ساختار سرمایه
The impact of capital structure on
the performance of microfinance
institutions
Anthony Kyereboah-Coleman
University of
Abstract
Purpose – The purpose of this paper is to examine the impact of capital structure on the performance
of microfinance institutions.
Design/methodology/approach – Panel data covering the ten-year period 1995-2004 were
analyzed within the framework of fixed- and random-effects techniques.
Findings – Most of the microfinance institutions employ high leverage and finance their operations
with long-term as against short-term debt. Also, highly leveraged microfinance institutions perform
better by reaching out to more clientele, enjoy scale economies, and therefore are better able to deal
with moral hazard and adverse selection, enhancing their ability to deal with risk.
Originality/value – This is the first study of its kind in the sector, especially within sub-Saharan
Keywords Microeconomics, Financial institutions, Capital structure,
Paper type Research paper
Introduction
The existence of separation between ownership and control of firms and the resultant
agency cost presents an embodiment of critical issues in modern corporate governance
in both financial and non-financial corporate entities. In such circumstances, managers
may pursue an objective function which is at variance with the firm or owners’
objectives. Hence agency cost arising out of the dichotomy between ownership and
control is measured as the resultant lost value due to managers pursuing their set goals
against those of the firm. To deal with this situation and in the process mitigate against
agency cost, several mechanisms have been proposed. One such theory is the use of the
firm’s capital structure. The capital structure of a firm is basically a mix of debt and
equity which a firm deems as appropriate to enhance its operations. Thus, theory point
out that high leverage or low equity/asset ratio reduces agency cost of outside equity
and thus increases firm value by compelling managers to act more in the interest of
shareholders, (Berger and Bonaccorsi di Patti, 2006). Therefore capital structure is
deemed to have an impact on a firm performance against the position held by
Modogliani and Miller in their seminal work of 1958. Modigliani and Miller (1958)
argue on the basis of the following assumptions; existence of perfect capital market;
homogenous expectations; absence of taxes; and no transaction cost, that, capital
structure is irrelevant to the value of a firm. This position has been supported by others
such as Hamada (1969), and Stiglitz (1974). It must however be pointed out that this
The current issue and full text archive of this journal is available at
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The author is very grateful to the anonymous referees for their invaluable comments. The usual
caveat for responsibility applies.
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The Journal of Risk Finance
Vol. 8 No. 1, 2007
pp. 56-71
q Emerald Group Publishing Limited
1526-5943
DOI 10.1108/15265940710721082
conclusion does not hold in the real world where these restrictive assumptions are not
applicable. Consequently, studies by Jensen and Meckling (1976); Myers (1977);
Williams (1987); Harris and Raviv (1990); Grossman and Hart (1982); and Jensen (1986)
have debunked the assertion made by Modigliani and Miller.
Studies however, on the impact of capital structure on firm performance have been
few and have in most of the cases been carried out in developed economies on large and
listed firms. It is in this vacuum that this study is being carried out especially within
Sub-Saharan region. We must however indicate that there are two studies on the
Ghanaian economy regarding capital stricture. While Abor (2005) looked at the effect
of capital structure on profitability of listed firms, Boateng (2004) looked at the
determinants of capital structure in international joint ventures. Thus, the authors
deem it appropriate to investigate how capital structure of microfinance institutions
(MFIs) affects their performance. The microfinance sub-sector has evolved as a
development tool intended to provide credit and financial services to the productive
poor who do not have access to formal financial intermediation and are engaged in
small and micro enterprises. In the beginning, such microfinance institutions were set
up through state-run subsidized credit schemes and therefore were directly controlled
by the state. Through their evolution, MFIs have benefited from the establishment of
mutual funds as part of shareholder structure and/or the connection of such
organizations with capital markets. These developments have several implications for
their capital structure, operations and performance essentially because the presence of
debt exerts pressure on management to ensure efficiency and profitability and to be
able to honour such debt obligations.
Microfinance is not a recent phenomenon in
that less than 15 percent of the population in developing countries, including
has access to the mainstream financial services Aryeetey (1995). It is in this regard that
microfinance is very crucial. Formal MFIs in
Community Banks (R and CBs), 200 Credit Unions, 8 Savings and Loans Companies,
ROSCAS and Regular Savings and Credit Associations (RESCAS) and some
commercial and development banks, especially the Agriculture Development Bank,
Ghana Commercial Bank Ltd, and SSB Bank. These service small-scale farmers,
artisans, fishermen and small-scale traders. GHAMFIN was established in 2000 with
the aid of the World Bank to partly regulate and keep database of MFIs in
membership includes over 70 regulated and non-regulated MFIs serving over 260,000
clients. A number of studies (Aryeetey, 2001; Quainoo, 1999; Ansah, 1999; World
Bank,1997) commissioned by the World Bank give some insight into some MFIs in
These studies point to a generally successful situation with potential future benefits
and recommend more studies into MFI activities. For example, in 2001, the total
number of depositors recorded by all rural and community banks was 1.2 million and
with about 150,000 borrowers. Again, by 2002, eight savings and loans institutions had
over 160,000 depositors, 10,000 borrowers, and offerings savings and credit products
similar to rural and community banks. Furthermore, in 2002, private deposits with
MFIs in
(2004). The government of
MFIs and has through the Central Bank and the Ministry of Finance and Economic
Planning began the implementation of a Rural Finance Services Project (RFSP) with
Microfinance
institutions
57
funding from donor agencies (World Bank, African Development Bank, UNDP, IFAD,
etc.) aimed at training and building capacity, as well as institutional development and
product design of MFIs. In summary therefore, it must be pointed out that
microfinance has for the past few years been used as a way of providing credit to the
poor in small-scale enterprises, in order to improve upon their income levels thereby
reducing poverty. Thus, the critical role played by the sector motivated us to examine
how their capital structure influences performance.
The paper seeks to address two-fold problem: the first is to provide an insight into
the capital structure of microfinance institutions in
how this structure impacts on performance. Not only will the findings of the study help
the ongoing debate on issues related to microfinance institutions, but it will also serve
as a foundation for further studies in this important sector. The rest of the paper is
organised as follows: section two reviews both theoretical and empirical literature;
section three discusses methodology and the data; section four discusses empirical
results and section five concludes and offers recommendations emanating from the
findings of the study.
Review of research literature
Theoretical underpinnings
One of the important financial decisions confronting a firm is the choice between debt
and equity according to Glen and Pinto (1994). The linkage between capital structure
and firm value has engaged the attention of both academics and practitioners. Indeed,
the famous seminal paper by Modigliani and Miller (1958) set the stage for numerous
propositions that have been developed to provide the theoretical underpinnings of this
crucial concept. Theoretical advancement with emphasis of shaping capital structure
models based on tax balancing and information asymmetry, product market, corporate
governance have aided in understanding the financing behaviour of corporate entities.
Argument amongst others has centred on the determination of an optimal capital
structure for a specific firm and also as to whether the quantum of debt usage in
relation to equity is irrelevant to a firm’s worth.
After their initial presentation stating that capital structure is irrelevant to firm
value, Modigliani and Miller in 1963 revised their position by incorporating tax
benefits as determinants of capital structure. In this new dimension, the essential
characteristic of taxation is the recognition of interest as a tax-deductible expenditure.
To strengthen this argument, Modigliani and Miller explain that a firm that honours its
tax obligation benefits from partially offsetting interest called “tax shield” in the nature
of payment of lower taxes. This therefore is a tacit admission that capital structure
influences firm value. They, thus, state that firms should use as much debt as possible
in order to maximize their value.
Subsequent to this, several studies have looked at the linkage between capital
structure and firm value and more especially after the paper by Jensen and Meckling in
1976. There is the argument that greater financial leverage has the possibility of
affecting managers and reducing agency cost through the threat of liquidation which
causes personal losses to managers’ salaries, reputation, perquisites etc. (e.g. Grossman
and Hart, 1982; Williams, 1987), and also through pressure to generate cash flows to
pay interest expenses (Jensen, 1986). Emanating from the foregoing discussion, higher
leverage is considered an appropriate method to employ in order to mitigate conflicts
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between shareholders and managers concerning the type of investment to undertake,
(Myers, 1977), the amount of risk to undertake, (Jensen and Meckling, 1976; Williams,
1987), the conditions under which the firm is liquidated, (Harris and Raviv, 1990), and
even decisions regarding dividend policy, (Stulz, 1990).
Berger and Bonaccorsi di Patti (2005), state that, whereas increased leverage may
reduce the agency costs of outside equity, the opposite effect may occur for the agency
costs of outside debt arising from conflict between debt holders and shareholders, and
that when leverage becomes relatively high, further increases may generate significant
agency costs of outside debt from risk shifting or reduced effort to control risk that
result in higher expected costs of financial distress, bankruptcy, or liquidation. Such
agency costs leads to higher interest expenses from firms to be able to compensate debt
holders for their expected losses. Thus, capital structure which is defined as total debt
to total assets at book value, impacts on both the profitability and riskiness of a firm
(Bos and Fetherston, 1993), and when a firm exhibits greater gearing, it has a higher
possibility for failure in the event that cash flows fall short of the required volume to
honour debt obligations. According to Jensen and Meckling (1976), the influence of
leverage on total agency cost is expected to be non-monotonic. Therefore, at low levels
of leverage, increases will produce positive incentives for managers and reduce total
agency costs by reducing agency costs of outside equity. Berger and Bonaccorsi di
Patti (2006) show however that at some point where bankruptcy and distress become
more likely, the agency costs of outside debt overwhelm the agency cost of outside
equity, and therefore further increases in leverage lead to higher total agency cost. In
all this debate, one important conclusion that has emerged is the fact that the structure
of a firm’s capital has implications for its operations and impacts on its performance.
Though much of the debate on capital structure has centred on the determination of
an optimal composition of debt and equity for firms, it lacks theoretical foundation and
that empirical results show that firms with diverse idiosyncrasies require what is
considered an acceptable level of debt and equity mix taking into consideration their
peculiar characteristics and the environment within which they operate for effective
operation and to deal with agency cost.
Empirical literature
There have been a number of studies investigating into the determinants of capital
structure of firms in different businesses such as, joint ventureships (Boateng, 2004),
manufacturing sector (Long and Malitz, 1985; Titman and Wessels, 1988), electricity
and utility companies (Miller and Modigliani, 1966), the non-profit hospitals, (Wedig
et al. 1988) and in agricultural firms (Jensen and Langemeier, 1996). In these studies,
one of the main findings is that industrial or sectoral classification is an important
determinant of capital structure. Thus, firms in different sectors employ different mix
of debt and equity for their operations. However, studies emphasizing on linkage
between capital structure and performance have been scanty. For instance, Berger and
Bonaccorsi di Patti (2006) using data on commercial banks in the
higher leverage or lower equity capital ratio is related to higher profit efficiency, and
Abor (2005) on capital structure and profitability of SMEs in
short-term debt ratio is positively correlated with return on equity. In a similar study,
Chiang Yat Hung et al. (2002), on capital structure and profitability of the property and
construction sectors in
Microfinance
institutions
59
related to asset, it is negatively related to profit margins. The separation of ownership
and management of any corporate entity leading usually to divergent objectives, raises
questions on how much debt and equity should be employed. A clear case of agency
costs which could be viewed from different perspectives by management and owners.
From the foregoing analysis, it is clear that agency cost and capital structure is an
important research agenda. Whiles, it raises several research question regarding the
banking sector, because of the sector’s role as a financial intermediary for monetary
policy, and due to their fundamental nature of being informationally opaque, (Berger
and Bonaccorsi di Patti, 2006), it raises larger concerns in the microfinance sub-sector.
The problem is compounded in this sub-sector where information asymmetry is
rampant. The sector, apart from being a critical component of the financial system, is
also regarded as a poverty reduction strategy for developing countries such as
An investigation therefore into their capital structure and subsequent linkage with
firm performance is not only appropriate, but a necessity to aid effective policy design
and formulation. One main motivation for this exercise is the shift of most MFIs from
donor dependence to accessing capital from capital markets. This raises fundamental
questions regarding organizational funding and with obvious implications for their
capital structure necessitating this study.
Hence, this is yet another study to contribute to the debate on capital structure and
its application to a sector which has not been visited.
Methodology
As already noted, the capital structure of a firm affects its performance. In examining
the effect of capital structure on the performance of microfinance institutions, panel
data from 52 MFIs drawn from
selected due to data availability and accessibility. The MFIs are drawn from formal,
semi-formal, and informal sectors. We must, however, indicate that the sampling
adopted does not lead to problem of biasedness because the institutions are spread over
the country and are dealing with different clientele base. Thus, findings of the study
which is not compromised by this limitation invariably is a reflection of the sector in
The data
The data are annual in nature collected from the selected institutions and it covers the
ten-year period 1995-2004. The following set of data is captured to represent both the
dependent and the independent variables.
Dependent variables. Studies on performance employ various measures to test the
predictions of different agency cost hypothesis. Some of the measures of performance
that have been used over the years include financial ratios (Demstz and Lehn, 1985;
Gorton and Rosen, 1995; Mehran, 1995), stock market return and their volatility
(Saunders et al., 1990; Cole and Mehran, 1998) and also, Tobin’s q (Morck et al., 1988;
McConnell and Servaes, 1990, McConnell and Servaes, 1995; Mehran, 1995;
Himmelberg et al., 1999; Zhou, 2001).
In this study we use unique data of Outreach and Default rate as the dependent
variables. Both Outreach and Default rates are essential variables that capture the
success and sustainability of microfinance institutions (Aryeetey, 1995).
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Independent variables. With regards to the independent variables, we employ
short-term debts, long term debts and total debts as a ratio of total assets. To make up
for other omitted variables we employ, firm size, risk level, and firm age as control
variables.
Analytical framework
In carrying out the analysis, we employ the basic panel data regression equation
yit ¼ a bX = uit ; i ¼ 1; . . .N; t ¼ 1. . .T ً1ق
where i denotes the individual microfinance institutions and t denoting time. In this
case, i represents the cross-section dimension and t represents the time-series
component. a is a scalar, b is a Kx1 vector and Xit is the itth observation on the K
explanatory variables. In estimating a panel data model, most applications make use of
a one-way error component model for the disturbances, with
uit ¼ mi nit ً2ق
where mi represents the unobservable individual-specific effect and nit denotes the
remainder of the disturbance.
Model specification
Following the econometric model by Miyajima et al. (2003) because the model presents
itself as the most appropriate, we estimate the following specific multiple regression
model:
Performanceit ¼ a bDebtRit fControlit uit ً3ق
where DebtRit represents the debt ratio of firm i in time t, and Controlit represents the
control variables of firm i in time t. Following from equation 3, the following equations
are estimated
DEFit ¼ a0 a1SDRit a2 ln SZEit a3RSKit a4 ln AGEit uit ً4ق
DEFit ¼ a0 a1LDRit a2 ln SZEit a3RSKit a4 lnAGEit uit ً5ق
DEFit ¼ a0 a1TDRit a2 ln SZEit a3RSKit a4 lnAGEit uit ً6ق
OUTit ¼ a0 a1SDRit a2 ln SZEit a3RSKit a4 ln AGEit uit ً7ق
OUTit ¼ a0 a1LDRit a2 ln SZEit a3RSKit a4 ln AGEit uit ً8ق
OUTit ¼ a0 a1TDRit a2 ln SZEit a3RSKit a4 ln AGEit uit ً9ق
where
DEFit is annual amount of loan defaults divided by the annual amount of loan
disbursement of firm i in time t;
Microfinance
institutions
61
OUTit is outreach measured by the annual rate of change of clientele base for
firm i at time t;
SDRit is short-term debt divided by total capital for firm i in time t;
LDRit is long term debt divided by total capital for firm i in time t;
TDRit is leverage measuring total debt divided total capital for firm i in time t;
lnSZEit measures the size of the firm and it is the natural log of asset base of firm
i in time t;
RSKit is risk of firm i in time t and it is measured by the deviation from mean
profitability;
lnAGEit is natural log of age of firm i in time t; (age in this wise is measured by
the number of years of operation using the year of incorporation as the reference
point), and Uit is the error term.
Estimation dilemma and diagnostics
There exist a number of approaches for estimating any basic panel model. However,
the most appropriate technique for estimating the basic model is dependent on the
structure of the components of the error term (refer to equation 2) and also the
correlation between the error term and the observed explanatory variables. In
considering a situation where there are no firm specific and time effects, the basic
pooled OLS is most appropriate because it ignores the panel nature of the data set, and
treats observations as being serially uncorrelated for a given firm with homoskedastic
errors across individuals and time periods (Johnston and DiNardo, 1997).
However, unobservable effects can be accommodated using one of two techniques.
The basic question to address remains “is it fixed or random effect?” Thus, in order to
reduce the number of parameters to be estimated, it is recommended to justify treating
the individual fixed effects as being drawn from some distribution. The estimation of
the parameters of this distribution is based on the assumption that the unobservable
effects are included in the error term. Thus, the variance-covariance matrix of the
resulting non-spherical errors is transformed to obtain consistent estimates of the
standard errors. The random effects estimator under such circumstances is the most
appropriate (Hsiao, 1989). Otherwise, the fixed effects is appropriate by including a
dummy variable for each firm, though it is less efficient.
Resolving the dilemma: a choice between random or fixed effects
In dealing with the situation, Hausman (1978) specification test is carried out to make a
choice between random or fixed effects in tandem with Greene (1997). Hence, we carry
out the Hausman specification test based on a contrast vector H and results reported in
later:
H ¼ bGLS 2 bW _ _=
_V_bw_2V_bGLS__
21
bGLS 2 bw _ _ ً10ق
where b GLS are the random effect parameter estimates and b W are the fixed effect
parameter estimates.
Table I shows the capital structure and performance of MFIS.
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OUT ROA ROE RSK DEF AST LDR SDR TDR LNSZE LNAGE
OUT 1.0000
ROA 0.0500 1.0000
ROE 20.0036 0.0075 1.0000
RSK 20.0751 0.0111 0.0387 1.0000
DEF 20.0267 0.0077 20.0221 20.0668 1.0000
AST 0.0035 0.0035 0.0078 20.1149 20.0268 1.0000
LDR 0.0015 0.0144 0.0632 0.0370 20.0800 0.0715 1.0000
SDR 0.0559 20.0056 20.0445 20.0734 0.0104 0.0426 20.6785 1.0000
TDR 0.0696 0.0117 0.0269 20.0415 20.0898 0.1430 0.4571 0.3432 1.0000
LNSZE 20.1014 0.0421 20.0064 20.0926 20.0635 0.6471 0.1988 20.1242 0.1038 1.0000
LNAGE 20.0749 0.0718 20.0401 20.0362 0.0463 0.3150 20.1471 0.2170 0.0746 0.4070 1.0000
Notes: OUT is the rate of outreach measured by the rate of change in clientele base on yearly basis; ROA is the return on assets measured by EBIT/Total
assets; ROE is Profit after interest and taxes/Equity; RSK is the risk level measured by deviation from mean profitability; DEF is the default rate
measured by the Annual amount in default/Annual total disbursement; AST is the asset structure and is calculated by Total fixed assets/Total assets;
SDR is the short-term debt ratio calculated by Total short-term debts/Total capital; LDR is long-term debt ratio which is calculated by Total long-term
debt/Total capital; TDR is the total debt ratio (gearing/leverage) calculated by diving Total debts/Total capital; LNSZE is the natural log of asset base
representing size; and LNAGE is the natural log of an institution’s age
Source: Author’s estimates
Table I.
Capital structure and
performance of MFIs:
correlation matrix
(between both dependent
and independent
variables indicating the
direction and level of
correlation)
Microfinance
institutions
63
Empirical results
Descriptive statistics
Table II offers the descriptive statistics with respect to both dependent variables and
regressors. While most of the microfinance institutions are highly leveraged, shown by
the mean total debt ratio of 0.76, most of these debts are long term as against short
term, suggesting a considerable dependence on long-term debt by MFIs for their
operations. The standard deviation coupled with the minimum and maximum values
of total debt ratio is an indication of a sector which is widely spread and highly
unevenly distributed with regards to leverage levels. Again, about 29 percent of all
assets of microfinance institutions under this study constitute fixed. Thus, most of the
microfinance institutions have a higher proportion of current and other forms of
intangible assets. This is again shown by the minimum and maximum values of 0.03
and 0.85 asset structure respectively.
The institutions studied have enjoyed satisfactory performance recording mean
values of 0.39 and 0.33 for ROA and ROE respectively. The standard deviation of 1.52
with respect to ROA suggests that whiles a few firms are doing well, most of them are
not. This is given more credence with 6 percent and 3500 percent representing
minimum and maximum ROA respectively. Thus, it could be argued that though on
the average these microfinance institutions are doing well in terms of ROA, the
performance is rather widely dispersed suggesting that the over all mean performance
could be driven by a few MFIs. Indeed, this story is not substantially different in the
case of ROE. Other performance variables such as outreach, risk, and default rates are
relatively encouraging suggesting that the institutions are evenly matched. With
noticeable different sizes measured by their assets base, these intuitions have been
operating for the past 41 years with average age of operation of about 18 years.
Discussion of regression results
As per the regression results, presented in Table III and Table IV, debt has a positive
impact on performance consistent with studies by Michaelas et al. (1999). Short-term
debt exerts pressure on management to deepen a MFI’s outreach. Though, long-term
debt equally shows a positive relationship with outreach, it is not significant. This
could be explained by the fact that with long-term debts, the pressure for repayment is
Variable Obs Mean Std dev. Min. Max
ROA 520 0.3916346 1.524334 0.06 35
ROE 520 0.3343462 0.4211618 0.14 6.3
OUT 520 0.2643846 0.0792669 0.12 0.45
DEF 520 0.3413846 0.1008156 0.2 0.63
RSK 520 0.2087692 0.3760601 20.6 0.7
AST 520 0.2864808 0.1986819 0.03 0.85
SDR 520 0.3549616 0.2872885 0.0003996 0.9929561
LDR 520 0.413822 0.3033911 0.0000435 0.8830535
TDR 520 0.7687836 0.2372909 0.0128895 1.717564
LNSZE 520 14.51752 1.901209 11.7712 18.72227
AGE 520 7.82692 5.024875 10 41
Note: Author’s estimates
Table II.
Capital structure and
performance of MFIs
descriptive statistics
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Dependent variable: outreach fixed effect estimates Dependent variable: default rates random effect estimates
Regressors Model 1 Model 2 Model 3 Model 1 Model 2 Model 3
Risk (RSK) 20.0174 (21.86) * * 20.187 (22.00) * * 20.0183 (21.96) * * 20.0204 (21.72) * * 20.0189 (21.61) * * 20.0205 (21.74) * *
Short-term debt (SDR) 0.0199 (1.53) * * 20.0106 (20.65)
Long-term debt (LDR) 0.0017 (0.14) 20.0161 (21.06)
Total debt (TDR) 0.0281 (1.91) * * 20.0380 (22.04) * *
Log of firm size
(LNSZE) 20.0012 (20.53) 20.0023 (21.01) 20.0025 (21.15) 20.0060 (22.29) * * 20.0048 (21.80) * * 20.0052 (22.04) * *
Log of firm age
(LNAGE) 20.0531 (22.52) * * 20.0416 (21.99) * * 20.0439 (22.20) * * 0.0365 (1.92) * * 0.0278 (1.48) 0.0341 (1.89) * *
Constant 0.4294 (8.77) * * 0.4191 (8.39) * * 0.4072 (8.30) * * 0.3330 (6.64) * * 0.3425 (6.73) * * 0.3532 (6.94) * *
R-squared 12.2 12.3 12.2 14.5 9.04 12.4
No. of obs. 520 520 520 520 520 520
Test of probability F(4, 506) ¼ 4.02
[0.0032]
F(4, 506) ¼ 4.02
[0.0089]
F(4, 506)
[0.0018]
Wald Chi2
(4) ¼ 8.60
[0.0718]
Wald Chi2
(4) ¼ 9.30
[0.0540]
Hausman test
Chi2 (4) ¼ 5.88
[0.2082]
Chi2 (4) ¼ 4.82
[0.3063]
Chi2 (4) ¼ 5.43
[0.2460]
Chi2 (4) ¼ 1.34
[0.8543]
Chi2 (4) ¼ 0.90
[0.9241]
Chi2 (4) ¼ 0.61
[0.9616]
Notes: All regressions include a constant. T-statistics are in parentheses and P-values in square bracket; * * Significant at 5 percent level
Table III.
Capital structure and
performance of MFIs
regression results
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institutions
65
Variable Mean Std dev. Min Max Observations
ROA
Overall 0.3916346 1.524334 0.06 35 N ¼ 520
Between 0.2089866 0.3094231 0.9853846 n ¼ 52
Within 1.51136 20.44375 34.406625 T ¼ 10
ROE
Overall 0.3343462 0.4211618 0.14 6.3 N ¼ 520
Between 0.0558842 0.2828846 0.4213462 n ¼ 52
Within 0.4178051 0.0599231 6.213 T ¼ 10
OUTREACH
Overall 0.2643846 0.0792669 0.12 0.45 N ¼ 520
Between 0.0084929 0.2490385 0.2780769 n ¼ 52
Within 0.0788556 0.1353462 0.4524615 T ¼ 10
DEF
Overall 0.3413846 0.1008156 0.2 0.63 N ¼ 520
Between 0.0095135 0.32733077 0.3546154 n ¼ 52
Within 0.10041 0.1867692 0.6433077 T ¼ 10
RISK
Overall 0.2087692 0.3760601 20.6 0.7 N ¼ 520
Between 0.0592046 0.125 0.3096154 n ¼ 52
Within 0.3718339 20.7008462 0.7576154 T ¼ 10
AST
Overall 0.2864808 0.1986819 0.03 0.85 N ¼ 520
Between 0.0090845 0.2730769 0.2988462 n ¼ 52
Within 0.1984945 0.0176346 0.8634039 T ¼ 10
SDR
Overall 0.3549616 0.2872885 0.0003996 0.9929561 N ¼ 520
Between 0.0383253 0.2907966 0.415901 n ¼ 52
Within 0.284974 20.057047 1.035222 T ¼ 10
LDR
Overall 0.413822 0.30333911 0.0000435 0.8830535 N ¼ 520
Between 0.0447416 0.367103 0.4949345 n ¼ 52
Within 0.3004015 20.0807664 0.9182805 T ¼ 10
TDR
Overall 0.7687836 0.2372909 0.0128895 1.717564 N ¼ 520
Between 0.0288348 0.7296816 0.8327454 n ¼ 52
Within 0.2357058 0.0314853 1.653603 T ¼ 10
LNSZE
Overall 14.51752 1.901209 11.7712 18.72227 N ¼ 520
Between 0.0146152 14.49912 14.53508 n ¼ 52
Within 1.901159 11.75364 18.7047 T ¼ 10
LNAGE
Overall 2.844473 0.2662127 2.302585 3.713572 N ¼ 520
Between 0.180451 2.553095 3.091751 n ¼ 52
Within 0.2037312 2.593963 3.757113 T ¼ 10
AGE
Overall 17.8269 5.024875 10 41 N ¼ 520
Between 3.02765 13.32692 22.32692 n ¼ 52
Within 4.121101 14.5 36.5 T ¼ 10
Notes: N is the overall observation (= nxT) where n is the cross-sectional observation (microfinance
institutions) and T is the time frame
Table IV.
Capital structure and
performance of MFIs.
Detailed descriptive
statistics
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relaxed giving management time to strategize their operations towards outlook
enhancement for profitability. However, Caesar and Holmes (2003), Esperance et al.
(2003), and Hall et al. (2004) also suggest a negative relationship between profitability
and both long-term and short-term debt.
Leverage, as expected, impacts positively on outreach. This could mean that with
microfinance institutions, the higher the leverage, the greater the outreach level, and
the higher the premium that is extractable from the credit advanced. This premium
then translates into the firm’s income flow and profitability which could be used to
service the debt. Again, greater outreach enables MFIs to enjoy economies of scale
essentially as a result of reduction in average cost of operation. Furthermore, an
increase in outreach could also lead to product diversification for different clientele
base and this enables an MFI to cushion itself against risk. These findings are
consistent with Bos and Fetherston (1993) who argue that the level of total debt to total
assets of a firm has influence on profitability of the firm. With the control variables,
whiles risk, showed the expected sign, the age of the firm, a measure of reputation,
showed otherwise. Thus, when outreach increases leading into higher income flow,
mean profitability deviation, measuring risk level reduces and enhances the total
profitability outlook of the firm. With age, the reason could be that, the poor do not
necessarily need a firm’s reputation to enjoy small credit. Though, the size of the firm
showed a negative relationship with outreach, this variable is not significant.
With default rates as the performance variable, both short- and long-term debts
showed the expected signs but are not significant essentially suggesting that maturity
may not necessarily be of essence. However, total debt to total assets, measuring
leverage is significant in explaining defaults rates. The results shows that a highly
leverage microfinance institution compels management to put in measures and
mechanisms to reduce annual default rates in order to improve on the institution’s
profitability and to be able to honour its debt obligations. Once again, this is in tandem
with Bos and Fetherston (1993) who indicated that a high leverage firm influences the
riskiness of a firm and that if a firm exhibits greater gearing, it has a higher potential to
fail if cash flows fall short of the needed levels to service outstanding debts. This is also
consistent with findings by Petersen and Rajan (1994) who find a positive association
between profitability and leverage. However, some studies find a negative relationship
between capital structure and profitability, (Friend and Lang, 1988; Barton et al., 1989;
Shyam-Sunder and Myers, 1999; Van de Wijst and Thurik, 1993; Chittenden et al., 1996;
Jordan et al., 1998; Mishra and McConaughy, 1999; Michaelas et al., 1999). Expectedly,
the size of a microfinance institution has a negative impact on default rates. This is
because as the firm expands, it is able to put in place structures to ensure repayment of
loans advanced and also are better placed to deal with problems of moral hazard and
adverse selection. Surprisingly, risk level is negatively related to default rate implying
a higher deviation from mean profitability leads to a lower default rate. However, this
could also mean that a higher risk level could influence management to work at
reducing default rates. There seems to be a bi-causality in this regard. Age has a higher
impact on default rate, which suggests that as a microfinance institution expands, it
encounters more repayment problems leading to defaults. Indeed, the age variable
could have either effect depending on other factors and measures put in place by the
firm to deal with repayment. Hence, if a firm is unable to ensure repayment as it grows
and reaches out to more clients, default rates are likely to increase.
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Conclusion
After the seminal paper by Modigliani and Miller in 1958 and the subsequent revision
of their initial position, several studies have shown that capital structure influences
performance of corporate entities. The microfinance sub-sector has woefully been
neglected in this whole exercise. As an evolving and a critical sector especially as a
development tool, an understanding of the linkage between capital structure and
performance is not only an appropriate addition to the ongoing debate for effective
policy formulation, but long over due. The main contribution of this study is its bold
attempt to examine this sector within the sub-Saharan region. The study thus explored
this linkage using panel data from Ghana on 52 microfinance institutions covering the
ten-year period 1995-2004. The results show that most microfinance institutions are
highly leveraged, have been operating for about 18 years and have about 71 percent of
their assets in current form. Again, the regression results point to the fact that highly
leveraged microfinance institutions perform better by reaching out to more clientele
base and reducing default rates consistent with other studies. Furthermore, the study
shows that highly leveraged MFIs enjoy scale economies and therefore are better able
to deal with moral hazard and adverse selection and also to accommodate risk.
From the findings of the study we recommend the development of appropriate
policies to enable MFIs to have access to long-term debt to enhance their operations. In
this regard, the Ghana Stock Exchange should have a look at their listing requirements
and work towards designing mechanisms that would enable MFIs to get listed and to
offer them the opportunity to access equity capital. Also, government and donor
agencies should consider developing a unique financial package for MFIs, taking into
consideration the peculiar rampant information asymmetry in the sector which
hampers their sustainability due to excessive exposure to default. Do the findings of
this study help us to understand optimal capital structure issues especially in MFIs?
Well, the obvious implication of our findings is that MFIs use more debt relative to
equity for financing their operations. Nonetheless, issues relating to capital structure
still remain contentious and a puzzle. In a study of this nature it would have been more
appropriate to examine all MFIs in Ghana. However, data availability and accessibility
was a limitation. In spite of this limitation, we would want to indicate that findings of
the study are not compromised. Recognizing the study limitations, we are of the
opinion that this study could serve as a framework for further studies in this area. As a
subsequent paper, we propose to look at the determinants of capital structure in this
sub-sector.
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Corresponding author
Anthony Kyereboah-Coleman can be contacted at: acoleman@usb.sun.ac.za, or
acoleman@ug.edu.gh
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