# **_Econometric Issues_**

While previous studies have been influential, the cross-country empirics they are based on have not always taken fully account of cross-country heterogeneity or the likely endogeneity between conflict and its determinants. The endogeneity bias may arise from measurement errors, omitted variables or potential reverse causality between the dependent variable, conflict onset, and our variable of interest, public spending. It is possible, for instance, that education enrollment drops as a civil war approaches because people flee their homes in anticipation of fighting. Likewise, expenditures may drop before an insurgency as the government diverts resources from social expenditures to the military in order to defend itself. Studies of conflict onset typically address this problem by lagging the explanatory variables so that conflict onsets in a given year are explained by the values on the explanatory variables in the previous year. However, it is possible that problems with reverse causality appear before t-1.

To take account of unmeasured regional effects, all estimations include regional dummies as in Fearon and Laitin (2003) for Asia, the Americas, Africa, and Europe (the Middle East being the reference region). We also use random effects logit models. Since the dependent variable is binary, fixed effects would drop all the countries that do not experience any conflict over the period of study. Yet, at the same time, it may be the case that the effect of interactions may differ systematically due to country specific conditions, even after the inclusion of our control variables. Therefore, as an alternative, we include country-level random effects to account for the likelihood that each country may have different effects with regard to the interaction term.

Finally, to test our results for military spending – as shown below, these are our most statistically significant results – we also employ instrumental variable (IV) estimations. It is not hard to imagine that increases in military spending could be driven by high risk of civil war. To mitigate the risk of reverse causality, we use IV estimations in which we first predict military spending with models including instrumental variables that are not clearly determined by the risk of civil war. Following Collier and Hoeffler (2007), we use two instrumental variables: neighboring countries’ military spending relative to home country’s GDP and a Cold War dummy.<sup>21</sup> From the first stage model we predict values of military spending per capita and subsequently estimate logit models of civil conflict onset that include: oil-gas value per capita, predicted military spending per capita, and the predicted interaction between military spending and oil wealth.<sup>22</sup>

> 21 We also include neighboring countries’ military spending relative to their own GDP and our results do not change. 22 Besides the exogenous instruments, we include the following independent variables in the first stage model: a country’s coup history and the years since the last coup, an indicator variable for military regimes, international war duration, and one year lagged values for Polity IV, per capita GDP, trade openness, oil-gas value per capita. To avoid the Wooldridge (2002) forbidden regression the predicted interaction between military spending and oil wealth comes from a similar model that also includes the interaction of predicted military spending and oil wealth.

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