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Available  online  at  www.sciencedirect.com
ScienceDirect
Review of Development Finance 7 (2017) 18–28
Financing the growth of SMEs in Africa: What are the contraints to SME
financing within ECOWAS?
Peter Quartey a,∗, Ebo Turkson a, Joshua Y. Abor b, Abdul Malik Iddrisu a
a Department of Economics, University of Ghana, Ghana
b Business School, University of Ghana, Ghana
bstract
This study attempts to provide some understanding about SMEs’ access to finance within the West African sub-region with particular interest in
stablishing whether there are similarities and/or differences in the determinants of SMEs access to finance across countries in SSA. For robustness
ake, we developed both subjective and objective measures of access to finance. Using data from World Bank’s Enterprise Survey data set, we
xamine the determinants of access to finance both at the sub-regional level and at the country-level. We found that, generally, at the sub-regional
evel, access to finance is strongly determined by factors such as firm size, ownership, strength of legal rights, and depth of credit information,
rm’s export orientation and the experience of the top manager. However, we found important differences in the correlates of firms’ access to
nance at the country level. The findings of this study therefore have important implications for policy.
 2017 Africagrowth Institute. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license
http://creativecommons.org/licenses/by-nc-nd/4.0/).eywords: Finance; SMEs; Growth; Constraints; ECOWAS; Africa
ontents
1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2. Overview of SME financing in SSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
3. Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.1. Data and summary statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
5. Analysis and discussion of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.1. Sub-regional analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.2. Country level analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
5.3. Robustness checks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
6. Conclusions and policy implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
Appendix A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.  Introduction∗ Corresponding author. a
eer review under responsibility of Africagrowth Institute. (
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879-9337 © 2017 Africagrowth Institute. Production and hosting by Elsevier t
.V. This is an open access article under the CC BY-NC-ND license c
http://creativecommons.org/licenses/by-nc-nd/4.0/). p
ttp://dx.doi.org/10.1016/j.rdf.2017.03.001 pA great deal of the development studies literature has focused
ttention on the difficulties small and medium-sized enterprises
SMEs) face in their daily operations. Generally, there is the
iew that SMEs decry their level of access to finance and the
ost involved in obtaining such funds where available. Obstacles
ncluding those created by commercial or equity banks, institu-
ional imperfections and SMEs themselves have been the main
hallenges. While a lot has been done to address this reoccurring
henomenon (see Abor and Quartey, 2010; Aryeetey, 1998), the
roblem is still pervasive, requiring further scrutiny given the
velop
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w Generally, within SSA, official schemes (where finance is
v provided to SMEs by government and/or other international bod-
f ies) and informal sources of finance remain the main alternatives
N
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a 1 This measure illustrates the distance of an economy to the “frontier”. The
frontier represents the best performing economy observed with regards to the
p ease of accessing credit across all economies and years included since 2005. An
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P. Quartey et al. / Review of De
prawling fields of economic resources waiting to be discovered
nd tapped by SMEs.
The need to finance the development and growth of SMEs
n Sub-Saharan Africa (SSA) economies has been of concern
o many policy makers for two main reasons. First, for SSA
o be able to compete effectively in the increasingly global-
zed environment, its micro and small enterprises should grow
nd transform into thresholds where they will be able to adapt
fficient production techniques. Indeed, the SME sector within
SA has been referred to as the ‘Missing Middle’ in the context
f financial inclusion or access to financial (including bank-
ng) services. SMEs are by their nature unable to provide the
equired collateral that large firms have in obtaining formal
anking sector loans and at the same time too large to ben-
fit from micro-finance loans and forms of financial support
chemes (Abor and Quartey, 2010; Quartey, 2002; Aryeetey,
998). Secondly, because the small scale end of the manufactur-
ng sector is a cornerstone to employment creation, micro, and
mall enterprises within that sector should be seen to be growing
nto thresholds that assure employment generation (by the adop-
ion of labour intensive production), increased incomes/earnings
nd, ultimately, poverty reduction and economic prosperity. This
s achievable once the needed finance is available for investment
o propel growth and employment.
Against this background, this paper seeks to examine SME
ccess to financing within the West African sub-region. The
otivation for this study is to add to the growing literature on
hat determines SMEs access to finance in SSA, in the fol-
owing ways: first, in spite of Africa’s specificities regarding
ME financing, not many studies have been undertaken to ascer-
ain why SMEs within SSA are considered riskier to lend to
nd therefore more financially constrained. Besides, to a large
xtent, most of the available studies, in Africa and elsewhere,
ave employed subjective measures of access to finance. This
tudy departs sharply from existing studies by employing an
bjective measure of firms’ access to finance to investigate the
eterminants of SMEs’ access to finance in SSA. We believe this
easure would yield more meaningful results than the subjective
easures.
.  Overview  of  SME  financing  in  SSA
The operations of small and medium-scale enterprises
SMEs) occupy an admirable position in the economic landscape
f most economies in the world, especially for developing coun-
ries. It is estimated that more than 95% of enterprises across the
orld are SMEs and they account for approximately 60% of pri-
ate sector employment (Ayyagari et al., 2011). SMEs account
or a greater share of businesses in South Africa, Ghana, and
igeria (Abor and Quartey, 2010; Gbandi and Amissah, 2014)
nd their contribution to GDP, in Ghana for example, stood at
bout 49% in 2012 (PWC, 2013).
The SME sector in West Africa has been exploding over the
ast three decades mainly as a result of the few formal avenues
e
or pursuing interest-bearing investment options. Most coun- r
ries within the sub-region have shallow stock markets, whilst o
t the same time interest rates have not been able to catch up dment Finance 7 (2017) 18–28 19
ith rising inflation. Under these circumstances entrepreneur-
hip has been the attraction for investing excess money holdings.
nfortunately the SME sector in West Africa is a mixture of self-
mployment outlets and dynamic enterprises that are involved
n an array of activities mainly concentrated in urban areas. In
ddition, they lack access to the financial resources needed to
xpand, grow and transform into higher size thresholds. Ques-
ions have been posed as to what might be the main reasons for
his.
According to Collier (2009) the lack of access to finance by
MEs in Africa is unfortunately as a result of two high risk char-
cteristics. First, the provision of finance for Africa is generally
ated as riskier than for other regions. Second, the provision
f finance for small firms is globally rated as riskier than for
arge firms. The ensuing sections discuss some stylized facts on
MEs within ECOWAS regarding access to finance and explore
ome of the alternatives that have been deployed within the sub-
egion by various countries to relieve SMEs of their financing
onstraints. The World Bank’s distance to 1 the frontier index
f 2014 shows that with the exception of Ghana and Nigeria,
ll other West African countries were at best 60 percentage
oints away from New Zealand (the best performing country
n the world). While Ghana and Nigeria were about 35 and 40
ercentage points away from the frontier respectively, Ghana’s
ndex was higher than the average for OECD high income and
urope and Central Asian countries (World Bank WDI, 2015).
The lack of access to credit by SMEs (who make up over
0% of the private sector) in the developing world is clearly
onfirmed from data on bank and domestic credit to the private
ector in West African countries, and among income groups and
ub-regions of the world (as shown in Table 1) between 2000
nd 2014. With the exception of high income countries, banks
ontribute over 90% of domestic credit to the private sector over
he period 2000–2014. The proportion of domestic credit to the
rivate sector contributed by banks was even higher for West
frican countries than other countries. Noticeably in almost all
he countries within the sub-region there has been an increase
n both domestic and bank credit to the private sector (as %
f GDP) over the period, with Cape Verde’s ratio more than
oubling and Guinea remaining stable at very low levels of less
han 6% of GDP. These stylized facts on credit to SMEs in West
frica necessitated the need for a study of this sort to unpack the
actors that explain why SMEs within West Africa have difficulty
n accessing finance. To do this, we begin by providing anecdotal
ccount of the various alternatives to SME financing within the
COWAS sub-region.conomy’s distance to frontier is indicated on a scale from 0 to 100, where 0
epresents the lowest performance and 100 the frontier. The measure on the ease
f getting credit is based on the sum of the strength of legal rights index and the
epth of credit information index.
20 P. Quartey et al. / Review of Development Finance 7 (2017) 18–28
Table 1
Bank and domestic credit to the private sector (ECOWAS, income and country groups).
Country/region/group Bank credit to private sector (% of GDP) Domestic credit to private sector (% of GDP)
2000 2005 2010 2014 2000 2005 2010 2014
Benin 11.6 16.3 23.3 25.1 11.6 16.3 23.3 25.1
Burkina Faso 11.6 16.4 17.3 28.0 11.6 16.5 17.3 28.0
Cape Verde 38.2 37.7 61.0 62.4 40.1 39.1 61.9 62.9
Cote d’Ivoire 14.9 13.0 16.5 20.3 15.1 13.2 16.6 20.3
Gambia, The 6.5 9.5 15.2 13.1 6.7 9.6 15.4 13.5
Ghana 13.8 15.4 14.6 15.8 14.0 15.5 15.3 16.8
Guinea 3.9 4.4 5.7 .. 4.0 5.7 5.7 ..
Guinea-Bissau 4.6 1.1 6.1 12.1 4.6 1.1 6.1 12.1
Liberia 3.1 6.3 14.4 19.5 3.3 6.7 14.8 19.6
Nigeria 12.3 13.2 15.4 14.5 12.4 13.2 15.4 14.6
Senegal 18.6 23.2 25.6 33.3 18.7 23.2 25.7 33.3
Sierra Leone 2.0 3.3 7.8 4.8 2.1 3.4 7.8 4.8
Togo 16.0 17.5 22.8 34.1 16.0 17.5 22.8 34.1
Niger 4.8 6.8 12.3 14.2 4.8 6.8 12.3 14.2
Mali 16.4 17.2 18.0 24.4 16.5 17.2 18.0 24.4
Europe & Central Asia .. 96.0 106.3 93.6 .. 96.0 106.6 93.8
Latin America & Caribbean 24.7 24.0 36.7 46.7 25.7 25.5 40.0 52.0
Middle East & North Africa 38.6 42.3 50.7 45.1 38.8 42.8 51.0 45.2
S
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ub-Saharan Africa 33.0 33.8 32.4 29.1 
ource: World Bank WDI (2015).
o banks and non-bank financial institutions financing of SMEs.
he official schemes are often set up with the primary motive of
ncreasing the flow of finance and credit to SMEs to enable them
ncrease their operational capacities, productivity and compet-
tiveness in the local and international markets. With regards
o informal sources of finance, many SMEs in SSA (especially
hose in Nigeria and Ghana) have made use of sources such
s owner’s savings, money lenders, friends and relatives, credit
nd savings associations, susu, etc. to finance their growth and
ransformation. See Appendix Table A1 for the key players in
he SME finance industry in Ghana.
For most economies in SSA, there are comprehensive SME
nance programmes that have been in place since the 1960s.
.  Literature
As already noted, the development of SMEs is often under-
ined by a number of factors, including inadequate finance, lack
f managerial skills, equipment and technology, poor access
o capital markets, among others (Steel and Webster, 1991;
ryeetey et al., 1994; Gockel and Akoena, 2002). A cursory
ook at the literature on SME development reveals that inade-
uacy of funds significantly constrains SME development (see,
ryeetey, 1994; World Bank, 1994; Parker et al., 1995; Arthur,
003; Mensah, 2004; Deakins et al., 2008; Okpara, 2011).
A survey of the extant literature in Africa particularly reveals
he existence of a finance gap in the SME sector (see, for
nstance, Sowa et al., 1992, for Ghana, and Daniels and Ngwira,
993, for Malawi). Aryeetey et al. (1994), for example, reported
hat 38% of the SMEs surveyed mention credit as a constraint.
2
t is also established that most SME loan applications in Africa
o
re not granted (see Osei et al., 1993; Dawson, 1993; and Bani,
H
003). In this vein, Aryeetey (1998) observed that only half of57.3 62.1 55.4 29.2
MEs’ applications for formal finance such as bank loans have
ny chance of success, and about two-thirds of loan applica-
ions by microenterprises were likely to be unsuccessful while
igsten et al. (2000) observed that about 90% of small firms
re denied credit from the formal financial sector due to their
nability to fulfill conditions such as collateral security. Consis-
ent with this, Osei et al.’s (1993) showed that about 95% of
he SMEs depend solely on personal resources and loans from
riends and relatives. Using data on firms in six African coun-
ries, Bigsten et al. (2003) found that among those firms which
pplied for a loan, small firms had substantially worse chances of
etting a loan from banks. Berg and Fuchs (2013) found that the
hare of SME lending in the overall loan portfolios of banks in
ve Sub-Saharan African countries varies between 5 and 20%.
Hansen et al. (2012) showed that about 39.6%, 18.3% and
.5% of small firms in Ghana, Kenya and South Africa respec-
ively cited access to finance as a barrier to SME growth.
untchev et al. (2012) used data from the World Bank’s
nterprise Survey which covered 13,685 companies across 38
ub-Saharan countries to examine SMEs access to finance. The
uthors observed a strong correlation between the size of a busi-
ess and their access to credit, where smaller businesses are more
ikely to be credit ‘constrained’ and thus depicting the difficul-
ies faced by small business entrepreneurs in securing loans from
ommercial sources. On the main sources of external finance for
MEs, Kuntchev et al. (2012) found that of the small businesses
n sub-Saharan Africa that obtained external financing, 6.3%
ook the form of equity, 48.5% was formal external debt, 17.4%
emi-formal financing and 27.8% informal financing. Gbandi
nd Amissah (2014) maintained that finance contributes about
5% to the success of SMEs in Nigeria and that more than 70%
f funds available to SMEs are from the informal finance sector.
aselip et al. (2014) investigated the financing of energy SMEs
velop
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P. Quartey et al. / Review of De
n Ghana and Senegal. They observed that the lack of access
o affordable finance is the predominant, persistent, barrier to
stablishing and scaling up a commercially viable energy SME
ector. Beck and Cull’s (2014) study concluded that compared
o other regions, enterprises in SSA are less likely to have a loan
han those in other developing regions of the world. However,
bor and Biekpe (2006) established that, most SME operators
ossess no knowledge of, and do not make use of the various
nancing initiatives available to the SME sector in Ghana. There
s also the perception that most of the schemes are difficult to
ccess and hence, alternative sources of financing available to
he SME sector remain largely untapped — a result that can be
artly attributed to the stringent eligibility criteria for accessing
hese funds (Abor and Biekpe, 2006).
With regards to the drivers of SMEs financial constraints, a
umber of studies have investigated the causes of SSA SMEs’
nability to acquire adequate external finance (Buatsi, 2002;
acerdoti, 2005; Ghandi and Amissah, 2014). For instance,
acerdoti (2005) noted that the inability of SMEs in SSA to pro-
ide adequate financial statements and quality collateral reduce
heir chances of accessing finance from formal financial institu-
ions. Also, the absence of credible credit reference bureaus in
ost countries in SSA and its attendant effect of interest rates
ndermine the chances of SMEs gaining access to finance (Bass
nd Schrooten, 2005). Using a sample data of over 10,000 firms
rom 80 countries, Beck et al. (2003) found that the size, age
nd ownership of firms are the factors that explain and predict
he obstacles that SMEs face. However, at the country level,
nstitutional development is the only country characteristic that
xplains the cross-country variations in the financial difficul-
ies faced by firms (Beck et al., 2003). Silva and Carreira (2010)
evealed that the size of a firm and its cash flow are highly signif-
cant in influencing the firm’s financial constraints whereas the
ge of the firm had no significant impact. A cross-country study
y Schiffer and Weder (2001) revealed that the size of a firm is
egatively related to the risk it poses to the lender. Beck et al.
2006), illustrate that larger, older firms and foreign-owned firms
njoy increased access to finance. In addition, Beck et al. (2004)
how that, in terms of access to external finance, small firms
enefit disproportionally from higher levels of property rights
rotection (see also Demirgüç-Kunt and Maksimovic, 1998).
nvestigating creditor protection, Love and Mylenko (2003) find
hat the presence of private credit registries in a country is asso-
iated with lower financing constraints and a higher share of
ank financing.
Important empirical insights are also provided by the litera-
ure studying finance as a barrier to firm entry. Bertrand et al.
2007) suggest that the banking reform in France during the
980s influenced product market competitiveness by increas-
ng entry and exit of firms and lowering industry concentration,
specially in bank-dependent industries. Guiso et al. (2004) anal-
se variations in financial development across Italian provinces
nd find that financial development enhances entrepreneurship.
etorelli and Strahan (2004) show that increased competition w
mong banks in the United States helped the creation of new a
rms due to enhanced access to finance. Similarly, Black and m
trahan (2002) employ US data and find that that entry of new Ament Finance 7 (2017) 18–28 21
rms increased following deregulation. Overall, these studies
rovide evidence that financial development – through bank-
ng competition – increases credit availability and enhances
ntry and efficiency in the corporate sector, thus confirming the
mportance of access to finance.
Thus, from the above discussions, access to credit remains
 challenge in the respective African countries reviewed. How-
ver, unpacking the reasons why SMEs are unable to access
redit over time and comparatively across selected African
ountries will provide enormous policy lessons for sustainable
evelopment and poverty reduction and this forms the focus of
he next section.
.  Methodology
Within the financing constraints literature firm’s access to
nance has normally been modeled based on financial state-
ents. For SMEs in developing countries this is impossible
ecause financial data is limited. This is so because SMEs are not
equired to file detailed financial reports because most of these
MEs do not raise equity or debt from public markets. What
as been readily available and has been mostly used are aggre-
ate measures of financial development. The problem with such
ggregate measures is that they do not provide the distribution
f financing among such firms. SMEs will have differing access
o finance and therefore the use of aggregate measures of finan-
ial depth masks the heterogeneity of firms’ access to finance.
s noted by Claessens and Tzioumis (2006) ‘the only way to
nvestigate firms’ problems accessing finance is through tailored
rm-level surveys directly addressing the issue of financing con-
traints’ (pg. 6). This motivated our choice of the World Bank
nterprises survey data for our study.
In order to achieve our research objective, we estimate func-
ionally the following:
Firm  access  to  crediti,k =  β0 +  β1firm  characteristicsi,k
+  β2country  characteristicsk +  εi,k (1)
Eq. (1) shows a functional relationship between firm’s access
o credit (on one hand) and, firm characteristics and country char-
cteristics (on the other). In this equation, the firm characteristics
s represented in a vector form comprising of firm’s attributes
ike age of the firm, size of the firm, gender of top manager, and
xperience of top manager as reported by firm i in country k. The
ountry characteristics variable also represents a set of dummies
hat controls for country-specific factors that are unobservable
nd may influence a firm’s access to credit. The dependent vari-
ble (firm access to credit) represents both specific and general
roblems faced by firm i in country k.
In line with the objective to investigate the determinants of
rm’s access to finance, we have used four measures of access to
nance from the enterprise survey data. For robustness checks,
e have developed both subjective and objective measures of
ccess to finance. Access to finance 1, 2 and 3 are subjective
easures whilst access to finance 4 is an objective measure.
ccess to finance 1 is a binary variable, which equals one if a
22 P. Quartey et al. / Review of Development Finance 7 (2017) 18–28
s size
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Chart 1. Firm
ource: World Bank (2015).
rm indicates that access to finance is an obstacle and zero if it
s not an obstacle. Access to finance 2 is derived from the top
hree obstacles that the firms face (ranked in order of severity).
ur measure of access to finance 2 is a relative measure taking
 value of 1 if the firm indicates access to finance as the third
bstacle, 2 if it is the second and 3 if it is the number one obstacle
aced by the firm. A firm that does not indicate access to finance
s an obstacle has a zero value. Access to finance 3 is a relative
easure of the severity of access to finance as an obstacle to the
rm. The severity index takes a value zero if it is not an obstacle;
 if minor, 2 if moderate, 3 if major and 4 if very severe. These
hree measures would be used for the robustness checks analysis.
With regards to access to finance 4, we attempt to obtain an
bjective measure of a firm’s access to finance which we derived
rom the shares of internal and external financial resources of
orking capital. It takes a value of zero if the firm uses internal
esources to finance at least 75% of its working capital, 1 if
nternal resources contribute between at least 50% but less than
5% of working capital, 2 if between at least 25% and below
0% and 4 if the internal resources are less than 25% of working
apital. According to the pecking order theory, because the cost
f financing increases with asymmetric information, when it
omes to methods of raising financial resources for working
apital, firms prefer financing that comes from internal funds,
efore resorting to external finance in the form of debt, and
ssuing new equity in that 2 order .
Accordingly, the model containing finance 1 as the depen-
ent variable is estimated using the binary probit model while the
odels containing finance 2 and 3 are estimated using the multi-
omial probit model. The last model which captures finance c 4
s the dependent is estimated using the ordered probit C model.
s
t
2 Source: Boundless (2014). “Pecking Order Consideration.” Boundless V
inance. Boundless, 30 Dec. 2014. Retrieved 16 Apr. 2015 from
ttps://www.boundless.com/finance/textbooks/192/capital-structure- m
3/capital-structure-considerations-102/pecking-order-consideration-440- i
966/. o distribution.
lso, the estimations are conducted both at the sub-regional
evel and at the country-level. Finally, we would discuss the
esults from the estimations that made use of access to finance 4
which is an objective measure of access to finance) as the depen-
ent variable. The other three subjective measures (access to
nance 1, 2 and 3) will be used for robustness checks/sensitivity
nalysis.
.1.  Data  and  summary  statistics
The Enterprise Surveys are conducted by the World Bank
nd its partners across all geographic regions and cover small,
edium, and large companies. The data is obtained from several
ross-country surveys across several years with an ‘access to
nance’ component. It also contains some questions regarding
ources of funds for investments and collateral requirements that
eveal substantial variation of financing practices across firms
nd countries.
Chart 1 shows the size distribution of firms in each country
hat constitute our sample. Generally there is a preponderance
f small firms in all the countries within the sub region and
elatively fewer large firms. Cape Verde, Burkina Faso and Togo
eported the highest percentage of medium and large firms of
9.3%, 43.4% and 42.6% respectively, while Guinea reported
he least of 12.1%.
Our study is based at the sub-regional level all the 15 member
ountries of ECOWAS and at the country level on 6 member
ountries including Ghana, Mali, Senegal, Gambia, Guinea, and
ote D’Ivoire. The choice of these six countries is based on data
uitability and the 2014 ranking of “getting credit” distance to
he frontier index of the countries in West Africa in 2014.
Strikingly, unlike most countries within the sub-region Cape
erde has the least percentage of firms and were mostly
icro/small-sized firms (50.7%), with Guinea Bissau account-
ng for the most of about 88%. Table 2 reports summary statistics
n a number of indicators relating to access to credit by sam-
P. Quartey et al. / Review of Development Finance 7 (2017) 18–28 23
Table 2
Summary statistics on measures of access to credit within ECOWAS.
Country Collateral as % Loans req. % of firms that % that use Bank % access to finance
of loan collateral (%) Bank for WC a major obstacle
B- Fasso 173.9 90.0 98.0 34.7 72.0
Benin 293.4 89.3 96.7 39.4 60.8
Cape Verde 157.4 93.2 92.2 45.5 39.2
Cote D’Ivoire 59.1 74.5 70.8 8.2 70.3
Gambia 192.2 86.2 72.4 14.4 40.8
Ghana 178.3 71.6 90.3 20.7 63.8
Guinea 95.0 53.8 53.4 2.7 60.1
Guinea Bissau 63.8 80.0 60.7 1.3 73.6
Liberia 53.3 73.3 66.4 17.3 35.4
Mali 176.1 63.5 79.5 13.6 55.8
Niger 200.9 83.1 96.6 35.9 51.0
Nigeria 129.0 80.2 – 4.9 56.0
Senegal 122.0 88.2 83.6 9.1 48.2
S
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ierra Leone 51.2 89.7 
ogo 265.9 80.0 
led firms operating within the various countries within the sub
egion.
.  Analysis  and  discussion  of  results
In this section, we discuss the determinants of firms’ access
o finance using our objective measure of access to finance at the
ub-regional level as well as at the country level. For robustness
ake, we juxtapose these findings with additional estimations
hat employ some subjective measures of access to finance.
.1.  Sub-regional  analysis
The results shown in Table A2 column 3 relate to access to
nance 4 after controlling for sector of activity and country spe-
ific effects. Generally, access to finance is significantly driven
y factors such as firm age, firm size, ownership, experience
f the top manager (at 10% level of significance), strength of
egal rights, depth of credit information, firm performance (at
0% level of significance) and sector of activity. Table 3 shows
stimates of the marginal effects after controlling for country
haracteristics. We find that firm’s age significantly increases
rm’s access to finance from external sources and this buttresses
he fact that older firms are less likely to have difficulties in
ccessing finance compared to newer firms.
Specifically, an increase in the age of a firm improves the
ikelihood that the firm would devote less than 75%3 of its inter-
al resources for working capital, indicating improved access
o external sources of financing working capital. Similarly, it is
videnced that larger firms are significantly more likely to gain
ccess to external finance relative to smaller firms in West Africa.
or instance, relative to smaller firms, larger firms are about U
4.0%, 17.0% and 16.0% significantly more likely to devote a
fi
e
3 This statement should be interpreted with reference to the base group. The i 
ase group is 0, representing the case when a firm uses at least t 75% of its internal
esources for working capital. w68.7 28.7 37.3
96.1 21.1 52.3
etween 50–75%, 25–50%, and less than 25% respectively of
heir internal resources for working capital.
This shows that size matters for access to external finance
y SMEs in West Africa. This is in line with the findings that
arger firms are more likely to gain access to finance than smaller
rms (see Beck et al., 2006; Buatsi, 2002; Bigsten et al., 2003;
untchev et al., 2012). Large firms do not face challenges in
ccessing external finance because, unlike small firms, large
rms have adequate collateral, able to prove creditworthiness,
dequate credit history and a developed bank-borrower relation-
hip necessary for securing external funding. Further, relative to
omestic ownership, foreign ownership significantly improves
rm’s ability to obtain external financing with foreign firms hav-
ng about 4.0% more likelihood to use between 50 and 75% of
heir internal resources as working capital relative to 75% of
heir internal resources.
Additionally, improvement in the strength of legal rights
ncreases firm’s access to external financing, while improve-
ent in the depth of credit information leads to about 0.4%
eduction in the likelihood of firms to use 50–75% of their inter-
al resources for working capital relative to using at least 75 of
heir internal resources for working capital (see, Table 3). This
mplies that, improvement in the depth of credit information
onstrains firm’s ability to access external sources of financing
nd so firms are forced to rely heavily on their internal resources
o finance working capital.
.2.  Country  level  analysis
In order to unmask important differences in the influential
actors across countries, country specific analysis is conducted.
sing access to finance 4 as a dependent variable, we find that
ccess to finance is significantly influenced by factors such as
rm age, firm size, gender of top manager, ownership, experi-
nce of top manager, formality, and firm performance at least
n one of the estimations as shown in Table A2. Specifically,
he determinants of access to finance varies across countries:
hereas in Ghana, access to finance is significantly influenced
24 P. Quartey et al. / Review of Development Finance 7 (2017) 18–28
Table 3
Pooled ordered logit results (marginal effects): access to finance 4 — ECOWAS.
Variables Dependent variable: access to finance 4
Outcome (1) Outcome (2) Outcome (3)
Firm age 0.003** 0.002** 0.002**  
(0.000) (0.000) (0.000)
Firm size 0.024*** 0.017*** 0.016***
(0.005) (0.004) (0.003)
Gender of top mgr. (fe) 0.016 0.012 0.012
(0.017) (0.014) (0.014)
Foreign ownership *** *** *** 0.040 0.033 0.034
(0.009) (0.009) (0.010)
Experience of top manager 0.000 0.000 0.000
(0.000) (0.000) (0.000)
Formality status 0.023 0.014 0.013
(0.083) (0.047) (0.041)
Exporter 0.002 0.002 0.002
(0.010) (0.007) (0.007)
Strength of legal rights index 0.013*** 0.009*** 0.008***
(0.002) (0.001) (0.001)
Depth of credit information index −0.004* −0.002* −0.002*
(0.002) (0.001) (0.001)
Firm performance −0.000 −0.000 −0.000
(0.000) (0.000) (0.000)
Sector (service) −0.006 −0.004 −0.004
(0.006) (0.004) (0.004)
Control for country effect Yes Yes Yes
Observations 5065 5065 5065
D esourc
b
b a
s p
m m
a c
e p
t c
fi f
c t
c
2 b
t v
t a
i t
u l
a m
T
fi fi
f r
s o
a t
i fi
u f
b r
r t
t l
c
fi
ependent variable: access to finance 4 = 0 if firm uses at least 75% internal r
elow 25%. Robust standard errors in parentheses.
y factors such as firm size and formality, access to finance is
ignificantly driven by; firm size, gender of top manager, and for-
ality in Mali; ownership, experience of top manager, formality
nd firm performance in Senegal; and firm age, ownership, and
xperience of manager in Gambia. These variations in the impor-
ance of the various covariates in explaining firms’ access to
nance across the countries reflects, in particular, country spe-
ific constraints in firms’ access to finance.
Also, consistent with the work of Beck et al., 2006; Buatsi,
002; Bigsten et al., 2003; Kuntchev et al., 2012, we observe
hat the older firms are significantly more likely to gain access
o finance compared to newer firms in Gambia. For instance, an
ncrease in the age of a firm improves the likelihood of the firm to
se less than 25% of its internal resources for working capital by
bout 0.5% relative to using at least 75% of its internal resources.
his may mean that older firms have wider networks including
nancial institutions such as banks and suppliers, making it easy
or them to access credit for their operations. Also, firm size is
ignificantly positively associated with the probability of having
ccess to finance in Ghana and Mali. In Mali, for example, an
ncrease in the size of a firm raises the probability of that firm
sing less than 25% of its internal resources for working capital
y a little over 3% relative to using at least 75% of its internal
esources for working capital (see Table 4). The importance of
he age and size variables in our model of access to finance is
onsistent with the results obtained by Beck et al. (2003). f
Strikingly, gender of top manager significantly determines r
rm’s access to finance but only in Mali. This result is strikinges for working capital; = 1 if between 50–75%; = 2 if between 25–50%; = 3 if
s it contradicts the conventional notion that women dispro-
ortionately face barriers to financial access compared to their
ale counterparts, thus requiring us to dig into what peculiar
haracteristics of the Malian economy might help explain this
henomenon. We explain this conundrum as follows: first, this
ould be due to government policy measures to tailor its support
or SMEs more in favor of women-owned businesses in order
o meet the Millennium Development Goals (MDGs); second,
ompared to men, Malian women are largely engaged in small
usinesses that do not require huge investments, thus providing
ery little incentive for them to solicit for external finance such
s borrowing from banks and other financial institution. Given
hese, it would be realized that female top managers are less
ikely to face difficulties in accessing finance compared to their
ale counterparts.
In addition, relative to domestic-owned firms, foreign-owned
rms are more likely to use less than 75% of their internal
esources for working capital compared to using at least 75%
f their internal resources for working capital in Senegal. Fur-
her, an increase in the experience of a top manager increases a
rm’s probability of using less than 75% of its internal resources
or working capital relative to using at least 75% of its internal
esources for working capital in Senegal. In particular, we find
hat, an increase in the experience of top manager raises the like-
ihood of a firm to use less than 25% of its internal resources
or working capital relative to using at least 75% of its internal
esources for working capital in Senegal by about 0.2% (Table 4).
P. Quartey et al. / Review of Development Finance 7 (2017) 18–28 25
Table 4
Ordered logit country level regressions (marginal effects).
Regressors Dependent variable: access to finance 4
Ghana Mali Senegal Gambia
Outcome (1) Firm age 0.000 0.001 −0.001 −0.002
(0.001) (0.002) (0.001) (0.001)
Firm size 0.044*** 0.115*** 0.008 −0.016
(0.014) (0.030) (0.023) (0.019)
Gender of top mgr. (fe) −0.022 ** 0.172
(0.035) (0.068)
Foreign ownership *** −0.017 0.030 0.082 −0.057
(0.034) (0.067) (0.016) (0.041)
Experience of top manager 0.000 −0.001 0.003* 0.002
(0.001) (0.002) (0.001) (0.002)
Formality status −0.067*** 0.196*** −0.038
(0.009) (0.020) (0.098)
Firm performance 0.000 0.001 * 0.002 −0.001
(0.000) (0.001) (0.001) (0.001)
Outcome (2) Age 0.000 0.000 * −0.000 0.005
(0.001) (0.000) (0.001) (0.003)
Firm size 0.028*** 0.015** 0.005 0.043
(0.009) (0.006) (0.013) (0.044)
Gender of top mgr. (fe) −0.013 0.035
(0.019) (0.026)
Foreign ownership −0.010 0.004 0.077** 0.087**
(0.019) (0.010) (0.033) (0.042)
Experience of top manager 0.000 −0.000 0.001* −0.005
(0.001) (0.000) (0.001) (0.003)
Formality status −0.101*** 0.021*** −0.109***
(0.018) (0.006) (0.023)
Firm performance 0.000 0.000 0.001* 0.002
(0.000) (0.000) (0.001) (0.001)
Outcome (3) Age 0.000 0.000 −0.001 0.005*
(0.001) (0.001) (0.001) (0.003)
Firm size 0.021***  0.033*** 0.006 0.045
(0.007) (0.010) (0.017) (0.045)
Gender of top mgr. (fe) −0.010 0.086 0.124*       
(0.014) (0.067) (0.070)
Foreign ownership −0.008 0.009 0.139*
(0.014) (0.022) (0.082)
Experience of top manager 0.000 −0.000 0.002*      −0.005*
(0.001) (0.001) (0.001) (0.003)
Formality status −0.102*** 0.041*** −0.334
(0.022) (0.009) (0.288)
Firm performance 0.000 0.000 0.002*  0.002
(0.000) (0.000) (0.001) (0.001)
Controlled for export status and sector of activity Yes Yes Yes Yes
Observations 894 509 407 177
Dependent variable: access to finance 4 = 0 if firm uses at least 75% internal resources for WC; = 1 if between 50–75%; = 2 if between 25–50%; = 3 if below 25%.
Robust standard errors in parentheses.
*** p < 0.01.
** p < 0.05.
r
l fi
w a
r fi
s
I
s
* p < 0.1.
Relative to non-formal firms, formal firms are significantly
ess likely to use less than 75% of their internal resources for
orking capital as against using at least 75% of their internal
esources for working capital in Ghana, indicating a relative
carcity of external financing options available to formal firms. n
n contrast, however, it is realized that, in Mali, formal firms are r
ignificantly more likely to devote less than 75% of their internal eesources as working capital compared to informal firms. This
nding lends credence to the fact that formality is often seen as
n important criterion to accessing external finance since formal
rms are to a large extent able to meet loan requirements than
on-formal firms. Firm performance is significantly positively
elated to access to external finance but only in Senegal. For
xample, an improvement in the performance of a firm raises
2 velop
( T
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7 fi
s 1
s
5 a
t
t f
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s s
s e
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s i
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v a
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s
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w a
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W s
i w
S e
t
e Appendix  A
s
a
c
fl
v
c
6 P. Quartey et al. / Review of De
about 0.2%) the likelihood of the firm to devote less than 25%
f its internal resources for working capital compared to using
5% and above of its internal resources for working capital as
hown in Table 4.
.3.  Robustness  checks
As a robustness check we estimated three different models of
he logistic regressions to predict the likelihood of the regressors
xplaining a firm’s access to finance. The three subjective mea-
ures used are access to finance 1, 2 and 3. We did this both for the
ub-regional level and the country level. 4 The results obtained ,
argely confirm what we found in our core model. At the sub-
egional level, therefore, access to finance is driven by factors
uch as larger firms, firms owned by foreigners and strength of
egal rights while variables such as age, firm size, ownership,
xperience of the top manager, depth of credit information, firm
erformance and sector of activity attained statistical signifi-
ance, at least in one of the equations, at the country level. Thus,
hereas the sub-regional level analysis obscures some important
ifferences in terms of the influence of the various explanatory
ariables on access to finance, the county level analysis delin-
ates these differential effects. For instance, it is only in Ghana
hat the depth of credit information is an important factor linked
o firm’s access to finance. This suggests that as lenders become
ore aware of the credit history of potential borrowers, firms
ith bad credit history find it difficult to obtain external finance,
n particular, bank loans.
.  Conclusions  and  policy  implications
The overall objective of this study is to examine SMEs access
o finance within the West African sub-region with particular
nterest in establishing whether there are similarities and/or dif-
erences in the determinants of SMEs’ access to finance across
ountries in SSA. Specifically, the study pursued the follow-
ng research goals: (1) explore alternatives to SME financing
ithin the ECOWAS sub-region; (2) predict SME access to
redit using firm specific characteristics and other controls for
elected countries in the ECOWAS sub-region.
In order to achieve the first research objective, we conducted
n extensive review of financing options available to SMEs in
est Africa. In most West African countries, long-term financ-
ng in terms of equity capital is virtually non-existent for the
ME sector. This makes debt financing the dominant channel
hrough which SMEs can access funds. Unfortunately, how-
ver, the SME sector in most countries within the ECOWAS
ub-region faces serious constraints in accessing formal finance
nd this problem is connected to factors such as SMEs’ lack of
ollateral, difficulties in providing creditworthiness, small cash
ows, inadequate credit history, high risk premiums, underde-
eloped bank-borrower relationships and high transaction costs.
4 Due to space limitations, we do not report these estimations. However, they
an be made available upon request.ment Finance 7 (2017) 18–28
o fill the finance gap of the SME sector, most economies,
specially those in Africa have developed comprehensive SME
nance programmes, many of which can be traced back to the
960s. Governments, over the years, have used interest rate sub-
idies, directed lending, guarantee funds and a variety of other
pproaches to fuel the financing needs of the SME sector but all
o no avail.
In line with the second objective of the study, we have used
our measures of access to finance from the World Bank’s Enter-
rise Survey data set. For robustness sake, we developed both
ubjective and objective measures of access to finance. Also, the
xamination of the determinants of access to finance is estimated
t the sub-regional level as well as at the country-level. Gener-
lly, at the sub-regional level, it is clear that access to finance
s strongly determined by factors such as firm size, ownership,
trength of legal rights, depth of credit information, firm’s export
rientation and the experience of the top manager. Further, it is
bserved that the use of different measures of access to finance,
n particular, the use of subjective vis a vis objective measures
f access to finance did not matter much for the importance of
he correlates of access to finance in SSA. The results obtained
t the country level, however, bring to bear some important dif-
erences in what explains firms’ access to finance at the country
evel.
The findings of this study therefore have important implica-
ions for policy: First, since formality strongly influences SMEs’
ccess to credit, the study suggests that SMEs in SSA must take
iant steps towards formalisation in order to increase their poten-
ial for accessing formal credits. Second, given that firm size and
epth of credit information are key to accessing credit, the study
ecommends that SMEs should join Business Associations and
eek group credit schemes. Related to the above, these associ-
tions should also promote credit information among potential
orrowers as a way of reducing information asymmetry in the
redit market. This would go a long way to boost creditor con-
dence as bad borrowers will be clearly identified and refused
redit. Third, the findings that ownership matters for the prob-
bility of accessing credit imply that African SMEs can learn
rom their foreign counterparts in order to position themselves
or institutional borrowing. Finally, past accounts of government
upported credit schemes to the SMEs sector indicates a seri-
us lack of efficiency and transparency in the operation of such
chemes. As a remedy, we maintain that future governments as
ell as international donor interventions must be administered
fficiently and transparently.
P. Quartey et al. / Review of Development Finance 7 (2017) 18–28 27
Table A1
Pooled ordered logit results: ECOWAS sub-region.
Regressors Dependent variable:
Access to finance 3 (severity index) Access to finance 4 (pecking order)
Firm age −0.00685** 0.000604*  
(0.00324) (0.000327)
Firm size −0.418*** 0.187*** 
(0.0505) (0.0535)
Gender of top manager (female = 1) −0.0190 0.0569
(0.130) (0.190)
Ownership (foreign = 1) −0.514*** 0.487***
(0.0979) (0.120)
Experience of top manager 0.00438 0.00642*
(0.00369) (0.00351)
Formality status −0.743 0.0172
(0.493) (0.745)
Exporter 0.0320 0.0467
(0.0999) (0.103)
Strength of legal rights *** index 0.0130 −0.286
(0.0559) (0.0840)
Depth of credit information index −0.0121 0.0130
(0.0198) (0.0222)
Firm performance −6.16e-05 0.00227*
(0.00107) (0.00138)
Sector of activity (services) −0.228*** −0.121** 
(0.0656) (0.0589)
Controlled for country effects Yes Yes
Observations 3428 5065
Dependent variable: access to finance 3 (severity of access to finance as an obstacle (1–4; 0 = no obstacle); access to finance 4 = 0 if firm uses at least 75% internal
resources for working capital; = 1 if between 50–75%; = 2 if between 25–50%; = 3 if below 25%. Robust standard errors in parentheses.
*** p < 0.01.
** p < 0.05.
* p < 0.1.
Table A2
Pooled ordered logit results: (access to finance 4) — country specific.
Regressors Dependent variable: access to finance 4
Ghana Mali Senegal Gambia
Firm age 0.000116 0.0114 −0.00724 ** 0.0407
(0.00917) (0.0157) (0.0142) (0.0207)
Firm size *** ***  0.380 0.887 0.0881 0.360
(0.113) (0.223) (0.241) (0.359)
Gender of top mgr. (F = 1) −0.188 1.290**
(0.288) (0.632)
Ownership ** ** (foreign = 1) −0.147 0.224 1.244 0.862
(0.278) (0.487) (0.497) (0.432)
Experience of top mgr. 0.00191 −0.0110 0.0281* −0.0387* 
(0.00950) (0.0172) (0.0151) (0.0220)
Formality status *** *** * −1.123 −12.69 −2.141
(0.174) (0.612) (1.207)
Exporter 0.0235 0.0628 0.447 −0.448
(0.184) (0.316) (0.308) (0.477)
Firm performance 0.000819 0.00394 0.0261** 0.0143
(0.00276) (0.00441) (0.0132) (0.0109)
Sector of activity (services) −0.142 −0.0190 0.153 0.167
(0.146) (0.214) (0.218) (0.430)
Observations 894 506 407 118
Dependent variable: access to finance 4 = 0 if firm uses at least 75% internal resources for working capital; = 1 if between 50–75%; = 2 if between 25–50%; = 3 if
below 25% Robust standard errors in parentheses.
*** p < 0.01.
** p < 0.05.
* p < 0.1.
2 velop
R D
A
A
D
A
D
A G
G
A
A G
H
A
H
B K
B
B L
B M
B O
B
O
B
B P
P
B Q
S
B S
B S
/. S
B
C
S
C
World Bank (1994). Industrial Structures and the Development of Small and
Medium Enterprise Linkages: Examples from East Asia, Meyanathan S.
(Ed.), EDI Series, World Bank.
C World Bank (2015). World Development Indicators.
D
8 P. Quartey et al. / Review of De
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