This paper proposes a Bayesian multilevel spatio-temporal model with a time-varying spatial autoregressive coefficient to estimate temporally heterogeneous network interdependence. To tackle the classic reflection problem, we use multiple factors to control for confounding caused by latent homophily and common exposures. We develop a Markov Chain Monte Carlo algorithm to estimate parameters and adopt Bayesian shrinkage to determine the number of factors. Tests on simulated and empirical data show that the proposed model improves identification of network interdependence and is robust to misspecification. Our method is applicable to various types of networks and provides a simpler and more flexible alternative to coevolution models.

International relations scholarship concerns dyads, yet standard modeling approaches fail to adequately capture the data generating process behind dyadic events and processes. As a result, they suffer from biased coefficients and poorly calibrated standard errors. We show how a regression-based approach, the Additive and Multiplicative Effects (AME) model, can be used to account for the inherent dependencies in dyadic data and glean substantive insights in the interrelations between actors. First, we conduct a simulation to highlight how the model captures dependencies and show that accounting for these processes improves our ability to conduct inference on dyadic data. Second, we compare the AME model to approaches used in three prominent studies from recent international relations scholarship. For each study, we find that compared to AME, the modeling approach used performs notably worse at capturing the data generating process. Further, conventional methods misstate the effect of key variables and the uncertainty in these effects. Finally, AME outperforms standard approaches in terms of out-of-sample fit. In sum, our work shows the consequences of failing to take the dependencies inherent to dyadic data seriously. Most importantly, by better modeling the data generating process underlying political phenomena, the AME framework improves scholars’ ability to conduct inferential analyses on dyadic data.

How pervasive is partisan sorting and polarization over public policies in the American public? We examine whether the barriers of partisan sorting and polarization seen in national politics extend to important local policies that shape economic development. To describe the extent of partisan sorting and polarization over local development policies, we employ conjoint survey experiments in representative surveys of eight US metropolitan areas and a hierarchical modeling strategy for studying heterogeneity across respondents. We find that strong partisans are sorted by party in some of their policy opinions, but rarely polarized. The same voters who disagree about national issues have similar preferences about local development issues suggesting a greater scope for bipartisan problem solving at the local level.

Building on past research, we implement a hierarchical latent class model to analyze political participation from a comparative perspective. Our methodology allows simultaneously: (i) estimating citizens’ propensity to engage in conventional and unconventional modes of participation; (ii) classifying individuals into underlying “types” capturing within- and cross-country variations in participation; and (iii) assessing how this classification varies with micro- and macro-level factors. We apply our model to Latin American survey data. We show that our method outperforms alternative approaches used to study participation and derive typologies of political engagement. Substantively, we find that the distribution of participatory types is similar throughout the continent, and that it correlates strongly with respondents’ socio-demographic characteristics and crime victimization.

Subnational analyses of political preferences are substantively relevant and offer advantages for causal inference. Yet, our knowledge on regional political preferences across Europe is limited, not least because there is a lack of adequate data. The rich Eurobarometer (EB) data is a promising source for European-wide regional information. Yet, it is only representative for the national level. This paper compares state-of-the-art methods for estimating regional preferences from nationally representative EB data, validating predictions with regionally representative surveys. Our analysis highlights a number of challenges for estimating regional preferences across Europe, such as data availability, variable selection, and over-fitting. We find that predictions are best using a Bayesian additive regression tree with synthetic post-stratification.

Much of the peace agreement durability literature assumes that stronger peace agreements are more likely to survive the trials of the post-conflict environment. This work does an excellent job identifying which provisions indicate that agreements are more likely to endure. However, there is no widely accepted way to directly measure the strength of agreements, and existing measures suffer from a lack of nuance or reliance on subjective weighting. We use a Bayesian item response theory model to develop a principled measure of the latent strength of peace agreements in civil conflicts from 1975 to 2005. We illustrate the measure's utility by exploring how various international factors such as sanctions and mediation contribute to the strength or weakness of agreements.