In order to interpret these results it is important to keep in mind that our estimates are coming from a country fixed effects regression. In this way we are comparing growth in a country during conflict to other years in the same country without conflict. The hope is that factors which affect the country generally are captured by the fixed effect. However, our empirical strategy also implies that if conflict affects growth in the postconflict period then _β_<sup>ˆ</sup> will provide an underestimate of the true economic costs of conflict. We will return to this point below.

# **2.3 Economic Impact at the Ethnic Group Level**

As a final piece of evidence, we draw from economic, population and ethnic conflict data compiled in the GROWup dataset. This dataset provides satellite night light data as a proxy for economic activity at the ethnic group level. Conflict incidence in the GROWUp data is defined through more than 25 fatalities, i.e. a fairly low threshold, in ethnic conflicts. This dataset provides information on 502 ethnic groups for the period 1992 to 2013. We run a regression of the following form: where _git_ is night light per capita growth of group _i_ in year _t_ , _incidenceit_ is conflict incidence, _θi_ are a set of 502 group fixed effects and _ηt_ are year fixed effects. The fixed effects in this context control for group-specific growth trends in the data. Note that we do not use the per capita model in this specification as violence seems to be a pure public bad locally.

Table 2 reports the results. We find a negative and statistically significant effect of incidence on per capita light growth. Light per capita growth is reduced by around 7 percentage points in years a group is engaged in conflict. This effect is robust to controlling for lagged light growth in column (2). In columns (4) and (5) we show that these reductions in light per capita growth are composed by two factors; light growth falls dramatically and this is partially offset by a negative effect on population growth. However, column (3) also shows that there is some evidence for negative spill-overs from conflict at the country level. Conflict in other groups in the same country lower light per capita growth for other groups in the same country by 0.7 percentage points.

Again we interpret these estimates in terms of GDP growth following Henderson et al. (2012). If a decrease of night light by 1 percent implies a decrease of GDP of about 0.25 percent, then our result from column (1) in Table 2 can be translated into a reduction of GDP growth of about 1 _._ 8 percentage points.

Overall this suggests that local effects are important and comparatively large in comparison to spill-overs. This is important for our discussion as they define the challenge that the country faces in the recovery from civil wars. Local effects of violence imply that civil conflict leaves a legacy of regional economic divergence. Another way to understand the size of this relative decline is to perform a second simulation of the GDP loss that focuses on group-level economic performance, following the same steps described above. Figure 4 displays the distribution of conflict lengths in the sample for which we also have the night light per capita data (years 1992-2012). We estimated 13