MALTHUS AND CIVIL CONFLICTS IN AFRICA: EVIDENCE FROM IV-ESTIMATES
Transcript of MALTHUS AND CIVIL CONFLICTS IN AFRICA: EVIDENCE FROM IV-ESTIMATES
MALTHUS AND CIVIL CONFLICTS IN AFRICA:
EVIDENCE FROM IV-ESTIMATES
by
Markus Brückner*
25 August 2008
Abstract: More than two centuries ago Thomas Robert Malthus argued that population
expansions may constitute a source of conflict, in particular in those countries that are
characterized by large masses of people living at subsistence income levels.
Investigating empirically Malthus's hypothesis is not straightforward though since wars
kill people and are often associated with waves of mass migration. This paper puts
Malthus's prediction to a test in a panel of 37 Sub-Saharan countries for the period
1981-2004, using severe droughts as an instrumental variable for population size. The
second stage regressions yield that population expansions significantly increased civil
conflict incidence and raised the region's risk of civil conflict onset.
Key words: Population Pressures, Civil Conflict, Reverse Causality
JEL codes: O0, P0, Q0
* Department of Economics, CAEPS, Universitat de Barcelona and Universitat Pompeu Fabra. Contact e-mail: [email protected].
"The prodigious waste of human life occasioned by this perpetual struggle for room
and food was more than supplied by the mighty power of population. ...An Alaric, an
Attila, or a Zingis Khan, and the chiefs around them, might fight for glory, for the fame
of extensive conquests...but the true cause was a scarcity of food, a population extended
beyond the means of supporting it." Thomas R. Malthus (1798) in: An Essay on the
Principal of Population (p. 23)
1. Introduction
In 1798 Thomas Robert Malthus published the first edition of his famous writing An Essay on the
Principal of Population, in which he argued that population expansions during times of food
shortages create a struggle for existence over fertile lands and valuable resources. Malthus's
thoughts inspired a broad spectrum of scientists ranging from evolutionists like Charles Darwin and
Alfred Wallace to economists like John Meynard Keynes. They became also readily absorbed in
the debates carried out within policy circles regarding issues of population planning and poverty
relief (Hart, 1992).
Today, the link between population and resource struggles continues to be a topic of
intensive discussion, in particular regarding causes of civil conflicts. Since the end of World War II
these events have resulted in three times as many deaths than wars between states. Total war
casualties have been estimated to sum to at least 16.2 million (Fearon and Laitin, 2003), with many
more killed or disabled due to violence against civilians and the spread of lethal diseases
(Ghobarah, Huth and Russeth, 2003). Civil conflicts posit also a major stumbling block for
developing countries in their quest to achieve economic prosperity: they destroy infrastructure, lead
to a deterioration in the quantity and quality of human capital, debilitate social networks, and deter
domestic and foreign investment (World Bank, 2003).
Essential for investigating whether Malthusian forces are partially responsible for causing
these devastating events is an econometric framework that can convincingly establish a causal
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relationship. The majority of recent empirical research has relied on the cross-sectional time series
analysis of panel data, but one may question whether the results obtained by these studies reflect a
truly causal effect, or whether estimates are just a product of spuriousness (see here for instance the
review of the civil conflict literature by Blattman and Miguel, 2008).1 On the one hand side, large
downward biases on the population variable may arise due to civil conflicts killing people and being
associated with waves of mass migration (Davenport, Moore, and Poe, 2003; Montalvo and Reynal-
Querol, 2007). Using a lagged population variable is unlikely to be an appropriate strategy of
dealing with this reverse causality bias since refugee movements to neighboring countries occur
often even before warfare activity has fully escalated (UNHCR, 2007). On the other hand side,
there are also many difficult to measure variables proxying for social fragmentation, institutional
quality or economic conditions that may be related to both civil conflict and population size.
This paper examines the existence of a causal effect going from population expansions to
civil conflict by drawing on the random occurrence of severe droughts in Sub-Saharan Africa as an
instrumental variable for changes in the size of the population stock. Sub-Saharan Africa is an ideal
region to test Malthus's prediction that around the proximity of subsistence income levels
population pressures may constitute a source of conflict: more than 40 percent of its 670 million
people live on less than 1 dollar per day with PPP adjusted per capita GDP accruing to just 1,690
dollars per year (WDI, 2008). Civil conflicts have been a real challenge for most Sub-Saharan
countries - over two-thirds have experienced in the past 25 years a civil conflict - making Africa the
world's continent with the highest mean incidence of civil conflict.2
The foundation for the paper's instrumental variable set-up is that Sub-Saharan economies
1 This line of empirical research stands in contrast to the case study analyses pioneered by Homer Dixon (1991, 1999)
that investigate the link between population pressures and civil conflict using a narrative approach. Despite its
richness case study analysis as a means of identifying causes of civil conflict has been heavily criticized for
suffering from selectivity bias and lack of generality (Gleditsch, 2001; Urdal, 2005).
2 See the PRIO/UPSALLA database on civil conflict for reference. Available at www.prio.no/cscw/armedconflict.
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depend heavily on the agricultural sector and that crop yield is highly vulnerable to rainfall (see for
instance the Intergovernmental Panel on Climate Change, 2001). A combination of government
subsidies and unemployment insurances usually cushion the adverse effects of drought in
economically developed countries but such buffer mechanisms are either widely lacking in Sub-
Saharan countries, or if present highly ineffective (Fafchamps, 2003).3 The impact of drought on
poor, credit-constrained, rural households is thus much more dramatic. So much so, that the
famines caused by extremely harsh and repeated drought may stimulate a reduction in birth rates,
encourage widespread migration, and, in the limiting case, cause death due to starvation (FAO,
2005; WHO, 2006; UNHCR, 2007).
Exploiting that population size in a panel of 37 Sub-Saharan countries during the period
1981-2004 is significantly negatively affected by episodes of severe drought, the second stage
regressions yield statistically significant and quantitatively strong evidence for Malthusian forces
causing civil conflict: increasing an African country's population size by one percentage point raises
the likelihood of observing in the following year a civil conflict by over 6.6 percentage points and
increases the risk of a conflict onset by over 5.1 percentage points.4 The estimates are based on a
set of rigorous control variables that include country fixed effects, country specific time trends, as
well as common year fixed effects. Furthermore, in order to come as close as possible to Malthus's
prediction of population pressures leading to conflict in the presence of per capita income falling
below subsistence levels, the second stage explicitly accounts for the role of per capita GDP in
3 This is not to say that rural households in Sub-Saharan Africa forpass opportunities to reduce risk by means of
diversification and village network effects. As the discussion in Fafchamps (2003) and papers cited therein makes
very clear, these mechanisms are indeed at work. However, they may not be well-suited to protect against collective
risk factors, such as large-scale droughts. Moreover, lack of well-established property rights, paucity of savings
instruments, and technological and environmental constraints peculiar to Sub-Saharan Africa posit additional
impediments that limit rural household's ability to smooth consumption and evade the deleterious effects of famine.
4 Similarly large and statistically significant point estimates are obtained when focusing on more specific measures of
population pressures such as population density, youth bulges or male population.
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potentially being related to both civil conflict and population size. Addressing arising issues of
endogeneity by drawing on the instruments used in previous studies by Miguel, Satyanath, and
Sergenti (2004) and Brückner and Ciccone (2007) for per capita GDP the second stage yields that
conditional on population size a one percentage point increase in income due to better rainfall
conditions or higher international commodity prices decreases the likelihood of civil conflict by
over 1.9 percentage points when focusing on conflict incidence and by over 1.3 percentage points in
terms civil conflict onset risk.
The paper bears thus two main messages. First, population expansions may act as potential
fuel to an already ongoing civil conflict. They also posit a serious threat for African countries to
become befallen by a new or recurrent civil conflict. Second, as African countries become richer
their likelihood to suffer from civil conflict diminishes. A direct policy implication following from
these results is that if African countries permit themselves increases in their population size then a
way to dampen the arising conflict potential is to ensure that population expansions are
accompanied by a substantial improvement in the prevailing economic environment.
The instrumental variable estimates are the basis for the message of this paper. In fact, the
least squares estimates on the population variable yield always insignificant point estimates that
appear to suffer from strong downward bias. One key advantage of the instrumental variable
approach is that it makes it credible that estimates reflect causal effects and are not just a product of
spurious correlations. Another key advantage is that it allows to take care of measurement error,
which is presumably large in data of Sub-Saharan national accounts statistics (Heston, 1994;
Deaton, 2005). Admittedly, a crucial assumption for the IV regressions to yield unbiased estimates
is that instruments have no effect on civil conflict other than through per capita GDP or population
pressures and that they are exogenous to the presence of Sub-Saharan civil conflict. The paper
discusses some of such possible channels, intending to outrule these. Since the second stage
regressions employ three instruments for two endogenous variables it is possible to empirically test
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the assumption of instrument validity using overidentification tests. These turn out to be always
highly statistically insignificant yielding no evidence that instruments are correlated to second stage
error terms.
The remainder of the paper is organized as follows: Section 2 discusses the estimation
methodology; the data is described in Section 3; Section 4 presents the main results and Section 5
concludes.
2. Estimation Framework
The econometric model employed in this paper to explore Malthus's hypothesis of population
expansions leading to an increase in the likelihood of conflict follows a two-stage instrumental
variable approach that treats both population size and per capita GDP as endogenous variables. In a
first stage regression population size and per capita GDP are regressed on the set of instruments and
control variables. Equations (1a) and (1b) show formally the functional specification,
log POP c , t=1, c1,c∗t1, t1,1 Drought c ,t1,2log Rainc ,t1,3 log Indexc ,t1,c ,t (1a)
log GDP c ,t=2, c2,c∗t2, t2,1 Droughtc , t2,2 log Rain c ,t2,3 log Indexc ,t2,c , t (1b)
where POP stands for population size; GDP is the level of (real) per capita GDP; Drought is a
dummy variable indicating episodes of drought; Rain is the amount of rainfall observed in a given
country-year; and Index is an index of international prices for exported commodity goods (see
Section 3 for a description of these variables). The control variables are: (i) country fixed effects
αc; (ii) country specific time trends βc*t; and (iii) year fixed effects γt. The first sub-indice "1" refers
to equation number "1" while the second sub-indice identifies the coefficient on the regressor; log
stands for the natural logarithm. The error terms εc,t are clustered at the country level to allow for
serial-correlation within countries across time.
The second stage captures the connection between measures of population pressure, poverty
and civil conflict. Using the fitted values from equations (1a) and (1b) the second stage is formally
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represented by equation (2),
Conflict c ,t=3,c3,c∗t3, t3,2 log Popc , t−13,3 log GDPc , t−13,c , t (2)
where Conflict is an indicator function that is one in the event of civil conflict and zero else. Note
that despite the presence of a binary dependent variable equation (2) is specified as a linear
probability model which is in two-stage instrumental variable estimation the preferred method
(Angrist and Krueger, 2001; Wooldridge, 2003). Although a linear model may potentially violate
Kolmogorov axioms it provides usually a good approximation of the average effect, while a
nonlinear specification would require strong identification assumptions.
Note also that population and per capita GDP are introduced in the second stage explicitly in
levels, rather than growth rates. On the one hand side, this guarantees that all possible information
contained in the levels of these variables is exploited for specific country-years in the second stage
civil conflict regressions. Clearly, Malthus's argument of population expansions increasing conflict
potential during times of food scarcity was about the (average) amount of food output available to
the population at a given point in time and not about changes relative to the previous period. The
other important advantage of a level specification is that it is immune to producing potentially
confounding results associated with a corresponding growth specification that arise due to strong
reversion in rainfall to its mean (see here Ciccone, 2008).
3. Data
The data on civil conflict is taken from the 2007 Armed Conflict Dataset of the Uppsala Conflict
Data Program (UCDP) and the Centre for the Study of Civil War at the International Peace
Research Institute, Oslo (PRIO). The UCDP/PRIO Armed Conflict Database defines civil conflict
as a "contested incompatibility which concerns government and/or territory where the use of armed
force between two parties, of which at least one is the government of a state, results in at least 25
battle deaths." Statistically, civil conflict incidence is captured by an indicator variable that is one
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in the event of civil conflict and zero else, while civil conflict onset is an indicator variable that
takes on the value of one each time a new conflict has started. For some summary statistics on civil
conflict see Table I, Panel A.
Data on population size and real per capita GDP is taken from the Penn World Tables 6.2
(Heston et al., 2006). Other measures capturing forces of population pressure - working age
population (ages 15-64), youth bulges (ages 0-14), male population, and population density - are
obtained from the World Development Indicators (2007). Summary statistics of these variables (in
log points) can be found in Panel B.
Following Miguel, Satyanath and Sergenti (2004) observations on rainfall for the Sub-
Saharan region come from the NASA Global Precipitation Climatology Project, Version 2 (Adler et
al, 2003). Based on this data, droughts are defined by an indicator variable that is one if the drop
over two consecutive years in the level of rainfall falls in the lower 5 percent quantile, capturing
time periods where countries experienced a severe and unexpected drop in agricultural output (in
Section 4.3.3 alternatives to this drought indicator are discussed). The index of international prices
for exported commodities is taken from Brückner and Ciccone (2007). This index is constructed
using fixed export shares, which has the implication that the index's time-series variation stems
entirely from fluctuations contained in the international commodity prices (these are obtained from
the IMF). For some summary statistics see Panel C.
The panel is strongly balanced with each of the 37 Sub-Saharan African countries containing
24 annual observations for the period 1981-2004.
4. Empirical Results
4.1 Rainfall, Commodity Prices, Population Size, and Per Capita GDP
The first stage estimates are reported in Table II, where the included control variables are country
fixed effects, country specific time trends, and common year fixed effects (all jointly significant at
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the 1 percent level). Column (1) shows that severe droughts in Sub-Saharan Africa lead to an
average decrease in the total population size of over 1.6 percentage points, significant at the 1
percent level. Minor changes in the level of rainfall, as captured by the linear rainfall term, had no
significant effect. The international commodity price index is also insignificant. Columns (2) and
(3) investigate whether this relationship is maintained when restricting attention to working age
population, or youth bulges. This yields virtually the same point estimates as in column (1), with
the drought dummy being highly significantly negative. Column (4) shows that droughts had also
an equally significantly negative effect on male population.
As an identification check column (5) repeats the first stage regression of column (1) but
includes as additional regressor an indicator variable that is one if the drop over two consecutive
years in the international commodity price falls in the lower 5 percent quantile. The definition of
this indicator variable is fully analogous to the indicator variable that captures the event of drought.
But whereas there exists ample documentation by media and international organizations of severe
droughts leading to a drop in the population size, say, due to mass migration or people due from
starvation, no such extreme consequences have been documented for slumps in prices of exported
commodity good (FAO, 2005; WHO, 2006). The commodity price crash dummy should hence be
insignificant. This is indeed the case as shown in column (5). Moreover, the drought indicator
remains highly significantly negative.
Column (6) reports the first stage estimates for the per capita GDP regression. As in
Miguel, Satyanath, and Sergenti (2004) the paper finds a positive effect of rainfall on economic
output: a ten percentage point increase in the amount of rainfall is associated with an average
increase in per capita GDP in that same year by 0.75 percentage points, significant at the 2 percent
level. Also, windfalls from commodity prices were a blessing for Sub-Saharan countries: a ten
percentage point increase in the Brückner and Ciccone (2007) commodity price index was
associated with an increase in per capita GDP by over 0.91 percentage points, significant at the 5
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percent level.5
To outrule that statistical inference is invalid due to the presence of unit roots in per capita
GDP, international commodity prices, or population size the Hadri (2000) Lagrange-Multiplier
(LM) test is computed for the residuals of the first stage regressions. The test statistic is based on
the null of stationarity around a country-specific deterministic trend and takes into account serial
dependence in the disturbance term by using a Newey-West kernel estimate of the long-run
variance. As can be seen from the p-values listed in Table II, there is no evidence that first-stage
residuals follow unit-root processes.
4.2 Population Pressures, Per Capita GDP, and Civil Conflict
4.2.1 Conflict Incidence
Table III presents estimates on the effect that changes in per capita GDP and population size exert
on civil conflict incidence. In columns (1) and (2) civil conflict incidence is regressed on a set of
cross-sectional control variables, the log of population size lagged one period, and the log of per
capita GDP, which is also lagged one period. The probit regression of column (1) is estimated
using maximum likelihood and reported coefficients are marginal effects evaluated at sample
means. It turns out that the only variable significant at conventional confidence levels is the log
share of mountainous terrain. Population size and per capita GDP are both insignificant. Also,
cross-sectional differences in ethnic fractionalization and the level of primary school attainment are
not significantly associated with a higher or lower average incidences of civil conflict. In column
5 Note that the drought dummy in the per capita GDP regression is insignificant. The first stage yields thus a linear
relationship between rainfall and per capita GDP (higher order terms of rainfall are insignificant, results not shown),
while for population size it is non-linear. Theoretically, one possible explanation for this nonlinearity in the
population variable is the necessity to satisfy basic biological needs: if income is not sufficient to finance the
purchase of goods necessary to fulfill alimentation requirements then people will die. Another explanation is that
whenever survival is at stake then people, which under normal circumstances act as if being risk averse, will be
ready to take the gamble and hence opt for risky strategies, such as migration for instance (Fafchamps, 2003).
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(2) exactly the same set of variables are included but the functional form is changed to a linear
probability model. Comparing these point estimates with the marginal effects of column (1) the
least squares and probit regressions yield quite similar coefficients. Moreover, the only variable
that is significant is a country's share of mountainous terrain.
The regressions in column (1) and (2) should be interpreted with care since there remain
many difficult to measure variables that reflect cross-sectional differences in social fragmentation,
institutional quality or geographic conditions that have only been captured imperfectly by the set of
included control variables. Also, some of these variables such as ethnic fractionalization and in
particular the level of primary schooling are likely to be endogenous to the incidence of civil
conflict. To address these issues column (3) implements country fixed effects to account for
unobserved cross-country heterogeneity. This still results in insignificant coefficients on the
population variable. And when additionally controlling in column (4) for country specific time
trends and year fixed effects the point estimate turns out to have even the wrong sign. In particular,
the negative coefficient is indicative of the presence of large downward bias that arises from civil
conflict reducing a country's population size.
Column (5) estimates the impact that population size exhibits on civil conflict incidence,
using a two-stage least squares country fixed effects approach. The excluded instruments are the
drought dummy, rainfall, and the international commodity price index. The coefficient on
population size is positive, almost ten times larger than the corresponding least squares estimate of
column (3), and highly statistically significant (p-value of 0.026): a one percentage point increase in
past year's population size increases the likelihood of civil conflict in the following year by over 1.2
percentage points. Per capita GDP enters this second stage country fixed effects regression with the
correct sign although still insignificant at conventional confidence levels (p-value of 0.119).
In column (6) country trends and common year effects are included as additional control
variables. This results in an even larger coefficient on the population variable: for each one
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percentage point increase in an African country's population size the likelihood for it to suffer in the
following year from a civil conflict increases by over 6.6 percentage points. And, despite the
substantial increase in estimation uncertainty (including time controls leads to an eight-fold increase
of the second stage standard error) this point estimate is still significantly different from zero at
conventional confidence levels (p-value of 0.095). Furthermore, once the time controls are included
the per capita GDP variable enters also as highly statistically significant (p-value 0.014): increasing
a typical African country's per capita income by one percentage point decreases the likelihood of
civil conflict incidence by over 1.9 percentage points. Column (7) repeats this regression but
raising the battle death benchmark to 1000, thus excluding minor civil conflicts. This results also in
a large and positive coefficient on the population variable, although not significant at conventional
confidence levels.
Table IV considers more specific measures of population pressure such as working age
population (column (1)), youth bulges (column (2)), male population (column (3)), and population
density (column (4)). Some voices in the literature have argued that these variables capture more
appropriately the channels through which population expansions fuel civil conflict.6 Panel A
reports second stage country fixed effects estimates. All four measures of population pressure are at
least significant at the 4 percent level with coefficients ranging from 1.122 (youth bulges) to 1.244
(male population). Panel B repeats these regressions including as additional control variables
country specific time trends and year fixed effects. This results in quantitatively very similar point
estimates to those of Table III, although some turn out to be less precisely estimated.
4.2.2 Civil Conflict Onset.
The dependent variable in Tables III and IV was the incidence of civil conflict, which incorporates
elements of conflict onset as well as elements of conflict duration. One possible interpretation of
the obtained results is hence that increases in population and lower per capita GDP act as fuel to an
6 See for instance de Soysa (2002), or Urdal (2005).
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already ongoing civil conflict. But an equally plausible interpretation would be that an increase in
an African country's population size increases the likelihood for this country to become befallen in
the following year by a new or recurrent civil conflict.
In order to explore specifically the impact that past year's population size exhibits on current
year's likelihood of civil conflict outbreak Table V presents second stage estimates for civil conflict
onset. The first two columns report the estimates of the least squares regressions that include as
control variables country fixed effects (column (1)) as well as country specific time trends and year
fixed effects (column (2)). In both least squares regressions is the population size variable
insignificant. Again the coefficient is negative, pointing towards strong downward bias that arises
from an onset of civil conflict reducing population size, say, due to migration for instance.
In columns (2)-(6) population size and per capita GDP are instrumented by a drought
dummy, rainfall, and the index of international commodity prices. Now the effect of population
expansions is positive and highly statistically significant. The point estimate of column (4), where
the control variables are country fixed effects, country specific time trends, as well as year fixed
effects, indicates that a one percentage point increase in past year's population size increases the
likelihood for an African country to experience an onset of civil conflict by over 5.1 percentage
points, significant at the 5 percent level. In turn, an increase of past year's per capita GDP due to
better rainfall conditions or higher international commodity prices significantly reduces the risk for
Sub-Saharan countries to become befallen by civil conflict. Thus, increases in Sub-Saharan
population size and lower per capita income increase the region's rate of conflict incidence, and, in
particular, increase the risk for African countries to become befallen by a new or recurrent civil
conflict.
4.3 Robustness
4.3.1 Overidentification
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On the statistical side the results of Tables III, IV, and V seem to bear a clear message: not
addressing the bias that results from reverse causality and possibly severe measurement error would
lead to the conclusion that Malthus's prediction of population pressures increasing the likelihood of
war failed in significantly explaining Sub-Saharan civil conflict. In turn, the instrumental variable
estimates provided stark evidence that population expansions not accompanied by significant
increases in per capita income place Sub-Saharan countries at a substantially higher risk of suffering
from civil conflict.
An important assumption underlying the validity of the IV-estimates is that the drought
dummy, rainfall, and the index of international commodity prices have no effect on civil conflict
other than through per capita GDP or population size. For instance, droughts may stimulate an
influx of food aid. The instantaneous average effect would be captured by the per GDP variable,
but aid could have effects on income distribution, generating a more egalitarian society. If a
reduction in inequality is associated with less conflict then this implies that both the per capita GDP
and population coefficient are over-estimated, e.g. the true impact would be larger (smaller) for
increases in per capita GDP (population size) than shown in Tables III, IV, and V. On the empirical
side, there is not much evidence though pointing towards a systematic effect of inequality on civil
conflict (see for example Hegre and Sambanis, 2006, or Blattman and Miguel, 2008). A somewhat
more concerning issue is that rainfall could affect warfare strategies. One such factor is for instance
troop mobility, which may be higher during times of low rainfall. Another factor may be that
heatwaves associated with episodes of drought make soldiers and civilians more aggressive,
increasing potential for violent action. Note however that second stage variables (and instruments)
are all lagged one period. Hence it would have to be the case that rainfall of the past year
significantly affects warfare possibilities of the following year.
Regarding the international commodity price index a concern may be that the instrument is
not entirely exogenous to Sub-Saharan civil conflict since large producers of world commodities
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may have an impact on international commodity prices. Note that if this were the case then the
second stage estimate of the per capita GDP variable would be an upper bound since civil conflict
will act as a supply shock to the international commodity market, increasing prices and hence per
capita GDP. Brückner and Ciccone (2007) addressed this concern by excluding large commodity
producers, running a reduced form and second stage regression without those commodities where
an African country's exports constitute more than 3% of world production. This did not
significantly change point estimates. Furthermore, in their second stage regressions international
commodity price growth conditional on per capita GDP growth was always insignificant.
The instrumental variable regressions in this paper employ three instruments for two
endogenous variables, implying that the system is overidentified. Hence, it is possible to formally
test instrument validity by running the Hansen J-test. This Lagrange-Multiplier test is based on the
null that instruments are jointly uncorrelated to second stage error terms. If the obtained test-
statistic is significantly different from zero then this would cast serious doubt on instrument
validity. But as can be readily seen from the computed p-values at the bottom of Tables III, IV, and
V the Hansen J-test statistic is always insignificant. Thus, the test does not provide evidence
against the assumption that the instruments fulfill the exclusion restriction and are exogenous to the
presence of Sub-Saharan civil conflicts.
4.3.2 Weak Instruments
The other important criteria in instrumental variable analysis is the instrument strength in providing
a sufficiently precise first stage fit. Staiger and Stock (1997) suggested as a rule of thumb criteria a
first stage F-stat of around 10, which for the population (per capita GDP) regression was 10.79
(8.75). The tabulations in Stock and Yogo (2002) show however that in the presence of multiple
endogenous regressors the first stage F-stat should increasingly exceed this rule-of-thumb value as
the number of instruments increases.
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To address this issue Table VI presents second stage estimates using Fuller limited
information maximum-likelihood estimators. These estimators have been shown to be more robust
to weak instruments than two-stage least squares (e.g. Stock, Wright, and Yogo, 2002; Hahn and
Hausman, 2003). The two Fuller limited-information maximum likelihood estimates are calculated
for Fuller constants 4 and 1. The Fuller 1 estimator yields the most unbiased estimator and is
recommended when one wants to test hypotheses; the Fuller 4 estimator minimizes the mean
squared error of the estimator (Fuller, 1977).
In columns (1)-(4) the dependent variable is civil conflict incidence, and in columns (5)-(8)
civil conflict onset. In all cases is the population coefficient positive and statistically significant.
The Fuller second stage conflict incidence country fixed effects estimates yield that a one
percentage point population increase raises the likelihood of civil conflict in the following year by
over one percentage point, with a p-value of 0.03 for the Fuller 1 estimator and 0.008 for the Fuller
4 estimator (columns (1) and (2)). Quantitatively these point estimates increase when including
country specific time trends and Africa wide year controls (columns (3) and (4)). Now the
coefficient is 5.98 for the Fuller 1 estimator and 4.13 for the Fuller 4 estimator.7 The Fuller second
stage estimates also highlight the quantitatively strong and statistically significant impact that
Malthusian forces exhibited on Sub-Saharan civil conflict onset: a one percentage point increase of
past year's population size increased the likelihood of a civil conflict outbreak in the following year
between 3.4 (Fuller 4) and 4.8 percentage points (Fuller 1), significant at least at the 4 percent level.
4.3.3 Alternative Drought Data
The so far presented instrumental variable estimates relied on the use of a drought indicator that was
defined as taking on the value of one if the drop over two consecutive years in the level of rainfall
7 Note that estimation uncertainty increases substantially when including country trends and year fixed effects
(standard errors for the population coefficients in columns (3) and (4) are seven times larger than the corresponding
standard errors in columns (1) and (2)). Nevertheless, point estimates would still be significant at 10 percent level.
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falls in the lower 5 percent quantile. As a robustness check Table VII, Panel A, columns (1)-(3)
show second stage fixed effects estimates that are based on extending the definition of the drought
dummy to include also the 10 percent quantile. The result is a significantly positive point estimate
on the population variable with coefficients ranging from 1.27 to 2.188 (the corresponding p-values
are 0.002 and 0.083 respectively). In columns (1)-(3) of Panel B where in addition to the country
fixed effects country specific time trends and year fixed effects are included coefficients are
quantitatively larger (ranging from 3.30 to 6.53) but are also very imprecisely estimated and not
statistically significant at conventional confidence levels. In general, extending the cut-off level
much beyond the 5 percent quantile, say, to the 15 percent quantile, would not yield significant first
nor second stage point estimates (results not shown). This negative result should not be too
surprising though given that it are severe and repeated droughts, rather than episodes of low rainfall
during which food output is below average, that lead to the famines associated with people dying in
Africa due to starvation or large streams of migration in search for food and better living conditions
(Sen, 1981; Fafchamp, 2003; FAO, 2005; WHO, 2006).
An alternative to the rainfall based drought indicator would have been to draw directly on
drought data provided by the Universite Catholique de Louvain's International Emergency Disasters
Database (EM-DAT, 2008).8 In this database droughts are reported if any of the following
minimum criteria are fulfilled: (i) ten or more people reported killed; (ii) hundred or more people
affected; (iii) a declaration of a state of emergency; or (iv) a call for international assistance. Table
VII, Panel A, columns (4)-(6) compute second stage country fixed effect estimates that use instead
of the GPCP rainfall based drought dummy the EM-DAT drought indicator as an instrumental
variable. This drought indicator is defined in complete analogy to the rainfall based drought
indicator, taking on the value of one for the 5 percent harshest droughts, as measured by number of
people affected according to EM-DAT. The result is a positive and significant effect on the lagged
population variable with coefficients ranging between 1.27 to 2.15 (the p-values are 0.008 and
8 The data is publicly available at www.emdat.be.
16
0.093 respectively). Finally, columns (7)-(9) combine EM-DAT drought data with the GPCP
rainfall based indicator generating a dummy variable that is one if both EM-DAT and GPCP agree
jointly on the event of drought. This yields somewhat smaller point estimates on the population
variable (coefficients range between 1.61 and 1.09), which are significant at the 5 percent level.
For the purpose of the instrumental variable analysis the rainfall based drought indicator has
at least two important advantages over the EM-DAT data: first, rainfall drought data is interpretable
as triggering famine, which in Sub-Saharan Africa has been associated with waves of mass
migration and people dying due to starvation; second, since rainfall is random the drought indicator
is completely exogenous to the presence of Sub-Saharan civil conflict. This stands in stark contrast
to the EM-DAT drought indicator, which is an outcome variable potentially endogenous to the
conditions of the local environment. Moreover, generating a drought indicator based on number of
people affected may produce confounding effects due to cross-country differences in the size and
the trend of the population variable. As columns (4)-(9) of Panel B in Table VII highlight the
second stage estimates based on the EM-DAT drought indicators are statistically much inferior to
the previous estimates once country specific time trends and year fixed effects are included as
additional control variables. Point estimates range from 0.98 to 8.47 with neither of these
coefficients being statistically significant at conventional confidence levels.
5. Conclusion
This paper addressed core issues at the heart of the debate of whether population expansions cause
civil conflict by exploiting randomness of drought as an instrumental variable for Sub-Saharan
population size. Taking Malthus seriously, the second stage regressions explicitly accounted for
year-specific cross-sectional differences in income levels by using rainfall and international
commodity prices as an instrumental variable for per capita GDP.
Quantitatively large and statistically significant empirical support is found for Malthusian
17
forces constituting a source of Sub-Saharan civil conflict: a one percentage point population
increase may raise the likelihood of observing in the following year a civil conflict by over 6.6
percentage points and increases the risk of a conflict onset by over 5.1 percentage points.
Comparing this instrumental variable estimate to the insignificant and quantitatively very small
least square estimates highlights the need to employ econometric methods that are able to
convincingly deal with the biases that arise when investigating empirically the link between
population and conflict.
From the policy perspective the paper bears a clear message: population expansions in Sub-
Saharan Africa increase these countries' likelihood of suffering from civil conflict. A way to
combat the higher risk of intra-state conflict is to ensure that population increases are accompanied
by a substantial improvement in the economic environment. As income rises the opportunity cost
of war increases and this reduces the incentives to engage in combat. Ultimately then, the future
role of Malthusian pressures in affecting Sub-Saharan civil conflict likelihood will depend on
whether increases in prosperity are associated with equally large percentage increases in population
size or whether Sub-Saharan Africa follows the path of western societies. This will be key to
understanding whether population planning as a means of preempting civil conflict is a necessary
let a lone effective strategy, or whether due to higher wages optimal household decision making
will endogenously solve the challenges arising from food scarcity and overpopulation that have
marked Sub-Saharan Africa, even in the most recent decades.
Author Affiliation: Universitat Pompeu Fabra and Universitat de Barcelona
18
6. References
Adler, R.F., G.J. Huffman, A. Chang, R. Ferraro, P. Xie, J. Janowiak, B. Rudolf, U. Schneider, S.
Curtis, D. Bolvin, A. Gruber, J. Susskind, P. Arkin, and E. Nelkin (2003). “The Version 2 Global
Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present).”
Journal of Hydrometeorology 4: 1147-1167.
Angrist, J. and A. Krueger (2001). “Instrumental Variables and the Search for Identification: From
Supply and Demand to Natural Experiments.” Journal of Economic Perspectives 15: 69-85.
Blattman, C. and E. Miguel (2008). "Civil War." Journal of the Economic Literature, forthcoming.
Brückner, M. and A. Ciccone (2007). "Growth, Democracy, and Civil War." CEPR Discussion
Paper No. 6568.
Ciccone, A. (2008). "Transitory Economics Shocks and Civil Conflict." Working Paper UPF,
Barcelona.
Davenport, C., W. Moore, S. Poe (2003). "Sometimes You Just Have to Leave: Domestic Threats
and Refugee Movements: 1964-1989." International Interactions 29: 27-55.
Deaton, A. (2005). "Measuring Poverty in a Growing World (Or Measuring Growth in a Poor
World)." Review of Economics and Statistics 87 (1):1-19.
De Soysa, I. (2002). "Paradise is a Bazaar? Greed, Creed, and Governance in Civil War, 1989-
1999." Journal of Peace Research 39 (4):395-416.
EM-DAT (2008). "EM-DATA: The OFDA/CRED International Disasters Database." Universite
Catholique de Louvain, Brussels, Belgium, available at www.emdat.net.
Fafchamps, M. (2003). Rural Poverty, Risk, and Development. Cheltenham: Edward Elgar.
FAO (2005). "Food Supply Situation and Crop Prospects in Sub-Saharan Africa." Food and
Agriculture Organization of the United Nations, Africa Report, April 2005.
Fuller, W. (1977). "Some Properties of a Modification of the Limited Information Estimator."
Econometrica 45: 939-954.
19
Gleditsch, N. (2001). "Armed Conflict and the Environment.", in P. Diehl & N. Gleditsch, eds,
Environmental Conflict. Boulder, CO:Westview (251–272).
Ghobarah, H., P. Huth, and B. Russet (2003). "Civil Wars Kill and Maim People Long after the
Shooting Stops." American Political Science Review 97 (2):189-202.
Hart, M. (1992). The 100: A Ranking of the Most Influential Persons in History. New York: Carol
Publishing Group.
Hegre, H. and N. Sambanis (2006). "Sensitivity Analysis of Empirical Results of Civil War Onset."
Journal of Conflict Resolution 50 (6):937-961.
Heston, A. (1994). "A Brief Review of Some Problems in Using National Accounts Data in Level
of Output Comparisons and Growth Studies." Journal of Development Economics 44: 29-52.
Heston, A., Summers, R., and B. Aten (2006). Penn World Table Version 6.2. Center for
International Comparisons of Production, Income and Prices, University of Pennsylvania,
September 2006.
Intergovernmental Panel on Climate Change (2001). Climate Change 2001: Impacts, Adaptation
and Vulnerability. New York, Cambridge University Press.
Malthus, Thomas Robert (1798/1997). An Essay on the Principle of Population. London: Electronic
Book.
Miguel, E., S. Satyanath, and E. Sergenti (2004). 'Economic Shocks and Civil Conflict: An
Instrumental Variables Approach.' Journal of Political Economy 112 (41):725-753.
Montalvo, J. and M. Reynal-Querol (2007). "Fighting Against Malaria: Prevent Wars While
Waiting For The Miraculous Vaccine." Review of Economics and Statistics 89 (1): 165-177.
UNHCR (2007). "Global Report 2006". United Nations High Commissioners for Refugees, June
2007.
Urdal, H (2005). "People vs. Malthus: Population Pressures, Environmental Degradation and
Armed Conflict Revisited." Journal of Peace Research 42 (4):417-434.
20
Sen, A. (1981). Poverty and Famines. Oxford: Clarendon Press.
Wooldridge, J. (2002). Econometric Analysis of Cross Section and Panel Data. Cambridge, Mass.:
MIT Press.
World Bank (2003). Breaking the Conflict Trap: Civil War and Development Policy. Oxford
University Press.
WHO (2006). "WHO Responding to the Drought Crisis in the Horn of Africa Region." World
Health Organization, ICP/EHA-HAC, May 2006.
21
Table I Descriptive Statistics
A. Measures of Conflict
Mean Std. Dev. Observations
Civil Conflict Incidence > 25 Battle Deaths 0.267 0.443 888
Civil Conflict Onset > 25 Battle Deaths 0.047 0.212 888
Civil Conflict Incidence >1000 Battle Deaths 0.123 0.328 888
Civil Conflict Onset >1000 Battle Deaths 0.023 0.148 888
B. Population Pressure and Per Capita Income
Total Population 15.841 1.150 888
Population Aged 15-64 15.186 1.158 888
Population Aged 0-14 15.040 1.159 888
Male Population 15.138 1.153 888
Real Per Capita GDP 6.996 0.734 888
C . Instrumental Variables
Rainfall (GPCP) 6.743 0.629 888
Commodity Price Index 4.094 0.494 888
Drought 0.050 0.217 888
Table IIDrought, Population Size, and Per Capita GDP
Population Size GDP
(1) (2) (3) (4) (5) (6) (7)
All Age 15-64 Age 0-14 Male All Per Capita GDP
Per Capita GDP
Drought, t -0.016***(0.005)
-0.017***(0.005)
-0.017***(0.006)
-0.016***(0.005)
-0.012***(0.004)
-0.001(0.014)
0.004(0.37)
Log Rainfall, t -0.006(0.012)
-0.006(0.015)
-0.007(0.011)
-0.004(0.013)
-0.010(0.012)
0.075**(0.029)
0.079***(0.030)
Log Index, t 0.012(0.010)
0.018(0.013)
0.011(0.11)
0.013(0.010)
0.010(0.012)
0.085**(0.041)
0.090**(0.045)
Commodity Crash, t 0.001(0.004)
0.026(0.020)
Hadri Unit Root test 0.8063 0.8082 0.79 0.8092 0.7367 0.7479 0.6562
Country Fixed Effects Yes Yes Yes Yes Yes Yes Yes
Year Effects and Trends Yes Yes Yes Yes Yes Yes Yes
No Observations 888 888 888 888 851 888 851Note: Method of estimation is least squares with Huber robust standard errors (shown in parentheses) clustered at the country level. *Significantly different from zero at 90 percent confidence, ** 95 percent confidence, *** 99 percent confidence.
22
Table IIIPopulation Size, Per Capita GDP, and Civil Conflict Incidence
Conflict> 25 Battle Deaths Civil War
(1) (2) (3) (4) (5) (6) (7)
Probit LS LS LS 2SLS 2SLS 2SLS
Log Population, t-1 0.022(0.43)
0.009(0.038)
0.149(0.144)
-0.628(0.731)
1.244**(0.560)
6.607*(3.962)
2.377(2.607)
Log per capita GDP, t-1 -0.092(0.062)
-0.064(0.050)
-0.163(0.103)
0.217(0.135)
-1.231(0.791)
-1.944**(0.787)
-1.216**(0.538)
Mountainous Terrain 0.079**(0.042)
0.083**(0.040)
British Colony -0.064(0.092)
-0.047(0.091)
Ethnic Fractionalization 0.106(0.249)
0.094(0.267)
Primary Education -0.180(0.115)
-0.188(0.123)
Overidentification . . . . 0.2051 0.5286 0.8222
Country Fixed Effects No No Yes Yes Yes Yes Yes
Year Effects and Trends No No No Yes No Yes Yes
No Observations 888 888 888 888 888 888 888Note: Method of estimation in columns (1)-(4) is least squares with Huber robust standard errors (listed in parentheses) clustered at the country level; columns (5)-(7) two-stage least squares. The excluded instruments in the 2SLS regressions are the drought indicator variable, the log level of rainfall, and the log level of the international commodity price index, all lagged one period. The Hansen J overidentification test result is provided for all second stage regressions in form of p-values. *Significantly different from zero at 90 percent confidence, ** 95 percent confidence, *** 99 percent confidence.
23
Table IVPopulation Size, Per Capita GDP, and Civil Conflict Incidence
Conflict > 25 Battle Deaths
Panel A: Country Fixed Effects
(1) (2) (3) (4)
2SLS 2SLS 2SLS 2SLS
Age 15-64 Age 0-14 Male Density
Log Population, t-1 1.227**(0.520)
1.122**(0.551)
1.241**(0.558)
1.225**(0.556)
Log per capita GDP, t-1 -1.196(0.743)
-1.077(0.775)
-1.227(0.787)
-1.226(0.794)
Overidentification 0.3270 0.1027 0.2034 0.1917
No Observations 888 888 888 888
Panel B: Country Fixed Effects + Time Controls
(1) (2) (3) (4)
2SLS 2SLS 2SLS 2SLS
Age 15-64 Age 0-14 Male Density
Log Population, t-1 6.100*(3.667)
6.160*(3.700)
6.614(4.101)
6.983(4.391)
Log per capita GDP, t-1 -2.167**(0.883)
-1.779**(0.729)
-2.024**(0.833)
-2.100**(0.869)
Overidentification 0.6806 0.4708 0.4758 0.5138
No Observations 888 888 888 888Note: Method of estimation is two-stage least squares. The excluded instruments are the drought indicator variable, the log level of rainfall, and the log level of the international commodity price index, all lagged one period. Control variables in Panel A are country fixed effects. Panel B includes in addition to the country fixed effects country specific time trends and year fixed effects. Standard errors are provided in parentheses. The Hansen J overidentification test result is provided for all second stage regressions in form of p-values. *Significantly different from zero at 90 percent confidence, ** 95 percent confidence, *** 99 percent confidence.
Table V Population Size, Per Capita GDP, and Civil Conflict Onset
Conflict Onset > 25 Battle Deaths Civil War Onset
(1) (2) (3) (4) (5) (6)
LS LS 2SLS 2SLS 2SLS 2SLS
Log Population, t-1 -0.004(0.056)
-0.234(-0.78)
0.720*(0.381)
5.179**(2.587)
0.362*(0.210)
2.164(1.448)
Log per capita GDP, t-1 0.001(0.030)
0.148(0.054)
-0.932*(0.532)
-1.376**(0.576)
-0.469(0.306)
-0.641(0.384)
Overidentification . . 0.5269 0.4432 0.8385 0.9549
Country Fixed Effects Yes Yes Yes Yes Yes Yes
Year Effects and Trends No Yes No Yes No Yes
No Observations 888 888 888 888 888 888Note: Method of estimation in columns (1)-(2) is least squares with Huber robust standard errors (listed in parentheses) clustered at the country level; columns (3)-(6) two-stage least squares. The excluded instruments in the 2SLS regressions are the drought indicator variable, the log level of rainfall, and the log level of the international commodity price index, all lagged one period. The Hansen J overidentification test result is provided for all second stage regressions in form of p-values. *Significantly different from zero at 90 percent confidence, ** 95 percent confidence, *** 99 percent confidence.
24
Table VI Population Size, Per Capita GDP, and Civil Conflict
Conflict Incidence Conflict Onset
(1) (2) (3) (4) (5) (6) (7) (8)
Fuller 1 Fuller 4 Fuller 1 Fuller 4 Fuller 1 Fuller 4 Fuller 1 Fuller 4
Log Population, t-1 1.272**(0.586)
1.033***(0.391)
5.980*(3.551)
4.134*(2.472)
0.658**(0.333)
0.489**(0.225)
4.773**(2.334)
3.387**(1.574)
Log per capita GDP, t-1 -1.273(0.832)
-0.923*(0.511)
-1.824**(0.725)
-1.453**(0.556)
-0.838*(0.458)
-0.584**(0.287)
-1.301**(0.536)
-1.029**(0.411)
Country Fixed Effects Yes Yes Yes Yes Yes Yes Yes Yes
Year Effects and Trends No No Yes Yes No No Yes Yes
No Observations 888 888 888 888 888 888 888 888Note: Method of estimation is the Fuller limited information maximum likelihood estimator. The excluded instruments are the drought indicator variable, the log level of rainfall, and the log level of the international commodity price index, all lagged one period. Standard errors are provided in parentheses. *Significantly different from zero at 90 percent confidence, ** 95 percent confidence, *** 99 percent confidence.
Table VII Population Size, Per Capita GDP, and Civil Conflict
10% Drought Shock EM-DAT Drought EM-DAT & 5% Joint
Panel A: Country Fixed Effects
(1) (2) (3) (4) (5) (6) (7) (8) (9)
2SLS Fuller 1 Fuller 4 2SLS Fuller 1 Fuller 4 2SLS Fuller 1 Fuller 4
Log Population, t-1 2.188*(1.277)
1.801**(0.835)
1.271***(0.417)
2.148*(1.278)
1.805**(0.886)
1.267***(0.447)
1.615**(0.840)
1.511**(0.732)
1.093***(0.394)
Log per capita GDP, t-1
-2.555(1.829)
-1.990*(1.167)
-1.225**(0.523)
-2.466(1.656)
-2.004*(1.127)
-1.284**(0.528)
-1.741(1.159)
-1.592(1.000)
-1.000**(0.483)
Overidentification 0.7974 0.7966 0.7966 0.7825 0.7698 0.7698 0.3234 0.4212 0.4212
No Observations 888 888 888 888 888 888 888 888 888
Panel B: Country Fixed Effects + Time Controls
(1) (2) (3) (4) (5) (6) (7) (8) (9)
2SLS Fuller 1 Fuller 4 2SLS Fuller 1 Fuller 4 2SLS Fuller 1 Fuller 4
Log Population, t-1 6.534(1.25)
5.605(4.420)
3.301(2.714)
8.474(10.330)
5.377(6.176)
0.984(2.287)
8.166(9.375)
5.275(5.801)
1.350(2.310)
Log per capita GDP, t-1
-1.936**(-2.23)
-1.785**(0.767)
-1.375**(0.553)
-2.182*(1.328)
-1.793**(0.847)
-1.161**(0.457)
-2.078*(1.130)
-1.743**(0.773)
-1.208**(0.473)
Overidentification 0.5153 0.5819 0.5819 0.5492 0.5016 0.5016 0.4694 0.5637 0.5637
No Observations 888 888 888 888 888 888 888 888 888Note: Method of estimation in columns (1), (4), and (7) is two-stage least squares; in columns (2), (3), (5), (6), (8), and (9) Fuller limited information maximum likelihood. The excluded instruments are the drought indicator variable, the log level of rainfall, and the log level of the international commodity price index, all lagged one period. Control variables in Panel A are country fixed effects. Panel B includes in addition to the country fixed effects country specific time trends and year fixed effects. Standard errors are provided in parentheses. The Hansen J overidentification test result is provided for all second stage regressions in form of p-values. *Significantly different from zero at 90 percent confidence, ** 95 percent confidence, *** 99 percent confidence.
25