Civil War and Economic Growth
James D. Fearon and Francine Anene
Department of Political Science
Stanford University
Stanford, CA 94305-6044
August 28, 2005
1
Introduction
Cross-national data and fancy statistics are hardly necessary to establish that civil war can have
horrible consequences for the people living in affected countries. The most lethal seven or eight
civil wars of the last 60 years have killed millions each. The next 15 to 18 killed more than 100,000,
and 50-odd wars yielded fatalities between ten and one hundred thousand.1 Refugee flows caused
by civil war are typically an order of magnitude greater than the number killed.2 Ghobarah, Huth
and Russett (2003) show that civil wars have seriously negative consequences for a population’s
health even for years after the war ends.
It is equally obvious that a large civil war can devastate a country’s economy. Recent cases
in point include Afghanistan, Somalia, Sierra Leone, and Liberia. Given such examples and the
remarkable prevalence of civil wars in the last 15 years, it is a natural next step to see violent civil
strife as a major obstacle to the economic development of the poorest countries, an important cause
of the extreme poverty of the “bottom billion.” For example, this is one of the central arguments
of the World Bank publication Breaking the Conflict Trap: Civil War and Development Policy by
Paul Collier and his collaborators at the Bank (Collier et al. 2003).
To date, however, there is surprisingly little research on the economic consequences of civil
1
These figures are based on the data collection effort discussed in Fearon and Laitin (2003). Some recent studies
(e.g., Roberts (2001) suggest that estimates of civil war-caused deaths are probably too low on average, especially if
one takes into account people who die as a result of starvation or disease that would not have occurred in the absence
of the war.
2
See, for example, the estimates in the appendix to Gurr (1994).
1
war. Much more has been written on the determinants of civil war onset and duration. Collier
(1999) and Sandler and Murdoch (2004) provide estimates of how civil war affects a country’s
growth rate by running regressions on a cross-section or panel of 70 to 90 countries. Controlling
for other variables, these papers correlate growth rate over a period of time (five, ten, or thirty
years) with measures of the presence or amount of civil war in the same period. Collier estimates
an average annual reduction in growth rate of 2.2%, while Murdoch and Sandler estimate that the
“short-run” effect of civil war is to reduce the growth rate by approximately 1% per year.3
In this paper we use annual data on civil war and growth rates for a larger set of countries
and years to examine how the relationships between war and growth differ across and within regions. While, unsurprisingly, countries do grow more slowly during civil war years on average,
the variation around the average is surprisingly large. Quite a few countries, especially in Asia,
have grown robustly during civil wars, in some cases even faster than when they were at peace.
We argue that the evidence suggests three typical patterns in the way that income per capita
evolves in civil war countries:
1. Development disasters. Some countries (typically in subSaharan Africa) were already on a
sharply downward income trajectory before the civil war started, and the war merely continued or aggravated this trend. For these cases it is at least as plausible that civil war is a
symptom of prior economic and institutional failures that led to economic decline as it is that
civil war was a random shock that knocked the country into a development trap.
2. Interruptions. Some countries show sharp declines in per capita income during the war, but
recover quickly after the war ends and seem to return to their prior growth trajectory. This is
the pattern neoclassical growth theory would predict. If war affects the economy primarily
by destroying or diverting physical and human capital during the war, then after the war the
marginal return to capital will be high and the growth rate accordingly higher. There is no
conflict trap in such cases, though war certainly has negative economic effects.
3. Peripheral insurgencies. Some countries, especially in Asia, maintain steady positive growth
paths during and after civil war. Often these are civil wars involving an ethnic minority on the
periphery of the country, fighting a war that may be devastating for the region but may have
little effect on the rest of the economy.
3
They give the figure of a 5% lower income for a five year period. Sandler and Murdoch include as controls
measures of investment in human and physical capital, which are clearly pathways by which civil war affects growth.
So their 1% figure should be seen as some sort of “direct” effect (though it is not clear what this means in this context).
See also Koubi (2005), who combines interstate and civil wars together in a cross-section to estimate the effect of war
experience on growth from 1960-89. She finds a negative impact that depends largely on the presence of the civil wars
in the sample.
2
2
The data
We begin with a sample of the 183 countries, which includes the 173 countries with economic data
in the Penn World Tables 6.1 (PWT6.1), and the ten countries missing from PWT6.1 that had a
population greater than 500,000 in 1990.4 For estimates of growth rates we use PWT6.1, which
gives purchasing-power-parity adjusted per capita income estimates in 1996 U.S. dollars, and an
income series based on PWT5.6 estimates (which are in 1985 dollars). For the latter, the PWT5.6
numbers are extended forward and backward using World Bank estimated growth rates and with
imputed values for other missing values using estimates of per capita energy consumption where
possible.5 The former series has data on 73% of the 7,409 country years from 1945 to 2000, the
latter on 86%.6
For data on civil war we use a slightly updated version of the list discussed in Fearon and
Laitin (2003), which covers 1945-2000. A civil war here means an armed conflict between the
state (or claimants to the state) and an organized group that kills at least 1000 over its course, with
an average of at least 100 per year and at least 100 killed on each side.7 For most cases, identifying
war starts and ends is straightforward. One codes when the killing starts and when it ends. In a
smaller set of cases the conflict begins slowly or erratically, parties may change, and the end may
be unclear. Fearon (2004) discusses the problems that arise and the set of rules used for these data.
Our list has 118 civil war starts in the period 1945-2000. About 13% (963) of the country
years have a war in progress for some or all of that year. One hundred and twelve countries had no
civil war at all, 71 had at least one. Forty two of the 118 wars have no economic data in PWT6.1
for any years the war was in progress; the number is only six using the PWT5.6-based data.
The last observation raises several questions about the quality and representativeness of the
economic data. Consistent with the pattern of missing cases for PWT6.1, it seems reasonable
that the bigger and more destructive the civil war, the harder it is to collect national accounts
data (possibly because the state in question is falling apart or has to focus on survival rather than
4
These ten are East Germany, Czechoslovakia, Bosnia, Libya, North and South Yemen, Afghanistan, North Korea,
Burma, and South Vietnam.
5 See Fearon and Laitin (2003) for details on the latter income series. Two thirds of the country years with data
are from PWT5.6, about one sixth from the World Bank growth rates, and about one sixth from the imputation using
information on energy consumption.
6
The two series are highly correlated, at .985, although this is a bit deceptive. At the country level, for one fifth of
the 130 countries with data on both measures the correlation is less than .67.
7 For further definitional conditions see Fearon and Laitin (2003, 76n4). For the analysis in this paper we omit the
problematic anticolonial wars fought by Britain, France, and Portugal in their various African and Asian colonies. See
the discussion in Fearon and Laitin (2003).
3
income statistics). Using PWT6.1 would then lead us to underestimate the negative effects of
civil war by omitting the experience of the worst cases. The PWT5.6-based series has much better
coverage, but even here it seems plausible that the measurement error for civil war periods and civil
war countries is significantly greater. Another, contrary, possibility is that if civil war shrinks the
formal sector and expands the informal sector, then accounts data collected in civil war countries
will understate actual incomes and so overestimate the negative effect of civil war. On top of these
civil war-related issues, there is the more general problem that the quality of the income estimates
for the poorer countries (where most civil wars take place) is typically poor, whether due to low
bureaucratic capacity, low literacy and formal accounting practices, or politicization.
There is not much that can be done except to use multiple estimates (here, the two PWTbased series), to pay careful attention to possible systematic biases (for example, by region), and
to outliers and particular cases that drive results.
3
Some bivariate and regional patterns
Table 1 summarizes information about the distribution of growth rates for all peace and war country
years in the sample. For both data series, the median growth rate in a war year is .6% lower than the
median rate in a peace year. This difference is small relative the range of estimated annual growth
rates, which are rather volatile (or erratically measured). But .6% per year would be substantively
significant for a war of moderate duration.
The gap in mean growth rates between peace versus war years is much larger, at 1.3% and
1.6% for the less and more complete growth series, respectively. The reason is that war does not
simply shift the distribution of growth rates downward by a fixed amount, but instead associates
with a markedly higher frequency of very bad years. As is clear from Table 1, the distribution
for war years is skewed somewhat to the right (fatter left tail).8 One might be inclined to see
the extreme values as “outliers” to be dropped from an analysis, and this might be part of the
justification for the standard practice in the growth literature of averaging over periods of five or
ten years. But as we will see in more detail below, the data suggest that particular war years are
systematically more likely to have extremely bad growth performance, so that for this analysis
simply dropping them as uninformative anomalies would be a mistake.
A last point evident from Table 1 is that many states grow quite well during some war years.
One quarter of all war years saw growth over 4% per year, which is almost twice the median rate.
Curiously, some of the fastest growing years in the data occurred during war.
8
Both annual growth rate series are also highly kurtotic (i.e., has fat tails). Kurtosis is 3 for a normal distribution; it
14.5 using PWT6.1 and 27.7 using PWT5.6+ !
4
Table 1. Distribution of annual growth rates by peace/war year.
1%
5%
25%
PWT6.1 data
All years Peace
War
-17.47 -15.67 -22.17
-7.72
-7.26 -12.04
-0.72
-0.63
-1.64
PWT5.6+ data
All Peace
War
-20.21 -17.89
-36.21
-9.25
-8.58
-13.99
-0.67
-0.45
-2.29
50%
Mean
2.24
1.89
2.30
2.05
1.73
0.79
2.29
1.97
2.40
2.20
1.83
0.60
75%
95%
99%
4.84
10.07
18.96
4.94
10.27
18.44
4.16
8.73
19.56
4.96
11.45
22.72
5.03
11.47
22.05
4.27
11.32
26.04
Table 2 presents the differences in average growth in peace and war years by region.9 The
first number that jumps out is the -20% per year difference in peace and war year growth rates
in Eastern Europe and the Soviet Union, using the more complete PWT5.6+ series. Several wars
broke out in the wake of the Soviet collapse, and at the same time the “transition” from socialist
economies took an enormous toll across the region. (PWT6.1 is missing data for most of these
countries in this period, hence the huge difference in estimates.) One might suspect that the 20%
figure is an artifact of comparing a time of economic collapse and a few wars to the more peaceful
and high-growth period of Eastern Europe and the former Soviet Union after World War II. But
this proves incorrect. Even comparing the war years to peace years in this region for 1991 through
1996, we also find a huge difference. Most of these countries suffered catastrophic economic
declines after 1991, but the several countries with civil wars – Yugoslavia, Moldova, Georgia,
Azerbaijan, Tajikistan, and to a lesser extent Russia – were by far the hardest hit.10
A second observation is that because there is hardly any civil war in the West in this period
– the long Northern Ireland conflict and the short Greek civil war – the estimates from this region
are not very informative or useful. In the analysis that follows, we will typically avoid pooling
the Western and Eastern European countries together with the others when examining patterns that
extend across regions.
9
“EEur” includes the former Soviet countries; “West” includes Japan; Sudan is coded in subSaharan Africa.
10
The estimates for these years in this region are based on the World Bank’s WDI growth rate series. They are so
large in some cases that it is difficult to know whether to believe them. For example: Yugoslavia 1991, -51%; Moldova
1992, -34%; Georgia 1992, -51% and -50% the next year; Azerbaijan 1992, -45%; Tajikistan 1992, -36%. Even if we
use medians instead of means, however, there remains a large difference in growth rates for war versus peace years in
the region and time period.
5
Third, notice that among the four regions in which civil war is quite common, Asia stands
out for having had a smaller difference in growth for peace vs. war years (using either series). Note
also that Asia had a much greater percentage of country years with civil war. This results from a
high frequency of wars per country, which were of longer average duration than those in the rest
of the world as well. Below we argue that the type of civil war often fought in Asia has been less
destructive to the economy and less symptomatic of institutional dysfunction at the center.
Table 2. Difference in Avg. Peace/War Growth, by Region
Region
West
EEur
Diff.
-0.81
1.24
Asia
NA/ME
SSA
LA/Ca
-0.89
-2.39
-2.07
-1.21
Region
West
EEur
Diff.
0.04
-19.98
Asia
NA/ME
SSA
LA/Ca
-0.44
-1.51
-1.94
-1.91
PWT6.1 data
p value # ctry yrs
0.057
1125
0.819
235
# war yrs
31
6
% war yrs
0.03
0.03
219
65
211
131
0.32
0.14
0.14
0.11
PWT5.6+ data
p value # ctry yrs # war yrs
0.973
1107
35
0.000
568
23
% war yrs
0.03
0.04
0.008
0.009
0.007
0.040
695
457
1495
1219
0.402
0.126
0.004
0.001
1021
821
1515
1165
342
104
253
137
0.33
0.13
0.17
0.12
Notes: The p value is for a two-sided t test against the null hypothesis
that the difference in growth rates is zero. Number of country and war
years are the number with data on growth.
The preceding tables compared growth rates in peace and war years across countries, so that
a country with no civil war at all, for example, has its growth rates figured into the average or
median for peace years. Table 3 calculates the difference between growth during peace and war
years for each country that had at least one civil war, and then averages (or takes the median of)
these differences. Thus, these figures answer the question, On average, what is the difference in
growth rates between peace and war years within a country that had a civil war?
The within-country comparisons generally show much larger, negative differences for growth
in war versus peace years. Using both growth series, war-affected countries in SubSaharan Africa,
6
North Africa/Middle East, and Latin America grew at least 2.25% more slowly per year during
war, but as much as 6% less in North Africa/Middle East (PWT6.1). The Asian countries look
even more anomalous here, as they actually grew slightly faster on average in their war years
versus their peace years. Once again, the median values are often markedly closer to zero than
the averages, which indicates that there are particular war years with especially steep economic
declines.11
Table 3. Avg. Within Country Growth Difference, War vs. Peace years
PWT6.1 data
Region
PWT5.6+ data
Avg. Diff Median Diff
#
Avg.
Median
#
World
-1.86
-0.47
45
-4.68
-0.98
65
West
EEur
-0.39
10.63
-0.39
10.63
1
2
3.03
-30.14
3.03
-29.17
2
6
Asia
NA/ME
SSA
LA/Ca
1.23
-5.93
-3.22
-2.25
1.37
-1.34
-2.50
-1.68
10
8
16
8
0.82
-4.48
-2.49
-3.91
0.39
-2.38
-1.43
-1.72
15
10
20
12
Notes: For each country with a civil war, we take the difference between average
growth in war and peace years, and then average over countries within each region.
The “median” column is the median of the country average differences. “#” is the
number of countries with a civil war and data.
Figure 1 visually summarizes the within-country data. A couple of the oddly large positive
estimates may result for using per capita energy consumption to impute a GDP estimate (Yemen
People’s Republic in the 1986-87 war, Korea regarding the war in the South prior to the Korean
War). Possibly the energy measure picks up war-related energy imports in these cases. Nigeria’s
economy did better on average during the Biafra war than before or after. Although growth plummeted 19% in 1967 (both series), oil exports must have come on line by 1969, causing two years
of rapid increase in total GDP (26% and 20%, by PWT5.6).
Another interesting feature evident from Figure 1 is that outside of Eastern Europe the four
countries that had the steepest drops in income during war were countries with sharp one-year
internal conflicts with third-party interventions: Cyprus 1974, which was an internationalized civil
11
The estimates for the Western and Eastern European/FSU countries are not very informative due to the small
number of war countries or the missing data issue noted above for PWT6.1.
7
war; Jordan 1971, which was also internationalized with Syrian intervention; Guinea Bissau in
1998-99, in which Senegal intervened to support the government against the coup leader and rebel
group; and the Dominican Republic in 1965, with U.S. intervention.
Overall, Figure 1 bears out the preceding tables: Either no effect or better performance on
average during civil wars in Asia; moderately bad performance during civil war in the three other
regions.
Fearon (2004) notes that Asia has had longer civil wars on average, and that they have often
been small-scale guerrilla wars in which a coherent central state dominated by a large ethnic group
fights against rebels from a small, peripheral ethnic minority. Often these wars show a “sons of
the soil” dynamic (Weiner 1978; Fearon and Laitin 2000). They involve conflict between landhungry majority group settlers and the local “sons of the soil,” or between the locals and a central
government over control of a regional natural resource such as oil. Perhaps Asian civil wars may
have been less economically destructive because they have often been smaller in magnitude and
fought on the margins of the core national economy.
Pursuing this hypothesis, Table 4 shows how growth rates have changed within countries
during different types of civil war, using the PWT5.6+ series for the four regions Asia, North
Africa/Middle East, subSaharan Africa, and Latin America.12 Three types are distinguished: (1)
wars in which the rebel leadership sought control of the central government, and did not organize
and recruit its forces along ethnic lines; (2) wars in which the rebel leadership sought control of the
center, and did organize and recruit along ethnic lines; and (3) wars in which the rebel leadership
espouses secession or greater autonomy for an ethnic group in a particular region of the country.13
12 The estimates are quite similar using PWT6.1. They also show the same pattern, though more extreme, when we
take the average instead of the median across wars of each type.
13
We used the codings for rebel aim and ethnic war discussed in Fearon and Laitin (2003), attributing the “ambiguously ethnic” conflicts to ethnic wars.
8
Table 4. Median Within-Country Difference in Growth by Type of War
Rebel Aim
Capture Center Secession/Autonomy
Not Ethnic War
-1.26
33
-1.26
33
Ethnic War
-3.32
27
0.23
35
-1.12
62
-2.14
60
0.23
35
-1.25
95
Notes: The bottom number in each pair is the number of civil wars of that type with data.
For the top number, we compute average growth during the war minus average growth
during peace for that country, and then take the median of the values for wars of that type.
The evidence in Table 4 supports the hypothesis that the “Asia difference” may stem from
ethnic conflicts over autonomy or secession being more common there. Autonomy-seeking conflicts have typically been much less destructive to economic growth than have conflicts over control
of the central government.14 Interestingly, ethnic wars in which the rebels aim at the center (e.g.,
UNITA in Angola) have been more destructive on average than “ideological” civil wars (e.g., El
Salvador). To some extent this is a contrast between civil wars in Latin America, which were
largely ideological, with cases in subSaharan Africa, which have been mainly fought for control of
the center and along ethnic lines.
A reasonable conjecture is that the weak condition of central governments in Africa encourages rebels from ethnic minorities to aim at the center, whereas the stronger central governments in
Asia favor secession and autonomy-seeking efforts by ethnic minority rebels there. Table 5 gives
the distribution of types of war by region.15 Note that in Asia, the modal civil war is an ethnic
secession or autonomy-seeking rebellion; in subSaharan Africa, a ethnically organized fight for
the center; and in Latin America, an ideological war for the center.
14
A two-sided Wilcoxon test rejects the null that the “capture center” and “secession” growth differences come from
the same distribution at the .002 level; the p-value for a two-side t test on the difference in means (-3.2 vs. -.59) is .04.
15 We recognize the problems involved in coding type of war. Several cases in Africa, for example, are ambiguously
“ethnic” or saw changes in the aims of the rebels over time (e.g., Mozambique, Sierra Leone, Ethiopia, Somalia). The
Guatemalan civil war can be viewed as ethnic or ideological or both. We err here on the side of too much clarity
to avoid cluttering up the tables with categories for “mixed/ambiguous,” which at any rate do not change the main
patterns visible here.
9
Table 5. Types of War by Region
Region
Ideological
Ethnic/Center
Ethnic/Autonomy
Asia
NA/ME
SSA
LA/Ca
12
8
0
14
1
4
25
1
21
6
11
0
Median Intensity
69
99
6
Notes: Table gives number of wars by type and region in the top portion. Intensity is the
median number killed per year per 100,000 population.
All but perhaps one of the 12 civil wars in this period in Eastern Europe/FSU were secessionist. Further, as noted above, the six with some economic data appear to have been highly destructive
of the national economy (Table 2). This exception may “prove the rule,” however. In contrast to
the ethnic separatist wars in the rest of the world, the post-Soviet civil conflicts were almost all
fought by conventional and militia forces rather than by bands of rural guerrillas. Conventional
armies make for shorter, higher intensity wars than do small irregular forces.
This proposition can be tested by looking at how economic growth varies with a measure of
war intensity, which we define here as the number killed per year per 100,000 population. For all
types of war, the median intensity in Asia was six, which compares to a range from 42 for Eastern
Europe/FSU to 77 for Latin America. For ethnic autonomy civil wars, the median intensities are:
Asia, 2.5; Eastern Europe, 42; subSaharan Africa, 12; North Africa/Middle East, 8; Latin America,
no cases. Thus, Eastern European secessionist wars were of far greater intensity than autonomyseeking civil wars elsewhere. The last line in Table 5 shows that in general, ethnic autonomy civil
wars have been of much lower intensity than the other two types. Note also that the pattern of
intensities mirrors the pattern of growth differences seen in Table 4.
Figure 2 shows the overall relationship between growth rate during a war (versus peace years
in that country) and the intensity of the war (intensity is on a log scale). The regression line implies
that increasing intensity ten-fold increases the gap between war and peace growth by almost 2%
per year on average.16
To sum up, growth rates during civil war year are, unsurprisingly, a good bit lower on average than during peace years. However, the range of variation is extremely large, and somewhat
The regression line is growth difference = −1.27 − .86 log(intensity), with a p value of .048 for intensity, and
an N of 98 wars. Dropping the Eastern European/FSU cases, which have extreme values, has little effect on the
coefficient but greatly improves the fit.
16
10
systematic. Civil wars in Asia correlate with little change in growth rate, or even a positive change
on average. We found that Asian civil wars have typically been ethnic separatist struggles, and that
such conflicts have everywhere but Eastern Europe/FSU produced fewer deaths per capita and year
and have associated with smaller changes in economic growth. By contrast, ethnic or ideological
contests for the center have been more intense in terms of deaths and much more costly in terms
of growth. The post-communist civil wars are actually consistent with this more general patterns –
they were fought more as conventional than guerrilla wars, and they produced far higher fatalities
per capita per year than separatist wars elsewhere. In general, greater war intensity associates with
bigger economic declines.
We have tried to be careful to avoid causal language in the preceding. It is natural and
reasonable to believe that some large portion of the economic declines observed during war years
was in fact caused by the war. However, war may also be partly as symptom or by-product of
economic decline that would have happened anyway. That is, the growth rates of some of these
countries might have been low and declining during war years even if there had not been a war.
This hypothesis is somewhat less plausible if growth resumes after the war ends. We consider this
issue in more detail in section 5 below.
4
Multivariate results
In the empirical growth literature, researchers usually average growth rates over five- or ten- or even
thirty-year periods for each country, creating a straight cross-section or a panel with two to eight
“waves.” The usual justification is to reduce measurement error that might arise from business cycle
effects. This procedure is less appealing when the objective is to estimate the effect of civil war on
growth rates, since averaging war years (or months) makes for an ecological regression of average
growth on some function of war experience.17 Since both growth rates and war involvement are
coded annually, it is more direct to use annual data and to try to control for other factors such as
business cycle influences. This approach also makes sense in light in the suggestion above that
particular war years may involve particularly large income declines.
A second issue for a multivariate analysis is what other determinants of growth should be
controlled for. Empirical growth theory in economics is large research area that has developed a
list of commonly used independent variables, most of which are motivated or justified by appeal
to variants on the Solow-Swann growth model.
[ ... more to come ...]
17
That is, the goal is to draw an inference about the causal effect of a month or a year of war, but in an ecological
regression this is done by using units that mix months or years of peace and war.
11
5
Patterns of economic growth in civil war-affected countries
The preceding sections compared growth rates in war versus peace years to estimate the magnitude
of the differences, and how these have varied across regions, types of wars, and timing within wars.
Of equal or perhaps greater interest is the question of how economic growth evolves after a
civil war has ended. If countries tend to bounce back, quickly resuming a prior upward trajectory,
then civil war would arguably be less important as an object of economic development policy than
if civil wars tend to be followed by continued flat or negative growth. The latter might suggest
enduring negative effects, and the possibility of a development “trap.”
Looking at before-, during- and after-war growth patterns can also help us address the endogeneity problem mentioned above: civil war might be a symptom of economic decline that would
occur even in the absence of civil war, so that we are overestimating the causal effect if we attribute all of the peace/war growth differences to war. This is more plausible if we often observe
countries that were in economic decline prior to the onset of civil war, and that merely continue the
decline during the war. It would seem less plausible as an important regularity if we more often
observe countries with decent economic growth prior to war that are “shocked” by war’s onset onto
a different and worse growth path.
Consider the following possible paths of per capita income in a country that gets a civil war.
1. Interruption: We might observe flat or increasing income prior to the war onset, a sharp
decline during the war, and a rapid recovery at the end of the war. This pattern is consistent
with a simple application of economic growth theory – war destroys or displaces human and
physical capital, increasing capital’s marginal return when the war ends, and thus raising the
growth rate while the country returns to its long-run equilibrium path.18
2. Shock with persistent negative effects: We might observe flat or increasing income prior to
war onset, followed by a negative shock during the war, and a failure to recover per war
growth rates post war. This would be a “trap” profile.
3. Continuation of pre-war decline: We might observe years of declining income prior to war
onset, and continued decline during and after the war. This pattern would suggest, albeit
not conclusively, that war was a symptom of prior economic and probably institutional failure. The war surely added to the decline, but some significant portion of it would probably
have occurred anyway. Further, since the war is in some significant part a symptom of prior
political and economic problems, ending the war is less likely by itself to make for growth.
18
This is the typical pattern observed for countries devastated by interstate wars (Organski and Kugler (1980); see
also Koubi (2005) for a good review of this literature).
12
4. No clear effect/continued growth: As we have seen above, some countries’ economies seem
to grow quite well during civil war. In such cases we would expect to see increasing income
before, during, and after the war. As argued, this should be most likely in countries with civil
wars that were peripheral insurgencies.
In principle one might devise multivariate specifications of regression models like those
considered in the last section to examine how growth paths sort into these various categories (or if
they do not fit them well at all). We take a different approach here, using the graphics to present
the data for visual inspection. As Figures 3a-f reveal, growth paths can be quite complicated. It
is much easier and probably less error-prone to sort them into categories based on case-by-case
assessment rather than trying to devise an algorithm that would average over fixed pre- and postwar periods. Nor would an algorithm easily take account of likely measurement errors in start and
end dates, or the varying quality of the different sources of income data.
Excluding the West and Eastern European/former Soviet countries, the graphs in Figures 3af plot the two income series for each country that had at least one civil war onset between 1945 and
2000. The PWT6.1 data is displayed in green. PWT5.6+ is red for years with data from PWT5.6,
and black for years with data from the World Bank or from the energy consumption estimates. The
blue horizontal lines at the bottom mark years in which a war is coded as ongoing.
Table 6 then shows how we coded the growth paths according to the four profiles just described. A few countries are listed in more than one category because of multiple wars, or in a
few cases because one might code a particular war either way. Question marks indicate the latter,
along with three other cases where the fit to the pattern is not so sharp. Overall, three of the four
categories proposed above fit easily and naturally a large majority of the cases. (Readers can check
to see if they agree by comparing codings in Table 6 to Figure 3.)
13
Table 6. Patterns of Before-, During-, and After-Civil War Economic Growth
Interruption
Cuba∗
Dominican Rep.
Guatemala (80s)
El Salvador
Peru?
Argentina?
Guinea-Bissau
Senegal?
Nigeria
Uganda (80s)
Burundi?
Rwanda
Ethiopia?
Mozambique
Zimbabwe (70s)
S. Africa?
Algeria?
Cyprus
Iran
Lebanon
Jordan
S. Korea
Pakistan (71)
Cambodia
Pre-war & cont. decline
No effect/Peri. Ins.
Guatemala (70s)
Colombia
Costa Rica
Trap?
Nicaragua
Bolivia
Liberia
Sierra Leone
C. Afr. Rep.
Chad
Congo
D.R.C. (90s)
Somalia
Senegal?
Uganda (90s)
Ethiopia?
Burundi?
S. Africa?
Morocco
Turkey
Iraq
Yemen A.R.∗
Yemen P.R.∗
Papua N.G.?
Afghanistan∗
China
India
Pakistan (90s)
Bangladesh
Burma
Sri Lanka
Nepal
Thailand
Laos∗
S. Vietnam∗
Philippines
Indonesia
Other patterns or no discernible pattern
Decline, war, recovery: Mali, Senegal?
No discernible pattern or conflicting or absent data: Haiti, Paraguay, Djibouti, Angola,
Zimbabwe (80s), Sudan, Afghanistan.
∗
energy consumption data only for war period.
The three paths that we see repeatedly are interruption, pre-war decline/continued decline,
and no effect/peripheral insurgency. Interruption is common in all regions except Asia. Pre14
war/continued decline has occurred almost exclusively in subSaharan Africa, and, as hypothesized
above, no effect/peripheral insurgency has occurred mainly in Asia. By contrast, the “trap path,”
wherein the country grew before the war but does not return to growth after it ends, appears infrequently and ambiguously.
6
Conclusion
Altogether, the evidence we have collected and analyzed supports the following hypotheses.
In countries with a reasonably coherent central government, a civil war directed at the center
is most likely to cause an interruption of growth (possibly quite serious) for the duration of the war.
But growth is likely to return, initially at a higher pace, after war ends.
If the war is an ethnic struggle for secession or autonomy, the national economy often continues to grow through the conflict, showing little apparent negative effect.19 This has been the
standard pattern for civil wars in post-45 Asia, where such “peripheral insurgencies” have been
the norm. But separatist conflicts in other regions tend to show the same pattern, except for Eastern Europe/FSU in the early 1990s, where the secessionist wars were fought as conventional or
militia-based conflicts rather than as rural guerrilla wars.
Finally, when the central government and the national economy are already on the road to
ruin, civil war may emerge as an effect of prior institutional failure, an effect that surely worsens the
economic decline. But ending the war in these cases would not necessarily return the country to a
positive growth path, because the same institutional disorder that helped bring on the initial decline
may well still be present. Although there have been many “interruption” cases in subSaharan
Africa, “pre-war decline” cases are increasingly common there, and have not yet appeared with
any frequency outside of this region. The median year of war onset for the “interruption” cases
in subSaharan Africa was 1982, whereas the median onset year for the “decline” cases was 1991.
The growth path for a number of other African countries – and most worryingly, for Nigeria, which
already has very high levels of communal violence – suggests that more such cases may be on the
way. Indeed, Ivory Coast’s 2002 war and its prior growth path (steep economic decline since 1980),
is another striking example of this pattern.20
By paying closer attention to regional variation and variation related to types of wars and
19 That is, the difference between growth in war years versus peace years is not large or is even positive, as we saw
for many of the Asian countries with peripheral insurgencies.
20
PWT6.1 has per capita income in Ivory Coast falling steadily from a high of $3,000 in 1979 to $1,869 in 2000.
Our data only goes to 2000, hence the omission of the recent Ivory Coast civil war in the analysis above.
15
their magnitudes, we get a much clearer and more refined picture than we do from the standard
approach of regression analyses that pool observations across continents and jump to multivariate
specifications before thoroughly examining the bivariate patterns. We also get an evidence-based
analysis that has suggestive policy implications for each type of war and growth path.
First, international efforts to end a center-seeking civil war in a state with a coherent central
government can reasonably expect to have major positive welfare benefits, if successful. Past
evidence suggests that in these cases the civil war often prevents the country from developing, and
further that ending the war would allow growth to resume quickly. Unfortunately, interest in, and
permission for, international intervention is less likely precisely where the central government is
relatively intact.21
Second, though this should certainly not take away from international efforts to end them,
the developmental consequences of ending separatist civil wars will typically be much smaller.
And of course in these cases international intervention efforts often run into major opposition from
the central governments involved.
Third, international intervention is more likely to be feasible in the case of collapsed states,
whose growth-and-war paths were characterized above under the rubric of “pre-war and continued
decline.” Unfortunately, while ending such wars can only be to the good, the evidence does not
suggest that this is the key to putting these countries on a growth path out of a war/poverty trap.
Rather, war in these cases is a symptom of prior economic and institutional failure that peace,
particular if it is imposed via outside intervention, is not likely to fix by itself.22
21 For example, Gilligan and Stedman (2003) show that United Nations peacekeeping missions are much less likely
to be sent for civil wars in countries where the central government has a strong army, and to civil wars in Asia.
22
Fearon and Laitin (2004) made this same general argument without the supporting evidence on economic growth
developed here.
16
References
Collier, Paul. 1999. “On the Economic Consequences of Civil War.” Oxford Economic Papers
51:168–183.
Collier, Paul, V. L. Elliott, Håvard Hegre, Anke Hoeffler, Marta Reynal-Querol and Nicholas Sambanis. 2003. Breaking the Conflict Trap: Civil War and Development Policy. Washington,
DC: World Bank and Oxford University Press.
Fearon, James D. 2004. “Why Do Some Civil Wars Last So Much Longer Than Others?” Journal
of Peace Research 41(3):275–301.
Fearon, James D. and David D. Laitin. 2000. “Sons of the Soil,Immigrants and the State.” Unpublished manuscript, Stanford University.
Fearon, James D. and David D. Laitin. 2003. “Ethnicity, Insurgency, and Civil War.” American
Political Science Review 97(1):75–90.
Fearon, James D. and David D. Laitin. 2004. “Neotrusteeship and the Problem of Weak States.”
International Security 28(4):5–43.
Ghobarah, Hazem Adam, Paul Huth and Bruce Russett. 2003. “Civil Wars Kill and Maim People
– Long After the Shooting Stops.” American Political Science Review 97(2):189–202.
Gilligan, Michael and Stephen John Stedman. 2003. “Where Do the Peacekeepers Go?” International Studies Review 5(4):3754.
Gurr, Ted Robert. 1994. “People Against States: Ethnopolitical Conflict and the Changing World
System.” International Studies Quarterly 38:347–77.
Koubi, Vally. 2005. “War and Economic Performance.” Journal of Peace Research 42(1):6782.
Organski, A. F. K. and Jacek Kugler. 1990. The War Ledger. Chicago, IL: University of Chicago
Press.
Roberts, Les. 2001. “Mortality in eastern Democratic Republic of Congo.” International Rescue
Committee Research Report.
Sandler, Todd and James C. Murdoch. 2004. “Civil Wars and Economic Growth: Spatial Dispersion.” American Journal of Political Science 48(1):138–151.
Weiner, Myron. 1978. Sons of the Soil: Migration and Ethnic Conflict in India. Princeton, NJ:
Princeton University Press.
17
Figure 1. Growth in war minus peace years
10
KOREA
5
NIGER
COSTA
0
CHINA
PAPUA INDIA LAOS
VIETN THAIL SRILA
NEPAL PHILI BANGL
PAKIS
AFGHA
INDON
ALGER
MOROC
IRAQ
TURKE
−5
LEBAN
CAMBO
YEMEA
UGAND
CONGODSOMAL
DJIBO
SENEG SUDAN
CENTR MALI ETHIO
CHAD
ZIMBA SOUTH
CONGOR
BURUN
LIBER RWAND
IRAN
ARGEN
PARAG CUBA
COLOM GUATE
BOLIV
ELSAL
PERU
HAITI
SIERR
−10
MOZAM
NICAR
−15
GUINE
−20
Avg. growth in war minus peace years (% diff)
15
YEMEP
DOMIN
JORDA
CYPRU
Asia
NA/ME
SSA
Region
18
LA/Ca
Figure 2. War Intensity and growth in war versus peace
YEPR
CHIN
INDI
−10
0
5 10
KORS
PHIL
NIGE
DERC
ARGE
UGAN CHIN
BURU
COST
CHIN
INDI
INDI
CHAD DERC
LAOS
PAPN
ETHI
SOMA
SRIL
CAMB
VIES
MORO
DJIB
SUDA
PARA
THAI
INDO NEPA
IRAQ
PAKI
BANG
CUBA
INDO
SENE
TURK
PAKI
CEAR CHIN SRIL
COLO
AFGH
MALI
GUATCOLOUGAN
INDO
INDO
ZIMB
CHAD
SUDA
PHIL
ETHI
ALGE SRIL
PHIL
BOLI
INDO
SOUA
SOMA
LEBA
IRAQ
INDO TURK
ZIMB
YEAR DERC
ELSACONG
RWAN
PERU
IRAN HAIT
NICA BURU LIBE
PAKI
SIEL
MOZA
RUSS
LEBA
YEAR
−20
TAJI
IRAN
BURU
RWAN
GUIB
DOMR
CAMB
JORD
CYPR
MOLD
NICA
−40
AZER
GEOR
−60
Peace growth minus growth during specific war
ALGE
YUGO
0.02
0.14
1
7
55
403
Deaths/Year/100,000 pop. (Cases are wars.)
19
2981
1000
1000
1950
1970
Year
1990
1990
1000
5000
1950
1950
1950
1970
1970
20
1970
Year
1990
Per cap. income
3000
5000
1000 2000 3000 4000
1000
3500
1970
3000
1990
Per cap. income
4000
2500
Per cap. income
1500
1950
10000
1970
1990
6000
1950
1970
2000
6000
Per cap. income
6000
3000
5000
2000
Per cap. income
1500
Per cap. income
4000
1000
1000
2000
Per cap. income
CUBA
2000
4000
Per cap. income
2000
1950
1970
Per cap. income
5000
3000
Per cap. income
1950
3000
Per cap. income
2000
Per cap. income
Figure 3
HAITI
DOMINICANREPUBLIC
1990
1950
1990
1950
1990
1950
1950
1970
Year
Year
1970
Year
COSTARICA
COLOMBIA
PERU
1970
Year
Year
Year
BOLIVIA
PARAGUAY
ARGENTINA
1970
Year
1990
Year
Year
Year
GUATEMALA
ELSALVADOR
NICARAGUA
1990
1990
1990
GUINEA−BISSAU
1990
2000
400
600
Per cap. income
800
1000
900
800
700
500
1980
1975
1985
1995
1960
1970
1980
1990
Year
Year
SENEGAL
LIBERIA
SIERRALEONE
2000
1980
1990
2000
1950
1970
1960
1980
NIGERIA
CENTRALAFRICANREPUBLIC
CHAD
500
1990
2000
1960
1970
1980
1990
2000
1990
2000
1000
500
Per cap. income
1500
Per cap. income
1980
1990
1500
Year
2000
Year
1000
1970
1970
Year
600
2000
1960
1970
1980
Year
Year
Year
CONGOREPUBLICOF
CONGODEMREP.
UGANDA
1960
1970
1980
Year
1990
2000
900
500
700
Per cap. income
800
600
400
200
500
1500
Per cap. income
2500
1100
1960
1000
1990
1000
1970
600
Per cap. income
900
500
700
Per cap. income
1600
1400
Year
1400
1960
Per cap. income
1970
1200
Per cap. income
1960
Per cap. income
MALI
600
Per cap. income
6000 10000
2000
Per cap. income
16000
CYPRUS
1960
1970
1980
21Year
1990
2000
1970
1980
Year
1990
2000
1000
600
2000
1970
1980
1990
2000
1960
1970
1980
Year
DJIBOUTI
ETHIOPIA
ANGOLA
1990
1995
2000
1950
1970
1500
1000
1990
1975 1980 1985 1990 1995 2000
Year
Year
MOZAMBIQUE
ZIMBABWE
SOUTHAFRICA
8000
6000
2000
4000
Per cap. income
2000
1000
800
1200
Per cap. income
1600
3000
Year
1965
1975
1985
1995
1950
1970
MOROCCO
ALGERIA
SUDAN
Year
1990
2000
1970
1980
22Year
1990
2000
700 800 900
4000
2000
Per cap. income
2000
1000
1980
1990
1100
Year
Per cap. income
Year
3000
Year
1970
2000
500
Per cap. income
200 300 400 500 600
Per cap. income
1985
1990
2000
Year
1800
Year
1975 1980 1985 1990 1995 2000
1960
800
Per cap. income
800 1000
400
1990
1400
1980
Per cap. income
600
Per cap. income
800
600
1980
1000
Per cap. income
1970
Per cap. income
SOMALIA
1200
RWANDA
400
Per cap. income
BURUNDI
1960
1970
1980
Year
1990
2000
0
500
1000
1950
1970
Year
1980
1990
1990
15000
1970
1950
4000
1970
1970
23Year
1990
3000
1950
2000
Per cap. income
1600
1990
4000
3000
5000
10000
15000
Per cap. income
1000
Per cap. income
2000
Per cap. income
1970
10000
1960
1200
1200
1970
1950
5000
1950
1990
Per cap. income
800
800
Per cap. income
400
1950
1970
2500
0
Per cap. income
1950
1500
3500
1500
Per cap. income
500
Per cap. income
2000
1000
1000
5000
4000
6000
Per cap. income
3000
7000
8000
5000
Per cap. income
3000
Per cap. income
IRAN
TURKEY
IRAQ
1990
1950
1990
1950
1990 1992 1994 1996 1998 2000
1970
1950
1970
Year
Year
Year
LEBANON
JORDAN
ISRAEL
1970
Year
Year
Year
YEMEN AR
YEMEN
YEMEN PE
1975
1980
1970
Year
1990
1990
Year
Year
Year
1985
AFGHANIS
CHINA
KOREAREPUBLICOF
1990
1990
500
1955
1950
1960
1965
Year
1970
1970
1975
1990
1960
1950
1970
1970
1980
1970
24Year
1990
1990
1500
1950
600
600
800
1000
1000
1400
3000
Per cap. income
2000
Per cap. income
400
1970
1000
1000
200
1950
500
1990
Per cap. income
1200
1970
400 600 800
7000
1990
Per cap. income
5000
Per cap. income
3000
1950
2500
1000
Per cap. income
1970
1500
3500
1400
Per cap. income
1000
Per cap. income
1950
1000 2000 3000 4000
600
Per cap. income
800
500
1500
1200
1600
Per cap. income
1000
Per cap. income
2000
1000 1500 2000 2500
Per cap. income
INDIA
PAKISTAN
BANGLADESH
1990
1975
1990
1950
2000
1960
1950
1985
Year
Year
Year
BURMA
SRILANKA
NEPAL
Year
Year
Year
THAILAND
CAMBODIA
LAOS
1970
1980
1970
Year
1995
1970
1990
Year
Year
Year
1990
VIETNAM,
PHILIPPINES
INDONESIA
1990
2000