sample (up to 147 countries) for the same period, both the terms for population size and GDP per capita show the expected effects. Furthermore, when I reran the model with the same sample as in Model 6, but no term for HI, the effects of population size and GDP per capita dropped to insignificant levels (see Table A3). Finally, the term for years of peace never reaches significance in any model, but that may be due to the short time-period (1986–2003).<sup>17</sup> In contrast to the control variables, the horizontal inequality terms reveal some interesting results. My first hypothesis (H1), assuming a higher risk of conflict for countries with severe socioeconomic horizontal inequalities is quite well supported in Table 1. All the terms show positive significant effects. Models 1 and 2 report the effects of HIs between the two largest ethnic groups in a country, Models 3 and 4 report the effects of HIs between the two largest religious groups in each country, and Models 5 and 6 report the effects of HIs between the capital region and the rest of the country. The reason for the differences in N is the different availability of data. All the DHS surveys include questions regarding regional affiliation, but several surveys exclude questions about ethnic and religious affiliations.

As expected, the term for horizontal asset inequality between ethnic groups shows a positive significant effect in Model 1. Although only significant at the 10% level, the marginal effect is quite strong. For a country with mean values on all the explanatory factors, the probability of onset of civil conflict in any given year is 2.3%. If we increase the level of horizontal asset inequality to the 95<sup>th</sup> percentile while maintaining the other variables at their mean, the probability of a conflict onset increases to 6.1%.<sup>18</sup> The effect for educational HI between ethnic groups (Model 2) is also positive with the probability of conflict increasing to 5.4% Focusing on HIs between religious rather than ethnic groups in Model 3 and 4, the effects seem quite similar.<sup>19</sup> Models 5 and 6 both provide strong support for H1, showing that interregional horizontal inequalities increase the risk of conflict both with regard to assets and education level. Both coefficients are positive at the 1% significance level, and their effects are very similar: If we increase the level of interregional horizontal inequality to the 95<sup>th</sup> percentile while maintaining the other variables at their mean, the probability of conflict increases from about 3.8% to 9.5%.<sup>20</sup> This finding corresponds well with related investigations, such as Murshed & Gates (2005) who find that Nepalese districts with severe gaps relative to Kathmandu in terms of schooling are associated with higher conflict intensity.

The data material presented here might be too scarce to convincingly conclude that regional inequalities matter more for conflict than HIs between ethnic or religious groups. However, since there is more data for regional HIs than ethnic or religious HIs, I conduct the rest of the analyses only with the former terms in order to maximize the size of the sample (and number of conflicts) when testing the effects of interactions with the institutional variables.

> 17 It is always the possibility that conflict history (peaceyears) may be explained by other independent variables. Hence, I also ran all the models 1-19 without the terms for peaceyears and splines (regressions not shown here), but there were no substantial changes in the results.

> 18 Marginal effects were calculated with the aid of CLARIFY software in Stata 8.2 (see Tomz et al., 2003).

> 19 Increasing the inter-religious HI term for assets and education respectively, the conflict risk increases from 2.9% to 7.2% and 7.1%.

> 20 When I ran Models 5 and 6 with the same sample as Model 1 and 2 (n=473) the results for the inter-regional HI largely hold, although the effects are slightly weaker.

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