Proceedings of Business and Social Sciences Research Conference
10 - 11 December 2015, Ambassador Hotel, Bangkok, Thailand, ISBN: 978-1-922069-90-0
Dynamic Panel Data Estimation of Determinants of Capital
Structure of Food Processing Industry in Pakistan
Agha Jahanzeba*, Pervaiz Ahmed Memonb and Javed Ali Tunioc
The objective of writing this paper is to analyze the association between determinants of leverage
amongst the sector of food processing in Pakistan. The logical validation towards the determinants
having an effect on the leverage is provided in this research. Different theories of leverage (i.e.
pecking order theory, trade-off theory, Modigliani and Miller theory) have been assessed for the
purpose of constructing proposition. Our investigation includes 33 companies from food processing
industry of Pakistan listed on Karachi Stock Exchange during the period of 2003 to 2012. As far as
the authors’ knowledge is concerned, the present study is in the midst of the exceptional wideranging researches in emerging economies that examines the relationship of determinants with
leverage in the division of food processing using the dynamic panel data evaluation and by
classifying leverage into short-term, long-term and total debt ratios. Thus, this research will
specifically give assistance to the owners, investors, and managers of a firm regarding the food
processing sector.
Keywords: Capital Structure, Leverage, Dynamic Panel Data Estimation and Corporate
Finance
Introduction
Leverage still has a perplexed nature, in spite of the fact that it has been under
investigation for over five decades. No “perfect” model is readily accessible in this
sphere which can be simply concurred over (Korteweg and Lemmon, 2012). Generally,
researches present the experimental results by taking the data of any country into
consideration all together, while the leverage determinants vary in different divisions.
Capital structure is not just the formation of attributes of a firm but also the result of
corporate governance, institutional environment, and legal framework (Deesomsak et al.,
2004). An important concern is if the firms encompass complete knowledge while
controlling their financial assets (trade-off theory), or it is simply an unmethodical
process that deals with the leverage through finding out historical investment and
profitability (pecking order theory).
The capital structure decision is regarded as of great significance as it has an
effect on the earnings per share or on the shareholders‟ wealth. Capital structure
appears to be the fundamental decision that every business needs to take, the benefits
and drawbacks of these decisions help in ascertaining the future of all business. The
modern capital structure theory was commenced by Modigliani and Miller (1958). In
accordance with Myers (2001), „there is no universal theory of the debt--equity choice,
and no reason to expect one‟. A lot of verified theories concerning the capital structure
assist in comprehending the mix of debt equity that the firms opt for. There can be two
categories of these theories– either they forecast the presence of the best possible ratio
of debt-equity for every firm (so-called models of static trade-off) or they affirm that no
distinct target capital structure is present (pecking-order hypothesis).
___________________________________________________________________
a
*Department of Business Administration, Sukkur Institute of Business Administration, Sukkur, Sindh, Pakistan, Email:
agha.jahanzeb@iba-suk.edu.pk
b
Department of Business Administration, Sukkur Institute of Business Administration, Sukkur, Sindh, Pakistan, Email: pervaiz@ibasuk.edu.pk
c
Department of Business Administration, Sukkur Institute of Business Administration, Sukkur, Sindh, Pakistan, Email:
javed.tunio@iba-suk.edu.pk
Proceedings of Business and Social Sciences Research Conference
10 - 11 December 2015, Ambassador Hotel, Bangkok, Thailand, ISBN: 978-1-922069-90-0
There are generally two procedural decisions that the management has to cope
with: financing and investment (or capital budgeting) decisions. While the decision of
capital budgeting is associated with what actual resources a firm is supposed to acquire,
the decision of financing is related to how these assets ought to be financed. It is noted
by Stephen R. Bishop et al. (2000) that a third decision might also take place as the
corporation starts to make profits: is the firm supposed to disburse a fraction or all of
earned revenues in the dividend form to the investors, or should all the profits be
reverted to the business.
The decision of capital structure is reckoned to be among the key decisions that
every company needs to take as they increase their capital. Substandard decisions
would induce unfavorable effects. A lot of financially affluent firms have gone off track
as a result of poor decision making. This paper focuses on the leverage of the food
processing companies during 2003 to 2012. Until now, there has been no such
comprehensive study available in the country which has presented findings on leverage
decisions in this sector. This study is among the rarest in developing economies which
has employed dynamic panel data estimation to analyze the data of this particular
sector.
Literature Review
Capital Structure Theories
In corporate finance, understanding of the manner in which corporations opt their
financing is an imperative subject of concern, and evidently no agreement is present on theories
that enlightens the perfect leverage of a firm (Seifert and Gonenc, 2008). The most primitive
study on the topic of leverage is initiated by Modigliani and Miller (1958) and it concluded that
the leverage makes no difference in a business world with no transaction costs, taxes, or other
imperfections of market. In other words, Modigliani and Miller (1958) stated that the firms‟
decisions of financing are take on with the same rate of interest and exclusive of tax.
Accordingly, equity cost is similar for companies which are both, non-leveraged and leveraged.
The theory of trade-off, by focusing its attention on the benefit and cost analysis of debt,
forecasts that the best possible debt ratio is there that helps in the value maximization of a firm.
The most favorable point can be achieved when the debt issuance benefits counterpoise the
growing present value of costs associated with more issuance of debt (Myers, 2001). Myers
(1984) offered the theory of Pecking order in which he elucidates that firms most probably have
a preference for financing new investments, initially in the midst of internally raised capital, for
instance retained earnings, after that with debt, and issuance of equity as a last option.
Factors which influence leverage
Firm Size
Rajan and Zingales (1995) have been in disagreement on the fact that the outsized
corporations tend to be more diversified, which facilitates less suffering from the costs of
bankruptcy. Hence, it is presumed that a positive association is present between leverage and
firm size. In this research, natural logarithm of sales is applied in order to determine the firm‟s
size, and this measure has also been applied by several other authors (e.g. Rajan and Zingales,
1995; Hernadi and Ormos, 2012; Titman and Wessels, 1988).
Proceedings of Business and Social Sciences Research Conference
10 - 11 December 2015, Ambassador Hotel, Bangkok, Thailand, ISBN: 978-1-922069-90-0
Tangibility
It is affirmed by Booth et al. (2001) that „The more tangible the firm‟s assets, the greater
its ability to issue secured debt and less information revealed about future profits‟. We employ
fixed assets over total assets (FA/TA) as an alternative in order to find out the tangibility of a
firm, as Chakraborty (2013) calculated. We look forward to find positive relationship between
tangibility and leverage.
Growth
Corporations that are experiencing growth stage usually finance their growth by means of
debt; they borrow money in order to grow more rapidly. A disagreement that occurs in the
presence of this course is that the profits of growth corporations are usually unproven and
unstable. As such, the higher load of debt is generally inappropriate (Sekar et al., 2014). This
research employed a technique that was used by Delcoure (2007) and Jahanzeb and Bajuri
(2014), i.e. by the use of the application of geometric average of three-year sales growth to total
growth of asset. A negative relationship is expected between growth and leverage.
Profitability
In proportion to the theory of trade-off, if a firm is more lucrative then the leverage may
be higher due to tax deductibility of payment of interest. Debt providers are always keen to
supply debt financing to firms that are profitable (Rajan and Zingales, 1995). Profitability (PROF)
is calculated as earnings before interest and tax on total assets (EBIT/TA) as calculated by Booth
et al. (2001) and Shah and Khan (2007) in earlier times. In this paper, we anticipate that
profitability and leverage are negatively associated.
Non-debt Tax Shield
In accordance with DeAngelo and Masulis (1980), non-debt tax shield (NDTS) is viewed
as a proxy of tax shield for the purpose of financing debt. Mixed results were reported by the
literature on this subject matter. Bradley et al. (1984) reported a strong correlation between
leverage and NDTS. However, Wald (1999) illustrated a strong negative correlation between
NDTS and leverage. Following Akhtar and Oliver (2009) and Jahanzeb et al. (2015), we define
non-debt tax shield as total annual depreciation expense divided by book value of total assets.
Firm Age
Not a much literature has been seen on the firm age (AGE), but still according to the few
studies, negative relationship is expected (Hall et al., 2004). AGE is measured by following
Akhtar and Oliver (2009), as the years from the date of incorporation.
Dividend Payout
Positive association between debt and payout ratio has been reported by Frank and Goyal
(2004). This study measures dividend payout (DPO) as dividend per share divided by earning per
share as previously measured by Al-Najjar (2011). We expect negative association between DPO
and leverage.
Business Risk
Excluding debt, business risk is the basic risk of the company's operations. The greater
the business risk, the lower the optimal debt ratio. Over the three years, standard deviation of
return on assets has been utilized as the proxy for the purpose of computing risk of business
(Booth et al., 2001; Jahanzeb & Bajuri, 2014).
Proceedings of Business and Social Sciences Research Conference
10 - 11 December 2015, Ambassador Hotel, Bangkok, Thailand, ISBN: 978-1-922069-90-0
Liquidity
This study hypothesizes negative relation between the leverage and liquidity (de Jong et
al., 2008). To measure liquidity, this study employs the ratio of current assets over current
liabilities (Mouamer, 2011).
Uniqueness
This study employs selling expenses to measure uniqueness, instead of R&D expenses,
because of the limitation and restriction in acquiring the data of R&D. Empirical findings on
uniqueness are still rare. Following Shahjahanpour et al. (2010), this study uses selling expense
over sales (SE/S).
Conceptual Framework
Research Methodology
Selection of dependent and independent variables has been made in accordance with the
literature. Consequently, we describe the methodology here to test different hypotheses and
analyze these variables empirically.
The model below assess the relationship between capital structure and its determinants
for balanced panel data:
Where
represents the debt/asset (capital structure) ratio for firm i in year t, and the
firm-level determinants are size (SIZE), tangibility (TANG), growth (GROW), profitability
(PROF), non-debt tax shield (NDTS), firm age (AGE), dividend (DIV), business risk (RISK),
liquidity (LIQ) and uniqueness (UNIQ). The error term is represented by and is considered to
be serially unrelated to main zero.
From the point of econometrics, pooled OLS potentially suffers from biases. Dynamic
panel data estimation which is also known as generalized method of moments (GMM), is used
by recent studies in this field (Chakraborty, 2010; Chakraborty, 2013; Mateev et al., 2013;
Drobetz et al., 2013) to analyze the capital structure in order to overcome the above stated
problems of techniques of pooled ordinary least squares. GMM efficiently deals with any
Proceedings of Business and Social Sciences Research Conference
10 - 11 December 2015, Ambassador Hotel, Bangkok, Thailand, ISBN: 978-1-922069-90-0
potential endogeniety issues. In accordance with Drobetz et al. (2013), this study follows the
model of dynamic panel data estimation that is presented by Blundell and Bond (1998) for
“System GMM Estimation” and applies “xtdpdsys” STATA estimation1.
(
)
Where LEVit is the debt of firm i in year t.
Data
Data were collected from State Bank of Pakistan (SBP) and Thomson
Reuters„ DataStream. There are currently a total of 21 food and personal care products
companies and 35 sugar and allied companies listed on Karachi Stock Exchange (KSE) (source:
www.ksestocks.com). Because of the delisting and unavailability of data of some companies, we
could only get data of 33 companies for the ten years (i.e. 2003-2012).
Dependent Variable
The term leverage may be very comprehensive and may be defined and measured
differently. We use debt to asset ratio to measure leverage. Hence, it would be appropriate to
discuss about the methodology employed in this study. We classify the debt ratio into three
different measures, i.e. short-term debt (STD), long-term debt (LTD) and total debt (TD).
Following Harris and Raviv (1991), Rajan and Zingales (1995), and Mateev et al., (2013),
short-term debt and long-term debt are measured as follows:
(
(
)
)
Following Mateev et al. (2013) and Jahanzeb et al. (2015), we measure total debt as:
(
)
Data Analysis
This sections hashes out the implications of empirical findings and poses the estimation
results. Table 1 below presents the summary of statistics of dependent and explanatory variables.
Total debt (TD) shows that the 52.17 percent of the assets of the firms are financed by total debt,
during the period of the study, which remained higher than some other developing countries, i.e.
Brazil, Malaysia, Mexico, Thailand and Zimbabwe (Booth et al., 2001). However, the results
show that short-term debt has been given much preference over long-term debt. Results of
GMM-system estimation (Table 3) show that there is the problem of second order serial
correlation with Model 1 (STD), therefore, its results are not valid to be discussed and this study
discusses the findings of Model 2 and Model 3 only.
1
Detailed overview of the model can be referred in Drobetz et al. (2013).
Proceedings of Business and Social Sciences Research Conference
10 - 11 December 2015, Ambassador Hotel, Bangkok, Thailand, ISBN: 978-1-922069-90-0
Table 1: Descriptive Statistics
Variable Mean Minimum Maximum Std. Dev.
0.2056
0
0.63
0.1492
SLEV
0.1416
0
0.62
0.1432
LLEV
0.5745
0.03
1.08
0.2017
TLEV
15.0793
9.61
18.19
1.1720
SIZE
0.4333
0
0.89
0.2161
TANG
0.0804
-0.98
0.95
0.3566
GROW
0.1099
-0.44
0.57
0.1377
PROF
0.0386
0.01
0.12
0.0202
NDTS
3.0742
0.69
4.08
0.6002
AGE
0.0495
0.01
0.12
0.0231
DIV
0.0412
0
0.13
0.0296
RISK
1.1362
-0.91
2.53
0.4685
LIQ
0.0480
0.01
0.17
0.0271
UNIQ

SLEV = short-term leverage, LLEV = long-term leverage, TLEV = total leverage, SIZE = firm size, TANG = tangibility,
PROF = firm profitability, NDTS = non-debt tax shield, AGE = firm age, DIV = dividend payout, RISK = business risk,
LIQ = liquidity, UNIQ = uniqueness
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Table 2: Correlation Coefficients
Variable
STD
LTD
TD
SIZE TANG GROW PROF NDTS AGE DPO RISK LIQ UNIQ
1
SLEV
LLEV .170** 1
TLEV .458** .480** 1
SIZE -.066 -.162** .009 1
1
TANG .195** .465** .115* -.091
**
.063
1
GROW .017 -.014 -.024 .240
**
**
**
**
**
.110*
1
PROF -.224 -.248 -.320 .366 -.170
*
**
-.046 .101
1
NDTS -.054 .007 -.115 .006 .229
**
*
**
-.011 .176 -.165** 1
AGE -.052 -.219 -.086 .109 -.084
.047 -.012 -.040 -.034 .072
-.009 -.143** -.096 .112* 1
DIV
.004 .151** .096 .166** .004 1
RISK -.191** -.064 -.066 -.031 .036
LIQ -.465** -.352** -.577** .046 -.447** -.011 .335** -.073 .014 -.129* .025 1
UNIQ -.139* -.087 -.154** -.119* -.147** -.080 .069 .111* .004 .063 -.023 .137* 1
** and * indicate significant levels at 1% and 5% respectively
Table 3: Results of Two-step System GMM Estimation1,2,3,4
Explanatory
Model 1
Model 2
Variables
(STD)
(LTD)
LEV
0.4650**
0.4144**
SIZE
0.0053
-0.0098
TANG
-0.0947*
0.1995**
GROW
-0.0936
0.0027
PROF
-0.0044
-0.1141**
NDTS
-0.6209**
-0.8138**
AGE
-0.0702**
-0.0733*
DIV
0.5870
-0.0538
RISK
-0.3246*
-0.0913
LIQ
-0.2115*
0.0617**
UNIQ
-0.0436
0.2750**
Constant
0.3580**
0.3277**
Serial Correlation 1
0.0031
0.0582
Serial Correlation 2
0.3831
0.1816
0.9950
0.9770
Sargan – Prob >
1
Model 3
(TD)
0.2570**
0.0177*
-0.2156**
-0.0356**
-0.2278**
-3.1144**
-.0735**
-0.2124
0.4329**
-0.1022**
-0.0004
0.7454**
0.0014
0.2051
0.9987
** and * indicate significant levels at 1% and 5% respectively.
LEV in Model 1, Model 2 and Model 3 is lagged variable of short-term debt, long-term debt and total debt
respectively.
3
For Arellano–Bond test Ho is: no autocorrelation. Rejecting the null hypothesis (p-value < 0.05) of no serial
correlation at order one in the first-differenced errors does not imply that the model is misspecified. Rejecting the
null hypothesis at higher orders implies that the moment conditions are not valid.
4
For Sargan test H0 is: overidentifying restrictions are valid. If p-value > 0.05, we confirm the null hypothesis
that the overidentifying restrictions are valid. Rejecting the null hypothesis implies that we need to reconsider our
model or our instruments.
2
Proceedings of Business and Social Sciences Research Conference
10 - 11 December 2015, Ambassador Hotel, Bangkok, Thailand, ISBN: 978-1-922069-90-0
Conclusion and Discussion
Pakistan is an agricultural country and agriculture accounted for 20.9% of the GDP in
2014-15. Agriculture is a source of living for 43.5% of the rural population (Pakistan Economic
Survey, 2014-2015). Hence, this study presents the findings on food processing industry which
highlights the issues related to this industry. In accordance with the findings of dynamic panel
data estimation (Table 3), most of the results remained highly significant. Second order
correlation and Sargan test validate the results of all three models, i.e. Model 1 (short-term debt),
Model 2 (long-term debt) and Model 3 (total debt). Results of Model 1 demonstrate that there is
a negative relation between capital structure and those firms that are tax shielded, older, riskier
having more tangible and liquid assets. Findings of Model 2 show that less profitable firms
having more tangible and liquid assets tend to increase their long-term debt. Most of the results
of Model 3 remained significant, i.e. positive relationship of size and business risk with capital
structure and negative relation of tangibility, growth, profitability, non-debt tax shield, firm age
and liquidity. Overall results suggest that most of the food processing firms mostly rely on their
internal financing and do not raise their debt ratio. This maybe because of higher cost of capital
or they might not have easy access to external financing.
No any detailed and comprehensive study has until been conducted on this particular
sector yet. Therefore, this study presents the findings on several leverage determinants so that it
could facilitate the managers of companies and investors in this sector while dealing with
financial decision-making. Food processing sector contributes 21% of GDP, but unfortunately,
this sector is facing continuous decline in the growth. The major reasons of decline in growth are
energy crisis, political instability, high cost of inflation and high cost of financing. The industry
also lacks research and development. Pakistan has been given duty-free access to European
markets from 1st January, 2014 (PES, 2013-2014). However, country still faces difficulties to
compete neighbor countries which are producing good quality FMCG (fast-moving consumer
goods) products. Gas and electricity tariff must be brought down to support this sector, and there
is a severe need of uninterrupted power and gas supply to this sector. Technology up-gradation,
capacity building, awareness about international quality standards, introduction of efficient
management techniques, and subsidizing the inputs may significantly assist the sector.
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