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Empirical Modelling of Capital StructureJordanian
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Journal of Emerging Market
Finance
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Empirical Modelling of Capital Structure: Jordanian Evidence
Basil Al-Najjar
Journal of Emerging Market Finance 2011 10: 1
DOI: 10.1177/097265271101000101
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Articles
Empirical Modelling of Capital
Structure: Jordanian Evidence
Basil Al-Najjar
This article provides evidence about the determinants of capital structure in
developing countries through studying non-financial Jordanian firms. We detect
that capital structure choice in Jordan is influenced by similar set of factors suggested
in the developed markets, namely, institutional ownership, profitability, business
risk, asset tangibility, asset liquidity, market-to-book and firm size. The findings
are consistent with the related studies in both developed and developing countries.
In addition, we report that Jordanian firms have target capital structure ratios
and that they adjust relatively quickly to their targets.
JEL Classification: G32, C33
Keywords: Capital structure, target, determinants, panel data, partial
adjustment model
1. Introduction
Capital structure theory is based on the seminal work of Modigliani and
Miller (M&M hereafter) (1958) and (1963). M&M (1958) suggest that in
a world without taxes, the firm value and its weighted average cost of capital
are not affected by its capital structure. M&M (1963) relax their assumptions
and incorporate the effect of corporate taxes. With corporate taxes, M&M
detect that leverage will increase the value of the firm because interest is a
tax deductible expense. M&M’s perfect market assumptions are not realistic
and cannot hold in the real world. This motivates researchers to relax such
assumptions and to provide more applicable theories and explanations for
capital structure.
Journal of Emerging Market Finance, 10:1 (2011): 1–19
SAGE Publications Los Angeles „ London „ New Delhi „ Singapore „ Washington DC
DOI: 10.1177/097265271101000101
2 / Basil Al-Najjar
The empirical research of capital structure has been largely restricted to
the US and other developed countries, with limited evidence about this issue
in developing countries. However, Booth et al. (2001) are considered among
the first to investigate this issue in developing countries (using 10 developing
countries: India, Pakistan, Thailand, Malaysia, Zimbabwe, Mexico, Brazil,
Turkey, Jordan and Korea). They argue that capital structure in developing
countries is affected by same type of factors that are found to be significant
in developed countries.
This article contributes to the literature in three ways. First, it provides
major evidence about the capital structure puzzle in Jordan. Second, it employs different statistical methodologies to investigate the determinants of
capital structure. Finally, it provides the first evidence about the target capital
structure issue in Jordan.
The results show that there is a significant negative relationship between
leverage and profitability, business risk and institutional ownership. In addition, there is a significant positive relationship between leverage and firm
size, market-to-book ratio, asset tangibility and liquidity. Mixed results are
reported for the effect of institutional ownership. However, the study could
not find evidence for a relationship between capital structure and dividend
policy. Finally, we report that firms have target leverage ratios and that they
relatively adjust quickly to their target ratios.
The study is structured as follows: Section 2 discusses the determinants of
capital structure, while Sections 3 and 4 demonstrate data and methodology.
Section 5 discusses the statistical results. Section 6 demonstrates the dynamics
of capital structure. Finally, Section 7 summarises this article.
2. The Determinants of Capital Structure
In this section, the investigated firm-specific factors in the econometric
models are discussed.
2.1 Dividends
Dividend payment can be seen as a signal of improved financial position
and, in turn, more debt-issuing ability (Bhaduri 2002; Kose and Williams
1985; Miller and Rock 1985). This argument is supported by the signalling
theory of capital structure. This study employs the dividend payout ratio
as an index for cash dividend. Agency models also show links between the
Journal of Emerging Market Finance, 10:1 (2011): 1–19
Empirical Modelling of Capital Structure / 3
dividend payments and leverage. These models envisage dividend payments
and debt financing as alternative mechanisms to mitigate agency problems.
Thus, from agency theory point of view, there is a negative relationship between dividend and leverage (Bhaduri 2002). Hence, we argue that there is
a relationship between dividend payments and capital structure.
2.2 Institutional ownership
Institutional investors are specialists in collecting and explaining information
relating to firms in which they invest. Agency theory argues that the optimal
capital structure and ownership structure are tools to alleviate agency costs,
and in turn, a negative relationship is expected between capital structure and
institutional ownership (Bathala et al. 1994; Chaganti and Damanpour 1991;
Grier and Zychowicz 1994; Jensen 1986; Jensen and Meckling 1976).
However, others report that managerial ownership (as an index for ownership structure) and leverage are positively related (e.g., Berger et al. 1997;
Chen and Steiner 1999; Leland and Pyle 1977). Institutional investors in
Jordan can be seen as an index for insider (managerial) ownership as they
are the main owners and they can control the firm. We posit that there is a
relationship between institutional ownership and capital structure. In this
study, two indices are produced to capture the ownership structure: The
first is the natural logarithm of the number of shares owned by institutional
investors, and the second is the percentage of institutional ownership from
the subscribed shares (Tong and Ning 2004).
2.3 Profitability
Myers and Majluf (1984) suggest that in the presence of asymmetric information, firms adopt a certain financing pattern to rank different financial
alternatives. They would select internal financing over the external financing,
but would use debt financing if such low-cost alternatives were exhausted
(because debt has lower flotation and information costs compared to equity
financing) and such firms tend to use covenants to minimise the information premium of the firm. The last option for the firm is to issue new equity
(Myers and Majluf 1984). Accordingly, a negative relationship is expected
between leverage and profitability of the firm (Bhaduri 2002; Booth et al.
2001; Cassar and Holmes 2003; Jensen et al. 1992; Ozkan 2001; Rajan and
Zingales 1995; Titman and Wessels 1988; Voulgaris et al. 2004).
Journal of Emerging Market Finance, 10:1 (2011): 1–19
4 / Basil Al-Najjar
2.4 Business risk
Firms with high business risk are more likely to face financial difficulties
and consequently are more likely to be bankrupted. Since debt involves a
legal commitment of periodic payments, highly leveraged firms are prone to
financial distress costs and are less able to obtain debt finances. Thus, firms
with volatile incomes are expected to use less debt in their capital structure
than those with stable incomes (e.g., Bhaduri 2002). Hence, we argue that
there is a negative relationship between business risk and capital structure
(it is worth noting that beta is not used because such information is not
available for our sample and that this article is interested in investigating
income variability).
2.5 Asset structure
Collateralised assets are considered to be an important driver that affects
the capital structure decision of the firm. Tangible assets could be used as
collateral; thus, the higher the proportion of tangible assets, the lower the
creditor’s risk, and, in turn, the higher the value of the assets in the case of
bankruptcy and liquidation. Booth et al. state that ‘The more tangible the
firm’s assets, the greater its ability to issue secured debt and less information
revealed about future profits’ (Booth et al. 2001: 101). Empirical studies that
support this relationship include those by Rajan and Zingales (1995) and
Titman and Wessels (1988).
On the other hand, the use of debt controls managers’ incentives to
consume more than the optimal level of perquisites by raising the threat of
bankruptcy. Grossman and Hart (1982) argue that managers are adverse to
bankruptcy because of its negative impact on their compensation plans and
job security. Thus, firms with fewer tangible assets may use more debt to
monitor managerial activity even though rising debt in such a situation is
costly. Hence, a trade-off between agency costs and expensive debt financing
occurs. Accordingly, a negative relationship is expected between leverage
and tangible assets (Bhaduri 2002; Jensen and Meckling 1976; Titman and
Wessels 1988). On the basis of the aforementioned contradicting arguments,
we include asset tangibility (Booth et al. 2001; Ghosh et al. 2000; Huang
and Song 2006; Rajan and Zingales 1995; Voulgaris et al. 2004) in the
models and posit that there is a relationship between asset tangibility and
capital structure.
Journal of Emerging Market Finance, 10:1 (2011): 1–19
Empirical Modelling of Capital Structure / 5
2.6 Asset liquidity
Liquid assets increase firms’ ability to obtain debt finances. Liquid assets can
be sold without significant loss of their value, making better collateral for the
lender. Therefore, debt is used as lenders face lower costs in financing such
assets. In the same vein, Ozkan (2001) states that ‘[f ]irms with higher liquidity
ratios might support a relatively higher debt ratio due to greater ability to meet
short term obligations when they fall due’ (Ozkan 2001: 182). The trade-off
theory of capital structure supports this relationship. Harris and Raviv (1990)
also suggest that there is a positive relationship between the liquidation value
and leverage. If the expected liquidation values are higher for more liquid
assets, then firm’s debt is positively associated with asset liquidity. To measure
this effect, the study uses the ratio of current assets to current liabilities as a
proxy for the liquidity of the firm’s assets (Ozkan 2001) and posits a positive
relationship between asset liquidity and capital structure.
2.7 Growth opportunities
Agency problems in growing firms are more severe due to the flexibility in
their future investments. Accordingly, the expected growth rate is negatively
related to (long-term) leverage (Titman and Wessels 1988). In the same
vein, Myers (1977) suggests that firms with higher growth rates tend to use
less long-term debt and more short-term debt in their capital structure in
order to reduce such agency costs. On the other hand, a positive relationship
between growth opportunities and leverage can be explained as ‘indicative of
the fact that growth opportunities add value to the firm and hence increase
long-term debt-taking capacity. Moreover, as growing firms require more
finance to support their planned capital expenditure, they are likely to be more
leveraged’ (Bhaduri 2002: 212). This study uses the market-to-book ratio
as an indicator of the firm’s expected growth rate (Booth et al. 2001; Huang
and Song 2006; Ozkan 2001) and hypothesizes that there is a relationship
between growth opportunities and capital structure.
2.8 Firm size
There is evidence that firm size plays a vital role in the capital structure
decision. Large firms can be seen as more diversified and less likely to experience bankruptcy. Therefore, there is a positive relationship between a firm
size and leverage (Bhaduri 2002; Titman and Wessels 1988). The natural
Journal of Emerging Market Finance, 10:1 (2011): 1–19
6 / Basil Al-Najjar
logarithm of total assets (ln (TA) is used as the proxy for firm size (Bhaduri
2002; Brailsford et al. 2002; Cassar and Holmes 2003; Rajan and Zingales
1995).
Table 1 presents a summary of the findings of selected studies in capital
structure arena; we conclude that different studies in different markets agreed
on the determinants of capital structure but with changes in the signs.
Table 1
Summary of Significant Findings of Selected Empirical Literature
The Study
The Independent Variables
Results and Relationship
Donaldson (1961)
Titman and Wessels (1988)
Profitability
Size
Profitability
Tangibility
Dividend payout ratio
Insider ownership
Business risk
Profitability
Market-to-book
Assets tangibility
Profitability
Size
Profitability
Assets tangibility
Size
Market-to-book
Growth rate
Liquidity
Profitability
Growth rate
Cash flow
Size
Profitability
Size
Tangibility
Growth rate
Profitability
Growth rate
Size
Liquidity
Negative
Negative
Negative
Positive
Negative
Negative
Negative
Negative
Negative
Positive
Negative
Positive
Negative
Positive/Negative
Positive
Positive/Negative
Negative
Negative
Negative
Positive
Negative
Positive
Negative
Positive
Positive/Negative
Positive
Negative
Positive
Positive
Negative/Positive
Jensen et al. (1992)
Rajan and Zingales (1995)
Booth et al. (2001)
Ozkan (2001)
Bhaduri (2002)
Cassar and Holmes (2003)
Voulgaris et al. (2004)
(Table 1 continued )
Journal of Emerging Market Finance, 10:1 (2011): 1–19
Empirical Modelling of Capital Structure / 7
(Table 1 continued )
The Study
The Independent Variables
Results and Relationship
Huang and Song (2006)
Profitability
Growth rate
Managerial ownership
Size
Tangibility
Negative
Negative
Negative
Positive
Positive/Negative
Source: Developed by the author.
3. Data
This study investigates the capital structure debate in emerging markets using
Jordanian non-financial companies. Firms that have reported their annual
accounts without significant gaps for this period are selected. Accordingly, a
sample of 86 non-financial Jordanian firms (110 manufacturing and services
firms provided their information in 2003) is included in the analysis. The
data set for this analysis is hand-collected from the Jordanian Shareholding
Companies Guide from 1999 to 2003. From this data set, 86 non-financial
Jordanian firms that have reported their annual accounts without significant
gaps for this period are selected. In 2003, there were 110 manufacturing firms;
these firms provided the required financial information for the period from
1994 to 2003. We argue that there is no significant survivorship bias, as the
number of firms has not changed significantly (86 for our sample compared
to 110 non-financial firms reported in 2003). It is worth noting that this
period of time provides us with the largest number of firms to represent the
entire population (860 firm-year observations). Our data is an unbalanced
panel due to missing observations. The total number of observations used
in the estimated models is 743.
Table 2 shows the descriptive statistics; from this table the following conclusions are found: low debt ratio (on an average firms use only 30 per cent
debt financing in their capital structure, one explanation is that Jordanian
firms tend to minimise the probability of bankruptcy by reducing debt
financing) and high percentage of institutional ownership (on an average
68.18 per cent of owners are non-individual owners [institutions]). Hence,
we expect that institutional ownership plays an important role in monitoring
the firm. Finally, we detect low profitability of Jordanian firms—on an average
1 per cent of the returns come from shareholders’ equity investment.
Journal of Emerging Market Finance, 10:1 (2011): 1–19
8 / Basil Al-Najjar
Table 2
Descriptive Statistics
Variables
Obs.
Minimum
Maximum
Mean
Std. Deviation
Leverage
DPO
PIO
ROE
TANG
LIQ
MB
LNSIZE
BR
826
826
853
826
826
826
744
826
853
0.00
0.00
0.0032
–2.85
0.00
0.01
0.00
13.68
0.01
0.93
12.50
1.00
0.48
0.96
4,421,470
7.53
20.10
0.17
0. 3048
0.2885
0.6818
0.0109
0.4365
23,802.6
1.1405
16.1547
0.0553
0.19784
0.7532
3.34007
0.17548
0.26281
291,047.94473
0.77549
1.21217
0.03357
Source: Developed by the author.
Notes: Leverage is measured as the total debt to total assets ratio, DPO is the dividend per
share divided by earning per share, IO is the natural logarithm of the number of shares
owned by institutional investors, PIO is the percentage of institutional ownership, ROE
is the return on equity measured by net income divided by owners’ equity, TANG is the
tangible asset ratio measured by fixed assets to total assets ratio , LIQ is the liquidity
ratio measured by current assets to current liabilities, MB is the market-to-book ratio
measured by market price per share divided by book price per share, BR is the business
risk measured by the standard deviation of the return on assets, LNSIZE is the natural
logarithm firm size measured by total assets.
4. Methodology
The study adopts two techniques to investigate the determinants of capital
structure—first, by using the pooled and panel data regression analysis:
Dit = α + β′ Xit + εit
(pooled model)
(1)
Dit = αi + β′ Xit + εit
(fixed effects model)
(2)
Dit = α + β′ Xit + (εit + µi )
(random effects model)
(3)
Dit = Total debt to total assets ratio, Total Debt , of firm i in year t.
Total Asset
αi = Intercept coefficient of firm i.
β′ = Row vector of slope coefficients of regressors.
Xit = Column vector of financial variables of firm i at time t, this vector is made
up of the following: DPO is dividend payout ratio, Dividend per Share ; IO is
Earning per Share
Journal of Emerging Market Finance, 10:1 (2011): 1–19
Empirical Modelling of Capital Structure / 9
the number of shares owned by institutional investors; PIO is the proportion
of institutional ownership in the firm; ROE is Return on Equity ratio,
Net Income
; BR is the standard deviation of the firm’s Return on Assets,
Owners Equity
Fixed Assets
Net Income
; TANG is Fixed Assets ratio (Tangibility),
; LIQ is
Total Assets
Total Assets
the Current ratio, CA ; MB is Market-to-Book ratio, Market Value per Share ;
CL
Book Value per Share
LNSIZE is firm size measured as the natural logarithm of the total assets;
εit is the error term.
Second, in order to validate the analysis of capital structure, we employ
the factor analysis approach to measure the ‘unobservable variables’ that are
captured by the ‘proxy variables’. Bhaduri (2002) states that ‘Factor analysis
is a statistical tool to determine a minimum number of unobservable common factors (which are smaller in number than the number of variables)
by studying the covariance among a set of observed variables’ (Bhaduri
2002: 207).
This method may provide the minimum number of factors that can
account for the observed correlation (Bhaduri 2002). The process which is
used in this section proceeds in two steps: The first is to extract the initial
factors. We employ the principal components method. The Kaiser rule of
thumb is used to extract the number of factors (which implies that the initial
eigenvalue must be greater than or equal to 1) (Bhaduri 2002). The second
step is to regress the factors against the debt ratios, for which the following
model is used:
Dit = α + ∑βi ωi + εi
(4)
where, ωi is a vector of the factor scores for the ith factor, βi is the regressor
coefficient of the ith factor, α is the intercept and εi is the error term.
5. Statistical Results
In this section, the empirical analysis of the capital structure drivers is
presented. First, Table 3 reports the results for the regression model using
pooled and panel models. In general, the coefficients have the predict sign
and are statistically significant. The Lagrange Multiplier test is 559.29 and
statistically significant, suggesting the suitability of panel models over the
Journal of Emerging Market Finance, 10:1 (2011): 1–19
10 / Basil Al-Najjar
Table 3
Regression Results without Including PIO
Dependent Variable: Leverage
Independent Variables
Constant
DPO
IO
PIO
ROE
BR
TANG
LIQ
MB
LNSIZE
Number of observations
R-square
Lagrange Multiplier test
Hausman test
Pooled Model
–0.7132∗∗∗
(0.000)
–0.0031
(0.5382)
–0.0395∗∗∗
(0.000)
0.987E-3∗∗∗
(0.0020)
–0.3121∗∗∗
(0.000)
–0.4841∗∗∗
(0.0063)
0.1399∗∗∗
(0.000)
0.537E-07∗∗∗
(0.000)
0.0284∗∗∗
(0.0001)
0.0954∗∗∗
(0.000)
743
36.38%
559.29∗∗∗
(0.000)
12.03
(0.1500)
Fixed Effects
Random Effects
0.961E–3
(0.8544)
–0.0218∗∗
(0.0132)
–0.0013∗∗
(0.0310)
–0.2382∗∗∗
(0.000)
–
–0.9299∗∗∗
(0.000)
0.246E–3
(0.9693)
–0.0256∗∗∗
(0.000)
–0.891E-3
(0.5089)
–0.2483∗∗∗
(0.000)
–
0.0934∗∗
(0.0144)
0.352E-7
(0.2650)
0.0188∗∗
(0.0275)
0.0985∗∗∗
(0.000)
743
69%
0.1065∗∗∗
(0.0001)
0.400E-7∗
(0.0860)
0.0197∗∗∗
(0.0055)
0.0960∗∗∗
(0.000)
743
35.79%
Source: Developed by the author.
Notes: The dependent variable is leverage measured as the total debt to total assets ratio and
the independent variables have the same definitions as in Table 2.
∗∗∗, ∗∗, ∗ indicate significance at 1%, 5% and 10% levels, respectively. The pooled
model and the fixed model are corrected for heteroscedasticity using Breusch–Pagan
and White methods, respectively.
Figures in parentheses are probability levels.
pooled model. The Hausman test is 12.03 and statistically insignificant,
indicating that the random effects model is ‘more preferable’ than the fixed
effects specifications.
Table 3 shows that the following are the main determinants of firm’s
capital structure:
Journal of Emerging Market Finance, 10:1 (2011): 1–19
Empirical Modelling of Capital Structure / 11
Dividend payout ratio (DPO): The results show that there is no evidence of
a relationship between dividend policy and leverage. Thus, the study cannot
find a support of the relationship between dividend payments and capital
structure. One explanation for such a result is that dividend payments for
Jordanian firms may not represent a good agency tool to substitute debt to
alleviate agency problems (according to Table 2, the average dividend is 0.29,
which is not that high).
Institutional ownership (IO and PIO): We detect a significant negative
relationship between the number of shares owned by institutional investors
and the debt ratio. This result is in line with agency theory, suggesting
that institutional owners are used as a significant mechanism to monitor
the firm’s managers and hence reducing the agency costs. Chaganti and
Damanpour (1991), Grier and Zychowicz (1994) and Bathala et al. (1994)
reported similar result. However, the pooled model shows that there is a
positive relationship between the percentage of institutional ownership
and leverage, indicating that institutional investors prefer firms with higher
debt levels. It can be argued that institutional owners can act as insiders
(managers) in the board of directors in the firm, and hence the institutional
ownership can reflect managerial ownership. Therefore, this result is similar
to the findings of Leland and Pyle (1977), Berger et al. (1997) and Chen
and Steiner (1999).
Profitability (ROE): We report a significant negative relationship between
profitability and leverage ratio, suggesting that firms prefer internal financing rather than debt. This result is in line with pecking order theory. Hence,
Jordanian firms follow a certain order when they finance projects. Other
studies in the financial literature reported the same result amongst them:
Donaldson (1961), Rajan and Zingales (1995) and Booth et al. (2001).
Business risk (BR): We detect a negative relationship between business risk and
the debt ratio. Debt financing is concerned with periodic payments. High
levered firms are more likely to face high financial distress costs. Thus, volatile
incomes will lead firms to be less levered. This result is consistent with the
findings of Bhaduri (2002) and is supported by the bankruptcy theory.
Asset structure (TANG): There is evidence for a positive significant relationship between asset tangibility and leverage, indicating that the more the
fixed assets, the more the use of such assets as collaterals to obtain debt. This
Journal of Emerging Market Finance, 10:1 (2011): 1–19
12 / Basil Al-Najjar
finding is supported by agency theory. Titman and Wessels (1988), Rajan
and Zingales (1995), Bhaduri (2002) and Huang and Song (2006) reported
similar results.
Liquidity (LIQ): The study finds limited evidence that liquidity has an
important role in determining firms’ debt level; the random effects model
and the pooled model show that there is a significant positive relationship
between asset liquidity and leverage. The trade-off models suggest that there
is a positive relationship between the liquidation value and debt position.
Thus, the expected liquidation values are more significant for firms with more
liquid assets, implying that debt is positively associated with asset liquidity
(Harris and Raviv 1990).
Growth opportunities (MB): We detect a significant positive relationship
between growth opportunities and leverage, and this result contradicts
the expected negative sign predicted by the agency theory, suggesting that
Jordanian firms with high growth opportunities tend to face different
financing alternatives and they prefer debt financing as a method to finance
their investments. Moreover, we argue that such firms have low likelihood
of bankruptcy and are more able to obtain debt than low growth firms. This
result is in line with the findings of Bhaduri (2002).
Size (LNSIZE): We report a significant positive relationship between firm
size and leverage, suggesting that large firms are more able to be diversified
and are less likely to face financial distress. This result is in line with the
bankruptcy theory of capital structure. Rajan and Zingales (1995), Booth
et al. (2001) and Bhaduri (2002) reported similar result.
Table 4 shows the Varimax rotated component matrix. The rule of thumb
here is that only loadings that are more than 0.32 are suitable for explanation;
the larger the overlap between a variable and a factor, the higher the chance
that this variable is a good measure of the specific factor. A loading for 0.70
or more is said to be excellent, 0.63 is very good, 0.55 is good, 0.45 is fair,
less than 0.32 is poor (Bhaduri 2002). From this table the following factors
are extracted:
1. Factor (1): Factor (1) is highly loaded in favour of LNSIZE, IO and
BR. LNSIZE is firm size and IO is the number of shares owned by
institutions (that can indicate firm size). In addition, we argue that
Journal of Emerging Market Finance, 10:1 (2011): 1–19
Empirical Modelling of Capital Structure / 13
Table 4
Varimax Rotated Component Matrix∗
Variable
1
LNSIZE
IO
BR
DPS
ROE
TANG
MB
LIQ
PIO
Eigenvalues
% of variation
Kaiser–Meyer–Olkin MSA
0.884832
0.873944
–0.39781
0.120454
0.163468
0.123081
0.311244
0.048095
–0.07
1.865
20.727
0.555
2
3
0.123863
–0.06617
–0.27552
0.787281
0.690399
–0.55278
0.488823
–0.14735
0.31024
1.855
20.606
–0.01445
0.028177
0.30873
0.038405
–0.11575
0.041337
0.217759
0.827991
0.407651
1.012
11.245
Source: Developed by the author.
Notes: ∗The definitions of the variables are the same as given for Table 2.
The cut-off point is 0.32.
MSA = measure of sample adequacy.
the larger the firm, the lower its business risk, which can be justified
in a way that large firms can be seen as diversified firms. Therefore,
this factor is our firm size factor.
2. Factor (2): Factor (2) is highly loaded in favour of DPO, ROE, TANG
and MB. ROE is our profitability index, while DPO is an index
for dividend payouts, TANG is assets tangibility and MB is growth
opportunities. We argue that firms with high growth opportunities are
profitable and more able to distribute earnings, and these firms do not
depend on the tangible assets (as collaterals). Therefore, this factor is
the profitability and the ability to distribute dividends (the economic
surplus).
3. Factor (3): Factor (3) is ambiguous. It is highly loaded in favour of
LIQ and PIO variables. This may reflect that the institutional investors
prefer to invest in firms with high liquidity, suggesting that these firms
are facing low risk and are more able to pay obligations (short-term).
Therefore, this factor is our institutional ownership factor. Another
way to explain this factor is that firms with high liquid assets are more
attractive to institutional investors. Hence, this factor can be seen as
a liquidity factor. We argue that the second explanation of the factor
is more favourable because LIQ is the highest load among the other
factors.
Journal of Emerging Market Finance, 10:1 (2011): 1–19
14 / Basil Al-Najjar
The second step is to regress the three factors against the leverage of the
firm. Table 5 presents the results of the regression model.
Table 5
Regression Analysis Using Factor Scores
Dependent Variable: Leverage
Independent Variables
Model
0.322∗∗∗
(0.000)
0.064∗∗∗
(0.000)
–0.024∗∗∗
(0.000)
0.018∗∗∗
(0.006)
743
13.6%
Constant
Factor (1)
Factor (2)
Factor (3)
Number of observations
R-square
Source: Developed by the author.
Notes: ∗∗∗indicates significance at 0.01.
Figures in parentheses are the significance levels.
Size factor has a positive sign. This result is in line with the previous
analyses. In addition, the economic surplus factor has a significant negative
sign. This result indicates that Jordanian firms follow a pecking order in
their finances. Finally, the liquidity factor has a positive significant sign;
this finding is in line with the findings in the previous analyses. However,
if this factor reflects institutional ownership then the positive reported sign
contradicts the previous findings. Thus, institutional investors have a dual
impact on the leverage decisions; they can act as agents to monitor firms as
well as acting as insiders.
6. Capital Structure Dynamics
Finally, we are interested in examining whether Jordanian firms have a target
leverage ratios and if they adjust to their targets. Table 6 reports the partial
adjustment model, which is related to Nerlove (1958). His hypothesis is called
the partial adjustment model (stock adjustment hypothesis):
Yt – Yt–1 = δ (Y ∗ – Yt–1),
Journal of Emerging Market Finance, 10:1 (2011): 1–19
(5)
Empirical Modelling of Capital Structure / 15
Table 6
Regression Analysis Using the Partial Adjustment Model
Dependent Variable: Leverage
Independent Variables
Pooled Model
Constant
Leverage (–1)
DPO
IO
PIO
ROE
BR
TANG
LIQ
MB
LNSIZE
Number of observations
R-square
Lagrange Multiplier test
Hausman Test
–0.2667∗∗∗
(0.000)
0.6832∗∗∗
(0.000)
0.0024
(0.5820)
–0.0148∗∗∗
(0.0002)
0.393E-3
(0.2172)
–0.1622∗∗∗
(0.000)
-0.2008
(0.1353)
0.0638∗∗∗
(0.0003)
0.195E-7
(0.1565)
0.0069
(0.2361)
0.0349∗∗∗
(0.000)
731
69.52%
9.55∗∗∗
(0.0020)
140.70∗∗∗
(0.000)
Fixed Effect
Random Effect
0.4512∗∗∗
(0.000)
0.0027
(0.5828)
–0.0140
(0.0217)
-0.858E-3
(0.1709)
-–0.1896∗∗∗
(0.000)
–
–0.3123∗∗∗
(0.000)
0.6717∗∗∗
(0.000)
0.0027
(0.5842)
–0.0146∗∗∗
(0.000)
0.434E-3
(0.6744)
–0.1565∗∗∗
(0.000)
–
0.0533∗
(0.0735)
0.431E-7
(0.1429)
0.0073
(0.3310)
0.0625∗∗∗
(0.000)
731
77.81%
0.0615∗∗∗
(0.0001)
0.196E-7
(0.1697)
0.0063
(0.2172)
0.0371∗∗∗
(0.000)
731
69.42%
Source: Developed by the author.
Notes: Leverage (–1) is the first lagged dependent variable, the other variables have the same
previous definitions.
∗∗∗, ∗∗,∗ indicate significance at 1%, 5% and 10% levels, respectively. Pooled model
and the fixed model are corrected for heteroscedasticity using Berusch–Pagan and
White methods, respectively.
Figures in brackets are probability levels.
where δ is the adjustment coefficient. If the adjustment coefficient is equal
to 1, it indicates that the actual stock adjusts to the desired instantly.
However, if the adjustment coefficient is zero, this suggests that the actual
Journal of Emerging Market Finance, 10:1 (2011): 1–19
16 / Basil Al-Najjar
stock is similar to the previous one (at time [t – 1]); δ should be between
0 and 1 (Gujarati, 2003). The following model is employed:
D∗it = αi + β′ Xit + εit
(6)
Dit – Dit–1 = λ (D∗it – Dit–1)
(7)
where 0 < λ < 1, Dit is the actual debt ratio and D∗it is the target debt ratio of
firm i at time t. Xit is Column vector of financial indicators of firm i at time
t, (D∗it – Dit–1) is the target change if only λ of the target change is achieved,
which is equal to (Dit – Dit–1). Rearranging the equation,
Dit = λ D∗it – λ Dit–1 + Dit–1
Dit = (1 – λ) Dit–1 + λ D∗it
(8)
(8.1)
If (1 – λ) = γ0 and λ = γ1, then
Dit = γ0 Dit–1 + γ1 D∗it
(9)
D∗it is obtained from the first stage.
Table 6 shows that the coefficient of the lagged dependent variable is
positive and significant.1 The adjustment coefficient is large (0.548),2 suggesting that the dynamics are not rejected and firms adjust their leverage
ratios ‘relatively quickly’ to achieve their targets. The adjustment speed can
indicate the costs paid by firms if they are away from achieving their targets.
The coefficient of the lagged dependent variable is (0.451), which is below
‘the middle range’ of 0 and 1. Thus, the process of adjusting to the target is
expensive, Ozkan (2001) states that ‘[t]his is consistent with the view that
1
The coefficients of the other independent variables in Table 6 have the same signs and
significance as in Table 3. In addition market-to-book ratio is positive (same sign as in Table 3)
but statistically insignificant.
2
The fixed effects model is used because the Lagrange Multiplier test indicates the preference of the panel model over the pooled model. In addition, the Hausman test indicates that
the fixed effects model is preferable to the random effects model.
Journal of Emerging Market Finance, 10:1 (2011): 1–19
Empirical Modelling of Capital Structure / 17
firms may trade-off between two different type of costs: costs of making
adjustment of their target ratios and costs of being in disequilibrium (being
off target)’ (Ozkan 2001: 188-89). Our result is in line with Ozkan (2001),
and hence Jordanian firms have target leverage ratios and adjust ‘relatively
quickly’ to their target ratios, suggesting that the disequilibrium costs and
the adjustment costs are both important.
7. Summary
This study investigates the determinants of capital structure choice in Jordan,
using Jordanian non-financial firms. The sample is composed by the firms
that maintained their identity and reported their annual accounts without
any significant gaps for the financial years of 1994 to 2003. Eighty-six firms
were selected as a sample of this study.
We employ different techniques to empirically investigate firms’ capital
structure. First, pooled and panel regression analyses are used to investigate
the determinants of the capital structure. The number of institutional ownership, the percentage of institutional ownership, return on equity, tangible
asset ratio, liquidity ratio, the market-to-book ratio, business risk and firm
size are the independent variables. Second, we employ the factor analysis
approach to validate the analysis. Finally, the partial adjustment model is
used to determine whether the selected firms have target leverage ratios and
if the firms adjust to these target leverage ratios?
The results indicate that there is a significant negative relationship between
leverage and both profitability and business risk, and a significant positive
relationship between firm’s size, market-to-book ratio, asset tangibility and
liquidity on one hand, and leverage on the other hand; also mixed results
of the effect of institutional ownership is found. However, the study could
not find support for a significant relationship between capital structure
and dividend policy. The factor analysis approach confirms the previous
results.
The study also provides evidence that Jordanian firms have a target leverage
ratio and that they relatively adjust quickly to their target ratios, indicating
that the disequilibrium costs and the adjustment costs are equally important
for firms. Therefore, the overall result of this study is that capital structure
in Jordan is affected by the same factors that determine the capital structure
decision in both developed and developing markets.
Journal of Emerging Market Finance, 10:1 (2011): 1–19
18 / Basil Al-Najjar
Basil Al-Najjar, Middlesex University Business School, Middlesex University, Hendon,
London, NW4 4BT, United Kingdom. E-mail: B.Al-Najjar@mdx.ac.uk
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