coefficients on transportation costs and the interaction between the high conflict dummy variable and transportation costs. In this high conflict scenario, a 10 percent increase in transportation costs increases the wealth index by 1.8 percent. This indicates that when there is high conflict near both the nearest market and the household, households that are farther away from markets are probably better off. This is likely because households farther away from markets are less affected by conflicts near markets. This is a new finding in the literature which suggests that in areas of high conflict, being near a road or a market may be detrimental to economic well-being. The coefficients of the control variables are similar to those reported for prior regressions.

To check whether the effect of reducing transportation cost and conflict on household wealth as estimated using EDS-DRC are robust, we provide estimates using local GDP (obtained from Ghosh et al. 2012). The results are in Table 5. Columns (1) and (2) present the OLS and IV results using data for all of DRC, while columns (3) and (4) present the results for only rural areas in DRC. Column (1) which presents the OLS results, shows a smaller elasticity of transport costs than the elasticity estimated using instrumental variables methodology. The IV estimate of transport cost elasticity indicates that when transportation cost decreases by 10 percent local GDP increases by 1 percent. This estimate is very close to the effect on wealth that was estimated for EDS-RDC<sup>24</sup> . Column (2) in Table 5 shows that a 10 percent increase in the number of fatalities will lead to a 1.2 percent decrease in local GDP. The first stage results presented in the second panel of Table 5 indicate that the instruments strongly predict transportation cost and fatalities in the area. All instruments pass the Angrist-Pischke F Test of Weak Identification, with the F statistic far exceeding 10, the rule of thumb. The results obtained for the entire sample are very similar to that obtained for the rural areas.

The regression results in for Table 6, seek to identify whether impacts differ by intensity of conflict as suggested by the DHS data. The regression includes a dummy variable for cases when the number of fatalities is higher than the mean<sup>25</sup> level of fatalities and an interaction term between this dummy and the natural logarithm of transport costs. The results suggest that when conflict (number of fatalities in the past five years) is below the mean, a ten percent decrease in transportation cost would increase local GDP by 1 percent. However, when conflict is above the average, a 10 percent reduction is transportation cost decreases local GDP by 2.2 percent. This indicates that when conflict is high, reducing transportation cost may not be a priority intervention, perhaps because as the model suggests, in these cases better roads enhance the payoffs from rebellion more than those from farming since opportunities for peaceful economic activities are limited in high conflict areas. When transportation costs are at the mean (US$45.1) a 10 percent increase in conflict leads to a 0.8 percent decrease in local GDP. Overall,

> 24 The OLS estimated a counter-intuitive positive effect of conflict on local GDP while the IV estimate of the effect of conflict on local GDP is negative, further evidence that it is necessary to instrument for conflict

> 25 The median value of conflict corresponding to the lights data set is zero hence the mean value is used instead which is very close to median value of conflicts near markets in EDS-RDC.

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