from Colombia’s price index would bias the _Px_ coefficient upward. Because of this reason we do not exclude any commodities from the price shock variables in the baseline specifications. Instead we check the robustness of the results to the exclusion of relevant commodities (according to different thresholds) as well as of some countries which are price-makers in at least one commodity (average share in global exports above the threshold).

The vector X contains controls that are likely to have an independent effect on conflict. First we include a dummy for whether the country has had any conflict since World War II, which captures the higher likelihood of starting a conflict for countries which already experienced one recently (World Bank, 2011). Second, in order to control for the spillover effects of conflict, we include a dummy for whether any of the neighboring countries has a civil conflict. The variable is lagged one year to reduce the endogeneity concerns. In some specifications we also include the incidence of coup attempts (whether successful or not) in the two years prior to _t_ , based on Powell and Thyne (2011). This can be interpreted as an indicator of a weak state (Kuhn and Weidmann, 2013), but it could also act as a trigger for a civil conflict itself.

To differentiate the effects across classes of commodities, we split the Px variables into different groups of commodities. In particular, we distinguish between point-source and diffused commodities as well as between commodities that are consumed in the country and those that are not. This yields four different export price variables, each with a lag structure.

We use the linear probability model as in B-B to estimate equations (5) and (6), although the results are very similar when using the conditional fixed effect logit estimator as well (results available upon request).

One problem with estimating equations (5) and (6) is that the dependent variables have a very large number of zeros relative to the number of ones, i.e. the models try to explain rare events.<sup>23</sup> That is inherent in the nature of this type of empirical analysis, although it is rarely acknowledged in other studies. Importantly, this problem can lead to biased coefficients’ estimates in finite samples (King and Zeng, 2001). In addition, the bias in the standard errors tends to go in the same direction as the bias in the coefficients. Thus the rare event bias may cause the underestimation of event probabilities. King and Zeng (2001) suggest that one way to correct the problem is to decrease the rareness of the event. That could be done, for example, by lowering the threshold of what constitutes an event or by expanding the data selection period. In our case this would strengthen the case for using _any civil conflict_ as the dependent variable. For this variable the number of events is 25 times smaller than the number of zeros while it is 50 times smaller for _major conflict onset._ We explore in more detail below the implications of this possible bias in interpreting the empirical results.<sup>24</sup>

> 23 King and Zeng (2001) define ‘rare events’ dependent variables as those for which the number of zeros are larger than that of ones by at least an order of magnitude in the dozens.

> 24 King and Zeng (2001) also propose a rare logit estimator to correct the rare event bias. Unfortunately the lack of convergence in the estimation of our model does not allow us to implement that estimator.

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