What causes civil wars?
Africa is the poorest continent and has for a long time failed to grow and catch up with other
regions. It is also the continent most affected by violence, with 29 countries out of 43 affected by
civil war during the 1980s and 1990s, the peak of violence (Figure 1). In this chapter we look at
violence and conflicts in terms of their effects and determinants. While making references in
passing to international wars, our focus will be essentially on civil wars, which are estimated to
have led to three times as many deaths as international wars since WWII (Fearon and Laitin 2003).
As in the rest of this course, the recurrent question will be how to identify causal mechanisms while
filtering out reverse causality and confounding influences.
Number of conflicts
Figure 1: The incidence of conflicts peaked in Africa in the 1980s and 1990s
Source: Strauss (2012)
1. The determinants of conflicts
1.1 Conflicts, poverty and income shocks
That poverty make people more prone to take arms is a widely share idea. For instance, in 2001
Germany’s chancellor Schröder declared that “[e]xtreme poverty, growing inequality between
countries, but also within countries themselves, are great challenges of our times, because they are a
breeding ground for instability and conflict. So reducing worldwide poverty is, not least, essential
to safeguarding peace and security” (quoted in Djankov and Reynal-Querol 2010).
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Whether this causal link is upheld by the data, and what are the underlying mechanisms, is the
focus of this section. In general, one can think of several channels through which poverty can make
civil war more likely
1. Grievances: Poor people may feel aggrieved by an unequal distribution of income and the
absence of trickle down; although Gini coefficients usually don’t fare very well as determinants
of civil war outbreak, see e.g. Collier Hoeffler 2004);
2. Opportunity cost: Poor young men have a lower opportunity cost when considering to join
armed militias (desertions used to be common during harvest season when European societies
were predominantly farming; this was observed e.g. during the Russian civil war in 1917-1920).
3. Failed states: States without resources may lack the means both to prevent rebellions (say by
providing basic services) and to stop them (by repression).
GDP per capita seems indeed to correlate negatively with the outbreak of civil war across countries
and time, as shown by Table 1. Fearon and Laitin (2003) also find that income per capita correlates
negatively with the probability of war.
Interestingly, there are two interpretations of the poverty effect: (i) a “greed” effect, whereby poor
individuals choose rationally to enlist in rebel militias to loot (a sort of “micro approach”); (ii) a
“grievance” effect, whereby the poor rebel against the dominant class because of the combination of
poverty, inequality and, possibly, ethnic or religious splits that overlap with income differences (the
rich belong to a different ethnic or religious group than the poor). Thus, the “greed vs. grievance”
issue relates to both the role of commodities (mines) and to that of ethnic fractionalization, which
we will discuss later on.
Collier and Hoeffler (2004) compare two regressions, one with the variables shown in Table 1
which they dub the “greed” model (based on micro motivations) and one with variables that have to
do with “grievances” (inequality, ethnic polarization, lack of democracy etc.) and do a J test (see
Box 1) to determine if one set of explanatory variables (“model”, although there is no real theoretic
model behind either—both are what we call “reduced-form” or “kitchen-sink” regressions)
dominates the other. The result is inconclusive. However, Collier and Hoefller’s narrative suggests
that they prefer the greed model, for me pretty much on ideological grounds (rebels are bad,
government is law and order and is good).
Box 1: Comparing models with Davidson and MacKinnon’s J test
The J test (Davidson and MacKinnon 1981) works like this. Suppose that a variable y is determined by a set
of explanatory variables x1 under model 1 and x2 under model 2. The procedure is
o
Regress y on x1 , retrieve ŷ1 (the predicted value of y based on x1 )
o
Regress y on x2 , retrieve ŷ 2 (the predicted value of y based on x2 )
o
Run two additional regressions: (1) y on x1 and ŷ 2 and (2) y on x2 and ŷ1 .
If ŷ 2 is not significant in (1) and ŷ1 is significant in (2), model 1 dominates model 2, and vice versa. If ŷ 2
is insignificant in (1) and ŷ1 in (2), then you are done in: both models are rejected and the test is
inconclusive. That often happens in small samples (the test is valid only asymptotically on large samples).
Alternatively, if both are insignificant, both models are accepted and the test is again inconclusive. That’s
what happened to Collier and Hoeffler.
2
Table 1: Income has a strong negative effect on the probability of civil war
Note: Sample: 5-year periods for all countries with data over 1965-1999; dependent variable is status dummy variable
equal to one when civil war breaks out in a given country in a given 5-year period; equal to zero before, and missing
value after.
Source: Collier Hoeffler (2004), Table 3
There are two big limitations to this exercise. First, the conflict data is very coarse as it records only
country-year observations with over 1,000 battle deaths. This is a very high threshold, with the
result that many low-scale incidents go unrecorded even though they are equally interesting to
study. Second and more damning, the scope for endogeneity bias is just massive. Many factors
(weather shocks etc.) can both depress economic activity and raise the likelihood of civil war (an
omitted variable problem). It is also possible that the anticipation of civil war due to a rise in
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tensions depressed investment and causes economic activity to shrink. The regression does not even
include country fixed effects, because the “grievance” model includes time-invariant country
characteristics like ethnic polarization. Thus, the scope for catching any kind of unobserved
heterogeneity is also appalling. Indeed, Djankov and Reynal-Querol (2010) find that the correlation
between GDP per capita and the incidence of civil conflicts goes up in smoke once country fixed
effects are added.
Miguel et al. (2004) improve on both counts (data and identification). For conflicts, they use the
PRIO/Uppsala dataset for 1981-99 which records all incidents with over 25 battle deaths, by
country and year. For identification, they instrument economic growth with variations in rainfall.
This makes sense in sub-Saharan Africa’s still largely agrarian societies where less than 1% of
arable land is irrigated, making agricultural yields (and hence incomes) heavily dependent on
rainfall. First-stage results confirm that the variation in rainfall affects significantly economic
growth the same year and the next (Table 2).
Table 2: Rainfall and economic growth
Note : Growth in rainfall at t + 1 is a placebo : If it was significant, it would make the whole relationship likely to be a
« spurrious correlation ».
Source: Miguel et al. (2004) Table 2.
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Using first-stage results, Table 3 shows the relationship between conflicts and economic growth
without instrumentation (columns 1-4) and with instrumentation by rainfall (columns 5-7).
Table 3: Conflicts and economic growth instrumented by rainfall
Source: Miguel et al. (2004) Table 4
So again, we have that negative income shocks raise the probability of conflicts (the old idea that
poverty breeds violence, possibly by reducing the opportunity cost of fighting), but now it is
identified correctly by looking only at the variation in incomes caused by exogenous weather
shocks.
The econometrics is all right, but pause for a second. Can we really believe that we have identified
a causal mechanism by relating rainfall averaged over a year and a whole country to the incidence
of conflicts, again aggregated at the country-year level? We are talking about pretty big countries in
cases like DRC, Kenya or Nigeria. The identification stretches a little bit the imagination. Harari
and La Ferrara (2014) conduct the analysis at a much more disaggregated level where the
identification is more credible. First, they look at subnational “cells” (squares of 110 km sides)
instead of countries. Second, they replace rainfall with a variable called “standardized precipitationevapotranspiration index” (SPEI) that takes into account the soil’s ability to retain water. Third,
they identify each cell’s main crop and look at deviations of the SPEI from its average level during
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that crop’s specific growing season (instead of average value over a year). Figure 2 shows that
rainfall and SPEI are two quite different things indeed.
Figure 2: Rainfall and SPEI, 1997-2011, by cell
Rainfall
SPEI
When regressing the outbreak of conflict on the SPEI’s deviation of its local average, they get
striking results:
o A drop in a cell’s SPEI by 2 standard deviations (i.e. to the lower bound of its distribution’s
95% confidence interval) during the main crop’s growing season raises the probability of
conflict in that cell, in the same year, from a baseline of 16% to 24%, a huge jump.
o Variations in the SPEI outside of the main crop’s growing season have no effect on the
probability of conflict, highlighting that it is really the shock to agricultural yields that triggers
the conflicts.
o Conflict spreads: When a cell experiences a conflict, neighboring cells are 3.6 percentage points
more likely to experience a conflict as well, independently of any weather shock in those cells;
in other words, a conflict in a neighboring cell, ceteris paribus, raises the probability of conflict
from the baseline 16% to almost 20%.
o Conflict is persistent: When a cell experienced a conflict last year, independently of any weather
shock this year, its probability of being in conflict jumps from the baseline 16% to 50%. We
will go back to the issue of the lingering effects of conflicts later on in this chapter.
Interestingly, they also use predictions of climate change to forecast the future incidence of
conflicts, which highlights vulnerability in Somalia (already a failed state) but also in Cameroon,
Guinea and Senegal (Figure 3).
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Figure 3: Incidence of conflicts and forecast SPEI shocks
Conflicts incidence, 1997-2011
Projected SPEI shocks, 2012-2030
Thus, all in all, the recent work highlights quite clearly the effect of negative income shocks on the
incidence of conflicts.
1.2 Conflicts and natural resources
In the paper already discussed, Collier and Hoeffler (2004) find a positive coefficient on the share
of primary commodities exports over GDP. What does that tell us about the effect of a country’s
dependence on commodity exports (oil and minerals) on the incidence of conflicts? Not much.
First, as already noted, their identification is across countries, with no fixed effects to control
unobserved heterogeneity. This was addressed by a second generation of papers using panel
(country-year) data.
However, country fixed effects improve identification only partly. The reason is the usual
endogeneity bias. Consider the fictitious data in Figure 4. Assume that extractive activities are
sheltered from the rest of the economy because mines are in heavily guarded enclaves and can
continue to operate even as the country descends into chaos.1 Assume that a shock, say the irruption
of armed guerillas from neighboring countries, precipitates an economic crisis around year nine.
Non-mining GDP shrinks, mechanically pushing up the share of mining in GDP from about 1/5
before year 10 to about 1/3 on average after, while violence episodes (“War”) rise, creating a
1
For instance, in Guinea, US- and Russian-owned bauxite mines are surrounded by heavily guarded fences and related
to a a deep-sea mineral terminal by a dedicated railway line. All employees are expatriates and consume only imported
stuff. As the country sunk into civil war in 2000-2003, mines largely continued operations.
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spurious correlation between the share of mining in GDP and the level of violence. Because the
confounding event in Figure 3 is time-variant, it cannot be picked up by fixed effects. It could only
be picked up by country-year fixed effects, but then, if all variables were at the country-year level,
there would be no degrees of freedom left.
0
20
40
60
80
100
Figure 4: War and the share of mining in GDP: The endogeneity bias illustrated
0
5
10
year
GDP
War
15
20
Mining
In order to be able to include country-year fixed effects, the analysis has to be taken to the subnational level, like in Harari and La Ferrara, using geo-referenced cells. That is what Berman et al.
(2014) do, looking at cells of 55  55 km. They look at whether the presence of mines correlates
with violent incidents, using the ACLED database which, unlike the PRIO/Uppsala database, is at
the level of these geo-referenced cells rather than at the country-year level. Now the identification
could be polluted by the fact that mines may—notwithstanding the argument of Figure 4—be
sensitive (endogenous) to violence. In order to eliminate that source of bias, they interact the
presence of mines with the world price of the mineral exploited in those mines, which, at least in
principle, should not be affected by local violent incidents.2 A priori, the location of mines in Africa
does not seem to fit very well with patterns of violence (Figure 5).
Figure 5: ACLED violent incidents and the location of mines in Africa
Violent incidents
Mines
2
Actually it could, if the mine is very large and the incident is serious, as commodity markets are quite speculative. In
order to control for that, in a robustness test they eliminate the largest mines, i.e. those that could affect world prices.
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Source: Berman, Couttenier, Rohner and Thoenig (2014), Figure 1
However, the econometrics suggests a very strong effect, shown in Table 4.
Table 4: Mines, mineral prices, and the incidence of conflicts
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Source: Berman, Couttenier, Rohner and Thoenig (2014), Table 4
Most striking, access to mining resources seems to fuel further violence (presumably because the
mining resources finance the purchases of weapons and new recruits). Berman et al. test this by
showing that when a rebel group wins a battle that gives it access to a mine, the probability that that
particular rebel group starts a violent incident in a different cell the following year is higher than
when the rebel group wins a battle that does not give access to a mine (Table 5).
Table 5: Winning a mine fuels further aggression
Source: Berman, Couttenier, Rohner and Thoenig (2014), Table 7
This all points to a “greed effect”: Rebels fight for control of natural resources which they then use
to finance further war. Well, could the story be slightly different? Azam and Bhatia (2012) propose
a quite different narrative, one that is grounded in anecdotal evidence but quite compelling. Their
account is about India’s Naxalite rebels, a maoist group active in Central India.
“According to a report by Bloomberg Business week ‘delays in approval for land and mines have
stalled 80$ billion of projects in India. Most of these projects are located in the tribal belt of India.’
The world’s biggest steel company “Mittal Steel” has threatened to pull out its 20$ billion project to
set up steel plants in Jharkhand and Orissa due to severe delays . Tata Motors have already pulled
out their Nano project from West Bengal over protest from farmers. The Orissa Mining Corporation
is waiting for clearance on five of its projects from the Environment and Forest Ministry. In an
interview to a newspaper, the previous Central Environment and Forest Minister, Jairam Ramesh,
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accepted that the state governments are under pressure to show that their economies are doing well
and hence some of the states are allowing the flouting of forest and land laws by mining companies.
These delays and roadblocks have given an incentive to the government to provoke a conflict. States
are in a rush to get their districts declared as ‘Naxalite affected’ by the Central Ministry of Home
Affairs. On various occasions, in Singur, Nandigram, Lalgarh, Niyamgiri etc. the State governments
have tried to declare a ‘Naxalite hand’ behind protests by local people. Districts which are declared
‘Naxalite affected’ have at their disposal the police force of the state and also Special Central Forces
like paramilitary, Commando Battalions for Resolute Action (CoBRA) and India Reserve (IR)
battalion. In addition the districts get special funds from the Centre under the Security Related
Expenditure (SRE) scheme for combating the Naxalites. These armed forces and funds are being
used rather arbitrarily by the State governments to clear the forest and hills of their inhabitants. The
Chhattisgarh State government has taken extreme steps to this effect. In 2006, the government of
Chhattisgarh actively supported the creation of a civilian vigilante army named ‘Salwa Judum’
(purification hunt). The Salwa Judum had very clear instructions from the government to burn down
the villages inside the forests and force the people to take shelter in big relief camps situated outside
the forests. Those who stayed behind were deemed as Naxalite supporters. A public interest
litigation petition filed in 2008 by a group of citizens in the Supreme Court records that, during this
cleansing act, at least 2,825 houses have been burnt, 99 women raped and approximately 100,000
persons were forcibly displaced by the Salwa Judum. To establish further control, the government
passed a law titled as Chhattisgarh Special Public Security Act, 2005 which makes it very difficult
for journalists to do any credible and independent reporting from these areas.” (Azam and Bhatia
2012, p. 7-8.
This channel of causation, whereby civilian populations, often from discriminated groups, are
forcibly chased out by terror methods in order to clear the land before oil or mineral extraction, is
largely left out of the literature. Yet anecdotal evidence is plentiful. India’s Salwa Judum have a
near-perfect equivalent in South Sudan under the name of Janjaweed, death squadrons that were
sent by the Sudanese government to burn, kill and rape until populations were entirely chased out
from oil-rich areas and oil-company executives could be flown by helicopter over the areas and
convinced they were empty, so that there would be no issue with indigenous people. The scale was
massive.3 In Sudan, the mechanism was direct. In India, it took the slightly more sophisticated form
of provocations through vicious violence designed to push indigenous people to rebel. Such tactics
have also been widely used in Latin America against indigenous people by death squadrons. They
remain to be explored systematically by the literature.
1.3 Conflicts, ethnicity, and media propaganda
We have already used ethnic fragmentation as an explanatory variable or instrument in a variety of
contexts like the emergence of social capital, corruption, the quality of institutions, and so on. A
natural conjecture is that ethnic fragmentation is one of the drivers of civil wars. However the index
typically works poorly in regressions of the determinants of civil war (as we saw with Collier
Hoeffler 2004, but there are others). Reynal-Querol (1998) proposed a different index, called
According to Wikipedia, “[b]y October 2007, only the United States' government had declared the Janjaweed killings
in Darfur to be genocide, since they had killed an estimated 200,000–400,000 civilians over the previous three years.
The UN Security Council called for the Janjaweed to be disarmed. On July 14, 2008 the prosecutor of the International
Criminal Court filed genocide charges against Sudanese President Omar al-Bashir, accusing him of masterminding
attempts to wipe out African tribes in Darfur with a campaign of murder, rape and deportation using the Janjaweed
tribes.”
3
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“polarization”, which measures how far away is a society from a 50/50 split between two different
groups. The index is
P  1  i si 1  2si 
2
instead of F  1  i si 2 for the fragmentation index. Reynal-Querol’s work shows that
polarization performs better as a predictor of the outbreak of civil war.
Many societies, while fragmented or polarized, do not sink into civil violence. Of course, many
other factors need to be there for that. But an important question remains open—namely, how does
ethnic polarization or fragmentation degenerate into violence? When do people get to act? An
important factor here is the media, in particular when manipulated by an extremist government to
spread hatred. Joseph Göbbels, Hitler’s minister of propaganda, once said that radio was “the most
important instrument of mass influence that exists anywhere” (Welsh 1992, cited in YanagizawaDrott (2014).
Yanagizawa-Drott (2014) looks at a particular instance where the influence of a radio on civil
violence was drastic: The 1994 Rwandan genocide, in which an estimated 500,000 Tutsis (the
minority) were massacred by the Hutus (the majority). The genocide was triggered by a terrorist
attack in which the plane of the Hutu Rwandan president, Habyarimana, was shot down. Soon after,
an extremist Hutu movement called “Hutu power” took power and actively called for the killing of
all Tutsis, stating that such killing would not be prosecuted and would actually be encouraged.
Rwanda had two radio stations, Radio Rwanda and Radio Television Mille Collines (RTMC). The
latter began broadcasting in 1993 and quickly became the most popular radio station, but also the
most inflammatory, regularly spreading violent anti-Tutsi messages. For instance, the Tutsis were
not referred to as Tutsis but as “cockroaches”, and messages included fabricated accounts of Tutsi
atrocities against Hutus portraying the latter as victims fighting for their self-defense.
In order to assess and quantify the effect of RTMC on mass killings, Yanagizawa-Drott uses a very
powerful identification strategy using variations in radio reception across Rwanda’s hilly terrain.
The idea is very simple. RTMC had two transmitters; villages that were facing one of the
transmitters had good reception, whereas villages on the other side of the hills had weaker
reception.4 The data is made of geo-referenced cells of extreme precision (90 m  90 m). Data on
the local incidence of violence come from subsequent prosecutions by so-called Gacaca courts,
which prosecuted 77,000 individuals for participation in militia violence and 433,000 for individual
violence.
While radio reception had no effect on individual violence, the effect on militia violence was highly
significant and quantitatively very large (Table 6). Going from no reception at all to full reception
yields a predicted increase in the number of people prosecuted for any type of violence (individual
or militia) of 62-69%.
Table 6: RTML’s effect on militia and individual violence
4
The characterization of reception areas in his paper is actually more complex and uses radio propagation software
available in ArcGIS.
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Source: Yanagizawa-Drott (2014), Table III
Interestingly, a “placebo exercise” on the influence of Radio Rwanda (which was much less
inflammatory) yielded non-significant estimates, confirming that it was the hate messages spread by
RTMC that had a causal effect on massacres.
Table 7: Radio Rwanda had no effect
13
Source: Yanagizawa-Drott (2014), Table III
Last noteworthy result, militia violence spread to neighboring villages, magnifying the impact of
RTMC, so that the quantitative effect, in the end, was massive.
Table 8: Spatial spillovers on militia violence
Source: Yanagizawa-Drott (2014), Table III
This clean identification of the effect of the media on violence is very compelling, suggesting that
they have huge influence and responsibility for civil violence and stand as a key mediating element
in the transformation of latent factors of distrust, like ethnic fragmentation or polarization, into the
acting out of violence.
1.4 Conflicts and history
It has been often observed that the strong ethno-linguistic fragmentation of modern African states is
largely the result of arbitrary borders negotiated between European colonial powers at the turn of
the XXth century with little regard to the continent’s ethnic and political structure, as European had
at the time only fragmentary information on all that and did not care that much (Figure 6). For
instance, Lord Salisbury thus described the process: “we have been engaged in drawing lines upon
maps where no white man’s feet have ever tord; we have been giving away mountains and rivers
and lakes to each other, only hindered by the small impediment that we never knew exactly where
the mountains and rivers and lakes were” (cited in Michalopoulos and Papaioannou, 2011).
Figure 6: Africa’s national borders have little to do with the continent’s ethnic structure
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Source: Michalopoulos and Papaioannou (2011).
Ethnic groups being split across national borders can be a cause of violence for many reasons. For
instance, they may have “irredentist” demands (i.e. demanding their re-unification into an
independent country); they may engage in smuggling across the border and thus attract repression;
they may—and often are—discriminated against by dominant ethnic groups in power. Any of these
channels can lead to violence, and indeed, Michalopoulos and Papaioannou (2011) show that split
ethnic groups have suffered a disproportionate incidence of civil wars. The trick in their
identification strategy is the usual one: By focusing on sub-national entities, they could both use
time-invariant characteristics (split ethnic group) while at the same time control for a bunch of
country-level factors through country fixed effects.
For all the evil done by the arbitrary partitioning of the African continent by the British and the
French, the historical roots of African violence seem to go much further in town. Using a similar
identification strategy (using sub-national cells and controlling for country-level events through
country fixed effects, Besley and Reynal-Querol (2013) show that between recorded wars between
African kingdoms between 1400 and 1700 have a significant influence on contemporary violence.
Note that in both cases, the fixed effects control for time-invariant country-level shocks; not for
time-variant country-level shocks like in Berman et al. (who use much more powerful country-time
fixed effects). Therefore in both these papers, the scope for confounding time-variant influences at
the country level is still uncontrolled.
2. The effect of conflicts
We all know that conflicts destroy wealth and capital. A number of papers suggest that they also
have long-lasting destruction effects on social capital.
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References
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Djankov, Simeon, and M. Reynal-Querol (2010), “Poverty and Civil War: Revisiting the
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