Dynamic Stochastic Blockmodel Regression for Network Data: Application to International Militarized Conflicts

Abstract

The decision to engage in military conflict is shaped by many factors, including state- and dyad-level characteristics as well as the state’s membership in geopolitical coalitions. Supporters of the democratic peace theory, for example, hypothesize that the community of democratic states is less likely to wage war with each other. Such theories explain the ways in which nodal and dyadic characteristics affect the evolution of conflict patterns over time via their effects on group memberships. To test these arguments, we develop a dynamic model of network data by combining a hidden Markov model with a mixed-membership stochastic blockmodel that identifies latent groups underlying the network structure. Unlike existing models, we incorporate covariates that predict dynamic node memberships in latent groups as well as the direct formation of edges between dyads. While prior substantive research often assumes the decision to engage in international militarized conflict is independent across states and static over time, we demonstrate that conflict is driven by states’ evolving membership in geopolitical blocs. Our analysis of militarized disputes from 1816 to 2010 identifies two distinct blocs of democratic states, only one of which exhibits unusually low rates of conflict. Changes in monadic covariates like democracy shift states between coalitions, making some states more pacific but others more belligerent. Supplementary materials for this article are available online.

Keywords:

• Democratic peace
• Hidden Markov model
• International conflict
• Mixed-membership stochastic blockmodel
• Social networks

Supplementary Materials

The supplementary materials contain complementary empirical analyses and derivations. Code and data needed to replicate the results presented in the main text, as well as all analyses presented in the supplementary materials, can be found at https://doi.org/10.7910/DVN/82CULX.

Notes

1 The MID data are available at https://correlatesofwar.org/data-sets/MIDs

2 As an alternative way to address geographic effects, we estimate a specification that includes a set of regional indicator variables (see Table S3 and Figures S5 and S6 in the supplementary material).

3 To ensure these patterns are not a function of ceiling effects—given that the number of states with the maximum polity score of 10 is increasing over the time period — we also calculate the effect of a one standard deviation decrease in polity (see Figures S11 and S12 in the supplementary material). The effects are substantively identical.