270 SAJEMS NS 10 (2007) No 2

THE DETERMINANTS OF CAPITAL STRUCTURE OF MICROFINANCE 
INSTITUTIONS IN GHANA 

Anthony Kyereboah-Coleman

University of Ghana Business School, Legon

Abstract

Using a panel data methodology, this study examines the determinants of capital structure of 52 
microfinance institutions (MFIs) in Ghana. The empirical results show that the MFIs are highly 
leveraged and that their capital structure is explained partly by standard finance theory and by 
other unconventional variables. Specifically, the study confirms that leverage is positively related 
to asset tangibility, with small MFIs using short-term and large MFIs using long-term debt. Though, 
the findings confirm that leverage is inversely related to risk, they also suggest that some MFIs enjoy 
long-term debt in spite of risk, while profitability is irrelevant in explaining the capital structure 
decisions of MFIs. Finally, the study shows that the reputation and board independence of MFIs 
significantly and positively affect their capital structure decisions. 

JEL G21; G32; C33

1 
Introduction

Since the landmark paper by Modigliani and 
Miller published in 1958, debate has raged 
about the theoretical basis for the determinants 
of capital structure in firms. Financial theory 
has made considerable progress in efforts 
at explaining capital structure decisions and 
the variables that determine these. In this 
quest, various theoretical models have been 
proposed to explain capital structure patterns 
across companies and countries, and many 
of these models have been empirically tested 
in the real business world. However, most of 
this research has concentrated on large and 
listed firms, mostly in the developed world, 
with the consequence that other critical sectors 
such as microfinance, especially in developing 
economies, have been neglected. 

Microfinance Institutions (MFIs) have 
evolved in recent years as tools for alleviating 
poverty. Understanding their operation is 
critical to maintaining this process of poverty 
reduction, the failure of which would have grave 
consequences for developing economies.

Following MFI successes such as the Grameen 
Bank of Bangladesh and Banco Solidario 
of Bolivia, considerable attention has been 
devoted to the sector by several players. These 
include governments, non-governmental 
organisations (NGOs), community activists 
and even some large commercial banks, which 
have re-channelled efforts and resources 
towards microfinance and microenterprise 
projects (Conning, 1999). Interest in the sector 
has touched on the objectives and methods 
of microfinancing (Rhyne, 1998; Morduch, 
1999), but most discussions focus on the nature 
and scope of potential tradeoffs between the 
outreach potential, impact and financial self-
sustainability in microfinance lending. Outreach 
must be recognised as a major performance 
indicator in the sector. Since the initial evolution 
of microfinance, when most microfinance 
operations were funded by governments, 
development-oriented and donor agencies, 
there has been a gradual but steady shift 
towards funding by capital markets, which has 
unavoidable implications for capital structure. 
This study therefore aims to investigate what 
goes into the financing decisions of MFIs.



SAJEMS NS 10 (2007) No 2 271 

Conning (1999) suggests that ‘all else equal, 
sustainable microfinance organisations that 
target the poorer borrowers must be less 
leveraged’, and Kyereboah-Coleman (2007) 
shows that the capital structure of MFIs impacts 
on their performance. This paper reports on 
a follow-up study to determine the correlates 
of capital structure of MFIs. Does the capital 
structure of MFIs follow standard finance theory 
or not? 

The paper is organised as follows: section two 
reviews the relevant literature, section three 
presents data collection and methodological 
issues, section four discusses the empirical 
findings, and section five concludes with the 
findings of the study.

2 
Literature review

The term ‘capital structure’ can be defined as 
the relative amount of debt and equity employed 
to finance a firm’s operations. It must be noted 
that, in finance, capital structure has been a 
hotly debated issue for years. One of the earliest 
important sources on capital structure is the 
work of Modigliani and Miller (1958). Their 
analysis of the irrelevance of capital structure to 
firm value is based on the premise that capital 
structure does not affect a firm’s cash flow. 
Indeed, in their paper, Modigliani and Miller 
show that the financing decisions of firms do not 
affect their value. Their conclusion, however, 
is based on what is termed ‘restrictive and 
unrealistic’ assumptions such as a world with 
perfect capital markets, absence of corporate 
and personal taxes, and independent firms’ 
financing decisions. Adapting these assumptions 
to more realistic expectations and repeating the 
analysis suggests that capital structure decisions 
do affect a firm’s value. Modigliani and Miller 
therefore revised their initial stance in 1963. 
Consequently, several studies have shown that 
capital structure significantly affect the value of 
a firm (Jensen & Meckling, 1976; Myers, 1977; 
Grossman & Hart, 1982; Jensen, 1986; Williams, 
1987; Harris & Raviv, 1990; Stulz, 1990; Harris 
& Raviv, 1991; Bos & Fetherston, 1993; Myers, 
2001; Boateng, 2004; Abor, 2005; Kyereboah-
Coleman, 2007). 

What has remained a puzzle in this debate is 
the identification of an optimal capital structure 
and its explanatory variables. The question has 
always been: ‘What motivates firms to choose 
levels of debt and equity or a combination 
of these?’ To explain this phenomenon and 
understand whether there is an optimal capital 
structure for a firm, a number of theories have 
been advanced. 

One of the fundamental propositions is agency 
theory, which states that debt financing creates 
an agency problem for firms. This is because 
debt financing leads to three difficulties: 

a) It creates an incentive for stockholders to 
accept sub-optimal and high risk projects 
and in the process transfer wealth from 
bondholders to stockholders. 

b) The presence of debt compels firms to avoid 
undertaking investments whose positive net 
present value is lower than the debt value. 

c) It increases the probability of bankruptcy 
cost with debt level because doubt is cast on 
the firm’s ability to generate the cash flow 
needed to meet its debt obligation. 

From this fundamental theory emerged other 
popular theories such as the static trade-off 
and the pecking order theories. Myers (1984) 
advances the static trade-off theory, which 
explains that a firm usually decides on an 
ideal debt-to-equity ratio and gradually moves 
towards this. The implication is that some 
optimal capital structure exists which will enable 
the firm to operate efficiently and also ensure 
that external claims on cash flow (such as taxes, 
bankruptcy costs and agency costs) are reduced. 
‘Thus, there is a transition from Miller’s (1977) 
postulate of tax neutrality – later refuted by 
DeAngelo and Masulis (1980) who introduced 
the concept of non-debt tax shield – to the static 
theory which postulates the optimum debt level 
as a consequence of a trade-off between tax 
advantages of borrowed money and financial 
distress costs’ (De Miguel & Pindado, 2001). In 
contrast, the pecking order theory suggests that 
the profitability of a firm affects its financing 
decisions, and therefore suggests that firms with 
no predetermined mix of debt and equity prefer 
internal to external financing and debt to equity 
if the firm issues securities.



272 SAJEMS NS 10 (2007) No 2

These theories have been used to explain 
the capital structure of firms. In the extant 
literature, both internal and external variables 
have been identified as relevant in influencing 
a firm’s capital structure. These variables 
include firm size, profitability, risk level, type 
of industry, tax shield, profitability margin, 
firm growth, whether a firm receives subsidy, 
ownership control and asset tangibility. As 
mentioned above, most of these studies of the 
capital structure of firms concentrate on large 
and listed firms in advanced economies. 

Microfinance is primarily the provision of 
credit to poor people, who lack access to credit 
from mainstream financial institutions. This kind 
of finance has evolved as a poverty reduction 
strategy in developing countries, such as Ghana. 
The enthusiasm with which microfinance has 
been greeted worldwide has led to an increase 
in the number of MFIs providing small credit 
to poor people in the developing world. The 
microfinance movement has been commended 
by both academic scholars, major development 
finance institutions such as the World Bank and 
development practitioners. Growing rapidly, 
the sector is moving away from its original 
non-profit, socially motivated agenda, with 
lenders seeking to reach as many of the poor 
as possible, to state profiteering using various 
techniques. The use of joint liability contracts 
and dynamic incentives has shown that MFIs 
can be profitable. This has attracted profit-
motivated institutions and other regular banks 
into the sector. The result has been that some 
of these institutions are moving away from 
government subsidies and donor dependence 
to accessing funds from the capital market. 
This move is also motivated by the MFIs’ 
desire to ensure institutional sustainability 
through minimising their subsidy and donor 
dependence and adopting the practices of good 
banking. This process has cost implications and 
has, therefore, further strengthened the profit 
agenda. The main advantage of profit is the 
attraction it offers to private investors into the 
sector. Thus, commercial microfinance lenders 
ought to achieve much better leverage on their 
equity than subsidised microlenders, allowing 
commercial lenders to greatly multiply the scale 
of outreach achieved through extra currency 

units contributed by donors to equity in the 
sector.

Obviously, these trends have implications 
for the capital structure of MFIs. Studies of 
capital structure in the microfinance sector are 
virtually non-existent, which is the fundamental 
motivation of this study. Kyereboah-Coleman 
(2007), in a study of capital structure and the 
performance of MFIs, found that MFIs are 
highly leveraged. Thus, an understanding of 
the correlates of capital structure in MFIs will 
contribute greatly to the on-going debate about 
capital structure. Are those determinants of 
capital structure identified in studies of large 
and listed firms applicable to MFIs? Do MFIs 
in developing countries such as Ghana exhibit 
the capital structure that conforms to standard 
finance theories? These are some of the 
questions this study seeks to answer.

3 
Data collection and methodology

This study uses data from a sample of 52 MFIs 
in Ghana. These MFIs are sampled primarily 
according to accessibility to data and records, 
since MFIs are noted for being difficult to 
acquire data from. The data is captured from 
annual financial statements and personal 
interviews and covers a 10 year period, from 
1995-2004. Analysis is carried out within a panel 
data framework. This is because panel data 
provides a relatively large number of data points 
and, therefore, additional degrees of freedom. 
We incorporate data from both cross-section 
and time series variables to reduce the problems 
associated with omitted variables, since we may 
not be able to fully specify a capital structure 
model. Variables relating to tax (such as non-tax 
shield and tax impact) and firm growth were 
not captured, due to data constraints. Thus, our 
basic model follows that of Delcoure (2006) and 
is specified as follows:

V
D Z

,

,
,
'

,
i t

i t
i t i t0 1= + +b b k  (1)

where i = 1......52; t = 1......10,

V
D

,

,

i t

i t  is a measure of debt ratios (where D repre-

sents the various debt levels and V representing 
total assets) explained below for firm i at time t. 



SAJEMS NS 10 (2007) No 2 273 

0b  is the intercept.

Z ,'i t  is a 1×k vector of observations on k 
explanatory variables for firm i at time t.

1b  is a k×1 vector of parameters.

,i tk   is the disturbance term measured by

v, ,i t
i

i tk +n= , (2)

where 
i

n  represents the unobservable individual 
effect and v ,i t  is the residual. 

3.1 Factors important in estimation

In any panel data model, choice of an appropriate 
technique for estimating the basic model is 
dependent on the structure of the error term, 
that is: 

v, ,i t
i

i tk +n=  (3)

and the correlation between the components 
of the error term and observed explanatory 
variables. 

The simplest and most basic estimator of the 
panel data set is the pooled OLS where there 
are no firm specific and time effects. However, 
Johnson and DiNardo (1997) argue that pooled 
OLS estimators ignore the panel nature of the 
data set, and so treat observations as being 
serially uncorrelated for a given firm, leading 
to homoscedastic errors across individuals and 
time periods. These effects could however be 
negligible and unobservable if accommodated 
using one of two techniques. 

The first technique is to reduce the number of 
parameters to be estimated and justify treating 
the individual fixed effects as drawn from some 
distribution. The parameters of this distribution 
then become the parameters to be estimated. The 
unobservable effects are therefore included in the 
error term. The variance-covariance matrix of the 
resulting non-spherical errors is then transformed 
to obtain consistent estimates of standard error. 
In this case, the random effects (RE) estimator 
is the most appropriate (Hsiao, 1989). 

However, a problem arises with the RE 
estimator if the unobservable effects which 
have been included in the error term are 
correlated with some or all of the regressors. 
This simultaneity makes the RE estimation 
inconsistent. Thus, as a consistent alternative 

to the RE estimator, a second technique can be 
used, namely a dummy variable can be included 
for each firm. This estimation approach is known 
as the fixed effects (FE) approach and yields 
consistent estimates regardless of correlation 
between firm-specific error components and 
the regressors. However, the FE is less efficient 
than the RE estimator. This inefficiency arises 
because the FE estimator requires a separate 
parameter to be estimated for each firm in the 
sample, rather than the single variance estimate 
required for the RE estimator. In deciding which 
estimation method to employ, the Hausman 
specification test is recommended.

Hausman (1978) suggests a test to check 
whether individual effects are correlated with 
the regressors (X

it
). Under the null hypothesis 

of orthogonality, that is, no correlation between 
individual effects and explanatory variables, both 
RE and FE estimators are consistent, but the RE 
estimator is efficient while FE is not. Under the 
alternative hypothesis, that individual effects 
are correlated with the explanatory variables, 
the RE estimator is inconsistent and the FE 
estimator is consistent and efficient. Greene 
(1997) states that under the null hypothesis the 
estimates should not differ systematically. Thus, 
the Hausman specification test is based on a 
contrast vector H:

H = [bGLS–bw]’[V(bw)–V(bGLS)]–1[bGLS–bw]   (4) 

Hence, in estimating our basic model, the 
Hausman specification test is carried out and 
a choice made between RE and FE estimation. 
In the Hausman specification test, if the 
probability value of the chi-square is ≥0.5, the 
RE estimation is accepted, otherwise the FE 
estimator is employed.

3.2 Variable description and  
 justification

The dependent variable is the debt ratio as 
suggested in the literature. Following Titman 
and Wessels (1988), we use three leverage 
measures calculated by finding the ratio of 
book value of short-term debt to total assets 
(short-term leverage), long-term debt to total 
assets (long-term leverage) and total debt to 
total assets (total leverage). 



274 SAJEMS NS 10 (2007) No 2

3.2.1 Asset structure
Lenders normally use tangible assets as security 
consistent with trade-off theory. Jensen and 
Meckling (1976) show that collateral serves to 
protect lenders from the moral hazard problem 
associated with shareholder-lender conflict. 
Also, Williamson (1988) points out that capital 
project financing is essentially dependent on 
asset tangibility. Similarly, Titman and Wessels 
(1988), Rajan and Zingales (1995) and Chen 
(2004) show that significant positive relationships 
exist between asset tangibility and firm debt. To 
measure asset tangibility, we use the ratio of net 
total fixed assets to total assets.

3.2.2 Volatility of earnings
This is a measure of risk, because according 
to the static trade-off theory, higher volatility 
of earnings increases a firm’s probability of 
financial distress. Thus, a firm’s debt ratio 
decreases when bankruptcy costs are higher in 
situations of higher earnings volatility. Studies 
by Marsh (1982), Bradley, Jarrell and Kim 
(1984), MacKie-Mason (1990) and De Miguel 
and Pindado (2001) find that there is an inverse 
relationship between a firm’s financial distress 
cost and debt ratio. We measure our risk 
(earnings volatility) as the standard deviation 
of the first difference of the ratio of Earnings 
before Interest and Taxes (EBIT) to total assets. 
Thus,

Risk = .Std deviation
TA

EBITDc m< F (5)
3.2.3 Firm size
Larger firms tend to be more diversified and are 
therefore able to absorb risk (Rajan & Zingales, 
1995). In addition, they tend to have easy access 
to credit and have more diluted ownership, 
leading to less control over managerial decisions. 
Friend and Lang (1988) find that, though, 
managers may influence debt ratios in order to 
protect their personal investment in a company, 
a firm’s debt maturity choice is less dependent 
on size. In this study we use the natural log of 
total assets as a measure of size.

3.2.4 Profitability
Concerning profitability, Jensen’s (1986) 
theory of the agency cost of financial structure 

considers debt as a disciplining device which 
compels managers to increase shareholders’ 
wealth rather than building empires. Therefore, 
studies on the links between capital structure 
and firm profitability have shown that there is 
an inverse relationship between capital structure 
and profitability (Friend & Lang, 1988; Rajan 
& Zingales, 1995; Wald, 1999; Chen, 2004). 
Return on total assets (ROA) measured by the 
ratio of EBIT to total assets is our measure of 
profitability.

3.2.5 Other variables
In addition to these variables drawn from 
standard finance theory, we also employ other 
unconventional variables that could impact on 
capital structure, particularly in the microfinance 
sector. First, we use the age of the firm as a proxy 
for reputation, on the premise that age should 
have a positive relationship with debt. An old 
firm generally is a reputable firm that with a 
reputation for reliability that commands trust. 
Firm age is captured using the number of years 
the institution has been in operation starting 
from the year of incorporation. 

We also use board characteristics such as size, 
independence, CEO duality and CEO tenure as 
possible explanatory variables. These translate 
into agency problems and costs with further 
implication for firms’ capital structure. For the 
size of the board, we use the number of board 
members, and for independence of the board the 
ratio of non-executive board members to board 
size. John and Senbet (1998) indicate that agency 
costs are higher when a board is dominated by 
executive members. In contrast, an independent 
board ensures effective monitoring and attracts 
private investment which has implications for 
debt levels. In the case of CEO duality, agency 
costs are deemed higher when a CEO doubles 
as a board chairman, and this discourages 
private investment and debt. In our study, this 
is a dummy variable which takes a value of 1 
when the CEO also acts as board chairman and 
0 otherwise. Finally, we use the tenure of the 
CEO as our last explanatory variable. 



SAJEMS NS 10 (2007) No 2 275 

4 
Empirical findings

4.1 Summary statistics

The oldest MFI in the sample has been in 
existence for 41 years; on average the firms 
have been operating for 18 years. In size the 
MFIs show a wide spread, with a relatively high 
standard deviation of 2.67+e07. Most of these 
MFIs have a greater percentage (about 71 
percent) of their assets in current form, though 
some have 85 percent of their assets as fixed 
assets. Most of these assets are financed with 
debt at a mean leverage of 0.76. Significantly, 
the leverage in these institutions is mainly driven 
by long-term rather than short-term debts. The 
MFIs appear to be moderately volatile in income 

flow, which suggests some degree of stability, 
with implications for lower bankruptcy costs 
and greater sustainability. This is supported by 
results for profitability performance of ROA, 
though these show a wider spread. Concerning 
institutional governance, the MFIs have a mean 
board size of 6 members, with a maximum of 10 
members. The independence of these boards 
is relatively low, according to the mean board 
composition of 0.48. This implies that about 52 
percent of the board members are executive 
directors. The leadership structure is equally 
divided, between separating CEO and chairman 
roles and a combination of the two. 50 percent 
of the MFIs have the two positions carried out 
by one person and the other 50 percent have two 
separate office holders. The longest CEO tenure 
is 4 years but most of them serve a term of up to 
3 years. A summary is presented in Table 1.

Table 1 
Summary of MFI statistics

Variable Obs Mean Std. Dev Min Max

Short-term leverage 520 0.35 0.29 0.0004 0.99

Long-term leverage 520 0.41 0.30 0.0000435 0.88

Leverage (total) 520 0.76 0.23 0.01 1.00

Asset tangibility 520 0.29 0.20 0.03 0.85

Earnings volatility 520 0.21 0.38 -0.60 0.70

Total assets 520 1.22e+07 2.67e+07 129470.00 1.35e+08

Return on assets 520 0.39 1.52 0.06 35.00

Firm age 520 17.83 5.02 10.00 41.00

Board size 520 6.23 1.83 3.00 10.00

Board independence 520 0.48 0.12 0.30 0.70

CEO duality 520 0.50 0.50 0.00 1.00

CEO tenure 520 2.85 0.72 2.00 4.00

Log of firm age 520 14.52 1.90 11.77 18.72

Log of total assets 520 2.84 0.27 2.30 3.71



276 SAJEMS NS 10 (2007) No 2

4.2 Regression results

Table 2 represents the regression results. 

4.2.1 Asset tangibility
Consistent with the static trade-off theory, short-
term, long-term and total leverage are positively 
correlated with asset tangibility. The argument 
is that tangible assets are used as collateral as 
a source of security for lenders in the event of 
financial distress. Jensen and Meckling (1976) 
argue that collateral protects lenders from the 
moral hazard problem caused by shareholder-
lender conflict. The use of tangible assets 
as collateral normally reduces lenders’ risk. 
While both short-term and total leverage are 
significant at five percent probability level, long-
term leverage is not. A possible explanation is 
that in most developing countries like Ghana, 
financial institutions regard short-term lending 
as relatively rewarding as compared to long-
term debt. This invariably makes long-term a 
limited option for several organisations. MFIs, 
considering their historical antecedents and 
social orientation, are likely to ward off long-
term lenders.

4.2.2 Earnings volatility (risk)
Once again, the trade-off theory stipulates that a 
higher risk increases the probability of financial 
distress, and so higher bankruptcy costs result 
in the impact of higher earnings volatility being 
negative on leverage. The study finds that there 
is a negative correlation between short-term and 
total leverage and earnings volatility (risk). The 
results of the study with regards to short-term 
and total leverage are consistent with other 
studies (e.g. Marsh, 1982; Bradley et al., 1984; 
MacKie-Mason, 1990; De Miguel & Pindado, 
2001), though total leverage is insignificant. 
However, the study also shows a significant 
positive correlation between institutional risk 
and long-term leverage, which contradicts 
standard finance theory. A possible reason 
could be the willingness of donor agencies to 
lend to such institutions in the interest of a 
social rather than financial agenda. This is a 
plausible explanation because, though most 
of these institutions are targeting a double 
bottom-line, some are heavily tilted towards 

their social mission at the expense of financial 
sustainability.

4.2.3 Firm size
Studies have found this variable rather 
inconclusive. While some find a positive 
relationship with leverage, others find a negative 
correlation. The pecking order theory suggests 
that larger firms exhibit lower information 
asymmetry and are therefore able to issue more 
equity, compared to small companies. Consistent 
with the findings of Titman and Wessels (1988), 
this study finds a negative relationship between 
short-term and total leverage and firm size. 
While short-term leverage is statistically 
significant, total leverage is not. The implication 
is that small firms use short-term leverage as 
suggested by Marsh (1982). In contrast, also 
supporting the findings of Marsh (1982), long-
term leverage is positively correlated with firm 
size, probably because large firms have relatively 
more diluted ownership and so lose control over 
managerial decisions. 

4.2.4 Profitability
At a glance, the pecking order theory seems 
applicable to short-term leverage because of its 
negative relationship with profitability, whiles 
the others show positive signs. However, this 
effect on MFIs’ leverage levels is insignificant. 

4.2.5 Age of firm
Using age as a proxy for reputation, the study 
shows that firms with good reputations attract 
investors and short-term debt in particular. 
Interestingly, the results show that the older 
the firm (the higher the reputation), the less 
the firm attracts long-term debt. Firms rather 
employ more short-term debt.

4.2.6 Institutional governance
Since size of board is inconclusively related to all 
types of leverage, its effects on MFI structures 
are insignificant. The independence of the 
board however is an attraction for investors. 
Thus, short-term, long-term and total leverage 
are positively related to board independence, 
though short-term leverage is insignificant at 5 
percent. The more independent a board is, the 
more shareholders feel their interest is protected 



SAJEMS NS 10 (2007) No 2 277 

and thus encourage debt in the operations of 
the MFI. While CEO duality does not appear 
to influence either short-term or total leverage, 
it is negatively related to long-term leverage. 
Indeed, in situations where the CEO doubles 
as the chairman, agency cost increases and 
has an adverse impact on the MFI’s ability to 
attract long-term debt. Finally, the study shows 
that CEOs with shorter tenure enable MFIs to 
attract short-term debt whiles MFIs with longer 
tenure for CEOs attract long-term debt. This is 

essentially due to the fact that a shorter tenure 
leads to frequent changes in leadership which 
obviously has implications for a firm’s policy and 
direction. Hence, investors are willing to commit 
long-term debt to the care of a firm only when 
satisfied that there will be less frequent changes 
in top management. However, the t-statistics 
of -1.61 and 1.53 for short-term and long-term 
leverage respectively in relation to CEO tenure 
appear rather low; the F-statistics and p-values 
make these acceptable in the estimation.

Table 2 
Regression results

Dependent variable

Variable Short-term leverage

(FE estimates)

Long-term leverage

(RE estimates)

Total leverage

(RE estimates)

Asset tangibility 0.2596891

(3.18)**

0.0037977

(0.04)

0.2533483

(3.73)**

Earnings volatility –0.0599206

(–1.89)**

0.0582923

(1.79)**

–0.0051144

(–0.20)

Log of total assets (size) –0.0634865

(–6.56)**

0.0471971

(4.93)**

–0.0045626

(–0.60)

Return on assets –0.0021889

(–0.28)

0.004655

(0.58)

0.0005246

(0.08)

Log of firm age 0.5156483

(7.48)**

–0.02830497

(–5.59)**

0.0440444

(1.09)

Board size 0.003356

(0.47)

–0.0039486

(–0.53)

–0.0008045

(–0.14)

Board independence 0.0473919

(0.46)

0.2816785

(2.67)**

0.2905328

(3.44)**

CEO duality 0.0297935

(1.23)

–0.066116

(–2.63)**

–0.0269775

(–1.34)

CEO Tenure –0.0285673

(–1.61)**

0.0284905

(1.53)**

–0.0051798

(–0.35)

Constant –0.2298125

(–1.17)

0.353496

(2.04)**

0.5237044

(3.79)**

R-squared 0.4335 0.2869 0.0619

No. of obs. 520 520 520

Test of prob. F(9, 501)=10.72

[0.0000]

Wald Chi2(9)=88.49

[0.0000]

Wald Chi2(9)=32.01

[0.0002]

Hausman test Chi2 (9) = 14.06

[0.1201]

Chi2 (9) = 6.29

[0.7103]

Chi2 (9) = 4.81

[0.8504]

Note: All regressions include a constant. t-statistics are in parenthesis and p-values in square bracket. 

** indicates significant at 5 percent level



278 SAJEMS NS 10 (2007) No 2

5 
Conclusion

This study aimed at identifying the determinants 
of capital structure of MFIs in Ghana, and 
to ascertain whether standard finance theory 
explains their financing decisions. This latter 
aim is especially important, considering the 
changing pattern of financing of MFIs, which 
has implications for their capital structure. A 
sample of 52 MFIs was selected from Ghana 
and data obtained for a 10 year period from 
1995-2004. Analysis was carried out within a 
panel data framework. The results show that 
leverage, irrespective of its maturity period, 
is positively related to MFIs’ asset tangibility. 
Similarly, the results confirm the finding of other 
studies that leverage is inversely related to firm 
risk. However, this study contradicts standard 
finance theory, since its results suggest that some 
MFIs could still use long-term debt in spite of 
high risk. Again, consistent with other findings, 
the study shows that small firms tend to use 
short-term leverage while large firms employ 
long-term leverage. Furthermore, the study 
finds that profitability is irrelevant in a MFI’s 
capital structure.

The results for more unconventional variables 
show that MFIs with good reputation enjoy short-
term leverage, and that older MFIs (with good 
reputations) employ more short-term than long-
term debt. Institutional governance variables 
produce mixed results; the independence of 
MFIs’ boards is crucial in determining capital 
structure, with a highly significant positive 
correlation found between board independence 
and all three types of leverage, and CEO duality 
impacts negatively on long-term leverage 
because of increased agency costs. Also, shorter 
tenure for the CEO encourages short-term debt 
and longer tenure attracts long-term debt. 

In conclusion, the capital structure of MFIs 
is therefore explained partly by standard 
finance theory and certain unconventional 
variables. These findings have important policy 
implications, but the relatively small sample and 
short period covered serve as the main limitation 
of this study. However, these limitations do not 
compromise the validity of the findings.

References

1 ABOR, J. (2005) “The effect of capital structure 
on profitability: An empirical analysis of listed 
firms in Ghana”, The Journal of Risk Finance, 6(5): 
438-447.

2 BOATENG, A. (2004) “Determinants of capital 
structure: Evidence from international joint 
ventures in Ghana”, International Journal of Social 
Economics, 31(1/2): 56-66.

3 BOS, T. & FETHERSTON, T.A. (1993) “Capital 
structure practices on the specific firm”, Research 
in International Business and Finance, 10: 53-66.

4 BRADLEY, M.; JARRELL, G.A. & KIM, E.H. 
(1984) “On the existence of an optimal capital 
structure”, Journal of Finance, 39: 857-878.

5 CHEN, J.J. (2004) “Determinants of capital 
structure of Chinese listed companies”, Journal of 
Finance, 56: 87-130.

6 CONNING, J. (1999) “Outreach, sustainability 
and leverage in monitored and peer-monitored 
lending”, Journal of Development Economics, 60: 51-77.

7 DEANGELO, H. & MASULIS, R.W. (1980) 
“Optimal capital structure under corporate and 
personal taxation”, Journal of Financial Economics, 
8: 3-29.

8 DELCOURE, N. (2006) “The determinants 
of capital structure in transitional economies”, 
International Review of Economics and Finance 
(forthcoming).

9 DE MIGUEL, A. & PINDADO, J. (2001) 
“Determinants of capital structure: New evidence 
from Spanish panel data”, Journal of Corporate 
Finance, 7: 77-99.

10 FRIEND, I. & LANG, A. (1988) “An empirical test 
of the impact of managerial self-interest on corporate 
capital structure”, Journal of Finance, 43: 271-281.

11 GREEN, W. (1997) Econometric Analysis (3rd ed.) 
Prentice-Hall: Eaglewood Cliffs, NJ.

12 GROSSMAN, S.J. & HART, O. (1982) “Corporate 
financial structure and managerial incentives”, In 
McCall, J. (ed.) The Economics of Information and 
Uncertainty, University of Chicago Press: Chicago. 

13 HARRIS, M. & RAVIV, A. (1991) “The theory 
of capital structure”, Journal of Finance, American 
Finance Association, 46(1): 295-335

14 HARRIS, M. & RAVIV, A. (1990) “Capital 
structure and the informational role of debt”, 
Journal of Finance, 45: 321-345.

15 HAUSMAN, J. (1978) “Specification tests in 
econometrics”, Econometrica, 46: 1251-71.

16 HSIAO, C. (1989) “Analysis of panel data”, 
Econometric Society Monographs, Cambridge 
University Press: New York.



SAJEMS NS 10 (2007) No 2 279 

17 JENSEN, M.C. (1986) “Agency cost of free cash 
flow, corporate finance and takeovers”, American 
Economic Review, 76: 23-339.

18 JENSEN, M. & MECKLING, W. (1976) “Theory 
of the firm: Managerial behaviour, agency cost 
and ownership structure”, Journal of Financial 
Economics, 43: 271-281.

19 JOHN, K. & SENBET, L.W. (1998) “Corporate 
governance and board effectiveness”, Journal of 
Banking and Finance, 22: 371-403.

20 JOHNSTON, J. & DINARDO, J. (1997) 
Econometrics Methods (4th ed.) McGraw Hill: New 
York.

21 KYEREBOAH-COLEMAN, A. (2007) “The 
impact of capital structure on the performance 
of microfinance institutions”, The Journal of Risk 
Finance, 8(1): 56-71.

22 MACKIE-MASON, J.K. (1990) “Do taxes affect 
corporate financing decisions?”, Journal of 
Finance, 45: 1471-1493.

23 MARSH, P.R. (1982) “The choice between equity 
and debt: An empirical study”, Journal of Finance, 
37: 121-144.

24 MODIGLIANI, F. & MILLER, M.H. (1958) “The 
cost of capital, corporate finance and the theory of 
investment”, American Economic Review, 48: 261-
297.

25 MODIGLIANI, F. & MILLER, M.H. (1963) 
“Corporate income taxes and the cost of capital: A 
correction”, American Economic Review, 53: 433-
443.

26 MORDUCH, J. (1999) “The microfinance 
promise”, Journal of Economic Literature, 37: 1569-
1614.

27 MILLER, M.H. (1977) “Debts and taxes”, Journal 
of Finance, 32: 261-276

28 MYERS, S.C. (2001) “Capital structure”, Journal 
of Economic Perspectives, 15(2): 81-102.

29 MYERS, S.C. (1984) “The capital structure 
puzzle”, Journal of Finance, 39: 575-592.

30 MYERS, S.C. (1977) “The determinants of 
corporate borrowing”, Journal of Financial 
Economics, 5: 147-175.

31 RAJAN, R.G. & ZINGALES, L. (1995) “What do 
we know about capital structure? Some evidence 
from international data”, Journal of Finance, 50: 
1421-1460.

32 RHYNE, E (1998) “The yin and yang 
of microfinance: Reaching the poor and 
sustainability”, Microbanking Bulletin, 2.

33 STULZ, R. (1990) “Managerial discretion and 
optimal financing policies”, Journal of Financial 
Economics, 20: 3-26.

34 TITMAN, S. & WESSELS, R. (1988) “The 
determinants of capital structure choice”, The 
Journal of Economic Theory, 42: 209-243.

35 WALD, J.K. (1999) “How firm characteristics 
affect capital structure: An international 
comparison”, Journal of Financial Research, 22: 
161-187.

36 WILLIAMS, J. (1987) “Perquisites, risk and 
capital structure”, Journal of Finance, 42: 29-49.

37 WILLIAMSON, O.E. (1988) “Corporate finance 
and corporate governance”, Journal of Finance, 43: 
567-591.