Investigating the determinants and growth of financial technology depth of penetration among the heterogeneous Africa economies

Figures & data

Table 1. Fintech depth of penetration determining variables

Variable names	Variable description and measurements	Variable type	Variable source	Data source
Depth of ATM (DATM)	Proportion of income devoted to ATM usefulness (ATM/Real GDP).	Dependent Variable (Model 1)	Mike (2010)	WBD (2019)
Depth of Internet Banking (DINTB)	Proportion of income devoted Internet Banking usefulness (Internet banking/Real GDP).	Dependent Variable (Model 2)	Haddad and Hornuf (2019)	WBD (2019)
Depth of Mobile Banking (DMB)	Proportion of income devoted to Mobile Banking usefulness (Mobile phone subscription/Real GDP).	Dependent Variable (Model 3)	Luarn and Lin (2005); Wu and Wang (2005)	WBD (2019)
Compatibility	A measure of users’ past experiences and benefits derived from the use of Fintech. This is measured using the first lag of dependent variables	Endogenously Predetermined Variable	Teo and Pok (2003)	WBD (2019)
Fintech Risk (FR)	This measure the degree users’ of Fintech perceive that it has no conflict with their needs and financial state. This is measured using the standard deviation of an index for Fintech	Endogenously Predetermined Variable	Ryu (2018); Abramova and Böhme (2016)	WBD (2019)
Complexity (COM)	A measure of the quality of human capital or Literacy rate. This is measured using the tertiary school enrolment.	Endogenous Explanatory Variable	Jarvenpaa et al. (2003) and Teo and Pok (2003)	WBD (2019)
Trail-ability and Observe-ability (TOB)	A measure of Fintech users’ financial strength which defines their ability and extent of use. It is measured with Real GDP per capita	Endogenous Explanatory Variable	E. Rogers (2003)	WBD (2019)
Relative Efficiency	The advantage current innovations have over previous ones. Rogers believes it can be measured with cost. Therefore, this study measured it with inflation rate.	Strictly exogenous control variable	Kleijnen et al. (2004); Teo and Pok (2003)	WBD (2019)
Financial Openness (FO)	This measure the degree to which financial operators adapt to changes. Measured with financial mkt access index.	Strictly exogenous control variable		IFS (2019)
Financial Development (FD)	This is the level of financial market resilience and fragility. It is measured with financial development index	Strictly exogenous control variable		IFS (2019)

Source: Based on theoretical and Empirical Literature.

Table 2. Heterogeneous system GMM: the main result

	One-step system GMM results			Two-step system GMM results
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
	DMPB	DATM	DINTB	DMPB	DATM	DINTB
DFintechit-1	1.346***	0.712***	1.131***	0.481***	0.453***	1.078***
	(14.38)	(29.49)	(15.65)	(10.82)	(15.68)	(21.15)
Constant	172.8*	−0.034**	0.320	987.7***	14.462***	−0.188
	(0.79)	(1.97)	(1.54)	(11.48)	(4.53)	(0.96)
Dummy Fron	866*	0.024**	−0.041	−208.5	0.333	−0.013
	(1.78)	(2.04)	(1.07)	(0.40)	(0.52)	(0.33)
Dummy Frag.	143.4*	0.012	−0.050	122.49*	0.303	−0.033
	(1.67)	(0.57)	(0.65)	(1.96)	(0.43)	(0.56)
Fintech Risk	−951.7*	−0.053	−0.083*	154.604***	0.040	−0.051
	(1.69)	(1.53)	(1.72)	(4.41)	(4.80)	(1.10)
Literacy Rate	−5.261	0.001***	0.0003	117.490***	0.0003	0.0002
	(0.79)	(5.74)	(0.51)	(3.72)	(0.22)	(0.53)
GDPPCit-1	0.001***	−0.000001***	1.28e-08	0.002**	−5.78e-08	−6.16e-10
	(3.57)	(2.96)	(0.84)	(2.17)	(1.30)	(0.05)
Fin Opennes	−245.8	−0.038***	−0.062	735.275	−0.115	−0.036
	(0.46)	(2.68)	(1.20)	(0.14)	(0.15)	(0.66)
Fin Dev.	3164.9***	0.080***	0.036	605.92	0.511	0.032
	(3.19)	(3.96)	(0.64)	(1.60)	(2.47)	(0.67)
Inflation	−26.302***	−0.0002	−0.001***	173.345***	−0.0002	−0.001
	(3.28)	(1.21)	(2.92)	(4.07)	(0.02)	(1.38)
F-Stat.	36.11***	94.97***	63.38***	2352.83***	32,546.35***	2046.8***
Year Dummy	Yes	Yes	Yes	Yes	Yes	Yes
No of Obs.	343	343	343	348	348	348
Groups/Instr.	30/26	30/26	30/26	30/21	30/21	30/26
AR(2)	0.652	0.251	0.972	0.151	0.088	0.658
Sargan Test	0.118	0.170	0.001	0.469	0.595	0.001
Hansan Test	0.169	0.545	0.099	0.186	0.685	0.099

Absolute value of t statistics in parentheses.

*** significant at 1%; ** significant at 5%; * significant at 10%.

Source: Estimation.

Table 3. Robust check for the heterogeneous system GMM: main result

	One-step system GMM result			Two step system GMM result
	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12
	DMPB	DATM	DINTB	DMPB	DATM	DINTB
DFintechit-1	1.346***	0.712***	1.131***	0.481***	0.453***	1.078***
	(16.92)	(33.34)	(18.87)	(2.92)	(6.28)	(9.68)
Constant	172.8	−0.034	0.320	987.7***	14.462	0.188
	(0.84)	(0.89)	(1.54)	(3.07)	(2.74)	(0.50)
Dummy Fron	866	0.024	−0.041	−208.5	0.333	−0.013
	(1.34)	(0.87)	(0.92)	(0.20)	(0.46)	(0.28)
Dummy Frag	143.4	0.012	−0.050	122.49	0.303	−0.033
	(1.12)	(0.27)	(0.79)	(0.50)	(0.38)	(0.64)
Fintech Risk(FR)	−951.7*	−0.053	−0.083	154.604	0.040	−0.051
	(1.77)	(1.15)	(1.69)	(0.91)	(0.56)	(0.54)
Literacy Rate(TSR)	−5.261	0.001	0.0003	117.490*	0.0000001	0.002
	(0.42)	(1.09)	(0.65)	(1.72)	(0.06)	(0.46)
GDPPCit-1	0.001**	−0.0000001	1.28e-08	0.002	−5.78e-08	−6.16e-10
	(2.15)	(1.26)	(0.94)	(0.83)	(1.05)	(0.05)
Fin Openness(FMA)	−245.8	−0.038	−0.062	735.275	−0.115	−0.036
	(0.27)	(0.79)	(1.08)	(0.05)	(0.15)	(0.71)
Fin Dev. (FDvI)	3164.9**	0.080**	0.036	605.92	0.511	0.032
	(2.22)	(2.13)	(0.72)	(0.35)	(0.87)	(0.58)
Inflation (INF)	−26.302**	−0.0002	−0.001**	173.345	−0.000	−0.001
	(2.44)	(0.62)	(2.22)	(0.94)	(0.00)	(0.61)
F-Stat.	94.97***	4924.95***	1259.03***	691.97***	890.13***	118.5***
Year Dummies	Yes	Yes	Yes	Yes	Yes	Yes
No of Obs.	343	343	343	348	348	343
Group/Instrumt.	30/26	30/26	30/26	30/21	30/21	30/26
AR(2)	0.550	0.173	0.946	0.522	0.598	0.679
Sargan Test	0.118	0.170	0.001	0.469	0.595	0.001
Hansan Test	0.169	0.545	0.099	0.186	0.685	0.099

Absolute value of t statistics in parentheses.

*** significant at 1%; ** significant at 5%; * significant at 10%.

Source: Estimation.

Table 4. Homogenous system GMM results (without Country’s dummy): baseline result

	One-step system GMM result			Two-step system GMM result
	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18
	DMPB	DATM	DINTB	DMPB	DATM	DINTB
DFintechit-1	1.252***	0.730***	1.130***	1.302***	0.724***	1.079***
	(16.17)	(30.29)	(23.21)	(33.82)	(38.80)	(26.32)
Constant	3226.4	−0.014**	0.174*	2237.35	−0.007	0.096
	(1.62)	(2.22)	(1.84)	(1.55)	(0.97)	(0.84)
Fintech Risk (FR)	−1,007.5*	−0.068*	−0.054*	−600.6	−0.065	−0.032
	(1.80)	(1.85)	(1.93)	(1.62)	(1.43)	(1.04)
Literacy Rate (TSR)	−1.615	0.001***	0.00002	−5.189	0.0003	0.0003
	(0.27)	(6.46)	(0.05)	(0.73)	(0.51)	(0.76)
GDPPCit-1	0.001***	−9.43e-09**	1.23e-08	0.0004*	−6.96e −09	3.55e-09
	(3.17)	(2.15)	(1.38)	(1.77)	(0.86)	(0.47)
Fin Openness (FMA)	−897.3**	−0.055***	−0.018	−471.360	−0.016	−0.027*
	(2.23)	(5.24)	(0.95)	(1.15)	(0.65)	(1.80)
Fin Dev. (FDvI)	1,758.1***	0.068***	0.057*	919.4	0.059**	0.043*
	(2.92)	(4.33)	(1.72)	(1.34)	(2.71)	(1.76)
Inflation (INF)	−24.695***	−0.0002	−0.001***	−21.86***	−0.0002	−0.0003
	(3.25)	(1.37)	(3.65)	(3.24)	(0.92)	(0.79)
Years	1.599	−0.000**	0.00009*	1.109	−0.000	0.00002
	(1.62)	(2.21)	(1.83)	(1.55)	(0.87)	(0.84)
F-Stat.	39.69***	136.5***	111.03***	1307.20***	10,661.56***	1664.62***
Year Dummies	Yes	Yes	Yes	Yes	Yes	Yes
No of Obs.	343	343	343	343	343	343
Groups/Instr.	30/26	30/26	30/26	30/26	30/26	30/26
AR(2)	0.581	0.236	0.906	0.543	0.218	0.441
Sargan/Hansan Tests	0.098	0.076	0.000	0.098/0.143	0.076/0.630	0.000/0.230

Absolute value of t statistics in parentheses.

*** significant at 1%; ** significant at 5%; * significant at 1%.

Source: Estimation.

Table 5. Long-run coefficients of the significant short-run variables in models 1 to 6

	Model 19	Model 20	Model 21	Model 22	Model 23	Model 24
	DMPB	DATM	DINTB	DMPB	DATM	DINTB
DFtchit-1	−3.889***	2.47***	−8.611**	0.926***	0.829***	−13.82
Constant	−4993	−0.116*	NSLS	1,902,639***	26.446***	NSLS
Fintech Risk	2749.5	NSLS	0.635	297.83***	NSLS	NSLS
Literacy Rate	NSLS	0.003***	NSLS	226.3.***	NSLS	NSLS
GDPPCit-1	−0.002***	0.00000002**	NSLS	0.005*	NSLS	NSLS
Fin Openness	NSLS	−0.130***	NSLS	NSLS	NSLS	NSLS
Fin Dev	−9143.1***	0.278***	NSLS	NSLS	NSLS	NSLS
Inflation	75.98***	NSLS	0.011*	333.9***	NSLS	NSLS

*** significant at 1%; ** significant at 5%; * significant at 10%.

NSLS = No Short-run and Long-run Significance.

Source: Estimation.

Table 6. Fintech growth rate in relation to its base years average penetration

Years	Coeff. of DMPB	Coeff. of DATM	Coeff. of DINTB	Grth rate of MPB	Grth rate of ATM	Grth rate of INTB
Formular	Β	Β	β	[100-e^β]%	[e^β-1]*100	[e^β-1]*100
2004	0	0	0	100	100	100
2005	0	0	0	100	100	100
2006	−3.67	0.008	−0.195	99.975	0.803	−17.717
2007	−3.83	−0.01	−0.192	99.978	−0.995	−17.469
2008	−3.77	0.002	−0.182	99.977	0.200	−16.640
2009	−3.63	−0.002	−0.18	99.973	−0.200	−16.473
2010	3.73	0.007	0.189	58.185	0.702	20.804
2011	3.86	0.003	0.191	52.318	0.300	21.046
2012	3.73	0.003	0.189	58.185	0.300	20.804
2013	3.86	0	0.191	52.318	0.000	21.046
2014	3.73	0.004	0.189	58.185	0.401	20.804
2015	3.86	0.003	0.191	52.318	0.300	21.046
2016	3.73	0.005	0.189	58.185	0.501	20.804
2017	3.86	0.003	0.191	52.318	0.300	21.046
2018	3.73	0.004	0.189	58.185	0.401	20.804

Source: Estimation.

Table 1A: Economic groups used in the study

Emerging markets	Frontier markets					Fragile markets
Egypt	Angola	Ethiopa	Malawi	Rwanda	Tunisia	Chad
Morocco	Botswana	Ghana	Mauritania	Senegal	Zambia	Cote d’Ivore
South Africa	BurkinaFaso	Kenya	Mauritius	Seychelles	Algeria	Niger
	Burundi	Madagascar	Mozambique	Swaziland	Nigeria	Sudan
	Cameroon	Mali	Namibia	Tanzania		Togo

Source: Author’s Compilation Based on FTSE and SPIP (2017) World Economic Groupings.

Table 2A. Bond et al. (2001) rule of thumb between the difference or system GMM estimator

	Coefficients of the lagged dependent variable
Estimators	DINTB	DATM	DMPB
Pooled OLS estimate	0.995	0.810	0.9898
FE estimate	0.776	0.786	0.865
Difference GMM (One-step)	0.714	0.728	1.283
Difference GMM (Two-step)	0.692	0.735	1.402

Decision Rule: If the estimate of δ in the difference GMM result is below or close to FE estimate, then it is downward biased, hence a System GMM is better, otherwise use the Difference GMM.

Source: Estimation.

Mike, L. (2010). ATM Potential in Africa. http://www.sparkatm.co.za/wp-content/uploads/2013/07/ATM-potential-in-Africa.pdf ( Accessed 18/04/2018)

 Google Scholar

Haddad, C., & Hornuf, L. (2019). The emergence of the global fintech market: Economic and technological determinants. Small Business Economics, 53(1), 81–105. https://doi.org/10.1007/s11187-018-9991-x

 Web of Science ®Google Scholar

Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioural intention to use mobile banking. Computers in Human Behavior, 21(6), 873–891. https://doi.org/10.1016/j.chb.2004.03.003

 Web of Science ®Google Scholar

Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & Management, 42(5), 719–729. https://doi.org/10.1016/j.im.2004.07.001

 Web of Science ®Google Scholar

Teo, T. S. H., & Pok, S. H. (2003). Adoption of WAP-enabled mobile phones among internet users. Omega, 31(6), 483–498. https://doi.org/10.1016/j.omega.2003.08.005

 Web of Science ®Google Scholar

Ryu, H. S. (2018). What makes users willing or hesitant to use Fintech?: The moderating effect of user type. Industrial Management & Data Systems.

 Google Scholar

Abramova, S., & Böhme, R. (2016), “Perceived benefit and risk as multidimensional determinants of Bitcoin use: A quantitative exploratory study”, Proceedings in International Conference on Information Systems, Dublin, 1–18

 Google Scholar

Jarvenpaa, S. L., Lang, K. R., Takeda, Y., & Tuunainen, V. K. (2003). Mobile commerce at crossroads. Commu-nications of the ACM, 46(12), 41–44. https://doi.org/10.1145/953460.953485

 Web of Science ®Google Scholar

Rogers, E. (2003). Diffusion of Innovations (5th ed.). The Free Press.

 Google Scholar

Kleijnen, M., Wetzels, M., & de Ruyter, K. (2004). Consumer acceptance of wireless finance. Journal of Financial Services Marketing, 8(3), 206–217. https://doi.org/10.1057/palgrave.fsm.4770120

 Google Scholar

Bond, S. R., Hoeffler, A., & Temple, J. R. (2001). GMM estimation of empirical growth models. Centre for Economic Policy Research (CEPR) Discussion Paper No. 3048.

 Google Scholar