significant, as in B-B (column 4). As explained above, we believe that using the level is more appropriate, therefore we take the positive _Px_ coefficient as more representative of the effect of export price shocks on conflict.

The non-significant results for _Pm_ are different from the positive impact found by Arezki and Bruckner (2011). However, they are consistent with the note of caution highlighted above, i.e. a developing country’s commodity imports account for only a limited share of total consumption of commodities, since a large share of consumption, especially among poorer households, comes from domestic production. At the same time, the lack of significance of the demand variable _MP_ suggests that the economic cycles in the export destination markets do not affect the probability of conflict at home, unlike other economic shocks such as rainfall (Miguel et al., 2004) and foreign interest rate movements (Hull and Imai, 2013).<sup>28</sup> In columns 5-8 we test for the effect of the same variables on major conflicts in the way we defined them above. The effects of _Px_ on major conflicts is not significantly different from zero overall, although its lagged coefficient is negative and significant (column 5). These results are unchanged when adding the other trade variables (column 6) as well as the control for coups (column 7). The other trade variables also have little impact on major civil conflicts. The trade coefficients are also not significant when computing the price indices with price changes instead of levels (column 8).

Taken at their face value these results suggest that export shocks could affect the eruption of minor conflicts but not of major civil wars. That would be the case, for instance, if these shocks influence local conflicts, which do not eventually spill into large scale civil wars. That would be consistent with evidence from Colombia (Dube and Vargas, 2013) and the DRC (Maystadt et al., 2014). However the lack of significance of the results for major conflicts could also be a product of the ‘rare event’ bias highlighted above. That bias is more likely to apply to the regressions with _major conflict onset_ as the dependent variable, as this is a much rarer event than any civil conflict onset. Given this possible interpretation, we consider the specifications with _any conflict onset_ as the dependent variable as our preferred ones.

# **5.1. Robustness**

These results are robust to a wide array of checks presented in tables 3 and 4. First, the results for _any conflict onset_ survive when adding country-specific time trends (column 1, table 3) as well as when constructing the price indices using a faster-moving average of the weights (i.e. the average between _t-4_ and _t-2_ ) (column 2). When we add countryspecific time trends to the latter specification, the contemporaneous _Px_ coefficient

> 28 This result appears to contradict those of two other recent studies, Berman and Couttenier (2014) and Chaudion et al. (2013). However the former considers only large economic downturns in trading partners (i.e. banking crises) and focuses on conflict eruption at the sub-national level. The latter uses the MP variable as an instrument for economic growth rather than as an independent regressor, and no lagged structure is used.

16