Lending methodologies and SMEs access to finance in Ghana; the mediating role of credit reference information

Figures & data

Table 1. Study population and sample sizes

Sector	General population	Target population	Accessible population	Sample
Research and development	2,211	564	544	226
HR services	1,231	421	401	196
Agric processing	908	231	277	123
Tourism	1,011	432	403	196
Beverage manufacturing	803	133	109	86
Manufacturing (others)	1,891	673	620	234
Total	8,055	2,454	2,354	1,061

Table 2. Basic owner and business characteristics

Variable	Level	Frequency	Percent
Gender	Male	552	65%
	Female	296	35%
	Total	848	100%
Age	26–35	96	11%
	36–45	232	27%
	46–55	324	38%
	Over 55	196	23%
	Total	848	100%
Education	High school	152	18%
	Diploma/Bachelor’s degree	228	27%
	Master’s	468	55%
	Total	848	100%
Ownership	Sole ownership	536	63%
	Partnership	172	20%
	Co-operate	140	17%
	Total	848	100%
Business category	Wholesale/retail	208	25%
	Manufacturing	196	23%
	Agriculture	100	12%
	Services	344	41%
	Total	848	100%
Business age	1–5 yrs	56	7%
	6–10 yrs	232	27%
	11–20	208	25%
	Above 20	352	42%
	Total	848	100%
Bank experience	1–5 yrs	56	7%
	6–10 yrs	200	24%
	11–20	284	33%
	Above 20	308	36%
	Total	848	100%

Source: Survey data, (2021).

Table 3. Summary statistics on lending methodology and its indicators

Variable	Min.	Max.	Mean	Median	SD
I was asked if I had a collateral	3	5	4.36	5.00	0.72
I was asked to provide my balance sheet information	3	5	4.33	4.00	0.73
I was interviewed on the policy, philosophy and performance of my business	3	5	4.35	5.00	0.73
I was asked to provide a copy of my business plan	3	5	4.32	4.00	0.75
I was asked to provide or discuss my financial performance target	3	5	4.32	4.00	0.74
I was asked about the historical records of my sales	3	5	4.35	4.00	0.71
I was asked about any debts I had	3	5	4.36	5.00	0.72
I was asked about the recognition of my business	1	4	3.18	3.00	0.85
Collateral and banking records	23	39	33.58	35.00	5.16
I was asked about my age	1	4	3.21	3.00	0.81
I was asked about my gender	1	4	3.15	3.00	0.84
I was asked if I was married	1	4	3.17	3.00	0.86
I was asked of my highest education	1	4	3.21	3.00	0.86
I was asked to indicate my experience in the business	3	5	4.35	5.00	0.73
The number of years the business had been in operation was ascertained	3	5	4.32	4.00	0.75
I was made to provide information on the ownership structure	2	5	4.29	4.00	0.80
I was asked about the size of my business	2	5	4.31	4.00	0.78
I was asked to describe the structure of the industry I operated in	2	5	4.28	4.00	0.81
I was asked to indicate the number of years I had banked with the bank	3	5	4.32	4.00	0.75
I was asked to indicate if I had received a loan from the bank in the past	3	5	4.29	4.00	0.76
I was asked to indicate how many times I had received loan in the past from the bank	3	5	4.33	4.00	0.73
Personal and business characteristics	32	56	47.24	48.00	7.21
Lending Methodology	55	95	80.82	81.00	12.08

Source: Survey data (2021).

Table 4. Summary statistics on credit reference information and its indicators

Variable	Min.	Max.	Mean	Median	SD
The procedure for obtaining bank loan has been simplified by the introduction of the use of credit referencing information	3	5	4.43	5.00	0.64
Banks are responsive to SME financing needs.	3	5	4.20	4.00	0.70
Sharing of credit information has reduced loan processing costs and increased lending volume significantly	3	5	4.41	4.00	0.64
Sharing of credit information has reduced interest rates charged on loans by commercial banks	3	5	4.18	4.00	0.71
Credit information sharing from Credit Reference Bureaus rewards and promotes good credit track record for firms.	2	5	4.39	5.00	0.70
Credit information sharing acts a deterrent to loan default.	3	5	4.24	4.00	0.70
Credit information sharing has impacted positively on loan repayment	3	5	4.42	5.00	0.66
Credit information sharing has effectively reduced non-performing loans on the institution’s financial statement	2	5	4.12	4.00	0.79
Credit risk information	24	40	34.40	36.00	4.52

Note: SD—standard deviation.

Source: Survey data (2021).

Table 5. Tests of data distribution and normality

Variable	Mean	SD	Skewness	Kurtosis	Shapiro Wilk’s Test
Lending Methodologies	80.82	12.08	−0.492	−0.87	0.912
Credit reference information	34.4	4.52	−0.441	−0.86	0.895

**p < 0.001.

Source: Survey data (2021).

Table 6. Factor loadings and variance of credit reference information from EFA

Statistics	Factor loading
Eigenvalue (5.34)
Variance (%, total = 66.34)
Indicator
The procedure for obtaining bank loan has been simplified by the introduction of the use of credit referencing information	0.97
(2) Banks are responsive to SME financing needs.	0.96
(3) Sharing of credit information has reduced loan processing costs and increased lending volume significantly	0.96
(4) Sharing of credit information has reduced interest rates charged on loans by commercial banks	0.51
(5) Credit information sharing from Credit Reference Bureaus rewards and promotes good credit track record for firms.	0.69
(6) Credit information sharing acts a deterrent to loan default.	0.94
(7) Credit information sharing has impacted positively on loan repayment	0.95
(8) Credit information sharing has effectively reduced non-performing loans on the institution’s financial statement	0.63

Source: Survey data (2021).

Table 7. Psychometric properties of lending methodologies and credit risk information scales

Variable	Factor	Cronbach’s alpha	Composite reliability	Average variance extracted	Maximum shared variance
Lending methodologies	Factor 1	0.937	0.954	0.621	0.212
	Factor 2	0.935	0.944	0.619	0.204
	Whole scale	0.966	—	—	—
Credit risk information	Whole scale	0.926	—	—	—

Source: Survey data (2021).

Table 8. Summary statistics on lending methodologies and their indicators

Variables	Scale			Mean
	Minimum	Maximum	Median	Statistic	SD
Collateral and banking records	8	40	24	33.58	5.16
Personal and business characteristics	12	60	36	47.24	7.21
Lending methodologies	20	100	60	80.82	12.08

Source: Survey data (2021).

Table 9. The one-sample t-test for a high level of application of lending methodologies

Variable	Test value	t	df	p	Mean difference	95% CI
						Lower	Upper
Collateral and banking records	24	54.077	847	0.000	9.58	9.23	9.93
Personal and business characteristics	36	45.392	847	0.000	11.24	10.75	11.72
Lending methodology	60	50.199	847	0.000	20.82	20.00	21.63

ƚ^a. Value equal to the median in Table 5.10; CI—confidence interval.

Source: Survey data (2021).

Figure 1. Standardised residuals and predicted values (the effects of lending methodologies on access to credit).

Source: Survey data (2021).

Table 10. Correlation matrix of relevant variable

Variables	#	1	2	3	4	5	6	7	8	9	10	11	12
CBRs	1	1	.904**	.967**	.312**	.494**	.270**	.479**	.446**	.216**	.238**	0.056	.251**
PBCs	2		1	.983**	.313**	.485**	.238**	.501**	.534**	.107**	.143**	0.061	.338**
Lending methodologies	3			1	.320**	.501**	.258**	.504**	.509**	.156**	.187**	0.06	.309**
Access to credit (₵)	4				1	.262**	.283**	.230**	.239**	.137**	.084*	.117**	.489**
CRI	5					1	.521**	.381**	.300**	.301**	.364**	.242**	.533**
Number of employees	6						1	.320**	.285**	.220**	.288**	0.059	.370**
Business age	7							1	.915**	.071*	.135**	.168**	.198**
Experience with bank	8								1	0.005	.095**	.115**	.153**
Fixed assets	9									1	.918**	.191**	.123**
Current assets	10										1	.160**	.089**
Annual sales 2019	11											1	.189**
Net income 2019	12												1

ƚ**p < 0.001; *p < 0.05; CBRs—collateral and business records; PBCs—personal and business characteristics; CRI—credit referencing information.

Source: Survey data (2021).

Table 11. The mediating influence of CRI on the relationship between lending methodologies and access to credit

Model	Predictor	Coefficients			95% CI	Tol.	Model fit
		B	SE	Β(t)			R^2	Adjusted R^2	Change in R^2	Durbin-Watson	F
1^a	(Constant)	2.51	0.10	(24.12)**	±0.41		0.122	0.121	0.001	—	117.67**
	CRI*Lmethodology	0.04	0.00	0.35(10.85)**	±0.01	—
2^b	(Constant)	4.18	0.26	(16.17)**	1.01		0.359	0.352	0.007	1.88	52.2**
	CRI*Lmethodology	−0.01	0.01	−0.06(−1.52)	±0.02	0.45
	Covariate
	Number of employees	0.00	0.02	0.01(0.18)	±0.09	0.64
	Business age	−0.23	0.05	−0.33(−4.44)**	±0.21	0.14
	Experience with bank	0.23	0.05	0.31(4.33)**	±0.21	0.15
	Fixed Assets	0.23	0.05	0.36(4.84)**	±0.19	0.14
	Current Assets	−0.28	0.04	−0.48	±0.17	0.14
	Annual Sales 2019	−0.09	0.05	−0.06(−1.88)	±0.18	0.86
	Net Income 2019	0.17	0.03	0.23(5.85)**	±0.11	0.52

ƚ **p < 0.001; ^a baseline model without covariates; ^b ultimate model with covariates; SE—standard error; CI—confidence interval (of B), CRI—credit referencing information; Tol.—tolerance; Tolerance ≥0.1 for each predictor.

Source: Survey data (2021).

Table 12. The mediating influence of CRI on the relationship between lending methodologies and access to credit

Model	Predictor	Coefficients			95% CI	Model fit
		B	SE	Β(t)		R^2	Adjusted R^2	Change in R^2	Durbin-Watson	F
1^a	(Constant)	2.51	0.1	(24.10)**	±0.41	0.122	0.119	0.00	1.78	58.47**
	CRI*CBRs	0.03	0.03	0.12(1.04)	±0.12
	CRI*PBCs	0.04	0.02	0.23(1.99)	±0.09
2^b	(Constant)	2.13	0.12	(17.79)**	±0.47	0.318	0.311	0.01	1.871	43.43**
	CRI*CBRs	0.15	0.03	0.56(4.71)**	±0.13
	CRI*PBCs	−0.1	0.02	−0.51(−4.11)**	±0.09
	Covariate
	Number of employees	0.05	0.02	0.07(2.16)*	±0.09
	Business age	−0.22	0.06	−0.31(−4.0)**	±0.22
	Experience with bank	0.35	0.06	0.47(5.97)**	±0.23
	Fixed Assets	0.25	0.05	0.38(4.99)**	±0.20
	Current Assets	−0.22	0.05	−0.38(−4.89)**	±0.18
	Annual Sales 2019	0.01	0.05	0.01(0.2)	±0.18
	Net Income 2019	0.34	0.03	0.45(12.26)**	±0.11

ƚ **p < 0.001; *p < 0.05; a baseline model without covariates; b ultimate model with covariates; SE—standard error; CI—confidence interval (of B), CBRs—collateral and business records; PBCs—personal and business characteristics; CRI—credit referencing information; Tolerance ≥0.1 for each predictor.

Source: Survey data (2021).