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Transnational Rebels: Neighboring States as Sanctuary for Rebel Groups

Published online by Cambridge University Press:  13 June 2011

Idean Salehyan
University of North Texas,
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To what extent do international factors affect domestic conflict processes? How do external conditions affect the state's repressive capabilities and the opportunities for opposition groups to mobilize, launch an insurgency, and sustain it? This article argues that because state strength is limited by international boundaries, rebel groups often organize transnationally in order to evade repression. External bases, refugee communities, and characteristics of neighboring states are expected to increase the likelihood of civil war onset and continuation. Importantly, external mobilization is difficult for states to monitor and verify, a factor that exacerbates bargaining problems and increases the probability of armed conflict. These claims are tested through a quantitative analysis of civil conflicts from 1951 to 1999. Results suggest that weak neighbors, rival neighbors, and refugee diasporas contribute to rebellion and that conflicts endure longer when rebels have access to external bases.

Research Article
Copyright © Trustees of Princeton University 2007

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22 Bapat, Navin, “The Internationalization of Terrorist Campaigns,” Conflict Management and Peace ScienceGoogle Scholar (forthcoming).

23 Several recent studies have begun to explore the relationship between external support for insur-gencies and conflict between states. In this regard, see Bapat (fn. 4,2006); Schultz, Kenneth, “War as an Enforcement Problem: Interstate Conflict over Rebel Support in Civil Wars” (Manuscript, Stanford University, 2007Google Scholar); Salehyan, Idean, “No Shelter Here: Rebel Sanctuaries and International Conflict,” Journal of Politics (January 2008CrossRefGoogle Scholar)

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30 Not all refugees flee because of government persecution. Situational refugees flee general conditions of violence in a country and do not necessarily have a stake in the conflict; see Kenyon Lischer, Sarah, Dangerous Sanctuaries: Refugee Camps, Civil War, and the Dilemmas of Humanitarian Aid (Ithaca, N.Y.:Cornell University Press, 2005Google Scholar). However, a significant subset of any refugee outflow is likely to include people who have a direct grievance against the state.

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32 Bapat (fn. 4, 2007); Dorff, Robert H., “Failed States after 9/11: What Did We Know and What Have We Learned,” International Studies Perspectives 6, no. 1 (2005CrossRefGoogle Scholar).

33 See Bapat (fn. 4, 2007); Byman (fn. 3), 260–62.

34 Lischer (fn. 30).

35 It is argued here that refugee camps are a source of recruits and bases for rebels. However, refugees may themselves complicate the bargaining environment, and that may lead to longer conflicts. States must offer credible commitments to allow refugees to repatriate and reintegrate back in the home country. They must also promise not to violate human rights again in the future, which may be difficult. Special bargaining problems posed by refugee communities are not addressed in depth here but are left for future work. On strategic issues involving refugee repatriation, see Zeager, Lester and Bascom, Johnathan, “Strategic Behavior in Refugee Repatriation: A Game Theoretic Analysis,” Journal of Conflict Resolution 40, no. 3 (1996CrossRefGoogle Scholar).

36 Filson, Darren and Werner, Suzanne, “A Bargaining Model of War and Peace: Anticipating the Onset, Duration, and Outcome of War,” American Journal of Political Science 46, no. 4 (2002CrossRefGoogle Scholar); Wagner, R. Harrison, “Bargaining and War,” American Journal of Political Science 44, no. 3 (2000CrossRefGoogle Scholar).

37 Byman (fn. 3), 70.

38 Fearon (fn. 6).

39 Walter (fn. 3).

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41 Kim, Julie, “Macedonia: Country Background and Recent Conflict,” CRS Report for Congress (Washington, D.C.:Congressional Research Service, 2001Google Scholar).

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44 Elbadawi and Sambanis (fn.3).

45 A high threshold for classifying binary events has important methodological limitations when using either a lagged dependent variable or counts of years at “peace.” With a threshold of one thousand deaths, an event that falls just short of the cutoff point would not be counted as a conflict and would be assumed to have no impact on the subsequent probability of violence. In practice, however, low-intensity conflicts are likely to be systematically associated with a higher likelihood of future large-scale conflict.

46 Peter Gleditsch, Nils, Wallensteen, Peter, Eriksson, Mikael, Sollenberg, Margareta, and Strand, Havard, “Armed Conflict 1946–2001: A New Dataset,” Journal of Peace Research 39, no. 5 (2002Google Scholar). I include all intrastate and internationalized intrastate disputes (type 3 and type 4 conflicts in U/PACD) that occur on a state's territory.

47 Alternative approaches (five-year intervals and no consolidation) were also considered, but results do not vary significantly.

48 Thompson, , “Identifying Rivals and Rivalries in World Politics,” International Studies Quarterly 45, no. 4 (2001CrossRefGoogle Scholar).

49 See Thompson (fn. 48) for details. I thank William Thompson for providing me with an electronic version of this data set.

50 I thank an anonymous reviewer for pointing this out. Results do not change substantially when using contemporaneous values.

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54 Fearon and Laitin (fn. 1).

55 Alternative measures indicating the GDP per capita of the poorest neighbor and the mean neighborhood GDP were also used, but this did not significantly change the results.

56 I thank Bela Hovy of the UNHCR for providing me with these data. However, the UNHCR does not keep track of figures for Palestinian refugees. Therefore, these data are supplemented with figures from the United States Committee for Refugees and Immigrants. Palestinian refugees are counted as originating from the state of Israel.

57 Davenport, Moore, and Poe (fn. 29); Neumayer, Eric, “Bogus Refugees? The Determinants of Asylum Migration to Western Europe, International Studies Quarterly 49, no. 3 (2005CrossRefGoogle Scholar); Schmeidl (fn. 29).

58 Because conflict data are available from 1945, the count of peace years since 1945 is taken.

59 Lagging refugees one year and including a peace years indicator presents a high hurdle and may understate the effect of refugees if conflict and refugee militarization occur simultaneously or unfold quickly. In addition, civilian populations may anticipate future conflict and flee before fighting begins. Endogenous relationships are difficult to disentangle and will require finer temporal units and alternative methodologies. This will be left for additional research.

60 In alternative models separate variables were created for refugees in rival states, civil war states, and all others. This analysis does not yield significant results, although there is a high degree of collinearity among the variables.

61 Collier and Hoeffler (fn. 1); Fearon and Laitin (fn. 1).

62 See Gleditsch (fn. 53).

63 Hegre (fn. 1); Muller and Weede (fn. 17).

64 Marshall, Monty and Jaggers, Keith, Polity IV Project: Political Regime Characteristics and Transi tions, 1800–2002 (College Park, Md.:Integrated Network for Societal Conflict ResearchGoogle Scholar, Center for International Development and Conflict Management, 2002),

65 Countries with special indeterminate codes ( -88, -77, -66) are assigned a value of zero, according to the standard practice in the literature and the recommendation of the Polity project.

66 Fearon and Laitin (fn. 1).

67 Elbadawi and Sambanis (fn. 3).

68 Nathaniel Beck, David Epstein, Simon Jackman, and Sharyn O'Halloran, “Alternative Models of Dynamics in BinaryTime-Series Cross-Section Models: The Example of State Failure” (Paper presented at the annual meeting of the Society for Political Methodology, Emory University, July 2001).

69 This approach is analogous to the dynamic probit used by Elbadawi and Sambanis (fn. 3), among others. In the dynamic probit model, a lagged dependent variable and interaction terms between each IV and the lagged DV are included on the right-hand side. A major advantage of the transition model is presentational. It is easier to interpret a sample broken into two different sets than it is to compare coefficients between interacted and nomnteracted variables.

70 Beck, Nathaniel, Katz, Jonathan N., and Tucker, Richard M., “Taking Time Seriously: Time-Series Cross-Section Analysis with a Binary Dependent Variable,” American Journal of Political Science 42, no. 4 (1998CrossRefGoogle Scholar).

71 There is a debate in the statistics literature on the utility of using tests of statistical significance for apparent populations. Normally, significance testing is used to give a measure of how confident the analyst or reader can be that the relationship in the sample holds true for the population to which one is generalizing. In the current study nearly all country-years since 1945 are analyzed, so the sample size approaches the entire universe of cases that the theory addresses. In this case, then, standard errors are not used to understand true population parameters but rather are used to determine the consistency of the statistical relationship in the observed data. They reveal how often the expected (probabilistic) relationship between the DV and IV occurs in practice. For a discussion, see Berk, Richard A., Western, Bruce, and Weiss, Robert E., “Statistical Inference for Apparent Populations,” SociologicalMethodology 25 (1995Google Scholar); and Bollen, Kenneth A., “Apparent and Nonapparent Significance Tests,” Sociological Methodology 25 (1995CrossRefGoogle Scholar).

72 For details, see Cunningham, Gleditsch, and Salehyan (fn. 43).

73 For methodological reasons, this variable was lagged. Because data on extraterritorial bases were collected onlyforcountry-years where the value of the dependent variable equals 1 (that is, when there i s a civil conflict), the model cannot be estimated with the variable itself because there is no variation on the DV. However, including lagged values of the extraterritorial bases variable eliminates this problem, and lagged values are very highly correlated with current values: R=.95.

74 The three coefficients fail to reach joint significance in a likelihood ratio test: p>chi-squared = .15

75 Results are available from the author.