Here, represents the wellbeing of the household, measured by the wealth index, the multi-dimensional poverty index, or local GDP. Transportation cost to the nearest market is given by ii and is instrumented WW as described earlier using the Natural-Historical Path variable. Conflict, measured in number of fatalities ii FF within a 50 km radius of the market and household, is given by and , respectively, also MM HH instrumented using the Fractionalization Index , and its squared term as well as the Euclidean distance to ii ii CC CC the eastern border as there is greater conflict along this border and because distance to this border should not directly impact income (for local GDP, the location of the household is merely replaced with the location of the centroid of the grid cell). The coefficients of these variables provide an indication of the differential effects of transport costs.

Finally , _X i_ denotes a vector of control variables that are likely to affect household wellbeing. Given the difference between household data and spatial data, different control variables were used in the regressions using the wealth index and the multi-dimensional poverty index, than with local GDP. For the first two, this vector includes agricultural variables, including agricultural potential (to account for the fact that areas with greater agricultural potential may naturally be more likely to have greater wealth) and a dummy indicating whether the household is engaged in agricultural activities (to account for the fact that agricultural households may accumulate wealth differently from others). We also control for household demographic characteristics such as the age of the household head, a binary variable indicating whether the household head is female, number of female members aged 15-49, number of male members ages 15-59, and number of children aged 0-5 (with all continuous variables estimated in log form). These variables help account for the fact that households with different demographic characteristics will have different propensities to accumulate wealth, as well as different levels of health and education. Finally, the regression includes a binary variable indicating whether the household is in a rural area and fixed effects indicating the geographic zone (to account for the unobserved characteristics of the area in which the household resides). For local GDP, controls include a quadratic term of population within the grid cell (to control for agglomeration benefits)<sup>21</sup> , the agro-ecological potential yield of several important crops within the gridcell<sup>22</sup> , the distance to the nearest mining facility<sup>23</sup> , and province fixed effects.

> 21 Population data is from Landscan and is available here: http://web.ornl.gov/sci/landscan/

> 22 Agro-ecological potential data is from GAEZ, a product of FAO.  It considers climate and soil conditions to estimate the maximum potential yields in each region for a large number of crops. The data used in this model assumes climactic conditions similar to the 1961-1990 baseline level, and is calculated assuming low input systems.

> 23 Distance to the nearest mining facility is included because mines tend to be areas of great economic activity. In addition to the economic activity at the mine, mines can often generate economic spillovers for industries which service the mine and the mine’s workers and their families. Data on mining facilities throughout DRC was obtained from the National Minerals Information Center of the USGS. The data set includes geo-referenced data on all mining facilities, active or closed, between 2006 and 2010. Because of the wide definition of what a mining facility actually is, we selected only a subset of mining facilities available to include in our data set. Facilities selected were

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