Fintech, bank funding, and economic growth in Sub-Saharan Africa

Figures & data

Figure 1. SEM path diagram.

Table 1. Descriptive statistics

Variable	Mean	Std. Dev	Min	Max
Levels
coredep_tl	0.66	0.22	0.22	0.98
ltd_tl	0.09	0.09	0.001	0.72
eq_tl	0.14	0.06	0.01	0.34
gvtdebt	30.99	21.21	4.66	129.31
inv_flows	1.2667	2.6279	−8.75	25.11
dom_cred	19.014	14.4075	2.26	106.04
gdp	2742.66	2269.78	315.7	11215.9
Fintech (in levels)
mma1kreg	640.07	608.38	0.06	2007.44
mmareg	12 million	14.6 million	893 000	66 million
mmt	435 million	658 million	3 218 000	353 million
vmmt	11.62	2.31	5.64	15.54
Log transformation
lncoredep_tl	−0.49	0.40	−1.55	−0.02
lnltd_tl	−2.83	0.89	−6.72	−0.33
lneq_tl	0.14	0.06	0.03	0.29
lngvtdebt	3.23	0.68	1.54	5.15
lninv_flows	−0.01	1.11	−3.00	3.22
lndom_cred	2.71	0.70	0.82	4.66
lngdp	4.03	1.34	1.34	7.05

mma1kreg = mobile money accounts active per 1 000 registered adults; mmareg = mobile money accounts registered; mmt = mobile money transactions; vmmt = value of mobile money transactions; coredep_tl=coredeposits to total liabilities; ltd_tl = long-term debt to total liabilities; eq_tl = equity to total liabilities; gvtdebt = government debt; invflows = investment flows; domcred = domestic credit provided by banks scaled by gdp; gdp = gross domestic product; ln =natural logarithm.

Table 2. Recursive model estimates

	Direct effects (1)	(2)	(3)	Total effects (4)	(5)	(6)
Variables	Coefficient	Standard error	Economic impact	Coefficient	Standard error	Economic impact
fintech <-
coredep	−0.0049	0.0218	—	−0.0049	0.0218	—
ltd_tl	0.0305	0.0505	—	0.0305	0.0505	—
eq_tl	0.094***	0.0242	0.0056	0.094***	0.0242	0.0056
gdp <-
coredep	0.1082	0.1349	—	0.1082	0.1349	—
ltd_tl	0.1315**	0.057	0.0873	0.1315**	0.057	0.0873
eq_tl	0.1745***	0.1225	0.0078	0.1745***	0.1225	0.0078
fintech	0.1727***	0.0509	0.1289	0.1926***	0.05	0.1437
gvtdebt	−1.3401***	0.081	−0.6801	−1.3401***	0.081	−0.6801
inv_flows	0.5263***	0.0449	0.4360	0.5263***	0.0449	0.4360
dom_cred	−0.6528***	0.0714	−0.3401	−0.6528***	0.0714	−0.3401

* p < 0.01** p < 0.05*** p < 0.1.

coredep_tl = coredeposits to total liabilities; ltd_tl = long-term debt to total liabilities; eq_tl = equity to total liabilities; gvtdebt = government debt; invflows = investment flows; domcred = domestic credit provided by banks scaled by gdp; gdp = gross domestic product; fintech = fintech index.

Table 3. Indirect effect results

Variables	Coefficient	Std.err	Economic impact
Fintech
coredep	0 (no path)	—	—
ltd_tl	0 (no path)	—	—
eq_tl	0 (no path)	—	—
Gdp
Coredep	0 (no path)	—	—
ltd_tl	0 (no path)	—	—
eq_tl	0 (no path)	—	—
Fintech	0.0199	0.0142	—
Gvtdebt	0 (no path)	—	—
inv_flows	0 (no path)	—	—
dom_cred	0 (no path)	—	—

* p < 0.01** p < 0.05*** p < 0.1.

coredep_tl=coredeposits to total liabilities; ltd_tl = long-term debt to total liabilities; eq_tl = equity to total liabilities; gvtdebt = government debt; invflows = investment flows; domcred = domestic credit provided by banks scaled by gdp; Fintech = Fintech index.

Table 4. Robustness test results

Variables	Bank size			NPL
	(1)	(2)	(3)	(4)	(5)	(6)
	Coefficient	Std.err	Economic impact	Coefficient	Std.err	Economic impact
fintech <-
coredep	−0.0049	0.0218		0.0033	0.0221	—
ltd_tl	0.0305	0.0505		0.0448	0.0546	—
eq_tl	0.094***	0.0243	0.0056	0.1026***	0.026	0.0062
gdp <-
coredep	0.1081	0.1214	—	0.0563	0.132	—
ltd_tl	0.1278**	0.0513	0.0849	0.1225**	0.0525	0.0814
eq_tl	0.2349**	0.1105	0.0105	0.2305*	0.1154	0.0103
fintech	0.1985***	0.0453	0.1481	0.2297***	0.0501	0.1714
gvtdebt	−0.9501***	0.0892	−0.4821	−0.9576***	0.0911	0.486
inv_flows	0.5861***	0.0412	0.4855	0.5484***	0.0436	0.4543
dom_cred	−0.3576***	0.0751	−0.1868	−0.3938***	0.0784	−0.1102
size	0.1971**	0.026	0.3412	0.211***	0.0274	0.3653
npl	—	—		−0.0144	0.0488	—

* p < 0.01 ** p < 0.05 *** p < 0.1.

coredep_tl = coredeposits to total liabilities; ltd_tl = long-term debt to total liabilities; eq_tl = equity to total liabilities; gvtdebt = government debt; invflows = investment flows; domcred = domestic credit provided by banks scaled by gdp; fintech = fintech index; size = bank size; npl = non-performing loans.

Table 5. 2SLS estimation results

Variables	Coefficient	Robust Standard Error	Economic impact
fintech <-
lncoredep_tl_lag	−0.089	0.1495	−0.0421
lnltd_tl_lag	−0.0342	0.0835	−0.0361
lneq_tl_lag	0.5024	0.1237***	0.2713
gdp <-
lncoredep_tl_lag	0.4501	0.1959**	0.1383
lnltd_tl_lag	0.3372	0.0762***	0.2312
lneq_tl_lag	0.3616	0.1537**	0.1268
gdp <-
fintech_lag	0.1563	0.0402***	0.1047
dom_cred_lag	−0.3864	0.0765***	−0.3751
gvtdebt_lag	−1.2405	0.0764***	0.7215
inv_flows_lag	0.3643	0.0547***	0.3762

* p < 0.01 ** p < 0.05 *** p < 0.1.

Table

Variables	coredep_tl	ltd_tl	eq_tl	gdp	gvtdebt	dom_cred	inv_flows	fintech
coredep_tl	1.0000
ltd_tl	−0.0355	1.0000
eq_tl	0.2177*	0.0345	1.0000
gdp	0.0261	0.0287	−0.053	1.0000
gvtdebt	−0.0372	0.1316*	0.1801*	−0.0595	1.0000
dom_cred	−0.0683	−0.0983	0.0724	−0.3830*	−0.1099*	1.0000
inv_flows	0.0461	0.0048	0.1875	0.3463*	0.0532	0.0541	1.0000
fintech	0.0571	0.2368	0.1200	−0.3641	0.5485	−0.2238	−0.0838	1.0000

NB: All variables are in log form

Table

	Country	Number of sampled banks	Income class
1	Angola	4	Lower middle income
2	Botswana	3	Upper middle income
3	Burkina Faso	2	Low income
4	Eswatini	1	Lower middle income
5	Ghana	7	Lower middle income
6	Ivory Coast Cote d’Ivoire)	2	Lower middle income
7	Kenya	8	Lower middle income
8	Malawi	4	Low income
9	Mali	2	Low income
10	Mauritius	2	Upper middle income
11	Namibia	2	Upper middle income
12	Niger	1	Low income
13	Nigeria	9	Lower middle income
14	Rwanda	1	Low income
15	Senegal	2	Lower middle income
16	South Africa	5	Upper middle income
17	Uganda	1	Low income
18	Zambia	2	Lower middle income
19	Zimbabwe	2	Lower middle income
	Total	56

Source: Individual countries’ central banks for the list of banks; S&P Capital IQ for the sample