Journal of Economic Studies
Microenterprise financing preference: Testing POH within the context of Ghana's rural
financial market
Eric Osei‐Assibey, Godfred A. Bokpin, Daniel K. Twerefou,
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JES
39,1 Microenterprise financing
preference
Testing POH within the context of Ghana’s
84 rural financial market
Received 12 April 2010 Eric Osei-Assibey
Accepted 25 January 2011 Graduate School of International Development,
Nagoya University, Nagoya, Japan and Department of Economics,
University of Ghana, Legon, Ghana
Godfred A. Bokpin
Graduate School of Economies, Osaka University, Osaka, Japan and
Department of Finance, University of Ghana Business School,
Legon, Ghana, and
Daniel K. Twerefou
Department of Economics, University of Ghana, Legon, Ghana
Abstract
Purpose – The purpose of this paper is to investigate the determinants of financing preference of
micro and small enterprises (MSEs) whilst distinguishing a broader range of financing sources beyond
what is typically the case within the corporate finance literature.
Design/methodology/approach – Under the framework of ordinal logistic regression,
the paper also tests whether there is evidence of hierarchical preference ordering as predicted by
pecking order theory (POH) using field survey data for 2009.
Findings – The authors relate that new enterprises are more likely to prefer low cost and less risky or
less formal financing such as internal or bootstrap finances. However, as the enterprise gets
established or matures, its capacity to seek formal financing increases, thereby becoming more likely
to prefer or being in a higher category of formal financing. While the paper affirms the POH, it is
argued that this order is a consequence of severe persistent constraints other than sheer preference.
The findings further reveal that, microentrepreneur’s and MSE’s-specific level socio-economic
characteristics such as owner’s education or financial literacy status, households tangible assets,
ownership structure, enterprise size, as well as sensitivity to high interest rates in the credit market, to
be important determinants of either past (start-up), present or future financing preference.
Originality/value – The main value of this paper is to analyse the determinants of financing preference
of MSEs within the context of rural financial market (RFM) from a developing country perspective.
Keywords Ghana, Developing countries, Financing, Financing preference, Micro enterprises,
Small enterprises, Pecking order theory
Paper type Research paper
1. Introduction
Journal of Economic Studies Modern financial intermediation in Ghana dates back to late nineteenth and early
Vol. 39 No. 1, 2012
pp. 84-105 twentieth centuries. But the financial services sector was characterised by a myriad
q Emerald Group Publishing Limited of problems embodying internal weaknesses and external setbacks. In 1983, the
0144-3585
DOI 10.1108/01443581211192125 Government initiated the Structural Adjustment Program aimed at reversing more than
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a decade of poor economic conditions and downturns in Ghana. The policy reform in Microenterprise
respect of the financial sector, however, began in 1988 and was referred to as the financing
Financial Sector Adjustment Programme (FINSAP). Prior to this, the country’s financial
system can best be described as shallow, fragmented and almost on the verge of preference
collapsing as a result of excessive state control and weak institutional framework
(Aryeetey and Udry, 1997). Mainly driven by liberalisation policies, FINSAP has led to
enhancing the soundness and competiveness of mainstream banking system through an 85
improved regulatory and supervisory framework, restructured and capitalised
distressed banks; deregulated interest rates, developed capital market, and allowed
massive entry of foreign banks among others. However, the rural financial market
(RFM), in particular, is still faced with a number of market imperfections that have
characterised it with high risk, high transaction cost and uncertainties (Nissanke and
Aryeetey, 2006). These have resulted in substantial market segmentations, where the
formal institutions co-exist alongside, semi-formal and informal traditional institutions
with very little linkages. In the paragraphs that follow, we briefly discuss key
developments in each segment and the challenges they face in meeting the financing
needs of microenterprises in Ghana.
The first is the conventional formal banking sector. This sector has experienced some
tremendous growth both in number and expansion of branch network resulting in
competitions since the FINSAP. For example, the number of banks has increased from
nine at the time of the reforms to 26 (with about 750 branch networks) at the end of 2009.
In addition to this are the Rural and Community Banks (RCBs). The RCBs have the main
objective of bringing the rural population into mainstream banking system under rules
designed to suit their socio-economic circumstances and the peculiarities of their
occupation in farming and microenterprise activities. Together with their branches, the
RCBs constitute the largest banking network in rural Ghana, now numbering 129 units
with more than 486 branches scattered across all the ten regions of the country.
However, despite this phenomenal growth, micro and small enterprises (MSEs) access to
formal finance is still very restricted. A recent study by the World Bank (2008) indicates
that the formal banking sector in Ghana reaches just about 5 percent of the population
and even much less for the smaller enterprises. Mensah (2004) attributes this low use of
formal finance in the country to the relatively undeveloped financial sector with low
levels of intermediation, lack of institutional and legal structures that facilitate the
management of small enterprise lending risk, high cost of borrowing and rigidities in
interest rates.
The next in the category is the Semi-Formal Finance. This mainly belongs to the
non-bank financial institutions (NBFIs) that are registered under the NBFI Acts 2008.
There are about nine categories of financial institutions under the NBFIs. Among them
are the Savings and Loans Companies, Credit Unions and some specialised MFIs, which
even though are restricted to a limited range of services, are most active in micro and
small-scale enterprise financing. These lenders, unlike conventional banks, appear more
willing to accept the greater screening and monitoring costs involved in overcoming
information asymmetry. For example, it is known that when formal banks lent to the rich,
these microbanking lent to the poor. When banks lent to men, they lent to women. When
banks made large loans, they made small ones. When banks required collateral, their loans
were collateral free. For these reasons, governments and development agencies have
persistently supported either directly or indirectly the promotion of these MFIs to stimulate
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JES the flow of funds to MSEs. However, these efforts have largely failed to reach the majority of
39,1 the intended beneficiaries. Among some of the underlying reasons are limited coverage,
over-dependence on government and donor funds, pervasive political patronage and
influence in as well as high default rates among the recipients (Mensah, 2004).
The third in the category is the informal financial institutions. By this, we refer to an
array of financial institutions that are not regulated and fall outside all the banking
86 laws of Ghana. These are the money lenders, pawnshops, SUSU, etc. The commonest
one, however, is the SUSU scheme. This is a long-time traditional saving scheme
currently undertaken by over 4,000 operators in Ghana and each serving between
400 and 1,500 customers daily (Barclays Press Release, 2005). The collectors offer very
flexible financial services to their clients, which are patronised mainly by small traders
at the market or roadside stalls. Beyond the provision of financial services, the informal
finance is said to offer stronger social capital that MEs derive enormous social
benefits which the formal financial institutions cannot offer (Alabi et al., 2007).
However, although it is the most patronised form of external financing, several studies
(Nissanke, 2001; Aryeetey and Udry, 1997) have found their services to be inimical to
small enterprises with growth potential. According to these studies, because the
informal financial institutions are underdeveloped, fragmented, disorganised and
charges astronomically high interest rates. They also offer short repayment periods
and limited loan size that do not meet the financing needs of MEs.
In sum, however, as diverse as the formal, semi-formal and informal finances are,
microborrowers in the country are preoccupied with the same issues as easiness,
flexibility, affordability, availability and successful outcomes of loan demand. Thus, the
majority are still constrained and often resort to unorthodox form of financing or what
is now known as “bootstrap financing”. Generally, bootstrap financing has been defined
as a variety of alternative routes or ingenious methods that owners can take to meet
businesses’ financial needs without borrowing or without any traditional institutional
commitments (Neeley, 2009). This is where business owners are encouraged to exploit
personal resources such as selling of properties, or to request funding from relatives,
to barter for services, to lease or hire equipment, or to obtain trade credit, etc. Although
Clark (1994) finds three forms of credit utilised among market traders in
Kumasi (Ghana), namely, advances of goods (i.e. trade credit or delayed payment),
advances of capital (like angels funds) and cash loans, she points out that the most
widespread is the advance of goods. This, according to her, is a form of advancement
seen as less risky and less shameful since cash indebtedness is perceived to reflect a
shaky financial condition. Figure 1 shows the structure of a wide range of financing
preferences available to a microentrepreneur within the RFM in Ghana.
However, existing empirical literature on financing preference of a small enterprise
has not taken these varieties of financing options and the access constraints they face
into consideration. In particular, as it is well acknowledged that access to mainstream
formal finance by MSEs in most developing countries is woefully limited, it is therefore
difficult to tell whether a particular financing pattern is just an issue of preference or
desperation borne out from limited access or constraints to formal finance. This further
raises two main questions. Does the relatively limited use of mainstream formal finance
(as proven in myriad of studies) a supply-side constraint or an issue of
microentrepreneurial preference? What determines MSE financing pattern, does it
conform to a hierarchical order as POH predicts?
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Universal/Retail Commercial Banks Microenterprise
Formal Finance financing
Development Banks Rural/Community Banks preference
Financial NGOs
RFM Semi-formal Finance
Savings and Loans Companies 87
Credit Union/Cooperatives
Gov. Credit Schemes
Informal Finance Friends & Relatives
Trade Credit
SUSU (ROSCA)
Leasing/Hire Purchase
Money lenders Figure 1.
Gift/Grants The structure of financing
Bootstrap Finance Sell of Properties sources within the
RFM in Ghana
Source: Authors
This study attempts to answer these questions by going beyond the conventional
capital structure theory of debt-equity decision of the firm to investigate the drivers of
the entire gamut of financing options available to a microentrepreneur within the rural
financial system of Ghana. The study also departs from the previous studies by
utilising both qualitative and quantitative analytical approaches not only at the
enterprise start-up and working capital financing levels, but also their exantes or
desired future financing needs. Our unique dataset also allows us to compare these
preferences for any evidence of hierarchical preference ordering as predicted by POH.
The study proceeds as follows: Section 2 briefly discusses the theoretical and empirical
literature, and formulates the study testable hypotheses. Section 3 presents data
description and some qualitative analysis while Section 4 presents econometric
specification and discussion of the estimation results. Finally, Section 5 concludes and
highlights policy and further research implications of the findings.
2. Theoretical and empirical review
A large body of theoretical and empirical literature have emerged on firm’s external
financing preference in the last three or more decades especially after the seminal work
of Modigliani and Miller (1958). However, most of these studies have been developed
within the corporate finance and capital structure frameworks of large and medium
firms with very little attention paid to the small and microenterprises. The most popular
among the theories are the static trade-off theory (STT) of capital structure pioneered by
Kraus and Litzenberger (1973) and the pecking order theory (POH) of capital structure
developed by Myers and Majluf (1984). The former explains the idea that a company
chooses how much debt finance and how much equity finance to use by balancing the
costs and benefits. This is done by considering a balance between the dead-weight costs
of bankruptcy and the tax saving benefits of debt. Although, this Static theory has been
vital and a sensitive research area for academics and practicing managers alike,
Gebru (2009) points out that its application for MSEs in particular is limited.
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JES This is because the STT requires a microentrepreneur to have financial sophistication
39,1 and substantial reliable data in application of such techniques as value optimization and
others, which is impracticable due to the peculiar nature of MSEs.
The POH, on the other hand, states that firms adhere to a hierarchy of financing
preference, where internal finance is first preferred to any form of external finance.
This is because internal finance incurs neither security issuing or flotation costs nor
88 disclosure of financial information and, thus no transaction cost. According to the
POH, if the external finance is needed, then debt, which is associated with less severe
information asymmetry, is preferred over equity or bond issue. The simplicity of this
hypothesis and the fact that SMEs are more faced with information asymmetry
problem seem to suggest that financing decisions of MSEs are better explained by the
POH than by the STT. Consequently, many recent studies (Gebru, 2009; Abor, 2008;
Green et al., 2002) of MSEs in Africa by exploring MSEs’ financing preference, have
attempted to explain in the context of POH. The conclusions, however, have been
mixed. Whereas some have found their preference to be consistent with POH, others
have not. For example, Green et al. (2002) studying MSEs’ debt-equity and gearing
decisions reach a conclusion that MSEs in Kenya obtain debt from a wide variety of
sources. Likewise, a recent study by Gebru (2009), which investigates the determinants
of financing preference of MSE’s owners in Tigray state of Ethiopia also within the
context debt-equity decision of the firm, concludes that MSE’s financing preference
generally conforms to the POH. However, Murray and Goyal (2003) have shown among
other things that POH fails where it should hold, especially for small firms where
information asymmetry is most probably an important problem.
However, the majority of these studies have paid little or no attention to the
heterogeneity of the socio-economic status of these MSEs and the diverse nature of
their economic activities. Neither have they considered the varying financing sources,
constraints nor the “bootstrap financing” that the majority of MSEs often resort to
when they are financially constrained. They have rather concentrated more narrowly
on the debt-equity financing preference as if their preferences also fit squarely into the
capital structure theories originally developed for corporate firms in developed
countries. Selvavinayagam (1995), for example, argues that such diversity among
MSEs would mean that their demand for external finance and financing pattern may
not be determined by a unique financial structure or a uniform approach. He points out
that much more focus has to be on the institutional mix, the product variety and the
operational approaches that is compatible with the characteristics of different
socio-economic categories if their demands for financial services are to be met
satisfactorily. This view point appears to be supported by Hamilton and Fox (1998).
They also argue that the diversity of small business and entrepreneurial firms
suggests that managerial beliefs and desires will play an especially important role in
determining capital structure and so “[. . .]models must include the role of management
preferences, beliefs, and expectations if we are to better understand capital structure
policy”. In the same vein, Gebru (2009) concludes that there are elements that could
determine MSE owners’ financing preferences that require better understanding before
a reliable prescriptive position on MSE’s financing can be reached.
In the light of the above, we argue that to better understand MSE’s financing
preference and for POH to better explain these preferences, it is important that research
is done within the context of the rural financial system and the access constraints
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emanating from information asymmetry and the complex socio-economic Microenterprise
circumstances of MSEs. Whereas in industrialised countries debt levels of MSEs financing
have been shown, in some cases, to reflect a demand-side preference ordering and are
not just the manifestation of severe supply-side deficiencies (Hamilton and Fox, 1998), preference
the same cannot be said of MSEs in developing countries.
2.1 Is there hierarchical preference ordering? 89
In this respect, as mentioned in Lean and Tucker (2001), we postulate that the rural
financial credit system exist on a continuum whereby their lending criteria can be
measured anywhere from purely non-commercial through to purely commercial or
purely formal to purely informal. At the commercial extreme, exists conventional
or mainstream formal and semi-formal banks whereas towards the informality or
non-commercial extreme exist informal or bootstrap finance to self-finance. According
to the POH, as also cited in Abor (2008) and Hussain and Matlay (2007), the order of the
preference is from the one that is least sensitive (and least risky) to the one that is most
sensitive (and most risky) that arise because of asymmetric information between
corporate insiders and less well informed market participants. Evidence abounds that
microentrepreneurs tend to rely heavily on their past savings, followed by informal
sources of credit from family and friends, particularly at business start-up stage
(Paul et al., 2007; Aryeetey et al., 1994).
From this discussion we hypothesized for the purposes of incremental preference
ordering as follows:
H1. At the very start of microenterprise establishment without any reputation or
collaterable assets, internal finance would be preferred to external finance as
it is of zero cost and has no problem whatsoever with problems relating to
information asymmetry. Or in a bid to avoid intrusion or external control
internal finance will also be preferred even for established enterprises.
H2. If internal finance depletes or is non-existent, as in some cases, and MSE
decides to hold any debt or borrow externally, non-cash debt such as
suppliers’ credit or other forms of bootstrap finance will be preferred over the
traditional informal sources of financing, not only because of the least cost or
almost the non-existence of information problem, but also because it is a
traditionally well patronised practice which is less perceived to be shameful.
H3. If there is the need to borrow money, informal type of financing would first be
preferred over the semi-formals because of familiarity, flexibility, easiness
and the social benefits it offers regardless of the cost as well as the system
inherent mechanisms of overcoming information problem such as application
of social sanctions.
H4. Over time as enterprise gets established, shows prospects of growth and gains
some experience, but still not having enough collateral or the capacity to
borrow from mainstream finance, then semi-formal financing becomes the
best option. Since most of the MFIs relax, while accepting some part of
screening and monitoring cost.
H5. As more time passes, enterprise matures, reputation is built and confidence is
gained, the desire for long-term finance for growth and expansion becomes
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JES more irresistible. Working capital or long-term finance for investment would
39,1 then be preferred or desired from mainstream formal finance, since the
enterprise then becomes more acceptable to the banks.
3. Econometric analysis
3.1 Model specification
90 From the discussion, we can assume an ordinal or incremental financing preference based
on the degree of formality or speed and ease of access as relates to information asymmetric
problem. Accordingly, we investigate this hierarchical or incremental preference ordering
in three important stages of firm’s financing, namely start-up, working capital/on-going
financing and exante or desire future financing preference. Thus, our dependent variable
for this analysis can be assumed as ordinal. When the dependent variable is ordinal,
its categories can be ranked from low to high allowing us to apply ordered probit or
ordered logit model (also known as ordered logistic regression or PLUM). Greene (2008)
observes that ordered probit or logit models have come into wide use as a framework for
analysing responses that can be ranked or inherently ordered.
Providing a simple explanation to ordered logit model, Aaron (2005) shows that the
ordered logit model depends upon the idea of the cumulative logit, which also in turn
relies on the notion of the cumulative probability. We can then think of the cumulative
probability Cij as the probability that the ith individual is in the jth or higher category:
Xj
Cij ¼ Prðyi # jÞ ¼ ½PrðyiÞ ¼ K ð6:1Þ
k¼1
This cumulative probability can then be converted into the cumulative logit:
Cij
LogitðCijÞ ¼ log ð6:2Þ
1 2 Cij
This ordered logit model can simply model the cumulative logit in a form of a linear
function of explanatory variables as:
LogitðCijÞ ¼ ai þ bxi ð6:3Þ
The coefficient, b suggests that a one-unit increase in the explanatory variable leads to
an increase in the log-odds of being higher than category j. We therefore apply an
ordered logit model to explore the determinants of MEs financing preference and to
examine whether there is incremental preference ordering among MSEs. We simply
re-write equation (6.3) as:
Y *ij ¼ bxij þ 1 ð6:4Þ
Where Y * represents the latent variable denoting the unobserved propensity of
microentrepreneur i for choosing external financing j. The variable x is a vector of
explanatory variables representing specific enterprise level and microentrepreneur’s
demographic characteristics which we have explained in the subsequent section. The
coefficients b is the parameters to be estimated.
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Although Y * is unobserved, we do observe an ordinal relationship as: Microenterprise
>8 financing> 0; if Y * # 0 ¼ internal finance ð6:6Þ< preference
> 1; if 0 , Y
* # m1 ¼ bootstrap finance ð6:7Þ
:>> 2; if m1 , Y * # m2 ¼ informal finance ð6:8Þ¼ 91Y
3; if m2 , Y * # m3 ¼ semi finance ð6:9Þ
4; if Y * . m3 ¼ formal finance ð6:10Þ
The mi’s are unknown parameters to be estimated with b a.
Where, 0 ¼ internal finance; 1 ¼ bootstrap finance; 2 ¼ informal finance;
3 ¼ semi-formal finance; 4 ¼ formal finance. A positive (negative) value indicates
that a one unit change in any of the explanatory variable increases (decreases) the odds
of being in a higher category. Equation (6.11), therefore, represents the final
substantive equation to be estimated with ordered probit model. Hereafter, we explain
in detail the explanatory variables and their hypothesized signs:
Yij ¼ b0 þ b1AgeðNew;Established;MatureÞ þ b2Size
þ b3Financial Viability þ b4Asset Structure
þ b5Ownership Structure þ b6Interest Sensi ð6:11Þ
3.2 Description of explanatory variables
Within the context of information asymmetry and RFM, credit evaluation literature
suggests five traditional characteristics that can be related to microenterprise
creditworthiness, which are five C’s: Capacity, Capital, Collateral, Character, and
Conditions (Wu and Guan, 2008). Accordingly, in what follows, we endeavour to
explain MSEs’ financing preference within the context of capital structure literature
and the peculiar institutional environment in the informal economy.
Age. Consistent with Hamilton and Fox (1998) classification of firms and in order to
test the hypothesis that there is a hierarchical preference ordering in microenterprise
financing, we model three age categories of microenterprises as “New” (i.e. aged 3 or less,
i # 3), “Established” (i.e. aged between 4 # i # 10 years) and “Mature” (i.e. aged more
than 10, i$ 11 years). Firm’s age has long been seen as a standard measure of reputation
within the capital structure literature (Diamond, 1989; Abor, 2008). As a firm age, it
establishes itself as a continuing business and it therefore increases its capacity to take
on more debt or external finance (Green et al., 2002). Thus, established and matured
enterprise’s preferred working capital or exante future financing preference are expected
to correlate positively with the likelihood of accessing funds from formal banks, whereas
the “New” enterprises are expected to be negative correlating more with informal finance.
Size. With regard to size of operation, the smaller the business the more likely
owner/managers will utilise or prefer internal finance or a less formal financing source
(Abor, 2008). The number of paid employee is therefore considered as a proxy for enterprise
size and it is expected to positively correlate with higher category of formality (bi . 0).
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JES Financial viability. Enterprise’s profit status or past sales growths is often a good
39,1 proxy for financial viability and loan repayment capacity of the enterprise.
Theoretically, whereas the POH predicts an inverse relation between profitability
and external financing preference because it is less risk relying on retained earnings,
the STT postulates a positive relationship. This is because STT model predicts that
profitable firms will employ more debt since they are more likely to have a high tax
92 burden and low bankruptcy cost (Abor, 2008). In this regard, the relationship could be
described as ambiguous (bi . , 0?).
Asset structure. Enterprise with title to assets such as land, building, etc. is more
likely to seek external finance, but using land ownership as a proxy for asset
tangibility, Green et al. (2002) argue otherwise. According them, ownership of or
ability to rent tangible assets is an indicator of wealth, which makes them more likely
to use their own equity or internal finance, at least to start a business. Consequently,
the expected relationship between the asset structure and financing preference is
ambiguous (bi . , 0?).
Ownership structure. It is often the case that many microentrepreneurs do not seek
external finance because they fear intrusion or do not want to lose ownership control
over their businesses. Gebru (2009) argues that MSE owners that are established as
either sole proprietors prefer to exhaust internal sources of finance before going for debt
or equity in conformity with POH. Using the proportion of profit that is kept by the
owner as a proxy for ownership type, we expect a negative relation with a higher
category of financing.
Interest sensitivity. Besides the above factors, the order of preferences is also widely
believed to reflect the relative costs and risk of various financing options (Myers and
Majluf, 1984). Likewise, among the local traders in Ghana, Clark (1994) contends that the
main determinants of each type of loan demand are interest rates, risk, terms of
repayment, and moral connotation which relates to one’s cultural or religious beliefs
towards the use of credit. Thus, we use microentrepreneur’s sensitivity to the prevailing
market interest rates to measure risk aversion. Interest sensitivity is expected to have a
negative relation (bi , 0) with the higher category of preferences since interest rates in
the country is generally believed to be much higher than the relative returns of MSEs.
Heterodox factors. The above factors are particularly important for ongoing finance
(i.e. working capital) or future financing preference. However, for start-up capitals, since
the owner may be unable to provide evidence of good financial performance, track record
or reputation, owner’s character or demographic characteristics, which are seen as
heterodox in corporate finance models, then becomes an important measure of repayment
ability (Hamilton and Fox, 1998; Green et al., 2002). Hamilton and Fox (1998) maintain that
such characteristics include their diversity, and the stage of development of their location.
Thus, we consider such variables as owner/manager’s in educational attainment, gender,
having bank deposit account (as a measure of relationship), keeps records of business
activities (proxy for transparency), location (proxy for proximity or traveling cost), etc.
3.3 Data source
The data for this paper was based on a field survey conducted in the Ashanti region of
Ghana, in August 2009. As described in detail the data sampling procedure in Chapter 5,
the choice of Ashanti region was appropriate in that besides being the most populous
region in Ghana, its capital, Kumasi has one of the highest informal economic activities
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second only to the capital, Accra (Aryeetey and Udry, 1997). Geographically, being Microenterprise
on the middle belt of the country, the region’s unique centrality makes it a traversing financing
point for migrants and traders from all parts of the country. The region also displays
additional character of modernity and tradition, the extreme poor and the wealthy, the preference
highly educated and the illiterates as well as a very large representation of both informal
and formal financial institutions.
Using a simple random sampling technique, we collected data based on the following 93
three strata – sector of activity, geographical location and enterprise size. We first divided
the enterprise population into three sub-strata – services, manufacturing (including
construction), and primary-related activities. On geographical distribution, three
socio-economically important locations were stratified namely the central business
district, sub-urban and rural location. Using structured questionnaires, we collected
quantitative usable data on some 176 microenterprises which employs ten or less persons
within the Kumasi metropolis and from ten villages across the region. To achieve the
study objectives, we obtained data on enterprise and owner’s socio-economic
characteristics, financial performance, choice of financing sources, perception of access
to and use of credits, collateral, etc.
4. Primary results and descriptive statistics
4.1 Enterprise start-up: what is the most binding constraint?
Limited access to finance is often mentioned as the greatest constraint to start-up capital
and growth of microenterprises. This was confirmed by our field survey as capital was
cited by microentrepreneurs as the greatest constraint (58.2 percent) they faced in setting
up their businesses (Figure 2). However, Figure 2 also shows that MSEs in the rural areas
(59.3 percent) are more likely to have problems raising capital than their counterparts in
CBD and suburb. This is not surprising; in that, the generally low income of rural dwellers
coupled with limited number of financial institutions and the high risk often associated
with them mean that they are more likely to be financially constrained (GSS, 2008)[1].
70.0
60.0 CBD
50.0 Urban
40.0 Rural
30.0 ALL
20.0
10.0
0.0
ts it w nin ed o tio t.
.. er
tra
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n
co ap al
k t r
e rm
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e
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n r p
ch nm o
te ve
r
din
g
go uil
b
d,
lan Figure 2.
MEs’ start-up constraints
Source: Field Survey, August 2009
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JES 4.2 Sources of start-up capital
39,1 Given the overriding evidence of MSEs’ limited access to mainstream formal finance,
due to the perceived high risk often associated with the sector and the inability of the
microentrepreneurs to provide viable business plan or collateral, especially at the
start-up, MSEs have to rely on personal or household savings and/or informal credit or
bootstrap financing sources. The survey results show that the most important source
94 of start-up capital is personal or household savings (67 percent), followed by financing
from informal financial institutions (19.3 percent) such as friends and relatives, SUSU
and money lenders (Table I). Among all MSEs only few sourced credit from the formal
commercial banks (3.4 percent) and semi-formal financial institutions (1.1 percent).
Start-up capital from bootstrap finance (9.1 percent) such as supplier’s credit is also
quite significant compared to the other sources.
4.3 On-going finance/working capital
With regard to working capital, only about three in 20 (15.9 percent) actually
borrowed money to finance their working capital needs. The majority, 65.9 percent,
utilised internal or self-raised finance, while a significant percentage (18.2 percent), used
bootstrap finance such as trade credit, leasing, etc. Among those who sought external
finance, the majority (53.5 percent) came from the formal financial institutions with
35.8 and 10.7 percent sourced from the semi-formal and the informal financial
institutions, respectively (Table I).
4.4 The desired or exante future financing preference
Looking forward, we asked microentrepreneurs what kind of finance (both in terms of
price non-price term and conditions as well as institutional sources) they believed
would help their businesses to grow, or they would choose if they were to make a
choice in the future. Beginning with non-price terms and conditions, the greater
number of the respondents (42.5 percent) mentioned long-term loan with the least been
an overdraft facilities (0.6 percent) (Table II). This should be expected since overdraft
facilities are rarely offered by any of the banks in Ghana. If it is even offered at all, it is
usually given to the large companies (Abor, 2008).
4.4.1 Price term: interest sensitivity. Interest rate or cost of borrowing in Ghana is one
of the highest in West Africa. At the time of the survey, lending rates within mainstream
credit market was averaging 35 percent, and even much higher those charged by the
informal financial institutions. The primary evidence reported here indicates that the
current high level of lending rates in the country is a major concern or a disincentive for
external financing preference. Almost all (82.6 percent) of the microentrepreneurs
Finance type Start-up capital (%) Working capital (%) Future preference (%)
Formal finance 3.4 8.5 42.6
Semi-formal 1.1 5.7 19.3
Informal 19.3 1.7 11.9
Bootstrap finance 9.1 18.2 17.0
Table I. Self-finance 67.0 65.9 9.1
MSEs’ preferred source
of finance Source: Field Survey, August 2009
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Microenterprise
Type of loan needed % Purpose for the loan %
financing
Long-term loan 42.5 Fixed capital for land, tools, stall 28.7 preference
Short-term loan 12.5 Renovation 6.8
Over draft facilities 0.6 Working or operating capital 34.2
Easy and faster access 15.6 Emergency needs 5.5
Suppliers credit, hire purchase 11.9 To support cost of living 15.1 95
Indifferent 3.8 To pay past debt 4.1
NA 9.4
Other 3.8 Other 5.5
Total 100.0 Total 100 Table II.
No. of observation 160.0 No. of observation 73 Non-price term and
conditions of preferred
Source: Field Survey, August 2009 loan and purpose
believed that the prevailing market interest rate for bank loans were too high for their
business to afford. When they were asked to indicate the rate of interest their business
can afford, an average lending rate of 9 percent were mentioned with almost five in ten
(48.5 percent) microentrepreneurs indicating between 1 and 10 percent (Figure 3).
Surprisingly, however, over 23 percent of ME owners or managers would prefer loans
with zero interest rate. Furthermore, a little over six in ten (63.5 percent) of all cases
generally disagreed with the statement that higher interest rate does not matter so long
as one gets easy and flexible access to formal bank loans. The high lending rates
constraint is also shown by the fact that more than seven in ten (76.2 percent) of
microentrepreneurs will not hesitate to apply for a bank loan if the prevailing interest
rates were to be cut by half.
4.4.2 Which of the external financing sources will MSEs prefer? Regarding a desired
future financing source, the highest preferred source was formal bank finance. While
approximately 45 percent of the microentrepreneurs would choose formal bank finance,
only 19.3 percent would prefer semi-formal finance with informal finance (11.9 percent)
being the least preferred (Table I). This outcome is quite surprising. However, if we
juxtapose that on the fact the majority would prefer long-term financing in the future as
mentioned above, then neither semi-finance nor the informal finance was the solution for
their financing need. It is the formal commercial banks, which are known to have the
capability to offer long-term debt financing ( Jaramillo and Schiantarelli, 2002); and the
MSEs appear to know that too well.
Average r = 9%
21% ≤ r ≤ 30% 4.3
11% ≤ r ≤ 20% 23.9
1% ≤ r ≤ 10% 48.5 Figure 3.
Lending rates
0% 23.3 microenterprises
can afford (%)
Source: Field Survey, August 2009
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JES Quite interestingly, the most common reason (31.75 percent) for preferring a particular
39,1 source was because he/she is a long-term customer of the institute or a group member
(Table III). This particular reason featured most prominently (37.5 percent) among those
who would choose formal banking source. This underscores the importance of
relationship banking and also group membership which, as previously mentioned, helps
to assuage agency problems. It is said to lead to low cost of information acquisition and
96 peer monitoring. However, over 30 percent of the respondents who chose formal finance
cited other as the reason for their choice. This may constitute those who choose either by
default or were made to open an account before loan is granted. As expected,
approximately half (50 percent) of MSEs who prefers informal finance, cited closeness
and convenience as the dominant reason.
4.5 Is there evidence of hierarchical order of preference?
From the preliminary results discussed so far; does it point to a hierarchical preference
ordering as predicted by POH? Table I reveals an emerging pattern that appears, on
the face value, to support this hypothesis. The evidence here affirms our earlier
believed that at the pre-start and start-up, microentrepreneurs have a strong preference
for using personal and informal sources of finance, and that the use of external debt
finance particularly bank loan becomes more common and most preferred once the
business is up and running (Wyer et al., 2007). At the start-up, besides almost
70 percent of capital raised from personal or household saving, those who decided to
seek external finance, slightly more than three quarters (76 percent) obtained it from
the existing informal institutions.
However, as the enterprise gets established, while formal bank debt consistently
increased from 3.4 percent at start-up to 8.5 percent as working capital to a much
higher 42.6 percent in the future, both the internal finance and informal finance
decreased. Preference for Internal-finance decreased drastically from an average of
66.5 percent between start-up and working capital to just about 9.1 percent in the
future preference. For the majority to reveal their future financing preference for formal
sources is an indication that the huge percentage of MSEs that use internal finance,
either as start-up or working capital, does so not because of preference, as is the case in
most part of developed world. But, as a consequence of severe constraints they face
either voluntarily or involuntarily in accessing external finance, particularly from the
formal finance (Table IV).
Reasons Formal Semi-formal Informal Trade credit Total
A long-time customer or a member of a group 37.5 33.3 25.0 100.0 31.7
Close and convenient 12.5 8.3 50.0 0.0 24.4
Did not require collateral 6.3 16.7 0.0 0.0 7.3
Interest rate low 0.0 8.3 0.0 0.0 2.4
Personal relationship 6.3 0.0 0.0 0.0 2.4
Offers easy and flexible service 6.3 33.3 25.0 0.0 17.1
Table III. Other 31.3 0.0 0.0 0.0 14.6
Reasons for Total 100.0 100.0 100.0 100.0 100.0
choosing a particular
financing source Source: Field Survey, August 2009
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Microenterprise
Hypothesized
Variables Description n Mean SD sign financing
preference
MSE size Average number of paid employees 111 2.8 0.782 þ
Age of the owner Mean age of the owner 176 36.7 0.310 þ /2
Educational attainment Mean number of years spent in 176 9.6 1.186 þ
school 97
Gender ¼ 1, if female; 0 male 175 0.313 0.463 2 /þ
Bank deposit A/C ¼ 1; 0 otherwise 176 0.333 0.489 þ
Awareness ¼ 1; if it is aware of alternative 176 0.455 0.499 þ
forms of financing; 0 otherwise
Negative perception ¼ 1; 0 otherwise 176 0.409 0.491 –
about debt
Net profit margins Net profit divided by total revenue 176 0.54 0.503 –
profit
Interest sensitivity ¼ 1; if owner thinks lending rate 176 0.633 0.498 –
matters more than easy access to
credit; 0 otherwise
Registered ¼ 1; 0 otherwise 176 0.415 0.499 þ
Location ¼ 1, if located in the CBD 176 0.250 0.475 þ
¼ 2, located in suburb 0.386 0.477 –
¼ 3, if located in rural area 0.364 0.464 –
Household assets ¼ 1, if ME household owns land or 176 0.614 0.500 þ
building
Assets structure Weighted index of assets of MSEs 168 1.9 0.698 þ
(natural logs)
Ownership structure Percentage of profits retained/ 176 0.902 0.191 –
shared by the owner (100% ¼
sole proprietor)
Age of enterprise New (# 3 years); ¼ 1 176 0.268 0.452 –
Established (4 # i # 10 years); ¼ 2 0.472 0.500 þ
Mature ($11 years) ¼ 3 0.259 0.434 þ
Skill training ¼ 1, if owner/manager has ever 174 0.561 0.499 þ Table IV.
received skill training Descriptive statistics of
the explanatory variables
Source: Field Survey Data, August 2009 and hypothesized signs
4.6 Regression results
The ordered logit or the PLUM-ordinal regression results for determinants of start-up
capital, working capital and future financing preference are presented in Tables V
and VI. We used the same set of explanatory variables in both working capital and
future financing models, which are both presented in Table V. However, for the
start-up model, we dropped certain variables such as profitability, age, size, etc. that we
believe are important only when the enterprise is on-going. Even though Green et al.
(2002) tested some of these variables in an initial capital determinants equation, they
were cautious about their interpretations since, according them; they all tend to have
retrospective and ambiguous effects.
The threshold portion of the estimation results shows the constants/intercept terms.
We emphasize here again that our interest is to determine the direction of the
relationship between each predictor and the ordinal nature of the categorical outcome.
A positive sign for the estimated parameters means higher categories are more
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JES
Working capital estimates Future finance estimates
39,1 Threshold Coefficient SE Coefficient SE
Internal ¼ 0 24.142 * * 1.993 25.435 * * * 1.481
Bootstrap ¼ 1 23.135 * 1.977 23.661 * * 1.426
Informal ¼ 2 22.934 1.975 22.964 * * 1.411
98 Semi-formal ¼ 3 22.230 1.973 21.599 1.384
Formal ¼ 4
Variables
Asset structure 0.622 0.447 20.182 0.331
Awareness 1.092 * 0.560 0.381 0.426
Book keeping 0.631 0.570 0.645 * 0.446
Deposit account 1.300 * * 0.584 0.301 0.448
Educational Attainment 20.328 * 0.202 0.408 * * 0.162
Enterprise size 0.797 * * 0.372 0.054 0.287
Established MSE 2.079 * * 0.924 2.189 * * * 0.507
Gender 20.373 0.579 20.382 0.476
Interest sensitivity 0.176 0.551 20.974 * * 0.442
Matured MSE 3.747 * * * 1.002 1.895 * * * 0.598
Negative perception 20.255 0.557 21.046 * * 0.438
Net profit margin 20.135 0.848 20.527 0.521
Ownership structure 1.161 0.933 21.344 * 0.773
Registration 1.202 * * 0.598 0.221 0.446
Rural location 0.101 0.634 0.315 0.536
Sub-urban location 21.057 * 0.643 0.203 0.516
Cox and Snell (R2) 0.364 0.420
22 log likelihood 164.323 251.553
No. of observation 141 138
Table V.
Ordered probit regression Notes: Significant at: *10, * *5, and * * *1 percent levels; NB: the estimates for age categories use the
results of MSEs’ “New variable” as a reference category while the estimates for Location variables use CBD as a
financing preference reference category
probable, whereas a negative sign means lower categories are more probable. However,
where necessary, we will endeavour to explain some of the results in terms of Odds
ratio (OR), which is simply by taking the exponential of the estimates.
Beginning with the age category variables, namely New,Established andMature, the
results are robustly significant and consistent with the study hypothesis. The positive
signs reveal that for Established and Mature enterprises, compared to New ones, have a
higher probability to be in a higher category in both ongoing and future financing
sources. This also suggests that new enterprises are more likely to prefer either internal
or less risky financing such as bootstrap or informal financing such as supplier’s credit
or SUSU schemes. However, as the enterprise gets established or matures its capacity to
seek formal financing increases and thus are more likely to prefer or being in a higher
category of formal financing. This result is consistent with Abor (2008)’s finding that
age is an important determinant of SMEs’ capital structure as older SMEs tend to depend
more on long-term debt. According to him, since SMEs do not have access to the public
equity market, long years of business could signify long business relationships with
external debt providers and that increases their chances of acquiring external long-term
debt finance. The results therefore appear to support the POH that firms adhere to
hierarchical preference ordering.
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Microenterprise
Start-up capital estimates
Threshold Estimates SE financing
preference
Internal ¼ 0 1.883 2.159
Bootstrap ¼ 1 2.481 2.163
Informal ¼ 2 4.461 * * 2.194
Semi-formal ¼ 3 4.731 * * 2.201 99
Formal ¼ 4
Variables
Age of the owner 0.352 0.571
Awareness 0.455 0.377
Bank deposit account 1.344 * * * 0.425
Education attainment 0.664 * * * 0.228
Gender (Female ¼ 1) 1.007 * * 0.389
Household assets 1.411 * * * 0.383
Interest sensitivity 0.027 0.390
Negative perception 20.582 0.415
Ownership structure 20.131 0.871
Registration 0.369 0.376
Rural location 20.917 * * 0.461
Skill training 20.484 0.368
Suburb location 0.516 0.465
Cox and Snell (R2) 0.261
22 log likelihood 275.399 Table VI.
No. of observation 157 Ordered probit regression
for determinants of
Note: Significant at: *10, * *5, and * * *1 percent levels start-up capital
Comparing the results of the education attainment variables in all the three models
appears to be mixed, but interesting. Whereas it shows up significantly positive in both the
start-up and future financing models, it is negative in the working capital model.
The positive signs suggest that one year increase in the number of years a
microentrepreneur spent in school will result in an OR, exp (0.664) ¼ 1.942 and exp
(0.409) ¼ 1.505 increase in odds of being in a higher category of formal finance for a
start-up capital and future financing, respectively. For start-ups, as there is no history of
enterprise’s performance, human capital then becomes “a collateral substitute” or a
measure of reputation and competence to guarantee loans from mainstream formal
finance as this may signal loan repayment ability of the would-be entrepreneur
(Cressy, 1990). On the contrary, the negative sign of education in the ongoing finance
estimation results, suggests that a highly educated microentrepreneur is less likely to
prefer formal finance. This is quite surprising, counter intuitive and inconsistent with most
previous studies thus making it difficult to assign any plausible reason for this outcome.
However, the awareness variable that also proxy for financial literacy is positive and
significant in the working capital model but not in the other two. Although weakly, it may
suggest that microentrepreneurs who are aware of the availability of alternative forms of
financing are more likely to be in the higher category. This seems consistent with the
argument that the decision on the choice of credit source is partly determined by the
information available to the potential borrower on the available sources and their specific
requirements (Kimuyu and Omiti, 2000). The results also reveal that bank deposit variable
is statistically significant and positive in both start-up and working capital models,
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JES but not in the future financing model. This finding underscores the importance
39,1 of relationship banking in mitigating information asymmetry problems as
microentrepreneurs who have a bank deposit account are more likely to prefer or gain
access to formal banking loans. Similarly, the MSE’s registration status variable is
significant with the expected positive sign in the working capital model. This seems to
suggest that if the enterprise is up and running, its legal status as proxy by registration
100 becomes important for accessing formal loans. If the MSE is registered, it might reflect a
compliance and more serious and organised business venture for a bank to have the
confidence to advance a loan.
The results of the study show significantly positive relationship between the book
keeping variable and formal finance in the working capital model but insignificant in
both start-up and future finance models. Although weak, the result suggests that book
keeping, which proxy for financial management capacity of MSE as well as a reflection
of transparency within the corporate finance literature (Greene et al., 2002), increases
the odds of being in the higher category or preferring a more formal financing.
The results further show that the interest sensitivity and negative perception of the
use of credit variables are significant with negative signs in the future financing
preference, but not in either start-up or ongoing finance models. Consistent with a
study by Clark (1994), these results suggest that MSEs with negative perception of
indebtedness or credit are less likely to seek external finance. Likewise, MSEs who are
sensitive to the high current interest rates in the credit market are less likely to demand
credit from a more formal financing source or more likely to use a less risky or low cost
financing such as internal or other bootstrap forms of financing. Notwithstanding the
fact that the pre-existing financing sources are not significant, the ever increasing
lending rates within mainstream formal financing system and even much higher
within the informal markets, suggest that MSEs’ future financing preference will be
more sensitive to high lending rates. They are more likely to prefer using their own
internal funds or at the very least bootstrap finance, if they are to seek external finance.
For Gender, females compared to males have a higher probability to be in a higher
category as the coefficient is statistically significant and positive albeit in the start-up
model only. This suggests that while at the business start-up, females are more likely
to have access to formal banking credit than their male counterparts, as the business is
up and running there is no major difference between the two as far as financing
preference is concerned. This is not only surprising, but also an indication of the fact
that many women are beginning to assert themselves in the credit market.
However, in terms of location, the results show that the rural location and suburban
variables compared to CBD have negative and significant relationship with higher
category of formal financing in the start-up and working capital models, respectively.
This suggests that MSEs located in the sparsely populated and low income communities
compared to those in the CBD are more probable to be in the lower category or use more
low cost financing at the start-up and as ongoing finance. The outcome seems to be
consistent with the POH and supports Carling and Lundberg (2005) argument that
information asymmetry problem increases with distance.
Unlike Abor (2008) and Green et al. (2002), the results on assets do not appear to
support the notion that asset structure of the firm is a significant determinant of debt or
external financing preference as the results in both working capital and future financing
models are not significant. However, when we used owner’s household assets such
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as building or land in the start-up model, the result is robustly significant and positive. Microenterprise
The plausible reason for these outcomes is that since most MSEs only work with simple financing
tools and movable assets which are of low collateral value and thus not acceptable by
banks, asset structure is unimportant factor in their financing preference. Nonetheless, if preference
microentrepreneur can post a title to a landed property or a building, especially at the
business start-up stage, it is more likely to get access to a formal finance. On the
enterprise size, the result is consistent with most previous studies and the study 101
hypothesis. The significant positive sign, albeit only in the working capital model, seems
to suggest that relatively bigger MSEs are more likely to be in the higher category of debt
financing.
In the case of ownership structure variable, however, the coefficient is negative and
statistically significant in the future financing preference model. This means that for every
1 percent point increase in profit that is not shared with anyone will result in OR of exp
(1.344) ¼ 3.88 increase in odds of being in a lower category of the ordinal financing outcome.
Consistent with the POH, the results suggests that as the level of interference/intrusion
increases from sole proprietorship, partnership to company, where profits have to be shared
among partners/equity holders, preference for formal finance increases. This finding
supports Hamilton and Fox (1998) and Gebru (2009) studies who generally conclude that
MSE owners operate without targeting an optimal debt-equity ratio, but rather reveal a
strong preference for those financing options that minimise intrusion into their business.
Robustness checks
In order to check the robustness of our results, we run two other alternative regressions,
using Logistic regression estimation model and ordinary least square method (OLS).
The dependent variable for these estimations was the same as the one used in the ordered
model, except that for the logistic model; we split the five financing preferences
(i.e. formal, semi-formal, informal, bootstrap and self-finance) into two binary choice
model as external versus internal finance (or like a debt-equity dichotomy). Thus, we
assigned the value one, if the financing preference can be categorised as external and
zero, if internal. The estimation results, as presented in the Tables VII and VIII, largely
support the findings discussed above and lend credence to the fact that MSEs’ financing
preference within the RFM conforms to the POH.
5. Conclusion
We have analyzed the determinants of MSE’s financing preference in the context of the
entire gamut of financing options available to a microentrepreneur within the RFM in
Ghana. We therefore categorised this range of financing choices broader than what is
usually the case in the capital structure literature into formal, semi-formal, informal,
bootstrap and internal finances. Basing this categorisation on the speed and ease of
access, the degree of formality, cost and risk as pertain to information asymmetry
problems; we tested whether there is an evidence of hierarchical ordering of financing
preference as predicted by POH among MSEs.
The analyses of the preliminary results reveal that at start-up, microentrepreneurs
have a strong preference for using personal, bootstrap and informal sources of finance,
and that the use of external debt finance, particularly formal bank loan, becomes more
common and most preferred as the business is up and running. This was somewhat
confirmed by the results of ordered regression estimation for our working capital
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JES
39,1
102
Table VII.
Determinants of
financing preference:
OLS and logistic
regressions results
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Logistic regression OLS regression
Future preference Working capital Future preference Working capital
B SE B SE B SE B SE
Asset structure 20.355 0.460 20.547 417 20.054 0.196 0.104 0.208
Awareness 0.245 0.668 0.912 0.615 0.159 0.247 0.324 0.262
Bank deposit account 0.002 0.734 1.372 * * 0.583 0.202 0.255 0.666 * * 0.271
Book keeping 0.340 0.733 0.078 0.600 0.232 0.254 0.471 * 0.270
Education attainment 0.149 0.226 20.114 0.194 0.174 * * 0.087 20.041 0.093
Established MSE 1.158 0.832 21.272 * * 0.662
Gender 0.048 0.771 20.332 0.616 20.006 0.268 20.086 0.284
Interest sensitivity 21.050 0.725 20.169 0.588 20.503 * * 0.256 0.034 0.272
MSE size 20.355 0.451 20.370 0.386 20.002 0.160 20.197 0.170
Mature 20.275 0.291 1.153 * * * 0.308
Negative perception 21.508 * * 0.681 0.130 0.589 20.693 * * 0.251 20.131 0.266
Net profit margin 20.197 0.838 20.463 0.875 20.204 0.293 0.180 0.311
New MSE 22.398 * * * 0.841 23.334 * * * 1.022 21.346 * * * 0.273 20.417 0.289
Ownership structure 23.990 2.576 2.784 1.759 20.853 0.618 0.520 0.655
Registration 0.435 0.693 0.827 0.611 0.147 256 0.532 * * .266
Rural location 20.132 0.791 0.689 0.685 20.123 0.288 0.419 0.305
Suburb location 0.464 0.738 20.823 0.665 20.217 0.293 0.353 0.311
Constant 6.239 * * 2.684 22.884 1.921 4.213 * * * 0.706 20.753 0.748
Durbin Watson 2.2 2.1
R-Square 0.204 0.305 0.446 0.369
22 log likelihood 72.267 93.606
Percentage correct 87 79.6
Observation 108 108 111 111
Note: Significant at: *10, * *5, and * * *1 percent levels
Microenterprise
Start-up capital
OLS regression Logistic regression financing
Variables B SE B SE preference
Age of the owner 20.013 0.263 0.180 0.613
Awareness 0.225 0.165 0.501 0.414
Bank deposit account 20.522 * * * 0.180 21.639 * * 0.471 103
Education attainment 0.163 * * 0.070 0.382 * * 0.196
Gender (Female ¼ 1) 0.485 * * 0.176 1.263 * * * 0.425
Household assets 0.556 * * * 0.166 1.096 * * 0.405
Interest sensitivity 20.073 0.171 20.181 0.431
Negative perception 20.247 0.183 20.434 0.444
Ownership structure 20.488 0.419 0.978 1.049
Registration 0.147 0.166 0.351 0.409
Rural location 0.231 0.205 0.974 * * 0.504
Skill training 20.221 0.165 20.333 0.404
Suburb location 0.070 0.200 0.762 0.499
Constant 0.628 1.074 23.916 2.677
Cox and Snell (R2) 0.232 0.204
22 log likelihood 164.895
Percentage correct 80 Table VIII.
Durbin Watson 1.91 Determinants of
No. of observation 157 157 start-up financing
preference: OLS and
Note: Significant at: *10, * *5, and * * *1 percent levels logistic regression results
and future financing preference models. We found that new enterprises were more
likely to prefer either internal or less costly and less risky financing such as bootstrap
or informal financing such as supplier’s credit or SUSU schemes. However, as the
enterprise gets established or matures its capacity to seek formal financing increases
and thus were more likely to prefer or being in a higher category of formal financing.
While this finding seems consistent with the POH as MSEs’ financing choice had
revealed to follow a hierarchical preference ordering – rising from internal, bootstrap,
informal, semi-formal to formal finance – we argue that this order is a consequence of
severe persistent constraints other than own preferences. This is because the study
found other microentrepreneur’s and MSE’s specific level socio-economic
characteristics such as owner’s education or financial literacy status, households
tangible assets, ownership structure, enterprise size as well sensitivity to high interest
rates in the credit market to be important determinants of either past (start-up),
present or future financing preference.
The conclusion drawn from this paper is that financing preference at all stages of the
MSE’s life were severely constrained. Thus, policy choice should not only be at supporting
further growth in established and mature enterprises, but also removing constraints that
start-up and newly established firms face in accessing finance particularly from
mainstream formal finance. The access to finance problem that is more binding on the
latter firms should be improved by creating an integrated rural financial system that is
more responsive to the varying financing needs of MSEs at all stages. This will mean that
future studies should focus on the relationship between the various financing preferences,
as well as strategies for creating linkage either directly or otherwise, between the
continuums of financing choices ranging from formal to the bootstrap finances.
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JES Note
39,1 1. Poverty in Ghana has remained a disproportionately rural phenomenon up until now. About
86 percent of the total population below the poverty line in Ghana lives in the rural area.
References
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teaching/G4075_Outline/node24.html
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Corresponding author
Eric Osei-Assibey can be contacted at: oassibey@yahoo.com
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