The results of our sensitivity tests are quite revealing, and show that our estimates are indeed robust to other measures of spatial inequality. In our first test, the results are also robust to using both constant and average mineral prices in the analysis. Of course, the magnitudes of coefficients do change considerably across MLD, Theil, and relative lights per capita (RLP), but the qualitative interpretations remain the same when compared with the baseline Gini. Worth noting, however, is a change in the R-squared across these different specifications: from 0.763 on the baseline Gini to 0.627, 0.677, and 0.464 on MLD, Theil, and RLP respectively. In our second test, when we model the data using multilevel analysis, although the magnitude varies across the different specifications, the qualitative interpretation of the results (unreported)<sup>24</sup> remains the same and robust.

Overall, although the aggregated analysis suggests that mining activities increase spatial inequality in favour of districts with natural advantages in mineral endowment, disaggregating the analysis to accommodate individual minerals shows that changes in inequality within districts and across countries are sensitive to the types of minerals and to whether mining activities are active or closed, small or large scale, of low or high value, and extractive or transformational. Based on these findings, we argue that mining activities can be both a curse and a blessing.

# **6 Conclusions**

This paper analysed the effects of mining activities on spatial inequality in 2,182 districts over 653 regions across 38 African countries. Our study employed novel spatial data enabling analysis at the local scale. District inequality was measured using night light data from the National Oceanic and Atmospheric Association’s National Geophysical Data Centre, while mineral production data came from the United States Geological Survey.

Our study offers three main conclusions. First, when the analysis is aggregated, mining activities significantly increase spatial inequality in Africa, a finding that is consistent with the standard resource curse literature. Second, when the analysis is disaggregated to include individual minerals, our findings suggest that mining activities impact on spatial inequality both positively and negatively, with the effects varying quite considerably across different minerals. This finding suggests that mineral wealth can be both a blessing and a curse. More importantly, this finding—a unique contribution of this study—reinforces the idea that disentangling the effects of individual minerals is more revealing of the effects of mining activities on development outcomes, and thus is important for eliciting the relevant policy responses.

Finally, our study shows that the effects of mining activities are leveraged by other important forces that underpin mining operations. For example, our analysis suggests that the coefficients of interest largely depend on whether mining activities are active or closed, small or large scale, of low or high value, and extractive or transformational. We argue that these are also relevant variables for informing appropriate policy responses aimed at addressing the nexus between mining activities and spatial inequality in Africa.

> 24 We have not reported these results here due to space constraints, but they are available from the authors upon request.

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