Table 6 Probit estimation between...

Table 6

Probit estimation between digital payments and determinants

Variables	Has made a digital payment (1)	Has made a digital payment (2)	Has made a digital payment (3)	Has made a digital payment (4)
LifeCycle: 18–25 years old	0.115***	0.106***	0.113***	0.0978***
LifeCycle: 18–25 years old	(0.0328)	(0.0340)	(0.0342)	(0.0344)
LifeCycle: 26–35 years old	0.0111	0.0829**	0.0836**	0.0533
LifeCycle: 26–35 years old	(0.0320)	(0.0336)	(0.0338)	(0.0341)
LifeCycle: 36–49 years old	−0.116***	0.0326	−0.00217	−0.0377
LifeCycle: 36–49 years old	(0.0338)	(0.0360)	(0.0363)	(0.0366)
LifeCycle: 50–64 years old	−0.151***	0.0736*	0.0280	−0.00752
LifeCycle: 50–64 years old	(0.0411)	(0.0444)	(0.0448)	(0.0451)
LifeCycle: Over 65 years old	−0.646***	−0.554***	−0.528***	−0.555***
LifeCycle: Over 65 years old	(0.0675)	(0.0686)	(0.0688)	(0.0692)
Is female	−0.238***	−0.233***	−0.193***	−0.172***
Is female	(0.0220)	(0.0229)	(0.0232)	(0.0234)
Are they in the workforce?	0.447***	0.367***	0.353***	0.341***
Are they in the workforce?	(0.0268)	(0.0278)	(0.0280)	(0.0282)
Secondary school completed		0.612***	0.544***	0.455***
Secondary school completed		(0.0273)	(0.0277)	(0.0285)
Tertiary education or more completed		1.300***	1.135***	1.068***
Tertiary education or more completed		(0.0362)	(0.0379)	(0.0384)
Income level: Quin2			0.135***	0.135***
Income level: Quin2			(0.0382)	(0.0383)
Income level: Quin3			0.181***	0.191***
Income level: Quin3			(0.0375)	(0.0377)
Income level: Quin4			0.330***	0.336***
Income level: Quin4			(0.0365)	(0.0367)
Income level: Quin5			0.512***	0.541***
Income level: Quin5			(0.0367)	(0.0371)
Country Category: 2				0.145***
Country Category: 2				(0.0277)
Country Category: 1				0.435***
Country Category: 1				(0.0285)
Constant	−0.0654*	−0.630***	−0.828***	−0.955***
Constant	(0.0361)	(0.0436)	(0.0492)	(0.0516)
LR χ²	823.73	2216.59	2450.42	2697.26
Pseudo R	0.0412	0.1123	0.1241	0.1366
Observations	14,499	14,324	14,324	14,324

Variables	Has made a digital payment (1)	Has made a digital payment (2)	Has made a digital payment (3)	Has made a digital payment (4)
LifeCycle: 18–25 years old	0.115***	0.106***	0.113***	0.0978***
LifeCycle: 18–25 years old	(0.0328)	(0.0340)	(0.0342)	(0.0344)
LifeCycle: 26–35 years old	0.0111	0.0829**	0.0836**	0.0533
LifeCycle: 26–35 years old	(0.0320)	(0.0336)	(0.0338)	(0.0341)
LifeCycle: 36–49 years old	−0.116***	0.0326	−0.00217	−0.0377
LifeCycle: 36–49 years old	(0.0338)	(0.0360)	(0.0363)	(0.0366)
LifeCycle: 50–64 years old	−0.151***	0.0736*	0.0280	−0.00752
LifeCycle: 50–64 years old	(0.0411)	(0.0444)	(0.0448)	(0.0451)
LifeCycle: Over 65 years old	−0.646***	−0.554***	−0.528***	−0.555***
LifeCycle: Over 65 years old	(0.0675)	(0.0686)	(0.0688)	(0.0692)
Is female	−0.238***	−0.233***	−0.193***	−0.172***
Is female	(0.0220)	(0.0229)	(0.0232)	(0.0234)
Are they in the workforce?	0.447***	0.367***	0.353***	0.341***
Are they in the workforce?	(0.0268)	(0.0278)	(0.0280)	(0.0282)
Secondary school completed		0.612***	0.544***	0.455***
Secondary school completed		(0.0273)	(0.0277)	(0.0285)
Tertiary education or more completed		1.300***	1.135***	1.068***
Tertiary education or more completed		(0.0362)	(0.0379)	(0.0384)
Income level: Quin2			0.135***	0.135***
Income level: Quin2			(0.0382)	(0.0383)
Income level: Quin3			0.181***	0.191***
Income level: Quin3			(0.0375)	(0.0377)
Income level: Quin4			0.330***	0.336***
Income level: Quin4			(0.0365)	(0.0367)
Income level: Quin5			0.512***	0.541***
Income level: Quin5			(0.0367)	(0.0371)
Country Category: 2				0.145***
Country Category: 2				(0.0277)
Country Category: 1				0.435***
Country Category: 1				(0.0285)
Constant	−0.0654*	−0.630***	−0.828***	−0.955***
Constant	(0.0361)	(0.0436)	(0.0492)	(0.0516)
LR χ²	823.73	2216.59	2450.42	2697.26
Pseudo R	0.0412	0.1123	0.1241	0.1366
Observations	14,499	14,324	14,324	14,324

Note(s): Standard errors are in parentheses ***p < 0.01, **p < 0.05, *p < 0.1

Column (1) shows the results of a probit model where the variable Has made a digital payment is related to life cycle, gender, and employment, while columns (2), (3) and (4) present the same probit model with other control variables, namely education, income level and country category. The country category groups countries based on fintech market penetration in each nation

Source(s): Authors’ elaboration

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