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Research Africa Network (RAN)
RAN Working Paper
WP/18/054
How Enhancing Information and Communication Technology has affected
Inequality in Africa for Sustainable Development: An Empirical
Investigation
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Forthcoming: Sustainable Development
Simplice A. Asongu
Department of Economics, University of South Africa,
Pretoria, South Africa.
E-mail:
asongusimplice@yahoo.com
Nicholas M. Odhiambo
Department of Economics, University of South Africa,
Pretoria, South Africa.
E-mail:
odhianm@unisa.ac.za
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This working paper also appears in the Development Bank of Nigeria Working Paper Series.
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2018 Research Africa Network WP/18/054
Research Department
How Enhancing Information and Communication Technology has affected Inequality in
Africa for Sustainable Development: An Empirical Investigation
Simplice A. Asongu & Nicholas M. Odhiambo
September 2018
Abstract
This study examines if enhancing ICT reduces inequality in 48 countries in Africa for the
period 2004-2014. Three inequality indictors are used, namely, the: Gini coefficient, Atkinson
index and Palma ratio. The adopted ICT indicators include: mobile phone penetration, internet
penetration and fixed broadband subscriptions. The empirical evidence is based on the
Generalised Method of Moments. Enhancing internet penetration and fixed broadband
subscriptions have a net effect on reducing the Gini coefficient and the Atkinson index,
whereas increasing mobile phone penetration and internet penetration reduces the Palma ratio.
Policy implications are discussed in the light of challenges to Sustainable Development Goals.
JEL Classification: G20; I10; I32; O40; O55
Keywords: ICT; Inclusive development; Africa; Sustainable development
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1. Introduction
Three factors motivate the positioning of this study which assesses how enhancing
information and communication technology (ICT) affects inequality in Africa, notably: the
growing policy syndrome of inequality in the light of challenges to Sustainable Development
Goals (SDGs)
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; the importance of ICT in contemporary development outcomes and gaps in
the literature.
First, in the light of SDGs, bringing the level of extreme poverty to a threshold below three
per cent of the global population by 2030 is reachable for other regions in the world but very
challenging for Africa. Some studies have suggested that the objective can be achieved if
growth rates attained during the period 2000 to 2010 are maintained until the year 2030
(Ravallion, 2013). Another stream of literature posits that progress in poverty alleviation at
the global level will decline in the coming years (Chandy et al., 2013; Yoshida et al., 2014).
The situation of Africa is quite distinct because despite experiencing more than two decades
of growth resurgence, the continent was considerably off-course from achieving the
Millennium Development Goals (MDGs) extreme poverty target (Tchamyou, 2018a, 2018b).
Two perspectives are apparent from the above insights. On the one hand, the fact that the
number of people living in extreme poverty have been increasing despite two decades of
growth resurgence is an indication that the fruits of economic prosperity have not been
trickling down to the poor factions of the population on the continent (Asongu & Kodila-
Tedika, 2017; Asongu & le Roux, 2018). On the other hand, even if the growth rates are
maintained as argued in Ravallion (2013), the extreme poverty target for 2030 is still not very
likely to be achieved unless inequality is reduced. The need to address inequality in order to
eradicate extreme poverty by 2030 is consistent with Bicaba et al. (2017): “This paper
examines its feasibility for Sub-Saharan Africa (SSA), the world’s poorest but growing region.
It finds that under plausible assumptions extreme poverty will not be eradicated in SSA by
2030, but it can be reduced to low levels through high growth and income redistribution
towards the poor segments of the society” (p. 93). This assertion on Sub-Saharan Africa is
relevant to North African countries (Ncube et al., 2014). The relationships between economic
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Consistent with Fosu (2013), policy syndromes are features that are not favourable to economic development.
These include: “administered redistribution”, “state breakdown”, “state controls”, and “suboptimal inter
temporal resource allocation”. With respect to Asongu (2017), in the light of challenges to 21
st
century
development, a knowledge economy gap between two countries represents a policy syndrome. Asongu and
Nwachukwu (2017) and Tchamyou et al. (2018) understand the concept of policy syndrome as growth that is not
inclusive. The conception and definition of policy syndrome in this study is inequality.
.
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growth, inequality and poverty build on the perspective that the response of poverty to growth
is a decreasing function of inequality, such that reducing inequality is crucial to extreme
poverty alleviation (Fosu, 2015). In this study, we consider the relevance of ICT in reducing
inequality because of the growing importance of information technology in development
outcomes in the continent (Asongu, 2013; Penard et al., 2012; Afutu-Kotey et al., 2017;
Asongu & Boateng, 2018; Efobi et al., 2018; Gosavi, 2018; Humbani & Wiese, 2018; Asongu
& Odhiambo, 2018a).
Second, in the light of the high potential for ICT penetration in Africa, compared to other
regions of the world where ICT penetration has reached saturation levels, there has been a
growing strand of literature on the importance of information technology in improving
macroeconomic and human developments (Abor et al., 2018; Tchamyou, 2017; Minkoua
Nzie et al., 2018; Isszhaku et al., 2018; Asongu & Nwachukwu, 2018a; Gosavi, 2018).
Unfortunately, to the best of our knowledge, empirical studies focusing on the nexus between
ICT and inequality are sparse.
Third, the recent inequality literature has focused on, inter alia the: nexus between foreign
investment and income inequality (Kaulihowa & Adjasi, 2018); relationships between
consumption, income and the wealth of the poorest factions in Sub-Saharan Africa (De
Magalhães & Santaeulàlia-Llopis, 2018); nexus between corruption and inequality (Sulemana
& Kpienbaareh, 2018); gender inequality (Bayraktar & Fofack, 2018; Mannah-Blankson,
2018; Elu, 2018); reinvention of foreign aid for inclusive development (Jones & Tarp, 2015;
Page & Söderbom, 2015; Asongu, 2016) and relationships between information sharing,
education, finance and inequality (Tchamyou, 2018a, 2018b; Meniago & Asongu, 2018).
Employing Ordinary Least Squares, Asongu (2015) has established a negative relationship
between mobile phone penetration and inequality with cross-sectional data which consists of
2003-2009 average growth rates. The corresponding findings are exploratory, from which
causality and solid policy inferences cannot be established. The present research departs from
the underlying study by: (i) using a panel data structure with an updated sample; (ii)
employing three income inequality indicators and (ii) adopting an estimation technique that is
robust to the control for endogeneity. In essence, this research is based on a panel of 48
countries in Africa for the period 2004-2014 and the empirical evidence is based on the
Generalised Method of Moments (GMM). The advantages of the GMM approach over the
OLS technique are discussed in the methodology section.
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Tchamyou (2018a) and Meniago and Asongu (2018) are also studies in the literature that are
closest to this research. Tchamyou (2018a) has examined the role of financial access in
moderating the impact of lifelong learning and education in African countries. The author
concludes that: (i) primary school enrolment interacts with all financial channels (depth,
efficiency, activity and size) to reduce income inequality and (ii) lifelong learning exerts net
negative impacts on income inequality through financial depth and efficiency mechanisms.
Meniago and Asongu (2018) have extended Tchamyou (2018a) by revisiting the finance-
inequality nexus in a panel of African countries in the light of the Kuznets hypothesis to: (i)
conclude that, with the exception of the financial stability mechanism, financial activity (or
credit access) and financial allocation efficiency reduce income inequality and (ii) confirm the
Kuznets hypothesis on the nexus between income levels and income inequality.
The present study is similar to the underlying two studies in the sense that three inequality
indicators are used, namely: the Gini coefficient, the Atkinson index and the Palma ratio. This
research also departs from the underlying two studies by considering ICT as a mechanism by
which income inequality can be reduced in Africa. The ICT indicators employed include:
mobile phone penetration, internet penetration and fixed broadband subscriptions. It is
relevant to note that the underlying studies have used education, finance and income levels as
channels for reducing income inequality. The research question this study seeks to answer is
the following: how does enhancing ICT affect income inequality in Africa?
The theoretical connection between ICT and inequality can be understood from neoclassical
models of knowledge creation and diffusion (Kwan & Chiu, 2015). Consistent with the
attendant literature, neoclassical growth models maintain that technology can be an important
source of economic and human development in poor countries (Abramowitz, 1986; Bernard &
Jones, 1996; Asongu et al., 2018a). According to the theoretical underpinning, information
technology enhances socio-economic development and the wellbeing of citizens (Muthinja &
Chipeta, 2018; Bongomin et al., 2018; Uduji & Okolo-Obasi, 2018a, 2018b; Asongu et al.,
2019a, 2019b). Arguments provided to support the importance of ICT in inclusive human
development include: (i) it offers enabling conditions to avoid physically moving from one
place to another by allowing users to perform activities from a distance (Ureta, 2008; Efobi et
al., 2018; Shaikh & Karjaluoto, 2015); (ii) ICT enhances access to relevant and timely
information which is crucial in development activities, essentially because it increases users’
cheap access to inputs of development, expands their capabilities and limits existing barriers
(Smith et al., 2011) and (iii) the highlighted positive development externalities are more
