Global Environmental Change 28 (2014) 239–250

Climate shocks and political violence§

Idean Salehyan a, Cullen S. Hendrix b,*a Department of Political Science, University of North Texas, 1155 Union Circle #305340, Denton, TX 76203-5017, USAb Korbel School of International Studies, University of Denver, 2201 S. Gaylord St., Denver, CO 80210, USA

A R T I C L E I N F O

Article history:

Received 24 October 2013

Received in revised form 3 July 2014

Accepted 17 July 2014

Available online

Keywords:

Climate change

Conflict

Environmental security

Political violence

Drought

A B S T R A C T

The dominant discourse on the security implications of climate change has asserted that acute

environmental scarcity—such as that caused by drought—causes political violence. In contrast, we argue

that there are good reasons why water scarcity might have a pacifying effect on armed conflict, and that

political violence should be more prevalent during periods of comparatively better agro-climatic

conditions. Political violence is more prevalent when basic needs are met and when the tactical

environment is more conducive to attacks—conditions that hold when water is comparatively abundant.

Empirically, this paper explores the relationship between environmental scarcity and political violence

in a global sample of countries, 1970–2006. We find that water abundance is positively correlated with

political violence, and that this relationship is stronger in less developed, more agriculturally dependent

societies. These findings are robust to several different operationalizations of our variables. We conclude

with a brief discussion of the policy implications of our findings.

� 2014 Published by Elsevier Ltd.

Contents lists available at ScienceDirect

Global Environmental Change

jo ur n al h o mep ag e: www .e lsev ier . co m / loc ate /g lo envc h a

Evidence is fast accumulating that, within our children’s lifetimes,

severe droughts, storms and heat waves caused by climate change

could rip apart societies from one side of the planet to the other.—Thomas Homer-Dixon, ‘‘Terror in the Weather Forecast,’’ New

York Times, April 24, 2007

Make forays in fertile territory in order to supply your army with

food.—Sun Tzu, The Art of War, Chapter XI

1. Introduction

Do climatic factors affect political conflict? Conventionalwisdom holds that resource scarcity leads to competition andviolence, and that natural disasters wreak havoc on communities,

§ We thank Jesse Hamner for processing of the drought data and Marshall Burke,

Josh Busby and Thomas Homer-Dixon for their comments and suggestions. Earlier

versions of this manuscript were presented at the annual meetings of the

International Studies Association and the American Political Science Association.

This material is based upon work supported by, or in part by, the US Army Research

Laboratory and the US Army Research Office under contract/grant number

W911NF-09-1-0077.

* Corresponding author.

E-mail addresses: idean@unt.edu (I. Salehyan),

cullen.hendrix@du.edu (C.S. Hendrix).

http://dx.doi.org/10.1016/j.gloenvcha.2014.07.007

0959-3780/� 2014 Published by Elsevier Ltd.

leading to social disorder. Several examples suggest that rainfallshocks (droughts/floods) and natural disasters can have profoundeffects on economic and social relations within society as well asplace strains on government services. The 2010 floods in Pakistancaused widespread displacement, put enormous strains onagricultural production, and stretched the country’s militaryservices thin as they simultaneously confronted a powerfulinsurgency. The poor response to Hurricane Katrina on the U.S.Gulf Coast undermined people’s trust in government and wasfollowed by isolated incidents of looting. Finally, the 2010–2011drought in the Horn of Africa crippled local economies by reducingexports of cash crops, threatening livelihoods among the poor andcausing acute food insecurity (McGregor, 2011). Such conditionscan give rise to popular discontent and weaken government abilityto respond to challenges, in turn making violent conflict morelikely—or so it is argued.

A growing body of literature has made alarming claims aboutthe security effects of climate change and resource scarcity (CNACorporation, 2007; Dalby, 2009; Kahl, 2006; Burke et al., 2009;Hsiang et al., 2011, 2013). It is argued that as natural resourcesbecome degraded due to over-exploitation and the effects of globalclimate change, distributional conflicts will arise as populationscompete for pieces of a dwindling pie. In addition, as livelihoodsare threatened by environmental stress, individual’s opportunitycost—the gap between the returns from participating in the legaleconomy and from joining militant groups—decline. Moreover,growing human populations will be forced to migrate internally orcross borders, sparking ethnic tensions, and economic conditions

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250240

will deteriorate, leading to conflict (Homer-Dixon, 2001; Reuveny,2007; Burke et al., 2009; Fearon and Laitin, 2011).

Yet, acute environmental stress may not always lead toheightened conflict—at least organized, armed conflict. Thepreceding discussion conceives of the effects of climate changein terms of exogenous shocks that make resources—especially foodand water—scarcer or more abundant. Scarcity may producecompetition and grievances, but grievances are neither a necessarynor sufficient condition for armed conflict; rather, aggrieved actorsmust be able to mobilize resources and people (Tilly, 1978; Collierand Hoeffler, 2004). While sporadic social unrest, such as riots andprotests, may emerge from conditions of scarcity, sustaining amilitant organization requires considerable planning andresources. In other words, even if people have the motive to fight,they also need the capability to do so, and environmental scarcitymay limit such capability, thus undermining the resource basenecessary for mobilizing armed violence.

Anecdotally, events in Somalia provide some prima facie

evidence of this assertion. As a terrible drought gripped Somaliain 2010–2011, the resulting humanitarian disaster and famineweakened Harakat al-Shabaab al-Mujahideen, the main armedopposition to Somalia’s Transitional Federal Government. Whileal-Shabaab has been implicated in food aid theft and the use of foodas a weapon, environmental stress may have undermined itsmilitary capacity. Drought forced farmers and herders to flee therural areas that are considered al-Shabaab strongholds, undercut-ting the economic base of the movement and making it harder toobtain the needed resources (Roble, 2011). Moreover, al-Shabaab’sblockage of food aid has angered tribal groups in areas it controls,and its resource constraints contributed to its unexpectedwithdrawal from Mogadishu in August 2011 and Kismayo in2012. In short, the prolonged drought and famine in the regionmade it more difficult for al-Shabaab to conduct militaryoperations.

We argue that organized, armed violence will increase withrainfall abundance, and that scarcity should have a pacifying effect.Using data on 165 countries from 1970 to 2006, we find aconsistent pattern indicating that violent events, includingterrorist attacks and killings, civil conflict incidence, and battledeaths in civil conflicts are more frequent when water is relativelymore abundant rather than during periods of drought. However,we find the relationship between conflict and water abundance ismediated by certain contextual factors, including level ofdevelopment and the centrality of agriculture to the nationaleconomy. This relationship holds even when we account for theindirect, positive effect of rainfall through economic growth and isrobust to a variety of modeling choices.

The next section summarizes the state of the literature onclimate change and conflict. The following section presents a shortreview of the literature on climate shocks and conflict, andpresents our theoretical argument and hypotheses. The nextsection introduces operationalizations of both climate shocks andpolitical violence. Then, we turn to describing our methodologyand summarizing our findings before concluding.

2. Environmental shocks and violence

As concern over global climate change intensifies, some haveargued that environmental degradation may give rise to armedconflict and state failure (Homer-Dixon, 2001; Faris, 2009; Kahl,2006; Dalby, 2009). The burgeoning quantitative literature onenvironmental security has focused on long-term, chronicenvironmental stressors, as well as specific environmental‘‘shocks’’ such hurricanes, droughts, floods, and earthquakes(Brancati, 2007; Hsiang et al., 2011; Nel and Righarts, 2008;Raleigh and Urdal, 2007; Theisen, 2008; Flores and Smith, 2013).

Although there is still considerable debate over whether and howenvironmental factors influence conflict (Gleditsch, 2012; Ber-nauer et al., 2012), there is growing evidence that climatic eventscan have far-reaching impacts on economic and political outcomes,including for security (Adger and Kelly, 1999; Barnett, 2003;Barnett and Adger, 2007; Busby, 2008; Dabelko, 2009).

Most of the literature has argued that the scarcity of renewableresources—particularly water and arable land—can lead to conflict(Percival and Homer-Dixon, 1996; Hauge and Ellingsen, 1998;Maxwell and Reuveny, 2000; Homer-Dixon, 2001; Kahl, 2006; seeLe Billon, 2001 for a review). First, according to the neo-Malthusianthesis, resource scarcity and environmental shocks generategrievances and fuel conflict over resource distribution as peoplefight directly over control of resources needed for survival (Bas andCoe, 2011). Researchers have tended to look at land, food, andwater to determine whether or not there is a direct link betweenscarcity and violence, or if environmental factors such as rainfallcan be used as instruments for growth in conflict models (Haugeand Ellingsen, 1998; Homer-Dixon, 2001; Miguel et al., 2004;Hendrix and Glaser, 2007; Raleigh and Urdal, 2007; Theisen, 2008,2012; Bergholt and Lujala, 2012; Fjelde and von Uexkull, 2012;Hendrix and Salehyan, 2012; Koubi et al., 2012).

Second, the opportunity cost model linking environmentalfactors and conflict (Maystadt et al., 2013; Miguel et al., 2004)argues that participation in rebellion should decrease as incomesrise; essentially, participation in violence is viewed as a problem ofunemployment. It is easier to recruit individuals to participate inviolence when they have fewer economic alternatives. Thesearguments link better climatic conditions with economic prosper-ity, and in turn, better wage-earning opportunities and higheropportunity costs to fighting (in Appendix 2 we examine therelationship between climatic shocks and growth). While thecausal mechanisms are different, both arguments predict thatviolent mobilization will increase during periods of acuteenvironmental scarcity.

Yet we argue that there are good reasons to believe that armed

conflict may subside during periods of relative scarcity. Whilesporadic social conflicts such as riots, interpersonal disputes,communal conflicts and mass demonstrations may erupt duringperiods of scarcity, organized militant activity requires additionalfunding, logistics, and planning. Indeed, a recent special issue ofthe Journal of Peace Research highlights the need to disaggregatevarious forms of violence. Contributions to the special issue findthat rainfall and natural disasters have little effect on conflict(Bergholt and Lujala, 2012; Koubi et al., 2012; Slettebak, 2012);that rainfall abundance increases conflict (Adano et al., 2012;Theisen, 2012); or that there is a curvilinear effect (Hendrix andSalehyan, 2012; Raleigh and Kniveton, 2012). This confusion stemslargely from different operationalizations of violence and a failureto think theoretically about how modes of conflict are distinct fromone another. A recently published meta-analysis (Hsiang et al.,2013) finds that deviations from normal precipitation andtemperatures are robustly linked to a variety of forms of conflict,from homicide to imperial conquest. However, comparing suchdifferent studies makes it difficult to draw firm conclusions aboutany specific set of conflict types, such as civil conflict.

Riots and sporadic communal fighting require far less planningand organization than sustaining a professional militant group; inshort, the degree of organization is critical. There are at least threereasons why organized, armed violence would increase duringperiods of relatively good climatic conditions. First, economicprosperity may increase resources available to militants: violencebecomes easier to sustain when resources are available and lootingbecomes more profitable (Adano et al., 2012; Butler and Gates,2012; De Soysa, 2002; Collier and Hoeffler, 2004; Witsenburg andAdano, 2009). Militant organizations do not grow their own food,

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250 241

but depend on voluntary or coerced contributions from thepopulation. Drought depresses rural incomes via reduced agricul-tural production making it more difficult to find willing donors andmaking civilians more likely to resist coercion. As rainfall increasesand more resources become available, violent actors find it easierto expropriate a share of the wealth from the population.

Second, more rain may affect the tactical environment,making it easier to coordinate violent attacks and evade captureby state forces. Even minimally capable governments aregenerally better armed and organized than opposition forces;the effects of rainfall on vegetation and rural infrastructure mayhelp to equalize this asymmetry of capabilities. Higher levels ofrainfall mean denser, lush foliage, which provides cover thatfacilitates ambush attacks and impedes the movement ofconventional government forces (Winters et al., 2001; Meieret al., 2007). Moreover, rainfall may wash out rural infrastruc-ture and make roads (especially dirt roads) impassable, creatingremote safe havens from which dissidents can mount attacks(Buhaug and Rød, 2006).

Third, participation in violence may be more feasible after basicmaterial needs are met (Tilly, 1978). Higher rainfall levels areassociated with better harvests, higher incomes, lower consumerprices for food, and the satisfaction of basic needs. This in turn freestime, energy, and resources for participating in other activities,including the satisfaction of political goals, community needs, andideological expression (Maslow, 1943; Inglehart, 1981). Forinstance, Lee (2012) finds militants in Bangladesh did not comefrom the poorest elements of society but from individuals who hadrelatively better standards of living. During periods of environ-mental distress, individuals may ‘hunker down’ and not have thenecessary time and resources to engage in dissident politicalbehavior. Likewise, during periods of environmental stress,communities will want to retain young, productive people toeither work in agriculture or participate in non-agricultural wagelabor (Reardon et al., 1988), rather than sending them to join theranks of an insurgency. In short, during drought and other naturaldisasters, individuals will be more concerned with basic survivalthan with politics.

Historians and military analysts have long highlighted theimportance of logistics and supplies for maintaining an army,although these insights have largely been lost on the environmen-tal security debate. Sun Tzu and Napoleon noted the importance ofabundant food for supplying an army. Researchers have found thatGenghis Khan’s empire expanded rapidly when rainfall wasabundant and pasture for horses was lush (Choi, 2012). Lest onebelieve that such factors only apply to pre-modern warfare, writingon the Syrian conflict for the New York Times, Chivers (2012)observes, ‘‘To succeed, guerrilla forces and insurgencies need tomeet many conditions. . . But in Syria, on several occasions, weheard people speak of something else: rain.’’ He recounts how aplentiful harvest around the Aleppo area provided enough food tosustain fighting units, indicating that even in modern urbanwarfare, food is an essential input.

Thus, the current literature and our theoretical argumenthighlight two competing hypotheses regarding the expecteddirection of the relationship between rainfall abundance/scarcityand the level of political violence. The first expectation is thatconflict will increase under conditions of scarcity because ofgrievances, resource competition, and lower opportunity costs forfighting. The second expectation is that conflict will increase underconditions of abundance as mobilization opportunities and thetactical environment become more favorable.

H1 (Scarcity hypothesis).Organized political violence is positively associated with droughtand water scarcity.

H2 (Mobilization hypothesis).Organized political violence is negatively associated with droughtand water scarcity.

Several of the mechanisms outlined above imply a significantcorrelation between climatic conditions and economic prosperity.This relationship is likely contingent on the degree to whichagriculture is a significant source of both household and nationalincome: economic conditions are less likely to be coupled toclimatic conditions in more developed economies in whichindustry and services account for a larger proportion of economicactivity. Thus, we expect that the correlation between climaticconditions and political violence will be largely confined to lessdeveloped, more agriculturally dependent societies, generating thefollowing conditional expectations:

H3 (Conditional scarcity hypothesis).Organized political violence is positively associated with droughtand water scarcity in less developed, more agriculturallydependent societies, but not in more developed, less agriculturallydependent societies.

H4 (Conditional mobilization hypothesis).Organized political violence is negatively associated with droughtand water scarcity in less developed, more agriculturallydependent societies, but not in more developed, less agriculturallydependent societies.

3. Data

We assess the impact of climatic variables on the prevalence ofpolitical violence, focusing on 165 countries, 1970–2006. Our unitsof observation are a panel of countries across calendar years(country/years). Some conflict researchers, including those doingwork on environmental conflict, have adopted units of analysisbelow the country level (Maystadt et al., 2013; Harari and LaFerrara, 2012). It is argued that local-level environmental factorslead to local-level fighting. We find this claim dubious fororganized, armed conflict. Local level scarcity or grievance maypredict where recruits come from, but not necessarily where theyfight as militant groups are quite mobile and will choose to attackwhere strategically advantageous. Nonetheless, because over-aggregation of our rainfall measure—particularly for very largestates—may present problems, we run additional models exclud-ing countries with the largest area (see Appendix 1). In theremainder of this section we discuss the variables used in ourempirical analysis.

3.1. Dependent variables

Our hypotheses focus on violence by organized, armed groupsrather than interpersonal disputes, non-violent protest, and crime.Therefore, we test our conjectures using a variety of data on civilconflict and terror attacks, which require some coordination andplanning.

We operationalize civil conflict in two ways. First, civil conflictincidence is a variable that has a value of 1 if an armed conflictoccurred in a country-year, zero otherwise. While environmentalconditions may spark the initial outbreak of a conflict (onset), wemainly argue that scarcity makes it more difficult to sustain anexisting militant organization, making conflict incidence theappropriate measure. Data are from the Uppsala UniversityConflict Data Program/Peace Research Institute, Oslo (UCDP/PRIO)Armed Conflict Database. UCDP/PRIO defines armed conflict as ‘‘acontested incompatibility which concerns government and/orterritory where the use of armed force between two parties, of

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250242

which at least one is the government of a state, results in at least 25battle-related deaths’’ (Gleditsch et al., 2002, p. 619). Second, weuse annual counts of battle deaths as a proxy for conflict intensity.As rebel organizations grow in size and capability, this will bereflected in their ability to inflict losses on the battlefield. Data onbattle deaths are from Lacina and Gleditsch (2005). We use the lowestimate of battle deaths as a conservative measure of intensity.

As an additional measure of organized violence, we include anannual count of violent attacks from the Global Terrorism Database(GTD 2011). The GTD contains information on over 87,000 terroristevents around the world since 1970. Although termed ‘‘terror-ism’’—which denotes different things to different people—theinclusion criteria for the GTD are quite broad. Events must: (1)be intentional, (2) entail violence or the threat of violence, (3) beperpetrated by non-state actors. Moreover, 2 of 3 additionalcriteria must be met: (1) the goal of the act must be political,economic, or social (as opposed to violent crime); (2) there must bean intention to coerce or intimidate a larger audience, other thanthe immediate victims; (3) the act must be outside the context oflegitimate warfare. As such, it is a broad measure, whichencompasses a variety of violent attacks against state and non-state actors by militant organizations. The variable GTD attacksindicates, for a country/year, the number of incidents that tookplace. We also estimate these models using the annual count ofGTD deaths. While the number of attacks and the number of deathsare clearly related, these are nonetheless different definitions ofthe magnitude or scale of violent conflict in society.

While the ACD and the Global Terrorism Database are distinctmeasures of political violence, they overlap considerably, both intime and space: Findley and Young (2012) find that most incidentsof terrorism in the GTD take place ‘‘in the geographic regions wherecivil war is occurring and during the ongoing war’’ (p. 286). A largeproportion of GTD attacks (between 56% and 63%, by theircalculations) are committed in zones of active rebellion.

Table 1 compares the two measures for the period 1970–2007.To make the measures more directly comparable, we dichotomizeGTD deaths. In particular we look at country-years in which theGTD records 25 or more fatalities, and cross tabulate this figurewith the civil conflict incidence variable, which also has a 25deaths threshold. As can be seen, the two measures agree on 486conflict-years. Yet, in 506 cases the ACD measure records a civilconflict, while the GTD does not indicate conflict at that codingthreshold; the GTD reports 180 cases of violence above 25 deathsbut which do not meet the ACD inclusion criteria. In our sample,years with active civil conflicts represent just 16.7% of country-years but account for 68% of all GTD attacks. The degree of overlapbetween armed conflict incidence and GTD attacks is substantial,although the measures are not identical.

GTD deaths and battle deaths are positively but only weaklycorrelated (r = 0.14). Much of the discrepancy between the twomeasures comes from the ACD’s requirement that the deaths bebattle-related, and not purely attacks on civilians, and from theGTD’s ‘‘legitimate warfare’’ criterion. For our purposes, however,we are interested in political violence in general. We employ bothmeasures in order to test the robustness of our findings againstdifferent operationalizations of violent conflict.

Table 1Comparing UCDP and GTD conflict variables, 1970–2007.

GTD � 25 deaths

No Yes Total

PRIO/UCDP conflict incidence

No 4621 180 4801

Yes 506 486 992

Total 5127 666 5793

3.2. Independent variables

Our independent variables are measures of water abundance/scarcity: the Palmer Drought Severity Index (PDSI) (Dai et al., 2004)and rainfall (Dell et al., 2012). The PDSI is a measure ofmeteorological drought, including information on precipitation,temperature and soil conditions. It is designed to measuredepartures from normal surface-level moisture supply and soildemand. The data range from �10 (dry) to +10 (wet), with zeroindicating normal conditions for the area. Our technique ofaggregating from grid cells to the country level produces valuesthat differ somewhat from the location-specific values: in oursample, the observed mean is �0.8, and ranges from �13.2 to 8.4.The PDSI models sometimes produce results outside of this range,given Dai et al.’s (2004) methodology. The data are collected at thelevel of 2.58 � 2.58 grid cells and are aggregated up to the country/year level by taking the average of PDSI scores across the country.These data, while widely used and commonly accepted, are notwithout drawbacks, as there is not a single consensus in themeteorological literature as to how drought should be measured(Guttman, 1998; Wells et al., 2004; Vicente-Serrano et al., 2010).Nonetheless, whereas many have focused exclusively on rainfall,the PDSI may be more effective at capturing drought conditionsthan precipitation alone since it accounts for temperature, whichcan destroy crops and reduce water availability (i.e. throughevaporation).

Our precipitation and temperature measures are from Dell et al.(2012), who base their data on the Terrestrial Air Temperature andPrecipitation: 1900–2006 Gridded Monthly Time Series, Version1.01. The raw data are at a resolution of 0.58 latitude by 0.58longitude and cover the time period 1900–2006. Dell, Jones andOlken then aggregate population-weighted average temperatureand precipitation levels to the country-year.

All climatic variables are transformed into anomalies fromlong-term means prior to analysis. To generate our standardizedclimate anomalies variables, we take the current year’s differencefrom the 10-year moving average of the indicator for a givencountry panel and divide it by the panel’s standard deviation. Moreformally, this is ðXit � XiÞ=si, where Xi is the 10-year movingaverage for country i, Xit is the current value for a climate variablein time t for country i, and s is the standard deviation for country i.The 10-year moving average is preferable to the overall panel meanbecause each of the climatic variables displays significant trendingover time. The temporal trending in the various climate measurescomplicates their use in regression analysis, as all are non-stationary. While one way to address this is with panel-specifictime trends, our approach allows us to control for temporal trendsin the climate variables without assuming a specific function form(linear, quadratic, sinusoidal) of the temporal trend and thenapplying it to all countries regardless of their specific climatictrends (see Appendix 3). The resulting variables, PDSI anomaly,precipitation anomaly, and temperature anomaly can be inter-preted in terms of differences (in standard deviations) in thecurrent year from historic trends. In Fig. 1, we compare PDSI andprecipitation anomalies. Although there is a positive relationshipbetween the two measures (r = 0.49), they are not identical. Thisgives us reasonable confidence that the measures capture the sametheoretical construct but are different enough to establishrobustness. For models testing the effect of precipitation, weinclude temperature anomalies in order to separate the effect oftemperature from that of rainfall, as higher temperatures areassociated with crop loss, and to facilitate comparison withexisting literature. In models reported in the appendix, we assesswhether or not there is a curvilinear relationship between ourclimate variables and conflict. It may be the case that very dryconditions as well as very wet ones are more conducive to violence.

Fig. 1. PDSI and precipitation anomalies. Variables are positively correlated

(r = 0.49, p < 0.01).

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250 243

However, we do not find broadly significant effects for quadraticterms (see Appendix 1, Tables A32 and A33).

By using a variety of independent and dependent variablesmeasuring the same general concepts (violence and waterscarcity), we can be more confident that our results are robustto alternative operationalizations.

3.3. Control variables

The inclusion/exclusion of control variables is one of the morecontested issues among those modeling the climate-conflict nexus.Some studies advocate the exclusion of control variables, such aseconomic growth, which might plausibly be affected by climaticfactors and thus produce biased coefficient estimates, and insteaduse and instead use panel fixed effects and time fixed effects and/ortime trends to identify the causal impact of contemporaneousclimatic fluctuations (Burke et al., 2009; Hsiang et al., 2013). Others(Buhaug, 2010; Theisen, 2012; O’Loughlin et al., 2014) argue that afull suite of control variables is necessary to place the relativecausal weight of climatic factors in context and to address theconfounding effects of factors (such as economic development)that may also display significant time trends. So that our results areas broadly comparable with the existing literature as possible, weestimate sparse models that exclude control variables and insteadrely on region-specific time trends (Africa, the Americas, Asia,Europe, the Middle East and Oceania), year fixed effects, and panelfixed or random-effects, as well as models with controls thatinclude a battery of standard variables drawn from models ofconflict and which are estimated via multilevel random effectsmodels.

First, we control for regime type. Democracies may be lessprone to armed conflict. Therefore we include a measure ofdemocracy from the Polity project (Marshall and Jaggers, 2009).Polity2 ranges from �10 (least democratic) to +10 (mostdemocratic); we include the squared term to address the potentialfor a curvilinear effect of regime type, as the most and leastdemocratic systems are the least conflict prone (Hegre et al., 2001).Second, we control for level of development and economic growth.The negative relationship between economic development andcivil conflict is the most robust finding to emerge from theconflict literature (Hegre and Sambanis, 2006). We measuredevelopment with GDP per capita. Moreover, various studiesindicate that economic growth is associated with a decrease inpolitical violence and protest (Collier and Hoeffler, 2004; Miguelet al., 2004). Thus, we include a measure of annual GDP growth in

addition to the GDP level. Data are from the Penn World Tableversion 6.3 (Heston et al., 2011). Some authors (e.g. Miguel et al.,2004; Bergholt and Lujala, 2012; Koubi et al., 2012) have usedrainfall as an instrument for economic growth in estimating theeffect of GDP growth on conflict onset. Yet we find the twomeasures to be only weakly correlated (r = 0.05) and we are moreconcerned with the direct effect of water availability. Instrumentalvariables analysis does not just require that rainfall correlatestrongly with economic growth, but further requires that rainfallnot affect conflict via any other mechanism than economic growth.Dunning (2008) and Sarsons (2011) have challenged this latterassumption. We will return to a discussion of potential mediatedand countervailing effects of rainfall on conflict via growth whendiscussing robustness.

Third, we control for a country’s population size, rate of growth,the presence of a ‘‘youth bulge’’, and oil exporter status. For anygiven level of grievance, we would expect that larger populationswould see more political violence (Fearon and Laitin, 2003), andsocieties—especially agrarian societies—experiencing rapid popu-lation growth have been found to be more conflict prone(Goldstone, 1991; Turchin, 2005). Countries with larger youthbulges—15–24 year olds as a share of total population—are moreconflict prone (Urdal, 2006). Data are from the Penn World Tableversion 6.3 and the UN Population Statistics division (Heston et al.,2011; United Nations Population Division, 2013). Following manystudies of civil conflict, we include a measure for oil economies;our measure, oil rents % of GDP, is from the World BankDevelopment Indicators (World Bank, 2013).

Finally, we include a time trend to capture any secular trend inthe conflict data, and a dummy indicator for the period before/after1998 is included to account for a systematic change in the way thatGTD reports terror events from the first to second phase of datacollection (see GTD 2011).

4. Methods and results

Given the degree to which modeling choices have drivendisagreement in the climate-conflict literature (see O’Loughlinet al., 2014) we use a variety of modeling strategies to reduceconcerns that our conclusions are model-dependent. For oursparse models, we use negative binomial regression for modelingthe count of GTD attacks, GTD deaths, and battle deaths becausetheir distributions are highly skewed and overdispersed. To modelcivil conflict incidence, we use logistic regression. All models areestimated with either fixed- or random-effects as indicated.Conditional fixed-effects models convert observed values for thedependent and independent variables into deviations from theirmean values within each unit (country), eliminating cross-sectional variance and time-invariant factors that affect eachcountry’s baseline propensity for violent conflict. Random effectsmodels account for non-independent errors within units, assumingthat the errors are uncorrelated with the independent variables,and produce very similar estimates to fixed-effects models in largesamples. The decision to use random- vs. fixed-effects in certainspecifications was driven by (a) the comparability of the estimatesderived from the two, and (b) the inability to compute severalmodels containing panel-specific fixed effects, year fixed effects,and regional time trends.

Because the conditional negative binomial estimator precludesthe use of robust errors, we also estimate models using fixed effectsPoisson regression for the count variables and fixed effects OLS forcivil conflict incidence and (log-transformed) battle deaths withrobust standard errors, clustered on countries, in order to avoidover-rejecting the null hypothesis due to within-panel correlationof the errors. For the models with controls, we use multilevelrandom effects (MLM-RE) negative binomial and OLS regression

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250244

(O’Loughlin et al., 2014). The MLM-RE model includes both time-variant covariates as well as panel means of the explanatoryvariables, thus modeling both within- and between-countryeffects. Unlike the FE estimator, it leverages cross-sectionalvariance (i.e., differences between countries), allowing us to placethe causal weight of climatic variables in context with other socio-demographic factors known to increase the propensity forviolence. By using these various approaches, we bridge theongoing divide between researchers using fixed effects vs.multivariate, cross-sectional-time-series techniques to studyingclimatic impacts on conflict. Our analyses are run on a sample of165 countries for the years 1970–2006, though the availability ofcontrol variables and different operationalizations of the depen-dent variable sometimes restrict sample size.

To probe the contingency of the relationship between climaticfactors and conflict, we estimate both the models with temporalcontrols and fixed effects (‘‘sparse’’ models) and standard controls(‘‘full’’ models) on a variety of samples/specifications: (1) a globalsample, (2) the African continent, (3) excluding African cases, (4)countries above the sample median in agricultural dependence, (5)countries below the sample median in agricultural dependence, (6)a lagged dependent variable, (7) the African and Asian continents,(8) countries below the sample median in GDP per capita, and (9)countries above the median in GDP per capita. Additionalregression tables are included in Appendix 1.

Table 2 reports the effects of climate anomalies on conflict in aglobal sample with panel-specific effects, time period dummies,and region-specific time trends.

We find the correlation between political violence and waterabundance to be positive and statistically significant under six ofthe eight specifications, providing evidence in favor of themobilization hypothesis. Fig. 2 reports regression coefficientsand 95% confidence intervals for PDSI and precipitation anomaliesin the four sub-samples for which agriculture ostensibly is a moresignificant determinant of social mobilization capacity: Africa(Appendix 1, Table A1), agriculturally dependent (Table A3),Africa and Asia (Table A6), and countries below median income(Table A7). These are the sub-samples for which non-null effectsare hypothesized. The coefficients on precipitation anomaly arepositive and significant (p < 0.1) in 16 of 16 specifications, whilethe coefficients on PDSI anomaly are positive and significant in 5 of16 specifications.

In contrast, Fig. 3 reports regression coefficients and 95%confidence intervals for the same variables in the non-Africa (TableA2), non-agriculturally dependent (Table A4) and above medianGDP per capita (Table A8) samples, i.e., the samples for whichagriculture is argued to be a less significant determinant of social

Table 2Climatic anomalies and political violence, 1970–2006. GTD attacks, killed, and UCDP b

Incidence models are estimated using panel random effects and time fixed effects.

(2.1) (2.2) (2.3) (2.4)

GTD attacks GTD killed UCDP incidence UCDP battle d

NBREG NBREG Logit OLS

PDSI anomaly 0.033* 0.042* 0.055 0.009

(0.018) (0.024) (0.055) (0.023)

Precip. anomaly

Temp. anomaly

Constant �1.138*** �2.027*** �3.998*** 0.741***

(0.124) (0.151) (0.416) (0.140)

N 5220 5010 5273 5273

Countries 164 157 165 165

* p < 0.10.** p < 0.05.*** p < 0.01.

mobilization capacity. PDSI anomaly is statistically significant inonly three of twelve specifications, with the sign switchingdirections. Precipitation anomaly is positive and significant inthree of twelve specifications. Thus, we do not find strong evidenceof a water abundance-conflict link in less agriculturally dependent,more economically developed countries.

The results of the fixed-effects models with clustered errors areless robust but still provide some evidence of a positive waterabundance-conflict relationship (Appendix 1, Tables A11–A21).The coefficient estimates on precipitation anomaly are alwayspositive and statistically significant in eight of 16 specifications forwhich non-null effects were hypothesized (Africa, agriculturallydependent, Africa and Asia, below median income) with the leastrobust findings regarding logged battle deaths (never significant)and the most robust regarding GTD deaths (significant in globalsample plus three of four sub-samples).

In Appendix 1 (Table A34) we focus on the incidence of highlevel (over 1000 deaths) civil wars, as Couttenier and Soubeyran(2013) find negative relationship between water abundance andcivil war incidence. Consistent with Couttenier and Soubeyran, wefound a negative relationship between PDSI and civil war incidencein the Africa sub-sample, though we note that this negativerelationship between water abundance and conflict was onlypresent at this higher coding threshold and in two sub-samples(the Africa and non-agriculturally dependent sample). At least forAfrica, these findings suggest there may be significant scale effects,i.e., differential impacts of water abundance and scarcity on largerand smaller conflicts. In general, the findings for precipitationanomalies were more robust than for PDSI anomalies.

These findings are consistent with those of several recentstudies linking more plentiful rainfall to conflict (Witsenburg andAdano, 2009; Buhaug, 2010; Theisen, 2012). These previous studieswere limited to particular countries or Africa, while we find aglobal effect. The sub-sample specifications confirm, however, thatthe effect is stronger for Africa and when looking at Africa and Asiatogether, the world regions with the highest level of agriculturaldependence. When stratifying on agricultural dependence andlevel of economic development, a similar pattern emerges. Thepositive water abundance-conflict relationship is largely aphenomenon of poorer, more agriculturally dependent societies.

Temperature anomalies, which have been linked to civil conflict(Burke et al., 2009), demonstrate a less robust effect on politicalviolence in our sparse models, only attaining statistical signifi-cance in half (8/16) the sparse specifications for which climaticfactors would be expected to exert significant effects. We findsome evidence that in Africa (and, to a lesser extent, Africaand Asia), higher temperatures are associated with an increased

attle death models are run on a global sample with panel and time fixed effects.

(2.5) (2.6) (2.7) (2.8)

eaths GTD attacks GTD killed UCDP incidence UCDP battle deaths

NBREG NBREG Logit OLS

0.042** 0.066*** 0.116** 0.039*

(0.018) (0.023) (0.054) (0.022)

0.029 0.057* 0.029 0.009

(0.022) (0.029) (0.070) (0.027)

�1.035*** �1.923*** �3.946*** 0.559***

(0.119) (0.143) (0.418) (0.143)

5330 5082 5432 5432

162 154 163 163

Fig. 2. Sparse model coefficient estimates and 95% confidence intervals for precipitation and temperature anomalies and political violence for Africa, Agriculturally

Dependent, Africa and Asia, and Below Median GDP per capita sub-samples. Circles = GTD events, triangles = GTD deaths, squares = UCDP conflict incidence,

diamonds = logged battle deaths.

Fig. 3. Sparse model coefficient estimates and 95% confidence intervals for precipitation and temperature anomalies and political violence for �Africa, Non-Agriculturally

Dependent, and Above Median GDP per capita sub-samples. Circles = GTD events, triangles = GTD deaths, squares = UCDP conflict incidence, diamonds = logged battle deaths.

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250 245

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250246

frequency of GTD attacks and deaths (Table A1), and someevidence that civil conflict incidence is more likely and battledeaths more frequent when temperatures are higher in the non-agriculturally dependent sample (Table A4).

The ‘‘full’’ models with a battery of controls and multilevelrandom effects provide further evidence of a positive relationshipbetween water abundance and political violence. Table 3 reportsthe results of the global sample. For the sake of space, we will notdiscuss the results of the control variables. Current precipitationanomalies are positively correlated with violence, though thefindings are not significant for PDSI anomalies. The findings forprecipitation anomalies are robust across operationalizations of

Table 3Multilevel random effects estimates of climate anomalies and political violence, 1977–

(3.1) (3.2) (3.3)

GTD

attacks

GTD

killed

UCDP

incidence

PDSI anomaly 0.011 0.030 0.000

(0.019) (0.025) (0.004)

PDSI, panel mean 0.123** 0.184*** 0.028

(0.053) (0.054) (0.028)

Precip. anomaly

Precip., panel mean

Temp. anomaly

Temp., panel mean

ln populationt�1 0.975*** 1.735*** �0.061

(0.263) (0.367) (0.048)

ln population, panel mean �0.819*** �1.501*** 0.121**

(0.262) (0.367) (0.050)

Pop. growtht�1 �0.040*** �0.039** �0.005**

(0.011) (0.017) (0.003)

Population growth, panel mean 0.199*** 0.037 0.017

(0.065) (0.061) (0.030)

ln GDP per capitat�1 0.156 0.109 0.026

(0.118) (0.142) (0.022)

ln GDP per capita, panel mean �0.047 �0.146 �0.071*

(0.136) (0.156) (0.041)

GDP growtht�1 �0.006** �0.005 �0.002***

(0.003) (0.004) (0.001)

GDP growth, panel mean �0.013 �0.006 0.002

(0.020) (0.021) (0.010)

Polity2t�1 0.028*** 0.015** �0.001

(0.006) (0.008) (0.001)

Polity2 t�1 0.048*** 0.075*** 0.012**

(0.011) (0.012) (0.005)

Polity22t�1 �0.009*** �0.007*** �0.002***

(0.001) (0.001) (0.000)

Polity22t�1, panel mean 0.005** �0.005* �0.000

(0.002) (0.003) (0.001)

Youth bulget�1 4.270*** 5.394*** 0.519**

(1.296) (1.695) (0.241)

Youth bulge, panel mean �6.964*** �6.282*** 0.212

(1.961) (2.333) (0.732)

Oil rents % GDPt�1 �0.001 0.002 �0.002**

(0.004) (0.005) (0.001)

Oil rents % GDP, panel mean 0.013** 0.013** 0.006**

(0.006) (0.006) (0.003)

After 1998 �0.819*** �0.799*** �0.048***

(0.066) (0.086) (0.015)

Time trend 0.008 0.002 0.002

(0.007) (0.010) (0.001)

Constant �17.416 �6.313 �4.621

(14.023) (19.445) (2.924)

N 3847 3847 3906

Countries 150 150 151

Standard errors in parentheses.* p < 0.10.** p < 0.05.*** p < 0.01.

the dependent variable when including political and economiccontrols.

Further investigation of split samples confirms the contingentnature of the water abundance-conflict relationship. Fig. 4reports regression coefficients and 95% confidence intervals forprecipitation and temperature anomalies in four sub-samples:Africa (Table A22), agriculturally dependent (Table A24), Africa andAsia (Table A27), and finally countries below median income(Table 28). The coefficient estimate on precipitation anomaly ispositive and significant in 15 of the 16 specifications for whichnon-null effects were hypothesized. Temperature anomalies weresignificant in only three of the 16 specifications, with the sign

2005.

(3.4) (3.5) (3.6) (3.7) (3.8)

UCDP battle

deaths

GTD

attacks

GTD

killed

UCDP

incidence

UCDP battle

deaths

�0.021

(0.025)

0.174

(0.160)

0.043** 0.069*** 0.009** 0.049**

(0.019) (0.024) (0.004) (0.024)

�0.023*** �0.028*** �0.002 �0.005

(0.006) (0.006) (0.003) (0.020)

�0.007 0.021 0.001 0.024

(0.022) (0.028) (0.005) (0.028)

0.001 0.009 0.001 �0.009

(0.008) (0.008) (0.004) (0.026)

�0.653** 1.019*** 1.778*** �0.070 �0.629**

(0.275) (0.256) (0.354) (0.046) (0.265)

1.008*** �0.858*** �1.517*** 0.137*** 1.023***

(0.287) (0.256) (0.355) (0.048) (0.276)

�0.043*** �0.043*** �0.040** �0.005** �0.044***

(0.015) (0.011) (0.017) (0.003) (0.015)

0.121 0.183*** 0.011 0.016 0.144

(0.172) (0.069) (0.064) (0.031) (0.178)

0.167 0.147 0.138 0.027 0.190

(0.124) (0.115) (0.139) (0.021) (0.118)

�0.470** �0.008 �0.119 �0.064 �0.448*

(0.235) (0.133) (0.153) (0.040) (0.232)

�0.017*** �0.007** �0.005 �0.002*** �0.017***

(0.003) (0.003) (0.004) (0.001) (0.003)

0.015 �0.008 �0.001 0.003 0.016

(0.058) (0.020) (0.021) (0.010) (0.058)

�0.004 0.028*** 0.018** �0.001 �0.006

(0.007) (0.006) (0.007) (0.001) (0.007)

0.064** 0.052*** 0.082*** 0.013** 0.072**

(0.030) (0.011) (0.012) (0.005) (0.031)

�0.012*** �0.008*** �0.007*** �0.002*** �0.011***

(0.001) (0.001) (0.001) (0.000) (0.001)

0.001 0.005** �0.006** �0.000 �0.000

(0.006) (0.002) (0.003) (0.001) (0.007)

1.385 4.053*** 4.899*** 0.518** 1.235

(1.384) (1.299) (1.665) (0.239) (1.371)

2.396 �5.669*** �5.021** 0.257 3.273

(4.236) (1.949) (2.270) (0.728) (4.216)

�0.008** 0.002 0.003 �0.002*** �0.008**

(0.004) (0.004) (0.005) (0.001) (0.004)

0.030** 0.008 0.010 0.006** 0.030**

(0.015) (0.006) (0.006) (0.003) (0.015)

�0.236*** �0.832*** �0.799*** �0.050*** �0.238***

(0.085) (0.066) (0.085) (0.015) (0.083)

0.014* 0.006 �0.001 0.003* 0.014*

(0.008) (0.007) (0.010) (0.001) (0.008)

�28.335* �14.137 �0.504 �5.524* �29.929*

(16.811) (13.821) (19.000) (2.844) (16.312)

3906 4013 4013 4101 4101

151 151 151 153 153

Fig. 4. Full model coefficient estimates and 95% confidence intervals for precipitation and temperature anomalies and political violence for Africa, Agriculturally Dependent,

Africa and Asia, and Below Median GDP per capita sub-samples. Circles = GTD events, triangles = GTD deaths, squares = UCDP conflict incidence, diamonds = logged battle

deaths.

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250 247

flipping across samples. In contrast, precipitation anomalies wereonly significant in three of 12 specifications for which null effectswere hypothesized (�Africa, non-agriculturally dependent, abovemedian GDP per capita), and temperature anomalies werepositive and significant in four of 12 specifications (see Appendix).These findings provide significant support for the conditionalmobilization hypothesis.

PDSI anomalies were less robustly associated with violentoutcomes: the coefficients are generally insignificant in both thesparse models with robust errors and the full models. At first blush,the differences across specifications of water scarcity are puzzling.The PDSI contains information on both precipitation abundanceand temperatures. When precipitation and temperature aredisaggregated, we find relatively strong evidence for precipitationaffecting conflict and relatively little evidence of strong tempera-ture effects; thus, a measure that combines both would be biasedtoward insignificance. We note that parsing precipitation andtemperature and estimating their effects independently is thestandard approach in crop yield models (Lobell et al., 2011).

The substantive effects, estimated using the global and Africasample sparse models, are presented in Table 4. A one (two)standard deviation increase in precipitation anomaly is associatedwith a 4.3% (8.8%) increase in the number of GTD attacks, a 6.9%(14.2%) increase in the number of deaths associated with thoseattacks, and a 12.3% (26.0%) increase in the probability of civilconflict incidence in the global sample. Our theory argues thatclimatic variability will affect the intensity of insurgent andterrorist activity as climatic conditions affect the logistical andtactical environment; as such, we interpret this finding as someevidence of this mechanism. In other words, dissidents will find iteasier to stage violent attacks and inflict losses during periods ofenvironmental plenty, when it is easier to provide for larger

fighting forces. The substantive effects are larger for the Africa,agriculturally dependent, and below median income samples, witha +1 SD increase in precipitation anomaly associated with a 17.0%,10.3%, and 9.1% increase the number of terror attacks in the Africa,agriculturally dependent, and below median income samples,respectively. That the relationships are not just statistically moresignificant but of larger magnitude for the poorer, moreagriculturally dependent, and African split samples is consistentwith the conditional mobilization hypothesis.

Finally, we discuss the relationship between economic growthand political violence. The coefficient estimate on economicgrowth is negative and significant in the GTD attacks and armedconflict incidence models, indicating that economic growthsuppresses both forms of political violence. The marginal effect,however, is modest: a one-percentage point increase in economicgrowth is associated with a 0.7% decrease in the frequency of GTDattacks. Water abundance, in addition to being positivelycorrelated with conflict, is itself a significant determinant ofeconomic growth (Miguel et al., 2004; Barrios et al., 2010).Indeed, some previous work uses rain as an instrument forgrowth. Therefore, part—though not all—of the effect of rainfallmay work through economic growth, and it is useful to considerthis causal pathway. Table 5 reports reduced-form results offixed-effects OLS models of the relationship between waterabundance and economic growth. Both PDSI and precipitationanomalies are correlated with economic growth in the currenttime period. For precipitation anomalies, the relationship iscurvilinear: periods of extreme water abundance and scarcity areassociated with lower growth, though the effect of waterabundance is associated with higher growth between the valuesof �2.5 and 1 (see Appendix 2, Table A35). For PDSI, therelationship is positive and linear.

Table 4Substantive effects of climate anomalies conflict.

DV IV Sample +1 SD +2 SD

GTD attacks Prec. anomaly Global +4.3% (3.2%, 5.5%) +8.8% (5.2%, 12.6%)

GTD attacks Prec. anomaly Africa +17.0% (13.6%, 20.4%) +36.8% (24.8%, 49.9%)

GTD deaths Prec. anomaly Global +6.9% (5.1%, 8.6%) +14.2%(8.7%, 20.0%)

GTD deaths Prec. anomaly Africa +19.7% (15.7%, 23.7%) +43.3% (27.9%, 58.7%)

Conflict incidence Prec. anomaly Global +12.3% (11.2%, 13.3%) +26.0% (21.2%, 31.0%)

Conflict incidence Prec. anomaly Africa +32.4% (29.6%, 35.2%) +75.2% (60.7%, 91.1%)

log battle deaths Prec. anomaly Global +4.1% (2.0%, 6.1%) +8.3% (2.4%, 13.7%)

log battle deaths Prec. anomaly Africa +9.6 (5.5%, 13.2%) +19.2% (7.8%, 29.1%)

Table 5Contemporaneous effect of climatic anomalies on economic growth. Dependent variable is economic growth, per Penn World Tables (2011).

Estimator Water variable Additional controls Coef. SE

OLS w/FE PDSI anomalyt Lag DV, ln RGDPCP (lag), ln population (lagged), region-specific time trends 0.303*** (0.113)

PDSI anomalyt2 �0.015 (0.073)

OLS w/FE Precip. anomalyt Lag DV, ln RGDPCP (lag), ln Population (lagged), region-specific time trends 0.138 (0.098)

Precip. anomalyt2 �0.116** (0.046)

* p < 0.10.** p < 0.05.*** p < 0.01.

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250248

Thus, water abundance exerts countervailing effects—a curvi-linear direct effect and a negative indirect effect—on conflict. Whilewe have shown a positive association between rainfall andviolence, part of this is counteracted by improved economicgrowth during wetter years. We address the issue of mediatedcausality by first calculating marginal effects for the direct effect ofwater abundance on violence; then, we calculate the cumulativeeffect of water abundance on violence, which includes the indirecteffect through growth as well. While water abundance affects thelevel of growth, neither growth nor political violence affects ourmeasures of water abundance, as the weather is exogenous. UsingTable 3, model 5 as our baseline, a �1 SD below mean to +1 SDabove mean in precipitation anomaly predicts a 8.9% increase inGTD attacks when only the direct effect is modeled. The indirecteffect, however, would be to increase the rate of GDP growth byroughly third of a percentage point. This has only a slightmoderating effect. When taking into account the effect operatingthrough economic growth, the increase of GTD attacks decreasesfrom 8.9% to 8.7%. Thus, the positive, direct effect of waterabundance on conflict dominates the negative, indirect effectoperating through economic growth.

5. Discussion and conclusions

Across different operationalizations of the dependent variable,samples, and modeling strategies, we provide evidence of arelationship between short-term water abundance and conflict.Moreover, the direction of the relationship is positive: more wateris associated with increases in organized political violence—especially in less developed, more agricultural economies—whichsupports the mobilization hypothesis. This is contrary to someexpectations in the literature, but consistent with some recentstudies linking plentiful rainfall to violent conflict (Witsenburg andAdano, 2009; Theisen, 2012). Scarcity may indeed provideincentives for violence—and increase low-level social unrest—but it may also have the countervailing effect of reducing thefighting capabilities of organized, militant actors.

While heightened grievances and lowered opportunity costsmay provide a motive for joining militant groups, climatic stressmay reduce the organizational capacity of combatants via its effecton logistics, tactical environments, and human terrain of

dissidents. Thus, climatic shocks can affect conflicts that are inno obvious way ‘‘about’’ resource scarcity, as they make conflictitself more or less costly for dissidents. Periods of acute scarcitymay incline individuals to focus on immediate survival rather thanengage in costly, destructive fighting, while periods of relativeabundance free up resources and labor, and create moreadvantageous tactical environments, for conflict. That food denialis often undertaken during counterinsurgency operations suggeststhat acute, severe scarcity should suppress insurgent activity andattacks. Active denial of food and emergency food assistance hasbeen a part of counterinsurgency strategies in conflicts rangingfrom the Boer Wars to present day Sudan (Valentino et al., 2004;Downes, 2007).

Our definition of conflict is limited to organized armed violence.Resource scarcity may provoke other forms of conflict includingunorganized riots, interpersonal disputes, or communal clashesbetween ethnic groups. It may also be the case that longer-termenvironmental distress and climatic changes cause conflictsthrough other channels not explored here. Drought may lead tomigration to other parts of the country, weakened state capacity, orpoor health conditions. These factors may in turn lead to conflictseveral years down the road, and our research is not designed tocapture longer-term effects or causal paths. Future research mustconsider alternative pathways from environment to conflict aswell as take care to specify which types of violence are more or lesslikely to emerge as resources decline. Future research should alsopay greater attention to how environmental conditions affect bothmotivations to fight as well as the opportunity or capability to doso, providing explanations for observed correlations that do notrequire reductive, environmentally deterministic assumptionsabout human behavior (Raleigh et al., 2014).

What do our findings imply about current debates regarding thesecurity impacts of climate change? Most importantly, we haveshown that analysts and policy planners should not look forsignificant increases in armed violence during periods of acutewater scarcity. Climate change may cause certain regions of theworld to be more drought-prone, but such droughts are not likelyto cause fighting to erupt—at least in the short term. It would bemore appropriate to focus on humanitarian concerns, capacitybuilding, and development needs in order to assure that drought-stricken communities are able to adapt to a more uncertain

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250 249

climate. As we have shown, economic development and a moveaway from resource dependent livelihoods can break the linkbetween environmental conditions and violence.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at doi:10.1016/j.gloenvcha.2014.07.007.

References

Adano, W., Dietz, T., Witsenburg, K., Zaal, F., 2012. Climate change, violent conflictand local institutions in Kenya’s drylands. J. Peace Res. 49 (1), 65–80.

Adger, W.N., Kelly, P.M., 1999. Social vulnerability to climate change and thearchitecture of entitlements. Mitig. Adapt. Strat. Glob. Change 4, 253–266.

Barnett, J., Adger, W.N., 2007. Climate change, human security and violent conflict.Polit. Geogr. 26 (6), 639–655.

Barnett, J., 2003. Security and climate change. Global Environ. Change 13 (1), 7–17.Barrios, S., Bertinelli, L., Strobl, E., 2010. Trends in rainfall and economic growth in Africa:

a neglected cause of the African growth tragedy. Rev. Econ. Stat. 92 (2), 350–366.Bas, M., Coe, A., 2011. Environmental change and conflict. Paper Presented at the

2011 Meeting of the International Studies Association, Montreal, Canada.Bergholt, D., Lujala, P., 2012. Climate-related natural disasters, economic growth,

and armed civil conflict. J. Peace Res. 49 (1), 147–162.Bernauer, T., Bohmelt, T., Koubi, V., 2012. Environmental changes and violent

conflict. Environ. Res. Lett. 7 (1), 015601.Brancati, D., 2007. Political aftershocks: the impact of earthquakes on intrastate

conflict. J. Confl. Resol. 51 (5), 715–743.Buhaug, H., Rød, J.K., 2006. Local determinants of African Civil Wars, 1970–2001.

Polit. Geogr. 25 (3), 315–335.Buhaug, H., 2010. Climate not to blame for African Civil Wars. Proc. Natl. Acad. Sci. U.

S. A. 107 (38), 16477–16482.Burke, M.B., Miguel, E., Satyanath, S., Dykema, J.A., Lobell, D., 2009. Warming

increases the risk of Civil War in Africa. Proc. Natl. Acad. Sci. U. S. A. 106(49), 20670–20674.

Busby, J.W., 2008. Who cares about the weather? Climate change and US NationalSecurity. Security Stud. 17 (3), 468–504.

Butler, C.K., Gates, S., 2012. African range wars: climate, conflict, and propertyrights. J. Peace Res. 49 (1), 23–34.

Chivers, C.J., 2012. In the Syrian fight, rebels prospects can change with the weather.New York Times: At War, Notes from the Front Lines August 29,. Online at:http://atwar.blogs.nytimes.com/2012/08/29/in-the-syrian-fight-rebels-pro-spects-can-change-with-the-weather/

Choi, C., 2012. Reign check: abundant rainfall may have spurred expansion ofGenghis Khan’s empire. Sci. Am., March 21.

CNA Corporation, 2007. National Security and the Threat of Climate Change. CNACorporation, Alexandria, VA.

Collier, P., Hoeffler, A., 2004. Greed and grievance in Civil War. Oxf. Econ. Pap. 56,563–595.

Couttenier, M., Soubeyran, R., 2013. Drought and Civil War in Sub-Saharan Africa.Econ. J. 121 (575), 201–244.

Dabelko, G.D., 2009. Planning for climate change: the security community’s pre-cautionary principle. Clim. Change 96 (1), 13–21.

Dai, A., Trenberth, K.E., Qian, T., 2004. A global dataset of palmer drought severityindex for 1870–2002: relationship with soil moisture and effects of surfacewarming. J. Hydrometeorol. 5 (6), 1117–1130.

Dalby, S., 2009. Security and Environmental Change. Polity, Cambridge, UK.De Soysa, I., 2002. Ecoviolence: shrinking pie or honey pot? Glob. Environ. Polit. 2

(4), 1–34.Dell, M., Jones, B.F., Olken, B.A., 2012. Temperature shocks and economic growth:

evidence from the last half century. Am. Econ. J.: Macroecon. 4 (3), 66–95.Downes, A.B., 2007. Draining the sea by filling the graves: investigating the

effectiveness of indiscriminate violence as a counterinsurgency strategy. CivilWars 9 (4), 420–444.

Dunning, T., 2008. Model specification in instrumental-variables regression. Polit.Anal. 16 (3), 290–302.

Faris, S., 2009. Forecast: The Surprising, and Immediate, Consequences of ClimateChange. Holt and Company, New York.

Fearon, J.D., Laitin, D.D., 2003. Ethnicity, insurgency, and Civil War. Am. Polit. Sci.Rev. 97 (1), 75–90.

Fearon, J.D., Laitin, D.D., 2011. Sons of the soil, migrants, and Civil War. World Dev.39 (2), 199–211.

Findley, M.G., Young, J.K., 2012. Terrorism and Civil War: a spatial and temporalapproach to a conceptual problem. Perspect. Polit. 10 (2), 285–305.

Fjelde, H., von Uexkull, N., 2012. Climate triggers: rainfall anomalies, vulnerabilityand communal conflict in sub-Saharan Africa. Polit. Geogr. 31 (7), 444–453.

Flores, A.Q., Smith, A., 2013. Leader survival and natural disasters. Br. J. Polit. Sci. 43(4), 821–843.

Gleditsch, N.P., Wallensteen, P., Eriksson, M., Sollenberg, M., Strand, H., 2002. Armedconflict 1946–2001: a new dataset. J. Peace Res. 39 (5), 615–637.

Gleditsch, N.P., 2012. Wither the weather? Climate change and conflict. J. Peace Res.49 (1), 3–9.

Global Terrorism Database, 2011. START (accessed 12.01.11).Goldstone, J.A., 1991. Revolution and Rebellion in the Early Modern World. Univer-

sity of California Press, Berkeley.Guttman, N., 1998. Comparing the palmer drought index and the standardized

precipitation index. J. Am. Water Resour. Assoc. 34 (1), 113–121.Harari, M., LaFerrara, E., 2012. Conflict, Climate and Cells: A Disaggregated Analysis.

Innocenzo Gasparini Institute for Economic Research Working Paper 461.Hauge, W., Ellingsen, T., 1998. Beyond environmental scarcity: causal pathways to

conflict. J. Peace Res. 35 (3), 299–317.Hegre, H., Sambanis, N., 2006. Sensitivity analysis of the empirical literature on Civil

War onset. J. Confl. Resol. 50 (6), 937–961.Hegre, H., Ellingsen, T., Gates, S., Gleditsch, N.P., 2001. Toward a democratic civil

peace? Democracy, political change, and Civil War, 1816–1992. Am. Polit. Sci.Rev. 95 (1), 33–48.

Hendrix, C.S., Salehyan, I., 2012. Climate change, rainfall, and social conflict in Africa.J. Peace Res. 49 (1), 35–50.

Hendrix, C.S., Glaser, S.M., 2007. Trends and triggers: climate, climate change, andcivil conflict in Sub-Saharan Africa. Polit. Geogr. 26 (6), 695–715.

Heston, A., Summers, R., Aten, B., 2011. Penn World Tables, Version 6.3. Center forInternational Comparisons of Production, Income and Prices at the University ofPennsylvania.

Homer-Dixon, T., 2001. Environment, Scarcity, and Violence. Princeton UniversityPress, Princeton, NJ.

Hsiang, S.M., Burke, M., Miguel, E., 2013. Quantifying the influence of climate onhuman conflict. Science 341 (6151), 1234567.

Hsiang, S., Meng, K., Cane, M., 2011. Civil conflicts are associated with the globalclimate. Nature 476, 438–441.

Inglehart, R., 1981. Post-materialism in an environment of insecurity. Am. Polit. Sci.Rev. 75 (4), 880–900.

Kahl, C.H., 2006. States, Scarcity and Civil Strife in the Developing World. PrincetonUniversity Press, Princeton, NJ.

Koubi, V., Bernauer, T., Kalbhenn, A., Spilker, G., 2012. Climate change and civilconflict. J. Peace Res. 49 (1), 113–127.

Lacina, B., Gleditsch, N.P., 2005. Monitoring trends in global combat: a new datasetof battle deaths. Eur. J. Popul. 21 (2–3), 145–166.

Le Billon, P., 2001. The political ecology of war: natural resources and armedconflicts. Polit. Geogr. 20 (5), 561–584.

Lee, A., 2012. Who becomes at terrorist? Poverty, education, and the origins ofpolitical violence. World Polit. 63 (2), 203–245.

Lobell, D.B., Schlenker, W., Costa-Roberts, J., 2011. Climate trends and global cropproduction since 1980. Science 333 (6042), 616–620.

Marshall, M.J., Jaggers, K., 2009. Polity IV Project: Political Regime Characteristicsand Transitions, 1800–2008. Version p4v2009e. University of Maryland, CollegePark, MD. , In: http://www.cidcm.umd.edu/inscr/polity/index.htm.

Maslow, A., 1943. A theory of human motivation. Psychol. Rev. 50 (4), 370–396.Maxwell, J., Reuveny, R., 2000. Resource scarcity and conflict in developing coun-

tries. J. Peace Res. 37 (3), 301–322.Maystadt, J.-F., Ecker, O., Mabiso, A., 2013. Extreme Weather and Civil War in

Somalia. International Food Policy Research Institute Discussion Paper #10243.McGregor, S., 2011, February 3. Kenya Drought Cuts Coffee, Tea Output; Growth

May Slow. Bloomberg. Available at: http://www.bloomberg.com/news/2011-02-02/kenyan-growth-may-slow-as-drought-reduces-black-tea-produc-tion.html (accessed 15.02.11).

Meier, P., Bond, D., Bond, J., 2007. Environmental influences on pastoral conflict inthe horn of Africa. Polit. Geogr. 26 (6), 716–735.

Miguel, E., Satyanath, S., Sergenti, E., 2004. Economic shocks and civil conflict: aninstrumental variables approach. J. Polit. Econ. 112 (4), 725–753.

Nel, P., Righarts, M., 2008. Natural disasters and the risk of violent civil conflict. Int.Stud. Q. 52 (1), 159–185.

O’Loughlin, J., Linke, A.M., Witmer, F.D.W., 2014. Model and data choices swayconclusions about climate-conflict links. Proc. Natl. Acad. Sci. U. S. A.. early viewversion: http://www.pnas.org/cgi/doi/10.1073/pnas.1323417111.

Percival, V., Dixon, T.-H., 1996. Environmental scarcity and violent conflict: the caseof Rwanda. J. Environ. Dev. 5 (3), 270–291.

Raleigh, C., Kniveton, D., 2012. Come rain or shine: an analysis of conflict andclimate variability in East Africa. J. Peace Res. 49 (1), 51–64.

Raleigh, C., Urdal, H., 2007. Climate change, environmental degradation, and armedconflict. Polit. Geogr. 26 (6), 674–694.

Raleigh, C., Linke, A., O’Loughlin, J., 2014. Extreme temperatures and violence. Nat.Clim. Change 4, 76–77.

Reardon, T., Matlon, P., Delgado, C., 1988. Coping with household-level foodinsecurity in drought-affected areas of Burkina Faso. World Dev. 16 (9),1065–1074.

Reuveny, R., 2007. Climate change-induced migration and violent conflict. Polit.Geogr. 26 (6), 656–673.

Roble, M.A., 2011. Somalia’s famine contributes to popular revolt against al-Sha-baab militants. Terror. Monit. 9 (32), 3–4.

Sarsons, H., 2011. Rainfall and conflict. Paper presented at the Northeast Universi-ties Development Consortium (NEUDC) Conference, Yale University.

Slettebak, R., 2012. Don’t blame the weather! Climate-related natural disasters andcivil conflict J. Peace Res. 49 (1), 163–176.

Theisen, O.M., 2008. Blood and soil? Resource scarcity and internal armed conflictrevisited. J. Peace Res. 45 (6), 801–818.

Theisen, O.M., 2012. Scarcity and organized violence in Kenya, 1989–2004: a ‘fitting’or a ‘mis-fitting’ case of environmental security theory. J. Peace Res. 49 (1), 81–96.

Tilly, C., 1978. From Mobilization to Revolution. McGraw-Hill, New York.

I. Salehyan, C.S. Hendrix / Global Environmental Change 28 (2014) 239–250250

Turchin, P., 2005. Dynamical feedbacks between population growth and sociopo-litical instability in Agrarian States. Struct. Dyn. 1 (1) , Article 3.

United Nations Population Division, 2013. World Population Prospects, In: http://www.un.org/popin/data.html (accessed 01.03.13).

Urdal, H., 2006. A clash of generations? Youth bulges and political violence. Int.Stud. Q. 50 (3), 607–629.

Valentino, B., Huth, P., Balch-Lindsay, D., 2004. ‘Draining the sea’: mass killing andguerrilla warfare. Int. Org. 58 (2), 375–407.

Vicente-Serrano, S.M., Beguerıa, S., Lopez-Moreno, J.I., 2010. A multiscalar droughtindex sensitive to global warming: the standardized precipitation evapotrans-piration index. J. Clim. 23 (7), 1696–1718.

Wells, N., Goddard, S., Hayes, M., 2004. A self-calibrating palmer drought severityindex. J. Clim. 17 (12), 2235–2351.

Winters, H.A., Galloway Jr., G.E., Reynolds, W.J., Rhyne, D.W., 2001. Battling theElements: Weather and Terrain in the Conduct of War. Johns Hopkins, Balti-more.

Witsenburg, K.M., Adano, W.R., 2009. Of rain and raids: violent livestock raiding innorthern Kenya. Civil Wars 11 (4), 514–538.

World Bank, 2013. World Development Indicators. World Bank, Washington, DC.