# **3 Empirical Strategy**

With several waves of survey data combined with detailed information on mines, the estimation relies on a spatial-temporal estimation strategy, using multiple definitions of the mine footprint area based on different proximity measures and alternative definitions of the control group.

Assuming that people seek employment at any mine falling within a cut-off distance, our main identification strategy includes three groups with the baseline distance 20 km: (1) within 20 kilometers from at least one active mine, (2) within 20 kilometers from an inactive mine (defined as a mine that is not yet active), but not close to any active mines or suspended mines, and (3) more than 20 kilometers from any mine. The baseline regressions are of the form: where the outcome _Y_ of an individual _i_ , cluster _v_ , and for year _t_ is regressed on a dummy _(active)_ for whether the person lives within 20 kilometers of at least one active mine, a dummy _(inactive)_ for whether the person lives close to a mine that has not started producing at the time of the survey, region and year fixed effects, region-specific linear time trends, and a vector _X_ of individual level control variables. In all regressions, we control for living in an urban area, age, years of education, and indicators for religious beliefs.

Interpreting the coefficient only for _active_ (20 km) builds on the premise that the production state ( _active_ or _inactive_ ) of the mine is not correlated with the population characteristics before production starts, i.e., that a mine does not open in a given location because of the availability or structure of the labor force in that geographical location. This is a potentially strong assumption because wage labor and population density may influence mining companies’ investment decisions or could jointly vary with a third factor such as accessibility or infrastructure. Including the dummy variable for inactive mines allows us to compare areas before a mine has opened with areas after a mine has opened, and not only between areas close to and far away from mines. For all regressions, we therefore provide test results for the difference between _active_ (20 km) and _inactive_ (20 km). By doing this we get a difference-in-difference measure that controls for unobservable time-invariant characteristics that may influence selection into being a mining area.

Exploiting within-country variation leads to more robust causal claims (e.g., Angrist and Kugler, 2008; Buhaug and Rød, 2006; De Luca et al., 2012; Dube and Vargas, 2013 on conflicts, Wilson, 2012 on sexual risk taking behavior in Zambia’s copper belt; and Aragon and Rud, 2013b on the local economy in Peru). With region fixed effects, we expect that only timevariant differences within regions are a threat to this identification strategy. That is, we control for time-invariant regional mining strategies, institutions, level of economic development, sectoral composition, and norms regarding female work force participation. Nonetheless, the exact location of a mine within a country or region may still be influenced by factors other than abundance of resources. The placement of mineral deposits is random (Eggert, 2002), but the 9