We assess the accuracy of procedural and bargaining models in predicting the outcomes of the reforms of the economic governance of the European Union (EU) that took place between 1997 and 2013. These negotiations were characterized by high costs of failure. We confirm the accuracy and robustness of the compromise model, but a procedural model with a costly reference point performs well, indicating that misestimation of the no-agreement cost may be a reason for its commonly reported poorer accuracy. However, this model is more sensitive to measurement errors. We also show how both models contribute to understanding bargaining success and how the conditional influence of the European Parliament should not be ignored. We conclude by discussing the implications of these results for our understanding of the EU.

We develop a theoretical framework that accounts for complex dependence in foreign direct investment (FDI) relationships. Conventional theories of FDI focus on firm-, industry-, country-, or dyad-level characteristics to account for cross-border capital movements. Yet, today's globalized economy is characterized by the increasing fragmentation and dispersion of production processes, which gives rise to complex dependence among production relationships. Consequently, FDI flows should be represented and theorized as a network. Specifically, we argue that FDI relationships are reciprocal and transitive. We test these hypotheses along with conventional covariate determinants of FDI using an exponential random graph model (ERGM) for weighted networks. We find that FDI networks exhibit strong reciprocity and transitivity. Our network approach to studying FDI provides new insights into cross-border investment flows and their political and economic consequences, and more generally the dynamics of globalization. In addition to our substantive findings, we offer a broad methodological contribution by introducing the ERGM for count-weighted networks in political science.

Which factors explain voters’ evaluations of policy responses to economic shocks? We explore this question in the context of mass preferences over the distribution of disaster relief and evaluate three fairness-based explanations related to affectedness, need, and political ties. We analyze experimental data from an original survey conducted among American citizens and find that affectedness and need are key drivers of voters’ preferred disaster responses. We then compare these patterns with observed disaster relief distributions (1993–2008). The results suggest that observed relief allocations largely mirror the structure of voter preferences with respect to affectedness and need, but not to political ties. These findings have implications for an ongoing debate over the electoral effects of natural disasters, voters’ retrospective evaluations of incumbent performance, and the extent to which divide-the-dollar politics decisions align with mass preferences.

Focusing on one specific aspect of immigrant political integration—how authorities deal with their political right to demonstrate—we show in a large-scale survey experiment that liberal policy decisions permitting demonstrations lead to a polarization in attitudes: citizens who agree with a permission become more sympathetic, while those in favor of banning become more critical of immigrants. This notion of opinion backlash to policy decisions adds a new perspective to the literature on immigration attitudes which has either assumed a congruence between public opinion and policy or ignored political sources of anti-immigrant sentiment altogether. By exploring the unintended consequences of policy decisions, we provide an alternative view and demonstrate the inherent dilemma of balancing citizen opinion and minority rights.

A robust economy is assumed to bolster leaders' standing. This ignores how benefits of growth are distributed. Extending the partisan models of economic voting, we theorize executives are more likely rewarded when gains from growth go to their constituents. Analyses of presidential approval in 18 Latin American countries support our pro-constituency model of accountability. When economic inequality is high, growth concentrates among the rich, and approval of right-of-center presidents is higher. Leftist presidents benefit from growth when gains are more equally distributed. Further analyses show growth and inequality inform perceptions of personal finances differently based on wealth, providing a micro-mechanism behind the aggregate findings. Study results imply that the economy is not purely a valence issue, but also a position issue.

Research shows that government-controlled media is an effective tool for authoritarian regimes to shape public opinion. Does government-controlled media remain effective when it is required to support changes in positions that autocrats take on issues? Existing theories do not provide a clear answer to this question, but we often observe authoritarian governments using government media to frame policies in new ways when significant changes in policy positions are required. By conducting an experiment that exposes respondents to government-controlled media—in the form of TV news segments—on issues where the regime substantially changed its policy positions, we find that by framing the same issue differently, government-controlled media moves respondents to adopt policy positions closer to the ones espoused by the regime regardless of individual predisposition. This result holds for domestic and foreign policy issues, for direct and composite measures of attitudes, and persists up to 48 hours after exposure.

Conventional OLS fixed-effects and GLS random-effects estimators of dynamic models that control for individual-effects are known to be biased when applied to short panel data (T ≤ 10). GMM estimators are the most used alternative but are known to have drawbacks. Transformed-likelihood estimators are unused in political science. Of these, orthogonal reparameterization estimators are only tangentially referred to in any discipline. We introduce these estimators and test their performance, demonstrating that the unused orthogonal reparameterization estimator in particular performs very well and is an improvement on the commonly used GMM estimators. When T and/or N are small, it provides efficiency gains and overcomes the issues GMM estimators encounter in the estimation of long-run effects when the coefficient on the lagged dependent variable is close to one.

This study presents a pattern overlooked in previous research on measuring sensitive political outcomes: over the course of data collection, responses tend to shift in the direction of support for the local incumbent power. We suggest that, whereas earlier responses are largely devoid of this social desirability bias, word of the research spreads across enumeration areas, and individuals interviewed later in the process alter their responses out of fear of retribution for inappropriate answers. We document the pattern using original data from two surveys on support for violent extremism conducted in three different countries in the Sahel region of Africa. We rule out a host of alternative explanations and further confirm that the pattern can arise not just with overt survey measures but even with covert, experimental ones. We then demonstrate the same pattern using out-of-sample data from a separate well-known study. The findings offer a cautionary note to both conventional and experimental approaches to measuring sensitive attitudes.

Decades of research has debated whether women first need to reach a “critical mass” in the legislature before they can effectively influence legislative outcomes. This study contributes to the debate using supervised tree-based machine learning to study the relationship between increasing variation in women's legislative representation and the allocation of government expenditures in three policy areas: education, healthcare, and defense. We find that women's representation predicts spending in all three areas. We also find evidence of critical mass effects as the relationships between women's representation and government spending are nonlinear. However, beyond critical mass, our research points to a potential critical mass interval or critical limit point in women's representation. We offer guidance on how these results can inform future research using standard parametric models.

Many believe primary elections distort representation in American legislatures because unrepresentative actors nominate extremist candidates. Advocates have reformed primaries to broaden voter participation and increase representation. Empirical evidence, however, is quite variable on the effects of reform. I argue that when institutional reform narrows one pathway of political influence, aggrieved actors take political action elsewhere to circumvent reform. I use a difference-in-differences design in the American states and find that although changing primary rules increases primary turnout, campaign contributions also increase with reform. Implementing nonpartisan primaries and reforming partisan primaries lead to estimated 9 and 21 percent increases in individual campaign contributions per cycle. This suggests actors substitute action across avenues of political influence to limit effects of institutional reform.

Using survey vignettes and scaling techniques, we estimate common socio-cultural and European integration dimensions for political parties across the member states of the European Union. Previous research shows that party placements on the economic left-right dimension are cross-nationally comparable across the EU; however, the socio-cultural dimension is more complex, with different issues forming the core of the dimension in different countries. The 2014 wave of the Chapel Hill Expert Survey included anchoring vignettes which we use as “bridge votes” to place parties from different countries on a common liberal/authoritarian dimension and a separate common scale for European integration. We estimate the dimensions using the Bayesian Aldrich–McKelvey technique. The resulting scales offer cross-nationally comparable, interval-level measures of a party's socio-cultural and EU ideological positions.

Although covert warfare does not readily lend itself to scientific inquiry, new technologies are increasingly providing scholars with tools that enable such research. In this note, we examine the effects of drone strikes on patterns of communication in Yemen using big data and anomaly detection methods. The combination of these analytic tools allows us to not only quantify some of the effects of drone strikes, but also to compare them to other shocks. We find that on average drone strikes leave a footprint in their aftermath, spurring significant but localized spikes in communication. This suggests that drone strikes are not a purely surgical intervention, but rather have a disruptive impact on the local population.

When separation is a problem in binary dependent variable models, many researchers use Firth's penalized maximum likelihood in order to obtain finite estimates (Firth, 1993; Zorn, 2005; Rainey, 2016). In this paper, I show that this approach can lead to inferences in the opposite direction of the separation when the number of observations are sufficiently large and both the dependent and independent variables are rare events. As large datasets with rare events are frequently used in political science, such as dyadic data measuring interstate relations, a lack of awareness of this problem may lead to inferential issues. Simulations and an empirical illustration show that the use of independent “weakly-informative” prior distributions centered at zero, for example, the Cauchy prior suggested by Gelman et al. (2008), can avoid this issue. More generally, the results caution researchers to be aware of how the choice of prior interacts with the structure of their data, when estimating models in the presence of separation.

Delegation is a well-known feature of policymaking in separation of powers systems. Yet despite the importance of this activity, there is little systematic evidence about how many major laws in the United States actually delegate policymaking authority to administrators in federal agencies. Using a database of agency regulatory activity along with text searches, we examine significant US federal enactments from 1947 to 2016 to see which of these laws delegate to agencies. We find that nearly all major laws—more than 99 percent—contain delegation. We also find that the number of agencies receiving delegation in each law has increased over time.

We examine citizens' evaluations of majoritarian and proportional electoral outcomes through an innovative experimental design. We ask respondents to react to six possible electoral outcomes during the 2019 Canadian federal election campaign. There are two treatments: the performance of the party and the proportionality of electoral outcomes. There are three performance conditions: the preferred party's vote share corresponds to vote intentions as reported in the polls at the time of the survey (the reference), or it gets 6 percentage points more (fewer) votes. There are two electoral outcome conditions: disproportional and proportional. We find that proportional outcomes are slightly preferred and that these preferences are partly conditional on partisan considerations. In the end, however, people focus on the ultimate outcome, that is, who is likely to form the government. People are happy when their party has a plurality of seats and is therefore likely to form the government, and relatively unhappy otherwise. We end with a discussion of the merits and limits of our research design.