of being available on a yearly basis independently of the quality of local statistical offices and data gathering. While it comes with its own problems it can shed light on local economic activity where gathering of statistical data is incomplete.<sup>7</sup> This makes it a great fit for measuring growth in a context of civil conflict. Conflict incidence is measured through the number of battle-related deaths from UCDP/PRIO dataset. We run the following regression for country _i_ at time _t_ : where _git_ is economic performance per capita growth of country _i_ in year _t_ , _incidenceit_ is conflict incidence, _μi_ and _ηt_ are respectively country and year fixed effects.

A cross-country analysis as in equation (1) bears considerable potential for both reverse causality and omitted variable bias. Thus, a priori, a convincing causal link is hard to establish. However, here we expect the resulting bias to be small for two reasons. First, the cross-country literature has not found that negative, contemporaneous shocks to growth systematically lead to violence.<sup>8</sup> Second, we have run a large number of robustness checks by adding time trends or lagged growth to our specification and controlling for rainfall shocks directly.<sup>9</sup> The upshot from this is not only that results remain significant but also that the estimated coefficients barely change. This does not mean that a causal link from falling growth to conflict can be ruled out. But it is unlikely to drive the macro relationship we see in the data.

In order to further explore the relationship between violence and country-level output we run two specifications of the model described above. In the first model conflict in country _i_ at time _t_ is defined by any violence, i.e. if at least one battle related deaths occurs. In the second specification, conflict is defined by a higher threshold, by 0.008 deaths per 1000 population.<sup>10</sup> We expect to get different results from the two specifications. From the analysis of Figure 1 we know that economic damage of civil war increases with the severity of conflict. The estimated impact from the second model should therefore be more acute.

Table 1, panel A and B, reports the results. Each column contains one of our measures for economic growth. In all specifications, conflict incidence correlates negatively with country-level economic performance. The estimated coefficients of conflict incidence are statistically significant and negative. We also find that the coefficients

> 7For a discussion see Henderson et al. (2012). In order to calculate light per capita we use population data that is provided by a World Bank dataset.

> 8The standard reference here is Miguel et al. (2004). Ciccone (2011) shows that high rainfall levels three years earlier seem to be best predictors of conflict in the reduced form. Miguel and Satyanath (2011) argue that lagged negative growth shocks are a predictor of conflict onset. In any case, there is no evidence from this literature that contemporaneous growth declines cause conflict. Bazzi and Blattman (2014) corroborate the view that the relationship between income shocks and conflict is not straightforward. They do not find evidence of an effect of price shocks on conflict onset and only weak evidence on incidence.

> 9Results from this are presented in the Appendix.

> 10We take the threshold from Mueller (2016) who shows that a threshold like this leads to a similar number of coded civil wars as the threshold of 1000 battle-related deaths often used in the conflict literature. In the context here, this is a conservative approach as it is not the threshold which yields the biggest difference between conflict and non-conflict countries.

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