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: markus.bruckner@upf.edu.

"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

1

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.

2

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

5

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

6

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

7

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

8

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).

9

(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

10

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).

11

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.

15

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

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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