a crude approximation of the actual figure. An instrumental variables two-stage least squares approach (IV 2SLS) is used to address possible measurement error. The foreign aid variable is therefore instrumented based on more reliable information about USAID aid flows at the local level. The first-stage regression also includes provincial fixed effects.

The preferred estimation includes the maximum number of control variables and corrects for measurement error (table 3a). As an additional robustness check, the indicator on conflict intensity is replaced by with the proportion of people at the provincial level who raised concerns about bad security conditions in their village (table 3b). The signs and magnitudes of the estimated coefficients are similar, although the high correlation between conflict-related indicators makes them less precise than would be ideal. However, the conflict-related indicators are jointly significant at the 5 percent level when using the indicator on casualties, and at the 1 percent level when using the indicator on perceptions of bad security.

In line with previous research, expenditures per capita increase with foreign aid and with the presence of troops, and decrease with conflict intensity (figure 6). Impact estimates vary across specifications. For instance, when controlling for the 2007 poverty rate at the district level, the positive impact of foreign aid is amplified and the negative impact of conflict intensity is substantially dampened. However, the positive impact of troops is relatively stable across specifications. In the case of Afghan troops, it is significant at the 1 percent level in all cases. The impact is two to three times higher than that of international troops. This is possibly a result of Afghan troops procuring and spending more at the local level.

# **6. The overall impact of conflict**

The estimates for Model 2 above show that conflict-related variables have opposite effects on household expenditures. Foreign aid and the presence of troops have a positive effect, whereas conflict intensity has a negative effect. But these conflict-related variables are not independent from each other. Indeed, the estimates for Model 1 imply that there is a strong correlation between conflict intensity and the presence of troops. Previous research by Chou (2012), Douglas (2012), and Johnson, Ramachandran, and Walz (2012), also points out to a strong correlation between conflict intensity and the level of foreign aid. Because of these correlations, assessing the impact of conflict on household expenditures requires considering all conflict-related variables together, and not in isolation.

The provincial database constructed for this paper sheds additional light on the correlation patterns linking conflict-related variables (figure 7). Contemporary correlations at the province level between conflict intensity, foreign aid and the presence of troops, range from 0.2 to 0.4. But based on the time structure of the correlation coefficients, conflict intensity precedes aid, which is in turn followed by an increase in the number of troops, both international and Afghan. This dynamic suggests that mobilizing more financial resources is easier than handling the logistics of a larger military presence on the ground.

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