Assessing the impact of non-random measurement error on inference : a sensitivity analysis approach

Many commonly used data sources in the social sciences suffer from non-random measurement error, understood as mis-measurement of a variable that is systematically related to another variable. We argue that studies relying on potentially suspect data should take the threat this poses to inference seriously and address it routinely in a principled manner. In this article, we aid researchers in this task by introducing a sensitivity analysis approach to non-random measurement error. The method can be used for any type of data or statistical model, is simple to execute, and straightforward to communicate. This makes it possible for researchers to routinely report the robustness of their inference to the presence of non-random measurement error. We demonstrate the sensitivity analysis approach by applying it to two recent studies

Polarisation, accountability, and interstate conflict

Voters constrain democratic leaders’ foreign policy decisions. Yet, studies show that elite polarisation restricts the choices available to voters, limiting their ability to punish or reward incumbent governments. Building on a comparative elections and accountability perspective, we hypothesise that the governing context moderates the effectiveness of domestic punishment and reward. The rise of elite polarisation in many democracies undermines voters’ ability to sanction leaders through elections. Linking data on international crises to domestic polarisation, we find that leaders are more likely to be involved in the initiation of inter-state disputes, resulting disputes will be more likely to result in prolonged conflict, and ultimately that foreign policy outcomes exhibit greater variance. Results from our analysis and extensive robustness checks demonstrate evidence that increased dispersion of preferences among key actors can lead to extreme and negative foreign policy outcomes as electoral mechanisms fail to reign in and hold governing parties to account

More dangerous than dyads : how a third party enables rationalist explanations for war

For the bargaining model of war, in the absence of incomplete information and commitment problems, war is irrational. But this finding rests on a simple and rarely dis- cussed assumption – that bargaining is between exactly two participants. When we relax this assumption, in a three-player bargaining game, war is an equilibrium. Thus, a key finding of the bargaining model – that there is always an agreement that all states prefer to war – is an artifact of dyadic analysis. By removing this limitation, we can find new factors that affect the risk of war: the number of actors, divergence in state preferences, alliance dynamics, and the issue being bargained over

A network approach to measuring state preferences

State preferences play an important role in international politics. Unfortunately, actually observing and measuring these preferences are impossible. In general, scholars have tried to infer preferences using either UN voting or alliance behavior. The two most notable measures of state preferences that have flowed from this research area are ideal points (Bailey et al.2017) and S-scores (Signorino & Ritter, 1999). The basis of both these models is a spatial weighting scheme that has proven useful but discounts higher-order effects that might be present in relational data structures such as UN voting and alliances. We begin by arguing that both alliances and UN voting are simply examples of the multiple layers upon which states interact with one another. To estimate a measure of state preferences, we utilize a tensor decomposition model that provides a reduced-rank approximation of the main patterns across the layers. Our new measure of preferences plausibly describes important state relations and yields important insights on the relationship between preferences, democracy, and international conflict. Additionally, we show that a model of conflict using this measure of state preferences decisively outperforms models using extant measures when it comes to predicting conflict in an out-of-sample context

Antigovernment networks in civil conflicts : how network structures affect conflictual behavior

In this article, we combine a game-theoretic treatment of public goods provision in networks with a statistical network analysis to show that fragmented opposition network structures lead to an increase in conflictual actions. Current literature concentrates on the dyadic relationship between the government and potential challengers. We shift the focus toward exploring how network structures affect the strategic behavior of political actors. We derive and examine testable hypotheses and use latent space analysis to infer actors’ positions vis-à-vis each other in the network. Network structure is examined and used to test our hypotheses with data on conflicts in Thailand from 2001 to 2010. We show the influential role of network structure in generating conflictual behavior

Network competition and civilian targeting during civil conflict

Building on recent developments in the literature, this article addresses a prominent research question in the study of civil conflict: what explains violence against civilians? We use a novel computational model to investigate the strategic incentives for victimization in a network setting; one that incorporates civilians' strategic behavior. We argue that conflicts with high network competition - where conflict between any two actors is more likely - lead to higher rates of civilian victimization, irrespective of the conflict's overall intensity or total number of actors. We test our theory in a cross-national setting using event data to generate measures of both conflict intensity and network density. Empirical analysis supports our model's finding that conflict systems with high levels of network competition are associated with a higher level of violence against the civilian population

Networks of violence : predicting conflict in Nigeria

Civil conflicts are complex: multiple warring parties compete for control of territory both against each other and the government. These processes are often dynamic; changing over time and space. In this study, we embrace these complexities through a network based approach. By considering important relational patterns, such as reciprocity and transitivity, and tying them together with existing theoretical developments in the conflict processes literature, we answer the question of 'who fights whom and when' during civil conflict. Further, using the case of Nigeria, we offer novel theoretical insights about how the entrance of a new, aggressive actor can decisively alter the trajectory of conflict. In addition, we show that our approach is better at predicting 'who fights whom and when' in an out-of-sample context than extant approaches

Cabinet formation and portfolio distribution in European multiparty systems

Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se from the distribution of key government pay-offs, such as cabinet portfolios, between members of the resulting government. Models of government formation are necessarily specified ex ante, absent any knowledge of the government that forms. Models of the distribution of cabinet portfolios are typically, though not necessarily, specified ex post, taking into account knowledge of the identity of some government ‘formateur’ or even of the composition of the eventual cabinet. This disjunction lies at the heart of a notorious contradiction between predictions of the distribution of cabinet portfolios made by canonical models of legislative bargaining and the robust empirical regularity of proportional portfolio allocations – Gamson’s Law. This article resolves this contradiction by specifying and estimating a joint model of cabinet formation and portfolio distribution that, for example, predicts ex ante which parties will receive zero portfolios rather than taking this as given ex post. It concludes that canonical models of legislative bargaining do increase the ability to predict government membership, but that portfolio distribution between government members conforms robustly to a proportionality norm because portfolio distribution follows the much more difficult process of policy bargaining in the typical government formation process

Cabinet Formation and Portfolio Distribution in European Multiparty Systems

Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se from the distribution of key government pay-offs, such as cabinet portfolios, between members of the resulting government. Models of government formation are necessarily specified ex ante, absent any knowledge of the government that forms. Models of the distribution of cabinet portfolios are typically, though not necessarily, specified ex post, taking into account knowledge of the identity of some government ‘formateur’ or even of the composition of the eventual cabinet. This disjunction lies at the heart of a notorious contradiction between predictions of the distribution of cabinet portfolios made by canonical models of legislative bargaining and the robust empirical regularity of proportional portfolio allocations – Gamson’s Law. This article resolves this contradiction by specifying and estimating a joint model of cabinet formation and portfolio distribution that, for example, predicts ex ante which parties will receive zero portfolios rather than taking this as given ex post. It concludes that canonical models of legislative bargaining do increase the ability to predict government membership, but that portfolio distribution between government members conforms robustly to a proportionality norm because portfolio distribution follows the much more difficult process of policy bargaining in the typical government formation process.Peer Reviewe

Learning from the past and stepping into the future : toward a new generation of conflict prediction

Developing political forecasting models not only increases the ability of political scientists to inform public policy decisions, but is also relevant for scientific advancement. This article argues for and demonstrates the utility of creating forecasting models for predicting political conflicts in a diverse range of country settings. Apart from the benefit of making actual predictions, we argue that predictive heuristics are one gold standard of model development in the field of conflict studies. As such, they shed light on an array of important components of the political science literature on conflict dynamics. We develop and present conflict predictions that have been highly accurate for past and subsequent events, exhibiting few false-negative and false-positive categorizations. Our predictions are made at the monthly level for 6-month periods into the future, taking into account the social–spatial context of each individual country. The model has a high degree of accuracy in reproducing historical data measured monthly over the past 10 years and has approximately equal accuracy in making forecasts. Thus, forecasting in political science is increasingly accurate. At the same time, by providing a gold standard that separates model construction from model evaluation, we can defeat observational research designs and use true prediction as a way to evaluate theories. We suggest that progress in the modeling of conflict research depends on the use of prediction as a gold standard of heuristic evaluation