over 10 years of observations as in Glick and Taylor (2005) and Martin, Mayer et Thoenig (2005). The data comes from the Correlates of War project. The sign of the coefficients is negative but not always statistically significant, possibly because we are working on a different panel at the product level. In the next tables we’ll see that the impact of war differs across types of products. The inclusion of this measure of cross-border security however, reduces only slightly the magnitude of the coefficient on past terrorism incidents.

Second, there are also some reasons to believe that two countries sharing the same types of political and economic institutions on the one hand could also share lower transaction costs and thus make more trade. On the other hand, this institutional proximity could lower the occurrence of terrorism attacks between them. In order to control for this effect, we add a dummy variable constructed from PolityIV dataset that takes on 1 when the polity variable (a grade that measures the degree of good governance) is as high as that of the US<sup>15</sup> , and 0 otherwise. But the effect, although positive, does not appear to be significant and leaves the variable of terrorism incidents unaffected.

We next introduce a series of controls related to insecurity that originate specifically from the exporting country. The objective, here again, is to isolate all the forces that affect both bilateral trade and terrorism incidents. The progressive inclusion of a civil war dummy, a newstate exporter dummy, a proxy of good governance (i.e. polity2 variable in PolityIV, varying from -10 to 10), measures of ethnic or religion fractions (from Alesina et al (2003) dataset), reduce further by a third the magnitude of the coefficient on past frequencies of incidents, although without affecting its high significance in the pooled regression (i.e. estimators reduced from 80 to 57).<sup>16</sup> . Column 9 introduces almost all of the control variables together<sup>17</sup> and shows further that the impact of third countries incidents variable is still significant with a semi-elasticity that reaches 47. Finally, as mentioned earlier, we run a within type regression that accounts for (country*product) dyadic effects in order to account for country specialization. The effect of the terrorism variable based on third countries, appear again with the a magnitude similar to that obtained from the prior regression, if one accounts for standard errors. To sum up, if the true semi-elasticity is say around 40, the inferred elasticity _η_<sup>_m_</sup> computed at the median point (1 per thousand of incidents originating from half of the countries) is around 0.015. This is still not a high figure. However, those exporting countries which happen to be at the origin of high terrorism activities over the period like Colombia (more than 20% share of total incidents in some years), tend to be associated with an elasticity of at least 2.8, almost three times as much as that estimated earlier.

> 15The US grade is 10, the maximum that could be obtained by a ranked country

> 16Notice however, that most of these variables appear to be statistically insignificant. Religion Fractions in a exporting country seems however to be good for trade with the US. This result is consistent with Alesina et al (2003) findings concerning the role of this variable on various outcomes.

> 17To avoid multicollinearity, we have removed Ethnic fractions and newstate exporter dummy from the regression

20