Foreign direct investment and financial development in Africa: A causality assessment in the frequency domain

Linking foreign direct investment to financial deepening: Evidence from sub‐Saharan Africa

“…In the literature, other variables found to affect financial deepening include GDP growth, inflation, trade openness, education, ease of doing business, legal and regulatory frameworks, financial liberalization, demography, institutional quality and environmental factors (Aibai et al, 2019;Edo et al, 2019;McDonald & Schumacher, 2007;Mlachila et al, 2016). Mbratana et al (2021) examined the causality between FDI and financial development in 47 Africa countries. They employed the frequency domain Granger causality test.…”

“…Mbratana et al (2021) examined the causality between FDI and financial development in 47 Africa countries. They employed the frequency domain Granger causality test.…”

“…Besides the global economic downturn caused by COVID 19 pandemic and the Russia–Ukraine war, factors that could have led to the shallowness of SSA financial development include political risk, weak crisis management, rising public debt that is crowding out foreign direct investment (FDI) via increases in financial risk, weak institutions and widespread macroeconomic instability. Also, the growth rate of GDP, the level of domestic investment, macroeconomic stability, trade openness, institutions, environment, health, legal and regulatory frameworks are commonly cited in the literature as factors that determine financial depth (Abusomwan & Agbonkhese, 2023; Abusomwan, 2019; Aluko & Ibrahim, 2020; Bittencourt, 2011; Donou‐Adonsou, 2022; Hami, 2017; Iheonu et al, 2020; Mbratana et al, 2021). However, few studies have investigated the role that FDI could play in the financial deepening of SSA, a region dominated by low‐ and lower middle‐income countries where domestic saving is relatively low.…”

“…Lately, there seems to be a rising interest in the relationship between FDI and financial development (see Henri et al, 2019; Mbratana et al, 2021). This study takes a different empirical route from the few previous studies by employing the dynamic cross‐sectionally augmented distributed lag (second generation, CS‐DL) estimation technique which is robust for cross‐sectional dependence (CD), endogeneity, heterogeneity and structural breaks in panel data analysis.…”