3.7 Hazard Model
3.7.1 Hazard Model: Empirical Analysis:
We use identical set of regressors as in the ordered logit model, but in this case we use a Cox proportional hazard model in order to estimate the risk of the hazard to transit from a certain level of violence to another. As said in the previous section, semiparametric models combine individual binary-outcome analysis at each of the failure times.
After setting our dataset with the "survival language", we rst run a specication test where each type of violence is included. Hence, we estimate the probability that a country shifts from any level of violence to another. Before running the "full" specication, we do some univariate analysis in order to explore whether or not to include the predictor in the nal model.
For the categorical variables we use the log-rank test of equality across strata which is a non-parametric test. For the continuous variables, instead, we use a univariate Cox proportional hazard regression. If the variable shows a p-value of 0.2 or less, it means that it is relevant to the model. From these tests, we realize that dummy variables related to gold and diamonds production, the Gini indicator for oil and the indicator of autocracy should not be included in the model. Other two variables show a p-value greater than 0.25 in the univariate analysis:
economic growth and the dummy for environmental conict. Of course, these two variables are important from a theoretical point of view, so we will include them as control variables anyway.
Another important element concerns the number of observations. Our ethnic-level dataset include 9988 observations. Yet in the ordered logit we have seen a deep reduction of observations used in the regressions. This was simply due to missing values in some variables. In the hazard model, the number of observations is even more reduced because of the imposed condition in the model: since we set our analysis starting from a particular degree of violence and experi-encing a transition to a specic other degree of violence, the algorithm needs to nd the exact correspondence in order to estimate the likelihood to transit. This is data consuming and limits our analysis to few observations (compared to our overall dataset). This problem becomes much more serious when we apply survival analysis to the dataset of non-ethnic groups and country.
Therefore, we cannot run survival analysis in the case of country and non-ethnic groups dataset.
As stated above, in the Cox proportional hazard model, covariates act as factors multiplying the hazard rate, which is the probability of experiencing the event in question during the next interval.
In Table 3.25 we have reported our estimates in four dierent specications, all of them rep-resenting the general case. Although dierent specications, we have similar results: economic growth, soil fertility and diamond production always aect the likelihood to verify a transition toward peace, regardless the starting point. On the contrary, population growth (lag), autoc-racy and public spending reduces this probability. Excepts for public spending, other coecients conrm previous results and are absolutely plausible.
We run some tests in order to verify our analysis. The rst test is a simple linktest, which does not provide any signicant p-value in each specication. We will report also a plot of the scaled Schoenfeld assumption in order to see graphically if our predictors are good or not.
We test for the adequacy of the proportional hazards assumption (Table 3.51), by estimating the same regression with time-varying covariates. Time-varying test tells us that public spending, population growth (lag) and diamond production vary over time but Schoenfeld and scaled
Table 3.16: Cox Proportional Hazard Model: General transitions toward Peace
1 2 3 4
Low Political Rights Quality 0.127 0.199 0.264 0.193
(0.164) (0.208) (0.202) (0.159)
High Political Rights Quality -0.118 -0.085 0.063 0.020
(0.192) (0.240) (0.240) (0.196)
Public Spending -0.018 *** -0.017 **
(0.007) (0.007)
Oil Production (Lag) -0.130 -0.159 -0.183 -0.179
(0.179) (0.185) (0.178) (0.171)
Autocracy -0.312 ** -0.324 ** -0.159 -0.155
(0.154) (0.161) (0.159) (0.152)
Population Growth (Lag) -0.176 *** -0.157 ** -0.097 * -0.119 **
(0.061) (0.067) (0.058) (0.052)
Religious Conict 0.472 *** 0.482 *** 0.351 *** 0.388 ***
(0.108) (0.146) (0.114) (0.096)
Environmental Conict -0.032 -0.026 0.113 0.082
(0.124) (0.149) (0.114) (0.099)
Political Conict -0.142 -0.195 * -0.090 -0.057
(0.106) (0.111) (0.107) (0.097)
Mountainous Terrain 0.215 0.267 -0.307 -0.369
(0.389) (0.411) (0.296) (0.289)
Oil Gini 0.219 0.255 0.257 0.295
(0.240) (0.276) (0.245) (0.231)
Soil Fertility 0.805 *** 0.749 ** 0.669 ** 0.705 **
(0.282) (0.317) (0.322) (0.278)
Africa Region (Est) 0.068 0.089 -0.007 -0.024
(0.154) (0.169) (0.143) (0.132)
Africa Region (Center) -0.167 -0.144 -0.082 -0.159
(0.163) (0.204) (0.185) (0.163)
Gold Production 0.140 0.197 0.023 -0.000
(0.131) (0.145) (0.132) (0.124)
Diamond Production 0.402 *** 0.456 *** 0.277 ** 0.275 **
(0.121) (0.133) (0.141) (0.127)
Economic Growth (Lag) 1.850 ** 1.792 ** 0.748 * 0.754 *
(0.757) (0.804) (0.408) (0.408)
Fixed Eects No Yes Yes No
N 489 489 604 604
Schoenfeld residuals test for proportionality assumption is veried, meaning that any variable violets the assumption, both globally and one by one22.
In the graph, a slope signicantly dierent from zero would be evidence against proportion-ality: an increasing trend would indicate an increasing hazard ratio over time. The horizontal line in graph 3.3 is the indication that there is no violation of the proportionality assumption and in the second graph, the lines are parallel, so we have further indication that the predictors do not violate the proportionality assumption.
We run also a log-log plots to test proportionality which gives us a further indication that the predictors do not violate the proportionality assumption.
In Table 3.26 we report estimated transitions toward armed violence and in Table 3.27 those related to civil war, with same specications as before. Here, public spending, population growth (lag) and oil exports increase the transition likelihood toward armed violence and civil war. On the contrary, only inequality in terms of natural resources and mountainous terrain result to reduce the probability to experience armed violence. Economic growth, soil fertility and diamond production reduce the odds of war.
22In the Appendix we have included Tables and 3.49 and Figure 3.2 where test results are shown.
Table 3.17: Cox Proportional Hazard Model: Genereal Transitions toward Armed Violence
1 2 3 4
Low Political Rights Quality -0.033 0.156 0.296 0.188
(0.480) (0.513) (0.509) (0.493)
High Political Rights Quality 0.455 0.522 0.491 0.440
(0.573) (0.572) (0.678) (0.700)
Public Spending 0.060 *** 0.053 ***
(0.020) (0.021)
Oil Production (Lag) 0.666 * 0.668 0.921 ** 0.953 **
(0.401) (0.408) (0.400) (0.392)
Autocracy 0.462 0.279 0.197 0.406
(0.437) (0.428) (0.550) (0.571)
Population Growth (Lag) 1.059 *** 0.961 *** 0.218 0.322
(0.230) (0.255) (0.211) (0.200)
Religious Conict -2.938 *** -2.688 *** -1.827 ** -2.239 ***
(0.874) (0.883) (0.758) (0.769)
Environmental Conict 0.889 0.765 -0.529 -0.374
(0.600) (0.585) (0.602) (0.596)
Political Conict -0.101 -0.226 -0.066 0.051
(0.391) (0.448) (0.371) (0.362)
Mountainous Terrain -22.952 *** -21.319 *** -5.903 -6.992
(4.094) (4.134) (5.410) (5.379)
Oil Gini -2.085 ** -1.754 * -1.280 -1.706 *
(0.837) (0.972) (1.046) (0.933)
Soil Fertility 0.351 -0.110 -0.398 0.263
(0.923) (0.982) (0.826) (0.784)
Africa Region (Est) 2.096 *** 1.745 ** 0.706 1.090
(0.666) (0.696) (0.742) (0.714)
Africa Region (Center) 2.111 ** 1.474 -0.132 0.625
(0.928) (0.998) (1.046) (0.957)
Gold Production -0.184 -0.142 0.332 0.323
(0.359) (0.393) (0.395) (0.362)
Diamond Production -0.324 -0.124 0.073 -0.080
(0.412) (0.406) (0.341) (0.347)
Economic Growth (Lag) -4.668 -3.825 -2.546 -3.312
(3.232) (3.105) (2.186) (2.424)
Fixed Eects No Yes Yes No
N 489 489 604 604
Tests are done as before. Briey, the linktest on armed violence is not veried for the last two specications.
Time-Varying and Residual Test, instead, provide good results, because each variable re-spects the proportionality assumption.
Same test for transitions to war, instead, are less good, because several variables seem to change over time (Table 3.53 and gure 3.4 and 3.5).
Also the Residual Test tells us that some variables vary over time, namely we cannot rely on oil production (lag), population growth (lag), mountainous terrain, diamond production and economic growth.
Hence, in the following section we rene these results by imposing specic directions of transition.
From Peace to other Degree of Violence
In this section we study the likelihood of violence transitions in case of peaceful countries.
In other words, we study the probability of violence escalation when there is peace. We will proceed as before. In order to have comparable estimates, we report specication without public
Table 3.18: Cox Proportional Hazard Model: General Transitions toward War
1 2 3 4
Low Political Rights Quality -0.529 -0.833 -1.081 * -0.699
(0.612) (0.611) (0.641) (0.637)
High Political Rights Quality 0.164 -0.147 -0.701 -0.333
(0.663) (0.681) (0.655) (0.658)
Public Spending 0.065 *** 0.063 ***
(0.019) (0.021)
Oil Production (Lag) -1.414 *** -1.250 ** -0.659 -0.723
(0.539) (0.534) (0.600) (0.619)
Autocracy 0.779 1.018 * 0.393 0.229
(0.494) (0.555) (0.522) (0.519)
Population Growth (Lag) 0.568 ** 0.523 ** 0.321 0.379
(0.249) (0.247) (0.214) (0.237)
Religious Conict -1.378 * -1.334 -0.674 -0.829
(0.776) (0.879) (0.576) (0.544)
Environmental Conict -0.751 -0.758 -0.406 -0.345
(0.536) (0.512) (0.489) (0.513)
Political Conict 0.578 0.659 0.224 0.174
(0.378) (0.419) (0.342) (0.313)
Mountainous Terrain 4.086 *** 3.495 ** 1.820 2.826 *
(1.459) (1.547) (1.488) (1.453)
Oil Gini -0.175 -0.210 -0.261 -0.374
(1.014) (1.114) (1.011) (0.949)
Soil Fertility -3.017 *** -1.842 -0.889 -2.229 **
(1.082) (1.483) (1.258) (1.008)
Africa Region (Est) 0.021 0.336 0.149 -0.112
(0.545) (0.559) (0.486) (0.532)
Africa Region (Center) 1.228 1.409 1.093 1.010
(0.843) (0.975) (0.816) (0.760)
Gold Production -0.411 -0.509 -0.360 -0.223
(0.319) (0.331) (0.399) (0.398)
Diamond Production -1.174 ** -1.359 ** -0.827 * -0.700
(0.508) (0.574) (0.488) (0.468)
Economic Growth (Lag) -5.977 * -7.145 * -5.434 * -4.813 *
(3.584) (3.748) (2.891) (2.907)
Fixed Eects No Yes Yes No
spending (as previous models), but in the Appendix B we provide other specications in which also public spending is included23.
In Table (3.28) each column represents a transition from peace to another degree of vio-lence (namely armed viovio-lence and civil war), excepts the rst one which reports the persistence probability.
Although dierent from the general case24, our result are very plausible and coherent with previous probabilities: if an ethnic group is in peace, an increase in population density reduces the probability of remaining in peace. On the contrary, we are condent at 99% that ethnic groups living in a fertile land, with diamond reserves and with some religious tensions, are more likely to remain in peace if they are already in peace. This makes sense: these variables represent primary drivers to economic and food security. If we are in peace, an increase of these drivers is redistributed within a community, giving less reasons to ght. Moreover, an increase in soil fertility reduces deeply the likelihood of war. On the contrary, an increase in oil production in previous year (because we use lag) increases the odds that an armed conict will occur. As explained in literature, also a community living in a mountainous area is more likely to move to war, even in peaceful period.
In the Appendix B we show results with time-varying covariates in order to see whether it rejects the proportional assumption.
Several variables result to be signicant and so to reject the Markov fundamental assumption.
Since diamond production, population growth (lag), autocracy, inequality in terms of natural resources and oil production (lag) are signicant as xed covariates, we cannot rely on their signicant eect.
In the second column (i.e. when we take into account the transition from peace to armed violence) we see a signicant coecient (with a strong impact) of economic growth. However, this variable is also highly signicant as time-varying variable, meaning that we should be suspicious of its eect.
The linktest (Tukey, 1949) tells us that the prediction squared is not signicant, so our specications do not need to be improved.
As before, we now proceed with the residual test. In the case of transition from peace to peace, the proportionality assumption is never violated. On the contrary, the Chi-squared value is signicant for some variables in the last two cases: political rights, oil production (Lag), population growth (lag) and economic growth. Therefore, we should be suspicious about the eect of those variables when we analyse transitions from peace to armed violence and to war.
Finally, we plot the test of goodness of t (Fig. 3.6, 3.7, and 3.8). As we can see, the best result is obtained when we estimate transition probabilities from peace to armed violence.
We have seen that when we are in peace the main root of conict is the religious determinant.
For this reason, we nd interesting to study in depth transition probabilities from peace to other degrees of violence taking into account only religious conicts (Table 3.33). What emerges is that religious conicts are more likely to occur in a mountainous terrain and if there is an increase in population or an increase in oil production (Lag). On the contrary, the more political rights quality increase, the more we remain in peace. Although economic growth has a large standard error, it has also an impressive eect in the probability reduction of war. Therefore, we have found out that religious conicts are mainly related to a problem of population growth (Lag) and inhospitable soil fertility.
We think that, although not signicant, the other roots of conict are important as well.
Therefore, in Table 3.41 and 3.43 we report also survival analysis for transitions from peace by taking into account only the environmental and political conicts.
23As said before, we nd interesting to include in the analysis public expenditures as well. However, analysis with the non-ethnic groups and country dataset could not nd convergence, so we had to remove this variable.
24In the Appendix B we have included also the case in which we apply xed eects (Table 3.29) where we have similar results.
Table 3.19: Cox Proportional Hazard Model: Transitions from Peace to other states
P eace → P eace Peace → ArmedV iolence Peace → CivilW ar
Low Political Rights Quality 0.227 0.085 -0.749
(0.153) (0.490) (0.617)
High Political Rights Quality 0.052 0.365 -0.387
(0.192) (0.727) (0.670)
Oil Production (Lag) -0.228 1.063 *** -0.405
(0.158) (0.380) (0.541)
Autocracy -0.139 0.368 0.221
(0.147) (0.577) (0.583)
Population Growth (Lag) -0.115 ** 0.339 * 0.368
(0.051) (0.203) (0.224)
Religious Conict 0.373 *** -2.231 *** -0.776
(0.097) (0.774) (0.538)
Environmental Conict 0.102 -0.390 -0.389
(0.098) (0.595) (0.487)
Political Conict -0.056 0.038 0.163
(0.096) (0.367) (0.301)
Mountainous Terrain -0.358 -7.529 2.783 **
(0.287) (5.528) (1.371)
Oil Gini 0.294 -1.712 * -0.394
(0.229) (0.915) (0.891)
Soil Fertility 0.724 *** 0.249 -2.312 **
(0.277) (0.798) (0.972)
Africa Region (Est) -0.031 1.200 * -0.110
(0.127) (0.727) (0.527)
Africa Region (Center) -0.140 0.625 0.965
(0.160) (0.991) (0.750)
Gold Production -0.007 0.378 -0.203
(0.123) (0.367) (0.373)
Diamond Production 0.290 ** -0.122 -0.752 *
(0.126) (0.350) (0.440)
Economic Growth (Lag) 0.705 * -2.806 -4.652
(0.391) (2.024) (2.899)
N 604 604 604
Table 3.20: Cox Proportional Hazard Model stratied with religious conicts (Ethnic Fixed Eects): Transitions from Peace to other states
P eace → P eace Peace → ArmedV iolence Peace → CivilW ar
Low Political Rights Quality 0.287 0.342 -1.111 *
(0.192) (0.497) (0.664)
High Political Rights Quality 0.102 0.602 -0.864
(0.237) (0.624) (0.729)
Oil Production (Lag) -0.224 0.903 ** -0.381
(0.171) (0.420) (0.570)
Autocracy -0.144 -0.109 0.273
(0.157) (0.549) (0.572)
Population Growth (Lag) -0.101 ** 0.316 ** 0.279 *
(0.051) (0.154) (0.164)
Mountainous Terrain -0.281 -3.662 1.883 *
(0.261) (2.342) (1.093)
Oil Gini 0.168 -1.532 ** 0.676
(0.217) (0.715) (0.695)
Soil Fertility 0.721 *** -1.225 -1.296
(0.273) (0.826) (1.049)
Gold Production 0.021 0.371 -0.479
(0.133) (0.335) (0.393)
Diamond Production 0.281 ** -0.202 -0.668
(0.131) (0.305) (0.420)
Economic Growth (Lag) 0.737 * -1.624 -5.377 **
(0.389) (1.607) (2.601)
N 457 457 457
Table 3.21: Descriptive Summary of Results: Transitions from Peace more population → less peace
more diamond production → more peace more soil fertility → more peace more oil production → more violence
From Armed Violence to other Degree of Violence
In this section, we study the likelihood of transitions in the case of conictual countries. There-fore, the starting point is a country living turmoil and we estimate its probability to move toward peace or to civil war. Given previous probabilities, we expect a signicant eect of population growth (lag), oil production (lag) and Gini indicator in terms of natural resources.
We proceed as before, so Table 3.30 reports coecient estimates of transition probabilities from armed violence. Starting from the rst column, only population growth (lag) reduces the likelihood to go to peace from armed violence, with a 95% of condence level. Other relevant results are that ethnic groups joying more fertile lands and more diamond production are more likely to reach peace. Hence, it seems that diamonds foster peace more than oil. Evidently, when we are in turmoil, oil is used as a primary resource of money to buy weapons, both in the legal market (from government) and in the illegal one. Therefore, if there are some violent turbulences, an increase in oil production (lag) can improve the illegal market and so laundering activities.
It is interesting to see that, in such a situation (where people have begun to rebel with substantial violence), soil fertility behaves as in previous case: an increase of fertility helps to control violence because people nd in it a chance against their poverty. In other words, people are suering because of social/political/environmental reasons and are looking for solutions.
Table 3.22: Cox Proportional Hazard Model: Transitions from Armed Violence to other states
V iolence → P eace Violence → V iolence Violence → CivilW ar
Low Political Rights Quality 0.071 0.076 0.334
(0.134) (0.474) (0.999)
High Political Rights Quality -0.123 0.469 0.664
(0.187) (0.679) (0.916)
Oil Production (Lag) -0.226 1.056 *** -0.845
(0.176) (0.395) (0.597)
Autocracy -0.180 0.598 0.277
(0.161) (0.578) (0.491)
Population Growth (Lag) -0.081 * 0.313 0.066
(0.047) (0.209) (0.234)
Religious Conict 0.326 *** -2.243 *** -0.850
(0.097) (0.753) (0.630)
Environmental Conict 0.082 -0.334 0.007
(0.115) (0.567) (0.533)
Political Conict -0.050 -0.019 0.071
(0.099) (0.387) (0.367)
Mountainous Terrain -0.249 -7.572 2.719
(0.410) (5.810) (1.753)
Oil Gini 0.259 -1.731 * 0.073
(0.225) (0.978) (0.985)
Soil Fertility 0.654 ** -0.154 -1.405
(0.258) (0.820) (1.402)
Africa Region (Est) -0.123 1.304 * 0.256
(0.124) (0.730) (0.670)
Africa Region (Center) -0.104 0.387 0.727
(0.162) (1.042) (0.788)
Gold Production -0.019 0.455 -0.155
(0.126) (0.373) (0.411)
Diamond Production 0.283 ** -0.030 -0.785 *
(0.122) (0.350) (0.465)
Economic Growth (Lag) 0.614 -3.001 -5.036 *
(0.407) (2.378) (2.975)
N 558 558 558
Often, fertile territories are a crucial richness for poor people.
To conclude this rst transition, religious conict seems to be peace-prone, since we are condent at 99% that this root of conict increases the likelihood to transit towards peace.
We run the usual test and, although time-varying covariates test (Table 3.35) tells us that autocracy, population growth (Lag) e diamond production vary over time, the residual test does not nd any proportionality assumption violation.
Also the linktest does not provide a signicant prediction squared meaning that the speci-cation is good. However, the goodness of t plot (Fig. 3.9) shows pretty far slopes.
The second column conrms the previous one: oil production (lag) increases the odds for armed violence, while religious conict helps to go back to peace. On the contrary, the Gini indicator reduces the probability of persistence. This result is supported by the time-varying covariates test, because only population growth (lag) has a signicant p-value, while the residual test nd economic growth and political rights quality changing with time. However, oil produc-tion (lag) and religious conict do not violete the assumpproduc-tion, hence we can trust on the results.
Moreover, the goodness of t shows that our data ts well the specication (although the link test nd a signicant prediction squared).
For what concerns the probability to go to war, our estimates tell that economic growth and diamonds production reduce the likelihood to transit to war. Therefore, the role of dia-monds production is conrmed: holding all other variables constant, a unit-increase in diamond
production, increases the rate of relapse (to peace) by 54.3%25. Economic growth, instead, con-tributes by 99.3%. However, time-varying covariates tell us that economic growth and diamond production vary over time (together with oil production (lag) and gold production).
The residual test conrms that economic growth violetes the proportionality assumption, so we cannot be completely sure about the real (and deep) eect of economic growth to the transition to peace. The residual test nd also oil production (lag), mountainous terrain and Gini indicator as critical.
Finally, the link test shows any explanatory power, but the goodness of t test provides opposite conclusion (Fig. 3.10 and 3.11).
As before, we run same specication by applying ethnic xed eects. In the Appendix we show results in Table 3.57. However, we see that the only dierence with Table 3.30 is on the signicant eect of population growth (lag). Therefore, we can consider our results pretty stable.
We include also a survival analysis for the specic case of religious conicts, since it is the only signicant root of conict. The following Table (3.31) reports estimated transition probabilities from armed conict to peace and war, when an ethnic group has a religious conict.
Table 3.23: Cox Proportional Hazard Model stratied with religious conicts (Ethnic Fixed Eects): Transitions from Armed Violence to other states
V iolence → P eace Violence → V iolence Violence → CivilW ar
Low Political Rights Quality 0.081 0.216 0.106
(0.171) (0.497) (0.929)
High Political Rights Quality -0.148 0.605 0.265
(0.211) (0.568) (0.882)
Oil Production (Lag) -0.258 0.946 ** -0.792
(0.196) (0.443) (0.655)
Autocracy -0.187 0.053 0.153
(0.167) (0.524) (0.520)
Population Growth (Lag) -0.057 0.305 * 0.019
(0.050) (0.157) (0.213)
Mountainous Terrain -0.491 -3.274 2.637 *
(0.353) (2.452) (1.451)
Oil Gini 0.272 -1.810 ** 0.566
(0.229) (0.713) (0.656)
Soil Fertility 0.710 *** -1.610 * -1.310
(0.269) (0.826) (1.172)
Gold Production -0.018 0.449 -0.263
(0.132) (0.312) (0.412)
Diamond Production 0.343 *** -0.194 -0.807 **
(0.124) (0.292) (0.390)
Economic Growth (Lag) 0.595 -1.601 -5.058 *
(0.396) (1.824) (2.802)
N 558 558 558
When an ethnic group is ghting for religious purposes, oil production and population growth are crucial for the continuation of armed violence. This means that oil prots might be used from the government and/or from the ethnic group to nance the ght. Since data on oil production refers to ocial information, we guess that oil revenues support the ght in the legal market and so it is more probable that it is the government who prots from oil revenues.
Other highly signicant variables are soil fertility, Gini Indicator and diamond production. As we can see, the Gini indicator reduces the likelihood to remain in armed violence but, at the same time, tough not signicant, it increases the odds for war. Diamond production and soil
25This value comes from this procedure: we rst calculate the hazard ratio as exp(-0.785). Then we subtract 100% - 0.4562973%
fertility, do not seem to be logically related to religious conicts. However, they help to reach peace. Humans are constantly confronted by scarcity and they are willing to use any means possible, including appeals to the supernatural, to achieve their objectives. Given people's desire for religious goods and services, the appearance of specialist claiming the ability to satisfy these desires is not surprising. As acknowledged by North, Wallis and Weingast (2009) all of the earliest human civilizations were theocracies, governed by priest-politicians. Of course, religious communities are very close and sympathetic within a community, so soil fertility is a tool for religious community to increase the solidarity and community's strength. Think of the Islamic Courts in Somalia. They are not only a religious community. They are the community.
They manage civil rules, taxes, juridical debates, food and markets. More fertile land is a tool to control the community and solve troubles. Of course this is an extreme case, however, Africa is a very spiritual continent in which religion has a crucial role for the community and for linking the living with the dead. As Mcbride et al. (2012) says, religion engenders conict and cooperation.
Table 3.24: Descriptive Summary of Results: Transitions from Armed Violence more population → less peace
more diamond production → more peace more soil fertility → more peace more oil production → more violence more resources inequality → more violence more economic growth → more peace
From Civil War to other Degree of Violence
This is the last section in which we run standard Cox regression in order to estimate the propor-tional likelihood for an ethnic group to transit from civil war to peace or to remain in violence.
We proceed as before. From the univariate analysis we nd out that many variables should not be included in the model. Among them, (previous) public spending should not be included.
However, we have run the regressions without this "problematic" variables and the models do not result better than the full model. Hence, we will show same specication as before to see what happens when an ethnic group is experiencing a civil war.
From our model (Table 3.32) it turns out that if we are in civil war, a 1-year increase in diamond production is associated with an increase in the hazard rate. This means that if we are in war, diamond production has a crucial impact on the likelihood to reach peace, while economic growth is non-signicant. So far, we have coherent results because diamond production is always associated with a return to peace. Other coherent result is population growth (lag), which reduces the likelihood to reach peace, if we are in war. This is not only coherent with previous results, but it is also pretty logical: in war, more population pressure, cannot help to
x problems and more people will be engaged in war.
Time-varying covariates nd population growth (lag) and diamond production changing over time, but the Schoenfeld residual test do not support this result. Finally, the goodness of t, reports a plot in which only in the rst part of our estimates, data t well the chosen model.
Generally speaking, we can consider the model pretty good.
As the literature underlines, population growth (lag) and mountainous terrain are important features in civil war. In this analysis we conrm this result, because these two covariates have a positive impact on the likelihood to remain in civil war.
Also in the case of soil fertility, it proves to be always negatively related to war, while positively related to peace. In a banal way, a more fertile land is able to feed more people thus reducing famines, which are linked to conicts.
For the rst time, also political rights quality shows a highly signicant eect, meaning that