Civil Wars and Economic Growth: Spatial Dispersion
James C. Murdoch University of Texas at Dallas
Todd Sandler University of Southern California
This article quantifies the impact of civil wars on economic growth at home and in nearby countries. Three alternative
measures of nearness—contiguity, length of contiguous borders, and distance of closest approach—are used to capture the
spatial dispersion of civil war consequences. We present short-run panel estimates (at five-year intervals) and long-run
(1961–95) panel estimates for the world. Generally, the distance measures, novel to this study, and not contiguity provides
the most accurate measure of the diffusion of the negative economic consequences of civil wars on other countries. Unlike
earlier studies, we also investigate the temporal influence of civil wars on growth at home and in nearby countries. Both the
duration and the timing of civil wars have an economic impact.
T
he 1990s have ushered in an era of civil wars, the
bulk of which are located in the less-developed
countries (LDCs; Sollenberg, Wallensteen, and
Jato 1999). Much attention has been devoted to investigating the determinants (e.g., Collier and Hoeffler 1998,
2002; Doyle and Sambanis 2000; Elbadawi and Sambanis
2002) and the duration of civil wars (e.g., Collier, Hoeffler,
and Soderbom 2001; Elbadawi and Sambanis 2001). Yet
civil war can have a profound negative influence on the
economic fortunes of a country or its neighbors, owing
to a loss of human capital, a destruction of infrastructure,
and reductions in investment, trade, and daily market activities. But with the exception of Murdoch and Sandler
(2002) and Elbadawi and Ndung’u (2000), little effort has
been expended to isolate the dynamic influences of civil
wars on economic growth.
This article extends the approach of Murdoch and
Sandler (2002) by further explicating spatially and intertemporally the relationship between economic growth
and civil war at home and in nearby nations. In particular, novel measures of nearness indicate that the collateral
economic damage of a civil war may extend beyond immediate neighbors. Unlike Murdoch and Sandler (2002), we
demonstrate that the duration and timing of the civil war
within the period of measurement have economic consequences. We scale the civil war variables to better identify
their relative impacts. A battery of tests also distinguishes
between direct and indirect influences of civil wars in both
the short run and long run. We also extend the analysis
into the early 1990s, a period during which the borders
of some countries changed and civil wars in Africa figure
more prominently, and by using World Bank data, enlarge
the country sample beyond the 1961–85 data of the Penn
World Tables (version 5.6; Summers and Heston 1991).
Theoretical Model
To derive the reduced-form growth equation for the estimations, we utilize a Solow (1957) neoclassical growth
model that is augmented to include human capital
(Mankiw, Romer, and Weil 1992) and a civil war influence.
Such models employ a production relationship for which
diminishing returns (i.e., decreasing marginal product)
apply to each input as it is varied in isolation. Constant
returns to scale hold so that as all inputs are varied proportionately, output changes by the same proportion. That
is, a doubling of all inputs leads to a doubling of output.
The augmented Solow growth model hinges on two
components: a neoclassical production function and capital transition equations. The production function relates
income per capita, y(t), to physical capital (i.e., machines,
plants, residential structures, and equipment) per capita,
k(t), and human capital per capita, h(t). Labor is in the
James C. Murdoch is Dean of the School of Social Sciences, University of Texas at Dallas, Richardson, TX 75083 ([email protected]).
Todd Sandler is the Robert R. and Katheryn A. Dockson Professor of International Relations and Economics, University of Southern
California, Von Kleinsmid Center 330, Los Angeles, CA 90089-0043 ([email protected]).
The research for this article was funded, in part, by a grant from the World Bank. All opinions expressed are solely those of the authors
and in no way reflect those of the World Bank. We have profited from the comments of four anonymous referees and the journal editors
on earlier drafts. The data are available from James Murdoch.
American Journal of Political Science, Vol. 48, No. 1, January 2004, Pp. 138–151
C 2004
138
by the Midwest Political Science Association
ISSN 0092-5853
139
CIVIL WARS AND ECONOMIC GROWTH
denominators of the income and the capital input terms,
since everything is in per capita terms. Technological
change is embodied in the improvement in labor effectiveness. The capital transition equations—one for physical capital and one for human capital—indicate that the
growth of physical or human capital equals the share of income saved, s k and s h , and devoted to augmenting physical
or human capital less a composite labor term that detracts
from capital per capita formation. This composite labor
term equals the sum of the growth in labor (n), labor’s enhanced efficiency (g) from technological change, and capital depreciation (). Anything that bolsters labor—labor
growth or its improved efficiency—increases the denominator of capital per capita, and so reduces its growth and
that of income per capita. Depreciation or the gradual
wearing down of capital through use or age also limits
capital growth.
Based on a series of substitutions involving the production function and the transition equations, we can
derive an equation for the long-run steady-state growth
of income per capita (gr):1
g r = a + b1 ln s k + b2 ln s h − b3 ln(n + g + )
− b4 ln y(0),
(1)
where ln denotes the natural logarithm, a is a constant,
bi s are coefficients, y(0) is the initial level of income per
capita, and other terms are as previously defined. In Equation (1), long-run income per capita growth depends
positively on the income shares of physical and human
capital devoted to investment, while this growth depends
negatively on both (n + g + ) and the initial level of
per capita income. Any form of investment augments income per capita growth, while (n + g + ) reduces this
growth by limiting net investment per capita. The initial level of income per capita, y(0), creates a negative
influence on growth, owing to convergence—the process whereby growth in poorer countries outpaces growth
in richer countries as diminishing returns are muted
when capital stocks are smaller (Barro 1991; Barro and
Sala-i-Martin 1992, 1995).2
Migration can be easily incorporated into the model
by merely replacing composite labor terms with (m + n +
g + ), where m is the growth of labor due to migration.
For example, the inflow of refugees from a nearby civil
war can lead to in-migration and may adversely affect in1
The growth rate is the difference of two natural logarithms of
income per capita at two points of time—i.e., an end period t and an
initial period 0, so that g r = ln y(t) − ln y(0). Steady-state growth
is where the growth of physical and human capital is zero.
2
Convergences depend, in part, on countries facing similar production functions and transition equations.
come per capita growth. Migration may, however, bolster
growth if the migrants bring in human capital, so that s h
increases.
From a theoretical perspective, civil wars can adversely affect income per capita growth at home through
a number of avenues. First, a civil conflict can destroy
physical and human capital. Second, by disrupting trade
flows and day-to-day marketing activities, civil wars can
inhibit growth. Third, civil wars may divert the inflow
of foreign direct investment (FDI) owing to heightened
perceived risks of investors. Because FDI is an important
source of savings that finances investment in LDCs, a fall
in FDI results in reduced growth. Heightened instability
and risks will also limit investment at home and cause a
flight of savings abroad. Fourth, civil wars redirect government expenditures from productive social overhead
capital (e.g., roads, schools, and bridges) to less productive defense spending. Fifth, such wars may cause the internal displacement of people as their homes either come
under rebel control or are destroyed, so that income per
capita is adversely influenced. Sixth, civil wars often result
in the breakdown of the health infrastructure leading to
a lack of medical care, less clean drinking water, and reduced sanitation, all of which have negative consequences
on economic activities and growth.
There are analogous considerations that may reduce
income per capita and its growth in countries in proximity
to those experiencing an internal war. Propinquity may
result in collateral damage to infrastructure and capital
from battles fought in neighboring states, especially when
battles are close to the border. Neighboring states must allocate resources to secure their borders from rebel incursions, which direct resources away from more productive
activities. Refugee inflows may stretch a government’s resources thin, while increasing population growth, which
can reduce income per capita. Negative spillovers are anticipated to be greater for nations close to many civil
wars—e.g., central Africa. The diffusion is apt to be beyond immediate neighbors owing to regional economic
integration and regional multiplier effects (Easterly and
Levine 1998). In fact, these regional multiplier effects
can lead to negative economic consequences resonating
through reduced trade and reinforcing flights of capital. Thus, economic impacts may even increase further from some conflicts as nearby countries reduce
trade with others in the region, and potential investors
brand even nonneighboring countries as poor investment
risks. As countries in a conflict-ridden region contract
their economic activities, the multiplier can amplify the
civil-war induced downturn at relatively great distances
from the conflict. Sufficient time may, however, enable
an impacted country to erect a firewall to insulate its
140
JAMES C. MURDOCH AND TODD SANDLER
economy from the harmful effects of nearby long-running
conflicts.
In terms of Equation (1), there are four potential
channels—human capital, physical capital, labor growth,
and an intercept shift—by which civil wars can influence
income per capita growth in other nearby countries. Tests
are conducted to identify the channel. By testing for an
interaction between the civil war spillover measure and
human capital, we can determine whether the channel
is going through the latter. For physical capital, we add
an investment equation that partly depends on civil war
spillovers to ascertain if this is the channel. To investigate the labor growth avenue, we first introduce the civil
war spillover measure as a proxy for migration in the labor term in Equation (1) and then estimate the resulting
equation. When the impact of nearby civil wars is just
a country-specific influence, its effect can be captured
by an intercept term. A nonlinear joint test between the
migration-based and the intercept-based estimates identifies the appropriate channel.
Empirical Specification and Data
The point of departure for the empirical work is Equation (1). For a particular starting point (time = 0) and
observation period (time = t), we can parameterize the
model as:
g r = 0 + 1 ln(y0) + 2 ln(invest) + 3 ln(school)
+ 4 ln(netlabor),
(2)
where gr is the rate of growth in income per capita in
the observation period; y0 is the income per capita in the
initial period; invest and school are, respectively, measures
of the shares of physical and human capital in the observation period; and netlabor is the effective growth rate of
labor plus depreciation (i.e., n + g + ).3 Based on the
parameterized structure above, where the coefficients
are similar for groups of countries, Equation (2) suggests
a regression model:
g r i = 0 + 1 ln(y0i ) + 2 ln(investi ) + 3 ln(schooli )
+ 4 ln(netlabori ) + εi ,
(3)
where the i subscript denotes the country and εi indicates
an unmeasured random country-specific effect.
We are interested in presenting both long-run and
short-run growth estimates of Equation (3). In the former,
the growth of income per capita is for 35 years (1961–
95) and better represents the long-run convergence to a
We follow Mankiw, Romer, and Weil (1992) and set g + equal
to 0.05 for all countries.
3
steady-state level as specified by the theoretical model.
For the short run, Equation (3) forms the basis for the
estimates but involves five-year observational periods, so
that the long-run steady-state assumption, used to derive
the model, is less likely to hold (see Burnside and Dollar
2000; Forbes 2000).
Time-series data that facilitate measurements of gr,
y0, invest, school, and netlabor are readily available. Income per capita (y) is measured as the real GDP per capita
in constant dollars and is available in the Penn World
Tables Mark 5.6 (PWT) for the years 1960–92 and from
the World Bank (http://www.worldbank.org/research/
growth/GDNdata.htm) after 1992. To distinguish the various years, we attach the last two digits of the year; thus,
y60 is the measure of income per capita in 1960. Growth
rates are obtained as the difference in the natural logarithms of the ending and beginning incomes per capita
for the period. The average investment share (invest) is
the arithmetic average of the investment shares over all
years in the period. Annual population growth (n) is
computed as the natural logarithm of the ratio of the
ending value to the starting value divided by the number of years. For example, the annual growth of labor
for 1961 to 1995 is ln(pop95/pop61) ÷ 35. Both the annual investment shares and the population data are from
PWT 5.6, and the corresponding updates are from the
World Bank.
Human capital accumulation (school) is measured
by the percentage of the population older than 25 that
has attained secondary school (see Barro and Lee 2000).
Mankiw, Romer, and Weil (1992, 418) indicate that the
human-capital-augmented growth model may rely on either the rate of human-capital accumulation or the level,
but that some care must be exercised in interpreting the
coefficients. Thus, our model corresponds most closely
with their Equation (12). The attainment data is available
quinquennially with the last two digits of the year identifying the year, so that school60, for example, denotes the
attainment in 1960.
With data on y0, invest, school, netlabor, and economic growth, the parameters of Equation (3) are easily
estimated. Unlike Mankiw, Romer, and Weil (1992) and
others, who are interested in testing the theoretical relationship between the independent variables and growth,
we take this economic structure as given and look for
additional factors, in particular, civil war, that may directly affect long-run and short-run economic growth.
In a standard economic application, such factors are generally lumped into the catchall error term. Our goal is
to offer estimates of the growth effects on the war-torn
country and its neighbors by augmenting the model with
measures of civil war.
141
CIVIL WARS AND ECONOMIC GROWTH
We apply two alternative measures of the presence
of a civil war during the observational period: (i) a
(0, 1) dummy indicating whether or not a country or
its neighbor(s) experienced a civil war (civilwar and
neighbor civilwar, respectively); and (ii) the number of
months, if any, that a country or its neighbor(s) experienced a civil war over the observational period (tmonths
and neighbor tmonths, respectively). The data on tmonths
come from Collier and Hoeffler (2002) and are fully described in their appendix. Essentially, they offer an update
to the Correlates of War Project (Singer and Small 1993)
through most of the 1990s.
The construction of the neighbor civilwar and
neighbor tmonths variables is particularly interesting.
Generally, we want these measures to reflect the existence
and duration, respectively, of civil war in “neighboring”
countries. The issue is how to define neighbor. We employ three different definitions. In the first, two countries are neighbors if they share a common border; i.e.,
mere contiguity. The second definition builds on the first
(contiguity) by making an allowance for the length of the
common border. The third is based on the actual distance, using closest approach, between nations. If two
countries are within some stated distance, then we define them to be neighbors. This distance-based definition
is implemented for distances of 100 km, 300 km, and
800 km.4
These definitions provide the basis for computing
weighted averages of the civil war measures (civilwar and
tmonths) in neighboring nations. As an example of how we
compute the various measures, consider a hypothetical
country called “A” that has three contiguous neighbors
“B,” “C,” and “D,” two neighbors (“E” and “F”) that are
not contiguous but within 100 km, and three neighbors
(“G,” “H,” and “I”) that are farther away than 100 km but
within 300 km. Next, assume that in the sample period
under study, there are months of civil war as follows: A =
0, B = 0, C = 24, D = 6, E = 0, F = 36, G = 0, H = 12,
and I = 48. Using the contiguity definition of neighbors,
our measures would be:
1
1
+ 1×
neighbor civilwar = 0 ×
3
3
1
2
(4)
+ 1×
=
3
3
4
Our distance measure goes beyond measures of “neighbors” used
in the literature (see, e.g., Most and Starr 1980; Starr and Most
1983; Siverson and Starr 1990), insofar as we allow for dispersion
to nonneighbor states some distance away. Given our interest in the
diffusion of the economic consequences of civil wars, impacts may
spread far beyond immediate neighbor in a geographic or colonial
sense.
and
1
1
+ 24 ×
neighbor tmonths = 0 ×
3
3
1
+ 6×
= 10.
3
(5)
In Equations (4) and (5), the denominator in the fractions
is determined by the number of neighbors. Weighted averages computed in this fashion are commonly referred
to as “spatial” weighted averages, since the weight reflects
spatial arrangements.
As an alternative to simple contiguity, we also consider a contiguity definition of neighbor that includes
the length of the common border. Let the common borders between A and B, A and C, and A and D equal
200 km, 300 km, and 500 km, respectively. Using the
border lengths, we have:
200
300
+ 1×
neighbor civilwar = 0 ×
1000
1000
500
4
(6)
+ 1×
=
1000
5
and
200
300
+ 24 ×
neighbor tmonths = 0 ×
1000
1000
500
(7)
+ 6×
= 10.2.
1000
Compared to the simple contiguity definition, we see that
in the border lengths definition, the weights are determined by the lengths of the common borders in relation
to the total length of the common borders.
Our third-neighbor definition is based on distance
between countries. If we define a neighbor to be any country within 100 km, then country A has five neighbors (B,
C, D, E, and F). Thus, our measures would be
1
1
1
+ 1×
+ 1×
neighbor civilwar = 0 ×
5
5
5
1
1
3
(8)
+ 0×
+ 1×
=
5
5
5
and
1
1
neighbor tmonths = 0 ×
+ 24 ×
+ 6×
5
5
1
1
+ 0×
+ 36 ×
= 13.2.
5
5
1
5
(9)
In Equations (8) and (9), the distance is used to define the
number of neighbors, which, in turn, is used to construct
the weights. A quick comparison of Equations (4) and (5)
142
to Equations (8) and (9) reveals that simple continuity is
a special case of the distance definition where the distance
is zero.
Expanding the reach to 300 km does two things. First,
country A will have three additional neighbors. Second,
we are using the weighted average to define the measures, so that the denominator in the weights will increase,
thereby giving less weight to any neighbor’s influence.
(Understanding this is essential for interpreting the estimated coefficients presented below.) In our hypothetical example, neighbor civilwar will change to 5/8, while
neighbor tmonths will change to 15.75.
There are at least two reasons that, a priori, we may
anticipate greater economic consequences from civil wars
as the distance measure of neighborliness increases. First,
there may be more civil wars as the reach is extended.
More wars make for greater potential economic impacts.
Second, there are more avenues at enhanced distances for
the economic spillovers to reach a specific country. For
example, a reference country may have six contiguous
neighbors, none of which have a civil war. Under contiguity, our spillover measure is then zero. Extending the
reach to 300 km, we may find a civil war in a country that
is contiguous with, say, three of the reference country’s
immediate neighbors. As these neighbors are negatively
affected by the civil war, the reference country is also negatively influenced through all of them. This type of regional
multiplier suggests that the economic spillovers may be
more pronounced when “neighbors” encompass not only
contiguous, but also more distant neighbors. The appropriate distance to capture these spillovers is an empirical question that can be answered by trying alternative
distances and finding the one with the most significant
outcome.
To find the border lengths between all contiguous
countries in our sample, we consult the CIA Factbook
(2000), which gives border lengths as of 2000. In cases
where a country’s borders changed over the observational
period (e.g., Yugoslavia), the country is dropped from
the respective period. The borders in the CIA Factbook
are irrelevant for some countries prior to 1990 because
of subsequent division of the country. To include these
cases in the early observational periods, we used the independent states definitions in Gleditsch and Ward (2000)
and various paper atlases to reconstruct the geography (in
terms of border lengths) of the countries during the sample period. Thus, the number of observations per sample
period is not necessarily the same.5 For distance, we use
5
The issue concerning the number of observations is only relevant
when pooling over five-year periods. Some countries may not exist
in all of the periods. When we examine long-run growth, we are
restricted to those countries that existed for the entire 1960–95
period.
JAMES C. MURDOCH AND TODD SANDLER
the raw distance matrices, developed by Gleditsch and
Ward (2000), which provide the minimum distance between all pairs of countries up to 950 km.
With data on civilwar, tmonths and the various definitions of neighbor civilwar and neighbor tmonths, we can
simply insert combinations of these variables as regressors
in Equation (3) and then test variations of the model.
Results
We first present the long-run estimates, followed by the
short-run estimates.
Long-Run Estimates
In Table 1, estimations for variants of Equation (3) are
displayed for the world sample of 84 countries over the
1961–95 period. There are ten variants of the model that
allow for the five alternative calculations of spatial average
and the two measures of civil wars. The primary economic
determinants of economic growth (invest, netlabor, and
school) as well as the initial level of income per capita (y0)
enter every specification and are remarkably robust to the
different specifications of the civil war effects.6
Because the dependent variable in each regression is
the growth of income per capita over the 35-year period,
the estimated coefficients reflect the contribution of that
variable to long-run economic growth. For example, investments in physical and human capital are both positive and significant influences on the long-run growth of
per capita income, while the measure for the growth in
the labor force net of depreciation and augmentation is
a negative but insignificant long-run influence. The investment coefficient is approximately 0.5, indicating that
a unit change in the natural logarithm of invest would
have an influence of approximately one-half of a percent
in economic growth, hardly an intuitive measure. In order
to gain more intuition on the size of the marginal investment effect, we calculate the derivative of gr with respect
to invest as:
2
d(g r )
=
,
d(invest)
(invest)
where 2 is the coefficient on invest as in Equation (3).
Clearly, this derivative depends on the estimated coefficient and the value of invest. Using an estimated value of
6
To establish that the civil war variables improve the estimates, we
first estimated the growth model without these variables. The coefficients on y0, invest, and school are almost identical to those in
the various models in Table 1. Moreover, netlabor remains insignificant. Most importantly, the adjusted R2 is smaller (0.6012) when
the civil war variables are excluded.
CIVIL WARS AND ECONOMIC GROWTH
0.5 and the mean of invest of approximately 16.0, we get
a marginal effect of 0.5/16 = 0.031 or 3.1%.7 This means
that if the investment ratio increases by 1.0, real GDP per
capita is predicted to increase by 3.1%. Another way to
gain some insight into the size of the effect is to calculate
an estimate of the “elasticity”; i.e., the percentage change
in gr ÷ percentage change in invest,
d(g r ) (invest)
.
d(invest) g r
Based on the mean of gr (0.55) and our approximate value
of 0.5 for the coefficient, the elasticity with respect to investment is estimated as 0.5/.55 = 0.91. Thus, a 1% change
in the investment share is associated with slightly less than
a 1% change in economic growth. Similar calculations for
school (using a point estimate of 0.23 and the mean of
21.75) give a marginal effect of approximately 1.3% and
an elasticity estimate of approximately 0.42.
The estimates on y0, the initial level of GDP per capita,
suggest that (in the limit) we should see convergence in
income per capita because countries with smaller initial
levels of per capita income tend to grow faster. This finding
implies that countries whose economies are devastated by
a civil war may tend to grow faster after the conflict is over.
The estimates on the dummy variable civilwar indicate that regardless of the representation for the spatial
diffusion, the presence of a civil war causes about a 0.17
fall in per capita income growth in the home country.
Given the overall mean growth of 0.55, this implies that
the (conditional) mean growth falls by 0.17/0.55 = 31%
owing to civil war. The estimates on the duration measure
(tmonths) are never statistically significant, so that civilwar appears to be the better measure in terms of goodness
of fit. This is a surprising result that we investigate in detail
below.
One of our primary concerns, the spatial reach of
spillovers from civil war, can be analyzed by comparing
the five sets of models in Table 1, which only differ by
the construction of the measures of civil war neighboring
nations. Looking first at contiguity, we see that neither
the spatial average of neighbor civilwar nor the spatial
average of neighbor tmonths is statistically significant at
conventional levels. This same finding is observed in the
border lengths measures and when neighbors are defined
as countries within 100 km. However, with the 300 km
definition and even more so with the 800 km definition
of neighbors, the consequence of civil war in neighbors
appears to have a strong negative effect on home-country
=
7
The predicted effects calculated here and elsewhere in the text
represent the point estimates of the prediction. In general, the predicted effects are random variables so that one could also calculate
the interval estimate.
143
economic growth. Using the R-squares as a benchmark,
the best fit of the data appears in the 800 km model,
consistent with the notion that the effects of civil wars
can reach significant distances with time. This is a striking and novel result that demonstrates the importance
of expanding the distance measure of neighbors to capture the regional multiplier and the inclusion of more
wars.
The 800 km estimated coefficient on neighbor
civilwar (−0.4228) implies that a unit change in the
neighbor civilwar term is associated with a 0.4228 decline
in long-term economic growth; i.e., a decline that practically equals the average increase in growth of 0.55 for
the sample. Some care, however, must be exercised when
interpreting this coefficient. Owing to its construction as
a spatial average, the range is the unit interval so that
a change of one unit is a radically large change. There
are two additional ways to analyze this effect. The first
is to use an elasticity measure. For the 800 km models,
the mean of neighbor civilwar and gr are 0.29 and 0.55,
so that the elasticity is approximately −0.23, indicating
that neighborhood consequences and the reach of civil
war can be quite substantial. Recall that the elasticity of
school was approximately 0.42; hence, the magnitude of
the spillover effect is a little over one-half the size of the
effect of schooling.
A more intuitive way to study the size of the
neighbor civilwar effect is to calculate the predicted impact from an additional civil war in a neighbor. The advantage of this approach is that we can then compare the
neighbor effect directly to the own-country effect. By construction of the neighbor civilwar, the effect of an additional civil war in a neighbor will depend on the estimated
coefficient and the number of neighbors. For example,
using 100 km to define neighbors, the average number
of neighbors is approximately four, making the average
weight in the spatial average 0.25. If we assume an additional civil war in a neighbor, the neighbor civilwar term
would increase by 0.25. The predicted change (point estimate) on gr would be the estimated coefficient (−0.18)
times the change neighbor civilwar (0.25); i.e., −0.18 ×
0.25 = −0.045. With the reach expanded to 300 km,
the average number of neighbors is approximately five,
so that an extra civil war in a neighbor would change
neighbor civilwar by 0.20. The corresponding coefficient
from Table 1 is −0.2556 so that economic growth is predicted to fall by 0.2556 × 0.20 = 0.051, slightly larger than
the estimate in the 100 km model. In the best-fitting model
(800 km), the average number of neighbors is approximately eight so that an additional civil war in a neighbor
yields an increase in neighbor civilwar of 0.125. With an
estimated coefficient of −0.4228, the predicted decline in
1.2005
(1.22)
0.6377
−0.3739
(−4.15)
0.5181
(7.19)
−0.0858
(−0.21)
0.2366
(4.00)
−0.1626
(−2.05)
0.0221
(0.20)
−0.0006
(−1.23)
0.00003
(0.03)
0.9300
(1.01)
0.6385
−0.3586
(−3.88)
0.5210
(6.43)
−0.1316
(−0.31)
0.2369
(4.15)
Contiguity
1.6040
(1.85)
0.6479
−0.3686
(−4.28)
0.5234
(7.46)
0.0520
(0.14)
0.2273
(4.33)
−0.1759
(−2.35)
−0.1993
(−1.74)
−0.0007
(−1.33)
−0.0006
(−0.90)
1.1699
(1.34)
0.6303
−0.3489
(−3.85)
0.5314
(6.82)
−0.0195
(−0.05)
0.2305
(4.16)
Border Lengths
2
1.5613
(1.81)
0.6447
−0.3695
(−4.22)
0.5187
(7.39)
0.0327
(0.08)
0.2299
(4.30)
−0.1726
(−2.28)
−0.1800
(−1.38)
−0.0006
(−1.32)
−0.0003
(−0.46)
1.1208
(1.30)
0.6286
−0.3503
(−3.83)
0.5257
(6.76)
−0.0423
(−0.10)
0.2329
(4.22)
Within 100 km
3
1.4841
(1.74)
0.6514
−0.3572
(−4.13)
0.5232
(7.67)
0.0240
(0.06)
0.2237
(4.27)
−0.1830
(−2.45)
−0.2556
(−2.07)
−0.0007
(−1.42)
−0.0011
(−1.84)
1.1956
(1.39)
0.6344
−0.3443
(−3.90)
0.5357
(6.75)
−0.0019
(−0.00)
0.2293
(4.13)
Within 300 km
4
1.8280
(2.09)
0.6568
−0.3805
(−4.38)
0.5329
(7.66)
0.0865
(0.23)
0.2258
(4.07)
−0.1648
(−2.28)
−0.4228
(−2.26)
−0.0007
(−1.41)
−0.0019
(−1.70)
1.2483
(1.48)
0.6372
−0.3606
(−4.07)
0.5393
(6.48)
−0.0337
(−0.09)
0.2276
(3.96)
Within 800 km
5
Note: t-ratios (in parentheses) are computed with White’s robust standard errors. y0, invest, netlabor, and school are entered as natural logarithms. The initial year is y0 in 1960 and the
final year is 1995 so that gr = ln(y95) − ln(y61).
R-square
constant
neighbor tmonths
tmonths
neighbor civilwar
civilwar
school
netlabor
invest
y0
Variable
1
TABLE 1 Estimated Coefficients of Long-Run Growth Regressions for Various Spatial Weight Matrices (World Sample: n = 84)
1961–95
144
JAMES C. MURDOCH AND TODD SANDLER
145
CIVIL WARS AND ECONOMIC GROWTH
economic growth is 0.4228 × 0.125 = 0.053, again a slight
increase from the previous model. In the 800 km model, a
civil war at home is associated with a decline in economic
growth of 0.1648, while an additional civil war in a neighbor is associated with a decline of approximately 0.05 or
about 30% of the home-country effect.
As discussed earlier, there is some question about
how civil wars in neighboring countries influence economic growth. The specifications presented in Table 1
assume that the influence is direct through an exogenous country-specific effect. To test for indirect avenues, we estimate other specifications that allow the
civil war spillover (based on the 800 km definition) to
work through invest, netlabor, or school. For the first indirect pathway, we estimate an auxiliary regression with
the natural logarithm of invest as the dependent variable
and civilwar and neighbor civilwar along with the natural logarithms of netlabor and school as the independent
variables:
ln(invest) = 1.272 − 0.182 ln(netlabor) + 0.326 ln(school)
(0.79) (−0.30)
(5.46)
− 0.353 civilwar + 0.404 neighbor civilwar
(−2.58)
(1.22)
R2 = 0.44
where the t-ratios are in parentheses. While schooling
and home-country civil war exhibit significant associations with investment, the estimate on neighbor civilwar
is insignificant, suggesting that the spillover is not working through the investment ratio. That civilwar is significantly associated with less investment is not surprising
since economic resources must be diverted from investment in order to fight a civil war. Additionally, the positive relationship between school and invest indicates that
investments in physical and human capital tend to complement each other.
A second indirect pathway for civil wars in neighboring countries to influence economic growth is through
migration. All else equal, migration should decrease economic growth by increasing netlabor. To test this hypothesis, recall that netlabor = n + 0.05. If neighbor civilwar
causes in migration, netlabor will be augmented accordingly. A simple model that allows for this possibility is a
redefinition of netlabor to be
n + 0.05 + neighbor civilwar,
where neighbor civilwar serves as a proxy for migration or a shock to labor growth. To distinguish whether
the neighbor civilwar term belongs in the net labor term
or as a separate direct effect (or both), we complete the
specification by making a “joint” model wherein (1 − )
neighbor civilwar replaces neighbor civilwar in the basic
specification:
g r i = 0 + 1 ln(y0i ) + 2 ln(investi )
+ 3 ln(schooli ) + 4 ln(ni + 0.05
+ neighbor civilwari ) + 5 civilwar
+ 6 (1 − )neighbor civilwar + εi .
(10)
The joint model includes all of the parameters from the
original model plus and can be estimated with nonlinear least squares. When is zero, the spatial average
enters linearly as a country-specific effect (i.e., the results
in Table 1 apply); however, when is one, the spillover
takes on the migration interpretation within the population term. Other values of suggest two avenues for the
effects of civil wars in neighboring countries: one through
migration and one direct effect, perhaps through disruption or uncertainty from the civil war spillovers. Based
on the 800 km definition for neighbors, the estimate for
is 0.02 with a t-ratio of 0.019, which is clearly not significantly different than zero, thus indicating no evidence
to support the migration interpretation of the spillover
effect.
The third avenue that we examine allows for migration into a country to depreciate the human capital stock.
In this case, we form a specification that includes the
neighbor civilwar interacted with the school term. The interaction term allows the growth returns to schooling to
change depending on the size of the influence of neighboring civil wars. Once again, we uncover no statistical
evidence in favor of the interaction term.
The results of these three sets of tests support that the
observed negative growth effects of nearby civil wars arise
from factors such as uncertainty and the direct disruption
of economic activity, rather than from the dilution of the
population’s human capital, enhanced population growth
from migration, or significant decreases in the longterm investment ratio. Thus, the estimates of Table 1 are
supported.8
8
If there is some process that causes economic growth to be spatially
correlated, meaning that the growth in one country depends on
the growth in neighbors, then the significance of neighbor civilwar
could be merely an artifact of that process. We tested for this
possibility by reestimating the model with the spatial average of
growth as an independent variable. This is a simultaneous model
often referred to as a spatial-lag model that can be estimated using maximum-likelihood methods (Anselin 1988). We found that,
while neighbors’ growth was statistically significant, the coefficient
was very small (0.0088). Although the magnitude of the estimate
fell to approximately 0.342, the estimate on neighbor civilwar remained statistically significant. Thus, we have some confidence that
the findings with respect to civil war in neighbors are not simply
picking up spillovers from economic growth.
146
JAMES C. MURDOCH AND TODD SANDLER
TABLE 2 Parameter Estimates for Testing the Influence of Duration and Timing of Civil War at
Home and in Neighbors within 800 km in the Long Run (1961–95)
Models
Variable
Brief Variable Description
(1)
TM12
DV; 1 if 0 < tmonths ≤ 12
TM < 48
DV; 1 if 12 < tmonths ≤ 48
TM > 48
DV; 1 if tmonths > 48
NeighborTM12
Spatial Average of Neighbors TM12
NeighborTM < 48
Spatial Average of Neighbors TM < 48
NeighborTM > 48
Spatial Average of Neighbors TM > 48
TM61-75
Months of civil war between 1961 and 1975
TM76-95
Months of civil war between 1976 and 1995
NeighborTM61-75
Spatial Average of TM61-75
NeighborTM76-95
Spatial Average of TM76-95
CivilWar ≤ 75
DV; 1 if TM61-75 > 0
CivilWar > 76
DV; 1 if TM76-95 > 0
NeighborCivilWar ≤ 75
Spatial Average of Neighbors
CivilWar ≤ 75
Spatial Average of Neighbors
CivilWar >76
NeighborCivilWar > 76
R-square
(2)
(3)
−0.1176
(−1.00)
−0.0042
(−0.04)
−0.2184
(−2.20)
−1.2343
(−2.04)
−0.4052
(−1.28)
−0.2488
(−0.79)
0.0016
(1.33)
−0.0013
(−2.26)
−0.0032
(−0.66)
−0.0012
(−0.48)
0.67
0.64
−0.0288
(−0.29)
−0.1732
(−2.44)
−0.1497
(−0.37)
−0.3383
(−1.31)
0.66
Note: Asymptotic t-ratios (using White’s robust standard errors) in parentheses. DV means “dummy variable.” All specifications include
the natural logarithm of y60, invest, netlabor, and school as independent variables.
Curiously, the performance of civilwar relative to
tmonths seems to indicate that the simple existence of civil
war, either at home or in neighbors, is the relevant factor in
explaining the impacts of civil war on long-run economic
growth. Recognizing that the influence of tmonths may be
more complicated than the simple linear model underlying the results presented in Table 1, we tested other specifications of tmonths. The results are presented as Model
(1) in Table 2, where we have replaced the continuous
variable tmonths with dummy variables (DV) that categorize duration into three groups: duration of a year or
less (TM12), more than a year but not more than four
years (TM < 48), and greater than four years (TM > 48).
The duration of civil wars in neighbors (within 800 km)
is classified in exactly the same fashion. In terms of homecountry civil war, the coefficient estimates indicate that
the significant effect is for wars of fairly long duration,
while in terms of neighbor civil wars, relatively short wars
appear to be the most important factor. This eventual insulation from the harmful economic consequences from
a nearby civil war is indicative of the erection of a firewall.
Such firewalls are not feasible against a home-grown civil
war.
That longer civil wars have a greater influence on the
host country’s long-run economic growth than shorter
wars is intuitive. Because we compute the growth over a
CIVIL WARS AND ECONOMIC GROWTH
35-year period, a country may be able to recover from
the economic consequences of a brief civil war early in
the sample period. More insight on this timing factor is
gained by looking at Models (2) and (3) in Table 2, where
we consider when the civil wars occur in terms of the first
or second half of the sample period. In Model (2), tmonths
since 1976 (TM76-95) is quite significant in stark contrast to the tmonths’ results presented earlier in Table 1.
Similarly, in Model (3), the important determinant is a
civil war in the second half of the sample period. There is
no evidence that these temporal distinctions provide additional insights into the relationship between neighbors’
civil wars and long-run economic growth, confirming our
prior finding that just the existence of civil war in neighbors matters. When the war drags out over a long period of
time, the war’s effects will diminish as the nation insulates
itself.
Short-Run Estimates
The results in Table 2 suggest that we may gain substantial insights into the relationship between civil war and
economic growth by looking at shorter time periods. In
Table 3, we display variations of Equation (3) where the
variables are defined over five-year time periods. With
35 years of data, we have seven five-year periods starting
with 1961 for the 84 countries in the long-run sample. Additionally, data from countries that had a missing value
in either 1961 or 1995, but not in other years, are included, making for 626 observations.9 Because countries
are observed over several five-year periods, the short-run
dataset is referred to as a “panel dataset.” There are ten
variants of the model displayed in Table 3, accounting for
the spatial transmission processes, where the dependent
variable is the five-year growth of income per capita. Time
fixed effects were included in all of the models but are not
displayed to conserve space. The qualitative conclusions
about the role of initial income per capita, investment in
physical and human capital, and net increases in the labor
force are essentially the same in the short run as in the long
run. Unlike the long-run estimates, the expected negative
impact of the netlabor term is significant in the short run.
In terms of civilwar and tmonths, the short-run estimates
appear to confirm the long-run findings that the mere
presence or absence of a civil war, and not its duration,
offers the best measure and that the spillovers from civil
wars in neighbors reach to 800 km.
9
For example, a country may have valid observations from 1986 to
1995. In the long-run study, the country would be dropped from
the sample, while in the short-run study, we get two additional
observations by including the country in the sample.
147
Civil war is a negative and significant influence on
short-run income per capita growth in Table 3, with the
coefficient estimate of approximately −0.05; i.e., the fiveyear growth in per capita real GDP is expected to be 0.05
less if there is a civil war in that country during the fiveyear period. Given that the mean five-year growth is approximately 0.06, countries that experience civil war are
predicted to have close to zero economic growth. In contrast to the long run where we found that a civil war over
the 35-year period would cause growth to fall by about
31% of the sample mean, we find that a civil war causes
short-run growth to decline by almost 85% (0.05/0.06)
of the sample mean. This result is sensible and illustrates
a fundamental characteristic of the growth process: over
long periods of time, an economy can recover, even catchup to the average, from a disruption like a civil war.
The spillover effect of nearby civil wars shows up with
the distance representation and is strongest at 800 km
just as in the long-run case. For the worldwide sample,
the diffusion of the negative economic influence is quite
far, thereby suggesting that dispersion is fairly rapid owing to regional multiplier effects. This concurs with the
results of Table 2. The spillover point estimate (−0.094)
is substantially smaller than in the long run. The spatial
average-number civil wars in any given five-year period is
also much smaller (0.12, compared to 0.29 in the long run)
because there are fewer civil wars in neighbors in any given
five-year period when compared to the entire 35-year period. Thus, the implied elasticity of neighbor civilwar in
Table 3 for the 800 km model is −0.19, which is somewhat
less than the long-run model, thus implying that spillover
effects may take longer than five years to have their full impact on economic growth. The implied marginal impact
of an additional civil war in a country within 800 km is approximately −0.012 (=0.125 × −0.094). Compared to the
effect of a civil war at home (−0.05), we see that a neighboring civil war influence is approximately 24% of the
home-country effect, thus confirming that the spillover
effects may take longer than five years to reach their maximum impact.
Additional tests with the short-run dataset confirm
our findings from the long run that civil war in neighboring countries does not seem to alter significantly the
investment ratio, the growth returns to schooling, or the
relationship between netlabor and economic growth.10
The auxiliary investment regression, including fixed effects for time and country, shows that neither the civilwar
nor the neighbor civilwar coefficient is a significant determinant of the five-year investment ratio. Recall that
10
Results for these tests are available at http://www.bruton.
utdallas.edu under the “civil wars” research link.
−0.1849
(−1.43)
0.2497
−0.0413
(−3.74)
0.0535
(4.81)
−0.1353
(−2.32)
0.038
(4.00)
−0.0488
(−2.57)
−0.0349
(−1.02)
−0.0004
(−1.07)
−0.0012
(−1.20)
−0.2086
(−1.59)
0.2428
−0.0391
(−3.62)
0.055
(4.85)
−0.1361
(−2.32)
0.0375
(3.90)
Contiguity
−0.2041
(−1.57)
0.2477
−0.0406
(−3.71)
0.0529
(4.74)
−0.1405
(−2.38)
0.0374
(3.93)
−0.0501
(−2.57)
0.0096
(0.52)
−0.0004
(−1.13)
0.0002
(0.60)
−0.2321
(−1.73)
0.2381
−0.0395
(−3.62)
0.0548
(4.78)
−0.1457
(−2.40)
0.0373
(3.85)
Border Lengths
2
−0.189
(−1.47)
0.2490
−0.0413
(−3.72)
0.0532
(4.79)
−0.1371
(−2.36)
0.038
(3.99)
−0.0489
(−2.58)
−0.032
(−0.83)
−0.0004
(−1.11)
−0.0008
(−0.79)
−0.2159
(−1.64)
0.2400
−0.0398
(−3.64)
0.0548
(4.83)
−0.1404
(−2.37)
0.038
(3.94)
Within 100 km
3
−0.1853
(−1.44)
0.2516
−0.041
(−3.74)
0.0538
(4.85)
−0.1351
(−2.32)
0.0378
(3.98)
−0.0497
(−2.60)
−0.0595
(−1.31)
−0.0004
(−1.18)
−0.0013
(−1.00)
−0.2118
(−1.60)
0.2417
−0.0397
(−3.63)
0.0552
(4.87)
−0.1383
(−2.32)
0.0382
(3.97)
Within 300 km
4
−0.1755
(−1.36)
0.2529
−0.0438
(−3.86)
0.055
(4.91)
−0.1388
(−2.39)
0.0376
(3.97)
−0.0502
(−2.61)
−0.094
(−1.87)
−0.0004
(−1.18)
−0.0014
(−1.01)
−0.2157
(−1.61)
0.2402
−0.0414
(−3.67)
0.0556
(4.87)
−0.1444
(−2.40)
0.0379
(3.93)
Within 800 km
5
Note: t-ratios (in parentheses) are computed with White’s robust standard errors. y0, invest, netlabor, and school are entered as natural logarithms. We use five-year observational periods
with initial years of 1960, 1965, 1975, 1980, 1985, and 1990. Thus gr in the first short-run observational period is ln(y65) − ln(y61) and similarly for other periods.
R-square
constant
neighbor tmonths
tmonths
neighbor civilwar
civilwar
school
netlabor
invest
y0
Variable
1
TABLE 3 Estimated Coefficients of Short-Run Growth Regressions for Various Spatial Weight Matrices, Using Seven Five-Year Periods
(World sample: n = 626) 1961–95
148
JAMES C. MURDOCH AND TODD SANDLER
149
CIVIL WARS AND ECONOMIC GROWTH
TABLE 4 Parameter Estimates for Testing the Influence of Duration of Civil War at Home and in
Neighbors within 800 km in the Short Run (1961–95)
Models
Variable
Brief Variable Description
(1)
TM12
DV; 1 if 0 < tmonths ≤ 12
TM < 24
DV; 1 if 12 < tmonths ≤ 24
TM > 24
DV; 1 if tmonths > 24
NeighborTM12
Spatial Average of Neighbors’ TM12
NeighborTM < 24
Spatial Average of Neighbors’ TM < 24
NeighborTM > 24
Spatial Average of Neighbors’ TM > 24
TM024
DV; 1 if 0 < tmonths ≤ 24
NeighborTM024
Spatial Average of Neighbors’ TM024
Lag(TM024)
One period lag of TM024
Lag(NeighborTM024)
One period lag of NeighborTM024
R-square
(2)
(3)
−0.0872
(−2.50)
−0.2063
(−2.64)
−0.0858
(−2.48)
−0.1848
(−2.35)
−0.0131
(−0.46)
−0.1154
(−1.22)
0.26
−0.0918
(−2.54)
−0.0801
(−1.11)
−0.0218
(−1.16)
−0.2101
(−2.29)
−0.1554
(−1.32)
−0.0396
(−0.56)
0.26
0.26
Note: Asymptotic t-ratios (using White’s robust standard errors) in parentheses. DV means “dummy variable.” All specifications include
six period dummy variables and the natural logarithm of y60, invest, netlabor, and school as independent variables.
in the long-run auxiliary model civilwar was negative
and significant. Evidently, the short-run period of five
years is not long enough for home-country civil war to
change the investment ratio. Forming the joint model with
neighbor civilwar entering directly and indirectly in the
netlabor term [Equation (10)], we again find that the
estimate for (0.10) is statistically insignificant from
zero (t-ratio = 0.42), suggesting that migration does
not explain the short-run negative consequences of civil
war in neighbors. Similarly, the interaction of school and
neighbor civilwar is insignificant with a point estimate of
0.072 and t-ratio = 1.10.
The estimates displayed in Table 3 seem to suggest that
tmonths is insignificant. Following the analysis developed
for the long-run model, we investigate this by looking at
classifications of tmonths. As displayed in the top third of
Table 4, we define three classes: civil wars up to one year
(TM12), more than a year but not over two years (TM <
24), and longer than two years (TM > 24). Using these
classifications instead of tmonths in the 800 km model, we
get the coefficient estimates as displayed under Model (1)
in Table 4. The coefficient on civil wars up to one year
in length is strongly significant and with approximately
the same magnitude as the civil war dummy variable in
Table 3. Additionally, the coefficient on the classification
for civil wars more than a year but not over two years,
while not significant, is of approximately the same magnitude. The magnitude falls substantially for civil wars
greater than two years. The coefficients on the neighbor
civil war terms display a similar pattern; i.e., the one-year
duration measure is significant and of approximately the
same magnitude of the next longest classification. For
wars greater than two years, the magnitude is substantially
less.
These patterns suggest that we test combining the first
two classifications and dropping the third for a more parsimonious specification. This requires four restrictions:
(1) the coefficient on TM > 24 equals 0, (2) the coefficient on NeighborTM > 24 is greater than 0, (3) the
coefficients on TM12 and TM < 24 are equal, and (4)
the coefficients of NeighborTM12 and NeighborTM < 24
are equal. The F-statistic for testing these restrictions is
150
JAMES C. MURDOCH AND TODD SANDLER
0.49. With four degrees of freedom in the numerator and
609 degrees of freedom in the denominator, the p-value
for the statistic is 0.74, suggesting that we fail to reject the
restrictions. The estimates from the parsimonious model
are presented in the Model (2) column of Table 4, where
the definition of TM024 captures civil wars up to two years
in length. This specification is actually “better” in a bestfitting sense than the model with the civil war dummy
variable and suggests that even in the short run, there is
some economic robustness with respect to lengthy civil
wars both at home and in neighbors. We can test for this
notion by including lagged values of the civil war measures in the model. If the economic effects persist, then
we would expect to see significant lagged values. However,
as shown in the Model (3) column of Table 4, there is no
evidence that consequences of civil war persist past the
current period. Like the long-run results, firewalls take
about two years to erect, and nations can recuperate from
the ravishes of civil wars.
Conclusions
By representing neighbors with a distance matrix, we are
better able to display significant civil war influences on
growth from civil wars in nearby, but not necessarily adjacent, countries in the short and long run. These civil war
influences on income per capita growth are quite substantial. For example, a country that experiences a civil war
and also has the misfortune to be nearby other countries
with civil wars is apt to experience negative annual growth
instead of their typical positive growth. Thus, policies to
bring peace to civil-war-torn countries have a return not
only for the conflict-ridden country, but also for its neighbors. The spatial diffusion of these detrimental influences
peaks at 800 km owing to the inclusion of more wars and
regional multiplier influences where economic impacts
are enhanced through trade and other factors. The presence of such collateral damage indicates that policy makers may wish to consider foreign aid in countries some
distance from a civil war, because growth-inhibiting consequences can diffuse widely.
When quantifying these civil war influences, we find
that a civil war at home can reduce a country’s growth
by 31% in the long run and by 85% in the short run.
This difference in impact highlights how time allows an
economy to repair itself. Moreover, each additional nearby
civil war can lower growth by approximately 30% of the
host-country effect in the long run and by 24% of the hostcountry effect in the short run. Thus, a country in a region
with three or more civil wars may be equally impacted as
a country experiencing a civil war. The temporal analysis
indicates that countries successfully erect firewalls within
a couple of years to limit these negative consequences to
their growth from nearby civil wars. In contrast, the host
country experiences greater harm as the war drags on.
Within the sample period, the timing of the war is crucial,
because nations rebound from the devastation once the
civil war ends. Wars occurring in the second half of the
long-run period have a significant impact on growth in
contrast to wars occurring in the first half of the period.
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