discovery of such deposits is not. In particular, the literature suggests that discovery depends on three other factors (Krugman, 1991 and Isard et al., 1998): (i) access to and relative price of inputs, (ii) transportation costs, and (iii) agglomeration costs. If selection into being a mining area, even within a country or region, is based on factors other than mineral endowments that are stable over time, we can control for such factors. We control for region fixed effects and region specific time trends and thereby allow for different time trends across sub-national regions.

The interpretation of the coefficients from our estimation strategy relies on the population being the same before and after mine opening. We are using a repeated cross-sectional dataset, and we discuss in the robustness section how we deal with this issue by using the available information on migration. Additionally, we worry that the control group in the baseline definition is inherently too different from the population living in mining areas. Several measures are taken to ensure that the results are not driven by such dissimilarities, including using region fixed effects and geographically limiting the area from which the control group is drawn. Furthermore, the estimation strategy could capture other changes that happen parallel to and irrespective of the mine opening. Mine industrialization and employment changes could be driven by improvements in infrastructure. We use the best available data on road networks in Africa and explore whether the results are stable. Different fixed effects, for the closest mines and for different types of minerals, are also included to verify the robustness of the results. We cluster the standard errors at the DHS cluster level, but we also present results where the standard errors are clustered at the regional level, at the level of the closest mine, and for multi-way clustering at both the DHS cluster and the closest mine.

# **4 Results**

We start by exploring the evolution of employment over time. Figure 2 shows the trends in service level employment for those within 20 kilometers and those between 20 and 200 kilometers from a mine. The treatment group follows a similar trend as the control group in service sector employment until mine opening, but at a lower level. Service employment increases sharply once the mine opens<sup>9</sup> . The levels equalize somewhat at the tenth year, which, in part could be due to a geographic dispersion of the effects with time (the control group is limited to within 200 km). Such a dispersion effect would explain the increase in service employment in the control group. The decline in the treatment group close to the tenth year may be a result of mine closings since mine length in our sample is, on average 10 years. This hypothesis is supported by the right-side figure showing that service employment is higher close to mines that are going to close, but have not yet done so. This difference in service level employment decreases as the date of closing appears and reverses once the mine closes. Similar trends are obtained if we have the residuals after controlled regressions instead of levels (figures are available upon request).

> 9An increase in service sector employment is noted shortly before mine opening, which corresponds to the investment phase of the mine.

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