Results of the Cross-sectional Analysis

Table 7 provides the results of the cross-sectional model using a NBM estimator. Compared to the results of the PM (see Table 26 in the appendix), the standard errors of the NBM are smaller.

Furthermore, alpha is significantly different form zero. This indicates a large improvement in the fit of the model compared to the PM.⁸³ Hence, the NBM seems to be a more appropriate parametric model and ensures efficiency gains.

The general picture from Table 7 is that the results largely support Hypotheses I.1 and I.2 which claim that (1) civil wars in which PMSCs operate are more likely to exhibit higher levels of conflict intensity and (2) civil wars in countries with natural resources production in which PMSCs operate are more likely to exhibit lower levels of conflict intensity. Model 1 and 2 include the presence of PMSCs, the production of gemstones and the respective interaction term. As predicted, the presence of PMSCs in a conflict is strongly associated with increased battle-related fatalities. The same holds true for the production of gemstones. The interaction term, however, is significantly associated with decreasing battle-related fatalities and, thus, strongly supports Hypothesis I.2. When omitting inequality, all models gain substantially more statistical power. This is due to the fact that inequality suffers from many missing values and causes a loss of observations. Since inequality seems to be irrelevant for the model, I have spared this variable in the following models. Thus, after excluding inequality from the analysis, all three independent variables become highly significant in Model 2.

Among the control variables only GDP, political regime and ethnic polarization are statistically significant and have a decreasing effect on the conflict intensity. Hence, as anticipated, a high level of economic and political development is strongly associated with lower battle-related deaths.

However, a large population, the conflict duration, inequality, insurgency groups, and the Cold War do not seem to be important factors. Models 3 and 4 include the presence of PMSCs, the production of drugs and hydrocarbons, respectively, and the corresponding interaction terms. Here, too, the results support Hypotheses I.1 and I.2: The presence of PMSCs and the production of drugs and hydrocarbons in a conflict are strongly associated with higher levels of conflict intensity, whereas the interaction between the presence of PMSCs and the production of drugs and hydrocarbons is significantly associated with lesser levels of conflict intensity. Model 5 includes the effect of a simultaneous production of all three types of natural resources on the conflict intensity and yields the same results. Model 6 includes the sum of battle-related deaths and shows exactly the same results – however, with the exception that in this model conflict duration becomes statistically significant and exhibits, as anticipated, an increasing effect on battle-related fatalities. In these models, the effect of the presence of PMSCs, the production of natural resources and the interaction

83 The Pseudo R² statistics is considered to measure the goodness of fit of the model. However, the NBM does actually not have an equivalent to the R-squared measure. Hence, I refrain from interpreting this statistic.

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between both variables remains always statistically significant. Hypotheses I.1 and I.2, hence, receive continually support.

Table 7: Relation between the Presence of PMSCs, Natural Resources and the Conflict Intensity of Civil Wars (1960-2000) – Cross-sectional Analysis⁸⁴

Negative Binomial Model

Battle Related Deaths Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8

PMSCs 1.40**

Observations 79 121 121 121 123 123 121 78

Notes: c oefficients are unstandardized negative Binomialcoefficients

Standard errors in parantheses; DV in Model 6: Sum of Battle Related Death; Model 9: observations after 1990

* p < 0.09, ** p < 0.05, *** p < 0.01

PMSC x G = mutual effect of PMSCs and gemstones PMSC x D = mutual effect of PMSCs and drugs PMSC x H = mutual effect of PMSCs and hydrocarbons PMSC x GDH = mutual effect of PMSCs and all types

84 Database: Lacina and Gleditsch (2005), Chojnacki et al. (2009), World Bank (2012), UT EHII (2004), Alesina et al. (2003), Marshall et al. (2011), Lujala (2009)

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Model 7 shows the complete model, including all resource and interaction dummies. In this model, the presence of PMSCs still has a statistically significant increasing effect on battle-related fatalities.

The same holds true for the hydrocarbons dummy and the dummy for all three types. The interaction term regarding the mutual effect of PMSCs and hydrocarbons also remains statistically significant and has a decreasing effect on the conflict intensity. In other words, contrary to the individual increasing effect of PMSCs and hydrocarbons on the conflict intensity, their combined effect in fact is strongly associated with a decreasing effect. However, the remaining interaction terms as well as the dummy for gemstones and drugs lost statistical significance, but still have the same positive or negative signs, respectively. Only the dummy for drugs became not only statistically insignificant but also exhibits a negative sign in this model. As for the control variables, only GDP, conflict duration and ethnic polarization remained statistically significant. Model 8, finally, serves as a robustness test and reports the results for the period from 1990 to 2000. The presence of PMSCs is still significantly associated with increasing conflict intensity. Due to the significant decrease in sample size, almost all other variables lost statistical significance, but the signs of the coefficients remained the same. The interaction variable depicting the mutual effect of all types of natural resources and the presence of PMSCs, though, constitutes an exception: This variable exhibits a positive coefficient and is statistically significant, indicating an increasing effect on the conflict intensity. However, taken as a whole this model confirms the results of the previous models. In conclusion, the results of the cross-sectional analysis provide strong support for the assumption that the prospect of a bonus increases the effort level of PMSCs.

Table 8 shows the factor change in the expected count of battle-related deaths with regard to the main independent variables. The calculation bases on the complete model (Model 7). Even though not all coefficients are statistically significant in this table, I report the factor change regarding each variable because all variables proved to be significant factors in the other models. When holding all other variables constant, the presence of PMSCs increases the expected number of battle-related deaths by a factor of 8.1. This result again supports Hypothesis I.1 and indicates that PMSCs increase the conflict intensity. The same holds true for gemstones, hydrocarbons and the impact of all three types taken together: The production of gemstones and hydrocarbons increase the number of battle-related deaths by a factor of 1.7 and 3.4, respectively, and the production of all three types by a factor of 5.7, ceteris paribus. However, in contrast to the effect of gemstones and hydrocarbons, the production of drugs decreases the number of battle-related deaths by 45%, ceteris paribus. The mutual presence of PMSCs and gemstones also decreases the number of battle-related deaths by 59%, the interaction between PMSCs and hydrocarbons decreases the number of battle-related fatalities by even 78%, the interaction between PMSCs and drugs by 17%, and the interaction between PMSCs and all three types taken together decreases the number of battle-related deaths by

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56%, ceteris paribus. These results confirm the hypothesized positive effect of a bonus on the effort level of PMSCs as postulated in Hypothesis I.2.

Table 8: Percentage Change in Expected Count⁸⁵

Battle Related Deaths b z P>|z| % %StdX SDofX

PMSC 2.08 3.40 0.00 8.07 2.65 0.47

Gemstones 0.44 1.16 0.25 1.56 1.25 0.50

PMSC x Gemstones -0.90 -1.35 0.18 0.41 0.70 0.40

Drugs -0.60 -1.12 0.26 0.55 0.75 0.47

PMSC x Drugs -0.18 -0.20 0.85 0.83 0.94 0.36

Hydrocarbons 1.22 2.55 0.01 3.38 1.82 0.49

PMSC x Hydrocarbons -1.54 -1.98 0.05 0.22 0.56 0.38

GDH 1.76 2.35 0.02 5.86 1.98 0.39

PMSC x GDH -0.83 -0.70 0.48 0.44 0.79 0.29

Notes: b = raw coefficient; z = z-score for test of b=0; P>| z| = p-value for z-test

% = percent change in expected count for unit increase in X

%StdX = percent change in expected count for SD increase in X SDofX = standar d deviation of X

The results of the cross-sectional analysis are robust over a range of different model specifications. I have tested alternative specifications in which I estimated a Poisson model with robust standard errors, replaced the dependent variable with the high and best estimates as well as with the sum of battle-related fatalities. Moreover, I have tested for outliers and influential observations and repeated the analysis after dropping those observations. The exclusion of these observations has an influence on the statistical significance of the natural resource dummies. The signs of the coefficients, though, remain on the whole the same. Additionally, I have created a sub-dataset and examined only the period from 1990 to 2000. Almost all coefficients lose statistical significance, but the signs of the coefficients remain on the whole the same. However, since the sample size decreased considerably after dropping all observations before 1990, these results need to be taken with caution (see Table 24 - Table 28 in the appendix).

85 Database: Lacina and Gleditsch (2005), Chojnacki et al. (2009), Lujala (2009)

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