Control Variables

My quantitative analysis builds on the empirical literature on civil wars. The literature on civil wars centres mainly on the exploration of civil war onset, its duration, intensity, and termination as well as the risk of recurrence. Theoretically, the literature has largely been driven by economic and political rational choice theories (see Blattman and Miguel (2010), Bussmann et al. (2009) and Hegre and Sambanis (2006) for excellent overviews). Economic theories have focused on the impact of economic modernization on the mobilization of social (or ethnic) groups (Newman, 1991), on economic conditions that facilitate the outbreak of violent conflict (Grossman, 1995) and on the consequences of violent conflict on economic growth (Hirschleifer, 1995). Two recent branches of the literature analyze the outbreak and duration of civil wars in view of (1) political, economic and

73 This was, however, only possible for the UK FCO (2002) dataset because contrary to the Chojnacki et al. (2009) dataset, this one offers further information.

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ethnic grievances (Collier and Hoeffler, 2000) and in view of (2) financial, organizational and political power (Fearon and Laitin, 2003).

Whereas the body of literature on civil war onset, duration and termination keeps constantly growing, research on the intensity of civil wars has remained disregarded. As Lujala (2009:58) puts it:

“There is a profound lack of theory in the studies on conflict severity.” There are, however, some empirical studies examining the impact of rebels (Benson and Kugler, 1998; Besançon, 2005; Kalyvas, 2006; Lacina, 2006; Weinstein, 2007; Lujala, 2009), political institutions (Krain, 1997; Lacina, 2006;

Heger and Salehyan, 2007), or economic grievances (Lu and Thies, 2011) on conflict intensity.

Preliminary work on the determinants of conflict intensity was undertaken by Lacina (2006), who investigated the impact of variables usually used in the civil war onset and duration literature on the number of combat deaths in internal conflicts from 1946 to 2002.⁷⁴ She found that ethnic homogeneity, the availability of foreign assistance and regime type (i.e. democracy) are negatively related to the severity of a conflict whereas she could not find any evidence for the influence of state strengths or ethnic or religious fractionalization on the intensity of internal armed conflicts.

Based on empirical studies on the determinants of conflict intensity and in particular the studies by Lacina (2006) and Lujala (2009), I include in addition to the main independent variables a number of economical, political and social control variables at the country-level that are commonly proved to affect the intensity of civil wars.

GDP: GDP per capita is the gross domestic product divided by midyear population as conditioned by the World Bank.⁷⁵ It is a natural logarithmised variable. Depending on the estimation strategy (cross-section or panel estimation) it is either the average GDP or a lagged variable depicting GDP per capita in the year preceding the first observation. According to Fearon and Laitin (2003) and Sambanis (2004), a low GDP per capita is a good predictor variable for civil wars because GDP indicates a state’s strength and capacity. Lacina (2006), however, finds no significant effect of GDP on the intensity of conflicts. I include GDP and test whether it contributes to an intensification of conflicts. The data are in current US dollars. The GDP in this sample ranges from $49.49 (Burundi in 1965) to $24893.49 (United Kingdom in 1998) with an average of $1383.71 (Israel in 1964 with a GDP of 1375.94 being closest to the average value).

POPULATION: Regarding civil war onset, large populations are found to increase the likelihood of civil wars (Fearon and Laitin, 2003). However, regarding the conflict intensity, Lacina (2006) shows that large populations are not related to the intensity of armed conflicts. In order to accommodate for

74 She examined the following variables: state strength, regime type, ethnicity, and religion.

75 GDP is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources (World Bank, 2012).

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these mixed results, I test whether Population is positively related to conflict intensity. This variable is a natural logarithmised variable. Like GDP, it depicts either the average population or is a lagged variable, which depicts the population in the year preceding the first observation. The country with the smallest population in the sample is Equatorial Guinea in year 1979 and the one with the biggest population is India in year 2000.

INEQUALITY: As Fearon (1998) points out, low income may lengthen civil wars by favouring insurgency, i.e. by favouring recruitment because attracting new members is easier when the economic alternatives are worse. Moreover, civil wars tend to be shorter in countries with higher income levels and less inequality whereas larger (in terms of population and territory) countries appear to have longer wars (Collier et al., 2004). Hence, I also include the variable inequality, measured as the estimated household income inequality (Texas, 2004)⁷⁶ to account for potential effects on conflict intensity. Depending on the estimation strategy, this variable either represents the average inequality within the observed period or is a lagged variable depicting the inequality in the year preceding the first observation. This variable, however, features a great many of missing values.

Thus, I exclude it from the analysis if necessary.

ETHNIC POLARIZATION: There is controversy in the literature whether it is ethnic fractionalization (the number of ethnic groups in a country) or polarization (large ethnic minority faces a large ethnic majority) that triggers societal conflict. In an often-quoted study, Montalvo and Reynal-Querol (2005) show that polarization is a significant explanatory variable for the incidence of civil wars.

Furthermore, Esteban and Ray (2008) argue that the probability of civil war onset is relatively low in highly polarized societies, however, once a civil war occurs its intensity is very severe. Whereas the probability of civil wars in highly fractionalized countries is very high but their intensity is relatively low. Following Montalvo and Reynal-Querol (2005) and Esteban and Ray (2008), I include ethnic polarization into the analysis and expect higher levels of conflict intensity in highly polarized societies. This variable is a dummy variable taken from Lujala (2009) and given the value 1 for high polarization in a country and 0 otherwise.

ETHNIC FRACTIONALIZATION: It is often argued that discrimination along the lines of ethnicity and religion creates grievances that in turn motivate armed conflict (Fearon and Laitin, 2003:79). Several studies argue furthermore that long-lasting insurgency is more common in ethnically diverse countries and that ethnic and religious motivated wars tend to be especially long (Fearon, 1998;

Licklider, 1995). In order to examine the effect of ethnic fractionalization on the conflict intensity, I include a variable measuring the degree of ethnic fractionalization in a country. The data is drawn

76 University of Texas Inequality Project. Estimated Household Income Inequality Data Set (EHII) - is a global dataset, derived from the econometric relationship between UTIP-UNIDO, other conditioning variables, and the World Bank's Deininger &

Squire data set.

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from Alesina et al. (2003). The dataset measures the degree of ethnic, linguistic and religious heterogeneity in various countries and contains data for only one year for each country. The least fractionalized country in the sample is the Comoros and the most fractionalized country is Yemen.

Table 4: Overview of Variables (Analysis I)

Variables Source Exp* Estimation

Dependent Variabl e

Battle Related Deaths PRIO BD 3.0 (2009) CSA / PA

Independent Variables

Natural Ressources Lujala (2009) CSA / PA

Production of Hydrocarbon Lujala et al. (2007)

Production of Gemstones Gi lmore et al. (2005); Flöter et al. (2005)

Production of Drugs Lujala (2003)

PMSCs

Presence Chojnacki et al. (2009); UK FCO (2002) CSA / PA

Number of PMSCs UK FCO (2002) PA

Interaction Vari able (Natural Ressources x PMSCs) CSA / PA

Cont rol Variables

GDP World Bank (2012) CSA / PA

Political Regime Marshall et al. (2011) CSA / PA

Population World Bank (2012) CSA / PA

Ethnic Fractionalization Alesina et al. (2003) CSA / PA

Ethnic Polarization Lujala (2009) CSA / PA

Insurgency UCDP Non-state Actor (2009) CSA / PA

Inequality UT EHII (2004) CSA / PA

Conflict Duration CSA / PA

Cold War CSA / PA

Notes: * expected effect on dependent var iable CSA = cross-sectional analysis / PA = panel analysis Research per iod: 1960-2000

INSURGENCY: Studies show that the interaction of rebels and government highly influence the progress and duration of civil wars. According to the state capacity argument, a state’s police, military and institutional capabilities are highly important for successful counter-insurgency operations (Fearon and Laitin, 2003:80). Insurgency groups can better survive if they face a weak government with weak security forces. The number of insurgency groups operating in a country, thus, indirectly represents a state’s capacity. In order to examine the effect of insurgencies on the conflict intensity of civil wars, I include data on the number of rebel groups per armed conflict. This data was drawn from the UCDP Non-state Actor Dataset (Version 1-2009). The country with the most insurgency groups (25) in the sample is Chad.

POLITICAL REGIME: The political regime of a country discloses opportunity costs for a rebellion. These tend to be higher in a democratic regime because there are alternative means for resolving problems. Lacina (2006), for instance, finds that civil wars in democracies are expected to result in half the battle deaths compared to civil wars in non-democracies. I use data from the Polity IV dataset in which the political regime type is measured as the openness of political institutions on a scale of +10 (strongly democratic) to -10 (strongly autocratic) (Marshall et al., 2011). Depending on the estimation strategy, this variable either represents the average polity score within the observed

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period or it depicts the polity score in the year of the observation. Based on previous empirical findings, I expect that high democracy scores as well as high autocracy scores negatively affect conflict intensity. The countries with the lowest polity score in the sample are Iran, Nepal, Oman, and Saudi Arabia. The countries with the highest score are Israel, the United Kingdom, Malaysia, and Spain.

CONFLICT DURATION and COLD WAR: Longer conflicts generally result in more battle-related deaths than shorter conflicts. Hence, I include a control variable counting the years of an ongoing conflict.

Following Lacina (2006) and Lujala (2009), who showed that conflicts during the Cold War period were more severe than those after the Cold War, I also include a dummy variable for conflicts that started during the Cold War. The descriptive examination of the data already indicates that longer civil wars result in more battle-related fatalities and civil wars during the Cold War also caused more battle-related deaths than those after the Cold War. The most severe civil wars are conflicts during the Cold War which last between 10 and 20 years (see Table 23 in the appendix).