Handbook of War Studies III by Midlarsky Manus I.;

Author:Midlarsky, Manus I.;
Language: eng
Format: epub
Publisher: University of Michigan Press

To investigate this claim further, we use the robustness checks introduced in Strand (2006, chap. 4). In the analysis reported so far, we have coded a new onset whenever the conflict was inactive for more than two whole calendar years. Inevitably, this includes a number of onsets that are commonly interpreted as a continuation and direct consequence of the previous conflict period. In Model 3, we reanalyze our data with a stricter requirement: new onsets are recorded only after eight years of inactivity. In Model 4, we include only those conflicts that exceed a total 1,000 battle-related deaths.

These robustness checks provide clear support for the instability hypothesis. With the stricter onset requirement (Model 3), we find that proximity to both independence and succeeding regime changes are significant contributors to risk of conflict. The inverted U-curve is present as well. However, neither elections nor difference from neighborhood are significantly associated with increase of risk in this model, although the coefficient for difference from neighborhood is almost as strong as in Model 1.

When we focus on conflicts that exceed 1,000 battle-related deaths (Model 4), proximity to regime change has almost exactly the same effect as in Model 3, but the uncertainty increases to just above the limit of significance. The substantive effect remains the same. On the other hand, proximity to independence is reduced from being very influential to an insignificant factor. Proximity to election is a very potent predictor in Model 4, in contrast to the other models. As expected, it is the first election in a political regime under the initial government that significantly increases the risk of conflict. Since this election very often comes in the initial period of a new political regime, this variable and proximity to regime change can be seen as measuring almost the same thing. The election variable is more sensitive to time, and the effect of an election is quickly reduced, whereas the proximity to regime change variable is much more persistent. Both are decay functions, but have half-life values of six months and 2.9 years respectively. However, they are both measures of political stability, and Model 4 further strengthens our hypothesis.

Table 5 shows correspondingly detailed results for models with severity as the dependent variable. The unit of analysis is a conflict-year, and the dependent variable is the natural logarithm of the estimated number of battle deaths in that conflict-year. We include the lagged dependent variable in the model. If the year before the year of observation was a peace year, we set ln(battle deaths) to 0. The interpretation of the estimates therefore indicates the change in battle deaths from the previous year. The estimates are exponentiated to be comparable to the risk and odds ratios presented earlier. The estimate for the SIP democracy index signifies that given last year's severity and the values for the other control variables, a war in a democracy on average has 55 percent of the fatalities of a war in an average autocracy.

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