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Geographic factors such as rugged terrain and distance from capital cities are widely believed to prolong civil wars by enabling rebel groups to resist total defeat. This article argues that prevalence of malaria can similarly serve to asymmetrically enhance rebels' defensive capabilities and thus prolong civil war. Malaria prevalence does so in three complementary ways. First, while malaria can inflict costs on both government and rebel troops, these costs are magnified for larger and denser human groups; thereby ensuring that the costs of malaria will often be higher among government troop deployments. Second, because government soldiers are rotated in and out of conflict zones whereas insurgents typically are not, the former are likely to have a higher nonimmune exposure rate than the latter, which further ensures that government forces will be more susceptible to contracting and spreading malaria. Third, malaria can also indirectly prolong civil war by helping to maintain a socio-geographic environment that is conducive to insurgency. These three complementary factors advantage rebel forces' abilities to resist defeat by government forces and prolong civil conflicts. I empirically test these arguments by examining the duration of civil wars and find strong support for a prolonging effect of malaria on civil conflict.

International relations scholars are often interested in nominal dependent variables, and commonly analyze such variables with multinomial logit (MNL) models that treat status quo outcomes (e.g. "peace") as a homogeneous baseline-choice category. However, recent studies of zero-inflation processes within international relations suggest that these baseline cases may often arise from two distinct sources. Specifically, some status quo responses are likely to correspond to observations that actively opted for this choice over all others, while the remaining status quo outcomes are likely to arise from observations that were unable to realistically register a non-status quo choice under any reasonable circumstances. Including both sets of responses within an MNL model's baseline category can bias the estimated effects of covariates, leading to faulty inferences. As a solution to this problem, this study considers a recently proposed baseline-inflated MNL (BIMNL) model that explicitly estimates and tests for heterogeneous populations of status quo observations. After discussing the model and its theoretical underpinnings, I demonstrate the BIMNL's utility through replications of two existing studies of political violence and cooperation within the areas of international relations and civil war.

AbstractThis note seeks to understand the extent of the disruptions to international relations caused by pandemics, focusing on one globally-endemic disease: malaria. We posit that longstanding diseases such as malaria have the potential to undermine the political ties of nation states, as well as the many benefits of these connections. Our argument is tested empirically using both directed-dyadic and monadic data, while incorporating methods that account for endogeneity and other relevant concerns. We find that the geographic malaria rates of a country not only serve to historically discourage foreign governments from establishing diplomatic outposts on a country's soil, but also lead to an aggregate decrease in the total diplomatic missions that a country receives. We then discuss the current implications of these findings.

Food security has attracted widespread attention in recent years. Yet, despite preliminary evidence connecting food insecurity to political violence, we lack a systematic understanding of the relationship(s) between local food resources and violence against civilians. This study develops a food-security based theory to explain the significant variation that we observe in violence against civilians across both time and subnational geographic space. We argue that combatants, be they government or rebel actors, often must turn to local agricultural resources for sustenance. During times of relative peace, armed actors and civilians have long time horizons, and the prospects of repeated interactions thereby promote a strategy of co-optation to obtain food resources. However, the existence of immediate conflict in a region leads armed actors to discount the benefits of future interactions in favor of obtaining food immediately, using violence if necessary. In estimating a series of statistical models on a sample of all African countries (1997–2009), we find robust support for our expectations: cropland increases the frequency of violence against civilians during periods of conflict, but has an added pacifying effect during times of peace.

Human rights monitoring reports play important roles both in the international human rights regime and in productions of human rights data. However, human rights reports are produced by organizations subject to formal and informal pressures that may influence the topics considered salient for attention and scrutiny. We study this potential using structural topic models (STMs), a method used for identifying the latent topical dimensions of texts and assessing the effects of covariates on these dimensions. We apply STMs to a corpus of 6298 State Department Country Reports on Human Rights Practices (1977–2012), identifying a plausible set of topics including killings and disappearances, freedoms of expression and movement, and labor rights, among others. We find that these topics vary markedly both over time and space. We also find that while US domestic politics play no systematic role in shaping topic prevalence, US allies tend to receivemoreattention to violations of physical integrity rights. These results challenge extant research, and illustrate the usefulness of STM methods for future study of foreign policy documents. Our findings also highlight the importance oftopical attention shiftsin documents that monitor and evaluate countries.

Researchers commonly employ multinomial logit (MNL) models to explain individual-level vote choice while treating "abstention" as the baseline category. Though many view abstainers as a homogeneous group, we argue that these respondents emerge from two distinct sources. Some nonvoters are likely to be "occasional voters" who abstained from a given election owing to temporary factors, such as a distaste for all candidates running in a particular election, poor weather conditions, or other temporary circumstances. On the other hand, many nonvoters are unlikely to vote regardless of the current political climate. This latter population of "routine nonvoters" is consistently disengaged from the political process in a way that is distinct from that of occasional voters. Including both sets of nonvoters within an MNL model can lead to faulty inferences. As a solution, we propose a baseline-inflated MNL estimator that models heterogeneous populations of nonvoters probabilistically, thus accounting for the presence of routine nonvoters within models of vote choice. We demonstrate the utility of this model using replications of existing political behavior research.

When are individuals more likely to support immigration? We suggest here that regional international organizations (IOs; for example, the European Union) publicly release reports about the scale and benefits of immigration to member states in the region in which these IOs operate. We argue that unlike individuals who are uninformed about immigration, informed individuals who have more knowledge of the main regional IO in which their country participates will be more likely to employ immigration reports released by their regional IO to construct their immigration attitudes. They will also perceive that these reports are credible. The credibility of these reports helps individuals with more knowledge about their region's main IO to view immigrants favorably, which translates to support for immigration. We test our prediction by developing a finite mixture model that statistically accounts for the econometric challenges that emerge when uninformed individuals "save face" by disproportionately opting for the middle "status quo" category in ordinal survey response variables of immigration support. Results from the finite mixture model corroborate our prediction and are more reliable than estimates from a standard ordered probit model. (International Interactions (London)/ FUB)