We show the marginal effect probit estimation of bank finance rejection for sole proprietor and partnership firms in Table 5.4a and Table 5.4b. We used the mfx
command in STATA to estimate the marginal effect after each probit regression run. Marginal effect provides the opportunity for simple percentage interpretation of effect. Again the study results are restricted to sole proprietor and partnership SMEs. In the model7, bank finance reject is defined as a combination of partial/outright overdraft rejections and partial/outright loan rejections. Overdraft reject is defined as partial/outright overdraft rejections. Loan reject is defined as partial/outright loan rejections.
This study shows in Table 5.4a that the gender of the owner manager has no significant effect on bank finance rejection rate. Hence, it is not possible to determine financial constraints on the basis of the gender of the owner manager of the business. This rejects hypothesis H3 (Male business owners are less likely to be rejected for bank finance than female business owners). Although research identified financial constraints (Bellucci et al., 2010) and risk aversion (Charness and Gneezy, 2012) as features of female entrepreneurs, Brindley (2005) indicate that differentiating between
6 If the model variables are highly correlated with one another, it may affect the estimates (Pevalin and
Robson, 2012, p. 290).
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genders in business can be problematic as different risk factors impact their start-up and existence. Therefore, this analysis finds no gender effect.
Table 5.4a shows that start-up firms were about 27% more likely to be rejected for bank finance in 2004 and 22% more likely to be rejected for bank finance in 2008, although the latter coefficient is found to be statistically insignificant. However, when this study estimates the model for overdraft and loan rejection separately we find that the coefficients become statistically significant. This supports hypothesis H2
Businesses with non-start-up status have lower probability of bank finance rejection. It also confirms existing research that start-up firms are more financially constrained, including during the financial crisis of 2007 (Saridakis et al., 2013; Cowling et al., 2012; Irwin and Scott, 2010).
Additionally, Table 5.4a shows that although the financially qualified owner manager variable carries a negative sign, the coefficient is found to be statistically insignificant for both 2004 and 2008 samples. Therefore, hypothesis H1 (Financial education of owner managers reduces bank finance rejection rate) is rejected. However, there is a strong statistical significance of self-confidence in finance supporting reduced bank finance rejection rate after the financial crisis. This suggests that although financial education is found to be useful (Disney and Gathergood, 2013; OECD, 2006), the self-confidence in finance makes a significant difference between success and failure in access to bank finance. This analysis therefore suggests that it may be inadequate to rely on financial education alone as a way to gain better access to bank finance. This study suggest that financially self-confident owner managers may produce better case for their business and present appropriate information to banks in their access to bank finance in times of economic hardship and financial distress. Thus, financially self-confident owner managers have the capacity to apply financial capability adequately to meet the needs of the business in accessing bank finance in times of crisis. It is possible to seek business advice as a solution to the absence of financial education of the business owner as suggested in research (Han et al., 2014).
We now consider the control variables. The results in Table 5.4a show that experienced owner managers had significantly reduced probability of bank finance rejection in 2004. Degree qualified owner managers are 8% less likely to be rejected for bank finance in 2008 but the effect is statistically insignificant. The findings of
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human capital researchers suggest that education and experience can act as a signal of potentially better business performance (Nolan and Garavan, 2016; Gruber et al., 2012; Unger et al., 2011; Segal et al., 2010; Colombo and Grilli, 2005; Colombo et al., 2004; Bates, 1990). Micro and small firms are less likely to be rejected for bank finance in 2008 with no statistical significance, hence no size effect. VAT registered firms are less likely to be rejected for bank finance in 2004 with no statistical significance. White British owner managed firms were 7% less likely to be rejected for bank finance in 2004 with no statistical significance. Owner managers with a net worth of less than £500,000 are more likely to be rejected for bank finance in 2004 with statistical significance. Hence, the greater the net worth of the owner manager, the less likely the firm was rejected for bank finance. The increased probability of bank finance rejection of 2008 could be associated with the financial crisis as banks become more selective in liquidity risk management (Smallbone et al., 2012; Cornett et al., 2011; Saridakis et al., 2008).
We considered industry as a control variable in Table 5.4b using marginal effect probit estimation of bank finance rejection. Agriculture, hunting and forestry industry was 8.5% less likely to be rejected for bank finance with strong statistical significance in 2004. Manufacturing industry was also 6.6% less likely to be rejected for bank finance with strong statistical significance in 2004. There was less likely to be overdraft rejection in 2004 for the following industry categories and with statistical significance: Agriculture, hunting and forestry; Manufacturing; Wholesale/Retail; and Real estate, renting and activities. However, all other industry categories in 2004 and 2008 had no statistically significant effect. It is possible that all industry categories were affected by the financial crisis as discussed in research (Smallbone et al., 2012; Cornett et al., 2011; Saridakis et al., 2008).