one-sided violence must be coded as having started.
(h) A peace treaty that produces at least 6 months of peace marks an end to the war.
(we) A decisive military victory by the rebels that produces a new regime should mark the end of the war. Because civil war is understood as an armed con ict against the government, continuing armed con ict against a new government implies a new civil war. If the government wins the war, a period of peace longer than months must persist before we code a new war (see also criterion k).
(j) A cease- re, truce, or simply an end to ghting can also mark the end of a civil war if they result in at least 2 years of peace. The period of peace must be longer than what is required in the case of a peace agreement because we do not have clear signals of the parties intent to negotiate an agreement in the case of a truce/cease- re.
(k) If new parties enter the war over new issues, a new war onset should be coded, subject to the same operational criteria. If the same parties return to war over the same issues, he generally codes the continuation of the old war, unless any of the above criteria for coding a war's end apply for the period before the resurgence of ghting.
Using these coding rules, Sambanis identi es 145 civil wars between 1945 and 1999 in the world. He uses two dierent coding war starts: in the rst one he considers simply civil war in a speci c country-period. This means that if in a country there are more than one con ict, he does not distinguish them. In the second version, on the contrary, he takes in consideration each civil war that starts, even if another war is going on. Of course, con ict onset is a dummy variable, where 1 means that a new war has started.
As in the previous dataset, also Sambanis inserts several control variables. These variables are the same as in FL. He adds only a new variable which measure time at peace since the last war and excludes the dummy variable of new states from FL.
Of course, since Fearon and Laitin, Sambanis, Collier and Hoe er datasets are all replication datasets, they are not annually updated. But they work a lot on con icts, so they update their dataset when they publish a new paper.
Other explanatory variables
As we could see, when we study civil war we must take into account country characteristics. The majority of the above mentioned variables are taken from the World Bank Database (WDI) which has a speci c dataset for the African Continent. In this database we can download contents such as: population, natural resource exports/imports and production, education, infrastructures, trade, gdp growth and per capita, agriculture, commodities, percent mountainous terrain and many others.
Usually, economic information are taken from the Penn World Table, which provides also information on the population in order to produce also per capita income.
Ethnic, religious and linguistic information are given by three main dataset: the ethnolinguis-tic fractionalization (ELF) index based on data from Atlas Narodov Mira 1964, Reynal-Querrol dataset, CIA Factbook, Alesina et al. (2003), Grimes and Grimes (1996), and Fearon and Laitin (2003).
Political information are provided mainly by the Polity IV Project and by the Database of Political Institutions (DPI).
variables. The last three speci cations use both country xed eects and country speci c time trends, but in the fourth and fth we include a speci cation test (as in MSS).
Table 2.14: Rainfall and Economic Growth: First-Stage (Dependent Variable: Economic Growth Rate, t)
I Ia II III
GDP Growth -0.676 -1.517 -2.644 *** -2.644 ***
(2.140) (1.263) (0.960) (0.960)
GDP Growth, t-1 -2.484 *** -1.545 *** -1.729 *** -1.729 ***
(0.843) (0.265) (0.356) (0.356)
GDP, 1979 3.122 3.122
(2.563) (2.563)
Quality Policy -0.001 -0.001
(0.001) (0.001)
Ethnic Fractionalization 17.195 17.195
(26.443) (26.443)
Religious Fractionalization 41.081 * 41.081 *
(22.887) (22.887)
Oil export 0.142 ** 0.142 **
(0.065) (0.065)
(Log)Population 0.006 0.006
(0.020) (0.020)
(Log)Terrain -3.202 -3.202
(3.262) (3.262)
N 1071 1071 959 959
R square adj 0.310 0.442 0.322 0.322
* p<0.1, ** p<0.05, *** p<0.01
From this rst stage, we can see that the instruments are highly signi cant in almost each speci cations. Even if rainfall variation shows a lower eect, compared to MMS, lagged rainfall variation are strongly and positively related to income growth. This is true also when we include country controls (regression 3 and 6) and xed eects (regression 2, 3, 4, 5 and 6). As expected, future predictions of rainfall variation do not aect income growth (regression 6). This con rms the identi cation check of MSS. Coherently with MMS, also growth in terms of trade is insigni cantly related to economic growth (regression 5).
Regarding the covariates, polity, oil exports, population and terrain variables change the sign (respect to MSS).
In this rst stage I do not use dummies for time period but, I can expect that some years might be particularly important for con ict onset. Over the African history, there have been some years during which some exogenous events have changed the conditions of some countries.
In order to establish if small changes to the model in uence the robustness of empirical results, I have replicated the same model with dierent time periods and then I drop, one by one, each country.
In general, we can say that the rst-stage relationship between rainfall and income growth is strongly positive and stable. Both current and lagged rainfall growth are positively and signi cantly related to income growth at over 95% con dence level, and this relationship is robust to the change of time period and also to the inclusion of country controls and time trends.
Hence, so far I can con rm the output of MSS: higher levels of rainfall are associated with positive economic growth.
Following MSS, in the following table (Table 2.15) I perform both linear least squares and non-linear models. Now my dependent variable is civil war. As we have seen in literature, I expect that gdp growth is the most important cause of civil war onset. In particular, I expect
that when there is an economic shock or a slow economic growth, the following year there are more possibilities of con ict. Therefore, lagged economic growth rates should be signi cantly related to civil war onset.
When I run an OLS regression of the incidence of civil war on economic growth, results change drastically. In MSS any contemporaneous or lagged economic growth rates are insigni cant, while with my data each current gdp growth, both in probit (column 2, 4 and 6) and OLS (column 1, 3 and 5), is signi cant and negatively correlated with the incidence of civil con ict.
Table 2.15: Probit and OLS: Economic Growth and Civil War
OLS probit1 OLS2 probit2 OLS3 probit3
GDP Growth -0.474 *** -2.484 *** -0.374 -1.694 ** -0.419 * -0.495 **
(0.150) (0.809) (0.232) (0.816) (0.227) (0.224)
GDP Growth, t-1 -0.038 0.036 0.148 0.395 0.115 0.073
(0.187) (0.728) (0.236) (0.822) (0.229) (0.219)
GDP, 1979 -0.022 * -0.195 * -0.023 * -0.052 *
(0.012) (0.112) (0.012) (0.030)
Quality Policy -0.004 * -0.011 -0.004 * -0.003
(0.002) (0.008) (0.002) (0.002)
Ethnic Fractionalization 0.219 0.997 0.230 0.280
(0.242) (0.800) (0.236) (0.215)
Religious Fractionalization -0.212 -1.087 -0.212 -0.294
(0.201) (0.711) (0.197) (0.191)
Oil export 0.074 0.250 0.065 0.061
(0.153) (0.483) (0.151) (0.132)
(Log)Population 0.036 0.139 0.032 0.034
(0.037) (0.133) (0.035) (0.035)
(Log)Terrain 0.081 ** 0.320 ** 0.083 ** 0.088 **
(0.037) (0.128) (0.037) (0.034)
N 1177 974 1017 1017 1017 1017
R square adj 0.443 0.106 0.105
Notes: Robust standard errors in parentheses. Signi cant at * p<0.1, ** p<0.05, *** p<0.01
Another dierence with MSS is that in my estimates, lagged economic growth rates are all positive, excepts for the rst OLS speci cation (which uses time dummies and country xed eects). On the contrary, in MSS, previous gdp growth is positively correlated with civil war only in the last two OLS speci cation (where we use within transformation). Among the covariates, as in MSS, terrain is statistically greater than zero at the 5% signi cance level.
According to the results, civil war onset is related to current rainfall levels and shocks but not signi cantly likely following low rainfall levels and negative rainfall shocks.
Only the rst OLS (which uses country xed eect and time dummies) tells us that ten-percentage point drop in previous annual economic growth increases the likelihood to have a civil con ict around 0.037 percentage points (with a standard error of 0.18).
In Table 2.16 the rst column reports a IV-2SLS with time dummies and country xed eects. If I follow MSS's procedure (who cluster by country), both current and lagged economic growth is non signi cant. Here, on the contrary, I have clustered by the mean level of GDP.
In the second column (Table 2.16) I report the same model with country xed eects and time trend. As before, previous economic growth is highly signi cant and negatively related to civil war onset. According to the adjusted R-square, this model ts very well our data. This is con rmed by the Kleibergen-Paap LM test which tells us that the structural equation is not overidenti ed. However, lagged gdp growth is badly instrumented. In fact, this instrument cannot refuse any statistical test because its F-statistic is far from the 25% maximal IV size and also p-value is low in both rst stage and second stage. Similarly, also here when I drop countries, I nd out that always Niger, Swaziland, Djibuti and Congo change drastically estimates. For
Table 2.16: IV2SLS: Economic Growth and Civil Con ict
I Ia II III
GDP Growth, t -0.676 -1.517 -2.644 *** -2.644 ***
(2.140) (1.263) (0.960) (0.960)
GDP Growth, t-1 -2.484 *** -1.545 *** -1.729 *** -1.729 ***
(0.843) (0.265) (0.356) (0.356)
GDP at 1978 3.122 3.122
(2.563) (2.563)
Quality Policy -0.001 -0.001
(0.001) (0.001)
Ethnic Fractionalization 17.195 17.195
(26.443) (26.443)
Religious Fractionalization 41.081 * 41.081 *
(22.887) (22.887)
Oil export 0.142 ** 0.142 **
(0.065) (0.065)
(Log)Population 0.006 0.006
(0.020) (0.020)
(Log)Terrain -3.202 -3.202
(3.262) (3.262)
N 1071 1071 959 959
R-square adj 0.310 0.442 0.322 0.322
* p<0.1, ** p<0.05, *** p<0.01
instance, when I drop Niger, one-point decline in (current and lagged) gdp growth increases the likelihood of civil con ict by over two percentage points.
In the last two columns I have added country controls. The dierence between the two speci cations is that in the rst one I use country xed eects, while in the second one I use only time trend. As we can see, current and lagged economic growth is highly signi cant and much more bigger than previous estimates. Hence, if I take into account country characteristics, the impact of economic variables become deep. At the same time, also oil exports and religious fractionalization show to aect civil war. Nevertheless, I have some doubts on these models, since religious fractionalization has strange values with huge standard error and because instruments are weak also in these cases. In fact, in the rst stage the structural equations are identi ed but the actual size of the t-test tells us that the point estimates on the endogenous variables equal zero at 25%. In the second stage, the Cragg-Donald Wald F statistic performs better, showing that the bias of the IV estimates are greater than 20% of the OLS bias.
In these two last speci cations, variations in the set of countries do not lead to strong changes.
Only Niger shows to aect deeply estimates, because, as before, when we remove this country, estimates change.
Each speci cation shows the same situation, excepts for the last two speci cations: models are identi ed, because null hypothesis that the structural equation is underidenti ed is rejected for the Kleibergen-Paap LM test p-value. Nevertheless, instruments are weak. In fact, both the Kleibergen-Paap and the Cragg-Donald test show high levels of p-value, far from the desired 0.05.
In the case of the last two IV2SLS, the Kleibergen-Paap LM test cannot reject the null hypothesis, meaning that the models are overidenti ed and have weak instruments.