remains positive and significant, while the sum of lagged _Px_ coefficients becomes significant only at the 15% level (column 3). In fact, the size of the _Px_ effect on conflict increases slightly with time trends, to a 2.5% rise in conflicts for a 10% increase in _Px_ (column 1). The results are also qualitatively similar to those in table 2 when running the same battery of checks for _major conflict onset_ (columns 4-6).

In Table 4 we also check the robustness of the results to the possible endogeneity resulting for countries that have a significant influence on the international prices of their exported commodity. We use two strategies to deal with this concern. First, we exclude from country X’s export basket the commodities for which X’s share in world exports is above a certain threshold (10% in column 1 and 20% in column 3), and obtain results that are similar to our baseline results in table 2. The main difference is that the coefficient of _Px_ is smaller but still significant at the 10% level. Second, we exclude the countries which are large exporters of at least one commodity (in one year) according to the 10% (column 2) or 20% (column 4) criterion. This approach has the advantage of not generating artificial biases in the countries’ export baskets. However, it also leads to a reduced sample that may be less representative than the full sample of developing countries as a group. The results are again the same as in our baseline results.

Finally, we test whether our results are different if we use the COW data set for the conflict data. In order to make the regressions comparable, we re-run the regression with PRIO data using the same battle deaths threshold of major conflict as in COW (i.e. at least 1,000 battle deaths over the entire course of the conflict). While we feel that the PRIO data is a more reliable data source, it is reassuring to see that the main results do not vary much across data sets. The comparison – reported in columns 5-6 – shows that the individual trade variables’ coefficients are not significantly different across the data sets. The only exception is that the contemporaneous coefficient on _Pm_ is positive and significant in COW and negative but not significant in PRIO. However, the sum of the _Pm_ variables is not significant with either data set.

# **5.2. Commodity types**

In table 5 we test for the effects of the different types of commodities on civil conflict by splitting the price indices into point-source and diffused commodities and into commodities that are consumed domestically and those that are not. To save clutter, for each price variable we only report the value and significance of the sum of the three coefficients (the contemporaneous and the two lagged variables).<sup>29</sup> This split suggests that the conflict-inducing impact of the export price rise is mainly driven by the price of point-source commodities, while the effect of diffused commodities on the probability of conflict is not significant (column 1). These results are consistent with the rapacity effect hypothesis, while they provide no support to the opportunity cost hypothesis of conflict.

In column 2 we split the commodities into those that are used for final consumption and those that are not, finding that only the former have a positive and significant impact on

> 29 Unless otherwise stated, we also do not report the coefficients of the other trade variables as they are little affected by the splitting of the price indices (results available upon request).

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