Fintech, Foreign Bank Presence and Inclusive Finance
Description
The research examined the direct effect of fintech and foreign bank presence on inclusive finance for 28 African countries spanning 2000-2018. We also determine the moderation role of foreign bank presence on the nexus between fintech and inclusive finance. We use two different proxies for fintech such as mobile phone used to make payments (age 15+) and mobile phones used to send money (age 15+) where the data was sourced from Global Financial Development Database (GFD). As a measure of the foreign bank presence (FBE), we used the percentage of assets of foreign banks in relation to the total assets of universal banks which was sourced from Bankscope. We employed Principal Analysis to generate Inclusive finance Index (IFI) using six variables such as commercial bank branches per 100,000 adults, depositors with commercial banks per 1000 adults, borrowers from commercial banks per 1000 adults, ATMs per 100,000 adults, bank branches per 100,000 adults and bank account per 1000 adults. The variables were collected from IMF. The control variables include; bank competition, bank spread, bank concentration, institutional quality, bank stability, education and population growth. Boone indicator is used to measure bank competition and was sourced from GFD. We proxy bank spread with bank lending-deposit spread which is sourced from GFD. Bank concentration (%) which was sourced GFD was employed as a measure of bank concentration. We considered population growth based on the extant literature that when there is increase in population growth, inclusive finance will reduce, hence, we used population growth rate (% annual) as a proxy for population changes. We utilized an average of six indicators of institutional quality to make a composite institutional quality index and was source from World Governance indicators (WGI). These six (6) indicators include 1. corruption control, 2. rule of law, 3. government effectiveness, 4. quality of regulation, 5. political stability and absence of violence, and 6. voice and accountability. When people attain higher education, the more likely they will get access to all financial resources, therefore, we utilized education as part of the predictors for the study and proxy it with secondary school enrolment. The stability of financial sector builds confidence for the financial sector which reduce fear and panic of depositors. Based on that premises and extant literature, we controlled for banks’ stability and proxied it with bank’s Z-score. Our results revealed that fintech increases the level of inclusive finance and the impact was higher when IFI when used as a proxy for inclusive finance as compared to the individual measure of inclusive finance. Unlike fintech, FBE only enhance some of the individual measure of inclusive finance whilst it does not have any statistical effect on IFI. Finally, the results showed that FBE serve as a catalyst which helped fintech to further enhance inclusive finance.
Files
Steps to reproduce
The authors collected data from the relevant data source, then cleaned the data collected. Due to some gap identified in the data, we ipolated the data using this command [by ID: ipolate variable name Year, gen(new variable name) epolate] in STATA 16. We then proceed to generate our dependent variable with PCA using [(pca variables with spacing between each variable); (rotate) and (predict financial inclusion index, score)]. After, we conducted diagnostics for the PCA using the following commands: [(factortest variables with spacing between each variable); kmo] We identify the descriptive summary using [sum variables with spacing between each variable]. The authors produce the final quantile results using the following command sqreg dependent variable variable of interest control variables , q(.25 .50 .75 .90). Notes: We used each variable of interest (i.e., mobile phone used for payment and mobile used to send money) in a separate regression. We first regress each variable of interest on IFI. Additionally we regress each variable of interest on each individual measure of financial inclusion. Similar approach was used for FBE and the interaction term (i.e., the multiplication fintech and FBE).