in our sample during 2001–2012. Moreover, for the status of mining activities, the table shows that out of the 17 per cent, active mines constitute about 15.3 per cent, and closed mines about two per cent. Classifying the districts’ mining sites by scale of mining operations, the table further shows that mining operations in Africa are predominantly large scale (about 16.2 per cent), with small-scale operations constituting only one per cent. This is not surprising in a sector that is capital intensive and on a continent where the mining sector has attracted significant direct foreign investment in recent years. Whereas most of the operations are large scale, the table shows that most of these districts’ operations are extractive industries (12.4 per cent) and of low value (13.4 per cent). High-value minerals and transformation industries constitute a small share—3.8 and 4.8 per cent respectively—of districts where mineral deposits and mining activities are present or closed.

Table 4 reports the summary statistics for all variables of interest in our analysis. These descriptive statistics cover the period 2001–2012. Overall, the table indicates that except for Gini, rainfall, NDVI, and population sizes, the dispersion of average district MLD, Theil, and mineral prices (in logarithms) varies quite considerably around their respective means. In the next section we present our empirical strategy.

# **4 Empirical strategy**

This section describes the empirical strategy we use to estimate the effects of mineral resources on district inequality. The analysis relies on panel data across districts during 2001–2012. Since the main covariates of interest are dummies, the first difference models of panel data analysis are inappropriate. Thus the empirical framework uses linear fixed effect estimators (i.e. least square dummy variable—LSDV) to model and estimate the effects of mining activities on district inequality. One natural advantage of LSDV is its ability to address endogeneity concerns. Given the hierarchical nature of our data, the use of LSDV is handy for capturing confounding and unobserved country and regional fixed effects in addition to district fixed effects, thus purging our estimates of any time-invariant unobservable biases. Our baseline model thus takes the following forms: where DI stands for district inequality (measured using spatial lights Gini index), c, r, d, t and m stand for country, region, district and time respectively, and i ∈ (c, r, d). Mine is a binary indicator—1 if a district has mineral deposits, 0 otherwise. Intensity denotes the number of mining sites available in a district in a given year. This model specification tests whether the presence of mineral deposits and mining sites has any measurable effects on district inequality in Africa. The coefficients of interest are α and θ, capturing the effects on district inequality of mineral deposits’ and mining sites’ presence respectively. Following the propositions of the resource curse literature, the underlying hypothesis of this specification is (α, θ) > 0: that is, districts with mineral deposits and mining sites are likely to be more unequal relative to those with none.

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