Human capital, social capital, gender and race: are they related to access to bank
finance, in a developing nation?
Abstract
Background: Financial capital is one of the crucial resources needed for a business to start
and operate. The financing decisions of SMMEs have important implications for the
economy, given the role that they play in employment growth, competition, innovation and
export potential. Conventional capital structure theory concludes that in any business there
must be a balance between the use of equity and debt. The static trade-off theory suggests
that SMMEs should use debt because of the tax advantages, the disciplining effect of debt,
and the ability it offers the owner of the business to maintain control.
The human and social capital of an entrepreneur may be central to securing scarce financial
resources in the context of developing economies. Gender and race are also issues in
accessing financial capital and likewise affect business growth.
Studies comparing the
relationship of race groups and gender in accessing financial capital are rare or non-existent
in developing nations, including RSA.
The aim of the study was to investigate whether the components of human and social capital
are associated with access to financial capital, from gender and race perspectives.
Methodology: The study employed a survey research design, in which a combination of
methods was used. These included a questionnaire survey, face to-face interviews, and a
literature review. The sample of 512 SMME owners was sourced from the main trading areas
in the City of Cape Town, in the Western Cape Province. The study was thus contextualized
in the SMME sector. Exploratory data analysis was used to make an initial assessment of the
individual variables.
Results: The findings of this research demonstrated that the human capital of an entrepreneur
is not strongly associated with success in securing bank finance or with business growth. The
social capital of an entrepreneur, however, is important in securing bank finance. Gender has
been found not to be a factor in securing bank finance. From a race perspective, white
entrepreneurs are more likely to be approved for bank finance than black entrepreneurs.
Implications: In summary, this study provided new evidence that non-bank finance, in the
form of internal equity, personal savings and finance from family or friends, are the major
sources of financing for SMMEs. An important policy implication of this study is that efforts
to improve financing and to promote the growth of SMMEs must include policies promoting
owners’ savings and business savings. While current policy for small business financing
addresses debt financing primarily from commercial banks, other policy considerations must
address such issues as improving business profitability and retained earnings.
1. Introduction
Financing provides the lifeblood of business ventures anywhere in the world. Research shows
that financial capital input levels are strong determinants of a new venture’s survival
prospects (Bates, 1990; Cooper et al., 1994) and growth (Wilklund and Shepherd, 2003). The
lack of financial capital is a critical impediment to entrepreneurial growth in developing
economies, in which the financial system is transforming towards market governance.
This study set forth two goals. The first goal is to explore the effect of human capital and
social capital on the likelihood of businesses use of bank financing in the context of South
Africa, a developing economy, almost twenty years into its democracy. The research follows
the lead of Carter et al. (2003), who investigated whether higher levels of human capital and
social capital are associated with particular financial strategies, in the context of women
business owners in the USA and Manolova et al (2006).
Human and social capital is critical initial ‘endowments’ which shape the entrepreneur’s
aspirations and choices about the strategic direction of the business (Greene et al., 1997;
Manev et al., 2005). They are also important instrumental ‘resources for business expansion’
(Cliff, 1998), used to leverage and transform other types of resources, especially financial
capital. Human and social capital may be even more critical for securing scarce financial
resources in the context of developing economies, characterized by high overall educational
attainment and high reliance on informal networks to countervail institutional voids (Khanna
and Palepu, 1997; Smallbone and Welter, 2001).This study, therefore, aims to offer fresh
perspectives to managerial theory and practice in a developing economy.
The second goal of this study is to explore the differential gender and race effects on the
impact of human and social capital in securing bank finance in the context of developing
economies. In relation to gender, despite the growing body of literature on women-owned
businesses, their financing strategies and the relationship of financing to growth and
performance ( see Carter et al., 2002;and Gatewood et al., 2003), extant research has not
established a set of relationships or patterns that would be unequivocally attributable to
gender. Treatments of the effects of gender and race on the access to entrepreneurial
financing in the context of developing economies, in particular, have been limited. This
study, therefore, aims to inform managerial practice and public policy about the differences
in how men and women entrepreneurs and the different race groups in developing economies
leverage their human and social capital in order to secure business finance. The following
section will provide a brief background on South Africa.
2. Background of South Africa, as a developing nation
In development terms, South Africa is classified as a developing nation (World Bank, 2008).
It is regarded as having a productive and industrialized economy, but also exhibits many of
the characteristics associated with developing countries, such as an uneven distribution of
wealth, a high level of poverty, unemployment and a low HDI rating.
Prior to 1994, South Africa was a developing country governed by apartheid policies which
resulted in dividing the various population groups. In 1994, it became a democratic nation,
popularly referred to as the ‘rainbow nation’. About 80 % of the South African population is
made up of black Africans, most of who can be classified as poor (Statistics South Africa,
2010).
The labyrinth of apartheid laws and policies in South Africa served to perpetuate a white
labour and political aristocracy, denying the black race group, which then comprised the
coloured, Indian and African groups, the opportunity to participate fully in the economy.
According to Statistics South Africa (2011) Africans are in the majority, making up 79.2% of
the population; coloured and white people each make up 8.9% of the total; and the
Indian/Asian/ other population 3%.
Recognition is also growing that women make a vital contribution to economic development.
Yet gender continues to have a negative impact on such development on the African
continent (SAWEN, 2005). South Africa’s own economic transition into the new millennium
has affected men and women differently. SAWEN (2005) highlights the fact that women still
do not have equal economic rights and access to resources such as finance. Under these
conditions, they are unable to take full advantage of the economic opportunities presented by
transition. The International Finance Corporation (IFC, 2006) maintains that, while access to
resources such as financial services continues to be largely racially defined, the gender gap
between men and women persists, and is likely to grow unless special efforts are undertaken
to address the underlying issues.
The article provides a literature review on access to finance, social capital, human capital
thereby developing the hypotheses. It then discusses the results, followed by conclusion and
future research.
3. Literature review
3.1 SMMEs’ access to finance
Any business, in order to undertake capital investments, meet working capital and develop
products, needs finance. This can come in the form of equity, debt or a hybrid of debt and
equity. The combination of the different financing options (debt and equity) constitutes the
capital structure of an enterprise.
Financial theories that apply to business finance are
included under the term ‘capital structure’ of a business In general, the static trade-off theory,
the agency theory and the pecking order theory all suggest the use of internal equity as the
first source of finance by new SMMEs.
In order to make use of their potential, enterprises need to be provided with an enabling
environment, one which encompasses access to capital, being one of the greatest obstacles to
business start-up (Green, 2003). Depending on their size and environment, enterprises see
access to formal finance as more or less challenging. Berger, Klapper and Udell (2001) point
out that developing and emerging economy may, however, be at a disadvantage both in their
access to credit and in the terms of loans. Notwithstanding the current situation, new
enterprises are born every day and/or existing enterprises are expanding by increasing their
productive assets. This involves an implicit decision to raise capital in order to finance
growth.
3.2 Bank finance
Bank finance has remained the main source of capital and credit for enterprises in both the
developed and developing countries (Cosh and Hughes, 2003, in Irwin and Scott, 2010). The
main source of external financing for small businesses in developed nations is conventional
bank lending, with almost half of such finance coming from overdrafts and term loans (Bank
of England, 2002; Observatory of European SMMEs , 2003).
Feakins (2005) points out that overdrafts and term loans are the two major products offered
by commercial banks to new SMMEs. Overdrafts are the most common type of short-term
finance, whereby a small enterprise is allowed by a bank to run a current account into deficit
up to an agreed limit. An overdraft is useful to an enterprise in meeting temporary financial
needs. Term loans can be short-term, medium- term or long-term and in RSA can cover
periods between one and thirty years. They are usually used to purchase fixed assets or to
extend productive capacity as well, as well as for purposes relating to a change in company
control or the acquisition of a business.
Hogan, Avram, Brown, Ralston, Skully, Hempel and Simonson (2001) and Nieman (2002)
state that the products offered to new SMEs by commercial banks also include leasing, hire
purchase and credit cards. The interest on hire purchase is usually fixed and is more
expensive than a bank loan. Furthermore, Bertocco (2007) and Feakins (2005) point out that
SMMEs may turn to alternative sources of bank debt financing if they find themselves unable
to obtain traditional forms of bank credit.
The limited extent of bank credit to SMMEs in sub-Saharan Africa is only partly
compensated by non-bank forms of credit, namely trade credit, loans from friends and
families, business relations, and, to a much smaller extent, loans from savings
associations. Between 75 and 90 per cent of SMMEs in the less developed countries rely
on their own savings, internal sources such as retained earnings, and borrowings from
relatives and friends (Lutabingwa, Cooley and Gray, 1996).
3.3. Social capital
Social capital develops through the personal relations and network relations within a social
structure (Granovetter, 1985). The success of an entrepreneur in raising external financial
capital may depend on who one knows or the amount of one’s social capital. Social capital
emerges from the norms, networks and relationships of the social structure in which an
individual lives, potentially producing useful resources for business through the development
of sets of obligations and expectations, information channels and social norms that reinforce
certain types of behaviour (Coleman, 1998).
Network ties are an important component of social capital. Having a diverse network, with a
mixture of strong and weak ties, also influences access to financial capital. Ties are
frequently attributed primarily to the individual agents involved. Weak ties are loose
relationships between individuals, as opposed to the strong ties that would be found in a
family. Weak ties are useful in obtaining information that would otherwise be unavailable or
difficult or costly to find. Granovetter (1973) highlights the importance of maintaining an
extended network of weak ties in obtaining resources. Strong ties, such as those derived from
family relationships, provide secure and consistent access to resources. Davidsson and Honig
(2003) suggests that the facilitation offered by business networks and associations may
provide the most consistent and effective support for emerging businesses. This support may
be in the form of business advice or financial capital.
In this study, I employ social capital broadly in terms of the social exchange between
individuals and groups, to examine the effects of exchange ties on access to financial capital.
Exchange effects may range from the provision of concrete resources, such as a loan secured
due to strong ties, to intangible resources, such as information on business advice. The study
is thus concerned with factors related to social relations, which consist of the patterns of
particular ties between individuals, where variations in the network or strength of the ties are
meaningful and consequential.
3.4. Human capital
Human capital derives from investments in formal education, work experiences and training
(Carter, et al., 2003), also includes training courses that are not part of traditional formal
educational structures. On the demand side, higher levels of education and longer previous
managerial experience result in the accumulation of explicit knowledge and entrepreneurial
skills (Davidsson and Honig, 2003). Similarly, Forbes (2005) asserts that entrepreneurs with
higher levels of education and work experience are likely to be more efficient in seeking,
gathering and analysing information about the availability of opportunities including
accessing financial resources. On the supply side, the level of education may serve as a proxy
for persistence, motivation and self-discipline, which impacts favourably on the impressions
of financial capital providers (Coleman, 2004). Bates (1990) finds that a college education
improves access to debt capital, most directly for commercial bank borrowers.
Gebru (2009) reports that small business owners with higher educational backgrounds have a
higher probability of choosing external sources of funds in the form of bank loans. However,
Irwin and Scott (2010) find that education made little difference to sources of finance, except
that those educated up to Grade 12 (A level) more frequently used friends and family and remortgaged their homes, and that graduates had the least difficulty in raising finance.
However, an earlier study by Coleman and Cohn (2000) found some evidence that education
was positively related to external loans.
In a later study, Coleman (2004) found that business owners with higher levels of education
were significantly less likely to have applied for a loan within the previous three years of the
start of a business operation. She offers possible explanations for her findings. First,
education is linked with business performance, implying that more highly educated owners
operate more profitable businesses. If this is the case, retained earnings may offset some of
the need for external sources of finance. Secondly, more highly educated owners may make
better use of trade credit as a source of financing, thus reducing their reliance on interestbearing loans.
The three components that guide the study are depicted in the framework below.
3.5 Research hypotheses
The following section gives a brief background to the development of the hypotheses for this
study.
Securing bank finance is influenced by the human capital variables of the entrepreneur
(Manolova, et al., 2006; Carter, et al., 2003). Human capital theory states that knowledge
increases people’s cognitive abilities, leading potentially to more productive and efficient
activity (Mincer, 1974; Becker, 1964). Previous knowledge and experience in a particular
environment play a critical role in the intellectual performance of an individual, assisting in
the integration and accumulation of new knowledge, as well as in integrating and adapting to
new situations (Weick, 1996). Thus, if opportunities for new and profitable economic activity
arise, those entrepreneurs with more or a higher quality of human capital will be better at
perceiving such opportunities and capitalizing on them.
According to Storey (1994), experience and educational levels may provide signals of a better
human capital. The better the human capital, the greater the likely viability of the start-up of
an enterprise, its growth and expansion, and consequently its access to financial capital. In
the context of this research, financial capital refers to an entrepreneur’s ability to secure bank
finance and his or her willingness to apply for it.
The variables that comprise human capital (education, business training, work experience,
previous ownership and work experience, and multiple ownership) are used to determine
whether there are associations with securing bank finance.
The above is background to the development of Hypothesis 1, to be examined in terms of
human capital and financial capital.
Hypothesis 1: Business owner’s human capital is positively related to securing bank
finance.
Shane and Cable (2002) found that networking can be used to reduce information asymmetry
in creditor/debtor relationships. In addition, business networks increase an enterprise’s
legitimacy, which in turn positively influences the enterprise’s access to external financing.
Kiggundu (2002) states that networks contribute to business success and continuity. This
suggests that lack of networking can negatively impact on the securing of bank finance by
both new and established SMMEs. According to Manolova, et al. (2006) and Carter, et al.
(2003), networks form part of social capital, and can be in the form of strong ties (family and
friends) or weak ties (bookkeeper, banker, other business owners, legal advisor, business
advisor or financial consultant). Consequently, it is hypothesized that:
Hypothesis 2: Business owner’s social capital is positively related to securing bank finance.
Studies by Lee and Denslow (2004) and Orser, et al. (2000) in industrialized countries reveal
that women are more concerned about access to finance than any other business problem, and
that it is more of a problem during the early stages of a developing an enterprise. Similarly,
Coleman (2000) found that acquiring financial capital and dealing with financial institutions
is particularly difficult for women business owners. Research also shows that women start
their businesses with smaller amounts of financial capital and are less likely to raise finance
from external sources (Fairlie and Robb, 2009; Constantinidis, et al., 2006).
In South Africa during the apartheid era, race and gender had an influence on the success rate
of the bank application process. Now a democratic nation, the country may still not
necessarily favour certain race groups or gender in bank loan applications. The hypotheses
developed below thus set out to examine gender and race in the bank application context.
Hypothesis 3: Male and female business owners are equally likely to be successful in their
applications for bank finance.
Hypothesis 4: Black and white business owners are equally likely to be successful in their
applications for bank finance.
4. Research Methodology
The study employed a quantitative method, using exploratory and descriptive research. In
obtaining primary data from SMME owners, the survey method was found to be the most
convenient. This article used some of the constructs devised by Manolova, et. al. (2006),
adapted for the South African context, using expert advice from three academics in the fields
of business and finance and a statistician. Data for the research study were gathered using a
combination of self-administered questionnaires and personal interviews.
The following rating scales as described by Sekaran (2003) were used: dichotomous
questions (to elicit a yes or no answer), a category scale (using multiple items to elicit a
single response), a Likert scale (to examine how strongly respondents agree or disagree with
statements on a 5-point scale) and a fixed sum scale (respondents are requested to distribute a
given number of points across various items).
The questionnaire was designed to retrieve information relating to the following sections, as
listed in the final questionnaire: Section A: Background on the business; Section B:
Biographic data of the entrepreneur and household; Section C: Financing the business and
Section D: Networking
Twenty five pilot questionnaires were subjected to an initial statistical analysis in order to
establish whether the variables and outcomes were appropriate. The questionnaire was then
refined, taking into account the responses from the respondents and the four academics.
Finally, results from the pilot study and comments from two academics in the field of SMME
were used in the formulation and preparation of the refined research instrument (final
questionnaire) that was used to collect data to investigate the problem under study
The target population was SMME owners who were operating in the main trading areas of
Cape Town Central, Bellville, Parow, Mitchells Plain, Wynberg, Goodwood, and a few other
suburbs around Cape Town Central. However, the main trading areas included in this study
could be generalizable to the Western Cape, as most SMMES are located in the City of Cape
Town.
Collecting the data
The study engaged the services of research assistants to administer the questions during the
fieldwork. The assistants were university students who had to have a background in business
management or finance. They were trained on how to make contact with business owners
and how to conduct the interviews when completing the questionnaires. Cold contacts were
made in the different trading areas to invite participation in the survey. The respondents had
to satisfy the following criteria: 1) Own 50% or more of the business; 2) be actively involved
in the business; and 3) have fewer than 100 employees.
5. Results and discussion
The following section provides summary results of the demographics of the SMME owner,
descriptive analysis; Chi squared analyses and regression analyses.
532 questionnaires were used in the analysis of the data. The response rate was 42% and 4%
of the questionnaires had to be discarded, due to incomplete responses from the subjects. The
42% response rate was well within the international standards, which range between 27% and
51% (Yang, et al. (2006).
5.1 Human capital variables
Education
Five categories of qualifications were used to describe the educational characteristics: below
Grade 12, Grade 12, diploma, degree and post-graduate. Most SMME owners had Grade 12
(34%) or Diploma (36%) qualification. It is interesting, although not significant, that females
seem to be more highly educated than males. This is evidenced by 37 percent of females
compared with 30 percent males having a diploma as a qualification. A higher proportion of
the white entrepreneurs are significantly more highly educated than black entrepreneurs, with
62% of white entrepreneurs obtaining a diploma or degree compared to black entrepreneurs
at 40%, (p< 0.05). Thus the level of education was found to be similar across gender, but
significantly different across race, white entrepreneurs having a higher level of education
than black entrepreneurs.
Business training
Of the total respondents, 46% had some form of business training. A higher proportion of
males (48%) than females (41%) had undergone business training. Similarly, a higher
percentage of white owners (50%) than black owners (46%) had undergone business training.
However, there was no significant difference in business training from a race perspective or a
gender perspective.
Business experience
Respondents were asked to indicate if they were the first-time owner of a business. 70% of
the subjects were first-time owners and there was no significant difference from race and
gender perspectives. A higher percentage of female owners (74%) than male owners (69%)
were first-time business owners. This supports the findings of early studies by Welsh and
Young (1984) where it was found that women business owners had less business experience
than their male counterparts.
Management experience
Respondents were required to indicate whether they had had previous management
experience before starting their current business. A significantly higher proportion of male
entrepreneurs (54%) than female entrepreneurs (41%) had had previous management
experience before starting their current businesses (p<0.05). Significantly more whites (66%)
than blacks (44%) had had previous management experience (p<0.05) before starting a
business. Thus there was a significant difference between previous management experience
across gender and race, implying that male entrepreneurs and white entrepreneurs have more
management experience than their counterparts.
Owning other businesses
Respondents were asked to indicate if they currently owned or had co-ownership in other
business/es. A significantly higher proportion of males (54%) compared to females (41%)
owned other businesses (p<0.05). However, there was no significant difference across the
race groups. It is interesting to note that about half the SMME owners either owned or were
co-owners in other business/es.
Previous work experience
Subjects were required to indicate if they had had previous work experience before starting
their current businesses. Seventy-six percent of the total respondents had had previous work
experience. This may indicate that these SMME owners probably saw business opportunities
while being employed. The role of previous work experience could be seen as crucial in
incubating future entrepreneurs. A significantly higher proportion of male owners (80%) had
previous work experience than female owners (70%) (p<0.05). Significantly more white
owners (85%) than black owners (76%) had previous work experience (p<0.05). Thus, more
white owners than black owners had previous work experience, and more males than females
had previous work experience.
5.2 Access to financial capital
Respondents were requested to indicate the percentage of different sources of finance they
used in starting up their businesses, and which formed part of the capital structure of the
business. A list of 10 sources of finance was given, namely own savings, business partners,
family/relatives, friends, employees, credit card, bank loan, government schemes, venture
capital, and stockvels. These were further categorized into bank, non-bank and a combination
of these, in order to facilitate the analyses, as required by regression analysis. It was noted
that 72% of the SMMEs had exclusively non-bank finance in the form of personal equity, 3%
had bank finance only, while 25% had a combination of non-bank and bank finance.
In relation to non-bank finance, further analyses revealed that 47% of the respondents had
only savings as part of their capital structure when starting a business. There were no
significant differences between males and females or between the race groups on utilizing
savings as the only source of finance. It was also found that 23% of the respondents sourced
finance from family or friends. However, there was a significant difference from a race
perspective; a higher proportion of black entrepreneurs (25%) than white entrepreneurs
(19%) sourced finance from family or friends. There was no significant difference between
type of finance across gender and race.
Human capital and financial capital
All the human capital variables were cross-tabulated with bank application and bank success
to establish if there were associations between human capital variables and bank finance
application and success in bank application.
Table 1: Human capital variables affecting bank application and success
Human capital
variables
Education
Business training
First business
Owning other
business/es
Management
experience
Work experience
*p<0.05
Bank application
Bank success
N
72
77
34
57
No
49%
53%
23%*
39%*
N
74
69
112
89
YES
51%
47%
77%*
61%*
N
50
60
24
45
NO
70%
78%
73%*
79%*
N
61
52
87
67
YES
83%
75%
78%*
75%*
74
51%
72
49%
56
76%
58
80%
31
21%
115
79%
23
73%
90
78%
A total of 27% of the respondents had applied for a bank loan in the previous three years of
business operation. Table 1 shows the human capital variables that were cross-tabulated with
bank application and bank success. The variables were dichotomous (yes or no).
Fifty-three percent of the total respondents were regarded as educated (having a qualification
above Grade 12), accounting for 51% of the bank applications, with 49% of the bank
applications from those who were classified as not educated. Of the 51% who were educated,
83% were successful in bank application, while of the 49% who were uneducated, 70% were
successful in their bank applications. Although not significant, being educated contributed to
the success of bank application.
Forty-six percent of the total respondents had some form of business training. Forty-seven
percent of the total number who applied for bank loans had undergone business training,
compared with 53% who had had no business training. Of the 47% who had had business
training, 75% were successful in their bank applications, while of the 53% who did not have
business training, 78% were successful in their applications. Thus business training was not
associated with success in bank application.
Seventy-seven percent of the total bank applications were from first-time business owners
and 23% were from non-first-time owners. Seventy-eight percent of first-time owners,
compared to 73% who were not first-time owners, were successful in their bank applications.
This was significant (p<0.05), indicating that first-time owners were more likely to apply and
be successful in securing loans than non-first-time owners.
Forty-nine percent of the total respondents had ownership in other business/es. Sixty-one
percent of the bank applications were from respondents who had ownership in other
business/es, while 39% were applications from those who did not have ownership in other
business/es. The bank application success rate was 75% for respondents who owned other
business/es, compared to 79% of the subjects not owning other business/es. This was also
significant (p<0.05), indicating that those who did not have ownership in other business/es
were more likely to apply and be successful in securing loans than those who had ownership
in other business/es. Owners of one business were thus more likely to apply successfully for
bank finance than those who had multiple ownership.
Fifty-percent of the total respondents had previous management experience. Forty-nine
percent of the total of those who had previous management experience had applied for loan.
The success rate for bank application was 80% for those who had previous management
experience, compared to 76% of those who had no such experience. Having previous
management experience was therefore not associated with success in bank application.
Seventy-six percent of the total respondents had previous work experience. Seventy- nine
percent of those who had previous work experience applied for bank loans. Seventy-eight
percent of those with previous work experience, compared with 73% of those with no
previous work experience, were successful in their loan applications. Thus previous work
experience was not a significant factor for success in the bank application process, especially
for new SMMEs.
Being educated, being a first-time business owner and owning one business were associated
with bank application and success in the bank application process. It is suggested that these
three variables may be seen as more favourable by the lending institutions, due to lower risk
propensity and the mitigation of information asymmetry.
5.3. Social capital and bank finance
Social capital variables were cross-tabulated with bank application (respondents who applied
to the bank for a loan) and bank success (success in bank application). The variables included
strong ties, a diverse network, belonging to a business network, strong assistance from a
business network, and a strong bank relationship, as shown in Table 3.
Table 2: Social capital variables versus bank application and bank success
Social capital
variables
Strong ties
Diverse network
Belonging to
business network
Strong assistance
from business
network
Strong bank
relationship
*p<0.05
Bank application
Bank success
N
143
58
92
No
98%
40%*
63%*
N
3
88
54
YES
2%
60%*
37%*
N
112
43
63
NO
78%
74%
68%*
N
1
71
49
YES
33%
81%
91%*
108
74%*
38
26%*
75
69%*
37
97%*
15
10%*
131
90%*
3
21%*
111
85%*
Ninety-six of the total respondents did not have strong ties, meaning that they did not consult
with other stakeholders, except family and friends (strong ties), when seeking financial
advice. Ninety-eight percent of bank applications were from business owners who did not
have strong ties, while 2% were from respondents who had strong ties (family or friends).
Seventy-eight percent of business owners who did not have strong ties (weak ties) were
successful in their loan applications, compared to 33% who had strong ties.
Twenty-eight percent of the total respondents had a diverse network, meaning they had a
combination of strong and weak ties. A significantly higher proportion of subjects (60%) who
applied for bank finance had a diverse network (p<0.05). Of the 60% who had a diverse
network, 81% were successful in their bank applications, compared to a 74% success rate for
those who did not have a diverse network. Having a diverse network was thus associated with
success in bank application.
Twenty-five percent of the total respondents belonged to a business network. Of the total
bank applications, 37% were from those who belonged to a business association and 63%
from those who did not belong to a business association. The bank success rate was
significantly higher (p< 0.05) for respondents who belonged to a business network than for
those who did not belong to a business network (91% versus 68%). This implies that
belonging to a business network is associated with success in bank application.
Nineteen percent of the total respondents who belonged to a business network had strong
assistance from their network in their efforts to secure bank finance. Of the total bank
applications, 26% were from those who had strong assistance from their business networks.
Of these, 97% were successful in their bank applications, compared with 69% for those who
did not have strong assistance from a business network (p< 0.05). Thus the strength of
assistance from a business network was associated with bank application and bank success.
Entrepreneurs who had strong assistance from their business networks were therefore likely
to be more successful than those who did not have such assistance.
Eighty-seven percent of the total respondents had a strong bank relationship. 90% of the bank
applications were from subjects who had a strong bank relationship. Of the 90% who applied
to a bank, 85% were successful, compared to 21% for those who did not have a strong bank
relationship. This was significant (p< 0.05), meaning that a strong bank relationship
influences the success of a bank application.
In summary, having a diverse network, belonging to a business network and having strong
assistance from the network and a strong bank relationship were associated with applying for
bank finance (p<0.05). In addition, belonging to a business network and having strong
assistance from the network and a strong bank relationship were associated with success in
bank application (p<0.05).
5.4. Application and success of bank loan by gender and race
Respondents were required to indicate if they had applied for a bank loan during the past
three years and to indicate whether they had been successful in their most recent application.
Table 3 shows the results from a gender perspective and a race perspective. Twenty-seven
percent of the total respondents applied for a bank loan.
Table: 3: Bank application and success from gender and race perspectives
Bank apply
Yes
No
Total
Success in
application
Yes
No
Total
*p<0.05
Gender
Male
Female
112
32%*
34
19%*
240
68%*
146
81%*
352
100%
180
100%
Male
85
27
107
76%
24%
100%
Race
Black
White
102
27%
44
29%
282
73%
104
71%
384
100%
148
100%
Female
27
7
34
79%
21%
100%
Black
71
31
102
69%*
31%
100%
White
41
3
44
93%*
7%*
100%
Total
27%
73%
100%
Total
79%
21%
100%
A significantly higher proportion of males (32%) than females (19%) applied for bank loans
(p<0.05). However, from a race perspective, the application rate for black SMME owners
was similar to that for white SMME owners. Thus there was no significant difference
between the race groups in terms of bank application. In terms of success rate, 93% of the
white entrepreneurs were successful, compared with 69% of black entrepreneurs. White
entrepreneurs are thus more likely to be successful in bank applications compared to black
entrepreneurs.
An explanation for the difference in application rates could be that female entrepreneurs had
less propensity to seek finance than men, reporting that they did not ‘need’ the finance ,
supporting a study by Orser, et al. (2006) that female-owned businesses are smaller, serviceoriented and ‘cheaper’ to finance (Heilbrunn, 2004). From a race perspective, the bank
application rates were similar. However, white entrepreneurs were more successful than
black entrepreneurs in applying to a bank. The results suggest that white entrepreneurs have a
greater chance of success than black entrepreneurs.
5.5 Reasons for rejection of bank loan
Respondents who were not successful in their loan applications were asked to indicate what
they perceived were the reason/s for rejection. The reasons given were insufficient collateral
(48%), inadequate business plan (33%), lack of trading history (27%), being over-indebted
(24%), and a poor bank-client relationship (24%).
5.6 Reasons for not applying for bank loan
From the options given, respondents were requested to tick all the reasons why they had not
applied for a bank loan during the past three years. The results highlighted four main reasons:
no need for additional finance (62%), high interest rate (28%), accumulating debt was too
risky and costly (27%), and fear of being denied the loan (11%). It was also found that more
blacks (9%) than whites (1%) needed finance, but feared that their application might be
denied. From a gender and race point of view, the reasons for not applying for a bank loan are
shown in Table 4. There were no significant differences between gender and race in the
reasons for rejection of bank loan. It should be noted that 46% of white entrepreneurs,
compared to 37% black entrepreneurs, had no need for additional finance. Similarly, 40% of
male entrepreneurs, compared to 38% of female entrepreneurs, had no need for additional
finance. The fact that 62% did not require additional external finance could be due to the use
of internal finance, as supported by the pecking order theory.
Table 4: Reasons for not applying for bank loan
Reasons for not applying
1) No need for additional finance
2) High interest rate
3) Accumulating debt is too risky
4) Need, but fear that application
may be denied.
5) Other
Gender
Male(N=239) Female(N=146)
96
40%
55
38%
41
17%
26
18%
38
16%
25
17%
Race
Black(N=284) White(N=105)
105
37%
48
46%
48
17%
20
19%
43
15%
22
21%
14
6%
12
8%
26
9%
1
1%
50
21%
28
19%
62
22%
14
13%
5.7. Human capital regressed with bank application and success in application
Human capital variables were regressed to assess which variables affected the likelihood of
applying for bank finance and resulting in success, as shown in Table 6. The independent
variables were level of education, business training, first business, and owner of other
business/es, previous management experience and previous work experience.
Table 5: Human capital and financial capital
Human capital
Education
Business training
First business
Own other business
Management
experience
Work experience
Constant
Model 1(bank application)
BANK APPLY
B
S.E.
Wald
Exp(B)
-.043
.095
.203
.958
-.026
.211
.015
.975
.527
.237
4.964
1.694*
.676
.203
11.068
1.966**
-.078
.220
.125
.925
Model 2 (success)
BANK SUCCESS
S.E.
Wald
.104
.000
.230
.239
.264
5.904
.222
6.131
.239
.614
B
-.002
-.112
.641
.549
.187
.245
.256
.916
1.277
.211
.284
.549
1.234
-1.740
.392
19.687
.176
-2.287
.438
27.213
.102
Exp(B)
.998
.894
1.899*
1.731*
1.206
*p<0.05, **<0.005
Models 1 and 2, containing all the predictors, were statistically significant [X 2 (6, N= 532) =
18.275, p=0.006] and Model 2 [X 2 (6, N= 532) = 13.851, p=0.031] indicated that the models
were able to distinguish between the human capital variables that contributed to applying for
bank finance and success in bank application. As shown in the Table 5, two independent
variables made a unique, statistically significant contribution to the model (first-time owner
and owner of other businesses). The strongest predictor of bank application was ‘owning
other business/es’, indicating that respondents who owned other business/s were 1.97 times
more likely to apply for bank loans than those who did not have other businesses, controlling
for all factors in the model. The strongest predictor of success in bank application was ‘firsttime owner’, indicating that respondents who were first-time business owners were 1.90
times more likely to be successful in their bank loan applications than those who were not
first-time owners, controlling for all factors in the model.
The implication of Model 1 and Model 2 is that SMME owners who were also involved in
other business/es were more likely to apply for bank finance. However, first-time SMME
owners were more likely to be successful in the bank application process than those who
were not first-time owners. The level of education, business training, management experience
and previous work experience were not associated with bank application and success in
obtaining bank finance. Hence human capital of an entrepreneur was not positively associated
with success in bank application.
5.8. Social capital versus bank application
Social capital variables were regressed to assess which variables affected the likelihood of
applying for bank finance and of success in the application, as shown in Models 3 and 4 in
Table 6. The independent variables were strong ties, a diverse network, belonging to a
business network, strong assistance from business network and a strong bank relationship.
Table 6: Social capital and financial capital
Social Capital
Strong Network ties
Diverse network
Belong to business
network
Strong assistance from
business network
Strong bank
relationship
Constant
*p<0.05, **<0.005
B
-1.097
.659
1.117
Model 3
BANK APPLY
S.E.
Wald
.638
2.961
.218
9.147
.380
8.621
-.638
.421
.512
-1.775
Model 4
BANK SUCCESS
S.E.
Wald
1.041
3.768
.239
7.003
.421
9.918
Exp(B)
.334
1.933**
3.056**
B
-2.021
.632
1.325
Exp(B)
.132
1.881*
3.761**
2.302
.528
-.429
.453
.897
.651
.334
2.345
1.668
2.053
.623
10.853
7.793*
.327
29.446
.169
-3.689
.628
34.569
.025
As noted in Table 6, Models 3 and 4, containing all predictors, were statistically significant,
[X 2 (5, N= 532) = 29.539, p=0.000] and [X 2 (5, N= 532) = 54.486, p=0.000], indicating that
the models were able to distinguish between the social capital variables that contributed to
applying for bank finance and success in bank application. ‘Diverse network’ and ‘belonging
to a business network’ made a unique, statistically significant contribution to Model 3 in
relation to bank application. The strongest predictor of bank application was ‘belonging to a
business network’, indicating that respondents who had belonged to a business network were
three times more likely to apply for bank loans than those who did not belong to a business
network, controlling for all factors in the model.
In relation to bank success, as shown in Table 6, ‘diverse network’, ‘belonging to a business
association’ and ‘strength of bank relationship’ made a statistically significant contribution to
Model 4, indicating that the model was able to distinguish between the variables that
contributed the success of a loan application. The strongest predictor of such success was
‘strong bank relationship’, indicating that subjects who had a strong bank relationship were
about eight times more likely to be successful in their applications than those who did not
have a strong bank relationship.
The implication of Model 3 and Model 4 is that SMME owners who belong to a business
network are more likely to apply for bank finance, probably due to the guidance and
assistance of the network. In addition, having a strong bank relationship greatly increases the
chances of success in the bank application process.
5.9. Gender, race and financial capital
The variables of financial capital were regressed with gender and race to establish if it was
influenced by gender and race.
As noted in Table 8, the full model on gender (Model 5) containing all the predictors of
financial capital (bank application and success of application) was significant [X
2
(2, N=
532) = 10.546, p=0.005], indicating that the model was able to distinguish between
respondents from a gender perspective who applied and were successful in the bank
application. However, there were no strong predictors of financial capital, implying that
gender is not significantly related to bank application. The results suggest that there is no
likely difference in the bank application rates between male and female entrepreneurs.
Table 7: Gender, race and financial capital
Model 5
Gender
Financial
B
Capital
Bank apply
-.816
Approved
.158
Constant
-.497
*p<0.05; **<0.005
S.E.
.418
.458
.105
Wald
3.817
.119
22.460
Model 6
Race
Exp(B)
.442
1.171
.608
B
-1.150
1.593
-1.002
S.E.
.546
.566
.115
Wald
4.439
7.926
76.195
Exp(B)
.317*
4.917**
.367
From a race perspective, the full model (Model 6) containing all predictors was significant [X
2
(2, N= 532) = 10.965, p=0.004], indicating that the model was able to distinguish between
respondents who applied and who were successful in the bank application process. As shown
in Table 7, bank application and bank success made a significant contribution to Model 6,
indicating that the model was able to distinguish between respondents who applied for and
were successful in their bank applications. The strongest predictor of race was bank success
rate, indicating that one race group (white) was about five times more likely to be successful
in bank application than the other race group (black).
The implication of Model 5 and Model 6 is that financial capital was not influenced by
gender. Male and female owners were equally likely to be successful in the bank application
process. However, financial capital was influenced by race. White entrepreneurs were more
likely than black entrepreneurs to be successful in their applications.
Results of the hypotheses
Hypothesis 1: Business owner’s human capital is positively related to securing bank
finance.
Only three human capital variables, namely ‘first-time business owner’, ‘owner of other
business/es’ and ‘management experience’ were associated with securing bank finance. In
particular, education was not associated with success in securing such finance. Thus it can be
concluded that human capital is not strongly associated with securing bank finance.
Hypothesis 2: Business owner’s social capital is positively related to securing bank finance.
Belonging to a business network, having strong assistance from the business network and
having a strong bank relationship were associated with bank application and success in bank
application, supporting this hypothesis , that a business owner’s social capital is positively
related to securing bank finance.
Hypothesis 3: Male and female business owners are equally likely to be successful in their
applications for bank finance.
Although male entrepreneurs had a stronger bank relationship than females, the success of bank
applications was not significantly different between male and female entrepreneurs thus
supporting this hypothesis.
Hypothesis 4: Black and white business owners are equally likely to be successful in their
applications for bank finance.
There were no differences in the bank application rates from a race perspective. However,
a significantly higher percentage of white entrepreneurs than black entrepreneurs were
successful in their bank applications, thus not supporting this hypothesis.
6. Conclusion
This article aimed to investigate if human capital and social capital were related to accessing
financial capital and also used gender and race as added variables in accessing financial
capital. In general, differences in human capital variables were noted from a gender and a
race perspective. A quarter SMMes sourced finance from a bank. Internal equity, in the
form of savings or finance from family or friends, was the major part of their capital structure
in terms of business finance.
Being educated, being a first-time business owner and owning one business were associated
with bank application and success in the bank application process.
SMME owners who belonged to a business network, had strong assistance from a business
network in securing finance and had a strong bank relationship were associated both with
bank application and success in bank application. It can therefore be concluded that social
capital was associated with bank application and success.
A higher proportion of males than females applied for bank finance and it is found that
gender is independent of success in bank application. From a race perspective, the bank
application rates were similar. However, white entrepreneurs were more successful than
black entrepreneurs in applying to a bank. The results suggest that white entrepreneurs have a
greater chance of success than black entrepreneurs.
Business training, management experience and previous work experience were not associated
with bank application and success in obtaining bank finance. Hence human capital of an
entrepreneur was not positively associated with success in bank application.
In relation to bank success, ‘diverse network’, ‘belonging to a business association’ and
‘strength of bank relationship’ made a statistically significant contribution to the success of a
loan application. It is found that having a strong bank relationship greatly increases the
chances of success in the bank application process.
In conclusion financial capital was not influenced by gender. Male and female owners were
equally likely to be successful in the bank application process. However, financial capital was
influenced by race. White entrepreneurs were more likely than black entrepreneurs to be
successful in their applications.
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