Who are more likely to borrow from non-primary resources?

To investigate this question, I employ Equation (3-1) following Cole et al (2004):

Prob(SWITCHBORROWi=1) = 𝛼0 + 𝛼1 × Firm and Owner Characters (X1i) + 𝛼2

× Most Recent Loan Application Characters (X2i) + 𝛼₃ × Primary Relationship

Characters (X3i) + 𝛼4 × Banking Market Concentration Characters (X4i) + εi

….……….……..Eq.(3-1)

where Prob(SWITCHBORROWi=1) is the probability of most recent borrowing from a non-primary lender and 𝛼₀ is the constant term, εi is the error term. ‘i’

indicates the ‘ith’ firm in the dataset. I have adopted Probit Regression for the estimation. Table 3-5 presents the results of the estimation.

180

Table 3-5 Determinants for Most Recent Loan Application Choice from Firms’ Non-primary Financial Institutions

Coe (S.E.) dy/dx

Constant 2.6457

***

(0.4307)

Industry Dummies YES YES

Region Dummies YES YES

YEAR98DUMMY 0.3587

***

(0.0835) 13.13%

Firm and Owner Characters

LOGTOTEM -0.0721 ** (0.0336) -2.56% CORPORAT _(0.0839) 0.0672 2.37% LOGFAGE _(0.0574) -0.0568 -2.02% STARTUP _(0.1622) 0.1152 4.18% INDYEARROA _(0.0014) -0.0013 -0.05% RISKYSCORE 0.0832 * (0.0449) 2.96% OWNERMANAGED _(0.1063) -0.1304 -4.74% FAMILY _(0.0876) -0.0111 -0.39% FEMALE _(0.0825) -0.0023 -0.08% EDUBA 0.1504 ** (0.0715) 5.34%

Loan Deal Characters

LOGMRLSIZE -0.1307 *** (0.0236) -4.64% LOCDUM -0.4908 *** (0.0711) -17.69%

Banking Relationship Characters

LOGPRIMEREL _(0.0358) 0.0260 0.92% LOGPRIMEDIST _(0.0271) -0.0085 -0.30% PRIMEBANK -0.5833 *** (0.1100) -22.14% PRIMESAVE -0.3247 ** (0.1460) -12.15%

Banking Market Conditions

HHIL 0.0892 (0.1483) 3.23% HHIH -0.1348 * (0.0711) -4.78% Observations 1765 Wald χ2 321.69*** Log Likelihhod -960.98 Pseudo R2 15.16% Predicted probability 31.46%

(Table 3-5 presents the Probit regressions for the determinants of Most Recent Loan Application (MRLA) Choice from Primary/Non-primary financial institutions. The dependent variable is SWITCHBORROW. Control variables including industrial and regional dummies are not reported in the table. “***”,”**”,”*” stand for the confidence level of 1%, 5% and 10% respectively. VIF for all regressions have been calculated, no multicollinearity problem has been found. Robust Standard Errors are used.)

181 Table 3-5 presented the model to answer the first research question: a) What is the likeliness of switching to alternative lenders for small businesses? The Chi2 statistic indicates a good model fitness for the regression. As presented in Table 3-5, firstly, it is shown that firm size negatively associates with “switching behaviour”. Larger size firms are less likely to borrow from non-primary financial institution and the marginal effect is -2.56%. This could be interpreted as small firms are supressed to the non-primary sources of finance when their quality has been identified by the primary bank. Secondly, more financially risky firms are not likely to borrow from their primary financial institution with a marginal effect of 2.96%, possibly due to the same reason as firm size. Moreover, it is found that if the owner of the firm has better educational background, it is 5.34% more likely for the firm to borrow from a non-primary financial institution.

In terms of the loan characters – loan size and loan type, both of which are significant at 1% level. Loan size is negatively related with “switching behaviour” with a marginal effect of -4.64%. Information advantage of primary financial institution plays important role here for the loan application with larger size. Secondly, business lines of credit are 17.69% more likely to be made from primary financial institution. Comparing with other types of loans, lines of credit are issued based on the accumulation of previous information and therefore, it is less likely for small business to apply lines of credit from non-primary financial institutions.

In terms of financial institution relationship characters in the regression, firstly, primary banking relationship and the distance between the firm and its primary financial institutions play insignificant role for the “changing” decision. The possible reason will be explored in section 3.3.5.1 for further analysis. Secondly,

182 when primary financial institution is a commercial bank, the firm is less likely to borrow from a non-primary financial institution and the likelihood of “switching behaviour” would be reduced by 22.14%, as the commercial banks have stronger market power to maintain the firms to borrow within the institution. A dummy of whether the firm is using any checking or savings service from its primary financial institution has been considered in the regression, as another proxy describing the primary banking relationship. It is shown that if a firm has already set up a bank account with its primary financial institution, the firm would like to borrow from the same financial institution. Such result is consistent with the theories from Norden and Weber (2010) and Sufi (2009), as such services could provide additional information to the service supplier financial institution and it saves the monitoring cost of the loan application as well as the information acquisition costs. In general, if a firm has a bank account in the primary bank, there is 12.15% less chance for the firm to borrow from a non-primary financial institution. These findings confirm that existing relationships would be able to retain the firm with the same financial resource for loan applications.

Moreover, HHI as a measurement of banking market concentration for the firm has been considered into the regression. Table 3-5 shows that there is a negative relationship between highly concentrated banking market and “switching behaviour”, indicating that in a more competitive banking market, small business is more probably to borrow from non-primary resources. This supports Petersen and Rajan’s (1994 and 1995) relationship lending and banking market concentration theory.

Overall, Table 3-5 supports Petersen and Rajan’s (1994 and 1995) relationship lending and banking market concentration theory. Firms with severe

183 financial risks and higher degree of asymmetric information problem are more likely to switch to non-primary source of finance. While the existing banking relationship and better informational transparency would be able to maintain the firm to borrow within the banking relationship. The adjusted R-square is 5.76% for only control variables included in the regression. And firm and owner characteristics, loan deal characteristics, banking relationship characteristics and banking market concentration contribute30 2.84%, 3.86%, 2.49% and 0.2% to the adjusted R-square respectively.

3.3.4.2 Does the behaviour of borrowing from non-primary financial institution