Post on 15-Sep-2018
« Drought and Civil War
in Sub-Saharan Africa »
Mathieu COUTTENIER,
Raphael SOUBEYRAN
DR n°2010-13
(revised version octobre 2012)
DROUGHT AND CIVIL WAR IN SUB-SAHARAN AFRICA∗
Mathieu COUTTENIER † Raphael SOUBEYRAN‡
Abstract
We show that civil war is strongly related to drought in sub-Saharan Africa. We consider
the effect of variations in the Palmer Drought Severity Index (Palmer 1965) - a cumulative
index that combines precipitation, temperature and the local characteristics of the soil - on
the risk of civil war. While the recent, contentious debate on the link between climate and
civil war has mainly focused on precipitation and temperature, without obtaining converging
results, the Palmer index describes social exposure to water stress in a more efficient way.
We show that it is a key factor of civil war in sub-Saharan Africa and that this result is
robust to various specifications and passes a series of sensitivity tests. Also, our results
indicate that agriculture, ethnic diversity and institutional quality are important factors to
link climate and civil war.
Keywords: Climate Change, Drought, Civil War.
JEL Codes: O10 , O55, P0, Q0.
∗We benefited from invaluable comments by Paola Conconi, Matthieu Crozet, James Fearon, David Laitin,Thierry Mayer, Edward Miguel, Elodie Rouviere, Julie Subervie, Mathias Thoenig, Thierry Verdier and par-ticipants in seminars at Stanford, Lausanne, DIAL and Montpellier. We thank Annie Hoffstetter for technicalassistance. Mathieu Couttenier would like to thank Sciences Po. Paris and the Stanford Political Science De-partment for their welcome. Raphael Soubeyran extends his thanks for financial support to the “RISECO”ANRproject, ANR-08-JCJC-0074-01.†University of Lausanne. Quartier UNIL-Dorigny Batiment Extranef 1015 Lausanne. Email: math-
ieu.couttenier@unil.ch‡INRA-LAMETA, Bat. 26, 2 Place Viala, 34060, Montpellier, France. Email: soubeyra@supagro.inra.fr
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1 Introduction
According to the Intergovernmental Panel on Climate Change (2007), changes in the global
climate will generate an increase in the number of abnormal climatic events across the world,
such as droughts and floods. These climatic anomalies might have disastrous consequences for
countries with a scarce fresh water supply and economies that depend on the local agriculture.
Given that agricultural activities account for between 60% and 100% of the income of the poorest
African households (Davis et al. 2007) and that these households often have no access to safe
water,1 sub-Saharan Africa is one of the regions most adversely affected by climate change in
the world. One of the possible consequences of climate change is an increase in conflicts. For
instance, there is now a consensus that drought has been a contributory cause of the civil war
in Darfur because it increased disputes over arable land and water, even if the conflict also had
an ethnic component since it opposed Arabs and Black Africans (Faris 2009).
The economic and political literature has started to analyze the link between climate change
and civil war, by considering sub-Saharan Africa more particularly. Evidence of the significant
impact of rainfall (Miguel et al. 2004) and temperature (Burke et al. 2009) on the risk of civil
conflict in sub-Saharan Africa has generated a contentious debate (Jensen and Gleditsch 2009,
Buhaug 2010, Burke et al. 2009, 2010a,b, Ciccone 2011, Miguel and Satyanath 2011). The
broader literature dealing with climate and violence (e.g. Hidalgo et al. 2010 and Bruckner and
Ciccone 2011) includes Hsiang et al. (2011) who focus on global climate variations instead of
on the idiosyncratic variations of rainfall and temperature and find that global climate has a
robust effect on a global measure of the risk of civil conflict.
In this paper, we focus on drought and not on rainfall or temperature shocks. We use the
most prominent meteorological index of drought - the Palmer Drought Severity Index (PDSI)
- developed in hydrology by Palmer (1965). This drought severity index is a function of the
duration and the magnitude of abnormal moisture deficiency. The PDSI captures meteorological
conditions on the ground. It also captures important effects that were missing in previous
studies: non-linearities - the effect of contemporaneous rainfall and temperatures depends on
the climate history -, interaction effects - e.g. low rainfall is more important in hot years -, and
threshold effects due to the limited capacity of the soil - e.g. rainfall water will runoff when the
soil layers are full.
We operationalize the idea that the impact of drought should be considered by exploiting a
large data set of PDSI values. While most previous studies in the literature focused on the 1980s
and the 1990s, our database covers a longer time period (1946-2005). Moreover, we show that
drought has been a key factor of civil war in sub-Saharan Africa (after independence) and that
the link between drought and the incidence of civil war in sub-Saharan African countries is very
robust. We find that the effect of PDSI remains significant even after controlling for rainfall
and temperature, suggesting that local meteorological conditions on the ground are essential.
Our results also suggest that agriculture, ethnic diversity and institutional quality are channels
for the link between drought and civil war.
The seminal paper by Miguel et al. (2004) refers to sub-Saharan African countries in the
1Many African people have no secure access to fresh water. Only 22% of Ethiopians, 29% of Somalis and 42%of Chadians have secure access to fresh water.
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1981-1999 period and shows that positive rainfall variations decrease the likelihood of civil war
through their positive impact on GDP.2 Burke et al. (2009) focus on the direct link between
climate and civil war. They study a reduced form relationship between rainfall, temperature,
and civil war and show that higher temperatures increase the likelihood of civil war. However,
the robustness of the link between contemporaneous climatic measures and civil war has been
challenged with two categories of arguments.
The first set of arguments relates to the lack of robustness to changes in the data and/or in
the coding choices. Jensen and Gleditsch (2009) show that spatial autocorrelation is important
and that taking it into account slightly weakens Miguel et al.’s result. In the present paper,
we follow Hsiang (2010) and use an estimator that takes into account both spatial and serial
autocorrelation (Conley 1999, 2008). Buhaug (2010) argues that Burke et al. (2009)’s result is
not robust to changes in the model specification (removal of fixed effects and trends), in the
rainfall and precipitation measures, or changes in the battle-death threshold (used to code civil
war indices). Buhaug et al. (2010) claim that the effect is not robust to removing a few very
influential and problematic observations, or to alternative sample periods (within the 1960-
2008 period). Burke et al. (2010a,b) answer that the inclusion of fixed effects and trends is
important to obtain unbiased estimates and that their original findings are robust to the use
of different climate data and to alternate codings of civil war. Our results cannot be subjected
to Buhaug (2010) and Buhaug et al. (2010)’s criticisms and are robust to the removal of the
most influential observations, to the use of alternative sample periods and to changes in the
battle-death threshold. However, we include country-specific fixed effects and country-specific
trends in our preferred specification.
The second category of criticism relates to the choice to model climate. Ciccone (2011)
argues that Miguel et al.’s findings rest on their choice to estimate the link between rainfall
variations and civil conflict. First, he claims that the negative link between rainfall growth
and civil war is in fact driven by a positive statistical link between lagged rainfall levels and
civil conflict in the data. Second, he uses the latest data and finds no link between rainfall
levels and civil conflict. Miguel and Satyanath (2011) answer that if the focus is on the causal
relationship between economic shocks and civil conflict, the use of rainfall levels rather than
rainfall variations does not affect their initial result.3 Our results cannot be subjected to the
same criticism. Indeed, we use a measure of drought that is based on a hydrological model.
Some recent papers report a link between climate and violence in other contexts. Hsiang
et al. (2011) show that the El Nino Southern Oscillation (ENSO) has increased the annual risk
of conflict4 from 1950 to 2004. Our analysis differs, as we focus on local drought using the
PDSI rather than a global scale measure of climate (ENSO) and we concentrate on the risk
of conflict within a country rather than on a planetary scale. We see the two approaches as
2Bruckner (2010) uses a similar approach and shows that civil war is more likely to occur following anincrease in population. Levy et al. (2005) use the Weighted Anomaly Standardized Precipitation Index (Lyonand Barnston 2005), a measure of precipitation deviation from normal. The WASP index is based on precipitationonly while the PDSI is based on precipitation, temperature, soil horizon thicknesses and textures, vegetation andtexture-based estimates of the available soil moisture.
3They also provide theoretical arguments to explain why they think that rainfall variations are a bettermeasure than rainfall levels.
4They define the probability that a randomly selected country in the data set will experience a conflict in agiven year.
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being complementary since ENSO affects local drought and the PDSI (e.g. see Dai et al. 2004).
Jacob et al. (2007) show that positive temperature shocks displace violent crime in time in the
United States. Bruckner and Ciccone (2011) find that negative rainfall shocks are followed by
democratic improvement in sub-Saharan Africa. Hidalgo et al. (2010) point out that agricultural
revenue losses due to negative rainfall shocks have increased conflict in Brazil.
The remainder of the paper is structured as follows. Section 2 describes the PDSI data, the
control variables and our estimation framework. Section 3 presents our results regarding the
effect of drought on the incidence of civil war. Section 4 concludes.
2 Data and Measurement
2.1 Palmer Drought Severity Index
Data Description
Our measure of drought is the Palmer Drought Severity Index (PDSI) developed in meteorology
by Palmer (1965). It is the most prominent meteorological drought index. This drought severity
index is a function of the duration and the magnitude of abnormal moisture deficiency. The
PDSI captures meteorological conditions on the ground and combines contemporaneous and
lagged values of temperature and rainfall data in a nonlinear model (with thresholds). First,
the index captures important interactions that were missing in previous studies. For instance,
low rainfall is more important in hot months because evapotranspiration is significant and there
is in turn less moisture recharge (or more loss if the layers are full). Indeed, high temperatures
can prevent abundant rainfall from recharging the soil moisture. Second, the index depends both
on the limited capacity of moisture accumulation of the soil and on the local characteristics of
the soil. As a consequence, abundant precipitation that reaches the accumulation capacity
of the soil will runoff (and will not be captured by the ground). Third, the PDSI takes into
account the heterogeneity in local conditions and the differences in local climate history. In
other words, the PDSI values for two different countries with the same current temperature and
rainfall levels may differ because of their differences in local conditions (e.g. the capacity of the
soil that depends on the location). PDSI values may also vary within a given country, even if
temperature and rainfall levels are the same (at two different dates) because the climate history
is different from one location to another.
The PDSI measures how moisture levels deviate from a climatological normal. It is based on
a supply and demand model of soil moisture and is calculated on precipitation and temperature
data, as well as on the local Available Water Content (AWC) of the soil. The available soil
moisture at the beginning of the period is used as a measure of past weather conditions. The
abnormal aspect of the weather is also important: deficiency occurs when the moisture demand
exceeds the moisture supply at some point in time, and an abnormal moisture deficiency occurs
when the excess of demand is large (i.e. compared to the average). All the basic terms in
the water balance equation can be determined, including the evapotranspiration, soil recharge,
runoff, and moisture loss from the surface layer.
The cumulative measure of the PDSI for month m in year t at location l is obtained with a
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weighted sum of the value of the previous month and a monthly contribution:5
Palmerltm = pPalmerltm−1 + qZitm, (1)
where p and q are calibrating weights and Zitm is the contribution of month m with m = 2, ..., 12.
The monthly contribution is calculated according to the supply and demand model of soil
moisture.6
We use the monthly PDSI grid cell data from Dai et al. (2004). This database covers
the world time series from 1870 to 2005; it is geolocalized and available at a resolution of 2.5
degrees by 2.5 degrees (about 250 km at the Equator). Figure 1 presents maps of the raw PDSI
data averaged over five periods of time: maps (a) to (e) represent the grid cell data averaged
over successive periods of time between 1946 and 2005; red represents the driest regions and
yellow represents the wettest regions of sub-Saharan Africa. The source for the PDSI data is
Dai et al. (2004) extended to 2005. The inputs to PDSI data are precipitation and temperature
time series over all months and climatological maps of the available water capacity of the soil
(AWC) at a given grid cell (2.5×2.5 degree). As in Burke et al. (2009), the climate data are
weather station data. Surface air temperature data come from the Climate Research Unit (Jones
and Moberg 2003); and precipitation data come from the National Centers for Environmental
Prediction (Chen et al. 2002). The available water capacity of the soil (AWC) is computed using
Webb et al. (1993) computations for the global distributions of soil profile thickness, potential
storage of water in the soil profile, potential storage of water in the root zone, and potential
storage of water derived from soil texture (see Webb et al. 1993 for a complete description).7
Before going further, let us provide some information regarding the accuracy of precipitation
and temperature data inputs for the PDSI. Brohan et al. (2006) report three categories of usual
potential errors: station error (the uncertainty of individual station anomalies), sampling error
(the uncertainty in a grid box mean caused by estimating the mean from a small number of point
values), and the bias error (the uncertainty in large-scale temperatures caused by systematic
changes in measurement methods). The Climate Research Unit surface air temperature data set
has been created by gridding data (0.5×0.5 degree grid cells) from 5,159 stations. The estimates
of accuracy reported by the CRU8 indicate that the annual values have been approximately
accurate to +/- 0.05◦C (two standard deviations) since 1951. They also report that the accuracy
is four times less accurate for the 1850s and improves gradually up to 1950. Our analysis
covers the period after independence for each country, thus covering the period with the highest
accuracy. The NCEP precipitation from Chen et al. (2002) is based on a previous version by
Dai et al. (1997) who report a 10% sampling error estimate in well-covered regions (in terms of
5The Palmer model is calibrated such that p = 0.897 and q = 1/3 (see Dai et al. 2004).6See Appendix B for a presentation of the PDSI hydrological model. See also Palmer (1965), Alley (1984),
Karl (1986), Wells et al. (2004) or Dai (2011) for other presentations of the PDSI model.7They computed these values by using: i) the data set of soil horizon thicknesses and textures, the Food and
Agriculture Organization of the United Nations/United Nations Educational, Scientific, and Cultural Organiza-tion (FAO/UNESCO) Soil Map of the World (includes the top and bottom depths and the percent abundance ofsand, silt, and clay of individual soil horizons in each of the 106 soil types cataloged or nine continental divisions).ii) the World Soil Data File (Zobler 1986) and iii) the Matthews (1983) global vegetation data set and texture-based estimates of available soil moisture. Data on soil characteristics is available at http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds id=548
8available at http://www.cru.uea.ac.uk/cru/data/temperature/, accessed in June 2012.
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the number of stations) including the Sahel and southern Africa, whereas the sampling error is as
high as 45% in poorly covered areas. They argue that the data need correction for station error
and bias error in high-latitude stations. Thus, potential errors in gauge records are relatively
limited in our context.
In practice, the PDSI values are most of the time between −10 and +10 (some values may
be outside this interval, because the index is not bounded theoretically), with negative values
referring to dry months and positive values to wet months. For the ease of exposition, we
choose to revert the initial scale such that the greater the value of the index, the drier the
climate. We also divide the values by 100 in order to avoid presenting 10−X coefficients in the
Tables. With these changes in mind and according to Palmer’s classification, PDSI = 0 means
a normal climate, PDSI > +0.04 means an extremely dry climate and PDSI < −0.04 means
an extremely wet climate. Palmer (Table 11, 1965) also defined 9 intermediate classes: severe
drought (0.03 to 0.04); moderate drought (0.02 to 0.03); mild drought (0.01 to 0.02); incipient
drought (0.005 to 0.01); near normal (0.005 to −0.005); incipient wet spell (−0.005 to −0.01);
slightly wet (−0.01 to −0.02); moderately wet (−0.02 to −0.03); and very wet (−0.03 to −0.04).
We make a country-year analysis of the effect of drought on the risk of civil conflict; in a
specific country-year, drought is measured as an average of the monthly grid cell PDSI values.
At the country level, the data contain observations for countries larger than one 2.5×2.5 grid
cell degree.9
Formally, the Palmer Drought Severity Index for country i in year t is:
PDSIit =1
Li
∑l belongs to i
1
12
∑m=1,...,12
Palmerltm
, (2)
where Li is the number of cells in country i.
Descriptive Statistics
The basic descriptive statistics of this measure are presented in Table 1. The number of meteo-
rological stations varies from 12 in the smallest countries (Benin, Liberia, Lesotho, Sierra Leone,
and Swaziland) to more than 300 in the largest countries (Democratic Republic of the Congo
and Sudan). The vast majority of sub-Saharan countries (76%) have experienced at least one
extremely dry year (maximum PDSI value above 0.04) and 23% have experienced at least one
extremely wet year (minimum value below −0.04). The average PDSI value is 0.0169 and its
standard deviation is 0.0247. The density curve of the PDSI for the sub-Saharan region moves
to the right over the decades (see Figure 2), i.e. the sub-Saharan climate has become drier.
The density curve becomes flatter and more right-skewed, i.e. the climate of more and more
countries is approaching extreme dryness.
The variation in the PDSI between and within countries is such that 54% of the PDSI is
explained by the specific year, whereas 44% is explained by the specific country; and 67% is
9The list of countries included in our analysis is presented in Table 1. The countries for which we have no PDSIdata are: Gambia, Rwanda, Burundi, Djibouti, Cape Verde, Sao Tome and Prıncipe, Comoros, and Mauritius.The largest country is Burundi - 27,000km2 - and its area is roughly a square measuring 160km on each side,which is smaller than one grid cell that measures 250km on each side.
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explained by both.10 We find that the contemporaneous country-year average of the PDSI
is significantly correlated with its lagged value within countries, which is consistent with the
recursive formula. Indeed, as mentioned before, the contemporaneous monthly value is the sum
of the value in the previous month and a monthly contribution. However, the PDSI is not
significantly correlated with its value at t− 2.11
The inputs of the Palmer index are temperature, precipitation and the available water ca-
pacity of the soil. This contrasts with previous studies that looked at the impact of precipitation
and/or temperature on the risk of conflict (e.g. Miguel et al. 2004, Burke et al. 2009). Time
series show that PDSI and rainfall generally vary in opposite directions (see Figure 3) and
that PDSI and temperature generally vary in the same direction (see Figure 4). Rainfall (with
lags) explains 50% of the PDSI variation12 and 70 − 85% of the within-country PDSI varia-
tion.13 Temperature (with lags) explain 60% of the PDSI variation14 and 50% to 80% of the
within-country PDSI variation.15 Temperature and rainfall together explain 60% of the PDSI
variation16 and 70% to 90% of the within-country PDSI variation.17 This is consistent with the
theoretical formula of the PDSI, which is based on precipitation, temperature and the available
water content of the soil. Notice that consistently with the Palmer model, we find that rainfall
decreases the PDSI, but less so during hot years (see Supplementary Table ST4).18
2.2 Data on Civil Wars
We use the latest UCDP/PRIO Armed Conflict Dataset (v4-2011), over the 1946 - 2010 period,19
where some errors have been corrected and the data has been extended compared to previous
versions. We also use data from the Correlates Of War (COW) as a robustness check of our
results (see Appendix D). We use the civil war incidence dummy variable, which is equal to 1
for years with a number of battle deaths greater than 1, 000, and 0 otherwise (we further discuss
our results for alternative coding such as the onset or intensity of civil war).
The UCDP/PRIO database includes two categories of civil conflict: internal and interna-
tionalized armed conflict. An internationalized civil armed conflict occurs between the gov-
ernment of a state and one or more internal opposition group(s) with intervention from other
states. An internal civil armed conflict occurs between the government of a state and one or
more internal opposition group(s) without intervention from other states. We follow Jensen
and Gleditsch (2009) who point out that internationalized civil armed conflicts should not be
included in the analysis20 and focus on internal conflicts.
10These values are the R2 of least square estimates of panel models including country fixed effects and/ or yearfixed effects. The estimates are not reported here.
11See Supplementary Table ST1 in Appendix A.12Estimates not reported here.13We run several regressions of the PDSI on rainfall and temperature with some lags (and we use two data
sources: the first one is the same as Hsiang et al. 2011 and the second one is the same as Ciccone 2011). Seecolumns 1 to 6 in Supplementary Table ST2 in Appendix A.
14Estimates not reported here.15See columns 7 to 12 in Supplementary Table ST2 in Appendix A.16Estimates not reported here.17See Supplementary Table ST3 in Appendix A.18We find that rainfall decreases the PDSI, and the interaction term between rainfall and temperature has a
positive effect.19available at http://www.pcr.uu.se/research/ucdp/datasets/ucdp prio armed conflict dataset/20They show that the estimated effect of economic growth on civil war is reduced compared to
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The history of sub-Saharan Africa reveals that there were important political changes
throughout the 20th century. Many African countries were colonized before World War II
and decolonized after World War II and the process of decolonization and the emergence of new
states present a theoretical and empirical challenge. This raises questions about the inclusion
or exclusion of anti-colonial civil wars in the analysis. We follow Fearon and Laitin (2003) who
argue that there are ways to include anti-colonial civil wars,21 but that the most conservative
strategy is to focus on civil wars in independent African states and to exclude the colonial
period. Hence, the time frame we consider is the period from a country’s independence to the
most recent year in our data, 2005. For example, the time frame for Ghana starts in 1957, for
Mozambique and Angola in 1975, and for the former French colonies in 1960.22
3 Estimation Framework
To estimate the effect of drought on the incidence of civil war, our baseline equation is the
following:
Warit = βPDSIit + ηi + θit+ εit, (3)
whereWarit is the the index of civil war in country i at time t and εit is the error term. PDSIit is
our country-specific measure of drought. The parameter β captures the effect of country-specific
drought on the risk of civil war. We also include country fixed effects (ηi) and country time
trends (θit) in our preferred specification. They serve as substitutes for variables which are
suspected of being endogenous (e.g. GDP), to control for unobserved heterogeneity, and to take
account of temporal trends in the causes of conflicts. We do not add other control variables to
the model in order to avoid the “bad control” problem (see Angrist and Pischke 2009).23
To give some insights into potential channels through which drought may affect the risk of
civil war (e.g. economic development, agricultural production, fractionalization indices, political
indices, food prices, etc.), we estimate the following equation:
yit = γPDSIit + yit−1 + ηi + θit+ ζit, (4)
Miguel et al. (2004)’s findings. They argue that according to Fearon and Laitin (2003), negative economicshocks should decrease the capacity of governments to send troops to civil wars in other states.
21The first way to code the data is to consider Ivory Coast as a single “state” for the whole 1946-2005 period(in fact it was part of the French empire from 1895 to independence in 1960). “States” that did not exist (suchas “Ivory Coast” from 1946 to 1960) are considered and the colonial empires are ignored. The second strategyis to consider colonial empires. For instance, Ivory Coast and Cameroon are categorized as belonging to theFrench empire before they gained independence (in 1960). Considering colonial empires requires the constructionof explanatory variables for whole empires. Fearon and Latin (2003) conclude that, although possible, it wouldbe very problematic to code variables such as GDP, ethnic fractionalization and democracy score. Also, in ourcontext it makes little sense to assign a climate value to a whole colonial empire. For instance, it would bemeaningless to assign the PDSI value of the French metropole (colonizer) to French colonies (such as Ivory Coastand Cameroon), or to assign the value of the PDSI averaged over the whole French empire to French colonies.
22Since different countries have different independence years, we have an unbalanced sample. However, we showthat our results are not influenced by this. In Supplementary Table ST5 (in Appendix A), column 1 shows ourbaseline estimates and column 2 shows the estimates of the same specification adjusted with sampling weights(weights that denote the inverse of the probability that the observation is included because of the samplingdesign): the significance of the effect is still 99% of confidence and the coefficient increases from 0.677 to 0.969.
23See section 3.2.3. Many variables that we may want to add are themselves outcome variables of droughtvariations. For instance, we know that drought affects GDP per capita (see Dell et al. 2012). The estimatedcoefficient of the PDSI will capture the effect of drought on the risk of civil war for a given level of GDP percapita. The problem is that this is not a causal argument.
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where, yit is the country-specific measure of the potential channel, PDSIit our measure of
drought, yit−1 the lagged dependent variable (see Miguel et al. 2004), ζit is the error term, ηi
the country fixed effect and θit the country-specific time trend.
To investigate the underlying mechanisms, we include interaction terms between drought
and country-specific characteristics (time-invariant or time-varying) in the right-hand side set
of explanatory variables of equation 3. These interaction terms capture the effect of latent
tensions and shed light on factors that create a spark which fuels tensions and can lead to civil
conflicts. Denoting Ci the country characteristic, we estimate:
Warit = δPDSIit + λPDSIit × Cit + ρCit + ηi + θit+ ξit, (5)
where ξit is the error term. The parameter δ captures the mean effect of drought and parameter
λ captures the differentiated effect of drought on the risk of civil war according to the country
characteristic.
As our index of civil war is a dummy, we could have used a logit model. However, we
have preferred to consider the linear probability model throughout the paper and to report
the estimated effects using least squares. On the one hand, the logit methodology is undoubt-
edly preferable to the linear probability model if we want predictions with more than marginal
changes in the variables. On the other hand, as argued by Wooldridge (2002) the linear proba-
bility model (in binary response models) should be seen as a convenient approximation to the
underlying response probability. All in all, the linear probability model does not always predict
values within the unit interval, but it is usual and it seems a convenient approximation in our
case. One of the advantages of the linear model is that it enables us to use econometric tools to
take into account serial correlation and spatial autocorrelation in the climate data. Moreover,
some studies (e.g. King and Zeng 2001) claim that logit and probit may provide biased esti-
mates when used with data such as civil wars, because the number of events is relatively small
(5.4% of civil war in our sample).
An important issue is the estimation of the significance of the effects. As mentioned before,
since we use climate data, serial correlation within countries and spatial correlation between
countries have to be corrected when we estimate the standard errors. We follow Hsiang (2010)
and deal with the two problems simultaneously in all our regressions, using a nonparametric
estimation of the variance-covariance matrix for the error term allows both for cross-sectional
spatial contemporaneous correlation and country-specific serial correlation. Weights in this
matrix are uniform up to a cutoff distance of 1, 000 km (Conley 1999). Linear weights that fall to
zero after a lag length of 4 years are used to take serial correlation into account (Conley 2008).24
24We checked for the sensitivity of our baseline estimates to the distance cutoff of 1,000 km and to thelag length cutoff. We considered different cutoffs, from 2 to 10 years for the temporal dimension and1,000km/2,500km/5,000km for the spatial cutoff. Supplementary Table ST6 reports the standard errors forthe various possible combinations of the cutoff values (we do not report the coefficient, which is 0.677). We alsoused a two-way clustering method to compute the standard errors (Cameron et al. 2011).
9
4 Empirical Results
4.1 Main Results
Table 2 presents our main results. First, we run a model with country fixed effects (column
1). Drought has a positive effect on the within variation of the probability of civil war. In
column 2, we run a standard difference-in-difference model with the inclusion of year fixed
effects. The PDSI is no longer significant (p-value = 0.16). However, year fixed effects might
soak up a lot of the variation of interest, as they capture many factors that can influence the
incidence of conflict (e.g. a natural resource price shock) and variations in the global climate
(Hsiang et al. 2011). We then follow the literature25 and include country time trends instead
of year fixed effects (column 3). We show that the effect of the PDSI on civil war is significant
at the 99% level of confidence. A one standard deviation increase in the PDSI increases the
annual risk of conflict by an additional 1.7% (the standard deviation of the PDSI is 0.025, see
Table 1). Note that in terms of Palmer’s classification, a one standard deviation increase in
drought may correspond to a change from a near normal climate (PDSI between −0.005 and
0.005) to a moderate drought (0.02 to 0.03). A greater change in climate such as a change from
normal (PDSI = 0) to extremely dry (PDSI = 0.4) would increase the annual risk of civil war
by 2.7%.
Assuming that without PDSI variations (PDSI = 0, normal climate), the probability of
civil war evolves linearly, i.e. the probability of civil war is explained by country fixed effects
and country specific time trends, we find that the PDSI may have affected one-fifth (21%) of
all civil wars (in our sample).26 Our model explains 35% of the variance of the risk of civil war.
Using out-of-sample prediction, we find that the model explains 26% of the variance of the risk
of civil war out-of-sample.27 To illustrate our result, we have picked two cases. Figure 5 plots
the PDSI level and reports periods of conflict for Sudan and Uganda. The time series of PDSI
values for Sudan reached a peak in the early 80s and the civil war began in 1983; the time
series of PDSI values for Uganda shows two large peaks, one during the 80s and one around
2003-2005. These periods correspond precisely to two periods of civil war in Uganda. These
two cases illustrate a positive correlation between drought and the incidence of civil war.
4.2 Robustness of the Main Result
4.2.1 Sensitivity Tests
One of the criticisms of Miguel et al. (2004)’s seminal work is that the link between rainfall and
civil war fails to pass several sensitivity tests (see Buhaug 2010). In this section, we show that
our results are much less sensitive.
25See eg. Miguel et al. (2004), Jensen and Gleditsch (2009), Burke et al. (2009, 2010a), and Ciccone (2011).26We project the observed sequence of PDSI realizations onto our linear model (dWarij/dPDSI = 0.667) and
find 19 civil war years were associated with the PDSI: (β∑t
∑i
PDSIit) divided by the total number of civil
war years in the sample (88).27This percentage is computed using the following procedure. We estimate equation 3 using 1, 643 different
samples. Each observation is excluded once. Each set of estimates is used to compute a predicted value of therisk of civil war for the observation that was excluded from the sample. We then use the standard R-squaredformula and find 26%.
10
Supplementary Table ST7 (in Appendix A) shows the results of several sensitivity checks.
First, we relax the hypothesis of linearity of the country specific time trends and we augment
equation 3 with a country specific time trend squared term (column 1). The effect of the PDSI
remains significant at the 99% level of confidence. Second, we introduce a common linear time
trend and the effect of the PDSI is significant at the 95% (column 2); we then add a common
linear time trend squared and the effect of the PDSI is significant at the 99% (column 3).
Column 4 shows our estimates when the lagged civil war incidence is included in the right-hand
side of equation 3 and the effect of the PDSI remains significant.28 Column 5 finds that the
effect is still significant at the 99% level of confidence when we follow Hsiang et al. (2011) and
drop the observations for 1989 (the end of the Cold War) from our sample.
An important issue in the literature is the inclusion of lags of the climate variable. This
question seems less crucial in our context, as the PDSI is defined by a recursive formula and
then takes past PDSI values into account. However, to check whether our baseline specification
(Table 2, column 3) needs to be augmented with lagged (or forward) values of the PDSI, we
estimate equation 3 augmented with lagged or forward PDSI values. Supplementary Table ST8
contains our results. Column 1 replicates our baseline estimate (same as in Table 2, column 3).
Columns 2 and 3 augment the specification in column 1 with lagged PDSI values. Column 2
shows that the PDSI at time t− 1 has an insignificant effect on the contemporaneous incidence
of civil war. Column 3 shows that the effect of the PDSI at time t − 2 is also insignificant.
Columns 4 and 5 augment our preferred specification with forward PDSI values. Column 4
includes the PDSI at period t + 1 and finds that the effect is insignificant. Column 5 shows
that the effect of the PDSI at t + 2 is also insignificant. To check whether a sequence of dry
years is much worse than a single dry year, we add a term that interacts the current and the
lagged PDSI (column 6) and find that this variable has an insignificant effect on the risk of
civil war.29 Likelihood ratio tests also suggest that the inclusion of lags is not necessary. These
results strengthen our choice to use the contemporaneous PDSI value and no PDSI lags as our
preferred specification.
We also test the sensitivity of our results to a change in the time frame of the sample
considered. The processes of democratization and the development of sub-Saharan Africa might
have induced changes in the relationship between drought and civil war during the post-World
War II period. Thus, the effect of drought on civil war may be sensitive to the time frame in
our sample. We use our preferred specification and re-estimate the effect of the PDSI on civil
war for each time interval of a minimum of 20 consecutive years (i.e. 861 estimates) between
1965 and 2005. The graph at the top of Figure 6 shows the sign of the PDSI coefficient (β) for
every possible start and end year in the time scale. Pale grey represents negative values and
black represents positive values of the estimated effect of the PDSI. In most cases, the value
of the estimated PDSI coefficient is positive. The graph at the bottom of Figure 6 shows the
28The results are not affected when we use a General Method of Moment (GMM) to estimate this dynamicequation (Wooldridge 2002, page 304 or Greene 2002, page 308).
29We also considered two alternative measures of drought. First, we computed the intra-annual PDSI variationfor each country (moments of order 1, 2 and 3). Second, we considered spatial PDSI variations within eachcountry by computing various moments of the PDSI using the 2.5 degree x 2.5 degree grid cell year averages(within year variation is eliminated). In both cases, the effect of the PDSI is positive and significant, but thehigher moments of the PDSI are insignificant.
11
significance of the estimated PDSI coefficient (whether significant at the 5% level of confidence
or not). The PDSI coefficient is most often positive and significant, but it is negative and
non-significant when the time scale ends early. This result may be due to the small number
of ongoing civil wars and to the high frequency of “normal” climatic conditions before the 80s
(there are 28 observations with Warit = 1 before 1980 in our sample; see Figure 2 for the
frequency of a “normal” climate). Overall, our result is very robust to a change in the time
frame, compared to estimates that use rainfall and temperature data.30
Even if our sample is larger than in previous studies, our result might be sensitive to the in-
clusion/exclusion of a small number of observations.31 Figure 7 shows a plot of our observations
and the two and the three standard error limits (the 2-sigma and the 3-sigma outliers are the
observations outside the “2 s.d.” and “3 s.d.” limits, respectively). Supplementary Table ST13
shows our results using our preferred specification with different samples. Column 1 reports
our estimates over the whole sample (identical to column 3 in Table 2), column 2 shows our
estimates over a sample without the 3-sigma outliers and column 3 shows our estimates over
a sample without the 2-sigma outliers. The effect of the PDSI is lower than in our baseline
estimates, but it remains significant at the 99% level of confidence.32
So far, we have made implicit the assumption that the response of the risk of civil war to
the PDSI is linear. We relax this assumption and first include the square of the PDSI in the
right-hand side of equation 3. We find that the effect of this additional variable is insignificant
(estimates are not shown here). Second, we use a non-parametric estimate of the effect of the
PDSI (see for instance Deschenes and Greenstone 2011): we compute the 10 deciles of the PDSI
and run our preferred specification , including a variable for each of the 2nd to the 10th decile
of the PDSI (9 bins, with the first decile being our reference). Figure 8 plots the estimated
response function linking civil war and the nine PDSI bin variables. The Figure also plots the
coefficients plus and minus two standard errors. Our response function increases slightly for
the last bins, meaning that the effect of the PDSI on the risk of civil war is greater for the
highest PDSI values, i.e. for PDSI values which are classified as moderate drought or drier
according to Palmer’s classification (above the 8th decile of the PDSI distribution, i.e. for a
PDSI > 0.027). Overall, the magnitude of the effect is such that PDSI values above the first
decile are associated with a 5% to 7% additional risk of civil war. A one standard increase in
the PDSI (from a normal climate) translates into a 5% increase in the risk of civil war, which
is slightly greater than in our baseline estimates (1.7%).
30See the discussion of Buhaug et al. (2010) on Burke et al. (2010a). Notice that our results are robust to theusual time frame of Miguel et al. (2004): 1981 - 1999.
31Buhaug et al. (2010) argue that the estimates in Burke et al. (2010a) regarding temperature data do notpass this test.
32We also use the dfbeta statistic for the PDSI to identify observations that are likely to exercise an overlylarge influence (see Buhaug et al. 2010). It measures the impact of each observation on the estimated effect of acovariate. The critical value is 2/
√n, where n is the number of observations. We find a threshold that amounts to
.05 and identify 74 observations with higher dfbeta values. When these observations are excluded, the estimatedPDSI coefficient is lower and less significant; however, it remains significant at the 10% level. This confirms therobustness of the effect of drought for the post-colonial period (see Supplementary Table ST13, column 4).
12
4.2.2 Other Climate Variables
In this section, we compare the effect of the PDSI to the effect of rainfall and temperature on
the risk of civil war.
Supplementary Table ST9 presents our estimates of the effects of the PDSI and rainfall on
civil war. We estimate equation 3 augmented with rainfall levels or rainfall growth measures.
We use the same rainfall data as Hsiang et al. (2011) (columns 1 and 2) and a second data set
with the same rainfall data as Ciccone (2011) (columns 3 to 6). Columns 1 and 3 present our
estimates of equation 3 augmented with contemporaneous rainfall and one lag. The effect of
the PDSI is significant at the 95% and 90% level of confidence, respectively, whereas the effect
of rainfall is insignificant. Columns 2 and 4 show our estimates when a two-year lag is added
and find that the effect of the PDSI is generally less significant. However, the effect of rainfall
remains insignificant. In columns 5 and 6, we augment equation 3 with the contemporaneous
growth of rainfall and one lag, respectively (as in Miguel et al. 2004). In both cases, the effect
of the PDSI is significant (at the 90% and 95% level of confidence, respectively). However,
the effect of rainfall growth is insignificant. Supplementary Table ST10 presents our estimates
of the effects of the PDSI and temperature on civil war. We use equation 3 augmented with
temperature measures. We use contemporaneous temperature (column 1), then we add one lag
(column 2), and finally we add two lags (column 3). The effect of the PDSI is always significant
at the 99% level of confidence and the effect of temperature is generally insignificant. To check
whether a large fraction of the effect of the PDSI is due to global climate variations such as
ENSO (see Hsiang et al. 2011), we include various measures of El Nino fluctuations in our
baseline specification. Supplementary Table ST11 contains our results. The effect of the PDSI
is always positive and significant at the 99% level of confidence. This is another argument in
favor of the complementarity of our analysis with Hsiang et al. (2011).
Thus, our results show that the effect of the PDSI is robust to the inclusion of other local
climate measures (rainfall and temperature) and to global climate fluctuations (various measures
of ENSO). They also confirm the predictive power of the PDSI regarding civil wars.
4.2.3 Onset and Intensity of Civil War
A usual alternative measure of civil war is the onset (or outbreak) of civil war index. It is set
at 1 for the first year of civil war, set to missing for the subsequent civil war years, and at
0 for peace years.33 Supplementary Table ST12 shows our results. The PDSI has a positive
and significant effect on the probability of the outbreak of civil war if we only include country
fixed effects (column 1). However, columns 2 and 3 show that the PDSI has a non-statistically
significant effect on the onset of civil war when year fixed effects or country time trends are
included, respectively, suggesting that the PDSI is not a strong predictor of the onset of civil
war index.
To investigate whether the PDSI affects the intensity of civil war, we use two different
strategies. First, we test whether the effect of the PDSI is sensitive to a change in the battle-
33We take multiple contemporaneous conflicts in the same country into account. In our sample, there is onlyone country with two contemporaneous civil wars: PRIO data report that Ethiopia has experienced severalcontemporaneous civil wars. The onset index is then set to 1 for Ethiopia in 1976, 1980 and 1981.
13
related death threshold. We re-code our dependent variable Warit for each threshold between 25
and 200, 000 battle-related deaths per year and re-estimate our preferred specification. Figure 9
reports the value of the estimated coefficient of the PDSI (and the 95% interval of confidence).
The PDSI has a positive and robust effect on civil war as long as the battle-death threshold is at
least 1, 000. The coefficient of the PDSI is positive for thresholds between 25 and 999 (number
of battle deaths each year), but not significant in most cases. The 1, 000 battle-related death
threshold corresponds perfectly to the usual limit considered in the literature to distinguish
between civil war and civil conflict. However, we cannot assert whether this threshold we find
is due to a real phenomenon or to a variation in the precision of the data. It remains that the
effect of the PDSI on civil war is positive and significant at the 95% level of confidence, no
matter the battle-death threshold we consider.34
Second, we maintain the same set of explanatory variables as in equation 3, but we replace
the left-hand side dependent variable with different measures of the intensity of civil war. Sup-
plementary Table ST14 contains our estimates. We use three different approximations for the
number of battle deaths: a lower bound, a higher bound and the “best” estimates (according to
the PRIO data set). We also alternatively use the square root of the number of battle deaths
or the number of battle deaths. Columns 1, 4 and 7 contain our results when the left-hand-side
dependent variable is the square root of the number of battle deaths. In all cases, the PDSI
increases the intensity of civil war and the effect is significant at the 99% level of confidence.
Columns 2, 5 and 8 report our results when the left-hand-side dependent variable is the number
of battle deaths. Column 2 shows our results for the lower bound measure. Our results indicate
that a one standard deviation increase of the PDSI leads to 180 additional battle deaths and
the effect is significant at the 99% level of confidence. Column 5 reports our estimates for the
higher bound measure. We find that a one standard deviation increase of the PDSI leads to 800
additional battle deaths and the effect is significant at the 99% level of confidence. Column 8
shows our results for the “best” measure. Our estimates show that a one standard deviation
increase of the PDSI leads to 114 additional battle deaths and the effect is significant at the 95%
level of confidence. Columns 3, 6 and 9 show our estimates when the left-hand-side variable is
the number of battle deaths, but we use a Poisson regression instead of least squares in order
to account for the discrete nature of the dependent variable and the presence of a large number
of 0. Again, the PDSI has a positive effect on the number of battle-related deaths. The effect
is significant at the 99% level of confidence.
4.3 Channels and Other Outcome Variables
To provide insights into the potential channels through which drought affects the risk of civil
war, we look at whether the PDSI affects other economic and political outcomes. Blattman
and Miguel (2010) argue that drought may increase the risk of civil war because it decreases
the opportunity cost of fighting among rural populations; however, crop failure may also reduce
government revenues and/or state capacity. We first present estimates of the effect of the PDSI
on various measures of economic productivity and production. Table 3 contains our findings
34The effect becomes non-significant for thresholds higher than 50,000 deaths per year, but in that case only2 observations are still considered as civil war years.
14
regarding the effect of the PDSI on agricultural and total economic production, and different
measures of government finances.35
We use the specification of equation 4 where (log of) cereal yields (tons/ha) is the left-hand-
side variable (column 1). The PDSI has a negative and significant impact on cereal yields,
as expected, and the effect is significant at the 95% level of confidence. We show the PDSI
reduces agricultural income per capita (column 2). A one standard deviation increase in the
PDSI leads to a 1.5% decrease in agricultural income per capita and the effect is significant
at the 95% level of confidence. Column 3 shows that a one standard deviation increase in the
PDSI leads to a 2.2% increase in local food prices and the effect is significant at the 99% level
of confidence. These three results show that the PDSI has a considerable effect on agricultural
outcomes. This is consistent with previous studies that documented the significant impact
of other climate measures such as temperature, precipitation and El Nino variations on crop
yields (e.g. Schlenker and Lobell 2010 and Hsiang et al. 2011). These results suggest that an
opportunity cost mechanism may explain the effect of drought on civil war, through the negative
effect of drought on agricultural production.
In columns 4 and 5, we report the effect of drought on the global economic activity. The PDSI
has a negative and significant effect both on GDP per capita and economic growth (columns 4
and 5, respectively). Our results indicate that a one standard deviation increase in the PDSI
leads to a 0.66% decrease in GDP per capita and to a 0.5 decrease in economic growth (that
is 14% of the average).36 These results are consistent with recent but growing evidence that
climate affects economic performance (Hsiang 2010, Barrios et al. 2010, Zivin and Neidell 2010,
Jones and Olken 2010 and Dell et al. 2012).37
To test whether state capacity is also a channel for the drought-civil war relationship,
we present estimates of the effect of the PDSI on various measures of government finances
(columns 6 to 10 in Table 3). Column 6 uses the specification of equation 4 to explain tax
revenue (% of GDP) as a function of the PDSI. We find that a one standard deviation increase
of the PDSI increases tax revenue by 0.6% of GDP, and the effect is significant at the 99% level
of confidence. We find that the PDSI has an insignificant effect on government revenue (% of
GDP) (column 7). Columns 8 and 9 present the effect of the PDSI on government consumption
expenditure (% of GDP) and government consumption (% of GDP per capita), respectively.
We find that the PDSI has no significant effect on either measure of government consumption.
We also show that the PDSI has no significant impact on the level of military expenditures
(column 10). Overall, among the various measures of government finances we used, the ratio of
tax revenue over GDP is the only one that has a significant (and positive) effect on the risk of
civil war. An increase in collected tariffs due to an increase in imports (e.g. compensating for a
drop in agricultural production) may explain this result. This suggests that drought has little
35See Appendix C for a description of dependent variables.36The average growth is 3.6% over our sample.37Hsiang (2010) looks at temperatures and cyclones and shows that they negatively affect economic output.
Barrios et al. (2010) look at agriculture and hydro-energy supply as two channels through which rainfall is likely tohave adversely affected the development of sub-Saharan Africa. Zivin and Neidell (2010) show that temperatureshave an impact on U.S. workers’ allocation of time. Jones and Olken (2010) show that temperatures reduce theexports of poor countries. Dell et al. (2012) show that temperature shocks reduce growth in poor countries (aswell as agricultural output, industrial output, and political stability).
15
or no effect on state capacity.38
Democratic change is an important political outcome related to civil wars. Bruckner and
Ciccone (2011) have shown that country-specific variations in rainfall are followed by significant
improvements in the democratic institutions of sub-Saharan African countries. Table 4 links the
PDSI to various indices of democratic change. We use the same specification as in equation 3,
but the left-hand-side outcome variable is a democratic change index instead of a civil war index.
From column 1 to column 9, we successively use 9 different indices of democratic change (see
Appendix C for a description of the indices). Column 1 shows that a one standard deviation
increase in the PDSI raises the variation in the polity score by 0.17 (the average variation being
0.07), and the effect is significant at the 95% level of confidence. Column 5 shows that a one
standard deviation increase in the PDSI raises the risk of a coup d’etat by an additional 2.4%,
and the effect is significant at the 90% level of confidence. Column 9 indicates that drought has
a positive effect on the democratization step. These results confirm the main result of Bruckner
and Ciccone (2011), which is that drought is followed by a democratic improvement. However,
we find that the PDSI has an insignificant effect on the variation in executive recruitment
(column 2), on the variation in political competition (column 3), on the variation in executive
constraint (column 4), on the occurrence of a coup d’etat in a democracy (column 6), on
democratic transition (column 7), and on autocratic transition (column 8).
4.4 Interaction between PDSI and Country Characteristics
The effect of drought on civil war may be due to latent tensions and drought may create a
spark which fuels tensions and may lead to the outbreak of civil conflicts. To investigate the
potential underlying mechanisms linking drought to civil war, we consider interactions between
drought and country characteristics. Various country characteristics may channel the effect of
drought on the risk of civil war. Table 5 contains our estimates of equation 5 on the effect of
the interactions between the PDSI and various country characteristics.
The development interaction result (we use the log GDP per capita in 1965 in order to avoid
reverse causality problems, column 1) indicates that relatively poor countries hit by drought
are as prone to civil war as relatively rich countries. This result differs from the (worldwide)
cross-country evidence that poor countries are more prone to conflict (Fearon and Laitin 2003),
but this is certainly due to our focus on sub-Saharan Africa and the fact that this region of the
world was relatively homogeneous in terms of economic conditions in 1965.
In column 2, we interact our measure of the PDSI with a measure of ethnic fractionalization.
Interestingly, we find that the effect of the interaction term is significant and positive: countries
which are relatively more ethnically fractionalized and are hit by a drought are more prone to
conflict than countries with less ethnic fractionalization (the total estimated effect is negative
for three countries with a low fractionalization in the sample - Equatorial Guinea, Lesotho,
and Swaziland). We also test for the effect of interaction terms between drought and linguistic
or religious fractionalization, but we find no significant results (we have not reported these
estimates). The role of ethnic groups in conflicts39 is a contentious issue and the debate is still
38The interpretation of these results need to be taken with caution because of the small sample size for somespecifications.
39Fearon (2006) reports that 709 minority ethnic groups are identified around the world and that at least 100
16
very active.40 This result contrasts with cross-country studies that find that ethnic diversity is
not significantly correlated to conflict across countries (Easterly and Levine 1997, Collier and
Hoeffler 1998, 2004 and Fearon and Laitin 2003). However, our result is consistent with recent
studies by Esteban et al. (2012a,b) who show (theoretically grounded) cross-country evidence
that civil conflict is correlated with ethnic polarization and fractionalization.41
In column 3, we test for the effect of an interaction term between drought and the percentage
of mountainous terrain in a given country. The interaction term is positive and significant. We
find that countries with a relatively higher percentage of mountainous terrain suffering from
drought are more prone to conflict than countries with a lower percentage of mountainous ter-
rain. This result is consistent with previous studies that found that mountainous terrain is a
correlate of civil war (Collier and Hoeffler 1998, 2004, and Fearon and Laitin 2003). We also
interact the PDSI with the population density in 1965 and find a positive effect, as expected,
but the effect is not significant. Again, this result contrasts with cross-country studies that
report a robust correlation between conflict and the size of the population (see Hegre and Sam-
banis 2006). In other words, we do not find evidence supporting the Malthusian view according
to which population pressure leads to resource scarcity and environmental degradation (see
Homer Dixon 1999 for a review). In columns 5 to 8, we interact the PDSI with indices of the
level of democracy. In column 5, we include an interaction term between the PDSI and the
aggregated level of democracy (polity score) and find that relatively democratic countries hit
by a drought are more prone to civil war than relatively autocratic countries. In columns 6, 7
and 8, we include an interaction term of the PDSI and three different indices of democracy: the
executive recruitment level, the level of political competition, and the level of executive con-
straint, respectively. The democracy interaction results indicate that countries with a relatively
low level of democracy which are hit by a drought are more prone to civil war than countries
with a relatively high level of democracy.
Another empirical strategy to investigate the potential underlying mechanisms linking drought
to civil war is to estimate the effect of drought on civil war on sub-samples split by country char-
acteristics. We focus on the agricultural share of GDP, GDP per capita, and ethnic and linguistic
fractionalization. For each of these variables, we plot the estimated coefficient (black curve)
and the 95% of confidence interval (see Figures 10 to 13). The x-axis represents the cutoffs of
the variable of interest used to define the sub-sample (cutoff of the agricultural share of GDP,
GDP per capita or ethnic fractionalization). The model is re-estimated for each sub-sample
from which the country with the lowest value is excluded, and countries are also excluded with
respect to increasing values for this variable. Our results show that countries with a relatively
large agricultural sector which are hit by a drought are more prone to civil war (Figure 10).42
Figure 11 presents the results when we split the sample based on GDP per capita in 1965. It
had members who engaged in an ethnically-based rebellion against the state between 1945 and 1998.40Blattman and Miguel (2010) provide a summary of the debate between “primordialists” (Horowitz 1985) and
“modernists” (Bates 1986 and Gellner 1983) who argue that ethnic conflict arises when groups excluded fromsocial and political power begin to experience economic modernization.
41Esteban et al. (2012a,b) also argue that ethnic conflicts are likely to be instrumental, rather than driven byprimordial hatreds.
42Note that the slight decrease for an agricultural share above 50% is notably due to the small size of thesample.
17
indicates that drought has a similar impact on the risk of civil war in relatively developed coun-
tries and in relatively less developed countries, except for the richest countries in the sample.
Figure 12 and Figure 13 suggest that relatively ethnically and linguistically diverse countries
hit by a drought are more prone to civil war than relatively ethnically and linguistically uniform
countries, respectively.
5 Conclusion
In this paper, we have used a meteorological measurement of drought, the Palmer Drought
Severity Index, with values computed according to a supply and demand model of soil moisture.
We have shown that drought (PDSI values) and the incidence of civil war are positively and
robustly linked for the independent sub-Saharan African countries. Furthermore, as discussed,
our analysis cannot be subjected to the criticisms leveled at studies that rely on rainfall and
temperature data, and we have shown that our results pass several sensitivity tests.
Our results suggest that drought is a key factor in security issues and that there are sev-
eral pathways through which climate variations may increase the risk of civil war. First,
consistently with recent evidence that civil conflict is associated with ethnic diversity (Este-
ban et al. 2012a,b), our results suggest that drought and ethnic diversity are interacting roots
of civil war. Second, we also find that democracy reduces the negative effect of drought on
civil war. Third, we find that agricultural production may play a part in the link between
drought and civil war. This result is consistent with growing evidence that the climate affects
economic performance (Schlenker and Roberts 2006, Hsiang 2010, Barrios et al. 2010, Jones
and Olken 2010, Schlenker and Lobell 2010, Zivin and Neidell 2010 and Dell et al. 2012), which
may in turn affect the risk of conflict (Miguel et al. 2004). African countries remain highly
dependent on agriculture for both employment and economic production, with agriculture ac-
counting for up to more than 60% of their gross domestic product.43 Lobell et al. (2008) and
Schlenker and Lobell (2010) show that increases in temperature and decreases in precipitation
have strong negative effects on staple crop production. Their projections indicate that Africa
is one of the regions in the world where the climate will be the most affected. As argued in
Burke et al. (2010b), the negative effects of climate fluctuations on agricultural productivity
and their importance for economic performance should push governments and aid agencies to
help the African agricultural sector face climate change. Burke et al. (2010b) suggest several
strategies to mitigate the effect of climate change on the likelihood of conflict. Those strategies
include technical solutions such as developing new crop varieties adapted to a drier climate,
to build irrigation infrastructures and to improve existing ones (World Bank 2007). They also
include mechanisms such as the development of insurance against catastrophic weather risk
events (Hess et al. 2005) to compensate for weak primary insurance markets. Miguel (2007)
suggests making international aid contingent on climate risk to prevent the emergence of violent
acts. Our results also suggest that on of the channels between climate and civil war is economic
performance in the agricultural sector.
43See World Bank (2011), World Development Indicators 2011. Available at: www.worldbank. org/data/.Accessed on December 15, 2011.
18
Tables
Table 1: Descriptive Statistics of PDSI
Country Mean PDSI SD PDSI Max PDSI Min PDSI # of stations
Angola .0071984 .0107785 .0274556 -.019194 216Benin .0411368 .03853 .1165667 -.036375 12
Botswana .0130075 .0248074 .0460139 -.0636972 108Burkina Faso .0345842 .0264598 .0965361 -.0195167 36
Cameroon .0205576 .0254717 .102646 -.019745 60Central. Afr. Rep .0164558 .0200309 .0598714 -.0265631 84
Chad .0147019 .014445 .0437842 -.0192711 228Congo .0029642 .0181206 .0462563 -.0263271 48
Dem. Rep. Congo -.0017612 .0086887 .0165686 -.0321497 360Equatorial Guinea .0285614 .0219395 .0995095 -.0004417 24
Eritrea .0019821 .0168117 .0204083 -.0247667 24Ethiopia .0054814 .0146646 .0299699 -.0368096 156Gabon .0104718 .0267633 .0933485 -.0452917 36Ghana .0253675 .0333554 .0830083 -.0424792 24
Guinea Bissau .0277526 .0261821 .0700375 -.0177313 48Ivory Coast .0286724 .0271378 .0729271 -.0381312 48
Kenya .0021568 .0196988 .0297635 -.0581708 96Lesotho .011265 .021537 .0538083 -.0356417 12Liberia .0189005 .0248124 .0721667 -.0319333 12
Madagascar .0037987 .014909 .0405948 -.0283302 96Malawi .0184314 .0290299 .097125 -.0272625 24
Mali .0191649 .0143314 .0484657 -.0060814 204Mauritania .0195388 .0120298 .0421617 -.0099106 180
Mozambique .0175786 .0205434 .0653042 -.016535 120Namibia .013263 .0106717 .0294477 -.0024326 132
Niger .0164668 .0146296 .0462526 -.0098866 192Nigeria .0232486 .0228473 .0727674 -.0195644 132Senegal .0344408 .0242616 .0745833 -.0158917 36
Sierra Leone .0244459 .0221126 .074225 -.019175 12Somalia .0021149 .0122863 .0198657 -.0420463 108
South Africa .008476 .0167565 .0388681 -.0440833 216Sudan .0204686 .0185983 .061752 -.0173794 408
Swaziland .0169079 .0298251 .0864917 -.0333917 12Togo .0206309 .0305059 .0749333 -.0614458 24
Tanzania -.0015626 .0161149 .0314391 -.0454378 156Uganda .0230863 .0308077 .0811417 -.0543583 48Zambia .0192661 .0264835 .0941342 -.0274933 120
Zimbabwe .028104 .0237525 .0756861 -.0209417 72
Total 0.0169 0.0247 0.1166 -0.0637 -
Note: This Table reports the mean, the standard deviation, the maximum and the minimum for PDSIvalues. We also report the number of stations for the measure of the PDSI. The descriptivestatistics are computed using the same sample as in our baseline estimates (Table 2, column 3).
19
Table 2: The Effect of Drought on Civil War
Specifications (1) (2) (3)Dep. Var. Civil War
PDSI 0.700*** 0.275 0.677***(0.149) (0.197) (0.201)
Country Fixed Effect Yes Yes YesTime Fixed Effect - Yes -Country Time Trends - - Yes
Observations 1,643 1,643 1,643R-squared 0.305 0.349 0.350
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ re-spectively denoting significance at the 1%, 5% and 10% lev-els. The dependent variable comes from the UCDP/PRIOArmed Conflict Dataset v4-2011 over the 1946 - 2010 pe-riod. Civil War is a dummy which is equal to 1 for a num-ber of battle deaths greater than 1,000. It includes onlyinternal civil wars. PDSI is the Palmer Drought SeverityIndex. Column 1 includes country fixed effects. Column 2includes country and year fixed effects. Column 3 includescountry fixed effects and country time trends.
Table 3: The Effect of Drought on Different Outputs
Specifications (1) (2) (3) (4) (5)Dep. Var. ln Cereal Yield ln Agricultural ln Local Food ln GDP/cap GDP Growth
Income/cap Prices
PDSI -132.6** -0.577** 0.871*** -0.265** -21.04**(58.15) (0.222) (0.227) (0.0992) (8.502)
Observations 1,447 1,177 32,569 1,543 1,375R-squared 0.853 0.951 0.957 0.989 0.214
Specifications (6) (7) (8) (9) (10)Dep. Var. Tax Revenue Gov. Revenue Gov. Consumption Gov. Consumption Military
(% of GDP) (% of GDP) Expenditure (% of GDP) Share Expenditure
PDSI 24.23*** 11.03 6.428 2.918 -0.496(8.134) (12.15) (4.902) (3.221) (0.959)
Observations 150 150 1,333 1,543 476R-squared 0.981 0.982 0.866 0.960 0.961
Note: Robust standard errors clustered at country level in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting significanceat the 1%, 5% and 10% levels.PDSI is the Palmer Drought Severity Index.All specifications include country fixed effects and country time trends. Column 3 includes also product fixed effects.ln Cereal Yield and ln Agricultural Income/cap come from Hsiang (2011). ln local food prices comes from FAO. lnGDP/cap and GDP Growth come from Penn World Table 7.0. Tax Revenue, Government Revenue, GovernmentConsumption and Government Consumption Share come from World Development Indicator. See Appendix C forfurther details on the dependent variables.
20
Tab
le4:
Th
eE
ffec
tof
Dro
ugh
ton
Dem
ocr
atic
Ch
ange
Sp
ecifi
cati
ons
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Dep
.V
ar.
∆P
olity
∆E
xec
uti
ve
∆P
oliti
cal
∆E
xec
uti
ve
Coup
Coup
inD
emocr
ati
cA
uto
crati
cD
emocr
ati
zati
on
Rec
ruit
men
tC
om
pet
itio
nC
onst
rain
tdem
ocr
acy
transi
tion
transi
tion
Ste
p
PD
SI
6.7
56**
-3.5
79
-2.7
95
-2.0
37
0.9
42*
-0.3
11
-0.1
66
0.8
01
0.6
85***
(3.3
74)
(23.8
7)
(23.8
8)
(24.3
6)
(0.5
12)
(0.2
35)
(0.2
92)
(0.7
45)
(0.2
57)
Obse
rvati
ons
1,5
50
1,5
52
1,5
52
1,5
52
1,6
43
1,6
43
1,1
02
467
1,4
74
R-s
quare
d0.0
46
0.0
08
0.0
08
0.0
08
0.2
35
0.1
19
0.1
41
0.6
92
0.4
58
Note
:C
on
ley
stan
dard
erro
rsin
pare
nth
eses
wit
h∗∗∗,∗∗
an
d∗
resp
ecti
vel
yd
enoti
ng
sign
ifica
nce
at
the
1%
,5%
an
d10%
level
s.A
llsp
ecifi
cati
on
sin
clu
de
cou
ntr
yfi
xed
effec
tsan
dco
untr
yti
me
tren
ds.
PD
SI
isth
eP
alm
erD
rou
ght
Sev
erit
yIn
dex
.D
epen
den
tvari
ab
les
are
con
stru
cted
than
ks
toP
olity
IVd
ata
.∆
Polity
cap
ture
sth
evari
ati
on
of
the
polity
score
fromt
tot
+1.
∆Execu
tive
Recru
itmen
tre
pre
sents
the
vari
ati
on
of
the
exec
uti
ve
recr
uit
men
tfr
omt
tot
+1.
∆PoliticalCompetition
rep
rese
nts
the
vari
ati
on
of
the
politi
cal
com
pet
itio
nfr
omt
tot
+1.
∆Execu
tive
Constraint
rep
rese
nts
the
vari
ati
on
of
the
exec
uti
ve
con
stra
int
fromt
tot
+1.Coup
isa
du
mm
yeq
ual
to1
ifth
ere
was
aco
up
ina
cou
ntr
yat
tim
et.
Coupin
Dem
ocracy
isa
du
mm
yeq
ual
to1
ifth
ere
was
aco
up
ina
dem
ocr
ati
cco
untr
yat
tim
et.
Dem
ocraticTransition
isa
du
mm
yco
ded
1if
aco
untr
yw
as
non
-dem
ocr
ati
cat
tim
et,
bu
td
emocr
ati
cat
tim
et+
1.AutocraticTransition
isa
du
mm
yco
ded
1if
aco
untr
yw
as
dem
ocr
ati
cat
tim
et
an
dn
on
-dem
ocr
ati
cat
tim
et+
1.Dem
ocratizationStep
isa
du
mm
yco
ded
1if
the
cou
ntr
yw
as
up
gra
ded
toei
ther
ap
art
ial
or
full
dem
ocr
acy
bet
wee
nt
an
dt
+1.
See
Ap
pen
dix
Cfo
rm
ore
det
ails.
21
Table 5: Interactions between Drought and Country Characteristics
Specifications (1) (2) (3) (4)Dep. Var. Civil War
PDSI 1.952 -0.807*** 0.141 0.486*(2.423) (0.274) (0.195) (0.286)
PDSI * ln GDP/cap ini. -0.246(0.480)
PDSI * Frac. Ethn. 2.130***(0.560)
PDSI * % Mountainous Terrain 0.395***(0.146)
PDSI * Pop Density ini. 0.00135(0.00138)
Observations 1,120 1,643 1,592 1,120R-squared 0.406 0.351 0.352 0.407
Specifications (5) (6) (7) (8)Dep. Var. Civil War
PDSI 0.829*** 0.683*** 0.684*** 0.667***(0.246) (0.190) (0.189) (0.192)
Polity t− 1 -0.000831(0.00135)
PDSI * Polity t− 1 0.0347(0.0337)
Executive Recruitment t− 1 0.0180(0.0169)
PDSI * Executive Recruitment t− 1 -0.00142**(0.000593)
Political Competition t− 1 0.0181(0.0169)
PDSI * Political Competition t− 1 -0.00142**(0.000593)
Executive Constraint t− 1 0.0190(0.0168)
PDSI * Executive Constraint t− 1 -0.00143**(0.000593)
Observations 1,555 1,552 1,552 1,552R-squared 0.355 0.375 0.375 0.375
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting significance at the1%, 5% and 10% levels. All specifications include country fixed effects and country time trends.PDSI is the Palmer Drought Severity Index. The dependent variable comes from the UCDP/PRIOArmed Conflict Dataset v4-2011 over the 1946 - 2010 period. Civil War is a dummy which is equalto 1 for a number of battle deaths greater than 1,000. It includes only internal civil wars. SeeAppendix C for more details on the independent variables.
22
Figures
Figure 1: Maps of PDSI
a. 1945-1960 b. 1960-1970
c. 1970-1980 d. 1980-1990
e. 1990-2005
This Figure represents the evolution of the PDSI over time. Deeper red indicates a drier climate (a higher PDSI level)
23
Figure 2: PDSI Density
02
46
81
0P
erce
nt
-.05 0 .05 .1 .15PDSI
1945 - 2005
01
02
03
04
0P
erc
ent
-.05 0 .05 .1 .15PDSI
1945 - 1960
05
10
15
20
Pe
rce
nt
-.05 0 .05 .1 .15PDSI
1960 - 1970
05
10
15
20
Pe
rce
nt
-.05 0 .05 .1 .15PDSI
1970 - 1980
05
10
15
Pe
rce
nt
-.05 0 .05 .1 .15PDSI
1980 - 1990
05
10
15
Pe
rce
nt
-.05 0 .05 .1 .15PDSI
1990 - 2005
Drought distribution by decades for Sub-Saharan Africa
Drought Distribution by decades
This Figure shows the distribution of the PDSI for the sub-Saharan region for the 1946-2005 period and its evolution over
the decades. A value of 0 refers to a “normal” climate, a value of +0.04 to an “extremely dry” climate, and a value of -0.04
to an “extremely wet” climate.
24
Figure 3: PDSI and Rainfall
-.02
0.0
2.0
4P
alm
er
2.4
2.6
2.8
33.
2R
ainf
all
1940 1960 1980 2000Year
Rainfall Palmer
This Figure plots the evolution of the PDSI and rainfall for sub-Saharan African countries (yearly averages over countries).
See Appendix C for more details on the data.
Figure 4: PDSI and Temperature
-.02
0.0
2.0
4P
alm
er
2122
2324
25T
empe
ratu
re
1940 1960 1980 2000Year
Temperature Palmer
This Figure plots the evolution of the PDSI and temperature for sub-Saharan African countries (yearly averages over
countries). See Appendix C for more details on the data.
25
Figure 5: Time Series of PDSI and Civil Wars for Sudan and Uganda-.
05
0.0
5.1
Pa
lme
r
1960 1970 1980 1990 2000 2010
Civil War Palmer
Sudan
-.0
50
.05
.1P
alm
er
1960 1970 1980 1990 2000 2010
Civil War Palmer
Uganda
The grey areas represent the years of civil war from the UCDP/PRIO Armed Conflict Dataset v4-2011 over the 1946 - 2010
period. Only internal civil wars are included. The y-axis reports the PDSI values. The black line represents the evolution
of the PDSI.
26
Figure 6: Drought Effect with Different Time Frame
194519501955196019651970197519801985
Sta
rt Y
ea
r
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
End Year
< 0
> 0
Size of Drought Coefficient
194519501955196019651970197519801985
Sta
rt Y
ea
r
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
End Year
NonSignifcant
Significant
Significance of Drought
The Figure shows (re-)estimates of our preferred specification (Table 2, column 3) for all possible time intervals of a
minimum of 20 consecutive years (i.e. 861 estimates). Each square represents the size of the coefficient of the PDSI ( β)
for every possible start and end year in the time scale. The graph at the bottom of the figure represents the degree of
significance of the estimated coefficient of the PDSI (whether significant at the 5% level of confidence or not).
27
Figure 7: Outliers
AGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGOAGO
AGO
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AGOAGOAGO
AGOAGOAGO
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ETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETHETH
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GABGABGABGABGABGABGABGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGHAGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGINGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQGNQKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENKENLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBR
LBR
LBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBRLBR
LBR
LBRLBRLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSOLSO
LSOLSOLSOLSOLSOLSOLSOLSOLSOLSOMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMDGMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLIMLI
MOZMOZMOZMOZMOZMOZ
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MOZMOZMOZMOZMOZMOZ
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MOZMOZMOZMOZMOZMOZMOZMOZMOZMOZMOZMOZMOZMOZMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMRTMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWIMWINAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNAMNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNERNER
NGANGANGANGANGANGANGA
NGANGANGANGA
NGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGANGA
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SDNSDNSDNSDNSDNSDNSDNSDNSDNSDN
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TCD
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TCD
TCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTCDTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTGOTZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZATZAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGAUGA
UGAUGAUGAUGAUGAUGAUGAUGA
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UGA
UGAUGAUGAUGAUGA
UGA
UGA
UGA
UGA
ZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAFZAF
ZAF
ZAF
ZAFZAFZAFZAF
ZAFZAF
ZAFZAFZAF
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ZARZAR
ZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZARZMBZMBZMBZMBZMBZMBZMBZMBZMBZMB
ZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZMBZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWEZWE
3 s.d.
2 s.d.
3 s.d.
2 s.d.
-1-.
50
.51
Res
idua
ls
1940 1960 1980 2000year
This figure reports 2-sigma outliers or 3-sigma outliers computed thanks to Table 2, column 3.
28
Figure 8: Non-Linear Effect of Drought
-.05
0.0
5.1
Est
imat
ed C
oeffi
cien
t
2 4 6 8 10PDSI Bin
We follow the same methodology as Deschenes and Greenstone (2011) and compute 10 deciles of the PDSI (bin 1 is the
reference). We estimate equation 3 with country fixed effects and country time trends. We plot the coefficient of each bin
(black line) and two standards errors (dashed lines).
29
Figure 9: Drought Effect and Intensity
-.5
0.5
11.
5D
roug
ht C
oeffi
cien
t
0 1000 2000 3000 10000 25000 100Battle Deaths
We re-code our dependent variable Warit for each threshold between 25 and 200,000 battle-related deaths per year and
re-estimate our preferred specification (Table 2, column 3). The figure reports the value of the estimated coefficient of the
PDSI at the 5% confidence interval (dashed lines).
30
Figure 10: Split with Agricultural Share
01
23
4D
roug
ht C
oeffi
cien
t
0.05 0.20 0.3 0.35 0.45Agricultural Share
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of agricultural share in 1965 is
excluded and countries are excluded with respect to increasing values of agricultural share in 1965. We report the value of
the effect of the PDSI on civil war (black line) and the confidence interval of 5% (grey area).
Figure 11: Split with GDP per capita
0.5
11.
5D
roug
ht C
oeffi
cien
t
39 75 110 135 175GDP per Capita 1965
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of GDP per capita in 1965 is
excluded and countries are excluded with respect to increasing values of GDP per capita in 1965. We report the value of
the effect of the PDSI on civil war (black line) and the confidence interval of 5% (grey area).
31
Figure 12: Split with Ethnic Fractionalization
01
23
Dro
ught
Coe
ffici
ent
0.65 0.75 0.850.05Ethnic Fractionalization
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of ethnic fractionalization is
excluded and countries are excluded with respect to increasing values of ethnic fractionalization. We report the value of
the effect of the PDSI on civil war (black line) and the confidence interval of 5% (grey area).
Figure 13: Split with Linguistic Fractionalization
0.5
11.
52
Dro
ught
Coe
ffici
ent
0.05 0.65 0.78 0.86Linguistic Fractionalization
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of linguistic fractionalization is
excluded and countries are excluded with respect to increasing values of linguistic fractionalization. We report the value
of the effect of the PDSI on civil war (black line) and the confidence interval of 5% (grey area) .
32
Appendix A: Supplementary Tables
Table ST1: PDSI Lags
Specifications (1) (2)Dep. Var. Palmer Drought Severity Index (PDSI t)
PDSI t− 1 0.660*** 0.629***(0.0313) (0.0445)
PDSI t− 2 0.0391(0.0382)
Observations 1,605 1,567R-squared 0.693 0.698
Note: Conley standard errors in parentheses with ∗∗∗,∗∗ and ∗ respectively denoting significance at the1%, 5% and 10% levels.All specifications include country fixed effects.The dependent variable is the contemporaneousPalmer Drought Severity Index.
33
Table ST2: The Effect of Rainfall/Temperature on PDSI
Specifications (1) (2) (3) (4) (5) (6)Dep. Var. Palmer Drought Severity Index (PDSI)
Hsiang et al. (2011)’s data Ciccone (2011)’s data
Rainfall t -0.0142*** -0.0114*** -0.0122*** -0.0591*** -0.0541*** -0.0555***(0.00217) (0.00249) (0.00247) (0.00442) (0.00387) (0.00390)
Rainfall t− 1 -0.00502* -0.00597* -0.0435*** -0.0427***(0.00272) (0.00314) (0.00467) (0.00470)
Rainfall t− 2 0.00480* -0.00597(0.00269) (0.00368)
Observations 973 936 899 891 855 819R-squared 0.736 0.752 0.766 0.799 0.840 0.847
Specifications (7) (8) (9) (10) (11) (12)Dep. Var. Palmer Drought Severity Index (PDSI)
Hsiang et al. (2011)’s data Ciccone (2011)’s data
Temperature t 0.0239*** 0.0185*** 0.0171*** 0.524*** 0.512*** 0.483***(0.00191) (0.00241) (0.00257) (0.0537) (0.0582) (0.0614)
Temperature t− 1 0.0110*** 0.00934*** 0.0403 0.0543(0.00249) (0.00268) (0.0613) (0.0681)
Temperature t− 2 0.00504** -0.121*(0.00242) (0.0637)
Observations 1,582 1,545 1,508 782 746 710R-squared 0.539 0.565 0.578 0.776 0.784 0.790
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting significance at the 1%, 5%and 10% levels.All specifications include country fixed effects.The dependent variable is the Palmer Drought Severity Index. Data on rainfall and temperature come fromHsiang et al. (2011) for columns 1 to 3 and 7 to 9 and from Ciccone (2011) for columns 4 to 6 and 10 to 12.
34
Table ST3: The Effect of Rainfall and Temperature on PDSI
Specifications (1) (2)Dep. Var. Palmer Drought Severity Index (PDSI)
Hsiang et al. (2011)’s data Ciccone (2011)’s data
Rainfall t -0.00975*** -0.0484***(0.00241) (0.00407)
Rainfall t− 1 -0.00551* -0.0443***(0.00323) (0.00544)
Rainfall t− 2 0.00381 -0.00964**(0.00279) (0.00384)
Temperature t 0.0118*** 0.342***(0.00319) (0.0471)
Temperature t− 1 0.00118 -0.0287(0.00323) (0.0565)
Temperature t− 2 -0.00143 -0.00823(0.00282) (0.0448)
Observations 899 710R-squared 0.775 0.861
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectivelydenoting significance at the 1%, 5% and 10% levels.All specifications include country fixed effects.The dependent variable is the Palmer Drought Severity Index. Dataon rainfall and temperature come from Hsiang et al. (2011) for col-umn 1 and from Ciccone (2011) for column 2.
35
Table ST4: The Cross Effect of Rainfall and Temperature on PDSI
Specifications (1) (2) (3) (4) (5) (6)Dep. Var. Palmer Drought Severity Index (PDSI)Rainfall Data Hsiang et al. (2011)’s data Ciccone (2011)’s data
Temperature t -0.000282 -0.00113 -0.00137 -0.0571 -0.123 -0.109(0.00299) (0.00306) (0.00315) (0.137) (0.123) (0.142)
Rainfall t -0.133*** -0.129*** -0.128*** -0.228*** -0.245*** -0.237***(0.0167) (0.0171) (0.0178) (0.0599) (0.0530) (0.0608)
Temperature t × Rainfall t 0.00507*** 0.00499*** 0.00493*** 0.0544*** 0.0622*** 0.0595***(0.000700) (0.000734) (0.000759) (0.0184) (0.0165) (0.0188)
Temperature t− 1 -0.00581 -0.00622 -0.0965 -0.105(0.00369) (0.00381) (0.145) (0.139)
Rainfall t− 1 -0.0662*** -0.0648*** -0.0626 -0.0592(0.0189) (0.0198) (0.0680) (0.0660)
Temperature t− 1 × Rainfall t− 1 0.00249*** 0.00242*** 0.00535 0.00495(0.000801) (0.000854) (0.0212) (0.0206)
Temperature t− 2 -0.00170 0.217*(0.00325) (0.123)
Rainfall t− 2 -0.00832 0.104*(0.0182) (0.0538)
Temperature t− 2 × Rainfall t− 2 0.000426 -0.0355**(0.000793) (0.0167)
Observations 973 936 899 638 609 580R-squared 0.769 0.790 0.801 0.819 0.857 0.866
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting significance at the 1%, 5%and 10% levels.All specifications include country fixed effects.The dependent variable is the Palmer Drought Severity Index. Data on rainfall come from Hsiang (2011)for specifications 1 to 3 and from Ciccone (2011) for specifications 4 to 6 .
36
Table ST5: Baseline without/ with Sampling Weights
Specifications (1) (2)Dep. Var. Civil War Civil WarMethodology No Weight Sampling Weights
PDSI 0.677*** 0.969***(0.241) (0.344)
Observations 1,643 1,643R-squared 0.313 0.316
Note: Robust standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denotingsignificance at the 1%, 5% and 10% levels.PDSI is the Palmer Drought Severity Index. The dependent variable comesfrom the UCDP/PRIO Armed Conflict Dataset v4-2011 over the 1946 - 2010period. Civil War is a dummy which is equal to 1 for a number of battle deathsgreater than 1,000. It includes only internal civil wars.All specifications include country fixed effects and country time trends.
Table ST6: Standard Errors using Various Spatial and Temporal Cutoffs
Year : 2 - Distance: 1000 (0.230)*** Year : 6 - Distance: 4000 (0.226)***Year : 2 - Distance: 2500 (0.275)*** Year : 8 - Distance: 1000 (0.167)***Year : 2 - Distance: 4000 (0.265)*** Year : 8 - Distance: 2500 (0.225)***Year : 4 - Distance: 1000 (0.201)*** Year : 8 - Distance: 4000 (0.213)***Year : 4 - Distance: 2500 (0.252)*** Year : 10 - Distance: 1000 (0.137)***Year : 4 - Distance: 4000 (0.240)*** Year : 10 - Distance: 2500 (0.204)***Year : 6 - Distance: 1000 (0.183)*** Year : 10 - Distance: 4000 (0.190)***Year : 6 - Distance: 2500 (0.238)*** Two-way Clustering (0.018)***
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectivelydenoting significance at the 1%, 5% and 10% levels. Reported Stan-dard Errors in parentheses for the estimates of our baseline (see Table 2,column 3) with different spatial and temporal cutoffs.
37
Table ST7: Robustness Checks
Specifications (1) (2) (3) (4) (5)Dep. Var. Civil War
Controls Country Specific Time Trend Time Trend + Lag War Year 6= 1989Time Trends Sq. Time Trend Sq.
PDSI 0.694*** 0.436** 0.572*** 0.346* 0.597***(0.210) (0.179) (0.204) (0.181) (0.208)
Lag War 0.509***(0.0601)
Observations 1,643 1,643 1,643 1,605 1,643R-squared 0.402 0.307 0.359 0.518 0.354
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting sig-nificance at the 1%, 5% and 10% levels. The dependent variable comes from theUCDP/PRIO Armed Conflict Dataset v4-2011 over the 1946 - 2010 period. Civil Waris a dummy which is equal to 1 for a number of battle deaths greater than 1,000. PDSIis the Palmer Drought Severity Index. All specifications include country fixed effectsand country specific time trends. Specification 1 includes a country specific time trendsquared, Specification 2 includes a common time trend, Specification 3 includes a com-mon time trends and a common time trend squared, Specification 4 includes a lag of thedependent variable, and Specification 5 excludes the year 1989.
Table ST8: The Effect of (Lags and Forward) PDSI on Civil War
(1) (2) (3) (4) (5) (6)Dep. Var. Civil War
PDSI 0.677*** 0.522** 0.522** 0.533** 0.555** 0.557**(0.201) (0.247) (0.251) (0.224) (0.224) (0.263)
PDSI t− 1 0.307 0.183 0.337(0.205) (0.225) (0.237)
PDSI t− 2 0.241(0.226)
PDSI t+ 1 0.379 0.198(0.247) (0.248)
PDSI t+ 2 0.331(0.246)
PDSI * PDSI t− 1 -1.435(3.989)
LR chi2(1) - 1.06 1.65 1.64 2.70 1.11Prob > chi2 - 0.3027 0.4382 0.1999 0.2596 0.5744
Observations 1,643 1,605 1,567 1,605 1,567 1,605R-squared 0.350 0.354 0.359 0.367 0.361 0.354
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting signifi-cance at the 1%, 5% and 10% levels.The dependent variable comes from the UCDP/PRIO Armed Conflict Dataset v4-2011over the 1946 - 2010 period. Civil War is a dummy which is equal to 1 for a number ofbattle deaths greater than 1,000. It includes only internal civil wars.PDSI is the Palmer Drought Severity Index.
38
Table ST9: The Effect of Drought and Rainfall on Civil War
Specifications (1) (2) (3) (4) (5) (6)Dep. Var. Civil War
Hsiang et al. (2011)’s data Ciccone (2011)’s data
PDSI 0.703** 0.593 0.892* 0.967* 0.794** 0.715*(0.344) (0.366) (0.505) (0.507) (0.389) (0.409)
Rainfall t 0.00698 -0.000444 -0.0314 -0.0196(0.0206) (0.0215) (0.0529) (0.0534)
Rainfall t− 1 0.00765 0.00418 0.0587 0.0609(0.0199) (0.0209) (0.0530) (0.0525)
Rainfall t− 2 -0.00499 0.0481(0.0193) (0.0555)
Rainfall Growth t -0.0461 -0.0572*(0.0293) (0.0327)
Rainfall Growth t− 1 -0.0265(0.0410)
Observations 936 899 725 725 725 725R-squared 0.550 0.559 0.561 0.561 0.561 0.561
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting significance at the1%, 5% and 10% levels.The dependent variable comes from the UCDP/PRIO Armed Conflict Dataset v4-2011 over the1946 - 2010 period. Civil War is a dummy which is equal to 1 for a number of battle deathsgreater than 1,000. It includes only internal civil wars.PDSI is the Palmer Drought Severity Index.Data on rainfall come from Hsiang et al. (2011) for columns 1, 2, 5 and 6 and from Ciccone (2011)for columns 3, 4, 7 and 8.
39
Table ST10: The Effect of Drought and Temperature on Civil War
Specifications (1) (2) (3)Dep. Var. Civil War
Hsiang et al. (2011)’s data
PDSI 0.702*** 0.700*** 0.662***(0.206) (0.211) (0.216)
Temperature t -0.00405 -0.000531 -0.00197(0.0156) (0.0162) (0.0165)
Temperature t− 1 -0.0158 -0.00899(0.0184) (0.0189)
Temperature t− 2 -0.0364**(0.0152)
Observations 1,582 1,545 1,508R-squared 0.356 0.360 0.366
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectivelydenoting significance at the 1%, 5% and 10% levels. The dependentvariable comes from the UCDP/PRIO Armed Conflict Dataset v4-2011 over the 1946 - 2010 period. Civil War is a dummy which isequal to 1 for a number of battle deaths greater than 1,000. It in-cludes only internal civil wars. PDSI is the Palmer Drought SeverityIndex. Data on temperature come from Hsiang et al. (2011).
Table ST11: The Effect of Drought and El Nino on Civil War
Specifications (1) (2) (3) (4) (5) (6)Dep. Var. Civil War
PDSI 0.734*** 0.694*** 0.716*** 0.689*** 0.729*** 0.703***(0.211) (0.212) (0.211) (0.216) (0.210) (0.208)
NINO3 May-Dec. 0.00361(0.00585)
NINO3 Jan-Dec. 0.00873(0.00795)
NINO12 May-Dec. 0.00492(0.00455)
NINO12 Jan-Dec. 0.00616(0.00566)
NINO4 May-Dec. 0.00526(0.00917)
NINO4 Jan-Dec. 0.00981(0.00949)
Observations 1,545 1,545 1,545 1,545 1,545 1,545R-squared 0.372 0.372 0.372 0.372 0.372 0.372
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting significance at the1%, 5% and 10% levels. The dependent variable comes from the UCDP/PRIO Armed ConflictDataset v4-2011 over the 1946 - 2010 period. Civil War is a dummy which is equal to 1 for anumber of battle deaths greater than 1,000. It includes only internal civil wars. PDSI is thePalmer Drought Severity Index. The data set on El Nino is obtained from Hsiang et al. (2011).NINO3, NINO4 and NINO12 measure equatorial Pacific sea surface temperature anomalies indifferent locations in the Pacific Ocean. NINO3 refers to location (5S-5N, 150W-90W), NINO4 tolocation (5S-5N, 160E-150W) and NINO12 to location (10S-Eq, 90W-80W). May-Dec. indicatesthat the values are averaged over the May to December period and Jan-Dec. indicates that thevalues are averaged over the year.
40
Table ST12: The Effect of Drought on the Onset of Civil War
Specifications (1) (2) (3)Dep. Var. Onset of Civil War
PDSI 0.296** 0.182 0.187(0.128) (0.147) (0.152)
Country Fixed Effect Yes Yes YesTime Fixed Effect - Yes -Country Time Trends - - Yes
Observations 1,583 1,583 1,583R-squared 0.080 0.115 0.114
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ re-spectively denoting significance at the 1%, 5% and 10% lev-els. The dependent variable comes from the UCDP/PRIOArmed Conflict Dataset v4-2011 over the 1946 - 2010 pe-riod. Onset of Civil War is coded 1 for the first year of civilwar (more than 1,000 battle deaths), set to missing for thesubsequent civil war years, and at 0 for peace years. PDSIis the Palmer Drought Severity Index. Column 1 includescountry fixed effects. Column 2 includes country and yearfixed effects. Column 3 includes country fixed effects andcountry time trends.
Table ST13: Robustness to the Exclusion of Outliers
(1) (2) (3) (4)Dep. Var. Civil WarSample All 3SD 2SD Dfbeta Stat.
PDSI 0.677*** 0.221*** 0.0650*** 0.131*(0.201) (0.0797) (0.0247) (0.0788)
Observations 1,643 1,573 1,544 1569R-squared 0.350 0.784 0.975 0.491
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗
respectively denoting significance at the 1%, 5% and 10%levels. All specifications include country fixed effects andcountry time trends. In specification 2, we exclude 3 stan-dard deviation outliers (labeled 3SD). In specification 3,we exclude 2 standard deviation outliers (labeled 2SD).In specification 4, we exclude observations using the Df-beta statistic. The dependent variable comes from theUCDP/PRIO Armed Conflict Dataset v4-2011 over the1946 - 2010 period. Civil War is a dummy which is equalto 1 for a number of battle deaths greater than 1,000. It in-cludes only internal civil wars. PDSI is the Palmer DroughtSeverity Index.
41
Table ST14: The Effect of Drought on the Number of Battle Deaths
Specifications (1) (2) (3)Dep. Var. # of deaths (low estimate)Methodology Square root - OLS Linear - OLS Poisson
PDSI 62.55*** 7,229*** 25.36***(15.37) (1,924) (0.0901)
Observations 1,643 1,643 1,351R-squared 0.362 0.217
Specifications (4) (5) (6)Dep. Var. # of deaths (high estimate)Methodology Square root - OLS Linear - OLS Poisson
PDSI 121.7*** 31,953*** 24.55***(30.42) (10,150) (0.0446)
Observations 1,643 1,643 1,351R-squared 0.422 0.210
Specifications (7) (8) (9)Dep. Var. # of deaths (best estimate)Methodology Square root - OLS Linear - OLS Poisson
PDSI 44.95*** 4,560** 18.22***(15.15) (2,048) (0.0752)
Observations 1,528 1,528 1,111R-squared 0.455 0.291
Note: Conley standard errors in parentheses (except for Poisson) with ∗∗∗, ∗∗ and ∗
respectively denoting significance at the 1%, 5% and 10% levels.The dependent variables come from the UCDP/PRIO Armed Conflict Datasetand are an estimate of the number of battle-related deaths. The UCDP/PRIOgives three estimates: a low one, a high one and a best estimate for battle-related deaths in the conflict, in the given year. The dependent variable incolumns 1, 4 and 7 is the square root of the number of battle-related deaths.We report least square estimates in columns 1, 2, 4, 5, 8 and 9. In columns 3,6 and 9, we use a Poisson model.PDSI is the Palmer Drought Severity Index.
42
Appendix B: Summary of the Palmer Model
In this appendix, we briefly present the way the PDSI is formulated (see Palmer 1965; Alley 1984;
Karl 1986; Wells et al. 2004, and Dai 2011). Palmer considered monthly values of precipitation
(P) and four other surface water fluxes: evapotranspiration (E) which is the return flow of water
to the atmosphere, recharge to soils (R), runoff (RO), and water loss of the soil layers (L). He also
considered the potential values of these four fluxes, namely PE, PR, PRO, and PL, respectively
(they depend on the available water capacity (AWC) of the soil, see Thornthwaite, 1948).
Palmer introduced the “climatically appropriate for existing conditions” (CAFEC) potential
values.
Evapotranspiration (E) in location i, year t and month m is computed as a function of
monthly average temperatures with the Thornthwaite (1948) method:
Eitm = cit × T aititm
ait = 6.75× 10−7I3it − 7.71× 10−5I2
it + 17.92× 10−3Iit + 0.49239,
cit = 1/Iit,
Iit =∑
m=1,...,12
(Titm
5
)1.514
,
and the potential evapotranspiration in location i, year t and month m is:
PEitm = 1.6
(10TitmIit
)ait.
Palmer considers a model with two layers, a surface layer s and an underlying layer u.
The moisture loss from the surface layer is:
LSitm =
{min {S′itm, PEitm − Pitm} if PEitm > Pitm
0 otherwise,
where S′itm is the available moisture stored in the surface layer at the start of the month. There
is no loss when potential evapotranspiration is lower than precipitation. When the difference
between potential evapotranspiration and precipitation is greater than the stock, the entire
moisture stock is lost. When the difference is smaller than the stock, the loss is equal to this
difference.
The loss from the underlying layer is:
LUitm =
{min
{U ′imt, ((PEitm − Pitm)− LSitm)
U ′imtAWCi
}if LSitm = Sitm
0 otherwise
where U ′itm is the available moisture stored in the underlying layer at the start of the month.
There is some loss of moisture in the underlying layer only if the surface layer is empty, i.e. if
LSitm = Sitm. In that case, the loss is either the entire stock, or the difference between potential
evapotranspiration and precipitation (net of the moisture stock of the surface layer) times the
stock of the underlying layer over the available water capacity of the soil (that depends on the
43
depth of the “effective root zone” and on the local characteristics. AWCi is computed using
Webb et al. (1993)’s computations for the global distributions of soil profile thickness, potential
storage of water in the soil profile, potential storage of water in the root zone, and potential
storage of water derived from the soil texture. The moisture recharge, Ritm, is defined in a
symmetric (complementary) way to the moisture loss.
The runoff, ROitm is the excess of precipitation relatively to the available capacity of the
soil,
ROitm = min {Pitm −AWCi, 0} .
The potential recharge is defined as:
PRitm = AWCi − S′itm − U ′imt,
that is the available water capacity minus the moisture stocks of the two layers.
The potential loss is the maximum possible loss, i.e., the moisture loss when the precipitation
level is set to 0:
PLitm = PLSitm + PLUitm,
where
PLSitm = min {Sitm, PEitm} ,
and,
PLUitm = min
{Uimt, (PEitm − PLSitm)
U ′imtAWCi
}.
The potential runoff is the difference between the potential precipitation and the potential
recharge. Palmer assumed that the potential precipitation is equal to the AWC, and then:
PROitm = S′itm + U ′imt
Given a specific month m, he defined the water-balance coefficients and they are computed
as follows:
αim =Eim
PEim, βim =
Rim
PRim, γim =
ROim
PROim, δim =
Lim
PLim,
where the upper-bar indicates that values where averaged over the calibration period (1950-
1979). These ratios measure the ratio of the long-term mean values of a water flux and its
potential value.
The CAFEC value is the potential value of a water flux times its calibrated ratio (e.g., αim×PEitm is the CAFEC value of evapotranspiration). The CAFEC precipitation (P ) represents
the amount of precipitation needed to maintain a normal soil moisture level for a single month
and it is computed as follows (location i, year t, month m):
Pitm = αimPEitm + βimPRitm + γimPROitm − δimPLitm.
The excess of precipitation or the moisture departure, d, is the difference between the actual
44
precipitation in a specific month and the CAFEC precipitation:
ditm = Pitm − Pitm
In order to make the moisture departure comparable in time and space, it is weighted using a
factor K, which is a general approximation for the climate characteristics of a location.
K ′im = 1.5 log
(Lim + 2.8
dim
)+ 0.5
where dim and P im are values averaged over the years; they are the average moisture precipi-
tation and the average precipitation for a location in a specific month. Lim is a measure of the
ratio of “moisture demand” to “moisture supply” in location i for month i:
Lim =PEim +Rim +ROim
P im + Lim
Kim =17.67∑
j=1,...,12
djmK′jm
K ′im,
where 17.67 is an empirical constant derived from data from the US. Furthermore, Palmer
defined the moisture anomaly index Z:
Zitm = Kimditm
This value is used in the recursive formula of the PDSI.
Appendix C: Summary of Other Variables
Political Variables
Our measure of democratic institutions is the revised combined Polity core (Polity2) of the
Polity IV database (Marshall and Jaggers 2005). We follow Bruckner and Ciccone (2011) for
the definition of the variables. ∆ Polity captures the variation of the polity score from t to t+1.
A positive score represents an improvement in democracy. ∆ Executive Recruitment represents
the variation of the executive recruitment from t to t + 1. A positive score represents an
improvement. ∆ Political Competition represents the variation of the political competition from
t to t+ 1. A positive score represents an improvement. ∆ Executive Constraint represents the
variation of the executive constraint from t to t+1. A positive score represents an improvement.
Coup is a dummy equal to 1 if there was a coup in a country at time t. Coup in Democracy
is a dummy equal to 1 if there was a coup in a democratic country at time t. Democratic
Transition is a dummy coded 1 if a country was non-democratic at time t, but democratic at
time t + 1. Autocratic Transition is a dummy coded 1 if a country was democratic at time t
and non-democratic at time t+ 1. Democratization Step is a dummy coded 1 if the country was
upgraded to either a partial or full democracy between t and t+ 1.
45
Other Variables
Our Cereal Yield variable is obtained from the Food and Agricultural Organization (FAO) and
Agricultural Income/cap comes from the United Nations Accounts database (same data as in
Hsiang et al. 2011). The GDP/cap, GDP Growth and Gov. Consumption Share variables are
obtained from Penn World Table 7.0. The Tax Revenue, Gov. Revenue and Gov. Consumption
Expenditure variables come from the World Bank (World Development Indicator). The data on
Military Expenditure is obtained from the Stockholm International Peace Research Institute.
The demographic data come from the World Bank (World Development Indicator). Ethnic
Fractionalization, Linguistic Fractionalization and % Mountainous Terrain are obtained from
Fearon and Laitin (2003).
Other Climate Variables
In the body of the paper, we refer to alternative climate variables regarding temperature and
rainfall as being the same as in Hsiang et al. (2011) or Ciccone (2011). Hsiang et al. (2011)’s
rainfall data come from the Climate Prediction Center (CPC) (see Xie and Arkin 1996) and
their temperature data come from the NOAA NCEP-NCAR Climate Data Assimilation System
I (Kalnay et al. 1996). Their data is available at:
http://www.solomonhsiang.com/computing/data.
For rainfall, we also use Ciccone (2011)’s data that come from the Combined Precipitation
Dataset of NASA’s Global Precipitation Climatology Project (GPCP) version 2.1 (Huffman and
Bolvin 2009) and their temperature data come from Buhaug et al. (2010) who use data from
the University of Delaware. These rainfall and temperature data are available at:
http://www.aeaweb.org/aej/app/data/2010-0064 data.zip.
The data set on El Nino is obtained from Hsiang et al. (2011). NINO3, NINO4 and NINO12
measure equatorial Pacific sea surface temperature anomalies in different locations in the Pacific
Ocean. NINO3 refers to location (5S-5N, 150W-90W), NINO4 to location (5S-5N, 160E-150W)
and NINO12 to location (10S-Eq, 90W-80W). May-Dec. indicates that the values are averaged
over the May to December period and Jan-Dec. indicates that the values are averaged over the
year. Data available at:
http://www.solomonhsiang.com/computing/data.
References
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[3] Barrios, S., Bertinelli, L. and Strobl, E. (2010). ‘Trends in Rainfall and Economic Growth
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Appendix D - Online Appendix - NOT FOR PUBLICATION:
Tables and Figures with Correlates of War Data
Table ST15: The Effect of Drought on Civil War (COW)
Specifications (1) (2) (3)Dep. Var. Civil War (COW)
PDSI 1.067*** 0.623** 0.907***(0.214) (0.282) (0.251)
Country Fixed Effect Yes Yes YesTime Fixed Effect - Yes -Country Time Trends - - Yes
Observations 1,643 1,643 1,643R-squared 0.305 0.349 0.350
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗
respectively denoting significance at the 1%, 5% and 10%levels. The dependent variable comes from the CorrelatesOf War data set. Civil War (COW) is a dummy which isequal to 1 for a number of battle deaths greater than 1,000.PDSI is the Palmer Drought Severity Index. Column 1includes country fixed effects. Column 2 includes countryand year fixed effects. Column 3 includes country fixedeffects and country time trends.
Table ST16: Baseline without/with Sampling Weights (COW)
Specifications (1) (2)Dep. Var. Civil War (COW) Civil War (COW)Methodology No Weight Sampling Weights
PDSI 0.907*** 1.252***(0.280) (0.367)
Observations 1,643 1,643R-squared 0.448 0.391
Note: Robust standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denotingsignificance at the 1%, 5% and 10% levels. The dependent variable comes fromthe Correlates Of War data set. Civil War (COW) is a dummy which is equalto 1 for a number of battle deaths greater than 1,000. PDSI is the PalmerDrought Severity Index.All specifications include country fixed effects and country time trends.
52
Table ST17: Interactions between Drought and Country Characteristics (COW)
Specifications (1) (2) (3) (4)Dep. Var. Civil War (COW)
PDSI 2.395 -1.271*** 0.630** 1.005***(2.283) (0.325) (0.320) (0.347)
PDSI * ln GDP/cap 1965 -0.267(0.431)
PDSI * Frac. Ethn. 3.126***(0.706)
PDSI * % Mountainous Terrain 0.213(0.170)
PDSI * Pop Density 1965 0.000605(0.00150)
Observations 1,148 1,643 1,592 1,120R-squared 0.484 0.507 0.506 0.507
Specifications (5) (6) (7) (8)Dep. Var. Civil War (COW)
PDSI 0.919*** 0.972*** 0.975*** 0.934***(0.317) (0.253) (0.254) (0.254)
Polity t− 1 -0.000187(0.00139)
PDSI * Polity t− 1 0.0104(0.0395)
Executive Recruitment t− 1 0.0472***(0.0160)
PDSI * Executive Recruitment t− 1 -0.00273***(0.000331)
Political Competition t− 1 0.0467***(0.0160)
PDSI * Political Competition t− 1 -0.00272**(0.000332)
Executive Constraint t− 1 0.0476***(0.0168)
PDSI * Executive Constraint t− 1 -0.00270**(0.000337)
Observations 1,555 1,552 1,552 1,552R-squared 0.512 0.544 0.544 0.544
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting significance at the1%, 5% and 10% levels. All specifications include country fixed effects and country time trends.The dependent variable comes from the Correlates Of War data set. Civil War (COW) is a dummywhich is equal to 1 for a number of battle deaths greater than 1,000.PDSI is the Palmer Drought Severity Index.
53
Table ST18: The Effect of Rainfall/Temperature/El Nino on Civil War (COW)
Specifications (1) (2) (3) (4) (5)Dep. Var. Civil War (COW)
PDSI 1.410*** 1.426*** 1.051*** 0.984*** 1.072***(0.453) (0.460) (0.267) (0.275) (0.258)
Rainfall 0.0835*** 0.0772***(0.0213) (0.0208)
Rainfall t− 1 0.0774*** 0.0711***(0.0241) (0.0232)
Rainfall t− 2 0.0592***(0.0200)
Temperature -0.0115 -0.0178(0.0191) (0.0192)
Temperature t− 1 -0.0180 -0.00962(0.0204) (0.0205)
Temperature t− 2 -0.0291(0.0197)
NINO 3 0.00245(0.00694)
Observations 936 899 1,545 1,508 1,545R-squared 0.721 0.729 0.531 0.539 0.543
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denotingsignificance at the 1%, 5% and 10% levels. The dependent variable comesfrom the Correlates Of War data set. Civil War (COW) is a dummy whichis equal to 1 for a number of battle deaths greater than 1,000. PDSI is thePalmer Drought Severity Index. Data on rainfall, temperature and El Ninocome from Hsiang et al. (2011). The data set on is obtained from Hsiang et al.(2011). NINO3 measures equatorial Pacific sea surface temperature anomaliesin different locations in the Pacific Ocean. NINO3 refers to location (5S-5N,150W-90W)
Table ST19: Standard Errors using Various Spatial and Temporal Cutoffs (COW)
Year: 2 - Distance: 1000 (0.259)*** Year : 6 - Distance: 2500 (0.293)***Year : 2 - Distance: 1000 (0.259)*** Year : 6 - Distance: 4000 (0.278)***Year : 2 - Distance: 2500 (0.318)*** Year : 8 - Distance: 1000 (0.195)***Year : 2 - Distance: 4000 (0.304)*** Year : 8 - Distance: 2500 (0.268)***Year : 4 - Distance: 1000 (0.251)*** Year : 8 - Distance: 4000 (0.251)***Year : 4 - Distance: 2500 (0.312)*** Year : 10 - Distance: 1000 (0.188)***Year : 4 - Distance: 4000 (0.297)*** Year : 10 - Distance: 2500 (0.263)***Year : 6 - Distance: 1000 (0.228)*** Year : 10 - Distance: 4000 (0.246)***
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectivelydenoting significance at the 1%, 5% and 10% levels. We use our baselinespecification, equation (3). The PDSI estimate is 0.907, see Table ST15,column 3. The dependent variable comes from the Correlates Of Wardata set.
54
Table ST20: Robustness Checks (COW)
Specifications (1) (2) (3) (4) (5)Dep. Var. Civil War (COW)
Controls Country Specific Time Trend Time Trend + Lag War Year 6= 1989Time Trend Sq. Time Trend Sq.
PDSI 0.907*** 0.785*** 0.685*** 0.186 0.840***(0.251) (0.251) (0.263) (0.201) (0.257)
Lag War 0.729***(0.0421)
Observations 1,643 1,643 1,643 1,605 1,643R-squared 0.506 0.512 0.584 0.767 0.507
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗ respectively denoting signifi-cance at the 1%, 5% and 10% levels. The dependent variable comes from the CorrelatesOf War data set. Civil War (COW) is a dummy which is equal to 1 for a number ofbattle deaths greater than 1,000. PDSI is the Palmer Drought Severity Index. All spec-ifications include country fixed effects and country specific time trends. Specification 1includes a country specific time trend squared, Specification 2 includes a common timetrend, Specification 3 includes a common time trend and a common time trend squared,Specification 4 includes a lag of the dependent variable, Specification 5 excludes the year1989.
Table ST21: The Effect of Drought on the Onset of Civil War (COW)
Specifications (1) (2) (3)Dep. Var. Onset of Civil War (COW)
PDSI 0.216 0.309* 0.283(0.137) (0.160) (0.178)
Country Fixed Effect Yes Yes YesTime Fixed Effect - Yes -Country Time Trends - - Yes
Observations 1,495 1,495 1,495R-squared 0.090 0.119 0.135
Note: Conley standard errors in parentheses with ∗∗∗, ∗∗ and ∗
respectively denoting significance at the 1%, 5% and 10%levels. The dependent variable comes from the CorrelatesOf War data set. Onset of Civil War (COW) is coded 1 forthe first year of civil war, set to missing for the subsequentcivil war years, and at 0 for peace years. PDSI is the PalmerDrought Severity Index. Column 1 includes country fixedeffects. Column 2 includes country and year fixed effects.Column 3 includes country fixed effects and country timetrends.
55
Figure 14: Drought Effect with Different Time Frame (COW)
194519501955196019651970197519801985
Sta
rt Y
ea
r
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
End Year
< 0
> 0
Size of Drought Coefficient
194519501955196019651970197519801985
Sta
rt Y
ea
r
19
65
19
67
19
69
19
71
19
73
19
75
19
77
19
79
19
81
19
83
19
85
19
87
19
89
19
91
19
93
19
95
19
97
19
99
20
01
20
03
20
05
End Year
NonSignifcant
Significant
Significance of Drought
We re-estimate our preferred specification (Table 2, column 3) for all possible time intervals of a minimum of 20 consecutive
years (i.e. 861 estimates). The dependent variable comes from the Correlates Of War data set.
Each square represents the size of the coefficient of the PDSI ( β) for every possible start and end year in the timescale.
The graph at the bottom of the figure represents the degree of significance of the estimated coefficient of the PDSI (whether
significant at the 5% level of confidence or not).
56
Figure 15: Non-Linear Effect of Drought (COW)
-.05
0.0
5.1
Est
imat
ed C
oeffi
cien
t
2 4 6 8 10PDSI Bin
We follow the same methodology as Deschenes and Greenstone (2011) and compute 10 deciles of the PDSI (bin 1 is the
reference). We plot the coefficient of each bin (black line) and two standards errors (dashed lines) The dependent variable
comes from the Correlates Of War data set.
57
Figure 16: Split with Agricultural Share (COW)
01
23
4D
roug
ht C
oeffi
cien
t
0 5 10 15 20 25Agricultural Share
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of agricultural share in 1965 is
excluded and countries are excluded with respect to increasing values of agricultural share in 1965. We report the value of
the effect of the PDSI on civil war. The dependent variable comes from the Correlates Of War data set.
Figure 17: Split with GDP (COW)
-.5
0.5
11.
5D
roug
ht C
oeffi
cien
t
0 5 10 15 20GDP per Capita 1965
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of GDP per capita in 1965 is
excluded and countries are excluded with respect to increasing values of GDP per capita in 1965. We report the value of
the effect of the PDSI on civil war. The dependent variable comes from the Correlates Of War data set.
58
Figure 18: Split with Ethnic Fractionalization (COW)
01
23
4D
roug
ht C
oeffi
cien
t
0 10 20 30Ethnic Fractionalization
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of ethnic fractionalization is
excluded and countries are excluded with respect to increasing values of ethnic fractionalization. We report the value of
the effect of the PDSI on civil war. The dependent variable comes from the Correlates Of War data set.
Figure 19: Split with Linguistic Fractionalization (COW)
0.5
11.
52
2.5
Dro
ught
Coe
ffici
ent
0 10 20 30Linguistic Fractionalization
This figure reports coefficient estimates of equation 3 that include country fixed effects and country time trends. The
equation is re-estimated for each sub-sample from which the country with the lowest value of linguistic fractionalization is
excluded and countries are excluded with respect to increasing values of linguistic fractionalization. We report the value
of the effect of the PDSI on civil war. The dependent variable comes from the Correlates Of War data set.
59
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