# **Introduction**

Mineral wealth extraction and its impacts on development outcomes, especially in the developing world, have received considerable attention in the economic development literature. Recent studies have shown the impacts of mineral wealth extraction on economic growth and development (Beny and Cook 2009; Papyrakis and Gerlagh 2004; Van der Ploeg 2011; Venables 2016), conflict and civil unrest (Collier and Hoeffler 1998, 2002, 2005; Lujala 2009; Van der Ploeg 2011), agricultural growth (Andersson et al, 2015; Chuhan-Pole et al. 2015), women’s employment (Kotsadam and Tolonen 2015), income and prices of goods (Aragón and Rud 2013), and revenues, public spending, and living standards (Caselli and Michaels 2013). Yet little is known about the relationship between mineral wealth and spatial inequality in resource-rich countries in general, or in producing regions in particular (see e.g., Ross 2007),<sup>4</sup> despite a widely held view that mineral-rich countries often exhibit large inequalities, both vertical and horizontal (see e.g., Auty 2001; Ross 2007).<sup>5</sup> This paper fills this gap in the literature by examining whether the presence of mineral deposits and mining activities affects spatial inequality. Our main focus is on Africa, a continent endowed with minerals and with recently growing mining activities (Beny and Cook 2009), and where the lack of empirical evidence is persistent and somewhat rooted in the legacy of unreliable and inconsistent data, especially at the local scale (Kim 2008). The analysis builds on two important tenets: first, the theoretical explanations of the causes of spatial inequalities that identify natural geographical advantages (including the presence of mineral wealth) as one key candidate among others (see Kim 2008; Venables 2005); second, a unilateral causation between mining and spatial inequality—the presence of mining deposits and activities influences spatial inequality, and not vice versa—as an identification strategy. With the aid of night-time lights data to measure inequality across space and time, and geocoded mine-level data, we confine our analysis at the district level, effectively allowing us to investigate the within-district inequality effects of mining activities across countries over time.

There were several reasons for our choice of districts as units of analysis. First, by focusing on districts we systematically examined the nexus between mining and spatial inequality in proximity to mines and over time. This is useful for untying the effects of mining on spatial inequality at the subnational level: understanding how mining affects economic activities at local scales, and identifying the drivers of spatial differences between mineral-rich and mineral-poor countries—a needed ingredient for eliciting appropriate policy responses. Second, the use of lower-level geographical administrative units allowed us to control for unobserved country and regional differences, which naturally cannot be handled in a setting where analysis is focused at the country level. In the context of our data, controlling for country and regional fixed effects is important if we are to understand the dynamic effects of mining

> 4 Producing regions can typically face forced displacement, population pressure due to in-migration, or environmental pollution and degradation (Akabzaa et al. 2007; Salami 2001), with little or no positive return in terms of higher incomes or better living standards.

> 5 Vertical inequality refers to inequality amongst individuals within countries or regions, while horizontal inequality refers to inequality between countries or regions. These inequalities can create frustrations and grievances in producing regions, leading to the so-called resource curse. In a worst-case scenario, attempts to gain more control over natural resource wealth can degenerate into violent regional conflicts or secessionist movements, as in the Democratic Republic of Congo, Indonesia, Myanmar, Nigeria, Sudan, or Yemen. For more detail, see e.g., Bannon and Collier (2003), or Ross et al. (2012).

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