The Return on Social Bonds: Social Hierarchy and International Conflict

This article takes a game-theoretic and latent variable approach to modeling the effect of international social hierarchies on conflict among states. I start with the premise that international states are social actors and are nested within informal social networks of friendly and conflictual relationships. Rather than lateral relationships among equals, networks among states tend to have a vertical or hierarchical structure. Although international hierarchical relationships may arise as a result of material power asymmetries, this article focuses on non-material asymmetries that stem from political legitimacy or policy innovation – a subject that has received less attention in scholarly research. I argue that, within these hierarchies, states adopt one of two roles – a dominant or a subordinate. Each resulting (dyadic) dominant–subordinate relationship is a social contract, in which the subordinate concedes some autonomy in exchange for the dominant’s protection. This social hierarchy affects the relationships among subordinates, as well as between a dominant and subordinates. The model predicts that a state’s degree of subordination reduces its probability of conflict initiation against other subordinates. Moreover, the decision to initiate conflict is influenced by the expectation that the dominant will intervene, which itself is affected by the target’s relative level of subordination to the dominant vis-à-vis the challenger. These predictions are supported by empirical analyses of the US hierarchy (1950–2000)

In search of growth: neoliberal and institutional models for economic growth in an age of globalization

The following study is an examination of the effects of globalization, regime type and macroeconomic policy on economic growth. 90 countries are examined between the years 1975-2003. While there are issues with the use of the proxy variable, the general effects can be explored. These effects are found to differ between high-income and low-income countries. The analysis finds that low-income countries should focus on improving inefficient distribution of services. High-income countries are able to choose from a range macroeconomic policies and are not forced to converge around any one policy

Strategic Binary Choice Models with Partial Observability

Strategic interactions among rational, self-interested actors are commonly theorized in the behavioral, economic, and social sciences. The theorized strategic processes have traditionally been modeled with multi-stage structural estimators, which improve parameter estimates at one stage by using the information from other stages. Multi-stage approaches, however, impose rather strict demands on data availability: data must be available for the actions of each strategic actor at every stage of the interaction. Observational data are not always structured in a manner that is conducive to these approaches. Moreover, the theorized strategic process implies that these data are missing not at random. In this paper, I derive a strategic logistic regression model with partial observability that probabilistically estimates unobserved actor choices related to earlier stages of strategic interactions. I compare the estimator to traditional logit and split-population logit estimators using Monte Carlo simulations and a substantive example of the strategic firm-regulator interaction associated with pollution and environmental sanctions

Rising Powers and Foreign Policy Revisionism: Understanding BRICS Identity and Behavior through Time

In Rising Powers and Foreign Policy Revisionism, Cameron Thies and Mark Nieman examine the identity and behavior of the BRICS (Brazil, Russia, India, China, and South Africa) over time in light of academic and policymaker concerns that rising powers may become more aggressive and conflict-prone. The authors develop a theoretical framework that encapsulates pressures for revisionism through the mechanism of competition and pressures for accommodation and assimilation through the mechanism of socialization. The identity and behavior of the BRICS should be a product of the push and pull of these two forces as mediated by their domestic foreign policy processes.State identity is investigated qualitatively through the use of role theory and the identification of national role conceptions. Both economic and militarized conflict behavior are examined using Bayesian change-point modeling, which identifies structural breaks in time series data, revealing potential wholesale revision of foreign policy. Using this innovative approach to show that the behavior of rising powers is governed not simply by the structural dynamics of power but also by the roles that these rising powers define for themselves, they assert that this process will likely lead to a much more evolutionary approach to foreign policy and will not necessarily generate international conflict.https://lib.dr.iastate.edu/pols_books/1000/thumbnail.jp

Peaceful Neighborhoods and Democratic Differences

Democracies are thought to behave differently than other states when cooperating in alliances, organizations, trade, and a host of other international institutions. We contend, however, that these democratic differences largely depend upon geopolitical environments that make cooperation possible. Though studies have demonstrated endogeneity between democracy and peace, few analyze the effects of this joint relationship on democratic differences in cooperative foreign policy behavior. We address this using the alliance literature as an example. We argue that alliances are used to either deter aggression or serve as conduits to advance other goals. Alliances that deter occur in dangerous environments, while those that serve other purposes cluster in peaceful environments. We find that alliances used to deter are particularly unreliable “scraps of paper”, and that the general reliability of alliances is concentrated among those existing in already-peaceful environments, which are unlikely to be invoked. By jointly modeling regime type and political environment using data on alliance termination from 1920–2001, we show that alliance reliability is a function of the latter rather than the former. Our argument has important ramifications for a host of literatures focused on regime type, as well as current debates over the effectiveness of democratic deterrence

Long Run Confidence: Estimating Confidence Intervals when using Long Run Multipliers

The recent exchange on Error Correction Models in Political Analysis and elsewhere dealt with several important issues involved in time series analysis. While there was much disagreement in the symposium, one common theme was the lack of power due to the few number of observations for much of this work. In this paper we highlight two well known but rarely discussed problems this has for inferences from standard time series techniques. First, one result of low power is inflated standard errors. One issue low powered time series can face is that the confidence interval on a lagged dependent variable, even when the series is stationary, includes values ≥ 1. This is particularly problematic when calculating the confidence interval of the long run multiplier. If the confidence interval of the lagged dependent variable includes 1, the standard error of the long run multiplier will be explosive. Second, the calculation of the long run multiplier is the ratio of coefficients, which makes the calculation of the uncertainty slightly more complicated. Unfortunately, the two standard approaches to calculating the uncertainty in the long run multiplier, the delta method and the Bewley transformation, are asymptotically accurate, but may have difficulties in small samples. As a solution, we suggest using a Bayesian approach. For autoregressive distributed lag models, the Bayesian approach can formalizes the stationarity assumption by using a beta prior that is strictly less than 1. With error correction models, the researcher can easily calculate the credible region of the long run multiplier from the posterior distribution of the ratio of the coefficients. As a result, we obtain theoretically informed estimates of the confidence regions for the distribution of the long run multiplier

The Effects of Dog-Whistle Politics on Political Violence

The election of President Trump marked significant changes in the content, outlets, and the level of civility of political rhetoric. The traditional left/right policy disagreements took on a more populist tone, activating extremist elements within society. We explore the consequences of political appeals to nationalist identity within the context of modern-day America. We argue that employed by elected officials, nationalist political rhetoric legitimizes extremist views and their expression. This effect is exacerbated by the social media, which provides an unmoderated channel for communication between elected officials and their extremist supporters. We test the link between nationalist rhetoric and hate crimes using data collected from Twitter, as well as an original dataset on daily hate incidents in the US, between February 2017–April 2018, and find strong evidence for our theory. Our results have important implications for the study of political communication and political violence

The Spatial Dimensions of State Fiscal Capacity The Mechanisms of International Influence on Domestic Extractive Efforts

This paper expands traditional predatory theory approaches to state fiscal capacity by adopting spatial analytical reasoning and methods. While previous work in the predatory theory tradition has often incorporated interdependent external influences, such as war and trade, it has often done so in a way that maintains a theoretical and empirical autonomy of the state. Theoretically, we suggest four mechanisms (coercion, competition, learning, and emulation) that operate to channel information through interstate rivalry and territorial contiguity, trade networks, and the political space associated with regime type and intergovernmental organization membership. We test our predictions using a multi-parametric spatio-temporal autoregressive model with four spatial lags capturing the four mechanisms. Our empirical results provide support for the coercion and learning mechanisms

Modeling Structural Selection in Disaggregated Event Data

Growing availability of disaggregated data, such as data on activity of subnational groups (e.g. protest campaigns, insurgents, terrorist groups, political parties or movements), has raised new types of theoretical and statistical challenges. In particular, rather than random, the observability and availability of disaggregated data are often a function of specific structural processes—an issue we refer to as structural selection. For example, domestic terrorist attacks or protester violence are conditional on the formation of domestic terrorist groups or protester movements in the first place. As a result, analytical inferences derived from subnational or other types of disaggregated data may suffer from structural selection bias, which is a type of sample selection bias. We propose a simple and elegant statistical approach to ameliorate such bias and demonstrate the advantages of this approach using a Monte Carlo example. We further illustrate the importance of accounting for structural processes by replicating three prominent empirical studies of government–opposition behavior and find that structural selection affects many of the inferences drawn from the observable data

Wanted dead or alive? The tradeoff between in-vivo versus ex-vivo MR brain imaging in the mouse

High-resolution MRI of the mouse brain is gaining prominence in estimating changes in neuroanatomy over time to understand both normal developmental as well as disease processes and mechanisms. These types of experiments, where a change in time is to be captured as accurately as possible using imaging, face multiple experimental design choices. Chief amongst these choices is whether to image ex-vivo, where superior resolution and contrast are available, or in-vivo, where resolution and contrast are lower but the animal can be followed longitudinally. Here we explore this tradeoff by first estimating the sources of variability in anatomical mouse MRI and then, using statistical simulations, provide power analyses of these experiment design choices