rather large income inequalities, and countries in other regions recording medium- to
low-income inequality (UNDP 2017) (Fig. 1.10). More importantly long-term
inequality trends vary significantly between countries, with some countries
experiencing a steady increase, others a steady decline, and others different
U-shape trends (UNDP 2017). However, countries in southern and central Africa,
and countries whose economies are dominated by the oil and mining sector have
experienced increasing inequality (UNDP 2017). Apart from income inequality,
there are substantial social inequalities in terms of, among others, access to health,
education, and employment, both within and between countries, with those trends
varying substantially between countries (Heaton et al. 2016).
Considering these rather different inequality trends, it is not easy to pinpoint an
exact set of challenges that drive inequality within and between SSA countries. In
fact, the perpetuation of inequalities in income, health and educational outcomes
possibly emanates from a complex combination of discriminatory social norms
(especially for women) and a skewed distribution of service provision. For example,
some of the most commonly associated challenges perpetuating inequality in SSA
include (a) dualistic economic structure (e.g. a minority ‘labour elite’ in the government, multinational companies and the resource extraction sector vs. the majority of
labourers in informal sectors or subsistence agriculture); (b) concentration of physical/human capital, and land in elites; (c) resource curse; (d) urban bias of public
policy; (e) limited or failed redistributive efforts from national governments;
(f) prevailing ethnic and gender inequalities (UNDP 2017; SDG Centre for Africa
2019).
Ongoing processes such as population growth, urbanization, conflicts and environmental change often accentuate these challenges. For example, the low level of
service delivery in rural areas compared to urban areas is often due to the sparse
settlements, which complicates infrastructure expansion (Frankema 2014). In turn,
the inadequate infrastructure and other outcomes associated with inequality
Fig. 1.10 Gini Index for selected SSA countries. Source: (World Bank 2018a)
24
D. Juju et al.
low-income inequality (UNDP 2017) (Fig. 1.10). More importantly long-term
inequality trends vary significantly between countries, with some countries
experiencing a steady increase, others a steady decline, and others different
U-shape trends (UNDP 2017). However, countries in southern and central Africa,
and countries whose economies are dominated by the oil and mining sector have
experienced increasing inequality (UNDP 2017). Apart from income inequality,
there are substantial social inequalities in terms of, among others, access to health,
education, and employment, both within and between countries, with those trends
varying substantially between countries (Heaton et al. 2016).
Considering these rather different inequality trends, it is not easy to pinpoint an
exact set of challenges that drive inequality within and between SSA countries. In
fact, the perpetuation of inequalities in income, health and educational outcomes
possibly emanates from a complex combination of discriminatory social norms
(especially for women) and a skewed distribution of service provision. For example,
some of the most commonly associated challenges perpetuating inequality in SSA
include (a) dualistic economic structure (e.g. a minority ‘labour elite’ in the government, multinational companies and the resource extraction sector vs. the majority of
labourers in informal sectors or subsistence agriculture); (b) concentration of physical/human capital, and land in elites; (c) resource curse; (d) urban bias of public
policy; (e) limited or failed redistributive efforts from national governments;
(f) prevailing ethnic and gender inequalities (UNDP 2017; SDG Centre for Africa
2019).
Ongoing processes such as population growth, urbanization, conflicts and environmental change often accentuate these challenges. For example, the low level of
service delivery in rural areas compared to urban areas is often due to the sparse
settlements, which complicates infrastructure expansion (Frankema 2014). In turn,
the inadequate infrastructure and other outcomes associated with inequality
Fig. 1.10 Gini Index for selected SSA countries. Source: (World Bank 2018a)
24
D. Juju et al.