political violence, interstate rivalry, and the diffusion of public health crises

Political Violence, Interstate Rivalry,and the Diffusion of PublicHealth Crises∗

Bryce W. Reeder, University of Illinois at Urbana-Champaign

Matthew R. Reeder, University of Utah

Objective. The objectives of the study were to expand the literature on politicalviolence and public health by exploring the possibility that the public health con-sequences of civil war not only apply to the host country, but also diffuse acrossinternational borders. We discuss and empirically test a diffusion mechanism ab-sent from the literature in this area—the incentive of a proximate state to reallocateresources in response to intrastate violence and the presence of interstate rivalry.Methods. Our hypotheses were tested using a directed dyad research design thatincluded all politically relevant dyads from 1997 to 2001. We used fixed-effects panelregression to estimate the influence of proximate intrastate violence and interstaterivalry on changes in infant mortality rate (IMR). Results. Our results indicated thatproximate intrastate violence and interstate rivalry are associated with increases inIMR. In addition, the influence of proximate intrastate violence on IMR increases asthe severity of the conflict increases. Conclusion. The public health consequences ofcivil war do diffuse across international borders, especially in cases in which interstaterivalry was present and the political violence in a proximate state was severe.

One of the most basic obligations of a national government is to provide forits citizens the fundamental resources to ensure an environment of physical,mental, and social well-being. Certain conditions are needed to ensure thehealth of a population at the most basic level. Of particular importance is thequality of food, water, energy, health services, and personal security available tothe population (Mann et al., 1994). Any force that has the potential to disruptthe infrastructure governing these conditions, however solid that infrastructuremay be, could have devastating and long-lasting effects for the health of itscitizens.

∗Direct correspondence to Bryce W. Reeder, Department of Political Science, 420 DKH,1407 West Gregory Drive, Urbana, IL 61801 〈[email protected]〉. All data, coding, andappendices can be found on the author’s website 〈www.brycereeder.info/research/〉. The authorswould like to thank Paul F. Diehl, Ashly Adam Townsen, and the journal’s reviewers for theircomments and suggestions.

SOCIAL SCIENCE QUARTERLYC© 2014 by the Southwestern Social Science AssociationDOI: 10.1111/ssqu.12108

2 Social Science Quarterly

The negative effects resulting from civil war on a country’s public healthsystem is one such example and has been well researched. These negative effectscan be long-lasting, with its citizens at increased risk for disease, disability,or death due to uncontrolled exposure to infectious diseases such as malaria,tuberculosis, and other respiratory diseases (Ghobarah, Huth, and Russett,2003). A reemerging incidence of sleeping sickness, a protozoan parasiticdisease transmitted by the tsetse fly vector affecting humans and livestock,has been observed in areas of Uganda during periods of civil conflict (Ford,2007). Outbreaks of vector-borne diseases, such as that of sleeping sicknessin Uganda, reflect the absence of quality medical, sanitary, and preventivehealth services (Mann et al., 1994). In Uganda, in particular, the inability tocoordinate intervention activities is constrained by the lack of cooperation ofadministrative and intersectoral officials (Ford, 2007).

Already fragile public health infrastructures further weakened by civil waralong with limited humanitarian resources lead to the increased morbidityand mortality of vulnerable populations that may include women, children,elderly, and those with chronic disabilities and illnesses. According to An-derson et al. (2011), women of child-bearing age and children were mostat risk for contracting malaria among refugees in Tanzania, with the annualincidence of malaria reported to be 399 per 1,000 refugees and 728 per 1,000refugee children. Consequently, malaria accounted for 16 percent of deaths inchildren younger than five years of age. Malnutrition, poor sanitary care, andlong-term stress lead to severe, often preventable diseases (Guha-Sapir andRatnayake, 2009; Tabbaa and Seimenis, 2013). Internally displaced personsare particularly vulnerable to the effects of poor sanitation, overcrowding, andpoor access to healthcare. Diarrheal diseases spread due to the lack of adequatedisposal facilities for human waste. Respiratory diseases are passed from per-son to person due to crowded living conditions and the inability of the healthsystem to detect and treat such illnesses. The shortage of vaccines impedes thelikelihood that children will receive important immunizations for preventableillnesses, such as measles, polio, diphtheria, and pertussis (Mann et al., 1994).The effects of disease transmission, however, are not isolated. When suitableconditions for disease transmission are present, migration of displaced personsfrom high to low endemic regions may result in the transmission of disease tosusceptible populations (Anderson et al., 2011).

Though the effects of political violence on public health outcomes in thehost country are well documented, the causal mechanisms underlying theobserved decline in public health outcomes in proximate states are poorlyunderstood and warrant further theoretical development. The current ex-planation of this relationship emphasizes the role of refugee flows, whichessentially infect neighboring states with many of the same health problemsthat the government embroiled in civil war is forced to cope with (Ghobarah,Huth, and Russett, 2003; Iqbal, 2010). The logic underlying this causal argu-ment is the notion that refugees have the potential to overwhelm the health-care system, increase the likelihood of epidemics, and/or adversely affect the

Political Violence, Interstate Rivalry, and Public Health 3

economy, making providing public services more difficult.1 The problem withthis theoretical approach, however, is that it fails to explain why states that donot experience a significant influx of refugees often see a decline in health-carespending, as well as the quality and quantity of healthcare afforded to thepopulation.

Take, for instance, the 2003 Iraq War. The effects of armed conflict onpublic health outcomes within the country have received significant attentionfrom scholars and several media outlets. In addition to direct fatalities resultingfrom armed conflict (Burnham et al., 2006), there is evidence that the war hasincreased birth defects and cancer rates (Kentane, 2012), with one hospitalreporting the prevalence of birth defects to be 17 times greater than that priorto the start of the war (Al-Sabbak et al., 2012). The amount of those internallydisplaced as a direct consequence of the war also have had severe consequenceson public health outcomes in the country (Morton and Burnham, 2008), witha large number fleeing across international borders. Among proximate states,Syria, Jordan, and Egypt reported the highest refugee inflows (in that order).2

Since 2003 all three states have seen a decrease in the percentage of GDPthat is dedicated to providing health-care services to the population, withSyria experiencing roughly a 25 percent decrease from 2003 to 2006. As theresources dedicated to public health services have declined, so have many of thevital indicators of public health.3 What is most important for the purposes ofthis study, however, is the observation that these changing patterns in health-care spending and public health outcomes were not restricted to those statesexperiencing high levels of refugee inflows. Saudi Arabia, Israel, and Turkey,for example, also saw a decrease in health-care spending and public healthoutcomes when compared to the years prior to the war, even though they didnot have to cope with significant inflows.

The question that we hope to answer in this study, then, is what explainsthe decline in public health outcomes in states not experiencing high levelsof refugee inflows? The current theoretical logic present in the literature, thatrefugees are the central causal mechanism, clearly is unable to explain thisempirical pattern. As such, we propose an indirect diffusion mechanism thatis theoretically grounded in the literature on threat perception and interstaterivalry. According to this theory, the presence of proximate political violenceincreases threat perception, incentivizing states to divert resources away fromsocial services and the maintenance of vital infrastructure. In addition, we

1Though not directly tested, this idea was present in the work of Murdoch and Sandler(2002).

2This was as of 2008. See 〈www.unhcr.org/487ef7144.html〉 for a visualization of a reportpublished by the World Health Organization.

3This information was derived by looking at the health indicators published by the WorldHealth Organization. We looked at nine different indicators: the availability of contraception,HIV infection rates, malaria infection rates, mortality rate, malnutrition, life expectancy,immunization rates, tuberculosis infection rates, and health expenditures as a percentage oftotal GDP. We looked at changes during the five-year period from 2002 to 2007 and comparedthem to changes in health outcomes during the previous five-year period (1995–2000).


posit that the presence of interstate rivalry, in which a pair of states sharesa history of conflict over contentious issues, will increase the likelihood thatongoing political violence will be deemed to be a threat to the regime inquestion. As resource diversion occurs in response to the ongoing violence inthe proximate state, the population loses access to vital health-care servicesthat were previously available. This, we argue, leads to a decrease in the overallhealth of the population.

In the following section we discuss this theory in greater detail and derivetwo testable hypotheses. We then outline our spatial and temporal domainand introduce the data that will be used in the empirical tests. Following thisprocess we present the findings from our statistical models, taking special careto discuss the policy implications. Finally, we conclude by discussing somepotential avenues for future research in this area.

External Threat as a Diffusion Mechanism

Increasing threat perception is inherent in the actions that are often neces-sary in order to address a growing domestic threat. In an attempt to designand implement an effective response to an internal threat, the state in questionis often forced to take immediate action to increase manpower and the avail-ability of war-fighting resources. This process requires policymakers to makequick decisions regarding what defense-related investments are worthwhile,and allocate funding to pay for them. A push by a regime to increase its abilityto cope with an internal threat is apt to continue into the foreseeable future, asthere is significant uncertainty as to when the threat might dissipate. Haltingthese efforts, or relying exclusively on material support from a third party, isa risky move because it increases the likelihood that state capabilities declinerelative to that of the rebels. This does not mean that a regime will neverrely on third-party support. Rather, it simply incentivizes the regime to avoidplacing all of its “eggs in one basket,” as it were. In the current Syrian civil war,for instance, the Bashar al-Assad regime willingly accepts arms from Russiaand Iran, but the incentive to produce arms for itself remains.

One important consequence of the push toward militarization is the po-tential of such actions to alter domestic political alignments. The presence ofhostility over a prolonged period of time is apt to increase the influence of hard-liners in the policy apparatus, making it easier for elites to convince the domes-tic audience that aggressive action is necessary. This passing of the torch fromthe so-called doves to the hawks is often accompanied by fundamental shifts inthe priorities of domestic institutions with the goal of easing the process of mil-itarization. This explains why an effort to improve the war-fighting ability of astate as a consequence of a growing threat tends to produce a situation in whichthe power of the executive branch increases relative to the rest of the govern-ing institutions (Evans, Rueschemeyer, and Skocpol, 1985; Friedberg, 2000).Though this process has traditionally only been conceptualized as being a con-sequence of interstate conflict, there is substantial evidence that “the threat or


eruption of internal wars can be just as influential as interstate war in moldingstate institutions, economy, and society” (Stubbs, 1999: 338).

The level of threat, as perceived by politically relevant states, grows out ofthese actions for three central reasons. The first, which is explicitly discussedabove, is the fact that the regime in question is now continually seeking toincrease its ability to wage war. Such actions have the potential to shift regionalpower distributions, leading nearby states to respond by increasing their ownwar-fighting capabilities. It might, under some circumstances, also incentivizedistant rival states to divert resources away from other programs in order tosolidify their ability to engage in, or respond to, aggressive interstate behavior.Second, the fundamental shift in the priorities of domestic institutions de-creases the time necessary for the regime in question to mobilize. It also is aptto make the onset of interstate conflict more likely as audience costs associatedwith the use of force are likely lessened by the presence of hardliners and aconstant internal threat. In this sense, the regime in question has not onlyincreased its opportunity to use force (via increased capabilities), but is alsomore willing to use it, as well.

These first two sources of increased threat perception are only exacerbatedby the presence of contentious issues that preceded the onset of civil conflict.A third-party state that has a history of conflictual relations with the regimein question is apt to view the movement toward increased militarization withextreme caution. The reason, which is well-articulated in the rivalry literature(Diehl and Goertz, 2000; Thompson, 2001; Colaresi, Rasler, and Thompson,2007), is that the recurrence of conflict fuels a psychological process thatleads the two sides to view one another as enemies. Over time, the issuesof contention become linked as both sides begin to seek out new sourcesof conflict. This explains why only one-quarter of all interstate disputes areisolated incidents that occur outside the confines of an active rivalry (Diehl andGoertz, 2000:60–63). In the context of increased militarization, therefore, thepresence of interstate rivalry will increase threat perception as the third-partystate will fear that the increase in military capabilities might provide a relativeadvantage for its rival. In addition, a third-party rival cannot be certain thatthe newly acquired resources will not be used against it in the future.

The third reason that proximate political violence increases threat percep-tion, which is not directly related to the increased war-fighting ability of aregime, is predicated on the commitment by a national government to engagerebels militarily. This, as previous research has shown, increases the likelihoodof spillover or externalization events (Gleditsch, Salehyan, and Schultz, 2008).These consequences of civil war are a source of increased threat perception,as they have the potential to inflict significant harm on the nearby state. Inthe context of this study, there are two specific risks to a third-party state thatwarrant discussion.

The first stems from the transnational dynamics of civil wars. Rebel groupsincreasingly seek to occupy border regions in an effort to establish a safehaven across international borders (Salehyan, 2009). This allows the group in


question to employ what amounts to “hit-and-run” tactics, in which it canattack government forces or civilians and then flee to a neighboring state.The probability for spillover or externalization events is increased as a result,as the government may (in some cases unintentionally) pursue rebels acrossinternational borders. The diffusion of violence poses a threat to the regimenot only because of a foreign military presence, but also because the regimeembroiled in civil war might demand action on the part of the third-partystate. Choosing to acquiesce and use force against the transnational rebel grouprisks undermining domestic stability, whereas failing to act at the request ofthe government involved in the civil war increases the likelihood of interstateconflict (Salehyan, 2008).

The second potential risk posed by civil war that is likely to produceincreased threat perception is the at-risk individuals who often flee the violencein search of safety and access to basic services (Davenport, Moore, and Poe,2003; Melander and Oberg, 2006). In addition to the direct public healthconsequences of refugees (Ghobarah, Huth, and Russett, 2003; Iqbal, 2010;Tabbaa and Seimenis, 2013), high inflows can have adverse effects on theeconomy of the receiving state. Unlike voluntary migrants, refugees from war-torn countries are not selected based on their skills and thus tend not to resultin significant economic activity (Cortes, 2004).

Thus, an influx of refugees has the potential to substantially decrease theamount of resources available to a regime. This often means that the refugeepopulation will be forced to compete with the citizenry for jobs and access toservices, which is apt to create conflict between the two groups (Martin, 2005).In addition, the likelihood of grievances is also increased by the possibility thatthe incoming refugees will exacerbate (or activate) ethnic cleavages (Weiner,1992). This might force the regime to have to cope with growing domesticdiscontent and violence, which increases the likelihood that the governmentbecomes embroiled in serious armed conflict with domestic actors (Salehyanand Gleditsch, 2006).

Faced with a growing threat due to ongoing political violence in a proximatestate, and the potential consequences that are often born out of proximatecivil conflict, a regime has a strong incentive to take preventative measures.It is this incentive, we believe, that functions as an indirect diffusion mecha-nism that can help explain the diffusion of public health consequences acrossinternational borders. This conceptual framework expects prolonged expo-sure to threat to incentivize the at-risk third-party state to improve the abil-ity of the regime to defend itself if the situation worsens (see Figure 1).This does not imply that the state in question is preparing (or expecting)to engage in direct conflict with the government embroiled in civil war,it simply means that manpower and supplies are necessary to respond tospillover events, externalization, and/or the influx of refugees. The decision bya regime to ignore the ongoing violence, especially when proximate civil warshave the potential to produce severe economic and political consequences, isunlikely. Rather, we expect at-risk states to constantly monitor the ongoing


FIGURE 1

Conceptual Framework: Intrastate Violence and the Diffusion ofPublic Health Consequences

situation and update their behavior accordingly. Any escalation in proximatepolitical violence is likely to be met with preventative preparations, whereasa move toward settlement should be associated with a reduction in suchactivity.


Preventative measures require the diversion of resources away from domesticspending in order to increase funding to the military, much like the reallocationof resources that takes place in a state facing a significant internal threat.Because of this, we expect the same potential for a fundamental shift in thealignment of the domestic political environment. As the level of threat posedby political violence in a proximate country increases, so will the influenceof hardliners and the probability that domestic institutions are altered in amanner to ease the process of militarization. Arguably, approving measures toincrease military spending when the potential threat is external will be easier,even in cases in which the shift in resource allocation requires approval via alegislative body. This is because external threats, and elite responses to theseevents, are apt to give rise to internal cohesion via a “rally-round-the-flag”effect. Due to this, we expect that as the intensity of violence increases in aproximate state so will the amount of resources that will be diverted away fromsocial programs and infrastructure in the name of national security. Becauseresource diversion reduces access to public health services and decreases theireffectiveness (Nelson et al., 2003; Ghobarah, Huth, and Russett, 2004; Furstet al., 2009; Iqbal, 2010), we expect the overall health of the population todecline. From this, we derive the following:

Hypothesis 1: An increase in the exposure to violence in a proximate state will beassociated with a decline in the overall health of the population, ceteris paribus.

As discussed above, we also expect the presence of interstate rivalry tobe associated with an increase in threat perception. As the regime currentlyembroiled in civil war improves its war-fighting capabilities and begins to shiftthe priorities of its institutions, a rival state will likely believe it is necessaryto respond. The reason is the mutual animosity that has developed over time,and also the potential for conflict to arise out of the civil conflict. In addition,the presence of rivalry implies that hardliners have already seen their influenceon policy increase over time and are able to convince the domestic audiencethat aggressive action is justified against the rival state. Any increase in thecapabilities of the rival, therefore, will likely produce broad public supportfor a response. As such, the domestic audience costs are significantly lower,making the diversion of resources—at high levels—more likely. As resourcesare reallocated in response to what is perceived to be a growing threat, publichealth services and vital infrastructure are likely to see a sharp reduction infunding. From this, we derive the following:

Hypothesis 2: The presence of intrastate violence in a rival state will be negativelyassociated with the overall health of the population, ceteris paribus.


Research Design

The empirical tests are concerned with exploring the relationship betweenthe escalation of political violence in proximate countries and changes in thegeneral health of the population. As such, we choose to employ a directed dyaddesign that is increasingly common among studies that seek to empiricallyexplore transnational processes (see Young and Findley, 2011; Findley, Piazza,and Young, 2012, for examples). Because the theoretical mechanisms thatwe have identified only apply to states that have the potential to influenceone another, we limit our sample to politically relevant directed dyads. Inaddition, we are forced to limit our sample temporally and spatially dueto some data limitations.4 The current set of analyses in which intrastateviolence is the independent variable includes all African states from 1997 to2001 (N = 936), whereas the models that use rivalry as the independentvariable include all politically relevant dyads globally from 1997 to 2001(N = 2,577).

The dependent variable in this study is the infant mortality rate (IMR),which is defined as the number of deaths in children less than one year ofage per 1,000 live births in any given year (Abouharb and Kimball, 2007).We chose this dependent variable for two primary reasons. First, there is alarge body of evidence that IMR is a good measure of the overall healthof a population (Macassa et al., 2003; Reidpath and Allotey, 2003). Thereason for this strong correlation is that many of the factors that explainvariation in IMR are predictors of the health of the general population aswell. Per the literature cited above, these factors include living conditions,gender inequality, environmental quality, and economic development, amongothers. The second reason for choosing IMR is to minimize the possibilitythat deaths resulting from violence are driving our results. Using the rawmortality rate (or other similar proxy), for example, would lead our em-pirical tests to capture the severity or onset of violence in the state underconsideration, rather than the changes in the health outcomes of the popu-lation. It would also include deaths resulting from crime and other factors.Only including children less than one year of age allows us to avoid theseissues.5

4The most significant of which are data on health expenditures, which is a vital controlvariable when exploring variation in the IMR. These particular data begin in 1997. We arelimited spatially because the event data that we use to create the intensity of intrastate violencemeasure are only available for African countries.

5Some scholars have questioned the use of IMR as a proxy for the overall health of apopulation. This criticism is based on policy making and not the use of the measure as a proxyin statistical models (Murray, 1996). Because we are simply using the measure for an empiricalanalysis, such criticism does not apply to our decision to use IMR as a dependent variable.Used in this way it remains a good proxy of population health, even when compared to someother popular alternatives (Reidpath and Allotey, 2003).


Intensity of Political Violence

The dominant approach in the political science literature of employinga dummy variable in order to operationalize the concept of active politicalviolence at the state level is problematic for the purposes of this study. Inexploring the relationship between IMR and violence in a proximate state, weare less concerned with the moment in time in which 24 battle deaths become25 (or, when 999 becomes 1,000), and are more concerned with measuring thelevel of exposure a state has to violence that is taking place near its territorialboundaries. The notion here is that as the exposure to violence increases, thelikelihood that the public health consequences of the ongoing civil conflict willinfect a neighboring state will increase as well. In the context of the theory de-scribed above, this means that as the conflict becomes more intense in a neigh-boring state, the stronger the incentive becomes for the state in question toreallocate resources. Exploring this question empirically requires the construc-tion of an intrastate conflict variable that is able to reliably measure the inten-sity of violence, rather than merely its presence (or lack thereof ) during a givenyear.

In order to accomplish this task we employ kernel density estimation, whichuses event data to calculate the intensity of political violence at the local level.6

After overlaying a series of grid cells over the study area, the kernel visits thecenter of each grid cell and calculates an intensity measure based on thenumber of events that are found within the bandwidth (spatial extent) of thekernel.7 This provides an estimate of intensity at each grid location, whichcan then be aggregated to the state level (see Figure 2 for an example).8 Inour case, we use the known locations of battle events in Africa and weigh eachpoint by the best-known estimate of battle deaths (Sundberg and Melander,2013). As the weight assigned to a given event increases, so does the influenceof that point in the creation of the intensity measure. This protects againstthe possibility that 10 events in which a single fatality was observed at eachevent will be deemed to be more intense relative to a single event in which500 people were killed. In aggregating this measure to the state level, we takethe mean value of the intensity measure in order to control for the size of thecountry.9

6We choose to rely on this technique rather than using raw battle deaths because the conceptof exposure, we believe, is best understood as being the product of the frequency of violentevents and the severity of those events. High numbers of battle deaths need not occur forthreat perception to increase. In addition, the technique below assumes that the clustering ofviolent events in space will be deemed to be more threatening than isolated incidents. Bothdimensions (frequency and clustering) are important in measuring exposure.

7We selected the size of the bandwidth of the kernel by minimizing the mean squared error(MSE), which is the norm for bandwidth selection in the literature.

8Please consult Bailey and Gatrell (1995) for a technical discussion of kernel estimation.Visualizations of the kernel estimates for each year included in this analysis can be found onlinein the web appendix.

9Note that in constructing this measure we include each event type included in the datadescribed in Sundberg and Melander (2013), as all forms of violence are likely to increasethreat perception.


FIGURE 2

Intensity of Political Violence in Africa, 1999

The primary purpose of creating this intensity variable was to create acategorical variable consisting of three groups: low violence, moderate violence,and high violence. This was accomplished using quantiles, which assign equalnumbers of observations to each group based on the severity of conflict duringthe year in question. This allows us to empirically evaluate whether increasingexposure to political violence in a neighboring state increases the influence ofthe violence on the general health of the population.10

Interstate Rivalry

The interstate rivalry variable is a dummy variable coded as a 1 if the statesthat comprise the dyad were rivals during the year in question; otherwise, itis coded as a 0. These data were taken from Klein, Goertz, and Diehl (2006)and operationalize rivalry as being those cases in which a previous interstatedispute has an influence on the evolution of a subsequent dispute. In otherwords, the issues at stake link the disputes.11

10The conflict variables are lagged by one year in order to control for the effect of time.11This variable is lagged by one year to control for the effect of time.


Political Violence in Rival State

In order to test our hypothesis concerning the relationship between politicalviolence in a rival state and changes in IMR, we combine our intensity ofpolitical violence measure with the rivalry data. Specifically, we create aninteraction term that is comprised of the rivalry variable and the intensity ofpolitical violence in the proximate state in question. The interaction term,therefore, only includes the intensity of proximate political violence if it isongoing in a rival state; otherwise, it will be given a value of 0.12

Control Variables

We also include a set of variables in our equations to account for factors thathave been shown to be associated with IMR in previous literature. Becausewe are predicting changes in the IMR of Country A over time, each controlvariable is the value for that country (not Country B). The first, healthexpenditures, was obtained from the World Health Organization NationalHealth Account Database13 and is the percentage of GDP that a state spendson providing healthcare to its citizens. The second, GDP per capita, is thenatural log of GDP per capita during the year of the observation (Gleditsch,2002). The third control variable is the natural log of the size of the populationduring the year of the observation. The fourth, which measures the degreeof income inequality in a society, is the GINI coefficient, as reported by theWorld Bank.14 In order to control for the level of gender inequality presentduring a given year, we include a variable that is the percentage of womenin the workforce. This variable was taken from the replication data providedby Caprioli (2005), who obtained the original data from the World Bank. Inorder to control for conflict within the country we include a dummy variablethat is coded as a 1 if the intensity of violence in Country A was placed ineither of the two last quantiles (per the method described above). Finally, weinclude the natural log of total refugee inflows to Country A. This was derivedfrom the figures reported in Salehyan and Gleditsch (2006).15

Empirical Tests

Method of Analysis

Given that our data are of a cross-sectional, time-series nature, thefirst step is to determine whether we should estimate our models using

12The range of the intensity measure is 0.003 to 2.299. The mean is 0.376.13〈http://data.worldbank.org/indicator/SH.XPD.TOTL.ZS〉.14〈http://data.worldbank.org/indicator/SI.POV.GINI〉.15Every control variable is lagged by one year in order to control for the effect of time.


fixed effects or random effects. Very simply stated, a fixed-effects model makesthe assumption that something within each panel (in our case the dyad) mayimpact or bias the predictors. Failing to control for this would make it diffi-cult to assess the net effect of the predictor because of omitted time-invariantcharacteristics. A random-effects model, on the other hand, assumes that thevariation across panels is random and uncorrelated with the predictors. Thus,if there is reason to believe that the differences across panels have some in-fluence on the dependent variable then the use of random effects may bejustified.

We rely on the Hausman test to determine which model is most appropriate,given our data. The null hypothesis of this test is that unique errors areuncorrelated with the regressors. In each model that is estimated in thisstudy, the null hypothesis is rejected—thus indicating the fixed-effects modelis the most appropriate.16 This, as described above, implies that there issome time-invariant characteristic within each dyad that is likely to bias ourpredictors. The next step was to determine whether or not fixed-time effectsare necessary. In order to accomplish this task, we employed an F-test forindividual effects. This test indicated that time-fixed effects were necessary forthe models estimated in this study.17 We report models with and without time-fixed effects as a robustness check. The findings presented below hold regardlessof whether time-fixed effects are used, which increases our confidence that theestimates reported below are robust.

Findings

The first hypothesis predicts that as the exposure to intrastate violencein a neighboring state increases, so will the likelihood that a decline in theoverall health of the population will be observed. According to the estimatesreported in Table 1, this is indeed the case (see Model 1 and Model 4). In bothmodels (fixed-time effects and no fixed-time effects), the dummy variable indi-cating low violence in a proximate country fails to reach statistical significance.Moderate and high levels of intrastate violence, however, are statistically signif-icant and positive. This provides support for the notion that ongoing violencein a proximate state has the potential to influence public health outcomesat home. Even more, the substantive importance of the variable increases bynearly 44 percent when moving from moderate to high violence in a proximatestate according to the estimates of the model (Model 1).

This finding provides preliminary evidence that the likelihood that theconsequences of civil war diffuse across international borders increases as theseverity of the violence increases. In addition, the effect of proximate politicalviolence also appears to increase in severity as the intensity of the violence

16The results from these tests can be found in the web appendix.17The results from these tests can be found in the web appendix.

14 Social Science QuarterlyTA

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−0.2

43−0

.212

0.12

2−0

.293

−0.2

48(0

.288

)(0

.307

)(0

.308

)(0

.313

)(0

.329

)(0

.329

)ln

refu

gee

inflo

ws(t

−1)

0.11

3∗∗∗

0.11

3∗∗∗

0.11

9∗∗∗

0.18

0∗∗∗

0.17

7∗∗∗

0.14

9∗∗∗

(0.0

35)

(0.0

35)

(0.0

22)

(0.0

37)

(0.0

37)

(0.0

23)

Hea

lthex

pen

ditu

res(t

−1)

−0.4

03∗∗

∗−0

.412

∗∗∗

−0.5

34∗∗

∗−0

.698

∗∗∗

−0.6

79∗∗

∗−0

.738

∗∗∗

(0.1

02)

(0.1

02)

(0.0

60)

(0.1

06)

(0.1

06)

(0.0

62)

lnG

DP

per

cap

ita(t

−1)

−0.4

39∗∗

∗−0

.464

∗∗∗

−0.4

80∗∗

∗−0

.152

−0.2

01−0

.413

∗∗∗

(0.1

17)

(0.1

17)

(0.0

64)

(0.1

24)

(0.1

23)

(0.0

68)

lnto

talp

opul

atio

n(t−

1)−3

.987

∗∗−4

.662

∗∗−4

.787

∗∗∗

−18.

291∗∗

∗−1

8.09

0∗∗∗

−18.

555∗∗

∗

(1.8

43)

(1.8

65)

(1.1

53)

(1.4

64)

(1.4

66)

(0.8

66)


TAB

LE1—

cont

inue

d

Mod

elS

pec

ifica

tion:

Tim

e-Fi

xed

Effe

cts

No

Tim

e-Fi

xed

Effe

cts

(1)

(2)

(3)

(4)

(5)

(6)

GIN

Icoe

ffici

ent(t

−1)

0.15

4∗∗∗

0.15

3∗∗∗

0.14

1∗∗∗

0.16

4∗∗∗

0.16

0∗∗∗

0.15

4∗∗∗

(0.0

22)

(0.0

23)

(0.0

14)

(0.0

24)

(0.0

24)

(0.0

14)

Wom

enin

wor

kfor

ce(t

−1)

−0.0

44∗∗

−0.0

43∗∗

−0.0

21∗

−0.0

21−0

.023

−0.0

15(0

.021

)(0

.021

)(0

.013

)(0

.023

)(0

.023

)(0

.013

)Fa

ctor

(yea

r)19

97−0

.731

∗∗∗

−0.7

19∗∗

∗−0

.738

∗∗∗

(0.2

14)

(0.2

14)

(0.1

30)

Fact

or(y

ear)

1998

−1.2

37∗∗

∗−1

.166

∗∗∗

−1.2

34∗∗

∗

(0.2

29)

(0.2

30)

(0.1

40)

Fact

or(y

ear)

1999

−2.0

80∗∗

∗−1

.965

∗∗∗

−1.7

50∗∗

∗

(0.2

42)

(0.2

44)

(0.1

44)

Fact

or(y

ear)

2000

−2.4

25∗∗

∗−2

.278

∗∗∗

−2.1

36∗∗

∗

(0.2

66)

(0.2

68)

(0.1

62)

Fact

or(y

ear)

2001

−3.2

80∗∗

∗−3

.117

∗∗∗

−2.9

90∗∗

∗

(0.2

89)

(0.2

93)

(0.1

79)

Ob

serv

atio

ns93

693

62,

577

936

936

2,57

7R

20.

486

0.48

10.

466

0.38

60.

393

0.38

9A

dju

sted

R2

0.38

90.

385

0.37

80.

311

0.31

70.

316

F-st

atis

tic44

.270

∗∗∗

43.4

29∗∗

∗13

0.29

7∗∗∗

43.0

62∗∗

∗44

.447

∗∗∗

148.

082∗∗

∗

(df=

16;7

50)

(df=

16;7

50)

(df=

14;2

,091

)(d

f=11

;755

)(d

f=11

;755

)(d

f=9;

2,09

6)

NO

TE:∗

p<

0.1;

∗∗p

<0.

05;∗

∗∗p

<0.

01.


increases. Though this is likely due to a combination of refugee inflows andthe incentive we outlined in this article, the fact that we control for refugeeinflows provides evidence that there is an indirect diffusion mechanism atwork. From a policy perspective, the fact that proximate intrastate violenceprovides an incentive for a government to redirect resources away from publicservices implies a need to address this incentive directly.18 If, for instance,an international organization or coalition of states could reduce the amountof threat inherent in the presence of civil war, either via peacekeeping forcesor another mechanism, perhaps the public health consequences could beminimized.

The second hypothesis tested in this study predicts that the presence ofpolitical violence in a rival state will be negatively associated with the over-all health of the population. This is because rivalry exacerbates the level ofthreat posed to the state in question due to an ongoing relationship that ischaracterized by mutual animosity. In other words, as the war-fighting abilityof the third-party state increases because of an ongoing domestic conflict,the regime in question is more apt to respond by redirecting resources awayfrom social programs to those that enhance national defense. The estimatesreported in Table 1 do not find support for this hypothesis. Rather, the resultsfrom the empirical tests indicate that the relationship between ongoing civilviolence and public health in a third-party rival is much more nuanced thanwe predicted. Though there is a positive and statistically significant relation-ship between the two variables that comprise the interaction term (intensityof violence in a proximate state and interstate rivalry), the interaction term isnegative and fails to reach statistical significant at the 0.5 level. In statisticalterms, therefore, there is no relationship between the presence of politicalviolence in a rival state and changes in IMR.

Though we did not anticipate this result, it does make some sense given thenature of interstate rivalry. Rivalry is characterized by constant competitionover a prolonged period of time. This implies that the level of threat perception,though subject to change over time, is constant during periods of activerivalry. As such, we should expect both governments to have chosen gunsover butter far before the onset of a civil conflict. This explains the positiveand statistically significant relationship between rivalry and IMR, which ispresent in the models with the interaction term (Model 2 and Model 5) andalso those that directly estimate the influence of rivalry on IMR (Model 3and Model 6). The fact that the direction of the relationship changes in the

18We do not show via our empirical models presented in this article that violence in aproximate state is related to changes in spending on the military and healthcare. We didestimate these models, however, and the results can be found in the web appendix. Consistentwith expectations, there is a negative and statistically significant relationship between proximatepolitical violence and health-care expenditures. Also notable, there is a positive and statisticallysignificant relationship between proximate political violence and military expenditures. In bothmodels, the substantive importance of proximate political violence increases as the severityincreases, which provides further support for our theoretical framework.


interaction term, we believe, indicates that if anything the onset of a domesticthreat in a third party presents the regime in question with an opportunity todecrease the current level of military spending and redirect those resources toserve other purposes. This is possible when the intensity of violence in a rivalstate is severe because the rival is distracted with the domestic threat and isunlikely to lash out at the government. In other words, the onset of intensecivil conflict creates a temporary lull in the rivalry, which has the potential toalter institutional priorities in the short term.

Taken together, these findings are preliminary evidence that interstate rivalryis not only a threat to regional peace and security, but also indirectly threat-ens to undermine public health. As states are unlikely to shift preferencesfollowing the “locking-in” of the rivalry, this implies a need for action fromthe international community to minimize the public health consequences ofcontentious interstate relations. Interestingly, however, the influence of rivalryis conditional on whether or not an intense civil conflict is ongoing in thedyad. The presence of severe political violence in one member of the dyadfundamentally changes the influence of rivalry on IMR.

Conclusion

In this article we have sought to contribute to the body of literature thatexplores the relationship between political violence and public health. In doingso, we proposed an indirect diffusion mechanism that can, in some cases,account for the fact that proximate states often suffer as a result of ongoingviolence. This mechanism, we argue, is indirect in the sense that ongoingviolence in a proximate state elicits security-seeking behavior, especially whenthe two states in question are rivals. This shift in preferences, which is imposedby the ongoing conflict, prioritizes guns over butter. As resources are divertedaway from social services and vital infrastructure, the quantity and qualityof healthcare is reduced. This, which is similar to what takes place in thecountry embroiled in the civil war, leads to a decline in the overall health ofthe population.

In testing our argument we utilized a spatial technique, kernel densityestimation, in order to construct a continuous measure of conflict severitythat was predicated on the number of events that are observed, as well asthe number of battle deaths that occurred at each event. This allowed us tonot only explore the relationship between intrastate violence in a proximatestate and changes in IMR, but it also afforded us the opportunity to utilize theconcept of exposure that is common in the public health literature. Consistentwith expectations, our analyses showed that increased exposure to intrastateviolence in a proximate state is associated with sharper increases in IMR. Thisconcept is central to understanding the effect of proximate violence on publichealth outcomes, as the incentive to adjust preferences increases along withthe amount of exposure a state has to the ongoing violence. In the context


of rivalry we predicted that the amount of exposure necessary to prompt achange in state behavior is lower, but the mechanism at work remains, for themost part, the same. Instead, we found some evidence that intense violencein an interstate rival creates a lull in the rivalry and temporarily suspendsthe public health consequences associated with interstate rivalry. The causalmechanism underlying this unexpected relationship, we believe, is the fact thatthe rival is forced to cope with a significant domestic threat and is thus unableto militarily engage the regime in question—though more work is necessaryto confirm that this is indeed the case. The influence of interstate rivalry onpublic health, however, is negative and was robust in each of our statisticaltests.

Taken together, we believe that these findings highlight the fact that therelationship between international politics and the quality of public health isan intimate one. Global and regional changes have the potential to produceshifts in state preferences. The consequences of these shifts not only haveimplications for the behavior of states in the international system, but alsoimpact the populations that regimes govern. There is a need for scholars ofinternational politics, therefore, to begin to theorize about how internationaland regional events might influence state priorities, producing importantchanges in state behavior at home. One reason, which was demonstrated bythe findings produced by this study, is the adverse effects that such changesoften have on people’s lives. Another reason, which is evident by events suchas the Arab Spring, is the fact that the causal arrow is often bidirectional. Aspeople begin to suffer at home, the likelihood of domestic instability increasesas the population begins to express a growing set of grievances.

In this sense, the quest to identify the specific characteristics of internationalpolitics that are apt to alter a regime’s investment in social services and vitalinfrastructure is a worthwhile goal. Doing so will allow scholars to identifythose states that are at most risk of suffering societal costs as a consequenceof interstate interactions, which will afford policymakers the informationnecessary to minimize the human suffering that often results.

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