mahira rafiq

Economics and Finance Review Vol. 1(4) pp. 50 – 58, June, 2011 ISSN: 2047 - 0401

Available online at http://wwww.businessjournalz.org/efr

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EFFECT OF PROFITABILITY & FINANCIAL LEVERAGE ON CAPITAL STRUCTURE: A CASE

OF PAKISTAN’S AUTOMOBILE INDUSTRY:

Mahira Rafique

Scholar, Faculty of Management Sciences International Islamic University Islamabad, Pakistan

E-mail: [email protected]

ABSTRACT

This paper focuses on investigating the effect of the profitability of the firm and its financial leverage on the

capital structure of the automobile sector companies in Pakistan. To proceed with this, the capital structure of

11 listed firms has been analyzed by adopting an econometric framework over a period of five years. Estimating

regression analysis and checking the relationship of the estimated model through Correlation Coefficient Test,

we found that the profitability of the firm and its financial leverage have an insignificant impact on the capital

structure of the studied firms during the examined period. Hence, the study is unable to establish any significant

relation between profitability and financial leverage effect on the capital structure of a firm.

Keywords: Profitability, Financial Leverage, Capital Structure

1. INTRODUCTION

Capital structure refers to the different options used by a firm in financing its assets. Generally, a firm can go for

different levels/mixes of debts, equity, or other financial arrangements. It can combine bonds, TFCs, lease

financing, bank loans or many other options with equity in an overall attempt to boost the market value of the

firm. In their attempt to maximize the overall value, firms differ with respect to capital structures. This has given

birth to different capital structure theories that attempt to explain the variation in capital structures of firms over

time or across regions. On the other hand, empirical evidence is also not sometime consistent in substantiating a

particular capital structure theory. This paper attempts to answer the question that how profitability and capital

structure impact capital structure of listed Pakistani firms belonging to the automobile industry. According to

the authors’ knowledge, it is the first empirical study to be conducted in Pakistan in this regard. Though H.

Jamal Zubairi has worked on the impact of capital structure on profitability of automobile firms in Pakistan but

no one has checked the relation conversely before.

Quite a large strand of theoretical and empirical research has focused on the area of capital structure since the

path-breaking paper on capital structure by Miller and Modigliani published in 1958. However, most of the

research work has been carried out in developed economies and very little is known about the capital structure

of firms in developing economies. With this very little research, we are not sure whether conclusions from

theoretical and empirical research carried out in developed economies are valid for developing countries too; or

a different set of factors influence capital structure decisions in developing countries? We are not sure whether

conclusions from research on capital structure are portable across countries in general. Rajan and Zingales

(1995) studied the G-7 countries while Booth et al (2001) extended this work by including some data from

emerging markets. The conclusions from these studies were that there were some common features in the capital

structures of firms in different countries but that further research was necessary to identify the determinants of

capital structure in particular institutional settings or countries.

Pakistan is a developing country with three stock exchanges, the Karachi Stock Exchange (KSE) being the

largest one. More than 700 companies are listed on KSE. Like other developing economies, the area of capital

structure is relatively unexplored in Pakistan. In all previous researches, capital structure has never been taken

as a dependent variable but would be discussed in this new way here.

mailto:[email protected]



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The paper is organized as follows. Section 1 introduces the paper. In the next section, after the background of

automobile industry in Pakistan, some of the theoretical literature concerning the determining factors and effects

of capital structure is reviewed. In Section 3 we describe our data, its sources and we justify the choice of the

variables used in our analysis. In Section 4 we estimate the model used in our analysis. The Fifth Section

presents the results and conclusion.

1.1. The Background of Pakistan’s Automobile Industry

Following international trends, the automobile industry in Pakistan showed substantial growth in the years under

review. The growth was aided by favorable government policies during this period and levy of lower import

duties on raw material inputs and on intermediate products. A significant rise in demand for automobiles,

propelled at least partly by easy availability of auto leases and loans from banks and leasing companies at low

financial cost, was instrumental in the fast growth of the sector. The expansion in the sector, besides boosting

the country’s industrial output, also provided significant direct and indirect employment opportunities.

In the past years, there has been a high growth of more than 40 percent per year in the automobile market. The

growth declined somewhat in 2008 and 2009 due mainly to a dip in demand because of rising prices and lease

financing becoming expensive for the consumers. This phenomenon resulted from steep depreciation of

Pakistan’s currency vis-a-vis international currencies and increase in market interest rates / inflation. The growth

in the automobile sector had naturally also given impetus to the allied automobile vendor industry, which also

faced problems due to the recent fall in demand.

Notwithstanding a manifold increase in car production in Pakistan during the last few years, Pakistan still stands

relatively low in terms of motorization when compared globally and even to its neighbors. It is clear that despite

a tremendous increase in demand of automobiles in the country; Pakistan still remains one of the “less

motorized nations” of the world with 11 cars per thousand persons. For instance, neighboring developing

country Iran currently has an availability of 23 cars per thousand persons. The auto-makers need to take this

crucial fact into account that there is still a significant gap between supply and demand for automobiles in

Pakistan.

2. REVIEW OF LITERATURE

Modigliani and Miller (1958) attempted to look into the relationship between capital structure and

earnings/market value. Their argument was that in an economy without corporate and personal taxes, capital

structure had no effect on firm value. In other words under some given restrictive assumptions, an un-leveraged

firm had the same market value as a leveraged firm. They subsequently included corporate taxes in their model

and showed that earnings and market value of the firm will be the maximum if 100% debt is used by a firm for

financing its assets. Their main assumption was that business risk can be fairly assessed by the standard

deviation of operating income (EBIT) and that all present and future potential investors share similar

expectations about corporate earnings and the chances of variation in those earnings. Another key assumption

was they assumed the companies’ stocks and bonds were traded in a perfect market. Yet another important

assumption was that rate of interest on debt was a risk-free rate for firms as well as individuals. Their model

with corporate taxes showed that debt brings benefits due to availability of tax shield due to interest being

treated as a tax deductible expense.

Mandelker and Rhee (1984) in their study discovered a relationship between Degree of Operating Leverage

(DOL), Degree of Financial Leverage (DFL) and beta. They were able to show empirically that DOL and DFL

explained between 38 to 48 percent changes in a cross-section of data. Profitability is a strong point of dissent

between the two theories presented by Myers (1984) i.e. Pecking Order Theory (POT) and Static Tradeoff

Theory (STT). Myers divided the contemporary thinking on capital structure into two theoretical currents. The

first one is the Static Tradeoff Theory (STT), which explains that a firm follows a target debt-equity ratio and

then behaves accordingly. The benefits and costs associated with the debt option sets this target ratio. These

include taxes, cost of financial distress and agency costs.



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Second, the Pecking Order Theory (POT) put forward by Myers (1984) and Myers and Majluf (1984), stated

that firms follow a hierarchy of financial decisions when establishing their capital structure. Initially, firms

prefer to finance their projects through internal financing i.e. retained earnings. In case they need external

financing, they first apply for a bank loan then for public debt. As a last resort, the firm will issue equity to

finance its project. Thus according to POT the profitable firms are less likely to incur debt for new projects

because they have the available internal funds for this purpose.

For the STT, the higher the profitability of the firm, the more reasons it will have to issue debt, thereby also

reducing its tax burden. On the other hand, the POT presupposes that larger earnings lead to increase in the main

source that firms choose to cover their financial deficit: retained earnings. Therefore, the STT expects a positive

relationship between profitability and leverage, whereas the POT expects exactly the opposite. Also for the

Static Tradeoff approach, the larger the firm, the greater the possibility it has of issuing debt, resulting in a

positive relationship between debt and size. One of the reasons for this is that the larger the firm, the lower is the

risk of bankruptcy. Large firms do not consider the direct bankruptcy costs as an active variable in deciding the

level of leverage because larger firms, being more diversified, have less chances of bankruptcy (see, for details

Titman and Wessels (1988)).

Signaling Theory originally developed by Ross (1977), explains that debt is considered as a way to highlight

investors’ trust in the company, that is, if a company issues debt it provides a signal to the markets that the firm

is expecting positive cash flows in the future, as the principal and interest payments on debt are a fixed

contractual obligation which the firm has to pay out of its cash flows. Thus, higher level of debt shows the

manager’s confidence in future cash flows. Another impact of the signaling factor in the Pecking Order Theory

is the problem of the under-pricing of equity. If a firm issues equity instead of debt for financing its new

projects, investors will interpret the signal negatively. Since managers have superior information about the firm

than investors, they might issue equity when it is overpriced.

Larry et al. (1995) reported that there exists a negative relation between leverage and future growth. This

relation is negative for firms whose growth opportunities are either not recognized by the capital markets or are

not sufficiently valuable to overcome the effects of their debt overhang. They also confirmed that leverage does

not reduce growth for firms known to have good profit opportunities. To examine the relation between leverage

and growth they used data set over a period of 20 years and they found a strong negative relation between them.

In Pakistan, Limited research work exists on the area of capital structure, like Booth et al (2001) studied 10

developing countries including Pakistan. However, this study was confined only to top 100 index companies.

Second study by Shah and Hijazi (2004) was an improvement on the first one as it included all non-financial

firms listed on KSE for the period 1997-2001. However, the second study too was basic in nature in terms of its

use of pooled regression model avoiding the fixed effects and random effects models. Attaullah Shah and

Safiullah Khan (2007) have extended the work of Shah and Hijazi (2004) by including more years, using

relevant models of panel data and including more explanatory variables.

Particularly in Automobile Industry of Pakistan, the only work found in this regard is by Zubairi and Zubairi and

Rashid. In both these papers, once again Profitability of this sector has been checked through different variables.

Thus this paper aims at targeting this knowledge gap by checking how profitability in turns impacts capital

structure along with financial leverage.

3. DATA AND VARIABLES

3.1. Source of Data

The study is based on the data taken from the State Bank of Pakistan publication “Balance Sheet Analysis of

Joint Stock Companies Listed on The Karachi Stock Exchange 2004-2009”. This publication provides useful

information on key accounts of the financial statements of all listed firms of KSE for six year period.



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3.2. The Sample

The study has focused on the automobile sector of Pakistan. Initially all the 20 firms listed on the Karachi Stock

Exchange were selected. However, after screening out the firms with incomplete data we were left with 11

firms. The study used the financial data of these firms over years 2005 to 2009 (the computation of the variables

are given in Annexure A). Hence, we have 55 firm-year observations. We wished to use the latest data up to

2010, but the data for the period 2010 onward has not yet been published by the State Bank of Pakistan.

3.3. Variable Description

3.3.1. Dependent and Independent Variables

After discussing the various theories of capital structure, now we discuss the potential dependent and

independent variables for our study. We take the debt to equity ratio as a proxy for capital structure (dependent

variable). For independent variables there can be many. However, we take only two main independent variables

namely, profitability (EBT/TA) and financial leverage (EBT/EBIT) of the firm.

3.3.2. Capital Structure

Capital Structure has been uniquely taken as the dependent variable here. It indicates the mix of equity financing

and debt financing supporting the assets side of the company’s balance sheet. In previous studies, it has never

been taken as a dependent variable. The typical debt to equity ratio has been used here as proxy for capital

structure measurement. The aim is to check if either profitability or degree of financial leverage or both have

any effect in bringing about capital structure change.

3.3.3. Profitability

We measure profitability as the ratio of net income before taxes divided by total assets. Previous studies have

used earnings before interest and taxes (EBIT) divided by total assets, as a measure of profitability as it is

independent of leverage effects. However we use the said measure as the data taken from the State Bank of

Pakistan publication does not permit us to calculate (EBIT).

3.3.4. Degree of Financial Leverage

Financial leverage results from the presence of fixed financial costs in a firm's income stream. The extent of the

presence of fixed financial costs in a firm's income stream is measured by the degree of financial leverage

(DFL). Financial leverage increases expected return on equity, but it also increases the risk faced by the

shareholders. The business risk part of total risk is affected by operating leverage, whereas financial leverage

affects financial risk thus affecting the total risk of the firm. Though capital structure theories consider long term

debt as a proxy for financial leverage but we measure “degree of financial leverage” (DFL) as the ratio of

earnings before taxes (EBT) to earnings before interest and taxes (EBIT).

Thus the hypotheses to be tested are as follows:

Hypothesis 1:

HO1: Profitability does not significantly affect Capital Structure

HA1: Profitability does significantly affect Capital Structure

Hypothesis 2:

HO2: Financial Leverage does not significantly affect Capital Structure

HA2: Financial Leverage does significantly affect Capital Structure

4.0. METHODOLOGY

4.1. The Regression Model

Regression models are used to predict one variable from one or more other variables. This study uses panel

regression analysis. Panel data analysis facilitates analysis of cross-sectional and time series data. We use the

pooled regression type of panel data analysis. The pooled regression, also called the Constant Coefficients

model, is one where both intercepts and slopes are assumed constant. The cross section company data and time



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series data are pooled together in a single column assuming that there is no significant cross section or inter

temporal effects.

Panel data follows a given sample of individuals over time, and thus provides multiple observations on each

individual in the sample. Panel data combines the features of time series and cross-section. It provides

information on a number of statistical units for a number of years. Panel data for economic research has several

advantages over cross-sectional or time- series sets. Panel data usually provides the researcher a large number of

data points, increasing the degrees of freedom and reducing the co-linearity among explanatory variables; hence

improving the efficiency of econometric estimates.

Therefore the equation for our regression model will be:

Where

CS = Capital Structure

PF = Profitability

DFL = Degree of Financial Leverage

= the error term

= the intercept of equation

= the change co-efficient for Profitability

= the change co-efficient for Degree of Financial Leverage

4.2. Correlation Coefficient

The most common measure of "correlation" or "predictability" is Pearson’s coefficient of correlation, although

there are certainly many others. Pearson’s r, as it is often symbolized, can have a value anywhere between -1

and 1. The larger r, ignoring sign, the stronger the association between the two variables and the more accurately

you can predict one variable from knowledge of the other variable. At its extreme, a correlation of 1 or -1 means

that the two variables are perfectly correlated, meaning that you can predict the values of one variable from the

values of the other variable with perfect accuracy. At the other extreme, an r of zero implies an absence of a

correlation i.e, there is no relationship between the two variables. This implies that knowledge of one variable

gives you absolutely no information about what the value of the other variable is likely to be. The sign of the

correlation implies the "direction" of the association. A positive correlation means that relatively high scores on

one variable are paired with relatively high scores on the other variable, and low scores are paired with

relatively low scores. On the other hand, a negative correlation means that relatively high scores on one variable

are paired with relatively low scores on the other variable.

5.0. RESULTS AND CONCLUSION

This section presents the descriptive statistics, the results of regression analysis and correlation coefficient. The

interpretation of the empirical findings is also reported in this section. Finally, important conclusions about the

results of the study have been drawn.

5.1. Descriptive Statistics

Prior to start of formal analysis, we present descriptive statistics in Table 1. The table shows the information at

the level of the variables. Table 1 presents the mean, median, maximum, minimum and standard deviation for

the variables.

5.2. Correlation Coefficient

To check for the possible multi-co-linearity among the independent variables, we calculate the Pearson’s co-

efficient of correlations for the independent variables. Table 2 presents the results.



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As we can see from the table, the multi-co-linearity problem is not too severe among the selected independent

variables. However, the table sheds light on some interesting correlations. Capital Structure and Profitability are

negatively correlated. As debt to equity ratio increases, a firm’s profitability decreases. Next up, capital structure

and degree of financial leverage are positively correlated. Hence as the debt structure increases, so does the

financial payable burden on the firm’s assets. Lastly, profitability and financial leverage are negatively

correlated. Thus as one increases, the other one decreases. So profitability is in negative relation with both

capital structure and degree of financial leverage, as proved by theory as well.

5.3. Regression Analysis

Using pooled regression technique, we ran the regression of the capital structure on the degree of financial

leverage and the profitability of the firm with the aim to investigate whether these two variables have significant

explanatory power. The estimated results are reported in Table 3.

It can be observed from the table that the estimated value of the R-squared is approximately 0.02. This implies

that the capital structure of the firm is very negligibly determined by the two said variables jointly. It shows that

only 2% of the variations in dependent variable (CS) are explained by the given two independent variables.

The value of F-statistic (0.54) shows the validity of the model. Its value is 0.54which is below its probability (F-

statistic) value of 0.58. Thus the overall model is not good. The Durbin-Watson statistic (1.23) is also close to 2,

which implies that the successive values of estimated residuals are not dependent on each other. This means that

there is evidence to accept the null hypothesis that there is no autocorrelation problem in the estimated model.

Regarding the significance of individual variables, the empirical results show that the firms’ capital structure is

very significantly negatively associated with profitability. The P-value is 0.34, as can be seen from the table.

This implies that the null hypothesis (HO1: profitability has no significant impact on capital structure) is accepted

at 1 percent level of significance. Thus empirically, profitability doesnot affect capital structure and we donot

find much evidence that this relationship is statistically significant.

The table also accounts for a positive relationship between capital structure and financial burden of firm, as is

indicated by the co-efficient value (1.48). But taking the significance level of probability to be 0.1, the p-value

of DFL was found to be 0.96. This shows highly insignificant results. the second null hypothesis is accepted

which states that degree of financial leverage has no significant impact on capital structure

Henceforth, it can be concluded that though firm’s profitability is strongly negatively related to capital structure

and financial leverage positively, as was found earlier through Pearson’s correlation coefficient, but statistically

in the light of p-value, both these findings were insignificant to establish any valid relationship of the two said

independent variables with the dependent variable of capital structure. Therefore, it can be safely said that in

automobile sector of Pakistan, profitability and financial leverage of firms are insignificant in bringing about

any changes in their capital structure.

REFERENCES

Fan, J. P. H., Titman, S., & Twite, G. (2010, September). An International Comparison of Capital Structure and

Debt Maturity Choices.

Frank, M. Z., & Goyal, V. K. (2003, April 17). Capital Structure Decisions.

Frank, M. Z., & Goyal, V. K. (2007, October 10). Capital Structure Decisions: Which Factors are Reliably

Important?

Guney, Y., Ozkan, A., & Yalciner, K. Dynamic Capital Structure Decisions: Evidence from Firms in an

Emerging Economy. The Turkish Economy, pp. 149-171.

Hatfield, G. B., Cheng, L. T. W., & Davidson, W. N. (1994). The Determination of Optimal Capital Structure:

The Effect of Firm and Industry Debt Ratios on Market Value. Journal of Financial and Strategic Decisions,

vol. 7, no. 3.

Hijazi, S. T., & Tariq, Y. B. (2006). Determinants of Capital Structure: A Case for the Pakistani Cement

Industry. The Lahore Journal of Economics, 11:1, pp. 63-80.



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Picu, A., Rotaru, A., & Covaci, B. Factors that Influence the Decision of the Capital Structure in Commercial

Company.

Qian, Y., Tian, Y., & Wirjanto, T. S. (2007, February). An Empirical Investigation into the Capital-Structure

Determinants of Publicly Listed Chinese Companies.

Shah, A., & Hijazi, T. (2004). The Determinants of Capital Structure of Stock Exchange-listed Non-financial

Firms in Pakistan. The Pakistan Development Review, 43 : 4 Part 2, pp. 605-618.

Shah, A., & Khan, S. (2007, October). Determinants of Capital Structure: Evidence from Pakistani Panel Data.

International Review of Business Research Papers, vol. 3 no. 4 pp. 265-282.

Waliullah, & Nishat, M. (2008, August 25). Capital Structure Choice in an Emerging Market: Evidence from

Listed Firms in Pakistan.

Zubairi, H. J. Impact of Working Capital Management and Capital Structure on Profitability of Automobile

Firms in Pakistan.

Zubairi, H. J., & Rashid, A. Leverage, Size and Profitability: The Case of Pakistan’s Automobile Industry.

Table 1: Descriptive Statistics

CAPITAL

STRUCTURE

PROFITABILITY FINANCIAL LEVERAGE

Mean 153.2434 0.092061 1.006875

Median 133.1000 0.113573 0.967753

Maximum 466.6000 0.360237 3.473118

Minimum 0.000000 -0.335779 0.145789

Std. Dev. 105.6824 0.137777 0.587475

Skewness 1.126558 -0.771186 2.147107

Kurtosis 4.187358 3.920189 9.093162

Jarque-Bera 14.32403 7.123330 122.7102

Probability 0.000775 0.028392 0.000000

Table 2: Estimated Correlations between Variables

Capital Structure Profitability Financial Leverage

Capital Structure 1

Profitability -0.111194088 1

Financial Leverage 0.123224758 -0.25022828 1

Table 3:Regression

Variable Coefficient Std. Error t-Statistic Prob.

PROFITABILITY -109.7593 113.0820 -0.970616 0.3364

FINANCIAL LEVERAGE

1.484041

26.52040

0.055958

0.9556

C

161.8537

34.80173

4.650739

0.0000

R-squared 0.021287 Mean dependent

variable

153.2434

Adjusted R-squared -0.017861 S.D. dependent

Variable

105.6824

S.E. of regression 106.6220 Akaike info criterion 12.23140

Sum squared residual

568412.6 Schwarz criterion 12.34292

Log likelihood -321.1320 F-statistic 0.543762

Durbin-Watson stat 1.232182 Prob(F-statistic) 0.583954



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Annexure A:

Year Capital Structure Profitability

Financial

Leverage

1) Al-Ghazi Tractors Ltd. 2005 131.9 0.22574323 0.995435457

2006 105 0.262451397 0.99853633

2007 77.3 0.280418321 0.998435463

2008 60 0.237507232 0.998398292

2009 36.2 0.360236834 0.999173243

2) Atlas Honda Ltd. 2005 190.2 0.154615068 0.932373386

2006 188.3 0.137743221 0.873519106

2007 171.5 0.100295795 0.748998043

2008 156.4 0.115560772 0.79933137

2009 126 0.047225644 0.583457403

3) Dewan Farooque Motors Ltd. 2005 382 0.081003153 0.78843485

2006 369.2 0.041144251 0.520867565

2007 309.1 0.01440374 0.206151288

2008 452.1 -0.098896217 2.205792683

2009 0 -0.335779408 1.075268817

4) Ghandhara Industries Ltd. 2005 0 -0.005108557 -0.816326531

2006 96.7 0.25111341 0.967753121

2007 79.1 0.0879986 0.882843352

2008 65.4 0.013724209 0.511304348

2009 66.3 -0.072991942 2.467486819

5) Ghandhara Nissan Ltd. 2005 236.7 0.103388619 0.888206785

2006 233.5 0.136624248 0.794470421

2007 135.1 0.119256109 0.715178795

2008 111.6 0.090226049 0.673729834

2009 99.2 -0.142840296 3.47311828

6) Ghani Automobiles Industries Ltd. 2005 2.5 -0.008608321 1

2006 31.8 0.013392857 1

2007 12.4 -0.268512111 1.127906977

2008 195.8 0.006256517 0.269662921

2009 290.2 -0.123792801 1.733606557

7) Hino Pak Motors Ltd. 2005 115.2 0.12055236 0.877011494

2006 141.8 0.123566479 0.913602285

2007 126.2 0.18427969 0.934690503

2008 157.8 0.023153054 0.145788745

2009 157.8 0.023153054 0.145788745



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8) Honda Atlas Cars (Pakistan) Ltd. 2005 466.6 0.02195824 0.977324263

2006 242.1 0.124022273 0.960681298

2007 246.9 -0.058464414 2.733825199

2008 113.3 0.009419152 0.213925328

2009 270.3 -0.063860132 1.557557558

9) Indus Motor Company Ltd. 2005 170.5 0.189010177 0.960742595

2006 153 0.257504868 0.969783556

2007 94.8 0.270055422 0.994661587

2008 45.7 0.257606506 0.999210021

2009 100.9 0.065057829 0.980685131

10) Millat Tractors Ltd. 2005 212.3 0.113573101 0.995563566

2006 202.3 0.144334605 0.997123504

2007 133.1 0.138190432 0.978911565

2008 139.9 0.157635604 0.977090101

2009 102.1 0.259533506 0.977793896

11) Pak Suzuki Company Ltd. 2005 140 0.188972531 0.967959296

2006 104.2 0.235544861 0.958858102

2007 53.2 0.205681562 0.967502825

2008 20.4 0.059865317 0.948833206

2009 23.8 0.024717037 0.971389646

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