INVESTIGATING THE LEVERAGE COMPOSITION OF PAKISTANI
FIRMS THROUGH THEIR DETERMINANTS
FARAH YASSER*
Abstract
To have an ideal mix of debt and equity in a balance sheet of an entity is
till to date a very complicated issue for managers as there is no such rule to
predict an optimal capital structure. An in-depth understanding is required for the
corporate culture, the degree of the development of the capital market and the
economy in which the firms operate. This study seeks to investigate the leverage
composition of Pakistani corporations through their determinants. Fixed effect
regression is used to show the relationship of determinants of capital structure on
leverage corporations listed on Karachi Stock Exchange (KSE) for the period of
2006 to 2013. The results suggest that agency cost, growth, age, and size are
significantly and negatively associated with the capital structure of Pakistan
firms, however, collateral value of asset is significantly but positively associated
with the capital structure of the firm. On the other hand, free cash flows, non debt
tax shield, profitability, business risk and bankruptcy cost are not significantly
associated with leverage composition of the firms and are against the signaling
theory and peaking order theory. The key importance of this study is that no prior
research was done for determinants like agency cost, free cash flows, bankruptcy
cost and age as determinants of capital structure for Pakistani firms among other
determinants. Further, this study does not confine to a particular sector rather it
covers all companies listed by Karachi Stock Exchange.
Key Words:
Leverage, Determinants of Capital Structure, Agency cost,
Collateral value of assets, Bankruptcy cost.
** Assistant Professor, School of Commerce and Accountancy, University of Management and
Technology, Lahore, Pakistan.
Email: farah.yasser@umt.edu.pk
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1.
Introduction
Capital structure irrelevance theory was introduced by Modigliani and Miller (1958)
and since then arguments have progressed for the leverage decision of the firm. MM
theory suggests that the value of the firm is independent of its capital structure under
certain assumptions. Even though this theory is based on unrealistic assumptions, there
are a number of variables that narrate the value to the firm and often identify as
determinants of the capital structure e.g. agency cost, collateral value of asset, growth,
free cash flows, and age of the firm, business risk, bankruptcy risk, profitability and non
debt tax shield. Hence, the main purpose of the firm is to maximize the wealth of the
stockholders by evaluating a suitable finance mix. Combination of debt and equity capital
becomes the most controversial corporate issue over the past four decades. The capital
structure decisions directly influence the market value of the firms and the cost of the
firm.
How to have an ideal mix of debt and equity in a balance sheet of an entity is till
to date a very complicated issue as there is no such rule to predict an optimal capital
structure. An in-depth understanding is required for the corporate culture, the degree of
the development of the capital market and the economy in which the firms operate. Firms
can only achieve their objectives through skillful intellectual managers and the
management can perform better without thinking about finance shortage or finance mix.
So soon after the arrival of (Modigliani & Miller, 1958), the western world started
financing their corporations without bothering the mode of finance i.e. debt or equity and
forgot about the equity debt mix and just focused on growth and achievement of
commercial business objectives. Companies achieved local and international remarkable
growth. Due to rapid growth historic numbers soon became meaningless. Corporations
started focusing more about their market values and future fund flows rather than the
book value of assets. Financial ratios are also designed on the market values of the
company. Human capital also becomes an important part of the company.
Pakistan is one of the developing countries with lot of issues involving unstable
micro and macroeconomic situations, political crisis, social behavior, geographical
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structure, complicated tax mechanism and non robust legal system. It is equally important
to find out those factors that influence a firm’s capital structure choice. However, in case
of developing economies, inadequate literature is available regarding capital structure of
corporations in Pakistan. Eldomiaty (2008) mentioned that because of the insufficient
information problems, the capital markets of the developing economies are not efficient
enough to compare with developed market. Therefore, the outcomes of the developed
countries cannot be generalized with the developing countries like Pakistan where
political risk is very high, foreign currency fluctuations are very frequent, business risk is
lofty, and the capital market is in developing phase. Most of the debate in the country on
low investment ratios has been centered around factors such as infrastructure, law and
order, skill shortages and bureaucratic hassles (Hussain, 2006).
The key importance of this study is that no prior research was done for
determinants like agency cost, collateral value of assets, free cash flows, bankruptcy cost
and age as determinants of capital structure for Pakistani firms. Further, this study does
not confine to a particular sector rather it covers all companies listed by Karachi Stock
Exchange. Therefore, this study shows an in depth analysis of determinants of capital
structure of Pakistani firms. Moreover, this study is very functional for the managers of
the corporations and provides guidelines for efficient use of the determinants of capital
structure in order to maximize firm performance. Government authorities, taxation bodies
and policy makers can also be benefited from the findings of the study. Hence, this study
also served the purpose of a rich contribution in the existing literature regarding the
determinants of capital structure of firms in Pakistan.
2.
Literature Review
Theories of corporate structure draw a closer attention in the world of finance
when Modigliani and Miller (1958) presented capital structure irrelevance principle. This
principle states that in a perfect market, whatever the mode of finance the firm uses, it is
irrelevance to the firm’s value. And since then, many other theories were presented with
the help of basic idea provided by Modigliani and Miller (1958) like trade off theory,
pecking order theory, OLI theory, signaling theory, etc. These theories highlight different
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determinants of capital structure and draw attention to their importance in the perspective
of capital structure.
Pecking order theory is based on asymmetric information of the firm. In this
theory, Myers (1984) explained that how a company prioritized its financing decisions.
The main theme of the theory is that the firm takes finances from easier sources first i.e.
internal finance. Pecking order theory also suggests that there is an inverse relationship
between profitability and leverage. Modigliani and Miller (1958) theorem was a very
simple utopian kind of model where it was assumed that there are no tax benefits, no
agency costs and bankruptcy cost. And it tried to convince its reader about being inert to
whether a capital is used or debt is employed for a firm’s operations. However, M&M
proposition II did take into account these costs and explained that although the firm’s
WACC decreases with debt inclusion, yet it becomes more risky. The signaling theory
talks about firm’s management decisions taken as being signals by the other stakeholders
to it. This allows good firms managers to signal to stakeholders about their firm’s value
and thus enable them to take decisions accordingly. Such popular signals involve
dividend policy, issue of bonus shares, stock splits.
2.1
Determinants of Capital Structure - Around the World
Al Amri and Al Ani (2015) examined determinants of capital structure for three
sectors (food, construction and chemical) of Omani industrial companies for the period of
2008 -2012. They found that there is a significant and positive relationship between risk
and tangibility and leverage and there is a significant but negative relationship between
growth rate and profitability and leverage, while there is no association with size. Baltacı
and Ayaydın (2014) explored Turkish banking sector and found that capital structure is
positively and significantly related with size, industry leverage and GDP growth. They
further find that capital structure is significantly but inversely related with financial risk,
profitability, tangibility and inflation.
Forte, Barros, and Nakamura (2013) investigated capital structure of Brazilian
firms and found that profitability is significantly and negatively related to capital
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structure. Also, growth is positively and significantly related to leverage. Further, size is
positively related and age is negatively related to the leverage.
Mac an Bhaird and Lucey (2010) explored 299 Irish enterprises and found that
age, intangibility, collaterals and size are the significant determinants of capital structure.
They further found that ownership structure, size, age and collateral are similar across
industry. Kouki and Said (2011) conducted research on 244 French listed companies and
found that trade off theory, pecking order theory and market timings are not significant.
Huang (2006) revealed an inverse relationship between leverage and profitability, non
debt tax shield, growth opportunities and managerial shareholdings.
2.2
Determinants of Capital Structure – Pakistan
Khan, Jan, and Khan (2015) explored cement sector of Pakistan. By using pooled
regression model, they found that there is an inverse relationship of firm size and
leverage of the firm which is against static trade off theory. Qadri (2015) investigated
Pakistani non financial firms listed on Karachi stock exchange for the period of 2004 to
2012. This study showed a significant and negative association between profitability and
leverage, supporting peaking order theory. Moreover, this study showed a significant and
positive association of tangibility and size with leverage supporting trade off theory.
Masnoon and Saeed (2014) examined automobile sector of Pakistan for the period
of 2008 to 2012. This study found that leverage has an inverse and significant
relationship with profitability and liquidity, whereas, leverage has a positive
insignificantly relationship with earning variability. Ahmed Sheikh and Wang (2011)
examined firms listed on Karachi stock exchange for the period of 2003 to 2007 and
found that profitability, liquidity, earnings volatility, and tangibility are negatively
associated to leverage, while firm size is positively associated to leverage. Non-debt tax
shields and growth opportunities do not appear to be significantly related to leverage.
Ahmad and Zaman (2013) analyzed textile sector listed on Karachi stock exchange of
Pakistan and revealed that profitability and size is significantly and negatively related to
leverage while tangibility and growth were positively related to leverage.
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Afza and Hussain (2011) investigated three sectors (automobile, engineering and
cable and electric goods) listed on Karachi stock exchange by using pooled data
regression model. The results of this study supported static trade off theory and pecking
order theory. Hijazi and Tariq (2006) investigated listed firms of cement industry of
Pakistan and found that firm size, tangibility , growth and profitability is associated to
leverage.Shah and Khan (2007) examined non financial firms listed on Karachi stock
exchange for the period of 1994 to 2002 and found that tangibility, volatility and non debt
tax shield are significantly related with leverage, hence confirming trade off theory and
profitability is significantly associated with leverage confirming pecking order theory,
whereas, size is insignificant to leverage.
From the above literature review, it can be wrap up that no prior research was
done for determinants like agency cost, collateral value of assets, free cash flows,
bankruptcy cost and age as determinants of capital structure for Pakistani firms.
However, these variables show a significant relationship with leverage in developed
countries Baltacı and Ayaydın (2014),
Forte et al., (2013), Mac an Bhaird and Lucey
(2010) etc. Therefore this study includes all those variables that have already tested in all
developed economies studies but first time include in developing ecomomy senario like
Pakistan.
3.
Theoretical Framework And Hypothesis Development
Agency Cost
Agency cost is one of the most important determinants of capital structure. It
begins with the conflicts of interest between debt holders and equity holders (Myers,
1977). Firm having high agency cost have high cost of debt and thus leads to have lower
debt ratio (Fama, 1980; Jensen, 1986; Titman, 1984).
Ho1: There is no significant relationship between agency cost and leverage of Pakistani
firms.
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Bankruptcy Cost
Bankruptcy cost depends on costs like legal fees, loss of sales, employees and
suppliers and the probability of its happenings. If financing through debt increase, the
probability of bankruptcy also increases and as a result bankruptcy cost increase. Firms
with higher bankruptcy cost have lower debt.
Ho2: There is no significant relationship between bankruptcy cost and leverage of
Pakistani firms.
Non Debt Tax Shield
It is usually argued that company with more non debt tax shields should have less
debt since the tax advantage of debt are comparatively less important (Akhtar & Oliver,
2009).
Ho3: There is no significant relationship between non debt tax shield and leverage of
Pakistani firms.
Profitability
Myers (1984) argued that if a firm is more profitable then it will have more
internal financing than external sources according to pecking order theory of capital
structure. Therefore it can be proposed that the firms with higher profit have higher
internal finance and hold less debt. Internal finance is less costly and easier whereas
external finance is more costly. Hence it can hypothesize that there is an inverse
relationship between profitability and leverage.
Ho4: There is no significant relationship between profitability and leverage of Pakistani
firms.
Size
Bigger firms usually have larger exposure to the public than smaller firms and
consequently need to provide more information to consumers, creditors, suppliers,
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forecasters and government personals (Cooke, 1991). Larger firms have more resources
to provide relevant information to stakeholders and as a result, larger firms have more
debt with more attractive terms as compare to smaller firms. Hence, a direct association is
expected between firm size and leverage. Empirical studies suggested size as a
determinant of capital structure (Ferri & Jones, 1979; Scott Jr & Martin, 1975) and
(Aggarwal, 1990).
Ho5: There is no significant relationship between size and leverage of Pakistani firms.
Collateral Value Of Assets
Rajan and Zingales (1995) found that tangibility of assets or collateral value of
assets is a determinant of capital structure. Corporations with more tangible assets are
expected to have more debt because having more tangible assets gets debt easily on more
favorable terms. On other side, Graham Jr. (1988) suggested that the corporations
having high intangible assets have lower cost of borrowings cause better security for debt
holders.
Ho6: There is no significant relationship between collateral value to assets and leverage
of Pakistani firms.
Business Risk
Business risk can be defined as the risk related with the future operations of the
company. Firms with less business risk, (the risk that is connected with the upcoming
business operations) are assumed more financial risks.
Ho7: There is no significant relationship between business risk and leverage of Pakistani
firms.
Growth
Theoretically it is suggested that the firm with higher growth rate will have lower
debt in their capital structure. A company that grows fast invests huge amount in research
and development.
Ho8: There is no significant relationship between growth and leverage of Pakistani firms.
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Age
As the firm grows, more information is available for the firm’s probable viability
in the future. More information cause less leverage in the capital structure of the firm.
Ho9: There is no significant relationship between age and leverage of Pakistani firms.
Free Cash Flows
Jensen (1986) define free cash flows as the cash flow that is left after all positive
NPV projects are funded. Harris and Raviv (1991) argued that firm with greater free cash
flows will have lesser debt and vice versa.
Ho10: There is no significant relationship between free cash flows and leverage of
Pakistani firms.
Table 1: Proxies for Variables
Variables
Leverage
Agency Cost
Free
Flows
Cash
Growth
Age
Non Debt Tax
Shield
Size
Proxies
(Long term debt) / (long term debt
+Market value of Equity)
(Cash and Marketable Securities) /
3 years of average total Assets
(EBIT+Depreciation+Amortization
-Tax-Interest-Dividends) / (Average
Total Assets)
(Change in Total Assets) / (Total
Assets)
Natural log (age of firm in years
from date of incorporation)
(Total
Annual
Depreciation
Expense) / Total Assets
Natural log of Total Sales
Collateral Value
of Assets
(Fixed Assets) / Total Assets
Profitability
(Net Income) / Total Sales
Business Risk
Volatility of Net Operating Income
Bankruptcy
Cost
SD of first Difference in EBIT /
Total Assets
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References
(Burgman, 1996),(Chkir & Cosset, 2001)
(Titman & Wessels, 1988)
(Jensen, 1986), (Akhtar, 2004)
(Jensen, Solberg, & Zorn, 1992) (Mehran,
1992), (Shah & Hijazi, 2004)
(Bracdley, Jarrell, & Kim, 1984), (Chaplinsky,
1984), (Lee & Kwok, 1988)
(Bradley, Jarrell, & Kim, 1984), (Titman &
Wessels, 1988)
(Ferri & Jones, 1979),(Scott Jr & Martin, 1975),
(Aggarwal, 1990),(Rajan & Zingales, 1995)
(Rajan & Zingales, 1995), (Friend & Lang,
1988)
(Chittenden, Hall, & Hutchinson, 1996)
(Doukas & Pantzalis, 2003), (Rajan & Zingales,
1995), (Shah & Hijazi, 2004)
(Burgman, 1996), (David M Reeb et al., 1998)
(Lee & Kwok, 1988)
(Bracdley, Jarrell, & Kim, 1984), (Lee & Kwok,
1988), (Chaplinsky, 1984)
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2016
Table 1 shows the proxies for dependent variable leverage and independent
variables agency cost, free cash flows, growth, age, non debt tax shield, size, collateral
value of asset, profitability, business risk, bankruptcy cost and foreign exchange risk.
4.
Research Methodology
4.1
Population
The initial data is collected from the publication of State Bank of Pakistan titled
as “Financial Statement Analysis of the Companies (Non-Financial) listed at Karachi
Stock Exchange”. The publication includes only non financial firms. Some data is also
collected from companies’ websites and annual reports.
4.2
Sample
In this study, random stratified sampling technique is used and data is collected
for the period of 2006 to 2013. SBP Publication contains 399 firms of different sectors.
Slovin’s sampling technique is used to determine the sample size for this study as used in
different previous studies. (Meyer, Mudambi, & Narula, 2011; Onimisi, 2010; Sharif,
Naeem, & Khan, 2012; Yasa et al., 2013). It is usually impossible to survey every
member in the population because of time or money constraint therefore, Slovin formula
is useful to determine the sample size at a given error tolerance.
Slovin’s formula is:
n = N/(1+Ne2)
Where;
n = Number of samples in the data
N = Total population
e = Error tolerance
I considered error tolerance 5%
Therefore:
n = __399_ _
1+399(.05)2
n = 199.77
= Approximately 200 firms
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Table 2: Industry-wise Sample Size by Using Slovin's Formula
Industries
TOTAL
SLOVIN’S FORMULA
SAMPLE
Textiles
Sugar and Other Food Products
Chemicals,
chemical
products
and
Pharmaceuticals
Electrical Machinery and Other manufacturing
Cement and other Mineral Products
Motor vehicles, trailers and auto parts
Fuel & Energy
Information, Communication, transport and
other services
Refined petroleum products
Paper, paperboard and products
Total:
155
54
43
(155*200)/400 = 77.5
(54*200)/400 = 27
(43*200)/400 = 21.5
78
27
22
39
28
22
18
22
(39*200)/400 = 19.5
(28*200)/400 = 14
(22*200)/400 = 11
(18*200)/400 = 9
(22*200)/400 = 1
20
14
11
9
11
9
9
399
(9*200)/400 = 4.5
(9*200)/400 = 4.5
(400*200)/400 = 200
5
5
202
From the table 2 it can be analyzed that Pakistan’s non financial sector is a
diversified sector with different nature of firms however, textile industry is the biggest
industry in Pakistan with highest number of companies and it ranges from spinning and
weaving to make up textile items. Sugar and other food is the second largest industry of
Pakistan and chemicals and pharmaceutical are the third largest industry. As Pakistan is a
developing country, high tech industries are smaller in number like motor vehicles, auto
parts, refined petroleum products, paper and board products, etc.
Table 3: Firms’ Years* Distribution of companies by Economic Groups
Economic Groups
Textiles
Sugar and Other Food Products
Chemicals, chemical products and Pharmaceuticals
Electrical Machinery and Other manufacturing
Cement and Mineral Products
Motor vehicles, trailers and auto parts
Fuel & Energy
Information, Communication, transport and other services
Refined petroleum products
Paper, paperboard and products
Total:
*Firms’ Years can be defined as the number of firms multiplied by the number
sample, i.e. 202 firms multiplied by 8years = 1616 firms’ years.
Total
624
216
176
160
112
88
72
88
40
40
1616
of years
%
38.0
13.4
10.9
10.4
6.9
5.4
4.5
5.5
2.5
2.5
100
in the
Table 3 represents the distribution of firm’s years with respect to economic
groups. Hence textile is the biggest economic group of Pakistan with total 624 firms’
years with 38% in total. The second largest economic group is sugar and other food
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products which have 216 firms’ years in total. Chemicals and pharmaceuticals and
electric machinery and other manufacturing are also big economic groups with 176 and
160 firm’s years respectively. In contrast refined petroleum products and paper, and
paper board products are the smallest economic groups with 40 firms’ years each.
Table 4: Descriptive Statistics of Leverage and Determinants of Capital Structure
LEV
AC
FCF
GRO
BC
AGE
NDT
S
PRO
F
CVA
BR
SIZE
N
Mean
Minimum
0.172
0.050
0.026
0.692
9.321
3.341
0.107
Std.
Deviatio
n
0.246
0.150
0.344
11.849
31.628
0.556
1.796
1612
1613
1615
1613
1613
1614
1613
Maximu
m
0.000
0.000
-0.277
-1.000
0.020
0.000
0.000
1.000
4.915
12.042
440.803
823.977
4.913
57.930
1595
-1.512
71.583
-2802.000
550.000
1613
1612
1595
0.473
-6.476
14.93
3
0.241
214.014
1.705
0.000
-6035.644
0.000
1.973
651.989
20.819
Table 4 shows the descriptive statistics of dependent variable (leverage) and
independent variables (determinants of capital structure) of six years from 2006 to 2013.
Leverage is 0.172 means on average generally Pakistani firms hold 17.2% debt in their
capital structure. Agency cost is 0.050 while free cash flows are 0.026. There is a positive
growth of corporations (69.2%) which is a good sign for Pakistan. Profitability is
negative (-151% ) which is a worrying sign on other hand. Financial crises, energy crises
and other unfavorable factors may be the reasons for negative profitability. Further,
average collateral value of asset is 0.473 and business risk of Pakistani firms is -6.476.
Average bankruptcy cost is 9.3321.
4.3
Ethical Issues
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As data is the secondary data, publically available on websites, no confidentiality
or anonymity issues will arise.
4.4
Data Analysis
Initial data is collected and entered into Microsoft excel worksheet. The collected
data has been entered accurately and systematically. The data for this research is the panel
data means the data is the combination of time series data and cross sectional data
therefore it has been organized accordingly and panel was created. In order to obtain the
accurate empirical results, this study is using STATA 12 which is a very useful statistical
tool for panel data. Different test have been applied in this research like descriptive
statistics for the comparison of mean of variables and regression analysis for the
relationship of variables.
5.
RESULTS AND DISCUSSION
Empirical model
LEV = α+ β1ACit+ β2BCit +β3NDTSit +β4PROFit +β5SIZit +β6CVAit + β7GROit +β8FCFit+
β9AGEit+ β10BRit +uit
Where, LEV = Leverage
AC = Agency Cost
BC = Bankruptcy Cost
NDTS = Non Debt Tax Shield
PROF = Profitability
SIZ = Size
CVA = Collateral value of Assets
GRO = Growth
FCF = Free Cash Flows
AGE = Age
BR= Business Risk
6.
Results And Finding
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Sample for this study contains data across firms and over time so panel data
analysis is appropriate. Panel data analysis has many advantages like it provide a hefty
number of data points, increasing the degree of freedom, also decreasing the co-linearity
among variables and helps in developing well-organized economic estimate (Hsiao,
1986).
Further, panel data has advantage of make out and determine those effects that
are simple not deductable in exclusive cross sectional or exclusive time series data
(Baltagi, 1995). Moreover, Hsiao (1986) mentioned that panel data allows the application
of variable intercepts models that initiate firm/industry type and/or time specific effects
into the regression equation that minimize or evade the omitted variable bias. The most
popular tools for analysis of panel data are fixed effect model and the random effect
model. In this thesis the author is using the following decision making criteria for
selection of the model presented by (Dougherty, 2011).
According to Figure 1, first of all, there is a need to perform both the fixed effect
regression and the random effect regression if the data is selected randomly. Then
Durbin-Wu Hausman’s (DWH) specification test is required. If the result of DWH test
rejects the null hypothesis, then fixed effect model should be used otherwise the random
effects model is required. And then, in case of random effects model, a further test is
required called Breusch Pagan Lagrange Multiplier (BPLM) Test to decide between the
random effects model and Pooled Ordinary Least Square (OLS) regression. Again, if null
hypothesis is rejected in case of BPLM test, the random effect model should be used
otherwise pooled OLS regression is required.
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Figure 1 Source: Adapted from (Dougherty, 2011). Decision making criteria for selection of model.
Source: Adapted from (Dougherty, 2011). Decision making criteria for selection of model.
Table 5.1: Fixed Effect Model
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LEV
Coef.
Std. Err.
t
P value
AC
-0.146
0.078
-1.860 0.063***
FCF
0.007
0.018
0.360 0.718
GRO
-0.002
0.001
-3.960 0.000*
AGE
-0.241
0.039
-6.220 0.000*
NDTS
0.000
0.004
-0.020 0.986
SIZE
-0.022
0.009
-2.580 0.010*
CVA
0.230
0.039
5.910 0.000*
PROF
0.000
0.000
0.760 0.447
BR
0.000
0.000
-0.380 0.705
BC
0.000
0.001
0.920 0.356
_cons
1.203
0.164
7.360 0.000
R-square within 0.0878, between = 0.1398, and overall = 0.098
F Statistics = 13.27, and Prob > F = 0.000, Variable is significant at
* 1, ** 5, and ***10% level of significance (two-tailed)
Table 5.2 :
Random Effect Model
LEV
Coef.
Std. Err.
z
P Value
AC
-0.1475
0.0618
-2.3800 0.017*
FCF
0.0051
0.0186
0.2700 0.7850
GRO
-0.0019
0.0005
-3.8800 0.000*
AGE
-0.0666
0.0161
-4.1400 0.000*
NDTS
-0.0001
0.0036
-0.0300 0.9780
SIZE
-0.0202
0.0056
-3.6100 0.000*
CVA
0.3210
0.0301
10.6700 0.000*
PROF
0.0000
0.0001
0.3300 0.7400
BR
0.0000
0.0000
-0.5200 0.6000
BC
0.0003
0.0004
0.7500 0.4550
_cons
0.5495
0.0990
5.5500 0.0000
R-square within 0.0711, between = 0.3148, and overall = 0.1920
Wald Chi2 = 210.19, and Prob > Chi2 = 0.000, Variable is significant at
* 1, ** 5, and ***10% level of significance (two-tailed)
Table 5.3 : Hausman Test
Fixed
Random
Difference
AC
-0.1458
-0.1475
0.0016
FCF
0.0066
0.0051
0.0016
GRO
-0.0022
-0.0019
-0.0003
AGE
-0.2414
-0.0666
-0.1748
NDTS
-0.0001
-0.0001
0.0000
SIZE
-0.0221
-0.0202
-0.0019
CVA
0.2298
0.3210
-0.0912
PROF
0.0001
0.0000
0.0000
BR
0.0000
0.0000
0.0000
BC
0.0005
0.0003
0.0002
Chi2 = 50.16, Prob > chi2 = 0.000
As mentioned earlier, data selected randomly for this study, there is a need to
perform both the fixed effect regression and the random effect regression according to
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Figure 1. Therefore, first of all these tests are applied to the sample of firms in Pakistan.
Both models are overall statistically good fit model as F test is significant in fixed effect
model and Chi2 is significant in random effect model in table 5.1 and 5.2. However, R 2
(within) is higher in fixed effect model as compare to random effect model (0.0878 vs
0.0711) and R2 between and overall is higher in random effect model as compare to fixed
effect model (0.1398 and 0.098 vs 0.3148 and 0.1920). In table 5.3, DWH test reject the
null hypothesis and shows significance at 1% level which means that this study entail the
fixed effect model and there is no need to further BPLM test and OLS test.
According to table 5.1, agency cost is significantly but negatively related to the
leverage (p-value = 0.063). Bankruptcy cost is not a significant determinant of capital
structure for Pakistani firms (p-value = 0.356). Non debt tax shield is not a significant
determinant of capital structure (p-value = 0.968). DCs (p-value = 0.453) or MNCs (pvalue = 0.591). Profitability is not significant (p-value = 0.447) which is against Pecking
Order Theory of Myers (1977). Size is a significant determinant of capital structure for all
Pakistani firms (p-value = 0.010). Collateral value of assets is a significant determinant of
capital structure (p-value = 0.000) and shows a positive relationship (coefficient = 0.023)
with leverage indicating that if collateral value of asset increases, the leverage of the
company also increases. Growth is a significant determinant of capital structure for the
sample of Pakistani firms (p-value = 0.000) at the significance level of 1%. The results
show a negative relationship with the leverage for firms (coefficient = -0.002). Age is
significant determinant of capital structure for firms (p-value = 0.0000) and shows a
negative relationship with the leverage. Business risk is not significant (p-value = 0.705).
Free cash flows is a significant determinant of capital structure for all firms (p-value =
0.0000) and for DCs (p-value = 0.0010). Free cash flows shows no relationship with
leverage for (p-value = 0.705).
Table 6: Pearson Coefficient Correlation
LEV
LEV
AC
FCF
GRO
AGE
NDTS
SIZE
1
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2016
CVA
PROF
BR
BC
AC
-0.1488
1
FCF
-0.0079
-0.0089
1
GRO
-0.0236
0.1248
-0.0024
1
AGE
-0.127
-0.076
0.0054
-0.0078
1
NDTS
0.011
-0.0021
0.7021
0.0194
-0.0018
1
SIZE
-0.1965
0.0365
-0.0183
-0.2376
0.0603
-0.0528
1
CVA
-0.0419
0.004
0.002
0.1836
-0.0054
0.0001
0.1809
1
PROF
0.4075
-0.0936
-0.0386
0.0606
-0.1035
-0.0108
-0.2405
0.0273
1
BR
-0.003
0.0093
-0.0018
0.0036
-0.0196
0.0016
-0.048
0.104
0.0107
1
BC
0.0065
0.6143
0.0456
0.3642
-0.0251
0.0942
-0.4449
-0.1493
-0.0048
-0.0087
6.1
1
Pearson coefficient correlation
Correlations among variables can cause multicolinearity which may create
problems in regression analysis. Table 6 shows correlations above 0.6 which explains
that there is no multicolinearity among variables in however a modest correlation
between free cash flows and non debt tax shield (0.702). Therefore, a multicolinearity
test is further required to check any dependence among these variables in case of
MNCs.
Table 7: Variance Inflation Factor
Variable
6.2
VIF
BC
AC
NDTS
FCF
SIZE
GRO
PROF
CVA
AGE
BR
2.88
2.12
2.00
1.98
1.61
1.28
1.14
1.10
1.03
1.00
Mean VIF
1.61
1/VIF
0.34683
0.47216
0.50092
0.50571
0.61978
0.78052
0.87522
0.91215
0.97221
0.99622
Multicollinerity test
When correlation among variables is high i.e. more than 0.6, either positive or
negative, then the problem of multicolinearity may arise. To check whether
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multicolinearity exists among variables, a variance inflation factor (VIF) test is applied
and hence the results show that (Table 7) that there is no multicolinearity exists among
variables.
7.
Conclusion
This study used 202 Pakistani companies and analyzed eight years data for the
annual periods of 2006 to 2013 and investigated the leverage composition of firms and
the determinants of capital structure namely agency cost, bankruptcy cost, profitability,
age, growth, collateral value of assets, non debt tax shield, free cash flows, size, business
risk and foreign exchange risk. Fixed effect model was used to regress the variables. This
study found that agency cost, growth, age, and size are significantly and negatively
associated with the capital structure of Pakistan firms, however, collateral value of asset
is significantly but positively associated with the capital structure of the firm. On the
other hand, free cash flows, non debt tax shield, profitability, business risk and
bankruptcy cost are not significantly associated with capital structure of the firms and are
against the signaling theory and peaking order theory.
From this study, one can conclude that firms in Pakistan are using collateral values
for getting more leverage out of their assets. Further, as agency cost, growth, age and size
of the firm increases, shareholders prefer to invest rather than taking external debt.
Therefore, this study demonstrates imperative policy implications for managers and
investors of the firms.
This fact must be taken into account while making the finance
mix decisions for securing the benefits of stakeholders.
Further research is required on several other factors that affect the capital structure
of the companies like diversification, i.e. product diversification and geographical
diversification. Both kinds of diversification not only affect capital structure of the
companies but also have an impact on the profitability of the firms. Political risk is also a
very important determinant of capital structure. Further, human capital is also a very vital
determinant of capital structure in modern world. Very less work has been done on
human capital. Firm’s specific factors may also affect the leverage of the firm therefore
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one can explore those firms’ specific factors. Impact of inflation on leverage can also be
assessed.
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