Explanation of the Variables

Accessibility in this study is measured using observations of SMEs’ borrowing such as “borrowed

microcredit” and “did not borrow microcredit”. The independent variables include owner-manager characteristics, enterprise characteristics, networking and distance. The list of explanatory variables and expected signs of how each variable influences microcredit access are shown in Table 4.1.

67

Table 4.1 Definitions of Independent Variables

Variable Definitions/measurement Expected

sign

Gender Gender of SME’s owner/manager is a vector of dummy variables

where: X1= 1 for male, 0= for female. +

Age Age of SME’s owner/manager is a vector of dummy variables

indicating age group where:

X2(1) = 1 for below 35 years of age, 0=otherwise;

X2(2) =1 for 36-45 years of age, 0=otherwise; and

X2(3) = 1 for 46 years of age and over, 0=otherwise.

- Marital status A vector of dummy variables for the marital status of

owner/manager of SMEs where: X3(1) = 1 if married; 0 otherwise. +

Ethnicity A vector of dummy variables for ethnicity of owner/manager of

SMEs where: X4= 1 if ethnicity is Malay, 0=otherwise. +

Education level A vector of dummy variables for educational level of owner/manager of SMEs where:

X5= 1 if higher than high school, 0 otherwise.

+ Financial training A vector of dummy variables of financial training where:

X6= 1 owner/manager received financial training, 0=otherwise +

Experience A vector of dummy variables of experience where:

X7= 1 if owner/manager has work or business experience before

running business and 0 otherwise). + Household size Number in the household is a vector of a dummy variable where:

X8(1) = 1 for 3 or fewer people, 0=otherwise;

X8(2) =1 for 4 people, 0=otherwise;

X8(3) =5 or more people, 0=otherwise.

-/+ Income earners A vector of dummy variables indicating number of income earners

in the borrower’s household where:

X9(1) = 1 for 1-2 people, 0=otherwise;

X9(2) =1 for 3-4 people, 0=otherwise;

X9(3) = 1 for more than 4 people, 0=otherwise.

-/+ Household

income Household annual income was divided into four levels: RM1000 to RM2000; RM2001 to RM3000; RM3001 to RM4000; and over

RM4000. +/-

Age of enterprise X11=Age of the firm (number of years established). +

Sector Sector the SME operated in is a vector of dummy variables where: X12(1) = 1 firm is in manufacturing, 0=otherwise;

X12(2) = 1 firm is in service, 0= otherwise; and

X12(3) = 1 firm is in agriculture, 0= otherwise.

+ Size of enterprise Size of the enterprise (based on number of employees in 2014). - Ownership Ownership of the firm is a vector of dummy where:

X14(1) = 1 if firm is sole proprietor; 0 otherwise -

Networking Network variable includes the extent to which the firm networks with a commercial bank, social organizations, NGO, microfinance organizations, and business associations measured on a scale of 0 = "Not at all" to 5 = "very extensive".

+ Distance Distance between borrower and microcredit provider (continuous

68 The choice of the explanatory variables used in the model is based on theory, previous studies and data availability. The expected signs indicate positive or negative hypothesised relationships between

the variables and SMEs’ microcredit accessibility. For example, the expected sign of (+) for gender

indicates it is hypothesized to positively affect the enterprise’s accessibility to microcredit.

Gender, age, education and household income are the common factors used to comprehend the factors affecting access to credit by individuals. The literature on gender and access to credit are quite interesting but have mixed results. In this study, gender is hypothesised to positively affect access to microcredit. It is common in Malaysia for men to make the important decisions since men are the main income earners of the household. Various studies showed that men and women differ in terms of access to credit and suggest that female-owned SMEs experience difficulty in obtaining

credit because credit institutions’ perceptions discriminate against female business owners, albeit that women demand credit (Okurut, Olalekan, & Mangadi, 2012; Riding & Swift, 1990). Women are perhaps prevented from obtaining microcredit because of the perception of low repayment capacity reflected by the weakness of control over economic resources compared with men. In contrast, entrepreneurial women are more likely to access credit with ease compared with male

entrepreneurs (Atta, 2012). Some studies found insignificant differences between male-owned and female-owned businesses (Baiyegunhi & Fraser, 2014; Watson et al., 2009). The age of the SME

owner is hypothesized to negatively affect an SME’s access to microcredit. Mohamed (2003) and Okurut (2006) show that as people age the probability of borrowing from credit sources reduces. Moreover, older owners or managers reduce their propensity to access microcredit because they may find it challenging and complex to comprehend the processes and loan conditions of microcredit programmes. The education variable has a positive sign regarding access to credit. This means a higher educational level increases SME owners’ chances to access microcredit. This specification of education is consistent with Peprah & Ayayi (2016) who conclude that the household head with higher education significantly influences access to microcredit. It is, therefore, established that a higher educational level fosters participation in microcredit. The SMES’ owners who are not

knowledgeable enough to make effective financial choices find their accessibility to credit hindered. Another important variable is financial training. Those owners who received financial training are hypothesised to be positively associated with access to microcredit compared with those who do not have such training. Financial training can be a proxy for financial literacy. Financial training can

enhance SME owners’ financial knowledge and increase their chances of success in borrowing

(Akudugu et al., 2009). SME owners with better financial knowledge may keep comprehensive financial records and are more likely to have access to credit. Furthermore, it is assumed that

69 financial training received by SME owners might help them to go through the borrowing process with a microcredit provider. Thus, financial training further develops financial literacy since experts agree that self-financial behaviour comes from a knowledge of finance (Hilgert, Hogarth, & Beverly, 2003).

Experience is hypothesized to positively affect SMEs’ access to microcredit. Experience is measured

by whether SME owners have work or business experience before starting their business. Hanedar, Altunbas, & Bazzana (2014) propose that the positive correlation between owners’ work experience

is likely to help reduce the firm’s financing obstacles. It is expected that the probability of access to

microcredit is greater for those who have work or business experience. Microcredit providers may require additional traits of borrowers such as work or business management experience since that lessens the chance of default or business failure.

Economic burden is always tied to the number of income earners and the needs of a family as determined by household size and composition (Voydanoff, 1990). Both the household size and income earner dummy variables exhibit ambiguous signs of effect on accessibility to microcredit. For instance, with a larger number of income earners it may be considered that the SMEs are less constrained financially but, depending on how many are in their families the reverse may be true. Respondents with a small household size may have fewer responsibilities, thus they have low demand for credit than those with larger households and more responsibilities on their business which forces them to look for credit to augment their working capital (Balogun & Yusuf, 2011). In this study, household income also exhibits ambiguous effects. For example, an individual with a higher income may be less constrained financially and does not require any additional credit. This has

a negative effect on SMEs’ demand for credit. Besides, the microcredit provider puts a limit on household income as a criterion for borrowing from a microcredit institution, i.e., the probability of accessing microcredit increases with a low household income. An often-hypothesized idea is that the ethnic majority can access credit more easily than a minority (Biggs et al., 2002; Bruder et al., 2011). The question in this study is whether there are differences between Malay-owned firms and ones owned by other ethnicities in accessing microcredit. Therefore, in this study, the ethnicity dummy variable hypothesises a positive correlation with access to microcredit. The literature suggests that the educational level of SME owners makes some difference to access to microcredit. The education dummy variable is hypothesized to have a positive effect on the probability of accessing microcredit. SME owners with a higher educational level are likely to borrow microcredit. Educated SME owners are viewed as possessing more skills and have more exposure to the external risks, hence they might require more credit to sustain their business than uneducated SME owners. In addition, educated

70 SME owners may have a better understanding of the terms and conditions of the micro loan and are willing to comply with the requirements set up by the microcredit providers (Duvendack, Palmer- Jones, & Vaessen, 2014; Okurut et al., 2012).

SME characteristics such as age of the enterprise, sector, size of the enterprise and ownership need to be considered because credit access may largely be influenced by such factors. Age of the enterprise is hypothesised to positively correlate with access to credit. In accessing credit from financial institutions business experience plays a vital role. Younger enterprises have a limited or no credit track record and are known as less experienced entrepreneurs (Harvie et al., 2013). Businesses with more years of experience are more likely to have access to credit than those with fewer years in business. This is because SME owners with more years in operation are in a better position to repay their loan than beginners. A study by Abunyuwah and Blay (2013) found that entrepreneurs with more years of experience have a higher probability of accessing credit from formal financial institutions than their counterparts with relatively few years of experience in business. In contrast, Abdullah and Manan (2011) found that the impact of firm age in Malaysia on accessibility to credit is not significant because access to credit relies on the profit of the firm. Sector is also expected to have a strong influence on accessing microcredit. This study includes sector to test whether there is a difference in accessibility to microcredit for different sectors of the economy. For instance, Le (2012) found that the probability of obtaining a bank loan is higher if firms were engaged in the service sector followed by some manufacturing industries. It is hypothesized that SMEs involved in the manufacturing or service sectors will have more access to microcredit than the agricultural sector because the lender perceives the risky nature of agriculture (Umoh, 2006).

Size of enterprise also determines SMEs’ probability of accessing microcredit. The relationship

between the dependent variable and size of the enterprise shows mixed results. According to Allen et al. (2012),Beck et. al (2008) and Nguyen & Luu (2013), large firms are most likely to have fewer constraints when accessing credit because they have many alternative funding sources. However, small sized firms have a higher credit risk that limits their access to bank credit. Therefore, in this study, the size of the enterprise is predicted to have an inverse relationship with access to

microcredit because microcredit providers in Malaysia generally target credit schemes at the SMEs of smaller size as indicated by sales turnover or number of employees (SME Corporation Malaysia, 2015a). On the other hand, Gemechu & Reilly (2011) found that access to credit is not affected by firm size. Similarly, ownership of the SME is hypothesized to negatively correlate with access to credit. The literature reveals different financing behaviour across firm ownership. Nguyen & Luu (2013) found no significant relationship between types of ownership of firms and credit constraints.

71 Networking with the commercial banks, non-governmental organizations, microfinance institutions and business associations are important factors in determining access to microcredit. The literature reveals networking positively affects accessibility with traditional banks, but there is no evidence specifically for microcredit. Therefore, networking is hypothesised to be positively related to access to microcredit.

This study sought to find the influence on SMEs of the distance to the nearest microcredit provider on access to microcredit. In the literature, distance is always associated with high transaction cost so it tends to decrease access to credit (Garikipati, 2012; Ibrahim & Bauer, 2013). Therefore, distance to the nearest microcredit provider such as an MFI, commercial bank or DFI is expected to be inversely related to access to microcredit. The nearer the distance between the borrower and credit

institution, the higher the probability of access to microcredit than for those residing far away from the microcredit providers.

4.2

Factors in Malaysia that Influence the Choice of Microcredit Provider by