# **3 Data and district inequality estimation**

# **3.1 Data**

We combine an array of data sources to construct a data set with the relevant important variables for estimating the effects of mining activities on district inequality: the National Oceanic and Atmospheric Administration, National Geophysical Data Center (NOAANGDC)<sup>9</sup> for data on night-time lights intensity; the United States Geological Survey (USGS)<sup>10</sup> for data on the spatial location of mineral deposits and mining activities in Africa and historical US mineral prices; the United States Global Land Cover Facility (GLCF)<sup>11</sup> for the Normalized Difference Vegetation Index (NDVI)—a proxy for agricultural productivity. We also use Tropical Applications of Meteorology using Satellite Data (TAMSAT)<sup>12</sup> to extract data on rainfall—a proxy for climatic shocks. Finally, we extract population data from the Gridded Population of the World (GPW v4).<sup>13</sup> Since our units of analysis are districts, the combined data set constitutes three clustered levels—districts, regions, and countries.

NOAA-NGDC data include annual time-series night-time lights intensity data, which are globally recorded across countries daily from 20:30 to 22:00 local time by a satellite orbiting the earth. The use of this data set follows recent literature that attempts to circumvent the absence of reliable and consistent subnational data (for a detailed discussion, see Chen and Nordhaus 2011; Henderson et al. 2012), particularly in Africa.<sup>14</sup> These data come in three main formats: cloud-free, average visible, and stable light composites. We use the stable light composites, which are clear of ephemeral events, background noise, summer light, and auroral activities. Moreover, we remove gas flares to eliminate potential bias in our estimates.

USGS data provide spatial locations of mineral deposits, and details of the types of mineral activities taking place across Africa (see Table 2 in Appendix 1 for the list of countries). These include the status (active or closed) of the mines; the type of mining activity (extraction or transformation); whether minerals of high or low value are extracted; whether the mining operations are large or small scale. These characteristics present unique exogenous variation that we exploit, as discussed in the empirical strategy. In general, we match USGS spatial mineral<sup>15</sup> data to 2,182 districts in 653 regions across 38 African countries. We measure mineral wealth using a binary indicator for districts with mineral production. However, one limitation of our study, as detailed in Section 5, is the lack of mine’s actual production, which

> 9 The data are available at: <u>www.ngdc.noaa.gov/eog/dmsp/downloadV4composites.html (accessed 16 June 2014).</u>

> 10 The data are available at: <u>minerals.usgs.gov/minerals/pubs/country/africa.html (accessed 5 July 2015).</u>

> 11 Sponsored by the University of Maryland, NASA, and Global Observation Forest and Land Cover Dynamics. Available at: <u>glcf.umd.edu/data/lc/ (accessed 30 December 2016).</u>

> 12 The data are available at: <u>www.met.reading.ac.uk/~tamsat/cgi-bin/data/rfe.cgi (accessed 5 July 2015).</u>

> 13 Available at: <u>beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4 (accessed 23 August 2015).</u>

> 14 For example, Elvidge et al. (2009) use night light data to construct a global poverty map, while Elvidge et al. (2012) develop a ‘night light development index’ to measure human development and track the distribution of wealth and income across countries. Other studies have used light data to measure economic growth (Henderson et al. 2012), income per capita (Alesina et al. 2015; Chen and Nordhaus 2011; Gennaioli et al. 2014; Hodler and Raschky 2014; Michalopoulos and Papaioannou 2013), and more recently regional income inequality (Mveyange 2015).

15 Our constructed data set contains over 40 mineral types. Using the standard international trade classification (SITC), we group these mineral types into 25 major types, as shown in Table 1 in Appendix 1.

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