately 41% of the asset. Even the standard deviation shows that 0.2245105 the firms had in the last five
years, focused more on debt financing than on equity financing. The average annual profitability ROA of the firms
under investigation was 0.0728817. And the dispersion was 0.1000582 which indicated that the individual, firm had
a constant profitability rate every year. The mean of asset composition was 0.3942559. This indicated that the firms
fixed assets represent only 39 percent of the total assets. The standard deviation was 0.230063. The firm’s total assets
have an average growth rate of 0.9498715 that is 95 percent for the five years of the study period. The standard
deviation was 3.489979. The age of the firms was varied from 7 years to 19 years. The mean was 3.559556 and
standard deviation also was 12.97518
Lastly, during the five years of the study period, the earning volatility variable values show that the firms mean value
of 504195.5 and standard deviation also was 4.05.
FIGURE 2
Descriptive Statistics
Sources: from STATA result 2018/19
-60%
-40%
-20%
0%
20%
40%
60%
80%
100%
120%
LEVERAGE TANGIBILITY SALES
GROWTH
FIRM SIZE ROA AGE EARNING
VOLI
percentage
variables
Obs Mean Std. Dev. Min Max
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6.2 Correlations between the Variables
Table 3 below depicts a correlation matrix of the variables. The table revealed that size, profitability’s, age, and sales
growth had a positive correlation with leverage. This implied that large-sized firms and firms with high levels of
ROA tend to use more debt. In contrast, tangibility’s and earning volatility had an inverse relationship with leverage.
This implied that tangibility’s and earning volatility use less debt.
It also reveals a little evidence for multicollinearity (a problem that occurs when the explanatory variables are very
highly correlated with each other). The highest correlation (nearly 39 %) observed between firm size variable and
leverage variable is not even significant enough to cause multicollinearity. According to Brooks (2008), in any
practical context, the correlation between explanatory variables will be non-zero, i.e., A small degree of association
between explanatory variables will almost always occur but will not cause too much loss of precision.
TABLE 3
Correlation Matrix of Variables
Variables
Leverage
Tangibility
Sales G
Firm size
ROA
Age
Earning
VOL
Leverage
1.0000
Tangibility
-0.0755
1.0000
Sales Growth
0.0716
-0.0400
1.0000
Firm Size
0.2749
-0.1549
-0.1438
1.0000
ROA
0.0996
-0.0790
-0.1402
0.1216
1.0000
Age
0.1542
-0.4173
0.1492
-0.0134
-0.0674
1.0000
Earning
Volatility
-0.0542
0.3155
-0.1331
-0.0040
-0.0148
-0.1162
1.0000
Sources: STATA result 2018/19 TABLE 4 (A)
Linear Regression Model
Sources: STATA result 2018/19
TABLE 4 (B)
Linear Regression
Variables
Co- efficient
Std. Error
T- Value
p>/t/
95% confidence interval
Tangibility
0.06855
0.09124
0.76
0.447
-0.11058
0.24968
Sales Growth
0.00353
0.00270
1.31
0.193
-0.00180
0.00887
Firm Size
0.05625
0.01448
3.88
0.000
0.02766
0.08484
ROA
0.18936
0.14852
1.27
0.204
-0.10384
0.48255
Age
0.009226
0.00434
2.13
0.035
0.00066
0.01779
Earning
Volatility
-3.76e-10
7.42e-10
-0.51
0.613
-1.84e-09
1.09e-1
Constant
-0.70846
0.27779
-2.55
0.012
-1.256867
0.16005
Sources: STATA result 2018/19
Table 4 (b) presents the regression results of determinants of Leverage MLs between 2014 and 2018. The regression
summary statistics pane (Table 4 (b) upper right) results and analyses are discussed as follows. The R squared is
0.119 which indicates that about 12 percent of the variability of Leverage ratio is explained by the selected firm’s
variable (Profitability, Tangibility, Size, Growth, Age and earnings volatility). In other words, about 12 percent of
the change in the dependent variable is explained by the independent variables that are included in the model.
6.3 Hypothesis Testing and Discussion of Results
Table 5 presents the summary of the regression results for the equation of firm’s leverage using the determinants of
capital structure as explanatory variables.
Results obtained from analysis, expressed in terms of the signs and statistical significance of the coefficients for the
selected six independent variables, are presented in Tables 5. The conducted hypothesis testing and discussed results
Source
SS
DF
MS
Numbers of observation = 175
Model
1.02214444
6
.170357407
F(6,168) = 3.81
Residence
7.50578941
168
.044677318
Probability > F = 0.0014
Total
8.52793386
174
.049011114
R-square = 0.1199
Adjusted R-square = 0.0884
Root MSE= .21137
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are categorized on the basis of these independent variables and focused on their relationships with capital structure
theories. TABLE 5
Firm Specific Analysis of Determinants of Capital Structure
Independent variable
Dependent variable Leverage
Value of the coefficient
T-test
Significant level
Tangibility
0.695537
0.76
(0.47)
Insignificant
Sales Growth
0.0035347
1.31
(0.193)
Insignificant
Firm Size
0.0562508
3.88
(0.00)
Significant 1% level
ROA
0.1893574
1.27
(0.24)
Insignificant
Age
0.009226
2.13
(0.035)
Significant 5% level
Earning Volatility
-3.76e-10
-0.51
(0.613)
Insignificant
Number of observations = 175
F-Statistics = 3.81
Prob > F = 0.0014
R2 = 0.1199
Source: STATA result 2018/19
In addition, to verify if capital structure decisions that are made in the firms to provide empirical support for existing
theories, regression results of this study, summarized in Table 5, are compared with the following table, Table 6, of
summaries of hypothesized, expected and observed theoretical signs of independent variables.
TABLE 6
Hypothesized, Expected and Observed Signs of the Independent Variables
Variables
Definition/ measurements
Relationship
with leverage
which expected
Actual Result
Dependent variable
Leverage = total debt / Total assets
Explanatory variables
Size
Total asset
+
Significant
+
Significant
Tangibility
fixed asset/total asset
+
Significant
+
Insignificant
Profitability
Earning before tax/ total asset
-
Significant
+
Insignificant
Growth
% change in total asset=
Total Assets t−Total Assets t−1
Total Assets t−1
-
Significant
+
Insignificant
Age
subtracting the business year of
establishment from each year
+
Significant
+
Significant
Earnings volatility
standard deviation of earnings before tax
(EBT)
-
Significant
-
Insignificant
Source: Researcher’s own computation 2018/19
Test of the research hypotheses was made based on the relationship of the dependent variable and the explanatory
variables. Therefore, the following subsections deal with hypothesis testing and the interpretation of the regression
results presented above.
6.4 Leverage with Profitability
Research hypothesis, one was formulated for the assessment of the relationship between leverage and profitability
based on pecking order theory. Beta coefficient associated with profitability (PR) accepted the first null hypothesis.
In this study, profitability is estimated to be positively related to the firms’ leverage ratio and this relationship is found
statistically insignificant. It implies that profitable firms in firms maintain a high debt to equity ratio. This result is
consistent with predictions of Pecking order theory which states that firms prefer to finance first with internal funds
before raising external financing. Further, this outcome is also consistent with most previous studies (Titman, S. and
Wessels, R. 1988; Rajan, R. G. et al., 1994; and Booth et al., 2001). Hence, with an inverse relationship between
profitability and financial leverage, it can be concluded that highly profitable firms maintain a high debt to equity
ratio and they utilize more equity source compared to debt for making their capital structure.
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6.5 Leverage with Tangibility
Research hypothesis two was formulated to estimate the relationship between tangibility and leverage based on static
trade-off theory. Beta coefficient associated with Tangibility (TN) accepted the second null hypothesis and proved
that there is a positive relationship between tangibility and capital structure of firms.
In this study, the sign of the tangibility variable coefficient was positive, but not statistically significant. This result,
tangibility being insignificant variable, contradicts with various previous research findings. However, the observed
sign coincides with the Static trade-off theory, pecking order theory and agency cost theory that theorize positive
relationship between leverage and tangibility.
6.6 Leverage with Size
Research hypothesis three was formulated to estimate the relationship between size and leverage based on static trade-
off theory. The result of beta coefficient linked with the size (SZ) accepted the third null hypothesis and proved that
there a positive relationship between leverage and size of commercial banks.
This study found the size to be highly statistically significant at the 1 percent level and have a positive impact on the
commercial bank’s leverage. This suggests that larger commercial banks in Ethiopia tend to have higher leverage
ratios and borrow more capital than smaller commercial banks do. To express it in figure, assuming other determining
factors constant, for a unit increase in size, there is a 1.95-unit positive increase in debt to equity ratio. The observed
result is consistent with the result of static trade-off theory. Major empirical studies also found a positive relationship
between size and leverage. For instance: Titman, S. and Wessels, R. (1988), Rajan, R. G. et al., (1994), and Booth et
al., (2001) provided the evidence of significant and direct relationship between size and capital structure measure.
Since the result of size variable indicated a significant statistic, it is estimated that size does have significant role in
making debt ratio and determining the capital structure of Ethiopian commercial banks.
6.7 Leverage with Growth
Research hypothesis predicted that a positive relationship exists between capital structure and growth, but the
regression result of beta coefficient linked to growth (GR) rejected the fourth null hypothesis favoring the alternate
hypothesis that infer a negative relationship between capital structure and growth variable. The negative result
contradicts with POT but supports STT and ACT. To conclude, growth is found to be insignificant factor for deciding
the capital structure issues in the MLs.
6.8 Leverage with Age
The result of beta coefficient linked to age variable accepted the fifth null hypothesis and proved the positive
relationship between capital structure and age of commercial banks in Ethiopia.
In this study, age is estimated to have a significant positive relationship with the leverage of commercial banks. The
positive relationship is statistically significant at 5 percent significance level. This implies that older commercial
banks use more debt than younger or newer ones do. Numerically, the 0.036 coefficients of age variable (making the
other variables constant) imply that every additional 1 year increases the leverage measure (DER) by 0.036.
This result in turn indicates that older banks have a reputation of credit and build a good relationship with creditors;
thus, they have better conditions to obtain debt and younger commercial banks rely more on equity financing, as they
are constrained by debt financing. The observed sign coincides with the static trade-off theory, but opposes the
pecking order theory. Accordingly, with 10 percent significance level and direct relationship between age and
leverage, it is expected that aged commercial banks in Ethiopia maintain a high debt to equity ratio and utilize more
debt source compared to equity source.
6.9 Leverage with Earning Volatility
The last research hypothesis, a hypothesis was developed to assess the relationship between leverage and earning
volatility. The result of beta coefficient associated with earning volatility variable accepted the null hypothesis and
proved that there is a negative relationship between capital structure and earning volatility of the MLs.
In this study, MLs is found to have a negative relationship with leverage and is not statistically significant. This result
is consistent only with STT for short term financing because banks are having more advantage from the tax-shields
by using more interest paying deposits. Operating in a developing country, most MLs use short term financing due
to macroeconomic factors, and the characteristics of the firm.
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VII. CONCLUSION AND RECOMMENDATIONS
7.1 Conclusion
Capital structure remains an important and significant issue for academicians and corporate managers. This area has
been researched by many prominent scholars, namely Modigliani and Miller, Stewart Myers, Stephen Ross, Michael
Jensen and William Meckling. However, the capital structure has extensively been studied in the developed countries,
but only few researches focus on developing countries like Ethiopia. In this research, the main objective is to study
the significant firm-specific determinants of capital structure in the context of the MLs. The researcher in trying to
understand and decipher capital structure have come up with many theories. Among the famous theories are
Modigliani and Miller propositions, Static tradeoff, Pecking order and Agency Cost. After reviewing the theories
involved in capital structure, Titman, S. and Wessels, R. (1988), Harris and Raviv (1991) and Frank and Goyal (2003)
also researched the determinants of capital structure.
In this study, firm-specific determinants (internal factors) were examined in the context of Ethiopia. The regression
results of the capital structure model verified that 12 percent of the change in the dependent variable (capital structure
measured by debt to equity ratio) are explained by the independent variables that are selected and included in the
model. This implies that the leverage ratio of MLs is highly explained by the selected firm specific variables. The
result also showed two of the explanatory variables (size, age and size) are the significant firm-specific determinant
factors of capital structure in the firms and Tangibility, ROA and Growth found to be positively related to debt to
equity ratio. On the other hand, both earning volatility variables were found to be negatively related to debt to equity
ratio.
As a result, profitability variable attained an inverse relationship with the capital structure measure that supports,
Static trade-off theory but opposes the Pecking order theory. This suggests that highly profitable commercial banks
in Ethiopia maintain a high debt to equity ratio and they utilize more equity sources as compared to debt sources for
making their capital structure. Tangibility variable has a direct relationship with financial leverage, but the researcher
could not get enough statistical significance. That is, tangibility variable does not have influence on MLs decisions,
but has positive relationship. This relationship is consistent with the three theories of capital structure. Size variable
displayed a positive relation with financial leverage and is found to be a more important determinant of the firm’s
financing pattern. Larger firms in the firm’s sector maintain high leverage ratios. Therefore, the size’s relationship
with financial leverage supports the static trade-off theory and Agency cost theory but contradicts with Pecking order
theory.
The negative relationship between growth and leverage was also found out as an insignificant determining variable
of firms’ financing decision. The negative relationship between growth and financial leverage supports Static trade-
off and Agency cost theories of capital structure. The positive and significant relationship between age and leverage
strongly supports the Static trade-off theory, but go up against Pecking order theory. Lastly, tax shield variable
displays a positive relation with financial leverage. This positive relation verifies that banks with high tax-shield use
more debt than equity. This evidence is consistent with the static trade-off theory for only short term debts.
As a concluding remark, this research found that profitability, size, age, tangibility and growth are some among the
firm-specific factors that determine MLs capital structure and are also found to be similar to the factors that influence
the capital structure of firms in developed and other developing counties that are studied by different researchers.
However, in acknowledging the influence of other pertinent factors, like corporate governance, legal framework and
institutional environment of the countries; that are not included in this study, capital structure decision is not only the
product of firms own characteristics but also the macroeconomic environment in which the firm operates.
7.2 Recommendations
The findings of the study are deemed to benefit investors, professional managers, lenders, academicians and policy
makers in the country.
1. External investors and shareholders should appreciate the discussed variables that determine the capital structure
of a particular firm and observe its performance before making decisions of whether or not to buy or sell its
particular inventory.
2. Before lenders seek to protect themselves from excessive use of corporate leverage through the use of protective
covenants, they should consider the capital structure determinant variables studied above to evaluate and predict
the risk associated with lending capital to their respective borrowers.
3. The lack of high-quality databases might constitute the major barrier to conducting capital structure research in
the Tigray regional state. Consequently, there is a need, for policy makers at different levels, to design policies
which guide organizations to develop validated databases as more data becomes available in future. Using such
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databases can help examining and identifying additional variables that could influence the financial behavior of
Tigray firms and other studies.
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