# **3.2 District inequality estimation and trends**

To estimate district inequality using night-time lights data, we followed Mveyange (2015) to calculate inequality indices for 2,182 districts in 653 regions across 38 countries in Africa. We computed several measures of district inequality as outcome variables: Gini and the entropy measures (mean logarithmic deviation (MLD) and Theil)). These measures have several desirable properties, e.g., symmetry, mean independence, Pigou-Dalton transfer sensitivity, and population size independence (Haughton and Khandker 2009). However, our main results are based on Gini—a commonly and widely used measure of inequality.

To calculate Gini at the district level, we identified (based on geographical administrative units’ data from the Global Administrative Areas database<sup>19</sup> ) and cut countries’ districts into 0.01 square decimal degree grid cells (about 1.1 km<sup>2</sup> at the equator—an equivalent to the size of a town or village). We then exploited the spatial and temporal variation of light intensity across these grid cells to estimate average inequality at the district level across countries for all the years between 2001 and 2012. We calculate Gini<sup>20</sup> as follows: where i is grid cell rank order, n is total number of grid cells, yi is grid cell value light intensity per capita, and P is grid cell population count. For the robustness checks we use the entropy<sup>21</sup> measures of inequality and a measure of spatial inequality proposed by Bonet (2006).<sup>22</sup>

# **3.3 District inequality trends and summary statistics**

Figure 3 plots the average trends of district light intensity Gini between 2001 and 2012. The figure shows the variation across districts with no mining activities, with active mines, and with closed mines. The figure suggests that overall there was a modest decline in spatial inequality in Africa between 2001 and 2012. The figure also reveals a clear difference in spatial inequality between districts with mining activities and those with none, inequality in the former being higher by a considerable margin. When districts with mining activities are divided into active mines and closed mines, the figure suggests that districts with closed mines experienced relative higher inequality than districts with active mines.

Turning to the summary statistics, Table 3 describes the composition of the mining sites based on the previously mentioned exogenous variations: status, scale, values, and types of mining activities. Overall, the table shows that mining sites constitute 17.2 per cent of all 2,182 districts

> 19 Available at: www.gadm.org/ (accessed 16 July 2014).

> 20 Similar to Damgaard and Weiner (2000).

> 21 MLD is calculated using the following formula: ���� �� ∑���� �� ~~�~~ <u>����</u> ~~�~~ and ������ �� ∑���� <u>����</u> �� ~~�~~ <u>����</u> ~~�~~ , where �� is grid cell value (i.e. light intensity per capita), ��  is the average grid cell lights, and �is the population size within the grid cells.

> 22 Defined as ���������,�,� �1� where, � is country,  � is district, � is year, ����,� is an average lights per capita at the district level, and ����,� is average lights per capita at the country level. Bonet (2006: 668) asserts that this measure of spatial inequality is based on the concept of relative lights per capita, with perfect equality achieved with the equality of district and national average lights per capita.

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