How does financial literacy impact on inclusive finance?

Financial Innovation

Table 9 Robustness checking with complementary log–log regression.

Covariates	Complementary log–log regression
Covariates	Banking	Microfinance	FinTech
Profession	2.498***	− 1.346**	− 0.068
	(0.502)	(0.619)	(0.201)
Income	2.415***	− 3.070***	0.266**
	(0.604)	(0.845)	(0.133)
Education	1.246***	0.650*	− 0.169
	(0.325)	(0.380)	(0.181)
Deposit & withdraw ability	6.086***	10.907***
	(0.866)	(2.697)
DPS & loan	0.624	2.788***
	(0.395)	(0.758)
DPS & loan interest rate	2.734***	2.449***
	(0.535)	(0.725)
Installment	0.217	0.617*
	(0.231)	(0.353)
Security money	− 0.343	2.467***
	(0.441)	(0.667)
Personal investment		− 1.832***
		(0.570)
FT send & withdraw			2.738***
			(0.188)
Billpay ability			− 0.309
			(0.393)
Software use ability			1.073***
			(0.342)
Online dealing ability			1.320**
			(0.531)
FinTech training			0.910**
			(0.379)
cons	− 12.68	− 10.016	− 1.684
	(1.974)	(5.102)	(0.423)

This model defines the robustness test of all models with Complementary Log–log regression. The banking model shows − 53.157 log-likelihood, LR chi² (8) is 1036.49, microfinance model shows − 46.57 log-likelihood, LR chi² (9) is 1077.69, FinTech model shows − 178.60 log-likelihood, LR chi² (8) is 687.76. The value of Prob > chi² is 0 for all models, and the observation of A & B is 852. ***, **, * refer significance level at 99%, 95%, 90%, respectively. The value within the first bracket is the standard error value