Replication with Non-Linear Models

In this section we will rst regress non-linear models with the same variables in order to see whether estimates change and ameliorate compared to IV2SLS.

The underlying table (Table 2.10) shows parameter estimates using the conventional variable of internal armed (i.e. we take the UCDP threshold of 25 battle-deaths) as the dependent variable. Although MSS's instruments have proven to be weak, we cannot use it anyway because it does not exist an instrumental variable Poisson or ZIP approach.

In probit models, the estimated marginal eect on civil war of a unit increases in current GDP growth ranges from -1.4 to -2.6. In the case of Poisson models, the rst model uses time trend and the second specication uses time dummies.

As we can see, excepts for the rst specication, all cases have signicant GDP per capita growth estimates. On the contrary, previous GDP per capita growth does not seem to have an impact on the likelihood of civil war. This nding goes in the opposite direction of our previous results, but remind that here we are not running instrumental variables models, so it can make sense that current GDP growth impacts the likelihood of civil war. When we instrument GDP growth by using rainfall variation over a year, it is more plausible that previous drought period aects negatively peace, while current drought should be less related to civil war. The ratio comes from the fact that during a drought people can still survive with their provisions or by developing emergency resilient activities. On the contrary, when we cannot use rainfall variation as instrument of economic shock, there is a more direct relationship between economic shocks and civil war. Hence, these results are not so unlikely.

As expected, country control variables have a very dierent eect compared to previous

ndings. Both probit and Poisson models show that covariates have a crucial impact on the likelihood of civil war. In particular, policy, religious fractionalization, terrain and population are signicantly related to civil war. Quality policy seems to have also a parabolic eect (rst increasing and then decreasing).

Therefore, in the standard probit, current GDP per capita growth decreases the probability of civil conict with a high level of signicance. Also mountainous terrain and policy are highly signicant and non linear (because there is a change of sign in the quadratic parameter).

Table 2.10: Probit and Poisson with 25-death threshold

Probit1 Poisson1 Probit2 Probit3 Poisson2

war

GDP Growth -1.956 *** -2.336 ** -1.878 ** -1.920 *

(0.739) (0.999) (0.755) (1.052)

GDP Growth, t-1 0.351 -0.120 0.471 0.657 0.420

(0.683) (0.972) (0.694) (0.710) (0.989)

Oil export 0.112 -0.562 0.085 0.020 -0.065

(0.155) (0.450) (0.154) (0.159) (0.255)

Quality Policy -0.012 -0.047 * -0.006 -0.021 * -0.075 ***

(0.010) (0.028) (0.010) (0.011) (0.021)

Quality Policy² 0.000 -0.000 0.000 0.000 -0.001 ***

(0.000) (0.000) (0.000) (0.000) (0.000)

Religious Fractionalization -0.544 * -52.668 -0.918 ** -0.852 **

(0.317) (157.493) (0.373) (0.382)

(Log)Population 0.175 *** -3.436 * 0.127 ** 0.095 0.148

(0.051) (1.765) (0.062) (0.064) (0.104)

(Log)Terrain 0.169 *** 17.982 0.234 *** 0.233 *** 0.204 ***

(0.038) (17.967) (0.057) (0.058) (0.079)

Ethnic Fractionalization -0.696 -0.554 -1.521 *

(0.427) (0.446) (0.778)

Ethnic Fractionalization² 1.324 *** 1.237 *** 1.702 **

(0.421) (0.437) (0.718)

N 1018 1018 960 928 960

In Table 2.11, we perform probit and Poisson models by applying time dummies (column 1) and country xed eects (column 2). As we can clearly see, current GDP growth is always highly signicant and negative related to the likelihood of civil war but, also mountainous terrain conrms its crucial role. In the third probit specication (with country xed eects and time trend), several covariates are highly signicant: Quality Policy, ethnic and religious fractionalization, population and terrain.

When, instead, we assume a Poisson distribution, estimates change. First of all, only GDP growth remains signicant. Particularly, estimates of GDP growth tell us that one point estimate change in GDP growth aects negatively the likelihood of peace of 2.1%. Contrary to the probit, grievance variables are not signicant when we use xed eects and time trend.

In a panel set, probit regressions are more suitable and provide a dierent picture on civil war, where both economic determinants and social-institutional determinants are relevant for the likelihood to be in war or in peace. In numerical terms, if a country increases its real GDP growth by one percent, the likelihood of the country being in peace would decrease by a factor between 1.7 and 3.8 point estimates. Generally speaking, the more a country is poor, densely populated and with mountainous terrain, the more chance it has of living a conict. Moreover, from probit specication we can see that Quality Policy has a parabolic trend, rst increasing and then decreasing; in particular, lagged and contemporaneous Quality Policy is signicant and negatively related to the likelihood to be in peace.

Table 2.11: Probit and Poisson with 25 battle-death threshold and Fixed Eects

probit1 probit2 probit3 poisson1

Conict

GDP Growth -1.731 * -3.855 *** -1.779 *** -2.112 **

(0.901) (1.019) (0.679) (0.863)

GDP Growth, t-1 0.796 -0.424 0.263 -0.270

(0.836) (0.847) (0.650) (0.883)

Oil export -0.171 0.738 0.218 0.235

(0.213) (0.514) (0.155) (0.406)

Quality Policy -0.034 **

(0.014) Quality Policy² -0.000 (0.000)

Quality Policy, t-1 0.039 ** -0.012 ** 0.001

(0.018) (0.006) (0.006)

Ethnic Fractionalization 0.278 0.000 1.004 *** 0.000

(0.520) (0.256)

Ethnic Fractionalization² 0.383 (0.490)

Religious Fractionalization -1.205 ** 0.000 -1.056 *** 0.000

(0.508) (0.306)

(Log)Population 0.312 ***

(0.084)

(Log)Terrain 0.278 *** 0.000 0.315 *** 0.000

(0.070) (0.044)

GDP, 1979 0.000 -0.185 *** 0.000

(0.047)

(Log)Population -0.295 0.122 ** -0.308

(0.246) (0.052) (0.276)

Time Trend 0.001

(0.006)

Constant -6.784 *** 6.639 -5.261 5.155

(1.198) (4.321) (12.221) (4.812)

N 927 813 1017 1017

In Table 2.12, we run ZIP models. Here, we follow Dunne and Tian (2015) procedure, so we have coded a new conict variable which can assume 2 values: the dependent variable takes on a value of 0 for all peace year observations and a 1 for civil war years with combat deaths ranging between 25 and annual battle deaths of above 1000. we rst regress probit and Poisson models and then we use ZIP regression to make see dierences among dierent approaches. In the context of the ination equation, the coecients represent the factor change in the probability of being always peaceful compared to unstable peace. Therefore, in the rst part of the table we have the response variables predicted by the full model. Here, the coecients of the output are interpreted as we would interpret coecients from a standard Poisson model. In the second part, instead, output refers to the logistic model predicting whether or not a country is always zero. So, the higher the value, the more likely the country is likely to be in peace.

When we use probit models, GDP growth has a negative impact. On the contrary, results reveal that in the case of the ZIP equation, GDP growth has a positive eect on the likelihood that a country-year to be among the always zero group (i.e. peaceful countries). In numerical terms, if a country increases its real GDP by one percent, the likelihood of the country being peaceful would increase by a factor ranging from 1.8 to 3.3. Oil exports as a share of GDP has a negative and signicant eect on the odds of being always zero. Assessing the non-economic variables reveals some more valuable insight on the dierences between the two types of zeroes or peace observations. In fact, each control variable now has an impact on the likelihood of being

Table 2.12: Probit and Poisson with New Threshold

probit1 poisson1 probit2 probit3 probit4 poisson2

Civil War

GDP Growth -1.846 *** -2.312 *** -1.808 *** -1.941 *** -1.650 **

(0.677) (0.686) (0.691) (0.694) (0.753)

GDP Growth, t-1 0.374 -0.339 0.439 0.361 0.459 0.940

(0.642) (0.681) (0.651) (0.655) (0.649) (0.688)

Oil export -0.026 -0.047 -0.079 0.207 -0.051 -0.023

(0.142) (0.320) (0.143) (0.157) (0.143) (0.162)

Quality Policy -0.019 ** -0.011 -0.019 ** 0.001 -0.013 -0.038 ***

(0.009) (0.016) (0.009) (0.013) (0.009) (0.013)

Quality Policy² 0.000 -0.000 0.000 0.000 0.000 -0.000

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Religious Fractionalization -0.664 ** 248.931 ** -0.935 *** -1.060 ***

(0.286) (101.095) (0.331) (0.307)

(Log)Population 0.276 *** 0.347 0.222 *** 0.371 ***

(0.046) (1.021) (0.056) (0.066)

(Log)Terrain 0.211 *** -14.604 0.268 *** 0.320 *** 0.302 *** 0.236 ***

(0.034) (11.317) (0.050) (0.044) (0.037) (0.049)

Ethnic Fractionalization 0.287 1.022 *** 0.590 * 0.422

(0.358) (0.257) (0.347) (0.416)

Ethnic Fractionalization² 0.393 0.260 -0.202

(0.341) (0.332) (0.380)

Time Trend -0.002

(0.007)

GDP, 1979 -0.200 ***

(0.049)

Quality Policy, t-1 -0.003

(0.009)

(Log)Population 0.121 **

(0.052)

Constant -5.176 *** -85.317 -4.648 *** -0.016 -1.776 *** -8.017 ***

(0.695) (62.377) (0.788) (13.571) (0.182) (1.033)

N 1018 1018 960 1017 923 960

completely peaceful. Among them, population results to be always highly signicant, both with and without time dummies.

The results reveal that in the case of ZIP models, GDP growth is mostly always signicant and positively aects the likelihood to be in peace. Together with economic variables, also grievances variables prove to be much signicant.

Finally, a Vuong (1989) test is run, rejecting the hypothesis that the traditional probit or Poisson models are better, thus favouring the zero-inated models as a less biased estimator.

Empirical estimations using the MSS updated data provide a strong case for arguing that the determinants of conict literature should consider moving from standard probit models to some form of a ZIP model. If not, researchers risk both underestimating the risk of civil conict and making erroneous conclusions regarding its signicance.