Debt Equity Choice of Life and Non-Life Insurers: Evidence from ...

Debt Equity Choice of Life and Non-Life Insurers: Evidence
from Pakistan
Talat Afza* and. Naveed Ahmed**
Abstract
Capital structure has attracted scholarly attention in corporate finance
literature over the past decades. However, in the context of financial
sector especially in insurance sector, it has received a little attention.
Current study provides the empirical evidence on firm level determinants
of capital structure of insurance sector of Pakistan over nine years from
2001 to 2009. Present study split the data set according to the type of
insurance companies such as; life, non-life and overall insurance sector
and accordingly three Ordinary Least Square (OLS) regression models
are applied to estimate the relationship between the dependant (Debt
Ratio) and independent variables i.e. size, profitability, tangibility, liquidity
and risk. The results revealed that both life and non-life insurance
companies in Pakistan followed Pecking Order patterns in case of
profitability and liquidity as both variables negatively and significantly
related with leverage in life and non-life insurance companies of
Pakistan. Moreover, positive and significant relationship between debt
ratio and size in all of the three models, support the Static Trade-Off
hypothesis. Furthermore, the results also indicate that profitable, more
liquid, more tangible and risky insurance companies focus on retained
earnings or equity than debt financing.
Keywords: Capital structure, Firm level determinants, Insurance sector of Pakistan.
1. Introduction
Over the past several decades, theories on a firm’s capital structure choice have
focused on many directions. Different models have been presented in the literature to
explain a firm’s financing behavior (Harris and Raviv, 1991). In 1952, Durand proposed
the concept of capital structure and he is considered to be the one of the pioneers who
analyzed it theoretically. According to his approach (Net income Approach), any firm
can increase its value and decrease the cost of capital by using debt financing. Later
on, Durand (1952) proposed another approach (Net Operating Income Approach)
related to capital structure and provided evidence that market value of the firm is
irrelevant of the debt-equity choice. These two approaches were purely definitional and
no economic and behavioral meanings could be derived from outcome of his work. In
order to fill these gaps, Nobel Prize winning finance theorist, Modigliani and Miller
(1958), presented behavioral approach and the modern concept of capital structure in
which they theoretically and algebraically analyzed the affect of capital structure on
firm’s value and concluded under certain assumptions that the firm’s value does not
depend upon the financing choices.
____________________
Corresponding author: Naveedahmed@ciitlahore.edu.pk.
* Professor and Director Academics, COMSATS Institute of Information Technology, Lahore, Pakistan.
** PhD Student, COMSATS Institute of Information Technology, Lahore, Pakistan
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MM provided path and guidelines for researchers to analyze the affects of capital structure and
later several hypothesis have been put forward by researchers related to the debt-equity choices,
but none of them exactly explains as to how much debt and equity should be used by any firm to
get the desired results (Zhou, 2008). The rationale of target debt-equity depends on many micro
and macro factors which are different across industries and regions. Therefore, keeping in view
all micro and macro factors the firm formulates its capital structure prior to starting its operations
and modifying it according to the requirements of business. For this purpose, firm requires the
services of financial experts or managers and board of directors for making right decision about
financing choices (debt and equity), because wrong decision may lead not only to increase cost
of capital but also to insolvency or bankruptcy of the firms. Hence, when management plays its
role in the selection of financing options, the core objective is to reduce the cost of debt and
equity along with increasing the benefits or value of the firm (Baral, 1996).
The measurement of Cost of debt and equity is also essential in determining the capital structure
because it provides link for increasing the value of shareholders along with attaining the financial
goals of the firm (Agymin, 1998) If a business raises its capital through large portion of equity
then it is attractive for the operational activities for two major reasons; first, It plays significant
role for corporate growth of every organization because it does not carry any fixed charges and
maturity period. Second, it enhances the credit worthiness due to reduction in gearing. But, on
the other hand, firms with poor prospects merely prefer to issue equity capital in order to bring
new investors to share losses. In addition, when an announcement for issuing new stock is made,
it gives negative signal to investors and as a result the market value of the stocks will decline.
Moreover, when equity is issued for financing the operational activities, different costs are
attached with it including the agency cost of equity. This cost arises in the firm due the conflict
of interests between shareholder and managers related to firm’s decision. Therefore, equity
should be the last option for raising funds (Aoun, 2006). Consequently, the management finds
right kind of solution or source of finance which minimizes the costs and increases the value of
firm.
One possible solution is to use debt capital for financing its assets which can be employed as a
tool which not only reduces the cost of equity but also enhances the market value of the firm.
When the firm shifts its capital structure towards more debt, then this forces managers to be
more disciplined in the operational activities. For the last sixty years, different approaches have
emerged regarding the use of debt. Debt acts as an engine for the growth of the firm and it
encourages the managers to get maximum returns that enable them to pay interest. Furthermore,
it minimizes free cash flows which make the manager to work less for their own interest and as a
result reduces the wasteful expenditures of business. Debt financing also provides tax shelter
which makes one to pay less taxes because the interest paid on debt is tax deductibles.
Under certain assumptions MM also proposed that a firm can increase its market value of stocks
if it is use 100% debt as a source of capital. But in the real world, the firm rarely uses 100% debt
in order to reduce or minimize agency and bankruptcy related costs. When a large potion of debt
is used by firms in the capital structure, different types of costs (agency and bankruptcy) are also
attached with it. The bankruptcy cost exists, when the firm feels that it would not be able to pay
interest on debt. As a result, the threat of bankruptcy also forces a firm to sell or liquidate its
assets less than their actual market value. In addition, lenders may demand more interest rates,
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suppliers may refuse to grant credit and employees may also jump towards alternative jobs due
to this threat.
Furthermore, the agency cost of debt arises; when there is some controversy between the
shareholders and debt holder in the firm. Thus, when the management is improperly (against
target ratio) used debt or equity in the formation of capital structure, it would be harmful for the
existence of the firm. Therefore, researchers have proposed the idea of optimal capital structure.
According to Myers (1984) the firm should have optimal capital structure and this can be
achieved through keeping a balance between the benefits and costs of equity and debt. It is the
mix of debt and equity in target proportion which not only maximizes the firm’s value but also
reduces the costs of financing choices.
Corporate finance literature reveals that some researchers describe capital structure in narrow
sense so as to include only long term financial instruments in its composition (Passilaki, 2008).
According to Devic and krstic (2001) “Capital structure is expressed as ratio of long term
liabilities to the sum of long term liabilities and firms equity”. “Capital structure is described as
long term debt divided by total assets” (Pandy, 2001; Omet, 2006; Delcour, 2007). But in the
words of Nemmers and Grunewald (2000) “capital structure refers to all financial resources
marshaled by the firm, it includes short as well as long-term, and all forms of debt as well as
equity”. It includes all liabilities (Mashharawe,2003; Gaud,2005; Joeveer,2006; Mitton,2007).
Nikolaos (2007) refers that “capital structure of firm is actually the relationship between the total
debt and assets of the firm”.
Insurance Sector of Pakistan
This paper attempts to investigate the determinants of capital structure of insurance companies of
Pakistan. In 1947, after the creation of Pakistan, the first step taken by government to promote
the insurance activities in Pakistan was the establishment of Insurance Association of Pakistan
(IAP) on 9 February 1949. The core objective of Insurance Association of Pakistan is to protect
the interests of its member insurance companies and to support their businesses. Another step to
further strengthen the insurance industry was taken in 1953 by establishing Pakistan Insurance
Corporation (now Pakistan Reinsurance Company Ltd) to boost the re-insurance activities. In
1955, when foreign insurers appeared as competitors, National Co-Insurance Scheme (NCIS)
was introduced to enhance the capacity of small insurance companies of local market. When East
Pakistan broke its links with west Pakistan in 1971, Insurance industry as well as the whole
economy faced financial crises and as a result Pakistan lost Rs.180 million in terms of premium
revenue of non-life insurance sector.
In 1972 all life insurance companies were taken over by the government through the policy of
nationalization but this process did not track out the growth of insurance sector of Pakistan. For
instance, insurance companies received premiums of Rs.290 million in 1973, Rs.335 million in
1976, Rs.820 million in 1980, Rs.3.357 billion, Rs.31.25 billion in 2005 and Rs.38.45 billion in
2006 (Insurance Year Book, 2007).
The year 2000 brought many changes in the insurance sector of Pakistan since Insurance Act
1938 was replaced by Insurance Ordinance 2000. This ordinance enhanced the financial health of
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insurance companies by increasing the paid up capital requirements of both non-life and life
insurance companies. Therefore, the minimum requirement of paid-up capital of general
insurance companies was enhanced to Rs.80 million from Rs.50 million whereas capital
requirement of life insurers was increased from Rs.100 million to Rs.150 million. Furthermore,
In 2002, Security and Exchange Commission of Pakistan and Ministry of Commerce notified and
implemented the Insurance Rules 2002 in insurance sector of Pakistan to support the financial
growth of insurance industry of Pakistan.
As a result of these financial reforms total assets and net premiums of life insurance companies
have reached Rs.163.13 billion and Rs.36.72 billion respectively in 2006 while assets and net
premiums of non-life insurers have reached Rs.72.88 billion and Rs.20.01 respectively in 2006
(Insurance Year Book, 2007). In addition, the share of Pakistani insurance sector in the world
also has reached 0.02% in 2009 while insurance density has touched the level of 6.9% in 2009
(World Insurance Report, 2010). Moreover, total market capitalization of insurance sector of
Pakistan has reached Rs.224 billion in 2009. Therefore, statistical figures show that financial
regulations have really contributed in the development of the insurance industry in Pakistan.
Objective and Significance of the Study
The Current study
‣ Identifies the firm level characteristics which have significant impact on capital structure
of insurance companies of Pakistan.
‣ Specifies the firm level factors that can affect the financing decisions of life insurance
and non-life insurance companies.
‣ Also helps to indicate the factors which have less significant or no impact on leverage of
insurance sector of Pakistan.
‣ Assists the regulators and management of insurance companies that how much debt and
equity should be used to formulate their capital structure for getting the desired results.
2. Determinants of Capital Structure
In this section, we provide a review of the factors which are considered as important
determinants of capital structure of insurance companies of Pakistan.
2.1 Size (SZ)
Size is considered a key factor that can influence the financial structure of the firm. It has been
extensively used by the corporate finance researchers as control variable in the empirical analysis
of determining the capital structure of the firm and found that proportion of debt and equity
formulates according to the size of the firm (Scott and Martin, 1976; Booth et al., 2001). Various
studies report a positive relationship between size and leverage (Hamaifer et al,1994;Al-
Sakran,2001;Antoniou et al,2002;Gaud, 2005) while several studies intended negative
relationship between debt ratio and firms size (Rajan and Zingales, 1995; Bevan and
Danbolt,2002). According to the Rajan and Zingales (1995), the relationship between size and
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leverage could be negative because larger firms have less asymmetric information that reduces
the chances of undervaluation of issuing new stock and thus they prefer to issue more equity than
debt.
On the other hand, Static Trade-off hypothesis explores a positive relationship between size and
firm because larger firms are normally diversified and considered less risky, hence, prefer to
utilize more debt. In addition, larger firms prefer to issue more debt because it reduces direct
bankruptcy costs due to market confidence (Warner, 1977). Moreover, smaller firms prefer to
acquire lower debt because, these firms might face the risk of liquidation at the time of financial
distress. (Ozkan, 1996). Consistent with the results of Static Trade-Off theory, current study
expects a positive relationship between size and debt ratio.
The natural log of sales or the natural log of assets is generally used as a proxy to determine the
size of the firm. Current study uses natural log of sale (premiums) to measure the size of
insurance industry of Pakistan.
“Therefore, first hypothesis is that there is a positive relationship between leverage and size of
the firm”.
2.2 Profitability (PF)
Theoretical literature has no consistent relationship between debt ratio and profitability. Jensen
(1986) argued that profitable firms use debt as a tool which enforces managers to invest in more
disciplined way and as a result reduces free cash flows, which implies a positive relationship
between leverage and profitability. Static Trade-off model also predicts a positive relationship
between profitability and debt ratio due to the tax shield benefits. But according to the Pecking
Order Theory (Myers and Majluf, 1984), firms prefer to use internal source of financing
(retained earnings), then debt and finally issue external equity if more funds are required.
Therefore, the more profitable the firms are the more retained earnings they will have, which
exhibit a lower debt may utilized in the capital structure. This shows a negative relationship
between profitability and leverage of the firm. In addition, profitable firms avoid to get loan in
inefficient markets due to disciplinary role of debt ( Agency Theory).Various studies (Gonedes et
al, 1988; Friend and Hasbrouck,1989; Shah and Khan, 2007) also reported a negative
relationship between profitability and debt ratio. Present study also expects a negative
relationship between profitability and leverage.
“Thus, second hypothesis is that there is a negative relationship between leverage and
profitability of the firm”.
2.3 Tangibility of assets (TG)
Tangible assets are considered to have an impact on borrowing decisions because they have
greater value in case of bankruptcy .A firm with large portion of fixed assets can easily raise debt
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at relatively lower rates by providing the collateral of these assets to the creditors. Having the
incentive of getting the loan at nominal rates, these types of firms are expected to borrow more
as compared to those firms where cost of borrowing is higher due to less proportion of fixed
assets (Suto, 1990). Hence, firms with large proportion of fixed assets prefer to employ more
debt for getting the advantage of this opportunity. On the contrary, negative relationship have
been reported between leverage and fixed assets in small and medium firms (Daskalakis and
Psillaki,2007) and in less developed economies ( Joever,2006 ). Therefore, according to the
nature of insurance industry, current study is expected to have negative relationship between
leverage and tangibility of assets and use ratio of net fixed assets to total assets for measuring the
tangibility of assets of insurance industry. The amount of net fixed assets indicates the cost of
fixed assets less depreciation.
“Therefore, fourth hypothesis is that there is a negative relationship between leverage and
tangibility of assets of the firm”.
2.4 Liquidity (LQ)
Liquidity ratio not only specifies the ability of the firm to cover its short term liabilities but also
shows the liquidity position of the firm. Liquidity ratio has a mixed impact on the leverage
decision. Firms with higher liquidity ratio prefer to acquire more debt because of their ability to
meet short term obligations (Ozkan, 2001). This shows a positive relationship between the debt
ratio and firms liquidity position. On the other hand, when firms have high liquidity ratios or
have more liquid assets then then may prefer to use these assets to finance their investments and
discourage to raise external funds (Pecking Order Theory). Therefore, the firm’s liquidity
position shows a negative relationship with capital structure of the firm. Current study is expects
to have a negative relationship between liquidly and leverage because insurance companies in
Pakistan seem to utilize only premium funds for paying claims. Current ratio (which is calculated
as current assets over current liabilities) is used as a proxy to measure the liquidity. It is given by,
“Therefore, fifth hypothesis is that there is a negative relationship between leverage and
liquidity of the firm”.
2.5 Risk (RK)
Risk is another key explanatory variable that may affect the capital structure of the firm. It is
considered to be either the inherent business risk or it may arise in the firm as a result of
inefficient management practices. Firms with high volatility in earnings might face higher risk
that forces the management to reduce the debt level because higher risk increases the chances of
bankruptcy (Pandy, 2001). This predicts a negative relationship between leverage and risk and
this result is also consistent with trade-off and pecking order hypothesis. Consistent with the
results of Pecking Order Theory and Static Trade-off Theory, present study is expected to have a
negative relationship between risk and leverage. Several proxies have used in empirical studies
to measure the risk of the firm such as standard deviation of the difference in operating cash
flows to total assets, standard deviation of returns on net income, and standard deviation of
percentage change in net income (Xiaoyan, 2008). According to the nature of business of
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insurance companies, current study use standard deviation of ratio of total insurance claims to
total premiums as a proxy to measure the risk.
“Therefore, seventh hypothesis is that there is negative relationship between leverage and risk of
the firm.
3. Methodology
Regression Model
Leverage = β0 + β1 (Size) + β2 (Profitability) + β3 (Tangibility) + β4 (Liquidity) + β5 (Risk)
+ ε
Where
Size = Natural Log of Premiums
Profitability = Net Income before Interest and Tax divided by Total Assets
Tangibility = Fixed Assets Divided by Total Assets
Liquidity = Current Assets Divided by Current Liabilities
Risk = Standard Deviation of Total Claims Divided by Total Premiums
3.1 Sample and Sources of Data
The study uses various sources have been used for data collection. The book value based yearly
financial data from 2001 to 2009 has been collected from the financial statements (Balance Sheet
& Profit and Loss A/c) of insurance companies and various “Insurance Year Books” published
by Insurance Association of Pakistan. Currently only 5 companies are providing the services of
life insurance in Pakistan, including; two domestic owned, two foreign and one government
owned. The life insurance companies comprising 59% of entire insurance sector in terms of total
assets in 2009 whereas total premium revenue of life insurance companies has also reached
Rs.35.59 billion in 2009(IAP, 2007). Non-life insurance industry in Pakistan comprises 45
private companies, out of which only 32 are currently operating while the remaining 13 non-life
insurance companies have closed their operations due to the limit of paid-up capital
requirements.
4. Empirical Results
4.1 Descriptive Statistics
Table 4.1 reports the descriptive statistics of leverage, size, profitability, tangibility, liquidity and
risk of Life insurance sector of Pakistan from 2001 to 2009. On average all life insurers are
highly leveraged over nine years with ratios of not less than 0.79. In 2006 leverage reaches at
maximum level i.e. 0.84 which shows the aggressive behavior of life insurers about utilization of
large portion of debt. On the other hand, variation in selection of debt capital also seems to be at
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maximum level in 2007 i.e. 0.30. Size is explanatory variable which is proxied by log of total
premiums. Statistics shows that on average size of life insurance companies are continuously
increasing from 2001 to 2009 which predicts that in Pakistan, people prefer to transfer their risk
by getting insurance policy. Terrorism aftermaths might be one of the key reasons of increasing
the life insurance policy premium revenues. Statistics also predict that with the enhancement of
size debt ratio of life insurers is also increases throughout nine years. On the other hand,
variation in size approximately same in all seven years i.e. around 2.06.
Profitability of life insurers is reported in third column of Table 4.1. Life insurers exhibit
uniformity with respect to the profitability from 2001 to 2006.i.e.approximately 0.02. But in
2007, average ratio jump into 0.07 which shows a healthy change in the profitability of life
insurance sector. In the same manner variation in profitability is considerably lesser and shows a
consistency from 2001 to 2006 except 2007. The average values of tangibility in Table 4.1 also
shows minor or small portion of tangibility of assets of life insurers and depicts consistency in
mean values from 2001 to 2009 approximately 0.03. The standard deviation of ratio of fixed
assets to total assets is slightly lesser i.e. around 0.02 and is not varied over nine years. Table 4.1
also indicated all life insurance companies, on average, continuously improving their liquidity
position through out nine years. This trend shows that life insurers keep large portion of funds in
liquid form for settlement of claims. The highest mean value of liquidity is observed in 2007
which is 6.36. But in the same year the value of standard deviation is at maximum level i.e. 8.63
among all the years which also predicts inconsistency in liquidity position.
Table 4.1 also describes that insurers seem to be risky from 2004 to 2009 with respect to the
settlement of claims. In 2003, the mean value of risk seems at minimum level with the ratio of
0.58 which reached at 7.23 in 2009. This continuously increasing trend over nine years predicts
that life insurance companies become more risky.Table 4.2 provides descriptive statistics of
variables leverage, size, profitability, tangibility, liquidity and risk for the same study period of
2001 to 2009 for non life insurance industry. The average value of leverage is approximately
0.50 over nine years which is quite lower than the mean values of life insurance industry. The
highest mean value of leverage is 0.54 in 2003 and lowest mean value is 0.45 in 2007.The inter
industry variation in selection of leverage is minimum in 2005 at 0.17 and maximum in 2001-02
at 0.22. The variation of non-life insurers is around 0.20 over nine years which is almost the
same as the life insurance industry.
Although the size of non life insurance industries is slightly lower than life insurance industry
but variable size is having the same growing trend throughout the study period. The maximum
mean value of size for non life insurers is 5.24 in 2007 which is almost 26% above than the size
in 2001. The variation in size of non-life insurers is slightly lower than the life insurance
companies over seven years i.e. 1.75. An increasing trend can be seen in the mean values of
profitability from the minimum of 0.05 in 2001 to a maximum of 0.22 in 2009. These values
show continuously improvement in profitability of non-life insurance companies. Table 2 also
indicates that the average values of profitability of non life insurance sector are much higher than
life insurance sector along with inter industry variability which is at its minimum level in 2004 at
0.06 and maximum in 2007 at 0.19.
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Consistent with the descriptive statistics of life insurers, table 4.2 shows that non-life insurance
companies also have less portions of fixed assets i.e. around 0.12 during nine years. On the other
hand, inter industry variability is on average 0.20 from 2001 to 2009 which is also consistent
with the standard deviations of life insurance industry. The maximum average value of liquidity
of non-life insurers is 6.10 found in year 2007 while on average, other eight years show
relatively lower level of liquidity i.e. around 2.25. In addition, average liquidity values of both
life and non-life insurers are approximately the same in all the years. Table 4.2 also depicts that
non-life insurance companies face higher risk (8.21) in 2004 as compare to other eight years.
Descriptive statistics also predicts that on average risk of non-life insurers is relatively higher
than the life insurers.
Table 4.3 provides the descriptive statistics of leverage, size, risk, tangibility, liquidity and
profitability of entire insurance sector (life plus non-life) of Pakistan. The average value of
leverage is approximately 0.55 over nine years of all insurance companies in Pakistan. In 2005
leverage reaches at maximum level i.e. 0.58 which shows the aggressive behavior of insurers
about utilization of large portion of debt. On the other hand variation in selection of debt capital
also seems to be at minimum level in 2005 i.e. 0.21 as compare to other years. The mean value
of size is at maximum level in 2009 i.e. 6.07 which shows around 73 % increase in premium
revenues from 2001. On the other hand, variation in size approximately same in all nine years i.e.
around 1.90. An increasing trend can be seen in mean values of profitability from the minimum
value 0.04 in 2001 to a maximum value 0.23 in 2009. In the same manner variation in
profitability is having increasing trend from 2001 to 2009.
As insurance companies face uncertainty for settlements of claims, therefore companies prefer to
keep large portion of current assets than fixed assets. The average values of Table 4.3 also shows
minor portion of fixed assets of insurers and depicts consistency in mean values from 2001 to
2009 approximately 0.11. The standard deviation of tangibility is around 0.19 over nine years.
Table 4.3 also shows insurance companies, on average, continuously improving their liquidity
position through out nine years. This trend shows that life and non-life insurers keep large
portion of funds in liquid form for settlement of claims. The highest mean value of liquidity is
observed in 2007 which is 6.13. But in the same year the value of standard deviation is at
maximum level i.e. 16.38 among all the years which also predicts inconsistency in liquidity
position. Statistics of Table 4.3 describes that in 2003, the mean value of risk seems at minimum
level with the ratio of 4.24 which reaches at 7.06 in 2007. On the other hand, in 2004, the value
of standard deviation is 10.91 which is the highest value over nine years.
4.3 Regression Analysis
Finally the impact of five explanatory variables size, profitability, liquidity, risk and tangibility
on capital structure of insurance companies of Pakistan has been examined by using three
ordinary least square regression models. These three regression models employ different data
sets according to the type of insurers. Model A uses the financial data of life insurance sector
while Model B and Model C are regress the data of five explanatory variables on capital
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structure of non-life insurance companies and entire insurance sector respectively over nine years
from 2001 to 2009. Table 4.4 of Model A reports the value of adjusted R square (0.768) indicates
that debt ratio is nearly 77% dependant on variables i.e. size, profitability, tangibility, risk and
liquidity. Therefore, leverage is mainly defined by these five variables of life insurers in Pakistan
over nine years.
Table 4.4 of Model A shows that coefficient of variable size is positive and statistically
significant at 1% level. This predicts that large size life insurance companies in Pakistan prefer to
utilize more debt in their capital structure. These results also confirm the notion that large firms
employ more debt because these are less risky and diversified in nature (Static trade- off
Theory). In addition, larger firms are prefer to issue more debt because it reduces direct
bankruptcy costs due to market confidence (Warner, 1977). Moreover, smaller firms prefer to
acquire lower debt because, these firms might face the risk of liquidation at the time of financial
distress (Ozkan, 1996).
The coefficient of profitability is found to be negative and statistically significant at 5% level.
This negative sign indicates the negative relationship between leverage and profitability and
predicts that, in Pakistan, profitable life insurance companies prefer to utilize small portion of
debt. This result confirms the notion that Pakistani life insurance companies follow the Pecking
Order pattern i.e. prefer to employ internal financing than debt. In addition, negative relationship
also confirms the implication of Agency Theory which predicts that profitable firms avoid to get
loan from inefficient markets due to the disciplinary role of debt. Table 4.4 depicts that the beta
value of explanatory variable tangibility of assets is 0.553 with the positive coefficient sign.
However, tangibility is not statistically significant with the large p-value. Although positive
relationship shows that a firm with the large portion of fixed assets can easily raise debt or
obtains more debt at relatively lower rates by providing collaterals of these assets to creditor but
due to the insignificant relationship of tangibility and leverage is not considered a powerful
explanatory variable to define the debt ratio of life insurance companies in Pakistan over nine
years. Results of regression model A indicate that the control variable liquidity with the negative
coefficient is statistically significant at 5% level. Therefore, Pakistani life insurance companies
with high liquidity ratios or more liquid assets are preferred to utilize these assets to finance their
investments and discourage to raise external funds.
Table 4.4 also shows that the coefficient of variable risk is positive and statistically significant at
5% level. This indicates that in order to fulfill the claims of the life insurance policyholder at the
time of death or expiry of the policy, companies acquire external funds. Table 4.5 of Model B
depicts the results of regression analysis of Pakistani non-life insurance companies from 2001 to
2009. The value of adjusted R square (0.713) indicates that debt ratio is nearly 72% dependant
on explanatory variables i.e. size, profitability, tangibility, risk and liquidity. Consistent with the
results of Model A, Table 4.5 of Model B shows that coefficient of variable size is positive and
statistically significant at 1% level. This predicts that large size non-life insurance companies in
Pakistan prefer to employ more debt in their capital structure. These results consistent with the
hypothesis that large firms employ more debt because these firms are less risky and diversified in
nature (Static Trade-off Theory).
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The coefficient of profitability is found to be negative and statistically significant at 1% level.
This negative sign indicates the negative relationship between leverage and profitability and
predicts that, in Pakistan, profitable non-life insurance companies prefer to utilize small portion
of debt. This result confirms the notion that Pakistani non-life insurance companies follow
Pecking Order pattern i.e. preferred to employ internal financing than debt. The results of
regression model B indicate that the coefficient of variable liquidity with the negative coefficient
is statistically significant at 1% level. This negative sign shows the inverse relationship between
the liquidity and capital structure of non-life insurance sector. Therefore, results predict that
Pakistani non-life insurance companies with high liquidity ratios or more liquid assets prefer to
utilize internal source of financing than debt. Negative coefficient of variable tangibility
specifies the negative relationship between tangibility of assets and debt ratio of non-life
insurance sector of Pakistan. This inverse relationship indicates that in Pakistan non-life
insurance companies with large portion of fixed assets are preferred to utilize small portion of
debt in their capital structure. Negative relationship has also been reported between leverage and
tangibility in small and medium firms (Daskalakis and Psillaki,2007) and in less developed
economies ( Joever,2006 ).
The variable risk is negative and statistically significant at 5% level. Negative sign indicates that
at the time of the destruction or loss of the subject matter, non-life insurance companies prefer to
employ their internal source of financing for settlement of claims than external financing. This
negative relationship between leverage and risk is also consistent with the results of trade-off and
pecking order hypothesis. Table 4.6 of Model C reports the results of regression analysis of
entire insurance sector (life plus non-life) of Pakistan from 2001 to 2009. The value of adjusted
R square (0.823) indicates that capital structure of Pakistani insurance sector is nearly 82%
dependant on control variables (size, profitability, tangibility, risk and liquidity). Therefore,
leverage is mainly defined by these five control variables of insurance sector of Pakistan over
nine years. Table 4.6 of Model C shows that coefficient of variable size is positive and
statistically significant at 1% level. This predicts that large size insurance companies (life plus
non-life) in Pakistan prefer to utilize more debt in formation of capital structure.
The coefficient sign of explanatory variable profitability is found to be negative and statistically
significant at 5% level. This negative sign shows the negative relationship between leverage and
profitability and predicts that, in Pakistan, profitable insurance companies (both life and non-life)
discourage to employ debt capital over seven years. This result confirms the notion that Pakistani
insurance companies follow the Pecking Order pattern i.e. preferred to employ internal source of
financing than debt. The negative relationship between the tangibility and debt ratio shows that
Pakistani insurance companies (both life and non-life) with large portion of fixed assets
discourage to employ debt capital. Al-Bahsh and Sentis (2008) also found the negative
relationship between tangibility and leverage by taking the sample of less developed economies.
Various studies like Joeveer (2006) and Daskalakis and Psillaki (2007) also predict the same
negative relationship between debt ratio and tangibility. The negative and statistically significant
relationship between liquidity and leverage ) indicates that Pakistani insurance companies (both
life and non-life with high liquidity ratios or more liquid assets prefer to utilize these assets to
finance their investments and discourage to raise external funds over nine years. Ozkan (2001)
and Mashharawe (2003) also show the inverse relationship between liquidity and debt ratio. In
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addition, the relationship between risk and debt ratio illustrates statistically insignificant results
of insurance sector of Pakistan.
Conclusion
This study investigates the determinants of capital structure of insurance companies of Pakistan
over the period of nine years from 2001 to 2009. Empirical results indicate that size, profitability,
liquidity tangibility and risk are important determinants of capital structure of insurance
companies of Pakistan. In addition, Pakistani insurers follow Pecking Order pattern in terms of
profitability, risk, tangibility and liquidity as leverage has a negative relationship with
profitability, risk, tangibility and liquidity while positive relationship between leverage and size
shows consistency with the Trade-off theory. Moreover, the results also indicate that the
management of profitable, more liquid, more tangible and risky non-life insurance companies
emphasize on retained earnings or equity rather than debt financing.
Limitations and Future Implications
This study considers only one financial sector i.e. insurance sector of Pakistan. Other financial
institutions of Pakistan like banks, mutual funds, modaraba companies etc. could be selected for
future research. In addition, present study selected only five explanatory variables (size,
profitability, risk, tangibility and liquidity), therefore, academicians may choose other
determinants of capital structure for future research.
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14

TABLE 4.1: Descriptive Statistics for Study Variables (Life Insurance)
Years Leverage Size Profitability
Mean SD Min Max Mean SD Min Max Mean SD Min Max
2001 0.80 0.21 0.45 0.99 6.02 2.12 3.06 8.93 0.02 0.01 0.00 0.03
2002 0.81 0.20 0.47 0.99 6.21 2.11 3.29 9.07 0.02 0.01 0.00 0.03
2003 0.82 0.19 0.51 0.99 6.50 2.08 3.57 9.20 0.02 0.01 0.00 0.03
2004 0.79 0.24 0.38 0.99 6.68 2.09 3.56 9.31 0.03 0.02 0.00 0.05
2005 0.83 0.21 0.47 0.99 6.95 2.03 3.96 9.53 0.02 0.02 0.00 0.05
2006 0.84 0.20 0.49 0.99 7.21 2.02 4.24 9.68 0.03 0.02 0.00 0.06
2007 0.79 0.30 0.26 1.00 7.51 2.06 4.50 10.03 0.07 0.07 0.00 0.17
2008 0.81 0.22 0.43 0.99 6.73 2.07 3.74 9.39 0.03 0.02 0.00 0.06
2009 0.80 0.27 0.39 0.96 6.84 2.09 3.85 9.47 0.04 0.03 0.00 0.07
15

TABLE 4.1 (Continued): Descriptive Statistics for Study Variables (Life Insurance)
Years Tangibility Liquidity Risk
Mean SD Min Max Mean SD Min Max Mean SD Min Max
2001 0.03 0.02 0.00 0.06 1.70 0.76 1.07 2.65 1.92 1.33 0.70 3.94
2002 0.03 0.02 0.00 0.06 1.73 0.86 1.14 3.01 0.83 0.47 0.40 1.34
2003 0.03 0.02 0.00 0.05 2.18 1.11 1.22 3.72 0.58 0.45 0.18 1.34
2004 0.02 0.02 0.00 0.04 2.24 1.77 1.09 4.85 3.34 3.08 0.00 7.23
2005 0.02 0.02 0.00 0.04 3.02 2.26 1.15 5.94 4.70 2.15 1.23 6.36
2006 0.02 0.01 0.00 0.03 3.98 2.72 1.36 7.37 3.60 3.86 0.51 9.72
2007 0.02 0.02 0.00 0.05 6.36 8.63 1.33 16.33 6.35 6.51 1.78 16.00
2008 0.03 0.01 0.00 0.06 3.03 2.59 1.19 6.27 6.85 2.55 0.69 6.56
2009 0.04 0.02 0.00 0.05 4.25 2.89 1.22 6.87 7.23 4.75 0.68 7.00
16

TABLE 4.2: Descriptive Statistics for Study Variables (Non-Life Insurance)
Years Leverage Size Profitability
Mean SD Min Max Mean SD Min Max Mean SD Min Max
2001 0.50 0.22 0.03 0.86 4.15 1.75 1.36 7.98 0.05 0.09 -0.21 0.20
2002 0.49 0.22 0.04 0.87 4.26 1.67 1.73 7.97 0.06 0.08 -0.14 0.24
2003 0.54 0.21 0.04 0.88 4.41 1.73 1.22 8.03 0.07 0.07 -0.14 0.26
2004 0.52 0.21 0.09 0.86 4.68 1.72 1.24 8.21 0.08 0.06 -0.03 0.21
2005 0.53 0.17 0.11 0.82 5.03 1.73 1.89 8.29 0.13 0.08 0.01 0.36
2006 0.49 0.20 0.08 0.91 5.23 1.72 2.37 8.60 0.17 0.15 0.00 0.71
2007 0.45 0.21 0.01 0.83 5.24 1.98 -0.20 8.72 0.19 0.19 0.00 0.86
2008
2009
0.50 0.21 0.06 0.86 4.71 1.76 1.37 8.26 0.21 0.10
0.49 0.20 0.05 0.87 4.81 1.76 1.38 8.30 0.22 0.11
-
0.07 0.41
-
0.05 0.44
17

TABLE 4.2 (Continued): Descriptive Statistics for Study Variables (Non-Life Insurance)
Years Tangibility Liquidity Risk
Mean SD Min Max Mean SD Min Max Mean SD Min Max
2001 0.14 0.21 0.00 0.94 2.50 2.04 0.51 8.63 5.32 5.63 0.25 22.51
2002 0.13 0.21 0.00 0.94 2.71 2.06 0.65 8.37 5.84 7.16 0.01 32.34
2003 0.12 0.20 0.00 0.93 2.00 0.71 0.59 3.79 4.90 4.97 0.07 21.44
2004 0.12 0.21 0.00 0.92 2.28 1.11 0.33 5.11 8.21 11.66 0.22 61.04
2005 0.12 0.19 0.00 0.89 2.23 1.52 -0.84 7.15 7.55 9.93 0.00 53.47
2006 0.12 0.21 0.00 0.97 2.96 2.08 0.30 9.85 4.38 3.62 0.28 15.18
2007 0.11 0.19 0.00 0.98 6.10 17.16 0.35 89.76 7.16 6.59 0.33 28.21
2008 0.12 0.20 0.00 0.94 2.97 3.81 0.27 18.95 6.19 7.08 0.17 33.46
2009 0.13 0.18 0.00 0.92 3.05 4.11 0.23 20.67 6.34 7.32 0.15 35.28
18

TABLE 4.3: Descriptive Statistics for Study Variables (Entire Insurance Sector)
Years Leverage Size Profitability
Mean SD Min Max Mean SD Min Max Mean SD Min Max
2001 0.55 0.24 0.03 0.99 4.43 1.90 1.36 8.93 0.04 0.08 -0.21 0.20
2002 0.54 0.25 0.04 0.99 4.56 1.85 1.73 9.07 0.05 0.08 -0.14 0.24
2003 0.58 0.23 0.04 0.99 4.73 1.91 1.22 9.20 0.06 0.07 -0.14 0.26
2004 0.56 0.24 0.09 0.99 4.99 1.89 1.24 9.31 0.08 0.06 -0.03 0.21
2005 0.58 0.21 0.11 0.99 5.32 1.88 1.89 9.53 0.11 0.08 0.00 0.36
2006 0.54 0.24 0.08 0.99 5.53 1.88 2.37 9.68 0.14 0.15 0.00 0.71
2007 0.50 0.26 0.01 1.00 5.58 2.13 -0.20 10.03 0.17 0.18 0.00 0.86
2008 0.53 0.24 0.06 0.99 5.97 1.92 1.37 9.39 0.19 0.10 -0.07 0.41
2009 0.52 0.22 0.04 0.89 6.07 1.82 1.38 9.47 0.23 0.11 -0.05 0.44
19

TABLE 4.3 (Continued): Descriptive Statistics for Study Variables (Entire Insurance Sector)
Years Tangibility Liquidity Risk
Mean SD Min Max Mean SD Min Max Mean SD Min Max
2001 0.12 0.20 0.00 0.94 2.39 1.92 0.51 8.63 4.77 5.31 0.25 22.51
2002 0.11 0.19 0.00 0.94 2.57 1.96 0.65 8.37 5.03 6.80 0.01 32.34
2003 0.11 0.19 0.00 0.93 2.02 0.76 0.59 3.79 4.24 4.83 0.07 21.44
2004 0.11 0.19 0.00 0.92 2.28 1.17 0.33 5.11 7.48 10.91 0.00 61.04
2005
0.10 0.18 0.00 0.89 2.34 1.61
-
0.84 7.15 7.12 9.21 0.00 53.47
2006 0.11 0.20 0.00 0.97 3.10 2.15 0.30 9.85 4.26 3.61 0.28 15.18
2007 0.09 0.18 0.00 0.98 6.13 16.38 0.35 89.76 7.06 6.48 0.33 28.21
2008 0.11 0.19 0.00 0.94 2.98 3.71 0.27 18.95 5.71 6.74 0.13 33.46
2009 0.14 0.17 0.00 0.93 3.07 4.01 0.23 20.67 5.87 6.97 0.12 35.28
20

Table: 4.4 Regression Coefficients & Significance level of Model A
(Life Insurance)
Variables
Unstandardized
Standardized
t-value
Sig.
Coefficients
Coefficients
B Std. Error Beta
(Constant) .321 .079 4.053 .001
Size .121 .009 .828 12.538 .000*
Profitability -1.132 .402 -.168 -2.940 .007**
Tangibility .553 .804 .041 .637 .481
Liquidity -.036 .005 -.262 -3.092 .008**
Risk .020 .004 .178 2.566 .012**
R Square 0.773
Adjusted R Square 0.768
F statistics 115.101
*Significant at 1% level
** Significant at 5% level
21

Table: 4.5 Regression Coefficients & Significance level of Model B
(Non-Life Insurance)
Variables
Unstandardized
Standardized
t
Sig.
Coefficients
Coefficients
B Std. Error Beta
(Constant) .375 .036 10.392 .000
Size .075 .006 .625 13.075 .000*
Profitability -.735 .072 -.444 -10.890 .000*
Tangibility -.170 .048 -.150 -3.345 .002**
Liquidity -.043 .005 -.235 -6.390 .000*
Risk -.043 .001 -.141 -3.056 .004**
R Square 0.720
Adjusted R Square 0.713
F statistics 85.101
* Significant at 1% level
** Significant at 5% level
22

Table: 4.5 Regression Coefficients & Significance level of Model B
(Entire Insurance Sector)
Model
Unstandardized
Standardized
t
Sig.
Coefficients
Coefficients
B Std. Error Beta
(Constant) .341 .033 10.388 .000
Size .069 .005 .539 16.098 .000*
Profitability -.747 .068 -.431 -12.499 .004**
Tangibility -.265 .047 -.147 -3.556 .017***
Liquidity -.036 .004 -.229 -6.526 .011***
Risk -.007 .001 -.127 -3.156 0.22
R Square 0.823
Adjusted R Square 0.818
F statistics 125.101
*Significant at 1% level
** Significant at 5% level
*** Significant at 10% level
23