Financial Inclusion for Sustainable Economy: Empirical Evidence from Nigeria

Abstract

The main objective of the study is to empirically examine how economic growth is impacted upon through financial inclusion. Economic growth per capital income is the study’s explained variables while, rural deposits, private sector deposits, rural loans, private loans, and number of banks branches are proxies for the explanatory variable. Secondary data was sourced from the Central Bank of Nigeria statistical bulletin and World Bank financial indicator and span thirty-five years (1982 to 2017). From the augmented dickey fuller (ADF) test results, autoregressive distributed lag (ARDL) regression was adopted. Findings shows that individually, rural deposits, and number of banks branches are significant in the short-run while, only the former is significant in the long-run. However, jointly, and from the Wald test result, a no significant relationship is established between the variables in the long-run. The study thus recommends a nurturing approach from primary to tertiary level of financial inclusion.

Effects of Financial Inclusion on Economic Growth, Poverty, Sustainability, and Financial Efficiency: Evidence from the G20 Countries

“…In Model 4, financial inclusion and population growth also show that they are statistically significant in explaining the gross domestic product. This outcome corroborates that of [62], where financial inclusion was found to significantly impact economic growth, as well as that of [52].…”

“…This informs the autoregressive distributed lag (ARDL) estimation procedure. Accordingly, [57,58,62] posited that when series exhibit different order of integration, such series qualify to be examined under the autoregressive distributed lag process. This is to check for the presence of a short or long-run relationship among the variables.…”

“…Whereas, Model 4, indicated that FII is not statistically significant in explaining GDP. However, the pitfalls inherent in PFE models, which [62,64,65] pointed out in their studies, informed taking a further examination of the significance of the variable by employing the randomization impacts using the PRE. Thus, what the PRE does is consider the heterogeneity, time-invariant, and individual country effect by allowing for the control of unseen heterogeneity through the general least squared method (GLS), where the error terms of observations are randomly distributed.…”