would have been useful in accounting for total mineral production and revenues for an unbiased estimates of mining activities on district inequality in Africa.

To partially address this limitation, we take advantage of the available time-series minerals prices data and use them as numeraires for minerals at the district level across countries over time. We thus use mineral prices data for the United States since 1900 available from USGS.<sup>16</sup> These price data are recorded in constant prices and annual average prices. As detailed in the empirical strategy, we use the constant prices to examine the main impacts of minerals’ constant unit dollar values on district inequality in Africa. We then use annual average prices for robustness checks on our main results. Our analysis is strengthened by the inclusion of mineral price data, since we are better able to account for the effects of mineral prices’ volatility on spatial inequality in Africa.

GLCF data on NDVI<sup>17</sup> were used to construct a measure of district-level vegetation, which has been shown to be a good proxy for agricultural output (see e.g., Labus et al. 2002 Ren et al. 2008).<sup>18</sup> High NDVI suggests a favourable vegetation landscape relative to low NDVI. NDVI is handy as a proxy, because consistent data on agriculture productivity across all districts in Africa are difficult to find. Obviously, district inequality is likely to be correlated with agriculture—a dominant sector in Africa. Andres and Ramlogan-Dobson (2011) and Chong (2004) assert that income distribution is relatively equally distributed across agriculturedependent economies. We therefore control for the district NDVI standard deviation to account for this fact.

Using TAMSAT data, we constructed annual district rainfall averages and standard deviations aggregated from monthly rainfall averages data. We use rainfall data in two ways: first, as a control for climate variability, and second, as a proxy for agricultural productivity variations, similar in spirit to Miguel, Satyanath, and Sergenti (2004).

Finally, we used Gridded Population of the World (GPW v4) data to construct data on district population counts. At a grid cell resolution of 30 arc-minutes (approximately 1 km at the equator), the GPW provides estimates of population count between 1990 and 2020 at a 5-year interval. We extracted these data at the district level and followed Hodler and Raschky (2014) to linearly interpolate the data to estimate population counts for the missing years, in turn allowing us to have population counts for the period 2000–2015.

> 16 We refrained from using mineral prices data from the Global Economic Monitor (GEM) of the World Bank because these do not cover all commodity categories in our sample, making the analysis less comparable. However, when we compare a similar set of minerals prices from the GEM and USGS, the overall correlation is 0.93 (unreported), suggesting that the use of US mineral prices is not unreasonable.

> 17 It is calculated as NDVI = (NIR - Red) / (NIR + Red), with NIR being near infrared (technical detail available at: <u>earthobservatory.nasa.gov/Features/MeasuringVegetation/measuring_vegetation_2.php,</u> accessed 18 Novermber 2014). Similarly to Andersson et al. (2015), we also use the MODIS Land Cover Type product (MCD12Q1) to extract the NDVI. These data are available for the period 2001–2012, and are provided at a 16day temporal resolution and a 250 m spatial resolution, making it possible to pair with other data at the district level.

> 18 For example, NDVI has been shown to be a good measure of vegetation greenness, primary productivity of vegetation, and leaf area index. NDVI is also useful for evaluating the evolution of vegetation cover over time (see Andersson et al. 2015 for details and more sources on technical studies in this area.)

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