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DOI: 10.1126/science.1235367
, (2013);341 Science
et al.Solomon M. Hsiang
Quantifying the Influence of Climate on Human Conflict
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13 SEPTEMBER 2013 VOL 341 SCIENCE www.sciencemag.org
Quantifying the Infl uence of Climate
on Human Confl ict
Solomon M. Hsiang,* Marshall Burke, Edward Miguel
Introduction: Despite the existence of institutions designed to promote peace, interactions between
individuals and groups sometimes lead to confl ict. Understanding the causes of such confl ict is
a major project in the social sciences, and researchers in anthropology, economics, geography,
history, political science, psychology, and sociology have long debated the extent to which climatic
changes are responsible. Recent advances and interest have prompted an explosion of quantitative
studies on this question.
Methods: We carried out a comprehensive synthesis of the rapidly growing literature on climate and
human confl ict. We examined many types of human confl ict, ranging from interpersonal violence
and crime to intergroup violence and political instability and further to institutional breakdown
and the collapse of civilizations. We focused on quantitative studies that can reliably infer causal
associations between climate variables and confl ict outcomes. The studies we examined are experi-
ments or “natural experiments”; the latter exploit variations in climate over time that are plausibly
independent of other variables that also affect confl ict. In many cases, we obtained original data
from studies that did not meet this criterion and used a common statistical method to reanalyze
these data. In total, we evaluated 60 primary studies that have examined 45 different confl ict data
sets. We collected fi ndings across time periods spanning 10,000 BCE to the present and across all
major world regions.
Results: Deviations from normal precipitation and mild temperatures systematically increase the
risk of confl ict, often substantially. This relationship is apparent across spatial scales ranging from a
single building to the globe and at temporal scales ranging from an anomalous hour to an anoma-
lous millennium. Our meta-analysis of studies that examine populations in the post-1950 era sug-
gests that the magnitude of climate’s infl uence on modern confl ict is both substantial and highly
statistically signifi cant (P < 0.001). Each 1-SD change in climate toward warmer temperatures or
more extreme rainfall increases the frequency of interpersonal violence by 4% and intergroup
confl ict by 14% (median estimates).
Discussion: We conclude that there is more agreement across studies regarding the infl uence of cli-
mate on human confl ict than has been recognized previously. Given the large potential changes in
precipitation and temperature regimes projected for the coming decades—with locations through-
out the inhabited world expected to warm by 2 to 4 SDs by 2050—amplifi ed rates of human confl ict
could represent a large and critical social impact of anthropogenic climate change in both low- and
high-income countries.
FIGURES AND TABLE IN THE FULL ARTICLE
Fig. 1. Samples and spatiotemporal resolu-
tions of 60 studies examining intertemporal
associations between climatic variables and
human confl ict.
Fig. 2. Empirical studies indicate that clima-
tological variables have a large effect on the
risk of violence or instability in the modern
world.
Fig. 3. Examples of paleoclimate reconstruc-
tions that fi nd associations between climatic
changes and human confl ict.
Fig. 4. Modern empirical estimates for the
effect of climatic events on the risk of inter-
personal violence.
Fig. 5. Modern empirical estimates for the
effect of climatic events on the risk of inter-
group confl ict.
Fig. 6. Projected temperature change by 2050
as a multiple of the local historical SD (σ) of
temperature.
Table 1. Primary quantitative studies testing
for a relationship between climate and con-
fl ict, violence, or political instability.
SUPPLEMENTARY MATERIALS
Supplementary Text
Figs. S1 to S4
Tables S1 to S4
References (140, 141)
−2 −1 0 1 2
−40
0
40
80
∆ Pixel temp. (°C)
∆ Conflict risk (% of mean)
−1 0 1 2
∆ Pacific Ocean temp. (°C)
−0.5 0 0.5 1
∆ Country temp. (°C)
Local violence
East Africa
Civil conflict onset
Global tropics
Civil war incidence
Sub-Saharan Africa
CBA
Climate and confl ict across spatial scales. Evidence that temperature infl uences the risk of modern human
confl ict: (A) local violence in 1° grid cells, (B) civil war in countries, and (C) civil confl ict risk in the tropics. The map
depicts regions of analysis corresponding to nonparametric watercolor regressions in (A) to (C). The color intensity
in (A) to (C) indicates the level of certainty in the regression line.
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Science 341, 1235367 (2013).
DOI: 10.1126/science.1235367
The list of author affi liations is available in the full article online.
*Corresponding author. E-mail: shsiang@berkeley.edu
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Quantifying the Influence of Climate
on Human Conflict
Solomon M. Hsiang,
1,2
*†‡ Marshall Burke,
3
† Edward Miguel
2,4
A rapidly growing body of research examines whether human conflict can be affected by
climatic changes. Drawing from archaeology, criminology, economics, geography, history, political
science, and psychology, we assemble and analyze the 60 most rigorous quantitative studies
and document, for the first time, a striking convergence of results. We find strong causal evidence
linking climatic events to human conflict across a range of spatial and temporal scales and
across all major regions of the world. The magnitude of climate’s influence is substantial: for
each one standard deviation (1s) change in climate toward warmer temperatures or more extreme
rainfall, median estimates indicate that the frequency of interpersonal violence rises 4% and
the frequency of intergroup conflict rises 14%. Because locations throughout the inhabited
world are expected to warm 2s to 4s by 2050, amplified rates of human conflict could represent a
large and critical impact of anthropogenic climate change.
H
uman behavior is complex, and despite
the existence of institutions designed to
promote peace, interactions between in-
dividuals and groups sometimes lead to conflict.
When such conflict becomes violent, it can have
dramatic consequences on human well-b eing. Mor-
tality from war and in terperso nal violenc e amounts
to 0.5 to 1 million deaths annually (1, 2), with
nonlethal impacts, including injury and lost eco-
nomic opportunities, affecting millions more. Be-
cause the stakes are so high, understanding the
causes of human conflict has been a major project
in the social sciences.
Researchers working across multiple dis-
ciplines including archaeology, criminology, eco-
nomics, geography, history, political science, and
psychology have long debated the extent to which
climatic changes are responsible for causing con-
flict, violence, or political instability. Numerous
pathways linking the climate to these outcomes
have been proposed. For example, climatic changes
may alter the supply of a resource and cause
disagreement over its allocation, or climatic con-
ditions may shape the relative appeal of using
violence or cooperation to achieve some precon-
ceived objective. Qualitative researchers have a
well-developed history of studying these issues
(3–7) dating back, at least, to the start of the 20th
century (8). Yet, in recent years, growing recog-
nition that the climate is changing, coupled with
improvements in data quality and computing, has
prompted an explosion of quantitative analyses
seeking to test these theories and quantify the
strength of these previously proposed linkages.
Thus far, this work has remained scattered across
multiple disciplines and has been difficult to syn-
thesize given the disparate methodologies, data,
and interests of the various research teams.
Here, we assemble the first comprehensive
synthesis of this rapidly growing quantitative lit-
erature. We adopt a broad definition of “conflict,”
using the term to encompass a range of outcomes
from individual-level violence and aggression to
country-level political instability and civil war.
We then collect all available candidate studies and,
guided by previous criticisms that not all corre-
lations imply causation (9–11), focus on only
those quantitative studies that can reliably infer
causal associations (9, 12) between climate var-
iables and conflict outcomes. The studies we
examine exploit either experimental or natural-
experimental variation in climate; the latter term
refers to variation in climate over time that is
plausibly independent of other variables that also
affect conflict. T o meet this standard, studies must
account for unobservable confounding factors
across populations, as well as for unobservable
time-trending factors that could be correlated with
both climate and conflict (13). In many cases, we
obtained data from studies that did not meet this
criterion and reanalyzed it with a common sta-
tistical model that did meet the criterion (see sup-
plementary materials). The importance of this
rigorous approach is highlighted by an exam-
ple in which our standardized analysis generated
findings consistent with other studies but at odds
with the original conclusions of the study in ques-
tion (14).
In total, we obtained 60 primary studies that
either met this criterion or were reanalyzed with a
method that met this criterion (Table 1). Collect-
ively, these studies analyze 45 different conflict
data sets published across 26 different journals
and represent the work of more than 190 re-
searchers from around the world. Our evaluation
summarizes the recent explosion of research on
this topic, with 78% of studies released since
2009 and the median study released in 2011 . We
collected findings across a wide range of conflict
outcomes, time periods spanning 10,000 BCE to
the present day , and all major regions of the
world (Fig. 1).
Although various conflict outcomes differ in
important ways, we find that the behavior of
these outcomes relative to the climate system is
markedly similar. Put most simply, we find that
large deviations from normal precipitation and
mild temperatures systematically increase the risk
of many types of conflict, often substantially, and
that this relationship appears to hold over a varie-
ty of temporal and spatial scales. Our meta-analysis
of studies that examine populations in the post-
1950 era suggests that these relationships contin-
ue to be highly important in the modern world,
although there are notable differences in the mag-
nitude of the relationship when different variables
are considered: The standardized effect of tem-
perature is generally larger than the standardized
effect of rainfall, and the effect on intergroup
violence (e.g., civil war) is larger than the effect
on interpersonal violence (e.g., assault). We con-
clude that there is substantially more agreement
and generality in the findings of this burgeoning
literature than has been recognized previously.
Given the large potential changes in precipitation
and temperature regimes projected for the coming
decades, our findings have important implications
for the social impact of anthropogenic climate
change in both low- and high-income countries.
Estimation of Climate-Conflict Linkages
Reliably measuring an effect of climatic condi-
tions on human conflict is complicated by the in-
herent complexity of social systems. In particular,
a central concern is whether statistical relation-
ships can be interpreted causally or if they are
confounded by omitted variables. To address this
concern, we restrict our attention to studies with
research designs that are scientific experiments or
that approximate one (i.e., “natural experiments”).
After describing how studies meet this criterion,
we discuss how we interpret the precision of re-
sults, assess the importance of climatic factors,
and address choices over functional form.
Research Design
In an ideal experiment, we would observe two
identical populations, change the climate of one,
and observe whether this treatment leads to more
or less conflict relative to the control conditions.
Because the climate cannot be experimentally ma-
nipulated, researchers primarily rely on natural
experiments in which a given population is com-
pared to itself at different moments in time when
it is exposed to different climatic conditions—
conditions that are exogenously determined by
RESEARCH ARTICLE
1
Program in Science, Technology and Environmental Policy,
Woodrow Wilson School of Public and In ternational Affairs,
Princeton University, Princeton, NJ 08544 , USA.
2
National Bu-
reau of Economic Research, Cambridge, MA 0 2138, USA.
3
Depart-
ment of Agricultural and Re source Economics, University of
California, Berkeley, Berkeley, CA 9472 0, USA.
4
Department of
Economics, University of California, Berkeley, Berkeley, CA
94720, USA.
*Present address: Goldman School of Public Policy, University
of California, Berkeley, Berkeley, CA 947 20, USA.
†These authors contributed equally to this work.
‡Corresponding author. E-mail: shsiang@berkeley.edu
www.sciencemag.org SCIENCE VOL 341 13 SEPTEMBER 2013 1235367-1
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Table 1. Primary quantitative studies testing for a relationship between
climate and conflict, violence, or political instability. “Stat. test” is Y if the
analysis uses formal statistical methods to quantify the influence of climate
variables and uses hypothesis testing procedures (Y, yes; N, no). “Large effect”
is Y if the point estimate for the effect size is considered substantial by the
authors or is greater in magnitude than 10% of the mean risk level for a 1s
change in climate variables. “Reject b =0” is Y if the st udy rejects an effect size
of zero at the 95% confidence level. “Reject b =10%” is Y if the study is able
to reject the hypothesis that the effect size is larger than 10% of the mean risk
level for a 1s change in climate variables. –, not applicable. SSA, sub-Saharan
Africa; PDSI; Palmer Drought Severity Index; ENSO, El Niño–Southern Os-
cillation; NAO, North Atlantic Oscillation; N. Hem., Northern Hemisphere.
Study
Sample
period
Sample
region
Time
unit
Spatial
unit
Independent
variable
Dependent
variable
Stat.
test
Large
effect
Reject
b =0
Reject
b =10%
Ref.
Interpersonal conflict (15)
Anderson et al. 2000* 1950–1997 USA Annual Country Temp Violent crime Y Y Y – (34)
Auliciems et al.1995† 1992 Australia Week Municipality Temp Domestic violence Y Y Y – (29)
Blakeslee et al. 2013 1971–2000 India Annual Municipality Rain Violent and
property crime
YYY – (42)
Card et al.2011†‡ 1995–2006 USA Day Municipality Temp Domestic violence Y Y Y – (37)
Cohn et al. 1997§ 1987–1988 USA Hours Municipality Temp Violent crime Y Y Y – (30)
Jacob et al.2007†‖ 1995–2001 USA Week Municipality Temp Violent and
property crime
YYY – (35)
Kenrick et al. 1986¶ 1985 U SA Day Site Temp Hostility Y Y Y – (27)
Larrick et al.2011†‡‖ 1952–2009 USA Day Site Temp Violent retaliation Y Y Y – (36)
Mares 2013 1990–2009 USA Month Municipality Temp Violent crime Y Y Y – (39)
Miguel 2005†‡ 1992–2002 Tanzania Annual Municipality Rain Murder Y Y N N (40)
Mehlum et al. 2006 1835–1861 Germany Annual Province Rain Violent and
property crime
YYY – (43)
Ranson 2012†‖ 1960–2009 USA Month County Temp Personal violence Y Y Y – (38)
Rotton et al. 2000§ 1994–1995 USA Hours Municipality Temp Violent crime Y Y Y – (31)
Sekhri et al.2013† 2002–2007 India Annual Municipality Rain Murder and
domestic violence
YYY – (41)
Vrij et al. 1994¶ 1993 Netherlands Hours Site Temp Police use of force Y Y Y – (28)
Intergroup conflict (30)
Almer et al. 2012 1985–2008 SSA Annual Country Rain/temp Civil conflict Y Y N N (65)
Anderson et al. 2013 1100–1800 Europe Decade Municipality Temp Minority expulsion Y Y Y – (63)
Bai et al. 2010 220–1839 China Decade Country Rain Transboundary Y Y Y – (50)
Bergholt et al. 2012‡# 1980–2007 Global Annual Country Flood/storm Civil conflict Y N N Y (75)
Bohlken et al. 2011‖# 1982–1995 India Annual ProvinceRain Intergroup YYNN(44)
Buhaug 2010
#
1979–2002 SSA Annual Country Temp Civil conflict Y N N N (22)
Burke 2012‡‖# 1963–2001 Global Annual Country Rain/te mp Political instability Y Y N** N (71)
Burke et al. 2009‡‖#†† 1981–2002 SSA Annual Country Temp Civil conflict Y Y Y – (64)
Cervellati et al. 2011 1960–2005 Global Annual Country Drought Civil conflict Y Y Y – (54)
Chaney 2011 641–1438 Egypt Annual Country Nile floods Political Instability Y Y Y – (70)
Couttenier et al. 2011
#
1957–2005 SSA Annual Country PDSI Civil conflict Y Y Y – (53)
Dell et al. 2012# 1950–2003 Global Annual Country Temp Political instability
and civil conflict
YYY – (21)
Fjelde et al.2012‡# 1990–2008 SSA Annual Province Rain Intergroup Y Y N** N (55)
Harari et al. 2013# 1960–2010 SSA Annual Pixel (1°) Drought Civil conflict Y Y Y – (52)
Hendrix et al. 2012‡‖# 1991–2007 SSA Annual Country Rain Intergroup Y Y Y – (46)
Hidalgo et al. 2010‡‖# 1988–2004 Brazil Annual Municipality Rain Intergroup Y Y Y – (25)
Hsiang et al. 2011‖# 1950–2004 Global Annual World ENSO Civil conflict Y Y Y – (51)
Jia 2012 1470–1900 China Annual Province Drought/flood Peasant rebellion Y Y Y – (56)
Kung et al. 2012 1651–1910 China Annual County Rain Peasant rebellion Y Y Y – (47)
Lee et al. 2013 1400–1999 Europe Decade Region NAO Violent conflict Y Y Y – (57)
Levy et al.2005‡‖# 1975–2002 Global Annual Pixel (2.5°) Rain Civil conflict Y Y N** N (49)
Maystad t et al. 2013# 1997–2009 Somalia Month Province Temp Civil conflict Y Y Y – (66)
Miguel et al. 2004#‡‡ 1979–1999 SSA Annual Country Rain Civil war Y Y Y – (48)
O’Laughlin et al. 2012‡‖# 1990–2009 E. Africa Month Pixel (1°) Rain/temp Civil/intergroup Y Y Y – (23)
Salehyan et al. 2012 1979–2006 Global Annual Country PDSI Civil/intergroup Y Y Y – (76)
Sarsons 2011 1970–1995 India Annual Municipality Rain Intergroup Y Y Y – (45)
Theisen et al. 2011‡# 1960–2004 Africa Annual Pixel (0.5°) Rain Civil conflict Y N N N (24)
Theisen 2012‡‖# 1989–2004 Kenya Annual Pixel (0.25°) Rain/temp Civil/intergroup Y Y N** N (14)
Tol et al. 2009 1500–1900 Europe Decade Region Rain/temp Transboundary Y Y Y – (60)
Zhang et al. 2007§§ 1400–1900 N. Hem. Century Region Temp Instability Y Y Y – (59)
Institutional breakdown and population collapse (15)
Brückner et al. 2011# 1980–2004 SSA Annual Country Rain Inst. change Y Y Y – (78)
Continued on next page
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the climate system (9, 15). In this research
design, a single population serves as both the
control population (e.g., just before a change in
climatic conditions) and the treatment population
(e.g., just after a change in climatic conditions).
Thus, inferences are based only on how a fixed
population responds to different climatic condi-
tions that vary over time, and time-series or lon-
gitudinal analysis is used to construct a credible
estimate for the causal effect of climate on con-
flict (12, 15, 16).
To minimize statistical bias and improve the
comparability of studies, we focus on studies that
use versions of the general model
conflict
variable
it
¼ b climate variable
it
þ
m
i
þ q
t
þ ∈
it
ð1Þ
where locations are indexed by i, observational
periods are indexed by t, b is the parameter of
interest, and ∈ is the error. If different locations
in a sample exhibit different average levels of
conflict—perhaps because of cultural, historical,
political, economic, geographic, or institutional
differences between the locations—this will be
accounted for by the vector of location-specific
constants m (commonly known as “fixed effects”).
The vector of time-specific constants q (a dum-
my for each time period) flexibly accounts for
other time-trending variables such as economic
growth or gradual demographic changes that could
be correlated with both climate and conflict. In
some cases, such as in time series, the q
t
parameters
AB
site
municipal
pixel
province
country
region
global
Spa
t
i
a
l scale
o
f
d
ependent variable
hour
d
a
y
we
e
k
month
ye
ar
decade
ce
n
tury
millenn
i
a
Duration of climatic event (log scale)
Hsiang et al.
(2011)
Continent
8000 BCE 0 1000 1800 1950 2000 2010
Years in study (log scale)
Kuper &
Kröepelin
(2006)
Vrij et al.
(1994)
N=10
Americas
Australia
Eurasia &
D’Angou et al.
(2012)
Africa
Global
Fig. 1. Samples and spatiotemporal resolutions of 60 studies examining
intertemporal associations between climatic variables and human con-
flict. (A) The location of each study region (y axis) plotted against the period of
time included in the study (x axis). The x axis is scaled according to log years before
the present but is labeled according to the year of the common era. (B)Thelevel
of aggregation in social outcomes (y axis) plotted against the time scale of climatic
events (x axis). The envelope of spatial and temporal scales where associations are
documented is shaded, with studies at extreme vertices labeled for reference.
Marker size indicates the number of studies at each location, with the smallest
bubbles marking individual studies and the largest bubble denoting 10 studies.
Study
Sample
period
Sample
region
Time
unit
Spatial
unit
Indepen-
dent
variable
Dependent
variable
Stat.
test
Larg-
e
ef-
fect
Re-
ject
b =0
Reject
b = 10% Re-
f.
Buckley et al. 2010‖‖ 1030–2008 Cambodia Decade Country Drought Collapse N –– –(85)
Büntgen et al. 2011‖‖ 400 BCE–2000 Europe Decade Region Rain/temp Instability N –– –(62)
Burke et al. 2010‡#1963–2007 Global Annual Country Rain/temp Inst. change Y Y Y – (77)
Cullen et al. 2000‖‖ 4000 BCE–0 Syria Century Country Drought Collapse N –– –(83)
D’Anjou et al 2012 550 BCE–1950 Norway Century Municipality Temp Collapse Y Y Y – (89)
Ortloff et al. 2003‖‖ 500–2000 Peru Century Country Drought Collapse N –– –(80)
Haug et al. 2003‖‖ 0–1900 Mexico Century Country Drought Collapse N –– –(84)
Kelly et al. 2013 10050 BCE–1950 USA Century State Temp/rain Collapse Y Y Y – (88)
Kennett et al. 2012 40 BCE–2006 Belize Decade Country Rain Collapse N –– –(87)
Kuper et al. 2006 8000–2000 BCE N. Africa Millennia Region Rain Collapse N –– –(81)
Patterson et al. 2010 200 BCE–1700 Iceland Decade Country Temp Collapse N –– –(86)
Stahle et al. 1998 1200–2000 USA Multiyear Municipality PDSI Collapse N –– –(82)
Yancheva et al. 2007‖‖ 2100 BCE–1700 China Century Country Rain/temp Collapse N –– –(79)
Zhang et al. 2006 1000–1911 China Decade Country Temp Civil conflict
and collapse
YYY – (58)
Number of studies (60 total): 50 47 37 1
Fraction of those using statistical tests: 100% 94% 74% 2%
*Also see (33). †Shown in Fig. 4. ‡Reanalyzed using the common statistical model containing location fixed effects and trends (see supplementary materials). §Also see discussion
in (32). ‖Shown in Fig. 2. ¶Actual experiment. #Shown in Fig. 5. **Effect size in the study is statistically significant at the 10% level, but not at the 5% level. ††Also see
discussion in (22, 132–137). ‡‡Also see discussion in (138, 139). §§Also see (61). ‖‖Shown in Fig. 3.
Study
Sample
period
Sample
region
Time
unit
Spatial
unit
Independent
variable
Dependent
variable
Stat.
test
Large
effect
Reject
b =0
Reject
b =10%
Ref.
Buckley et al. 2010‖‖ 1030–2008 Cambodia Decade Country Drought Collapse N –– –(85)
Büntgen et al. 2011‖‖ 400 BCE–2000 Europe Decade Region Rain/temp Instability N –– –(62)
Burke et al. 2010‡# 1963–2007 Global Annual Country Rain/temp Inst. change Y Y Y – (77)
Cullen et al. 2000‖‖ 4000 BCE–0SyriaCenturyCountry Drought Collapse N –– –(83)
D’Anjou et al 2012 550 BCE–1950 Norway Century Municipality Temp Collapse Y Y Y – (89)
Ortloff et al. 1993‖‖ 500–2000 Peru Century Country Drought Collapse N –– –(80)
Haug et al. 2003‖‖ 0–1900 Mexico Century Country Drought Collapse N –– –(84)
Kelly et al. 2013 10050 BCE–1950 USA Century State Temp/rain Collapse Y Y Y – (88)
Kennett et al. 2012 40 BCE–2006 Belize Decade Country Rain Collapse N –– –(87 )
Kuper et al. 2006 8000–2000 BCE N. Africa Millennia Region Rain Collapse N –– –(81)
Patterson et al. 2010 200 BCE–1700 Iceland Decade Country Temp Collapse N –– –(86)
Stahle et al. 1998 1200–2000 USA Multiyear Municipality PDSI Collapse N –– –(82 )
Yancheva et al. 2007‖‖ 2100 BCE–1700 China Century Country Rain/temp Collapse N –– –(79)
Zhang et al. 2006 1000–1911 China Decade Country Temp Civil conflict
and collapse
YYY – (58)
Number of studies (60 total): 50 47 37 1
Fraction of those using statistical tests: 100% 94% 74% 2%
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may be replaced by a generic trend (e.g., q t)
that is possibly nonlinear and is either common
to all locations or may be location-specific (e.g.,
q
i
t). Our conclusions from the literature are
based only on those studies that implement Eq. 1
or one of the mentioned alternatives. In select
cases, when studies did not meet this criterion
but the data from these analyses were publicly
available or supplied by the authors, we used
this common method to reanalyze the data (see
supplementary materials). Many estimates of
Eq. 1 in the literature and in our reanalysis ac-
count for temporal and/or spatial autocorrelation
in the error term ∈, although this adjustment was
not considered a requirement for inclusion here.
In the case of some paleoclimatol o g i c a l and ar-
chaeological studies, formal statistical analysis is
not implemented because the outcome variables
of interest are essentially singular cataclysmic
events. However , we include these studies because
they follow populations over time at a fixed lo-
cation and are, thus, implicitly using the model in
Eq. 1 (these cases are noted in Table 1).
We do not consider studies that are purely
cross-sectional; that is, studies that only compare
rates of conflict across different locations and
attribute differences in average levels of conflict
to average climatic conditions. Populations differ
from one another in numerous ways (culture, his-
tory, etc.), many of them unobserved, and these
“omitt ed variables” are likely to confound these
analyses. In the language of the natural experi-
ment, the treatment and control populations in
these analyses are not comparable units, so we
cannot infer whether a climatic treatment has a
causal effect or not (12, 13, 15–17). For example,
a cross-sectional study might compare average
rates of civil conflict in Norway and Nigeria,
attributing observed differences to the different
climates of these countries, despite the fact that
there are clearly many other relevant ways in which
these countries differ . Nonetheless, some studies
−30 −20 −10 0 10 20 30
−60
−40
−20
0
20
40
Civil conflict onset (Global)
Pixel−by−year: N = 3,809,432
Levy et al. (2005)
Pixel Standardized Precipitation Loss
(Weighted Anomaly Index)
Risk of any conflict
(% of mean)
−10 0 10
−20
−10
0
10
20
Rape (USA)
County−by−month: N = 1,434,832
Ranson (JEEM, in-press)
Mean Daily Max County Temperature
(Anomaly in ˚C)
Crime rate per capita
(% of mean)
−20 −10 0 10 20
−20
−10
0
10
20
Violent inter−group retalitation (USA)
Play−by−day: N = 595,500
Larrick et al. (PS, 2011)
Stadium temperature
(Anomaly in ˚C)
Risk of inter−team retaliation
(% of mean)
−2 −1 0 1 2
−50
0
50
100
Political & inter−group violence (East Africa)
Pixel−by−month: N = 91,656
O’Laughlin et al. (PNAS 2012)
Pixel Temperature
(Anomaly in ˚C)
Risk of conflict onset
(% of mean)
−0.5 −0.25 0 0.25 0.5
−100
−50
0
50
100
Political & inter−group violence (Kenya)
Pixel−by−year: N = 13,520
Theisen (JPR, 2012)
Pixel Temperature
(Anomaly in ˚C)
Risk of conflict onset
(% of mean)
−0.5 0 0.5 1
−20
0
Civil war incidence (Africa)
Country−by−year: N = 1,049
Burke et al. (PNAS, 2009)
Country Temperature
(Anomaly in ˚C)
Risk of civil war incidence
(% of mean)
−1 0 1
−20
−10
0
10
20
Political leader exit (Global)
Country−by−year: N = 5,491
Burke (BEJM, 2012)
Country Temperature
(Anomaly in ˚C)
Risk of leader exit
(% of mean)
−1 0 1 2
−20
0
20
40
60
Redistributive inter−group conflict (Brazil)
Municipality−by−year: N = 50,521
Hidalgo et al. (REStat, 2010)
Municipality Rainfall Deviation
(Absolute value in σ)
Land invasion risk
(% of mean)
−1 0 1 2 3
−20
0
20
40
60
Riot, political & inter−group violence (Africa)
Country−by−year: N = 1,344
Hendrix & Salehyan (JPR, 2012)
Country Rainfall Deviation
(Absolute value in σ)
Number of violent events
(% of mean)
(% change from prior year)
−1 0 1 2
−20
0
20
40
60
80
Civil conflict onset (Global tropics)
Annual observations: N = 54
Hsiang et al. (Nature, 2011)
Pacific Ocean Temperature
(NINO3 index May−Dec. in ˚C)
Annual conflict risk
(% of mean)
DCBA
HGFE
I LKJ
20
40
−40 −20 0 20
−80
−40
0
40
Inter−group riots (India)
State−by−year: N = 206
Bohlken & Sergenti (JPR, 2010)
State Rainfall Losses
Number of Hindu − Muslim riots
(% of mean)
−10 −5 0 5 10
−5
0
5
10
Violent personal crime (USA)
Jurisdiction−by−week: N = 26,567
Jacob et al. (JHR, 2007)
Jurisdiction Temperature
(Anomaly in ˚C)
Number of violent crimes
(% of mean)
Fig. 2. Empirical studies indicate that climatological variables have a
large effect on the risk of violence or instability in the modern world.
(A to L) Examples from studies of modern data that identify the causal effect of
climate variables on human conflict. Both dependent and independent variables
have had location effects and trends removed, so all samples have a mean of zero.
Relationships between climate and conflict outcomes are shown with nonpara-
metric watercolor regressions, where the color intensity of 95% CIs depicts the like-
lihood that the true regression line passes through a given value (darker is more likely)
(128). The white line in each panel denotes the conditional mean (129, 130).
Climate variables are indicated by color: red, temperature; green, rainfall deviations
from normal; blue, precipitation loss; black, ENSO. Panel titles describe the
outcome variable, location, unit of analysis, sample size, and study. Because the
samples examined in each study differ, the units and scales change across each
panel (see Figs. 4 and 5 for standardized effect sizes). “Rainfall deviation”
represents the absolute value of location-specifi c rainfall anomalies, with both
abnormally high and abnormally low rainfall events described as having a large
rainfall deviation. “Precipitation loss” is an index describing how much lower
precipitation is relative to the prior year’s amount or the long-term mean.
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use cross-sectional analyses and attempt to con-
trol for confounding variables in regression analy-
ses, typically using a handful of covariates such
as average income or political indices. However,
because the full suite of determinants of conflict
is unknown and unmeasured, it is probably im-
possible that any cross-sectional study can explic-
itly account for all important differences between
populations. Rather than presuming that all con-
founders are accounted for, the studies we eval-
uate compare Norway or Nigeria only to themselves
at different moments in time, thereby ensuring that
the structure, history, and geography of compar-
ison populations are nearly identical.
Some studies implement versions of Eq. 1 that
are expanded to explicitly control for potential
confounding factors, such as average income. In
many cases, this approach is more harmful than
helpful because it introduces bias in the coef-
ficients describing the effect of climate on con-
flict. This problem occurs when researchers control
for variables that are themselves affected by cli-
mate variation, causing either (i) the signal in the
climate variable of interest to be inappropriately
Europe
(Dry) 180
160
140
120
(Wet) 100
Ti count
China
Cambodia
1250 1300 1400 1450 1500 1550 1600
(Dry) −4
−2
0
2
(Wet) 4
PDSI
Empire of Angkor
city-state collapses
Mexico
Major phases of collapse for
the “Classic” Mayan empire
(Wet) 40
(Dry) 20
30
700
Major dynasties collapse
and transition
BA
C
1.2
1.6
(Wet) 2.0
(Dry) 0.8
Ice accum. (m)
Tiwanaku
abandonment
Peru
E
1350
900800
−2
−1
0
1
2
Temperature anomaly (°C)
−3000 −2500 −2000 −1500 −1000 −500 0 500 1000 1500 2000
Years B.C.E/C.E.
Migration
period
Celtic
expansion
30 Years
War
Modern
migration
Syria
(Dry) 8
6
4
(Wet) 2
Akkadian empire
collapses
Dolomite (%wt)
D
F
Roman
conquest
Great Famine
& Black Death
Fig. 3. Examples of paleoclimate reconstructions that find associations
between climatic changes and human conflict. Lines are climate recon-
structions (red, temperature; blue, precipitation; orange, drought; smoothed
moving averages when light gray lines are shown), and dark gray bars indicate
periods of substantial social instability, violent conflict, or the breakdown of
political institutions. (A) Alluvial sediments from the Cariaco Basin indicate sub-
stantial multiyear droughts coinciding with the collapse of the Maya civilization
(84). (B)ReconstructionofadroughtindexfromtreeringsinVietnam,the
Palmer drought severity index (PDSI), shows sustained megadroughts prior to the
collapse of the Angkor kingdom (85). (C) Sediments from Lake Huguang Maar in
China indicate abrupt and sustained periods of reduced summertime
precipitation that coincided with most major dynastic transitions (79). The
collapse of the Tang Dynasty (907) coincided with the terminal collapse of the
Maya (A), both of which occurred when the Pacific Ocean altered rainfall patterns
in both hemispheres (79). Similarly, the collapse of the Yuan Dynasty (1368)
coincided with collapse of Angkor (B), which shares the same regional climate.
(D) Tiwanaku cultivation of the Lake Titicaca region ended abruptly after a drying
of the region, as measured by ice accumulationintheQuelccayaIceCap,Peru
(80). (E) Continental dust blown from Mesopotamia into the Gulf of Oman
indicates terrestrial drying that is coincident with the collapse of the Akkadian
empire (83). (F) European tree rings indicate that anomalously cold periods were
associated with major periods of instability on the European continent (62).
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absorbed by the control variable or (ii) the esti-
mate to be biased because populations differ in
unobserved ways that become artificially cor-
related with climate when the control variable is
included. This methodological error is commonly
termed “bad control” (12), and we exclude results
obtained using this approach. The difficulty in
this setting is that climatic variables affect many
of the socioeconomic factors commonly included
as control variables: things like crop production,
infant mortality , population (via migration or mor-
tality), and even political regime type. T o the
extent that these outcome variables are used as
controls in Eq. 1, studies might draw mistaken
conclusions about the relationship between cli-
mate and conflict. Because this error is so salient
in the literature, we provide examples below. A
full treatment can be found in (12, 18).
For an example of (i), consider whether var-
iation in temperature increases conflict. In many
studies of conflict, researchers often employ a
standard set of controls that are correlates of con-
flict, such as per capita income. However, evi-
dence suggests that income is itself affected by
temperature (19–21), so if part of the effect of
temperature on conflict is through income, then
controlling for income in Eq. 1 will lead the
researcher to underestimate the role of temper-
ature in conflict. This occurs because much of
the effect of temperature will be absorbed by the
income variable, biasing the temperature coeffi-
cient toward zero. At the extreme, if temperature
influences conflict only through income, then con-
trolling for income would lead the researcher in
this example to draw exactly the wrong conclusion
ab o u t the relationship between temperature and
confl ict: that there is no effect of temperature on
conflict.
For an example of (ii), imagine that a measure
of politics (i.e., democracy) and temperatu re both
have a causal effect on conflict and both poli-
tics and temperature have an effect on income,
but that income has no effect on conflict. If poli-
tics and temperature are uncorrelated, estimates
of Eq. 1 that do not control for politics will still
recover the unbiased effect of temperature. How-
ever , if income is introduced to Eq. 1 as a control
but politics is left out of the model, perhaps be-
cause it is more difficult to measure, then there
will appear to be an association between income
and conflict because income will be serving as
a proxy measure for politics. In addition, this ad-
justment to Eq. 1 also biases the estimated effect
of temperature. This bias occurs because the types
of countries that have high income when tem-
perature is high are different, in terms of their av-
erage politics, from those countries that have high
income when temperature is low. Thus, if income
is held fixed as a control variable in a regression
model, the comparison of conflict across temper -
atures is not an “apples-to-apples” comparison be-
cause politics will be systematically different across
co u ntr ies at different temperatures, generating a
bias that can have either sign. In this example,
the inclusion of income in the model leads to two
incorrect conclusions: It biases the estimated rela-
ti ons h i p between climate and conflict and impli-
cates income as playing a role in conflict when it
does not.
Statistical Precision
We consider each study’s estimated relationship
between climate and conflict, as well as the esti-
mate’s precision. Because sampling variability and
sample sizes differ across studies, some analyses
present results that are more precise than other
studies. Recognizing this fact is important when
synthesizing a diverse literature, as some appar-
ent differences between studies can be reconciled
by evaluating the uncertainty in their findings.
For example, some studies report associations that
% change per 1σ change in climate
−8
−4
0
4
8
12
−8
−4
0
4
8
12
Ranson 2012
Jacob Lefgren Moretti 2007
Card and Dahl 2011
Ranson 2012
Jacob Lefgren Moretti 2007
Ranson 2012
Larrick et al 2011
Sekhri and Storeygard 2012
Sekhri and Storeygard 2012
A
uliciems and DiBartolo 1995
Miguel 2005
murder
property crime
domestic violence
assault
violent crime
rape
retaliation in sports
domestic violence
murder
domestic violence
murder
USA
USA
USA
USA
USA
USA
USA
India
India
Austrailia
Tanzania
16% 20%
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
All
studies
Temperature
studies
Median = 3.9%
Mean = 2.3%
Fig. 4. Modern empirical estimates for the effect of climatic events on
the risk of interpersonal violence. Each marker represents the estimated
effect of a 1s increase in a climate variable, expressed as a percentage change
in the outcome variable relative to its mean. Whiskers represent the 95% CI
on this point estimate. Colors indicate the forcing climate variable: A
coefficient is positive if conflict increases with higher temperature (red),
greater rainfall loss (blue), or greater rainfall deviation from normal (green).
The dashed line indicates the median estimate; the top solid black line denotes
the precision-weighted mean, with its 95% CI shown in gray. The panels on
the right show the precision-weighted mean effect (circles) and the
distribution of study results for all 11 results looking at individual conflict or
for the subset of 8 results focusing on temperature effects. Distributions of
effect sizes are either precision-weighted (solid lines) or derived from a
Bayesian hierarchical model (dashed lines). See the supplementary materials
for details on the individual studies and the calculation of mean effects and
their distribution.
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are very large or very small but with uncertainties
that are also very large, leading us to place less
confidence in these extreme findings. This intui-
tion is formalized in our meta-analysis, which ag-
gregates results across studies by down-weighting
results that are less precisely estimated.
The strength of a finding is sometimes sum-
marized in a statement regarding its statistical
significance, which describes the signal-to-noise
ratio in an individual study. However, in prin-
ciple, the signal is a relationship that exists in the
real world and cannot be affected by the researcher ,
whereas the level of noise in a given study’s
finding (i.e., its uncertainty) is a feature specific
to that study—a feature that can be affected by a
researcher's decisions, such as the size of the sam-
ple they choose to analyze. Thus, although it is
useful to evaluate whether individual findings are
statistically significant and it is important to down-
weight highly imprecise findings, individual studies
provide useful information even when their find-
ings are not statistically significant.
To summarize the evidence that each statisti-
cal study provides while also taking into account
its precision, we separately consider three ques-
tions for each study in Table 1: (i) Is the estimated
average effect of climate on conflict quantitative-
ly “large” in magnitude (discussed below), regard-
less of its uncertainty? (ii) Is the reported effect
large enough and estimated with sufficient pre-
cision that the study can reject the null hypothesis
of “no relationship” at the 5% level? (iii) If the
study cannot reject the hypothesis of “no rela-
tionship,” can it reject the hypothesis that the
relationship is quantitatively large? In the litera-
ture, often only the second question is evaluated
in any single analysis. Yet, it is important to
consider the magnitude of climate influence (first
question) separately from its statistical precision,
12340
Standard deviations
Fig. 6. Projected temperature change by 2050 as a multiple of the local
historical SD (s ) of temperature. Temperature projections are for the A1B
scenario and are averaged across 21 global climate models reporting in the
Coupled Model Intercomparison Project (CMIP3) (96). Changes are the difference
between projected annual average temperatures in 2050 and average temper-
atures in 2000. The historical SD of temperature is calculated from annual average
temperatures at each grid cell over the period 1950–2008, using data from the
University of Delaware (131). The map is an equal-area projection.
% change per 1σ change in climate
−20
−10
0
10
20
30
40
50
−20
−10
0
10
20
30
40
50
Theisen Holtermann Buhaug 2011
Bergholt and Lujala 2012
Buhaug 2010
Dell Jones Olken 2012
Burke 2012
Harari and La Ferrara 2011
Fjelde and von Uexkull 2012
Miguel Satyanath Sergenti 2004
Hidalgo et al 2010
Levy et al 2005
Burke et al 2009
Hendrix and Salehyan 2012
Hsiang Meng Cane 2011
Burke and Leigh 2010
Couttenier and Soubeyran 2012
O'Laughlin et al 2012
Maystadt Ecker Mabiso 2013
Dell Jones Olken 2012
Bohlken and Sergenti 2011
Theisen 2012
Bruckner and Ciccone 2010
civil conflict outbreak
civil conflict outbreak
civil conflict incidence
civil conflict incidence
leadership exit
civil conflict incidence
communal conflict
civil conflict incidence
land invasions
civil conflict outbreak
civil conflict incidence
riots and political violence
civil conflict outbreak
institutional change
civil conflict incidence
civil conflict incidence
civil conflict incidence
irregular leader exit
riots
intergroup violence
institutional change
SSA
global
SSA
global
global
SSA
SSA
SSA
Brazil
global
SSA
SSA
global
global
SSA
SSA
Somalia
global
India
Kenya
SSA
71% 93%
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
−
All
studies
Temperature
studies
Median = 13.6%
Mean = 11.1%
Fig. 5. Modern empirical estimates for the effect of climatic events on
the risk of intergroup conflict. Each marker represents the estimated effect
of a 1s increase in a climate variable, expressed as a percentage change in the
outcomevariablerelativetoitsmean. Whiskers represent the 95% CI on this
point estimate. Colors indicate the forcing climate variable : A coefficient is
positive if conflict increases with higher temperature (red), greater rainfall loss
(blue), greater rainfall deviation from normal (green), more floods and storms
(gray),moreElNiño–like conditions (brown), or more drought (orange), as
captured by different drought indices. The dashed line indicates the median
estimate; the top solid black line denotes the precision-weighted mean, with
its 95% CI shown in gray. The panels at right show the precision-weighted
mean effect (circles) and the distribution of study results for all 21 results
looking at intergroup conflict or for the subset of 12 results focusing on
temperature effects (which includes the ENSO and drought studies).
Distributions of effect sizes are either precision-weighted (solid lines) or
derived from a Bayesian hierarchical model (dashed lines). See the
supplementary materials for details on the individual studies and on the
calculation of mean effects and their distribution.
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because the magnitude of these effects tells us
something about the potential importance of cli-
mate as a factor that may influence conflict, so
long as we are mindful that evidence is weaker if
a study’s results are less certain. In cases in which
the estimated effect is smaller in magnitude and
not statistically different from zero, it is important
to consider whether a study provides strong evi-
dence of zero association—that is, whether the
study rejects the hypothesis that an effect is large
in magnitude (third question)—or relatively weak
evidence because the estimated confidence inter-
val (CI) spans large effects as well as zero effect.
Evaluating Whether an Effect Is Important
Evaluating whether an observed causal relation-
ship is “important” is a subjective judgment that
is not essential to our scientific understanding of
whether there is a causal relationship. Nonetheless,
because importance in this literature has some-
times been incorrectly conflated with statistical
precision or inferred from incorrect interpreta-
tions of Eq. 1 and its variants, we explain our ap-
proach to evaluating importance.
Our preferred measure of importance is to ask
a straightforward question: Do changes in climate
cause changes in conflict risk that an expert,
policy-maker , or citizen would consider large? To
aid comparisons, we operationalize this question
by considering an effect important if authors of a
particular study state that the size of the effect is
substantive, or if the effect is greater than a 10%
change in conflict risk for each one SD (1s)
change in climate variables. This second criterion
uses an admittedly arbitrary threshold, and other
threshold selections would be justifiable. How-
ever , we contend that this threshold is relatively
conservative, as most policy-makers or citizens
would be concerned by effects well below 10%
per 1s. For instance, because random variation in
a normally distributed climate variable lies in a
4s range for 95% of its realizations, even a 3%
per 1s effect size would generate variation in con-
flict of 12% of its mean, which is probably im-
portant to those individuals experiencing these shifts.
In some prior studies, authors have argued
that a particular estimated effect is unimportant
based on whether a climatic variable substantially
changes goodness-of-fit measures (e.g., R
2
)fora
particular statistical model, sometimes in com-
parison to other predictor variables (14, 22–24).
We do not use this criterion here for two reasons.
First, goodness-of-fit measures are sensitive to
the quantity of noise in a conflict variable: More
noise reduces goodness of fit; thus, under this
metric, irrelevant measurement errors that intro-
duce noise into conflict data will reduce the ap-
parent importance of climate as a cause of conflict,
even if the effect of climate on conflict is quan-
titatively large. Second, comparing the goodness
of fit across multiple predictor variables often makes
little sense in many contexts, because (i) longi-
tudinal models typically compare variables that
predict both where a conflict will occur and when
a conflict will occur, and (ii) these models typically
compare the causal effect of climatic variables
with the noncausal effects of confounding var-
iables, such as endogenous covariates. These are
“apples-to-oranges” comparisons, and the faulty
logic of both type s of comparison is made clear
with examples.
For an example of (i), consider an analyst
comparing violent crime over time in New York
City and North Dakota who finds that the number
of police on the street each day is important for
predicting how much crime occurs on that day ,
but that a population variable describes more of
the variation in crime because crime and pop-
ulation in North Dakota are both low. Clearly this
comparison is not informative, because the rea-
son that there is little crime in North Dakota has
nothing to do with the reason why crime is lower
in New York City on days when there are many
police on the street. The argument that variations
in climate are not important to predicting when
conflict occurs because other variables are good
predictors of where conflict occurs is analogous
to the strange statement that the number of police
in New York City is not important for predicting
crime rates because North Dakota has lower
crime that is attributable to its lower population.
For an example of (ii), suppose that both higher
rainfall and higher household income lower the
likelihood of civil conflict, but household income
is not observed, and instead, a variable describing
the average observable number of cars each house-
hold owns is included in the regression. Because
wealthier households are better able to afford
cars, the analyst finds that populations with more
cars have a lower risk of conflict. This relation-
ship clearly does not have a causal interpretation,
and comparing the effect of car ownership on
conflict with the effect of rainfall on conflict does
not help us better understand the importance of
the rainfall variable. Published studies that make
similar comparisons do so with variables that the
authors suggest are more relevant than cars, but
the uninformative nature of comparisons be-
tween causal effects and noncausal correlations
is the same.
Functional Form and Evidence of Nonlinearity
Some studies assume a linear relationship be-
tween climatic factors and conflict risk, whereas
others assume a nonlinear relationship. Taken as
a whole, the evidence suggests that, over a suf-
ficiently large range of temperatures and rainfall
levels, both temperature and precipitation appear
to have a nonlinear relationship with conflict, at
least in some contexts. However, this curvature is
not apparent in every study, probably because the
range of temperatures or rainfall levels contained
within a sample may be relatively limited. Thus,
most studies report only linear relationships that
should be interpreted as local linearizations of a
more complex, and possibly curved, response
function.
As we will show , all modern analyses that
address temperature impacts find that higher tem-
peratures lead to more conflict. However , a few
historical studies that examine temperate loca-
tions during cold epochs do find that abrupt cool-
ing from an already cold baseline temperature
may lead to conflict. T aken together , this collectio n
of locally linear relationships indicates a global
relationship with temperature that is nonlinear.
In studies of rainfall impacts, the distinction
between linearity and curvature is made fuzzy by
the multiple ways that rainfall changes have been
parameterized in existing studies. Not all studies
use the same independent variable, and because a
simple transformation of an independent variable
can change the response function from curved to
linear and visa versa, it is difficult to determine
whether results agree. In an attempt to make find-
ings comparable, when replicating the studies
that originally examine a nonlinear relationship
between rainfall and conflict, we follow the ap-
proach of Hidalgo et al.(25) and use the absolute
value of rainfall deviations from the mean as the
independent variable. In studies that originally
examined linear relationships, we leave the inde-
pendent variable unaltered. Because these two
approaches in the literature (and our reanalysis)
differ, we make the distinction clear in our figures
through the use of two different colors.
Results from the Quantitative Literature
We divide this section topically, examining, in
turn, the evidence on how climatic changes shape
personal violence, group-level violence, and the
br e a kdown of social order and political institu-
tions. Results from 12 example studies of recent
data (post-1950) are displayed in Fig. 2. These
findings were chosen to represent a broad cross
section of outcomes, geographies, and time pe-
riods, and we used the common statistical frame-
work described above to replicate these results
(see supplementary materials). Findings from
several studies of historical data are collected in
Fig. 3, where the different time scales of climatic
events can be easily compared. Table 1 lists and
describes all primary studies. For a detailed de-
scription and evaluation of each individual study,
see (26).
Personal Violence and Crime
Studies in psychology and economics have re-
peatedly found that individuals are more likely to
exhibit aggressive or violent behavior toward
others if ambient temperatures at the time of ob-
servation are higher (Fig. 2, A to C), a result that
has been obtained in both experimental (27, 28)
and natural-experimental (29–39) settings. Docu-
mented aggressive behaviors that respond to tem-
perature range from somewhat less consequential
[e.g., horn-honking while driving (27) and inter-
player violence during sporting events (36)] to
much more serious [e.g., the use of force during
police training (28), domestic violence within house-
holds (29, 37), and violent crimes such as assault
or rape (30–35, 38)]. Although the physiological
mechanism linking temperature to aggression
remains unknown, the causal association appears
robust across a variety of contexts. Importantly,
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because aggression at high temperature increases
the likelihood that intergroup conflicts escalate in
some contexts (36) and the likelihood that police
officers use force (28), it is possible that this
mechanism could affect the prevalence of group-
level conflicts on a larger scale.
In low-income settings, extreme rainfall events
that adversely affect agricultural income are also
associated with higher rates of personal violence
(40–42) and property crime (43). High temper-
atures are also associated with increased property
crime (34, 35, 38), but violent crimes appear to
rise with temperature more quickly than property
crimes (38).
Group-Level Violence and Political Instability
Some forms of intergroup violence, such as Hindu-
Muslim riots (Fig. 2D), tend to be more likely
after extreme rainfall conditions (44–47). This rela-
tionship between intergroup violence and rainfall
is primarily documented in low-income settings,
suggesting that reduced agricultural production may
be an important mediating mechanism, although
alternative explanations cannot be excluded.
Low water availability (23, 46, 48–57), very
low temperatures (58–63), and very high temper-
atures (14, 21, 23, 51, 64–66) have been as-
sociated with organized political conflicts in a
variety of low-income contexts (Fig. 2, E, F, H, I,
K, and L). The structure of this relationship again
seems to implicate a pathway through climate-
induced changes in income, either agricultural
(48, 67–69) or nonagricultural (20, 21), although
this hypothesis remains speculative. Large devia-
tions from normal precipitation have also been
shown to lead to the forceful reallocation of
wealth (25) (Fig. 2G) or the nonviolent replace-
ment of incumbent leaders (70, 71) (Fig. 2J).
Some authors recently suggested that contra-
dictory evidence is widespread among quantita-
tive studies of climate and human conflict (72–74),
but the level of disagreement appears overstated.
Two studies (22, 24) estimate that temperature
and rainfall events have a limited impact on civil
war in Africa, but the CIs around these estimates
are sufficiently wide that they do not reject a
relatively large effect of climate on conflict that is
consistent with 35 other studies of modern data
and 28 other studies of intergroup conflict. Within
the broader literature of primary statistical studies,
these results represent 4% of all reported findings
(Table 1). Isolated studies also suggest that wind-
storms and floods have limited observable effect
on civil conflicts (75) and that anomalously high
rainfall is associated with higher incidence of ter-
rorist attacks (76).
Institutional Breakdown
Under sufficien tly high levels of climatological
stress, preexisting social institutions may strain
beyond recovery and lead to major changes in
governing institutions (77–79) (Fig. 3C), a pro-
cess that often involves the forcible removal of
rulers. High levels of climatological stress have
also led to major changes in settlement patterns
and social organization (80, 81) (Fig. 3D). Final-
ly, in extreme cases, entire communities , civili-
zations, and empires collapse entirely after large
changes in climatic conditions (62, 79, 80, 82–89)
(Fig. 3, A to C, E, and F). These documented
catastrophic failures all precede the 20th century,
yet the level of economic development in these
communities at the time of their collapse was
similar to the level of development in many poor
countries of the modern world [see (26)fora
comparison], an indicator that these historical
cases may continue to have modern relevance.
Synthesis of Findings
Once attention is restricted to those studies able
to make rigorous causal claims about the relation-
ship between climate and conflict, some general
patterns become clear . Here, we identify , for the
first time, commonalities across results that span
diverse social systems, climatological stimuli, and
research disciplines.
Generality: Samples, Spatial Scales, and Rates
of Climate Change
Social conflicts at all scales and levels of organi-
zation appear susceptible to climatic influence,
and multiple dimensions of the climate system
are capable of influencing these various outcomes.
Studies documenting this relationship can be found
in data samples covering 10,000 BCE to the
present, and this relationship has been identified
multiple times in each major region, as well as in
multiple samples with global coverage (Fig. 1A).
Climatic influence on human conflict appears
in both high- and low-income societies, although
some types of conflict, such as civil war , are rare
in high-income populations and do not exhibit a
strong dependence on climate in those regions
(51). Nonetheless, many other forms of conflict
in high-income countries, such as violent crime
(35, 38), police violence (28), or leadership changes
(71), do respond to climatic changes. These forms
of conflict are individ ually less extreme, but their
total social cost may be large because they are
widespread. For example, during 1979–2009 there
were more than 2 million violent crimes (assault,
murder, and rape) per year on average in the
United States alone (38), so small percentage
changes can lead to substantial increases in the
absolute number of these types of events.
Climatic perturbations at spatial scales rang-
ing from a building (27, 28, 36) to the globe (51)
have been found to influence human conflict or
social stability (Fig. 1B). The finding that climate
influences conflict across multiple scales sug-
gests that coping or adaptation mechanisms are
often limited. Interestingly, as shown in Fig. 1B,
there is a positive association between the tem-
poral and spatial scales of observational units in
studies documenting a climate-conflict link. This
might indicate that larger social systems are less
vulnerable to high-frequency climate events, or it
may be that higher-frequency climate events are
more difficult to detect in studies examining out-
comes over wide spatial scales.
Finally, it is sometimes argued that societies
are particularly resilient to climate perturbations
of a specific temporal scale: Perhaps these so-
cieties are capable of buffering themselves against
short-lived climate events, or alternatively , they
are able to adapt to conditions that are persistent.
W ith respect to human conflict, the available
evidence does not support either of these claims.
Climatic anomalies of all temporal durations,
from the anomalous hour (28) to the anomalous
millennium (81), have been implicated in some
form of human conflict (Fig. 1B).
The association between climatic events and
human conflict is general in the sense that it has
been observed almost everywhere: across types
of conflict, human history, regions of the world,
income groups, the various durations of climatic
changes, and all spatial scales. However, it is not
true that all types of climatic events influence all
forms of human conflict or that climatic condi-
tions are the sole determinant of human conflict.
The influence of climate is detectable across con-
texts, but we strongly emphasize that it is only
one of many factors that contribute to conflict
[see (90) for a review of these other factors].
The Direction and Magnitude of Climatic
Influence on Human Conflict
We must consider the magnitude of the climate’s
influence to evaluate whether climatic events play
an important role in the occurrence of conflict
and whether anthropogenic climate change has
the potential to substantially alter future conflict
outcomes. Quantifying the magnitude of climatic
impact in archaeological and paleoclimatological
studies is difficult because outcomes of interest
are often one-off cataclysmic events (e.g., so-
cietal collapse), and we typically do not observe
how the universe of societies would have re-
sponded to similar-sized shocks. Modern data
samples, however, generally contain a large num-
ber of comparable social units (e.g., countries)
that are repeatedly exposed to climatic variation,
and this setting is more amenable to statistical
analyses that quantify how changes in climate
affect the risk of conflict within an individual
social unit.
To compare quantitative results across studies
of modern data, we computed standardized effect
sizes for those studies where it was possible to do
so, evaluating the effect of a 1s change in the
explanatory climate variable and expressing the
result as a percentage change in the outcome
variable. Because we restrict our attention to
studies that examine changes in climate varia-
bles over time, the relevant SD is based only on
intertemporal changes at each specific location
instead of comparing variation in climate across
different geographic locations.
Our results are displayed in Figs. 4 and 5
(colors match those in Figs. 2 and 3). Nearly all
studies suggest that warmer temperatures, lower
or more extreme rainfall, or warmer El Niño–
Southern Oscillation (ENSO) conditions lead to a
2 to 40% increase in the conflict outcome per
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1s in the observed climate variable. The consist-
ent direction of temperature’s influence is partic-
ularly notable because all 27 modern estimates
(including ENSO and temperature-based drought
indices, 20 estimates are shown in Figs. 4 and 5)
indicate that warmer conditions generate more
conflict, a result that would be extremely unlikely
to occur by chance alone if temperature had no
effect on conflict. It is more difficult to interpret
whether the signs of rainfall-related variables
agree because these variables are parameterized
several different ways, so Figs. 4 and 5 present
likelihoods for different parameterizations sepa-
rately. However, if all modern rainfall estimates
are pooled (including ENSO and rainfall-based
drought indices, 13 estimates are shown in Figs.
4 and 5) using signs shown in Figs. 4 and 5, then
the signs of the effects in 16 out of 18 esti-
mates agree.
Under the assumption that there is some un-
derlying similarity across studies, we compute
the average effect of climate variables across
studies by weighting each estimate according to
its precision (the inverse of the estimated var-
iance), a common approach that penalizes uncer-
tain estimates (91). We also calculate the CI on
this mean by assuming independence across studies,
although this assumption is not critical to our
central findings (in the supplementary materials,
we present results where we relax this assumption
and show that it is not essential). The precision-
weighted average effect on interpersonal conflict
is a 2.3% increase for each 1s change in climatic
variables(SE=0.12%,P < 0.001; Fig. 4 and table
S1 ) and the analogous estimate for inter group
conflict is 11.1% (SE = 1.3%, P <0.001;Fig.5
and table S1). These precision-weighted averages
are relatively uninfluenced by outliers because
outlier estimates in our sample tend to have low
precision and, thus, low weight in the meta-
analysis. The corresponding medians, which are
also insensitive to outliers, are comparable: 3.9%
for personal conflict and 13.6% for group con-
flict. If we restrict our attention to only the effects
of temperature, the precision-weighted average
effect is similar for interpersonal conflict (2.3%);
however , for intergroup conflict, the effect rises
to 13.2% per 1s in temperature (SE = 2.0, P <
0.001; Fig. 5). Regarding the interpretation of
these effect sizes, we note that whereas the aver-
age effect for interpersonal violence is smaller
than the average effect for intergroup conflict in
percentage terms, the baseline number of inci-
dents of interpersonal violence is dramatically
higher , meaning a small percentage increase can
represent a substantial increase in total incidents.
We estimate the precision-weighted proba-
bility distribution of study-level effect sizes in
Figs. 4 and 5 and in table S1. These distributions
are centered at the precision-weighted averages
described above and can be interpreted as the
distribution of results from which studies’ find-
ings are drawn. The distribution for interpersonal
conflict is narrow around its mean, probably be-
cause most interpersonal conflict studies focus on
one country (the United States) and use very large
samples and derive very precise estimates. The
distribution for inter group conflict is broader and
covers values that are larger in magnitude, with an
interquartile range of 6 to 14% per 1s and the
5th to 95th percentiles spanning –5to32%per
1s (table S1). We estimate that for the intergroup
and interpersonal conflict studies, respectively,
10 and 0% of the probability mass of the distri-
butions of effect sizes lies below zero.
Figures 4 and 5 make it clear that even though
there is substantial agreement across results, some
heterogeneity across estimates remains. It is pos-
sible that some of this variation is meaningful,
perhaps because different types of climate var-
iables have different impacts or because the so-
cial, economic, political, or geographic conditions
of a society mediate its response to climatic events.
For instance, poorer populations appear to have
larger responses, consistent with prior findings
that such populations are more vulnerable to cli-
matic shifts (51). However , it is also possible that
some of this variation is due to differences in how
conflict outcomes are defined, measurement error
in climate variables, or remaining differences in
model specifications that we could not correct in
our reanalysis.
To formally characterize the variation in esti-
mated responses across studies, we use a Bayesian
hierarchical model that does not require knowl-
edge of the source of between-study variation (92)
(see supplementary materials). Under this approach,
estimates of the precision-weighted mean are es-
sentially unchanged, and we recover estimates
for the between-study SD (a measure of the un-
derlying dispersion of true effect sizes across
studies) that are half of the precision-weighted
mean for interpersonal conflict and two-thirds of
the precision-weighted mean for intergroup con-
flict (median estimates; see supplementary mate-
rials, fig. S3, and tables S2 and S3). By comparison,
if variation in effect sizes across studies was
driven by sampling variation alone, then this SD
in the underlying distribution of effect sizes
would be zero. This finding suggests that true
effects probably differ across settings, and under-
standing this heterogeneity should be a primary
goal of future research.
Publication Bias
Publication bias is a long-standing concern across
the sciences, with a common form of bias arising
from the research community’s perceived prefer-
ence for positive rather than null results. Al-
though it is always possible that publication bias
played a role in the publication of a specific
analysis, there are multiple reasons why publica-
tion bias is unlikely to be driving our findings
about the literature on climate and conflict. First,
we include working papers in our analysis (as is
common practice in the social sciences), thereby
eliminating editorial selection. Second, the cen-
tral results presented here are replicated in mul-
tiple disciplines and across diverse samples. Third,
the large number of positive findings present in
the literature since 2009 could provide limited
professional incentive for researchers to publish
yet another positive finding, and benefits might
be higher to those who publish results with al-
ternative findings. Fourth, many analyses are not
explicitly focused on the direct effect of climate
on conflict but instead use climatic variations
instrumentally (25, 35, 48, 71, 77) or account for
it as an ancillary covariate in their analysis [e.g.,
(37)] while trying to study a different research
question, indicating that these authors have little
professional stake in the sign, magnitude, or sta-
tistical significance of the climatic effects they are
presenting. Fifth, we reanalyze the raw data from
many studies using a common statistical frame-
work, possibly undoing adjustments that authors
might be making to their analysis (consciously or
unconsciously) that make their findings appear
stronger. Partial support for this idea is provided
by individual studies that present significant re-
sults, but whose results are only marginally signif-
icant or no longer significant after our reanalysis
(see supplementary materials for details). Finally,
we look for evidence of publication bias by ex-
amining whether the statistical strength of indi-
vidual studies reflects their sample size (93)and
do not find systematic evidence of strong bias in
absolute terms or in comparison to other social
science literature (see fig. S4, table S4, and sup-
plementary materials).
Implications for Future Climatic Changes
The above evidence, taken at face value, makes
the case that future anthropogenic climate change
could worsen conflict outcomes across the globe
in comparison to a future with no climatic changes,
given the large expected increase in global surface
temperatures and the likely increase in variability
of precipitation across many regions over coming
decades (94, 95). Recalling our finding that a
1s change in a location’s temperature is associated
with an average 2.3% increase in the rate of in-
terpersonal conflict and a 13.2% increase in the
rate of intergroup conflict, and assuming that
future populations will respond to climatic shifts
similarly to how current populations respond, one
can consider the potential effect of anthropogenic
warming by rescaling expected temperature changes
according to each location’s historical variabil-
ity. Although not all conflict outcomes have been
shown to be responsive to changes in temper-
ature, many have, and the results uniformly indi-
cate that increasing temperatures are harmful in
regions that are temperate or warm initially. In
Fig. 6, we plot expected warming by 2050, com-
puted as the ensemble mean for 21 climate mod-
els running the A1B emissions scenario, in terms
of location-specific SDs (96). Almost all inhab-
ited locations warm by >2s,withthelargestin-
creases exceeding 4s in tropical regions that
are already warm and currently experience rel-
atively low interannual temperature variability.
These large climatological changes, combined
with the quantitatively large effect of climate on
conflict—particularly intergroup conflict—suggest
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that amplified rates of human conflict could rep-
resent a large and critical impact of anthropogenic
climate change
Two reasons are often given as to why climate
change might not have a substantive impact on
human conflict: Future climate change will occur
gradually and will, thus, allow societies to adapt,
and the modern world today is less susceptible to
climate variation than it has been in the past.
However, if slower-moving climate shocks have
smaller effects, or if the world has become less
climate-sensitive, it is unfortunately not obvious
in the data. Gradual climatic changes appear to
adversely affect conflict outcomes, and the ma-
jority of the studies we review use a sample period
that extends into the 21st century (recall Fig. 1).
Furthermore, some studies explicitly examine
whether populations inhabiting hotter climates
exhibit less conflict when hot events occur but
find little evidence that these areas are more
adapted (31, 38). We also note that many of the
modern linkages between high-temperature
anomalies and intergroup conflict have been char-
acterized in Africa (14, 23, 52, 64, 66)orthe
global tropics and subtropics (21, 51), regions
with hot climates where we would expect pop-
ulations to be best adapted to high temperatures.
Nevertheless, it is always possible that future
populations will adapt in previously unobserved
ways, but it is impossible to know if and to what
extent these adaptations will make conflict more
or less likely.
Studies of nonconflict outcomes do indicate
that, in some situations , historical adaptation to
climate is observable, albeit costly (97–100),
whereas in other cases there is limited evidence
that any adaptation is occurring (19, 101). To our
knowledge, no study has characterized the scale
or scope for adaptation to climate in terms of
conflict outcomes, and we believe this is an im-
portant area for future research. Given the quan-
titatively large effect of climate on conflict, future
adaptations will need to be dramatic if they are
to offset the potentially large amplification of
conflict.
Future Research
Given the marked consistency of available quan-
titative evidence linking climate and conflict, in
our view , the top research priority in this field
should be to narrow the number of competing
explanatory hypotheses. Beyond efforts to miti-
gate future warming, limiting climate’s future in-
fluence on conflict requires that we understand
the causal pathways that generate the observed
association. This task is made difficult by the
likely situation that multiple mechanisms con-
tribute to the observed relationships and that
different mechanisms dominate in different con-
texts. The rich qualitative literature (3–7) sug-
gests that a multiplicity of mechanisms may be
at work.
To date, no study has been able to conclu-
sively pin down the full set of causal mechanisms,
although some studies find suggestive evidence
that a particular pathway contributes to the ob-
served association in a particular context. In most
cases, this is accomplished by “fingerprinting”
the effect of climate on an intermediary variable,
such as income, and showing that the same sta-
tistical fingerprint is visible in the climate’s effect
on conflict. This approach, typically called “in str u-
mental variables” (12) in the social sciences, iden-
tifies a mechanism linking climate and conflict
under the assumption that climate’s only influence
on conflict is through the particular intermediate
variable in question. Because this assumption is
often difficult or impossible to test, evidence from
this approach is more suggestive than conclusive
in uncovering mechanisms (51).
An alternate and promising research design
that can help rule out certain hypotheses is to
study situations in which plausibly exogenous
events block a proposed pathway in a treated
subpopulation and then to compare whether the
climate-conflict association persists or disappears
in both the treatment and control subpopulations.
In an example of this approach, Sarsons exam-
ines whether rainfall shortages in India lead to
riots because they depress local agricultural in-
come (45). By showing that rainfall shortages
and riots continue to occur together in districts
with dams that supply irrigation, investments that
partially decouple local agricultural income from
temporary rain shortfalls, Sarsons argues that the
rainfall effect on riots is unlikely to be operat-
ing solely through changes in local agricultural
income.
Plausible Mechanisms
The following hypotheses have, in our judgment,
received the strongest empirical support in exist-
ing analyses, although the evidence is still often
inconclusive. A common hypothesis focuses on
local economic conditions and labor markets and
argues that when climatic events cause economic
productivity to decline (19–21, 68, 69, 102–104),
the value of engaging in conflict is likely to rise
relative to the value of participating in normal
economic activities (48, 52, 105–110). A compet-
ing hypothesis on state capacity argues that these
declines in economic productivity reduce the
strength of governmental institutions (e.g., if tax
revenues fall), curtailing their ability to suppress
crime and rebellion or encouraging competitors
to initiate conflict during these periods of relative
state weakness (61, 70, 71, 77–79, 84, 85).
A second set of hypotheses focuses on what
have, more generally, been termed “grievances.”
Hypotheses about inequality contend that when
climatic events increase actual (or perceived) so-
cial and economic inequalities in a society (111, 112),
this could increase conflict by motivating at-
tempts to redistribute assets (25, 34, 35, 43).
Evidence linking changes in food prices to con-
flict (61, 113–115) can be interpreted similarly—
for example, food riots due to a government’s
perceived inability to keep food affordable—
particularly when some members of society can
influence food markets (111, 116).
Climate-induced migration and urbanization
might also be implicated in conflict. If climatic
events cause large population displacements or
rapid urbanization (97, 117, 118,), this might lead
to conflicts over geographically stationary resources
that are unrelated to the climate (119) but become
relatively scarce where populations concentrate.
Changes in climate might also affect the logistics
of human conflict (76, 120), for example, by al-
tering the physical environment (e.g., road quali-
ty) in which disputes or violence might occur
(52, 120, 121). Finally , climate anomalies might
result in conflict because they can make cogni-
tion and attribution more difficult or error-prone,
or they may affect aggression through some
physiological mechanism. For instance, climatic
eve nt s may alter individuals’ ability to reason and
correctly interpret events (27, 28, 30, 31, 34–36),
possibly leading to conflicts triggered by mis-
understandings. Alternatively, if climatic changes
and their economic consequences are inaccu-
rately attributed to the actions of an individual
or group (63, 122–125)—for example, an inept
political leader (71)—thismayleadtoviolent
actions that try to return economic conditions to
normal by removing the “offending” population.
Selecting Climate Variables and
Conflict Outcomes
Climate variables that have been analyzed pre-
viously, such as seasonal temperatures, precipi-
tation, water availability indices, and climate
indices, may be correlated with one another and
autocorrelated across both time and space. For
instance, temperature and precipitation time se-
ries tend to be negatively correlated in much of
the tropics, and drought indices tend to be spa-
tially correlated (51, 126). Unfortunately, only a
few of the existing studies account for the cor-
relations between different variables, so it may be
that some studies mistakenly measure the influ-
ence of an omitted climate variable by proxy [see
(126) for a complete discussion of this issue].
Except for the experiments linking temperature
to aggression (27, 28), only a few studies demon-
strate that a specific climate variable is more im-
portant for predicting conflict than other climate
variables or that climatic changes during a spe-
cific season are more important than during other
seasons. Furthermore, no study isolates a partic-
ular type of climatic change as the most influ-
ential, and no study has identified whether temporal
or spatial autocorrelations in climatic variables
are mechanistically important. Identifying the cli-
matic variables, timing of events, and forms of
autocorrelation that influence conflict will help us
better understand the mechanisms linking cli-
matic changes to conflict.
A similar situation exists with the choice of
conflict outcomes. Most analyses simply docu-
ment changes in the rate at which conflicts are
reported in aggregate, but this approach provides
only limited insight into how the evolution of
conflict is affected by climatic variables. A path
for future investigation is to link climate data
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with richer conflict data that describes different
stages of the conflict “life cycle.” For example, fu-
ture studies could examine how often nonviolent
group disputes become violent. Two studies cited
in this paper (28, 36) demonstrate the usefulness
of selecting conflict variables other than total con-
flict rates. By examining the probability that an
initial confrontation escalates rather than just count-
ing the total number of conflicts, these studies
demonstrate that high temperatures lead to more
violence by increasing the likelihood that a small
conflict escalates into a larger conflict.
Conclusion
Findings from a growing corpus of rigorous quan-
titative research across multiple disciplines suggest
that past climatic events have exerted consider-
able influence on human conflict. This influence
appears to extend across the world, throughout
history, and at all scales of social organization.
We do not conclude that climate is the sole, or
even primary , driving force in conflict, but we do
find that when large climate variations occur,
they can have substantial effects on the incidence
of conflict across a variety of contexts. The me-
dian effect of a 1s change in climate variables
generates a 14% change in the risk of intergroup
conflict and a 4% change in interpersonal vio-
lence, across the studies that we review where it
is possible to calculate standardized effects. If
future populations respond similarly to past pop-
ulations, then anthropogenic climate change
has the potential to substantially increase conflict
around the world, relative to a world without cli-
mate change.
Although there is marked convergence of
quantitative findings across disciplines, many open
questions remain. Existing research has success-
fully established a causal relationship between
climate and conflict but is unable to fully explain
the mechanisms. This fact motivates our pro-
posed research agenda and urges caution when
applying statistical estimates to future warming
scenarios. Importantly, however, it does not im-
ply that we lack evidence of a causal association.
The studies in this analysis were selected for their
ability to provide reliable causal inferences and
they consistently point toward the existence of at
least one causal pathway. To place the state of this
research in perspective, it is worth recalling that
statistical analyses identified the smoking of tobac-
co as a proximate cause of lung cancer by the 1930s
(127), although the research community was un-
able to provide a detailed account of the mech -
anisms explaining the linkage until many decades
later . So although future research will be critical in
pinpointing why climate affects human conflict,
disregarding the potential effect of anthropogenic
climate change on human conflict in the interim
is, in our view , a dangerously misgu id ed inter p re -
tation of the available evidence.
Numerous competing theories have been pro-
posed to explain the linkages between the climate
and human conflict, but none have been con-
vincingly rejected, and all appear to be consistent
with at least some existing results. It seems likely
that climatic changes influence conflict through
multiple pathways that may differ between con-
texts, and innovative research to identify these
mechanisms is a top research priority. Achieving
this research objective holds great promise, as the
policies and institutions necessary for conflict
resolution can be built only if we understand why
conflicts arise. The success of such institutions
will be increasingly important in the coming
decades, as changes in climatic conditions am-
plify the risk of human conflicts.
References and Notes
1. C. Mathers, T. Boerma, D. Ma Fat, The Global Burden
of Disease: 2004 Update (World Health Organization,
Geneva, 2008).
2. R. Lozano et al., Global and regional mortality from
235 causes of death for 20 age groups in 1990 and
2010: A systematic analysis for the Global Burden of
Disease Study 2010. Lancet 380, 2095–2128 (2012).
doi: 10.1 016/S0140-6736(12)61728-0; pmid: 23245604
3. M. A. Levy, Is the environment a national security issue?
Int. Secur. 20,35–62 (1995). doi: 10.2307/2539228
4. T. F. Homer-Dixon, Environment, Scarcity, and Violence
(Princeton Univ. Press, Princeton, NJ, 1999).
5. J. Scheffran, M. Brzoska, H. G. Brauch, P. M. Link,
J. Schilling, Eds., Climate Change, Human Security and
Violent Conflict: Challenges for Societal Stability, vol. 8
(Springer, Berlin, 2012).
6. T. Deligiannis, The evolution of environment-conflict
research: Toward a livelihood framework. Glob. Environ.
Polit. 12,78–100 (2012). doi: 10.1162/GLEP_a_00098
7. K. W. Butzer, Collapse, environment, and society.
Proc. Natl. Acad. Sci. U.S.A. 109, 3632–3639 (2012).
doi: 10.1073/pnas.1114845109; pmid: 22371579
8. E. Huntington, Climatic change and agricultural
exhaustion as elements in the fall of rome. Q. J. Econ.
31, 173–208 (1917). doi: 10.2307/1883908
9. P. W. Holland, Statistics and causal inference. J. Am.
Stat. Assoc. 81, 945–960 (1986). doi: 10.1080/
01621459.1986.10478354
10. N. P. Gleditsch, Armed conflict and the environment: A
critique of the literature. J. Peace Res. 35, 381–400
(1998). doi: 10.1177/0022343398035003007
11. J. D. Angrist, J.-S. Pischke, The credibility revolution in
empirical economics: How better research design is
taking the con out of econometrics. J. Econ. Perspect. 24,
3–30 (2010). doi: 10.1257/jep.24.2.3
12. J. D. Angrist, J.-S. Pischke, Mostly Harmless Econometrics:
An Empiricist’s Companion (Princeton Univ. Press,
Princeton, NJ, 2008).
13. J. Wooldridge, Econometric Analysis of Cross Section and
Panel Data (The MIT Press, Cambridge, MA, 2002).
14. O. M. Theisen, Climate clashes? Weather variability, land
pressure, and organized violence in Kenya, 1989-2004.
J. Peace Res. 49,81–96 (2012). doi: 10.1177/
0022343311425842
15. D. Freedman, Statistical models and shoe leather. Sociol.
Methodol. 21, 291–313 (1991). doi: 10.2307/270939
16. W. H. Greene, Econometric Analysis, (Prentice Hall,
Upper Saddle River, NJ, ed. 5, 2003).
17. A. Gelman, J. Hill, Data Analysis Using Regression and
Multilevel/Hierarchical Models (Cambridge Univ. Press,
Cambridge, 2006).
18. J. D. Angrist, A. B. Krueger, in Empirical Strategies in
Labor Economics, vol. 3 (Elsevier Science, Amsterdam,
1999), chap. 23, pp. 1277–1366.
19. W. Schlenker, M. J. Roberts, Nonlinear temperature
effects indicate severe damages to U.S. crop yields under
climate change. Proc. Natl. Acad. Sci. U.S.A. 106,
15594–15598 (2009). doi: 10.1073/pnas.0906865106;
pmid: 19717432
20. S. M. Hsiang, Temperatures and cyclones strongly
associated with economic production in the Caribbean
and Central America. Proc. Natl. Acad. Sci. U.S.A. 107,
15367–15372 (2010). doi: 10.1073/pnas.1009510107;
pmid: 20713696
21. M. Dell, B. F. Jones, B. A. Olken, Climate change and
economic growth: Evidence from the last half century.
Am. Econ. J. Macroecon. 4,66–95 (2012). doi: 10.1257/
mac.4.3.66
22. H. Buhaug, Reply to Burke et al.: Bias and climate war
research. Proc. Natl. Acad. Sci. U.S.A. 107, E186–E187
(2010). doi: 10.1073/pnas.1015796108
23. J. O’Loughlin et al., Climate variability and conflict
risk in East Africa, 1990-2009. Proc. Natl. Acad.
Sci. U.S.A. 109, 18344–18349 (2012). doi: 10.1073/
pnas.1205130109; pmid: 23090992
24. O. Theisen, H. Holtermann, H. Buhaug, Climate wars?
Assessing the claim that drought breeds conflict. Int.
Secur. 36,79–106 (2011). doi: 10.1162/ISEC_a_00065
25. F. Hidalgo, S. Naidu, S. Nichter, N. Richardson, Economic
determinants of land invasions. Rev. Econ. Stat. 92,
505–523 (2010). doi: 10.1162/REST_a_00007
26. S. M. Hsiang, M. Burke, Climate, conflict and social stability:
What does the evidence say? Clim. Change 10.1007/s10584-
013-0868-3 (2013); available at http://papers.ssrn.com/sol3/
papers.cfm?abstract_id=2302245.
27. D. T. Kenrick, S. W. Macfarlane, Ambient temperature
and horn honking: A field study of the heat/aggression
relationship. Environ. Behav. 18, 179–191 (1986).
doi: 10.1177/0013916586182002
28. A. Vrij, J. Van Der Steen, L. Koppelaar, Aggression
of police officers as a function of temperature:
An experiment with the fire arms training system.
J. Community Appl. Soc. 4, 365–370 (1994).
doi: 10.1002/casp.2450040505
29. A. Auliciems, L. DiBartolo, Domestic violence in a
subtropical environment: Police calls and weather in
Brisbane. Int. J. Biometeorol. 39,34–39 (1995). doi:
10.1007/BF01320891
30. E. Cohn, J. Rotton, Assault as a function of time and
temperature: A moderator-variable time-series analysis.
J. Pers. Soc. Psychol. 72, 1322–1334 (1997).
doi: 10.1037/0022-3514.72.6.1322
31. J. Rotton, E. G. Cohn, Violence is a curvilinear function of
temperature in Dallas: A replication. J. Pers. Soc. Psychol.
78, 1074–1081 (2000). doi: 10.1037/0022-
3514.78.6.1074; pmid: 10870909
32. B. J. Bushman, M. C. Wang, C. A. Anderson, Is the curve
relating temperature to aggression linear or curvilinear?
A response to Bell (2005) and to Cohn and Rotton
(2005). J. Pers. Soc. Psychol. 89,74–77 (2005).
doi: 10.1037/0022-3514.89.1.74; pmid: 16060746
33. C. A. Anderson, B. J. Bushman, R. W. Groom, Hot years
and serious and deadly assault: Empirical tests of the
heat hypothesis. J. Pers. Soc. Psychol. 73, 1213–1223
(1997). doi: 10.1037/0022-3514.73.6.1213;
pmid: 9418277
34.C.Anderson,K.Anderson,N.Dorr,K.DeNeve,M.Flanagan,
Temperature and aggression. Adv. Exp. Soc. Psychol. 32,
63–133 (2000). doi: 10.1016/S0065-2601(00)80004-0
35. B. Jacob, L. Lefgren, E. Moretti, The dynamics of criminal
behavior: Evidence from weather shocks. J. Hum. Resour.
42, 489–527 (2007).
36. R. P. Larrick, T. A. Timmerman, A. M. Carton, J. Abrevaya,
Temper, temperature, and temptation: Heat-related
retaliation in baseball. Psychol. Sci. 22, 423–428 (2011).
doi: 10.1 177/0956797611399292;pmid:21350182
37. D. Card, G. B. Dahl, Family violence and football: The
effect of unexpected emotional cues on violent behavior.
Q. J. Econ. 126, 103–143 (2011). doi: 10.1093/qje/
qjr001; pmid: 21853617
38. M. Ranson, Crime, weather and climate change, J. Environ.
Econ. Manage. (in pre ss); http://papers.ssrn.com/sol3/
papers.cfm?abstract_id=2111377.
39. D. Mares, Climate change and levels of violence in
socially disadvantaged neighborhood groups. J. Urban
Health 90, 768–783 (2013). doi: 10.1007/s11524-013-
9791-1; pmid: 23435543
40. E. Miguel, Poverty and witch killing. Rev. Econ. Stud. 72,
1153–1172 (2005). doi: 10.1111/0034-6527.00365
41. S. Sekhri, A. Storeygard, Dowry deaths: Consumption
smoothing in response to climate variability in India,
working paper (2012); http://goo.gl/2pfZr.
13 SEPTEMBER 2013 VOL 341 SCIENCE www.sciencemag.org1235367-12
RESEARCH ARTICLE
on September 12, 2013www.sciencemag.orgDownloaded from
42. D. Blakeslee, R. Fishman, Rainfall shocks and property
crimes in agrarian societies: Evidence from India, SSRN
working paper (2013); http://papers.ssrn.com/sol3/
papers.cfm?abstract_id=2208292.
43. H. Mehlum, E. Miguel, R. Torvik, Poverty and crime in
19th century Germany. J. Urban Econ. 59, 370–388
(2006). doi: 10.1016/j.jue.2005.09.007
44. A. T. Bohlken, E. J. Sergenti, Economic growth and ethnic
violence: An empirical investigation of Hindu-Muslim
riots in India. J. Peace Res. 47, 589–600 (2010).
doi: 10.1177/0022343310373032
45. H. Sarsons, Rainfall and conflict, Harvard working paper
(2011); www.econ.yale.edu/conference/neudc11/papers/
paper_199.pdf.
46. C. S. Hendrix, I. Salehyan, Climate change, rainfall, and
social conflict in Africa. J. Peace Res. 49,35–50 (2012).
doi: 10.1177/0022343311426165
47. J. K. Kung, C. Ma, Can cultural norms reduce conflicts?
Confucianism and peasant rebellions in Qing China,
working paper (2012); http://ahec2012.org/papers/
S6B-2_Kai-singKung_Ma.pdf.
48. E. Miguel, S. Satyanath, E. Sergenti, Economic shocks and
civil conflict: An ins trumental variables approach. J. Polit.
Econ. 112, 725–753 (2004). doi: 10.1086/421174
49. M. Levy et al., Freshwater availability anomalies and
outbreak of internal war: Results from a global spatial
time series analysis, International Workshop on Human
Security and Climate Change, Holmen, Norway, 21 to 23
June 2005; www.ciesin.columbia.edu/pdf/waterconflict.pdf.
50. Y. Bai, J. Kung, Climate shocks and Sino-nomadic
conflict. Rev. Econ. Stat. 93, 970–981 (2011).
doi: 10.1162/REST_a_00106
51. S. M. Hsiang, K. C. Meng, M. A. Cane, Civil conflicts are
associated with the global climate. Nature 476, 438–441
(2011). doi: 10.1038/nature10311; pmid: 21866157
52. M. Harari, E. La Ferrara, Conflict, climate and cells: A
disaggregated analysis, working paper (2013); www-2.
iies.su.se/Nobel2012/Papers/LaFerrara_Harari.pdf.
53. M. Couttenier, R. Soubeyran, Drought and civil war in
Sub-Saharan Africa. Econ. J. 10.1111/ecoj.12042 (2013).
doi: 10.1111/ecoj.12042
54. M. Cervellati, U. Sunde, S. Valmori, Disease environment
and civil conflicts, IZA discussion paper no. 5614 (2011);
http://papers.ssrn.com/abstract=1806415.
55. H. Fjelde, N. von Uexkull, Climate triggers: Rainfall
anomalies, vulnerability and communal conflict in
sub-Saharan Africa. Polit. Geogr. 31, 444–453 (2012).
doi: 10.1016/j.polgeo.2012.08.004
56. R. Jia, Weather shocks, sweet potatoes and peasant
revolts in historical China. Econ. J. (2013). doi: 10.1111/
ecoj.12037
57. H. F. Lee, D. D. Zhang, P. Brecke, J. Fei, Positive
correlation between the North Atlantic oscillation and
violent conflicts in Europe. Clim. Res. 56,1–10 (2013).
doi: 10.3354/cr01129
58. D. Zhang et al., Climatic change, wars and dynastic cycles
in China over the last millennium. Clim. Change 76,
459–477 (2006). doi: 10.1007/s10584-005-9024-z
59. D. D. Zhang, P. Brecke, H. F. Lee, Y. Q. He, J. Zhang,
Global climate change, war, and population decline in
recent human history. Proc. Natl. Acad. Sci. U.S.A. 104,
19214–19219 (20 07). doi: 10.1073/pnas.0703073104;
pmid: 18048343
60. R. Tol, S. Wagner, Climate change and violent conflict in
Europe over the last millennium. Clim. Change 99,
65–79 (2010). doi: 10.1007/s10584-009-9659-2
61. D. D. Zhang et al., The causality analysis of climate
change and large-scale human crisis. Proc. Natl. Acad.
Sci. U.S.A. 108, 17296–17301 (2011). doi: 10.1073/
pnas.1104268108; pmid: 21969578
62. U. Büntgen et al., 2500 years of European climate
variability and human susceptibility. Science 331,
578–582 (2011). doi: 10.1126/science.1197175
pmid: 21233349
63. R. W. Anderson, N. D. Johnson, M. Koyama, From the
persecuting to the protective state? Jewish expulsions
and weather shocks from 1100 to 1800, SSRN working
paper (2013); http://ssrn.com/abstract=2212323.
64. M. B. Burke, E. Miguel, S. Satyanath, J. A. Dykema,
D. B. Lobell, Warming increases the risk of civil war in
Africa. Proc. Natl. Acad. Sci. U.S.A. 106, 20670–20674
(2009). doi: 10.1073/pnas.0907998106;pmid:19934048
65. C. Almer, S. Boes, Climate (change) and conflict:
Resolving a puzzle of association and causation, working
paper #dp1203 (2012); http://ideas.repec.org/p/ube/
dpvwib/dp1203.html.
66. J.-F. Maystadt, O. Ecker, A. Mabiso, Extreme weather and
civil war in Somalia: Does drought fuel conflict through
livestock price shocks? IFPRI working paper (2013);
www.ifpri.org/publication/extreme-weather-and-civil-war-
somalia.
67. W. Schlenker, M. J. Roberts, Nonlinear effects of weather
on corn yields. Rev. Agric. Econ. 28, 391–398 (2006).
doi: 10.1111/j.1467-9353.2006.00304.x
68. W. Schlenker, D. Lobell, Robust negative impacts of
climate change on African agriculture. Environ. Res. Lett.
5, 014010 (2010). doi: 10.1088/1748-9326/5/1/014010
69. D. Lobell, M. Burke, Climate Change and Food Security:
Adapting Agriculture to a Warmer World (Springer,
Dordrecht, Netherlands, 2010).
70. E. Chaney, Revolt on the Nile: Economic shocks, religion
and political influence, Econometrica (in press); http://
scholar.harvard.edu/chaney/publications/revolt-nile-
economic-shocks-religion-and-political-power-0.
71. P. J. Burke, Economic growth and political survival.
B. E. J. Macroecon. 12,1–43 (2012).
72. J. Scheffran, M. Brzoska, J. Kominek, P. M. Link,
J. Schilling, Climate change and violent confli ct. Science
336,869–871 (2012). doi: 10.1126/science.1221339;
pmid: 22605765
73. N. Gleditsch, Whither the weather? Climate change
and conflict. J. Peace Res. 49,3–9 (2012).
doi: 10.1177/0022343311431288
74. T. Bernauer, T. Böhmelt, V. Koubi, Environmental
changes and violent conflict. Environ. Res. Lett. 7,
015601 (2012). doi: 10.1088/1748-9326/7/1/015601
75. D. Bergholt, P. Lujala, Climate-related natural disasters,
economic growth, and armed civil conflict. J. Peace Res.
49, 147–162 (2012). doi: 10.1177/0022343311426167
76. I. Salehyan, C. Hendrix, Climate shocks and political
violence, Annual Convention of the International
Studies Association, San Diego, CA, 1 April 2012;
http://goo.gl/7RGEZd.
77. P. J. Burke, A. Leigh, Do output contractions trigger
democratic change? Am. Econ. J. Macroecon. 2, 124–157
(2010). doi: 10.1257/mac.2.4.124
78. M. Brückner, A. Ciccone, Rain and the democratic window
of opportunity. Econometrica 79, 923–947 (2011).
doi: 10.3982/ECTA8183
79. G. Yancheva et al., Influence of the intertropical convergence
zone on the East Asian monsoon. Nature 445,74–77 (2007).
doi: 10.1038/n ature05431;pmid:17203059
80. C. R. Ortloff, A. L. Kolata, Climate and collapse:
Agro-ecological perspectives on the decline of the
Tiwanaku State. J. Archaeol. Sci. 20, 195–221 (1993).
doi: 10.1006/jasc.1993.1014; pmid: 11303088
81. R. Kuper, S. Kröpelin, Climate-controlled Holocene
occupation in the Sahara: Motor of Africa’s evolution.
Science 313, 803–807 (2006). doi: 10.1126/
science.1130989; pmid: 16857900
82. D. W. Stahle, M. K. Cleaveland, D. B. Blanton, M. D. Therrell,
D. A. Gay, The lost colony and Jamestown droughts. Science
280, 564–567 (1998). doi: 10.1126/science.280.5363.564;
pmid: 9554842
83. H. Cullen et al., Climate change and the collapse of the
Akkadian empire: Evidence from the deep sea. Geology
28, 379–382 (2000). doi: 10.1130/0091-7613(2000)
28<379:CCATCO>2.0.CO;2
84. G. H. Haug et al., Climate and the collapse of Maya
civilization. Science 299, 1731–1735 (2003 ).
doi: 10.1126/science.1080444; pmid: 12637744
85. B. M. Buckley et al., Climate as a contributing factor in
the demise of Angkor, Cambodia. Proc. Natl. Acad. Sci.
U.S.A. 107, 6748–6752 (2010). doi: 10.1073/
pnas.0910827107; pmid: 20351244
86. W. P. Patterson, K. A. Dietrich, C. Holmden, J. T. Andrews,
Two millennia of North Atlantic seasonality and
implications for Norse colonies. Proc. Natl. Acad.
Sci. U.S.A. 107, 5306–5310 (2010). doi: 10.1073/
pnas.0902522107; pmid: 20212157
87. D. J. Kennett et al., Development and disintegration of
Maya political systems in response to climate change.
Science 338, 788–791 (2012). doi: 10.1126/science.
1226299; pmid: 23139330
88. R. L. Kelly, T. A. Surovell, B. N. Shuman, G. M. Smith,
A continuous climatic impact on Holocene human
population in the Rocky Mountains. Proc. Natl. Acad. Sci. U.
S.A. 110, 443–447 (2013). doi: 10.1073/pnas.1201341110;
pmid: 23267083
89. R. M. D’Anjou, R. S. Bradley, N. L. Balascio, D. B. Finkelstein,
Climate impacts on human settlement and agricultural
activities in northern Norway revealed through sediment
biogeochemistry. Proc. Natl. Acad. Sci. U.S.A. 109,
20332–20337 (2012). doi: 10.1073/pnas.1212730109;
pmid: 23185025
90. C. Blattman, E. Miguel, Civil war. J. Econ. Lit. 48,3–57
(2010). doi: 10.1257/jel.48.1.3
91. L. V. Hedges, I. Olkin, Statistical Method for
Meta-Analysis (Academic Press, London, 1985).
92. A. Gelman, J. B. Carlin, H. S. Stern, D. B. Rubin,
Bayesian Data Analysis (Chapman & Hall/CRC Press,
Boca Raton, FL, 2004).
93. D. Card, A. B. Krueger, Time-series minimum-wage studies:
A meta-analysis. Am. Econ. Rev. 85,238–243 (1995).
94. G. Meehl et al., Global climate projections, in Climate
Change 2007: The Physical Science Basis. Contribution
of Working Group I to the Fourth Assessment Report of
the Intergovernmental Panel on Climate Change
(Cambridge Univ. Press, Cambridge, 2007),
pp. 747–845.
95. Intergovernmental Panel on Climate Change, Managing
the Risks of Extreme Events and Disasters to Advance
Climate Change Adaptation (Cambridge Univ. Press,
Cambridge, 2012) .
96. G. A. Meehl et al., The WCRP CMIP3 Multimodel Dataset:
A new era in climate change research. Bull. Am.
Meteorol. Soc. 88, 1383 –1394 (2007). doi: 10.1175/
BAMS-88-9-1383
97. R. Hornbeck, The enduring impact of the American Dust
Bowl: Short and long-run adjustments to environmental
catastrophe. Am. Econ. Rev. 102, 1477–1507 (2012).
doi: 10.1257/aer.102.4.1477
98. G. D. Libecap, R. H. Steckel, Eds., The Economics
of Climate Change: Adaptations Past and Present
(University of Chicago Press, Chicago, 2011).
99. O. Deschênes, M. Greenstone, Climate change, mortality,
and adaptation: Evidence from annual fluctuations in
weather in the US. Am. Econ. J. Appl. Econ. 3, 152–185
(2011). doi: 10.1257/app.3.4.152
100. S. M. Hsiang, D. Narita, Adaptation to cyclone risk:
Evidence from the global cross-section. Climate Change
Econ. 3, 1250011–1250039 (2012).
101. M. Burke, K. Emerick, Adaptat ion to climate change:
Evidence from US agriculture, working paper (2013);
http://ssrn.com/abstract=2144928.
102. S. Barrios, L. Bertinelli, E. Strobl, Trends in rainfall and
economic growth in Africa: A neglected cause of the
African growth tragedy. Rev. Econ. Stat. 92, 350–366
(2010). doi: 10.1162/rest.2010.11212
103. J. Graff Zivin, M. Neidell, Temperature and the allocation
of time: Implications for climate change, J. Labor. Econ.
(in press); www.nber.org/papers/w15717.
104. B. Jones, B. Olken, Climate shocks and exports. Am. Econ.
Rev. Pap. Proc. 100, 454–459 (2010). doi: 10.1257/
aer.100.2.454
105. O. Dube, J. Vargas, Commodity price shocks and civil
conflict: Evidence from Colombia. Rev. Econ. Stud.
(2013); http://restud.oxfordjournals.org/content/
early/2013/02/15/restud.rdt009.abstract.
106. J. Angrist, A. Kugler, Rural windfall or a new resource
curse? Coca, income, and civil conflict in Colombia. Rev.
Econ. Stat. 90, 191–215 (2008). doi: 10.1162/
rest.90.2.191
107. S. Chassang, G. Padro-i-Miquel, Economic shocks and
civil war. Q. J. Polit. Sci. 4, 211–228 (2009).
doi: 10.1561/100.00008072
108. E. Dal Bó, P. Dal Bó, Workers, warriors, and criminals:
Social conflict in general equilibrium. J. Eur.
Econ. Assoc. 9,646–677 ( 2011). doi: 10.1111/
j.1542-4774.2011.01025.x
www.sciencemag.org SCIENCE VOL 341 13 SEPTEMBER 2013 1235367-13
RESEARCH ARTICLE
on September 12, 2013www.sciencemag.orgDownloaded from
109. E. Berman, J. Shapiro, J. Felter, Can hearts and
minds be bought? The economics of counterinsurgency
in Iraq. J. Polit. Econ. 119, 766–819 (2011).
doi: 10.10 86/661983
110. Y. Lei, G. Michaels, Do giant oilfield discoveries fuel
internal armed conflicts? CEP discussion paper (2011);
http://cep.lse.ac.uk/pubs/download/dp1089.pdf.
111. R. Grove, The Great El Niño of 1789-93 and its global
consequences: Reconstructing an an extreme climate
event in world environmental history. Mediev. Hist. J. 10,
75–98 (2007). doi: 10.1177/097194580701000203
112. J. K. Anttila-Hughes, S. M. Hsiang, Destruction,
disinvestment, and death: Economic and human losses
following environmental disaster, SSRN working paper
(2012); http://papers.ssrn.com/abstract_id=2220501.
113. M. Lagi, K. Bertrand, Y. Bar-Yam, The food crises and
political instability in North Africa and the Middle East,
arXiv working paper (2011); http://arxiv.org/
abs/1108.2455.
114. R. Arezki, M. Brückner, Food prices and political
instability, IMF working paper (2011); www.imf.org/
external/pubs/ft/wp/2011/wp1162.pdf.
115. C. B. Barrett, Ed., Food or Consequences? Food Security
and Its Implications for Global Sociopolitical Stability
(Oxford Univ. Press, New York, 2013).
116. B. Carter, R. Bates, Public policy, price shocks, and civil
war in developing countries, Harvard working paper
(2012); www.wcfia.harvard.edu/sites/default/files/
bcarter_publicpolicycivilwar.pdf.
117. S. Barrios, L. Bertinelli, E. Strobl, Climatic change and
rural-urban migration: The case of Sub-Saharan Africa.
J. Urban Econ. 60, 357–371 (2006). doi: 10.1016/
j.jue.2006.04.005
118. S. Feng, M. Oppenheimer, W. Schlenker, Climate
change, crop yields, and internal migration in the
United States, NBER working paper 17734 (2012);
www.nber.org/papers/w17734.
119. P. S. Jensen, K. S. Gleditsch, Rain, growth, and civil war:
The importance of location. Defence Peace Econ. 20,
359–372 (2009). doi: 10.1080/10242690902868277
120. J. Fearon, D. Laitin, Ethnicity, insurgency, and civil war.
Am. Polit. Sci. Rev. 97,75–90 (2003). doi: 10.1017/
S0003055403000534
121. C. K. Butler, S. Gates, African range wars: Climate,
conflict, and property rights. J. Peace Res. 49,23–34
(2012). doi: 10.1177/0022343311426166
122. C. Achen, L. Bartels, Blind retrospection. Electoral
responses to drought, flu, and shark attacks, Centro de
Estudios Avanzados en Ciencias Sociales working paper
(2004); www.march.es/ceacs/publicaciones/working/
archivos/2004_199.pdf.
123. D. Hibbs Jr., Voting and the macroeconomy, in The
Oxford Handbook of Political Economy, B. R. Weingast,
D. A. Wittman, Eds. (Oxford Univ. Press, New York,
2006), pp. 565–586.
124. A. J. Healy, N. Malhotra, C. H. Mo, Irrelevant events affect
voters’ evaluations of government performance. Proc.
Natl. Acad. Sci. U.S.A. 107, 12804–12809 (2010).
doi: 10.1073/pnas.1007420107; pmid: 20615955
125. M. Manacorda, E. Miguel, A. Vigorito, Government
transfers and political support. Am. Econ. J. Appl. Econ. 3,
1–28 (2011). doi: 10.1257/app.3.3. 1;pmid:22199993
126. M. Auffhammer, S. Hsiang, W. Schlenker, A. Sobel, Using
weather data and climate model output in economic
analyses of climate change. Rev. Environ. Econ. Policy 7,
181–198 (2013). doi: 10.1093/reep/ret016
127. H. Witschi, A short history of lung cancer. Toxicol.
Sci. 64,4–6 (2001). doi: 10.1093/toxsci/64.1.4;
pmid: 11606795
128. S. M. Hsiang, Visually-weighted regression, SSRN
working paper (2013); http://papers.ssrn.com/sol3/
papers.cfm?abstract_id=2265501.
129. G. S. Watson, Smooth regression analysis. Sankhya 26,
359–372 (1964).
130. E. A. Nadaraya, On estimating regression. Theory Probab.
Appl. 9, 141–142 (1964). doi: 10.1137/1109020
131. D. R. Legates, C. J. Willmott, Mean seasonal and spatial
variability global surface air temperature. Theor. Appl.
Climatol. 41,11–21 (1990). doi: 10.1007/BF00866198
132. A. E. Sutton et al., Does warming increase the risk of civil
war in Africa? Proc. Natl. Acad. Sci. U.S.A. 107, E102
(2010). doi: 10.1073/pnas.1005278107;pmid:20538978
133. H. Buhaug, H. Hegre, H. Strand, Sensitivity analysis of
climate variability and civil war, PRIO working paper
(2010); http://goo.gl/Ar3xox.
134. H. Buhaug, Climate not to blame for African civil wars.
Proc. Natl. Acad. Sci. U.S.A. 107, 16477–16482 (2010).
doi: 10.1073/pnas.1005739107; pmid: 20823241
135. M. Burke, J. Dykema, D. Lobell, E. Miguel, S. Satyanath,
Climate and civil war: Is the relationship robust? NBER
working paper 16440 (2010); www.nber.org/papers/w16440.
136. M. B. Burke, E. Miguel, S. Satyanath, J. A. Dykema,
D. B. Lobell, Climate robustly linked to African civil war.
Proc. Natl. Acad. Sci. U.S.A. 107, E185 (2010).
doi: 10.1073/pnas.1014879107; pmid: 21118990
137. M. B. Burke, E. Miguel, S. Satyanath, J. A. Dykema,
D. B. Lobell, Reply to Sutton et al.: Relationship between
temperature and conflict is robust. Proc. Natl. Acad.
Sci. U.S.A. 107, E103 (2010). doi: 10.1073/
pnas.1005748107
138. A. Ciccone, Economic shocks and civil conflict: A
comment. Am. Econ. J. Appl. Econ. 3, 215–227 (2011).
doi: 10.1257/app.3.4.215 ; pmid: 22199993
139. E. Miguel, S. Satyanath, Re-examining economic shocks
and civil conflict. Am. Econ. J. Appl. Econ. 3, 228–232
(2011). doi: 10.1257/app.3.4.228; pmid: 22199993
Acknowledgments: We thank C. Almer, D. Blakeslee,
D. Card, P. Burke, H. Buhaug, P. deMenocal, N. P. Gleditsch,
M. Harari, G. Haug, R. Larrick, H. Lee, L. Lefgren, M. Levy,
S. Naidu, J. O’Loughlin, M. Ranson, O. M. Theisen, and
N. von Uexkull for providing results and data. We also thank
three anonymous reviewers, I. Bannon, D. Card, M. Cane,
M. Kr emer, D. Lobell, K. Meng, S. Mullainathan, M. Oppenheimer,
M. Roberts, I. Salehyan, W. Schlenker, J. Shapiro, R. Singh,
D. Stahle, L. Thompson, D. Zhang, and seminar participants at
Columbia University, Harvard University, the International
Studies Association Annual Meeting, Oxford University, the
Pacific Development Conference, Princeton University,
University of California (UC) Berkele y, UC San Diego, and
the World Bank for comments, suggestions, and references.
We thank C. Baysan and F. Gonzalez for excellent research
assistance. S.M.H. was funded by a Postdoctoral Fellowship in
Science, Technology and Environmental Policy at Princeton
University. M.B. was funded by a Graduate Research Fellowship
from the NSF. E.M. was funded by the Oxfam Faculty Chair
in Environmental and Resource Economics at UC Berkeley.
S.M.H. served as consultant for Scitor, a company that has
been analyzing security risks that emerge from climatic
changes. Data and replication code for all results in this paper
are available for download at: http://cega.berkeley.edu/ass ets/
miscellaneous_files/HsiangBurkeMiguel-2013-data.zip. Author
contributions: S.M.H. and M.B. conceived of and designed
the study; S.M.H. and M.B. collected and analyzed data;
S.M.H., M.B., and E.M. interpreted the findings and wrote
the pa per.
Supplementary Materials
www.sciencemag.org/cgi/content/full/science.1235367/DC1
Supplementary Text
Figs. S1 to S4
Tables S1 to S4
References (140, 141)
18 January 2013; accepted 23 July 2013
Published online 1 August 2013;
10.1126/science.1235367
13 SEPTEMBER 2013 VOL 341 SCIENCE www.sciencemag.org1235367-14
RESEARCH ARTICLE
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