Causal Relationship Among Foreign Bank Presence, Financial Development and Inclusive Growth in Africa.
Description
The study examined 28 African countries using data collected between 2000 and 2015, and sourced data from Bank Scope (BS), World Bank and International Monetary Fund (IMF). We used principal component analysis to create inclusive growth index with a focus on inclusive growth based on the ADB framework. Due to data limitations, the study used 11 variables that were normalized before being used to generate an inclusive growth index. These variables include 11 such as Access to clean fuels and technology (% population), Control of corruption estimate, Government effectiveness estimate, Voice and Accountability estimate, Access to electricity (% population), GDP per capita (PPP constant 2017, US$), People using at least basic sanitation (% population), People using at least basic drinking water (%population), Human development index, Employment to population ratio (15 years and above), Mobile cellular subscriptions per 1000 adults. All these 11 variables were sourced from World Bank with exception of HDI which was sourced from Human development report office. The validity of the index was tested using various statistical measures. Additionally, the study employed the Svirydzenka financial development index, which contains nine indices to capture the multidimensional nature of the financial sector. This was also sourced from IMF. The measurement scale ranges from 0 to 1, where a variable closer to 1 indicates a higher level of development in the financial sector, while a variable closer to 0 suggests a less developed financial sector. Finally, the study used the share of bank assets held by foreign banks as a percentage of total assets to measure FBP, with data sourced from Bank Scope. The study limited the study period to 2015 because the estimation technique employed required no missing values, and the data on FBP was only available for that year. A variable closer to zero (0) indicates that there are more domestic banks, while the opposite is true for a variable further away from zero
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Steps to reproduce
1. Data was sourced from the available source (World Bank' World Development Indicators and World Governance Indicators; IMF and Bankscope). The foreign bank presence variable was computed by determining the percentage of foreign banks' assets to total universal banks' assets after downloading the relevant data from Bank Scope. 2. The data was integrated in Microsoft Excel to ensure that there were no string variables and minimal errors, before being imported into STATA 17 for analysis. 3. In STATA 17, the 11 variables used to generate the inclusive growth index were first normalized due to their different units of measurement. The inclusive growth index was then computed using PCA, with the values (scores) generated by the PCA serving as the index. We tested the validity of the index using number of eigenvalues, Kaiser-Meyer-Oikin (KMO) measure of Sampling Adequacy and Bartlett’s Test. 4. Descriptive summary and correlation matrix were conducted for the variables used in the study. 5. The stationarity test was conducted using Pesaran's (2007) Cross-sectionally Augmented Dickey-Fuller (CADF) panel unit root test. 6. After finding stationarity among the variables, the panel causality test was conducted using Dumitrescu and Hurlin's (2012) Granger non-causality approach. 7. Finally, the results were exported to Microsoft Word for interpretation