Does digitisation determine financial development? Empirical evidence from Africa

Figures & data

Table 1. Variable description, data sources and a priori signs.

Variables	Description	Measurement	Data Source	A priori Sign
	Dependent
Financial Sector Development (FD)	Financial Depth Financial Access Financial Activity Financial efficiency Financial size Financial stability	The ratio of private sector credit to GDP Depositors with commercial bank Issuance of private domestic loans from financial institutions The ratio of bank credit to bank deposit Bank assets on central bank assets Z-score of banks	World Bank Financial Development Database Trading Economics, 2022
	Independent Variables
Digitisation	1. ICT	1. Subscribers of mobile phone 2. Internet users	1. World Bank, 2021a 2. World Bank, 2021a	+
	2. Innovation (INN)	3. The number of scientific and technical journals 4. ICT-Innovation interaction	3. World Bank, 2021b 4. World Bank, 2021b	+ +
Controls	1. Institutional Quality (IQ) 2. GDPGR	1. Effectiveness of government 2. Gross Domestic Product growth rate	3. World Bank, WGI Database, 2022b 4. World Bank, 2021a	+/− +
	3. Financial Inclusion (FI)	Index of usage, barriers and access	World Bank Financial Development Database	+
	4. Inflation (INF)	Annual Consumer Price Index (CPI)	WDI	–
	5. Government Support (GS)	The ratio of government expenditures to the total output of the economy	WDI	+
	6. Economic Development	Using the human development index	WDI	+
	7. Unemployment Rate	The number of unemployed individuals by the total labour force and then multiplied by 100 to express it as a percentage.	WDI	–
	8. Population rate	The percentage change in the population over a specified period. This is expressed as the annual percentage change in population size.	WDI	+

Table 2. Descriptive statistics.

Variables	Mean	Std. Dev	CV	Observation
FD	2.143	1.939	0.905	1188
ICT	3.290	1.025	0.3115	1188
INN	3.717	2.824	0.7597	1188
FI	15.738	13.533	0.859	1188
ED	3.368	1.483	0.440	1188
IQ	3.539	1.499	0.424	1188
GS	2.210	0.408	0.185	1188
IFS	4.303	2.460	0.572	1188
GDPGR	15.731	5.403	0.343	1188
INF	9.500	3.500	0.368	1188
UEM	0.062	0.013	0.209	1188
POP	0.089	0.027	0303	1188

Source: Authors’ Computation (2023): Notes: NB; where ‘FD is the Financial Sector Development, ICT is the information communication technology, INN is the innovation, FI is the financial inclusion, ED is the economic development, IQ is the institutional quality, GS is the government support, IFS is the infrastructure, GDPGR is the Gross Domestic Product growth rate, INF is the inflation rate, UEM is the unemployment rate, and POP is the population rate ‘The sample size was all the 54 Africa with 1188 observations in the time intervals of 2000 to 2021.

Table 3. Correlation results.

S/N	Variables	1	2	3	4	5	6	7	8	9	10	11	12
1	FD	1
2	ICT	0.0944	1
3	INN	0.1589	0.0304	1
4	FI	0.0999*	0.5099*	0.0389*	1
5	ED	0.1248*	0.3920*	0.4904*	0.2509*	1
6	IQ	0.0929	0.0209*	0.0893***	0.0449	0.0049	1
7	GS	0.0309	0.0849*	0.0639	0.0379*	0.0829	0.5099*	1
8	IFS	0.004**	0.026*	0.0409**	0.0242	0.0493*	0.3920*	0.1442*	1
9	GDPGR	0.0904	0.093***	0.0994***	0.0324	0.0989	0.3089*	0.1360*	0.4332*	1
10	INF	−0.0842*	0.004	0.2094*	0.2409*	0.0999*	0.0994	0.1809*	0.3999*	0.0922	1
11	UEM	−0.094*	0.046	0.0409*	0.0094	0.0308	0.3449*	−0.0398	0.1989*	0.0240	0.2999*	1
12	POP	0.0928	0.049*	0.0994*	0.9949*	0.0298	0.3499*	0.0099	0.3422*	0.0198*	0.1792*	0.0399	1

Source: Authors’ Computation (2023): NB; where ‘FD is the Financial Sector Development, ICT is the information communication technology, INN is the innovation, FI is the financial inclusion, ED is the economic development, IQ is the institutional quality, GS is the government support, IFS is the infrastructure, GDPGR is the Gross Domestic Product growth rate, INF is the inflation rate, UEM is the unemployment rate, and POP is the population rate ‘.

Table 4. Cross-sectional dependence tests.

Test	Statistic	p-Value
Pesaran CD test	2.1967	0.0038
Pesaran scaled LM test	2.6302	0.0192
Breusch-Pagan LM test	20.4829	0.0054

Source: Authors’ Computation (2023).

Table 5. Panel unit root test.

Variables	LLC Level	IPS	ADF	PP	LLC First difference	IPS	ADF	PP
FD	0.465	8.319	46.104	68.435	−81.187***	−10.199***	781.311***	891.192***
ICT	−0.5472	−1.821	75.190	78.171	−21.217***	−23.183***	231.141***	664.119***
INN	−3.821***	−8.714***	94.972***	161.61***	−19.319***	−24.181***	745.161***	1510.11***
FI	−6.491***	−4.243***	45.215***	140.112***	−20.130***	−23.812***	389.164***	1219.917***
ED	−8.738***	−3.7651***	23.815***	32.472***	13.894***	13.892***	283.291***	2138.929***
IQ	1.483	10.835	54.198	76.576	−89.387***	−20.132***	681.381***	789.392***
GS	−1.837	−3.936	59.846	88.473	−29.419***	−33.875***	131.491***	558.889***
IFS	−7.5673***	−7.378***	84.572***	287.687***	−29.349***	−34.291***	775.861***	2510.893***
UEM	−8.5648**	−3.8352**	48.9201***	137.2617***	−23.2718**	45.6179***	378.0917**	1187.1982***
POP	−6.5637***	−8.4824**	56.5491***	88.7142***	−33.6179*	−12.1867**	541.8918**	1271.8854***
GDPGR	−6.745***	−4.378***	39.485***	210.312***	−10.230***	−33.882***	287.164***	1319.817***
INF	−9.746***	−3.845***	35.485***	42.472***	23.184***	23.772***	283.291***	2938.649***

Source: Authors’ Computation (2023): NB; where ‘FD is the Financial Sector Development, ICT is the information communication technology, INN is the innovation, FI is the financial inclusion, ED is the economic development, IQ is the institutional quality, GS is the government support, IFS is the infrastructure, GDPGR is the Gross Domestic Product growth rate, INF is the inflation rate’ Probabilities for Fisher tests are computed using an asymptotic Chi-square distribution. All other tests assume asymptotic normality.

Table 6A. Digitisation and other financial sector development determinants in African countries.

Variable	Model (1) FD	Model (2) ICT	Model (3) INN	Model (4) FI	Model (5) ED
ICT	0.0256*** (0.0061)	0.0418** (0.0178)	0.0163*** (0.0019)	0.0446** (0.0227)	0.0221*** (0.0049)
INN	0.0201*** (0.0065)	0.0360** (0.0182)	0.0548** (0.0225)	0.0354** (0.0177)	0.0671*** (0.0199)
FI	0.0344 *** (0.0112)	0.0954*** (0.0295)	0.0121*** (0.0035)	0.0541*** (0.0039	0.0758* (0.0426)
ED	0.236*** (0.0611)	0.0367** (0.0125)	0.0177*** (0.006)	0.0201*** (0.0071)	0.0299*** (0.0084)
IQ	0.0121*** (0.0035)	0.0105* (0.0055)	0.0177*** (0.006	0.0201*** (0.0071)	0.0299*** (0.0084)
GS	0.0177*** (0.0064)	0.0212*** (0.0073)	0.0256*** (0.0063)	0.0151** (0.0077)	−0.0254*** (0.0067)
IFS	0.0383** (0.0152)	0.0214** (0.0091)	0.0256*** (0.0061)	0.0171 (0.0120)	0.0257*** (0.0069
GDPGR	0.0477** (0.0187)	0.0439** (0.0161)	0.0344 (0.1110)	0.0344 (0.1110)	0.0344 (0.0112)
INF	−0.0609** (0.0289)	0.0411** (0.0251)	0.0446 (0.2110)	−0.0162 (0.0198)	−0.0254*** (0.0067)
POP	0.0167*** (0.0101)	−0.0391*** (0.0109)	0.0457** (0.0182)	0.0383** (0.0146)	−0.0208** (0.0089)
UEM	−0.196*** (0.0632)	−0.0162*** (0.0061)	0.0334*** (0.0023)	0.0684** (0.0415)	0.0224*** (0.0081)

Source: Authors’ Computation (2023): NB; where ‘FD is the Financial Sector Development, ICT is the information communication technology, INN is the innovation, FI is the financial inclusion, ED is the economic development, IQ is the institutional quality, GS is the government support, IFS is the infrastructure, GDPGR is the Gross Domestic Product growth rate, INF is the inflation rate, POP is the population rate, UEM is the unemployment rate’ ***, **, significant at 1% and 5% levels. Values in parentheses are standard errors.

Table 6B. Digitisation and other financial sector development determinants in African countries.

Variable	Model (6) IQ	Model (7) GS	Model (8) IFS	Model (9) GDPGR	Model (10) INF	Model (11) POP	Model (12)
ICT	0.0334*** (0.0069)	0.0120*** (0.0032)	0.0167** (0.0082)	0.0256*** (0.0061)	0.0257*** (0.0069)	0.0295*** (0.0079)	0.0256*** (0.0063)
INN	0.0413*** (0.0028)	0.0296*** (0.00621)	0.0477*** (0.0132)	0.0201*** (0.0065)	0.0249*** (0.0073	0.0301*** (0.0082)	0.0201*** (0.0064)
FI	0.0121*** (0.0034)	0.0103** (0.0044	0.0609** (0.0289)	0.0344 (0.0112)	0.0171 (0.0120)	0.0443 (0.0328)	0.0344 (0.1110)
ED	0.0177*** (0.0063)	0.0145** (0.0055	0.0477** (0.0187)	0.236*** (0.0611)	0.249*** (0.0627)	0.340*** (0.0676)	0.236*** (0.0601)
IQ	0.0177*** (0.0063)	0.0145** (0.0055)	0.0383** (0.0152)	0.0121*** (0.0035	0.0541*** (0.0039)	0.0758* (0.0426)	0121*** (0.0034)
GS	−0.0184** (0.0085)	0.239*** (0.0719)	0.0248** (0.0124)	0.0177*** (0.0064)	0.0201*** (0.0071)	0.0299*** (0.0084)	0.0177*** (0.0063)
IFS	−0.0295*** (0.0079)	0.0231** (0.0098)	0.0274** (0.012)	0.0383** (0.0152)	0.0532*** (0.0117)	−0.0260** (0.0103)	0.0383** (0.0146)
GDPGR	0.0526*** (0.0162)	0.0335* (0.0188)	0.0502** (0.0211)	0.0477** (0.0187)	0.0235* (0.0138)	0.0327* (0.0177)	0.0477*** (0.0184)
INF	0.0206*** (0.0064)	0.267*** (0.062)	0.0186*** (0.0063)	−0.0609 (0.0289)	0.0358** (0.0180)	−0.0468* (0.0244)	−0.0609** (0.0284)
POP	0.0161*** (0.0047)	0.0373** (0.0186)	0.0131*** (0.0034)	0.0167 (0.0101)	0.0520** (0.0254)	0.0802* (0.0416)	0.0167** (0.0078)
UEM	−0.0116 (0.0113)	−0.0162*** (0.0060)	0.0587*** (0.0128)	0.0158*** (0.0048)	0.0335* (0.0188)	0.0150 (0.0428)	0.0158*** (0.0047)

Table 7. GMM Estimation for Digitisation and other financial sector development determinants in African countries.

Dependent variable = Financial Sector Development
Variables	Coefficient
${\mathit{\text{FD}}}_{i,t-1 }$	0.0295*** (0.0079)
ICT	0.0301*** (0.0082)
INN	0.0443*** (0.0328)
FI	0.340*** (0.0676)
ED	0.0758** (0.0426)
IQ	0.0299*** (0.0084)
GS	0.0260** (0.0103
IFS	0.0327*** (0.0177
GDPGR	0.0468*** (0.0244)
INF	−0.0802*** (0.0416
POP	0.239*** (0.0719)
UEM	−0.0212*** (0.0073)
Hansen J-Statistic	0.435
Sargan Test	0.219
AR (1)	0.054
AR (2)	0.059

Source: Authors’ Computation (2023): NB; where ‘FD is the Financial Sector Development, ICT is the information communication technology, INN is the innovation, FI is the financial inclusion, ED is the economic development, IQ is the institutional quality, GS is the government support, IFS is the infrastructure, GDPGR is the Gross Domestic Product growth rate, INF is the inflation rate, POP is the population rate, UEM is the unemployment rate’ ***, **, significant at 1% and 5% levels.

Data availability statement

The study used publicly available data from WDI and Trading Economics which is available on their respective websites. However, the authors are ready to make the data and any materials associated with the paper available to anyone upon reasonable request.