The Insurgent Capacity Model substantiates whether rebels’ support from their ethnoreligious brethren and the government’s rival states instigates state-sponsored extensive mass killing and rebels’ exploitation of lootable resources inhibits it. Table 5.4 reports that in most of full models, ethnoreligious support is statistically significant and
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its sign of the coefficient estimate is positive. Even in the OLS model based on lowest numbers, p-value for ethnoreligious support is 0.151, which is close to the 0.1 threshold of statistical significance. This suggests that models addressing all control variables capture the effect of ethnoreligious support on variation in state-sponsored mass killing. I also performed the analyses without ELF, mountain, troop size, and distance. The simplified models also detect the effect of ethnoreligious support, which signifies that even when statistically insignificant controls are removed, ethnoreligious support still exercises an influence upon the scale of mass killing. Furthermore, percentage changes in expected count derived from a negative binomial regression manifest the effect of ethnoreligious support. When ethnoreligious support shifts from 0 to 1, the expected number of intentional civilian deaths increases by 214.1% for lowest number
specification, 347.5% for middle number specification, and 380.9% for highest number specification, holding all other variables constant.26 Therefore, I conclude that statistical analyses corroborate my hypothesis with respect to the relationship between rebels’ external support from their ethnoreligious brethren and state-sponsored extensive mass killing.
Ethnoreligious support tends to be rigid, which makes embattled rulers judge that their efforts to stem this support would not bear fruit. Furthermore, support from co-ethnics or co-religionists helps insurgent leaders induce civilians to join the
insurgency and deters the rebel leadership from mistreating civilians. The tenacity of ethnoreligious support and strong civilian support for insurgents might propel the
26
These rates are calculated based on full models. The percentage change rates for simplified models do not diverge much from those for full models.
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government to massacre civilian populations and fracture insurgents’ recruitment pool as a strategy for crippling rebel forces and forestalling the resurgence of rebellion. For instance, during the Burundian civil war (1993-2003), Hutu insurgent groups received support from their co-ethnics in neighboring Rwanda (Ngaruko & Nkurunziza, 2005).27 Rwandan Hutus, ex-soldiers and Interahamwe militias who fled Rwanda after the 1994 genocide, backed the Burundian insurgents, fighting alongside them and supplying them with weapons (Lemarchand, 2009; Ngaruko & Nkurunziza, 2005; Human Rights Watch, 1998). This kindred support can account for strong civilian support for the rebels. Insurgent leaders might convince their co-ethnics to join the armed struggle against Tutsis by stressing that Rwandan Hutu brothers would never abandon them; therefore, the rebels would be able to win the war. Rwandan Hutu sponsorship might also
constrain the insurgents from committing widespread abuses against the Hutu population. Embattled Tutsi rulers were not able to stymie the activities of Rwandan Hutus who frequently operated outside Burundi, which propelled the government to project massive violence against its Hutu citizens. It is estimated that combating Hutu insurgencies, Tutsi elites murdered 100,000 - 200,000 Hutu civilians (Valentino, 2004).
Table 5.4 reveals that models containing all control variables capture the effect of rival support on variation in state-sponsored mass killing. In most of full models, rival support is statistically significant and its sign of the coefficient estimate is positive. In the negative binomial model based on highest numbers, p-value for rival support is .117,
27 Hutu insurgents comprised the Forces for the Defense of Democracy (FDD), the National Forces of
Liberation (FNL), the National Liberation Front (FROLINA), the Union of National Liberation (ULINA), and other minor groups (Ngaruko & Nkurunziza, 2005).
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which is very close to the .1 threshold of statistical significance. Models excluding statistically insignificant control variables still detect the influence of rival support. In the OLS specification with highest numbers and negative binomial specifications with middle and highest numbers, dropping those variables enhances the effect of rival support on severity of mass killing. Furthermore, percentage changes in expected count also exhibit the influence of rival support. When rival support moves from 0 to 1, the expected number of intentional civilian deaths increases by 174.3% (lowest number specification), 132.3% (middle number specification), and 125.8% (highest number specification), holding all other variables constant.28 Therefore, I conclude that statistical results substantiate my hypothesis germane to rebels’ support from the government’s rival states.
Support from the government’s rival states tends to endure, even if the
government exerts diplomatic and military efforts to vitiate this support. Furthermore, rival states harbor strong desire for rebel victory, thus seeking to regulate insurgents’ behavior and to make civilians bolster the insurgency. The rigidity of rival support and civilian support for insurgents might propel embattled rulers to orchestrate extensive mass killing and wipe out rebels’ recruitment pool. The First Indochina war (1946-1954) illustrates this point. Viet Minh guerillas enjoyed a high level of civilian support, which prompted French colonial forces to massacre Vietnamese civilians. Rival support can explain why the Viet Minh rallied civilian support. This insurgent group received
28
These rates are calculated based on full models. The rates for simplified models are 224% (lowest numbers), 174.8% (middle numbers), and 163.8% (highest numbers). This indicates that when removing control variables that are not statistically significant, percentage changes in expected count rise by about 40-50%.
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significant support from communist China, France’s arch rival, which aspired to dislodge France from Indochina and eliminate (potential) capitalist threats (Zhai, 1993 & 2000). Mao Zedong sought to regulate Viet Minh’s military operations and implant his guerilla war doctrines into the Viet Minh, one of which was treating civilians well. Chinese military advisors deployed in Vietnam helped the guerillas mobilize the masses, ensuring that Viet Minh insurgents build civilian support (Zhai, 1993 & 2000).
Table 5.4 demonstrates that the effect of lootable resources hinges somewhat on model specifications. None of the OLS models detects the effect of lootable resources on variation in state-sponsored mass killing. Even dropping statistically insignificant controls does not boost the influence of lootable resources. In contrast, in the negative binomial models, lootable resources exert some effect upon the extent of mass killing. Of course, the p-values for lootable resources exceed the .1 threshold of statistical significance in full models based on middle and highest numbers. The p- values for those specifications are .145 (middle numbers) and .152 (high numbers), which is not far from the .1 threshold of statistical significance. When excluding variables that are not statistically significant, the effect of lootable resources inflates. This suggests that only negative binomial analyses vindicate my expectations regarding rebels’ exploitation of lootable resources. Resource wealth can spoil the rebel
leadership. Hence, when recruiting labor force and combatants, resource-rich insurgents eschew the distribution of their wealth and resort to coercive measures. Furthermore, rebel leaders might give free rein to resource producers, which clears the
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way for rampant civilian abuses. Rebels’ heinous behavior begets deep animosity toward the insurgents, which can inhibit embattled rulers from targeting civilians.
As shown in Table 5.4, all models capture the effect of insurgents’ civilian support on variation in state-sponsored mass killing. A high level of civilian support for insurgents is likely to propel the government to engineer extensive mass killing, which corresponds to my theoretical argument. In some OLS and negative binomial models, population size and the Cold War are statistically significant and their signs of the coefficient estimate are positive, which indicates that 1) during civil war, countries with large populations are likely to have a large number of intentional civilian deaths and 2) civil wars in the Cold War era generated more extensive mass killing than those in the post-Cold War era.
As shown in Table 5.5, statistical analyses without anti-colonial wars diverge little from those with all civil wars. Most specifications capture the effects of
ethnoreligious support and rival support on the severity of state-sponsored mass killing.29 Only in negative binomial models, lootable resources exercise an influence on variation in mass killing. Hence, when dropping anti-colonial wars, the Insurgent
Capacity Model still confirms my hypotheses on ethnoreligious support and rival support and partly confirms my hypothesis on lootable resources.
29 In the OLS and negative binomial specifications based on middle numbers, the p-values for
ethnoreligious support are .105 (full model) and .103 (simplified model), which is very close to the .1 threshold of statistical significance.
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