Beyond the Target State: Foreign Military Intervention and Neighboring State Stability

Abstract

Despite the abundance of research on the consequences of foreign military intervention for target countries, scant research has been devoted to the possible regional externalities of intervention. This article examines whether large-scale armed operations affect the likelihood of civil conflict onset in countries neighboring the target of intervention. We posit that interventions against the target regime reduce the government's ability to maintain full control over the entire national territory by diminishing its coercive and administrative capacity. This might, in turn, result in safe haven possibilities for neighboring rival groups in the target and facilitate the transnational spread of arms and other illicit activities that increase the risk of civil conflict onset in the contiguous countries. Armed interventions supportive or neutral toward the target state, on the other hand, bolster the government's coercive capacity and mitigate ongoing crises in the target. Such armed intrusions might therefore undermine the likelihood of internal armed conflict in neighboring countries triggered by the factors associated with “bad neighborhoods”: safe haven possibilities, transnational spread of arms, and refugee flows. To substantiate these claims, we use time-series, cross-national data for the 1951–2004 period. Results indicate that hostile interventions increase the probability of civil conflict onset in connected countries while supportive interventions have a regional pacifying effect, reducing the likelihood of domestic unrest in countries neighboring the target state. Neutral interventions, on the other hand, are unlikely to have any discernible effect on regional stability. Further, the primary motive of intervention, whether for humanitarian or other purposes, has no statistically significant impact on the stability of neighboring countries.

Keywords:

• civil conflicts
• foreign military interventions
• intrastate conflict
• regional stability

Acknowledgments

The authors thank A. Cooper Drury, Frederic S. Pearson, and three anonymous reviewers of International Interactions for helpful comments on earlier drafts of this manuscript. They also thank Yvonne M. Nzomo for research assistance. Replication materials (data and exact computer codes) can be found at http://dvn.iq.harvard.edu/dvn/dv/internationalinteractions.

Notes

¹Throughout the manuscript, military intervention refers to “the movement of regular troops or forces (airborne, seaborne, shelling, etc.) of one country into the territory or territorial waters of another country, or forceful military action by troops already stationed by one country inside another, in the context of some political issue or dispute” (Pearson and Baumann 1993:4).

²For comprehensive reviews of this literature see for example Sambanis (2002), Hegre and Sambanis (2006), and Olson, Lounsbery, and Pearson (2009).

³The data analysis excludes the intervention cases that “support or oppose third party government” or “support or oppose rebel groups in sanctuary.” This is because it is difficult to determine whether the interventions aimed at third party governments or rebel groups in sanctuary are supportive, neutral, or hostile to the target government.

⁴The question of “who intervenes?” might be another confounding factor in understanding the transnational consequences of armed intervention. We run several additional models that control for the interventions by “great powers” (France, Great Britain, the U.S., and Soviet Union/Russia), the neighboring countries of the target, and intergovernmental organizations (both by the UN and regional ones). In the models that we control for different types of interveners, we find no statistically significant association between any type of intervener and the possibility of civil conflict onset in neighboring countries.

⁵Diagnostic tests reveal that there was no issue with multicolinearity in any of the estimations. To check the model fit, we used the Hosmer-Lemshow goodness-of-fit test. The chi-square is not statistically significant in any of the estimations indicating goodness of fit.

⁶SPost Stata ado files by Long and Freese (2004) are used for the post-estimation interpretation of regression models for categorical outcomes.

⁷The natural log values on the x-axis were converted back to real numbers to make the interpretations of the substantive impact of the duration variables easier.