With respect to selection, it is also interesting to interpret the coefficient for _inactive_ as the correlation between living in a mining area and our outcomes before the mines have any industrial-scale production. The statistically significant results for _inactive_ show that there may be selection into being a mining area, which is not fully accounted for by including region fixed effects. We posit three possible reasons why the likelihood of women working is higher around inactive mines: (1) these are geographical areas with an agricultural focus, where women are more likely engaged in economic activities outside of the household; (2) the coefficient captures pre-opening effects (e.g., jobs generated in the prospecting and investment phase); and (3) the artisanal and small scale mining activities that may employ women directly, in addition to indirectly generating employment. The first hypothesis is supported by the baseline results, where a large share of the population engages in subsistence farming. We explore the second hypothesis by looking at trends in employment (Figure 2 and Figure A.2). According to the visual evidence, there is an increase in service sector employment and a decrease in agricultural employment during the pre-production phase, but the effects are small in magnitude and confined to the last years before mine opening. Regarding the third hypothesis, we explore direct employment in mining, and see that mining employment for women does not change with mine opening (see Table 5).

For partners, there is a decreased probability of working, driven by a drop in agricultural employment. A substantial and positive effect of mine opening on men’s employment in skilled manufacturing is identified.

We choose a baseline distance of 20 kilometers from the mine. Although this distance cut-off does not maximize the effect size, we find it reasonable for four reasons: (1) the geocoordinates in the DHS data are randomly displaced up to 5 kilometers, and for 1% of the sample up to 10 kilometers whereby small distance spans introduce more noise; (2) the geocoordinates in the mining data reflect the centroid of the mining area. With too small an area, we are likely to capture the actual mining site rather than the surrounding communities; (3) the sample size increases rapidly with distance, which increases the robustness of the results; and (4) using distances longer than 20 kilometers, we fail to capture the mine footprint. In Section A.2 in the Appendix we discuss this extensively and we show different results based on other cutoffs and specifications.

We next examine the effects of a mine closing on employment. The results are shown in Table 4. The effects are not entirely symmetrical to the effects of mine openings. Initially, mine openings induced an increase in the likelihood of service sector employment for women, an effect that is offset by the time of mine suspension. Agricultural self-employment increases, but the effect is not statistically significant, and the magnitude is much smaller than the decline induced by mine openings. These results indicate that the localized structural shifts spurred by mine openings are not reversible for women; i.e., women are inhibited from going back to is estimated to 236,241,202 people. In our sample, approximately 1.6 % percent live within 20 kilometers of an active mine. Our baseline estimates indicate that 2 % of the women close to mines benefit from service sector employment, amounting to 73,727 women, and that 202,749 women left the labor market. Using a 25 kilometer distance span from an active mine, we estimate that 94,402 women gained employment in the service sector, and 283,206 women left the labor market.

12