Finance, inequality and inclusive education in Sub-Saharan Africa

Abstract

This research complements the extant literature by establishing inequality critical masses that should not be exceeded in order for financial access to promote gender parity inclusive education in Sub-Saharan Africa. The focus is on 42 countries in the sub-region and the data is for the period 2004-2014. The estimation approach is the Generalized Method of Moments. When remittances are involved in the conditioning information set, the Palma ratio should not exceed 6.000 in order for financial access to promote gender parity inclusive "primary and secondary education" and the Atkinson index should not exceed 0.695 in order for financial access to promote inclusive tertiary education. However, when the internet is involved in the conditioning information set, it is established that in order for financial access to promote inclusive primary and secondary education, the: (i) Gini coefficient should not exceed 0.571; (ii) Atkinson index should not be above 0.750 and (iii) Palma ratio should be maintained below 8.000. Irrespective of variable in the conditioning information set, what is apparent is that inequality decreases the incidence of financial access on inclusive education. Hence, a common policy measure is to reduce inequality in order to promote inclusive education using the financial access mechanism. Policy implications are discussed in the light of Sustainable Development Goals.

The role of finance in inclusive human development in Africa revisited

“…Consistent with contemporary literature (Tchamyou, Erreygers & Cassimon, 2019;Asongu, Nnanna & Acha-Anyi, 2020a, 2020b, there are two main theories linking finance to inclusive development, notably: the intensive margin theory and the extensive margin theory.…”

Drivers of income inequality in Africa: Does institutional quality matter?

“…Our study examines income inequality in a dynamic model setting. This is because income inequality is known to exhibit a great degree of inertia as evidenced in studies by Anyanwu (2016); Anyanwu et al (2016); Asongu, Nnanna, and Acha‐Anyi (2020); Chu and Hoang (2020); Dincer and Gunalp (2012); Kunawotor et al (2020); Mahmood and Noor (2014). Thus, the current level of income inequality depends on its past value.…”

“…Also, GMM generates internal instruments that account for simultaneity bias or reverse causality. Fourth, inequality is known to be persistent and depends on its lags (see Anyanwu et al, 2016; Asongu et al, 2020; Cevik & Correa, 2015; Kunawotor et al, 2020; Shimeles & Nabassaga, 2018). This is also confirmed in our result as the first period lag of the income inequality appears statistically significant in Table 4.…”

“…The choice of GMM is motivated by five reasons in line with recent GMM-centred literature (see Agoba, Abor, Osei, & Sa-Aadu, 2019;Asongu et al, 2019Asongu et al, , 2020Fosu & Abass, 2019;Kunawotor et al, 2020;Ogbeide and Adeboje, 2020;Tchamyou, Asongu, & Odhiambo, 2019). First, the cross-sectional units (N) are higher than the time series (T).…”

Trade openness, FDI, and income inequality: Evidence from sub‐Saharan Africa

“…Four, to the best of our knowledge, the extant literature on inequality in Africa has fundamentally focused on 15 main strands, notably: the determinant of income inequality (Anyanwu, 2016; Bigsten, 2016); the relationship between economic structure, growth, and evolution of inequality and poverty (Fosu (2018); the relationship between inequality, information and communication technology (ICT), and financial access (Tchamyou et al, 2019a); the effect of ICT, education and lifelong learning on income inequality and economic growth (Tchamyou et al, 2019b); the nexus between inequality, poverty and growth (Fosu, 2015); the relationship between FDI and inequality (Kaulihowa & Adjasi, 2018); the relationship between inequality, ICT and inclusive education (Asongu et al, 2019); the linkage between inequality, gender inclusion and ICT (Asongu & Odhiambo, 2019c); the nexus between inequality, governance and gender economic inclusion (Odhiambo & Asongu, 2020); the relationship between education, lifelong learning, inequality and financial access (Tchamyou, 2018); the relationship between finance, inequality and inclusive education (Asongu et al, 2020); the effect of finance and CO 2 emissions thresholds on inequality (Asongu & Vo, 2020); the relationship between structural transformation and income inequality (Ibrahim et al, 2020) and the impact of mobile phone penetration on African inequality (Asongu, 2015). Thus, the relationship between trade openness, FDI and inequality is missing in contemporary African inequality literature.…”