Impact of Natural Resources on Africa`s Economic Growth

Over two decades, Africa has recorded a good performance in terms of economic growth with robust GDP growth of about 5.0 percent. Despite such a good performance, the problem of poverty remains unsolvable in the region (the level of poverty in the region 41 percent compared to other developing regions). It further states adds that although sub-Saharan Africa (SSA) achieved an average reduction in its unweighted Gini coefficient—from about 0.47 to 0.43 between 1991 and 2011— the region remains one of the most unequal in the world with 10 its counties listed among the 19 most unequal in the world. According to UNDP’s assessment three reasons explain the persistence of income inequality in African countries. First, the highly dualistic economic structure which explain by a limited capacity of high income sector (multinational firms and extractive sector) to generate employment compared to the informal sector. Second, high concentration of physical capital, human capital, and land, for example in Eastern and Southern Africa. Third, limited distributive capacity of state which explain by a natural resources curse, an urban bias of public policy and ethnic and gender inequality (Bigsten, 2016).
Globally, there has been a remarkable rise in the number of international migrants. The number of international migrants grew exponentially over the period 2000 and 2015. Based on International Migration report (2015), during this period, the number of people living in country other than where they were born reached 244 million for the world as whole, an increase of 71 million (41%) compared to 2000. While (Aboulezz, 2015) has argued that international migration has positive and negative effects on their home and host countries, but one generally positive benefit of migration is financial remittances. According to the World Bank (2019) report, overseas remittances to poor countries reached a record high in 2018. the official recorded annual overseas remittance flow to poor countries reached $529 billion in 2018, an increase of 9.6% compared to 2017. In 2018, considering Sub-Saharan Africa case, overseas remittances grew 10% to $46 billion.
The positive effect of international remittance on economic growth is expected to translate into economic development (Baldé, 2009; Mundaca, 2009). Thus, recent studies in the field of the remittance explore the spillover effect of international remittance on human capital development (Azizi, 2018; Bouoiyour & Miftah, 2016), poverty (Musakwa & Odhiambo, 2019; Qayyum & Javid, 2008), and income inequality (Bang, Mitra, & Wunnava, 2016). The latter is the focus of this article. In this article, our focus is on income inequality for two main reasons, First, the existence of inequality constitutes a huge challenge to achieve Sustainable Development Goals (SDGs) in 2030. Second, the level of human capital development of poor is low. (Folarin, 2019) have argued that the deprivation comes from the low level of human capital between the poor.
However, previous studies have used Gini coefficient as measure of income inequality (Franco & Gerussi, 2013; Ucal, Haug, & Bilgin, 2016). While (Ben Naceur & Zhang, 2016) have argued that using Gini coefficient present itself some limitations. One of these limitations, Gini coefficient did not consider the extremities of the income distribution for example the lowest and highest bounds of inequality. Thus, For robust estimation, in addition to Gini coefficient we provide two measurement that are tailored to consider the extreme ends of inequality distribution, namely the Atkinson index and the Palma ratio (Asongu & Odhiambo, 2019b, Tchamyou, Erreygers, & Cassimon, 2019). As explanation, The Atkinson index (also known as the Atkinson measure or Atkinson inequality measure) is a measure of income inequality developed by British economist Anthony Barnes Atkinson. The measure is useful in determining which end of the distribution contributed most to the observed inequality. Whereas The Palma ratio is defined as the ratio of the richest 10% of the population\'s share of gross national income divided by the poorest 40%\'s share.
This study differs fundamentally from the previous studies in that, firstly, it employs a GMM model approach because of the endogeneity problem ( see Folarin, 2019). Secondly, the study focuses on Sub-Saharan African countries by using panel data where inequality and extreme poverty are persistent. In this regard is unlike other studies that have relied on country level data, which is unable to sufficiently capture heterogeneity across the region. Thirdly, additionally to Gini coefficient we use two others measures of income inequality, namely the Atkinson index and the Palma ratio.
To the best of our knowledge, this study is the first to empirically to empirically examine the effect of international remittances on Gini coefficient, Atkinson index, and Palma ratio to enhance the understanding of this issue, using the available panel data of Sub-Saharan African countries spanning the period 2000–2015.