Replication with Dierent Thresholds

In the previous paragraph we have explained our reasoning about current denition of civil war and related coding rule. Namely, we have argued that the conict variable developed by the UCDP (and invented by COW) is not a proper variable because estimates are too sensitive to the coding rule of conict variable (Sambanis, 2004). To address the issue mentioned in the previous paragraph, in this section we report our ndings showing that battle-death threshold is A very fragile indicator: small changes of the threshold cause big changes in the estimates.

In the underlying gure (Fig. 2.3), we see how estimates depend on the battle-death thresh-old²¹: we have run for 3000 times the same IV-2SLS by changing each time the threshold.

Therefore, in the rst regression the dependent variable (civil war) assumes value equals to 1 with one death over a year; in the second regression conict variable corresponds to value 1 when there are two dead; and last regression, instead, assumes value 1 when we reach 3000 deaths.

10−1−2−3GDP Growth lagged

0

100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700 1800 1900 2000 2100 2200 2300 2400 2500 2600 2700 2800 2900 3000

Number of Deaths

Standard Error Non−significant Significant at 10% Sigificant at 5%

Loop of IV2SLS with Different Number of Fatalities

Fig. 2.6: Loop of IV2SLS: From 1 death to 3000 deaths

The graph shows that red points stand for non-signicant lagged GDP growth estimates.

Black and blue points, instead, indicate a 5% and 10% signicance level respectively. Therefore, each dot shows the signicance of the lagged GDP per capita growth coecient corresponding to a particular regression. For example, the rst dot shows that lagged GDP per capita growth is non signicant if the y assumes value equal to 1 when we have 1 dead over a year.

Estimates of (instrumented) lagged GDP growth are signicant in a cyclical way: we have signicant coecient in the interval ranging from 5 deaths to 200 and from 1000 to 1400 fatalities.

These changes might be due to endogeneity related to countries. In order to address this issue, we run regressions of the same model by removing countries with the highest fatalities. Then, we replicate same model with dierent thresholds and we test MSS's instruments to see if estimates are simply biased by weak instruments.

Table 2.7 shows that even the main important Database of conicts (i.e. UCDP) have some recording problem. In fact, as we can see, in the third column Ethiopia and Sudan²² have the highest number of deaths. We remind that Niger was a problematic country in previous regressions. Here, we discover that this country has not many fatalities.

21Informations about the number of deaths are taken from the UCDP Database.

22In Sudan there is a cruel dictatorship of Al Bashir who came to power in a coup in 1989 and in 2009 has been condemned for genocide by the International Criminal Court for the crisis in Darfur province. Moreover, Sudan is aected by regional instabilities, displacements and terrors with Al Qaeda allegedly eeing their Pakistan cells to Sudan and Somalia.

At the same time, we have also several zero values (1421 zero observations). First of all, these zeros can create estimation bias. Then, if we take a closer look at these zeros, we can easily say that, at least for some countries, they are missing values (i.e. they are not peaceful countries but countries with missing values). In fact, for example, it is quite unlikely that Lybia, Tanzania and Madagascar have no killings over the time span 1981-2009²³. Similarly, in South Africa, the apartheid was removed in 1990. Hence, it is very likely that South Africa's zero fatalities during the period 1981-2009 is a measurement bias.

Table 2.8: IV2SLS without Sudan

I Ia II III

GDP Growth -0.988 -1.597 -2.797 -2.797

(2.288) (1.668) (1.843) (1.843)

GDP Growth, t-1 -2.463 -1.530 -1.737 * -1.737 *

(1.562) (1.051) (1.029) (1.029)

GDP, 1979 3.052 3.052

(2.668) (2.668)

Quality Policy -0.001 -0.001

(0.002) (0.002)

Ethnic Fractionalization 19.707 19.707

(23.837) (23.837)

Religious Fractionalization 37.338 37.338

(27.553) (27.553)

Oil export 0.108 0.108

(0.095) (0.095)

(Log)Population 0.014 0.014

(0.029) (0.029)

(Log)Terrain -2.803 -2.803

(4.438) (4.438)

N 1044 1044 933 933

R square adj 0.275 0.406 0.259 0.259

Notes: Huber robust standard errors are in parentheses. Regression disturbance terms are clustered at the country level. A country-specic year time trend is included in all specications (coecient estimates not reported).

* Signicantly dierent from zero at 90 percent condence level.

** Signicantly dierent from zero at 95 percent condence level.

*** Signicantly dierent from zero at 99 percent condence level.

Table 2.8 shows estimates when we remove Sudan and similar results are obtained if we drop Ethiopia (instead of Sudan).

We see that Ethiopia and Sudan aect estimates²⁴. The reason is clear: in Ethiopia we have a dictatorship²⁵ and in Sudan we have a civil war. For this reason, we cannot remove those countries when we analyse civil war. However, we have to take into account that the number of

23Until 2011 in Lybia there was a dictatorship, then a revolution put down and killed the dictator Ghedda.

Although Ghedda was able to maintain peace and gave decent life to its population, he was a cruel dicta-tor inicting dozens of deaths. In particular, SCAD Dataset records conictual events since 1991. Similarly, Madagascar is a very unstable island with recurrent coup d'etat. According to World Bank Indicators, political stability in Madagascar has always been low but it is decreasing since 2006. In 2009 Madagascar had a deep political crisis and still now this big island has not recovered. Although Tanzania is rated as the most peaceful country in the region, it is still struggling with its internal economic dynamics. In Tanzania, from 1992 ethnic groups have started to kill each other with the aim to take the power. Tensions are high also for environmental issues (oil and land-grabbing for biomass) (Environmental Justice Atlas). There is not a proper civil war, but tension is still high among them.

24We have run also the overidentication and weak instrument test, following the same procedure as before.

Once again, instruments prove to be weak, although equations are not overidentied.

25In 1991 the Ethiopian People's Revolutionary Democratic Front (EPRDF) assumed democratically power after overthrowing President Mengistu. EPRDF has been ruling the country since 1991 and currently controls all but one seat in parliament. Human Rights Watch reports in 2015 that thousands of Ethiopians are jailed, killed and tortured by the State. "Ethiopia is one of the most restrictive environments for independent

investi-fatalities inuence estimates. Hence, if it is true, the bias from the missing values of conictual countries is much bigger than we expect.

Of course, if we rely on the number of fatalities to build our conict variable, we need to have reliable statistics.

In the following lines we will prove that estimates are very sensible to small changes of the threshold.

For example, let's see what happens if we run the usual IV2SLS specications by using 30 battle-deaths as threshold for the conict variable.

Table 2.9: IV2SLS with 30 Battle-deaths Threshold

I Ia III

GDP Growth -1.859 -2.217 -2.310

(1.545) (1.604) (1.448)

GDP Growth, t-1 -1.552 * -1.539 * -1.682 **

(0.939) (0.832) (0.774)

GDP, 1979 0.009 -0.026 ***

(0.007) (0.008)

Quality Policy -0.002 -0.007 ***

(0.001) (0.002)

Ethnic Fractionalization -0.206 0.052

(0.130) (0.189)

Religious Fractionalization 0.054 -0.175

(0.160) (0.169)

Oil export -0.052 0.093

(0.072) (0.103)

(Log)Population -0.002 0.004

(0.021) (0.026)

(Log)Terrain -0.024 0.046 **

(0.022) (0.023)

N 1071 959 959

R square adj 0.326 0.288 -0.123

Cragg-Donald F stat 4.595 4.033 5.902

Kleibergen-Paap F stat 4.286 3.974 5.495

Kleibergen-Paap LM test p-value 0.00262 0.00363 0.00106 H0: t − testsize > 10%(p − value)|KP 0.963 0.971 0.918 H0: t − testsize > 25%(p − value)|KP 0.231 0.267 0.128 H0: t − testsize > 10%(p − value)|CD 0.953 0.970 0.897 H0: t − testsize > 25%(p − value)|CD 0.200 0.260 0.104 H0: t − testrel − bias > 10%(p − value)|KP 0.660 0.700 0.505 H0: t − testrel − bias > 30%(p − value)|KP 0.181 0.213 0.0946 H0: t − testrel − bias > 10%(p − value)|CD 0.620 0.692 0.456 H0: t − testrel − bias > 30%(p − value)|CD 0.154 0.207 0.0752

Notes: Huber robust standard errors are in parentheses. Regression disturbance terms are clustered at the country level. A country-specic year time trend is included in all specications (coecient estimates not reported).

* Signicantly dierent from zero at 90 percent condence level.

** Signicantly dierent from zero at 95 percent condence level.

*** Signicantly dierent from zero at 99 percent condence level.

From Table 2.9, it is clear how estimates change drastically by adding only 5 fatalities to the conventional coding rule for conict variable.

As before we run weak instrument test and overidentication test for this regression. Once

gation, reporting, and access to information, earning the country a top-10 spot in the global ranking of jailers of journalists. For the past decade, the government has limited access to information by regularly threatening, imprisoning, and prosecuting individual activists, bloggers, and journalists and sending a clear public message that the media must self-censor and that dissent or criticism of government policy will not be tolerated." (Human Rights Watch Report, 2015).

again, the instrument is weak but the structural equation is identied. Therefore, we have not solved the problem yet, because instrument is always weak.