baseline specification allows us to evaluate whether, for example, the districts with active or closed mines experience higher or lower inequality compared with districts with no mines. The same logic also applies to the other sources of exogenous variation mentioned. The coefficient of interest, which disentangles α in equation [2], is θ<sup>j</sup> , with the underlying hypothesis, still following the resource curse literature, being θj > 0. ν is an error term.

One advantage of equation [4] is that it brings into the analysis the mentioned exogenous variation in mining activities, which is useful for explaining the mechanisms through which mining activities can affect district inequality in Africa. However, the impact of mining activities on district inequality is likely to be driven by the specific minerals commodities available (Boschini et al. 2007). This latent effect cannot be captured by Pricem,t in equations [2] and [4].

Obviously, controlling for minerals’ values is important to obtain unbiased estimates and explain the mining-spatial inequality nexus. As noted in Section 3, our analysis, which relies on USGS mineral data, suffers from the lack of data on mineral production at mining sites in Africa. However, the available mineral prices data offer a possibility to circumvent this limitation: the use of minerals unit prices as numeraires for minerals across districts where mineral deposits and mining activities are available. For districts where mineral deposits and mining activities are absent, we assign zero values in unit mineral prices. We therefore update equation [4] slightly differently to take into account the idea of using mineral prices as numeraires. The updated specification takes the following form: where Mineralc,r,d,t denotes the dummies for the major mineral commodities types (see Table 2) available in our sample, and ωc,r,d,t is an error term. A unique feature of equation [5] is the interaction term Mineralc,r,d,t � Pricem,t, which underlies the use of minerals’ unit prices as numeraires. In principle, equation [5] mimics the underlying structure of equation [2]: the interaction term Mineralc,r,d,t � Pricem,t captures coefficient estimates for all districts with mineral numeraires relative to the base (non-numeraire) districts, effectively measuring the effect of individual minerals’ unit values on district inequality in Africa. As an added value, this specification makes it possible to attribute changes in district inequality to changes in specific mineral numeraires, which is useful for identifying the specific types of mineral that influence the dynamics of district inequality during the period 2001–2012. As above, the underlying testable hypothesis is ψ > 0, suggesting that district inequality increases with increasing mineral numeraires.

Understanding how specific mineral commodities affect district inequality in Africa is undoubtedly necessary but not sufficient, especially if we are to understand the precise sources of the effects. We thus extend equation [5] one step further to pin down the precise mechanisms at work in the mining-spatial inequality nexus. The extended specification is of the following form: 12