When and why do electoral commitments enhance parties’ ability to implement their preferred policy in multiparty governments? We propose an audience costs theory whereby strong platform commitments enhance parties’ negotiating positions in multiparty cabinets but only when they are on a salient policy issue for core voters and the party controls the policy-relevant portfolio. Utilizing new data on portfolio allocations in eight parliamentary democracies over 40 years, we show that absent a strong platform commitment, control of the portfolio of social affairs by social democrats, alone, is not associated with changes in welfare generosity. Notably, our findings are independent of party size and hold in most multiparty legislative systems not dominated by three parties.

How do personality traits condition the effects of campaign messages meant to mobilize voters? With two nationally representative US survey experiments, I show that common aggressive metaphors mobilize or demobilize voters depending on their traits. Aggressive metaphors increase the mobilizing impact of motivations to participate among aggressive individuals but decrease that impact among low-aggression people. For example, the language mobilizes strong partisans with aggressive personalities but demobilizes strong partisans low in aggression. This heterogeneity showcases the nuanced power of metaphors in campaigns, reaffirms the importance of personality in political behavior, and reveals the hidden role of aggression in non-violent political behavior for the first time. In practice, the net effects of aggressive metaphors can be positive, negative, or null depending on average traits in an audience.

Do minority voters respond to co-racial or co-ethnic candidates? That is does the increased chance of substantive representation translate into increased participation? Here, we focus on this question among African American voters. While much of the empirical literature on this question has produced conflicting answers, recent studies suggest that minority candidates can significantly increase minority turnout. We argue that past work on this topic does not adequately account for the fact that minority voters in places with minority candidates may systematically differ in their level of participation than minority voters in places without minority candidates. In this study we address the weaknesses of previous research designs and offer a new design that exploits the redistricting process to gain additional leverage over this question. We find little evidence that African American voter turnout increases when voters are moved to African American candidates. We find some evidence that white voters, however, tend to vote at lower rates when they are represented by African American candidates.

We argue that democratic institutions influence property rights in attracting foreign direct investment (FDI) by providing: (1) a coherent logic to the property rights regime that is created in a state and (2) a legitimate way to manage conflicts that arise in dynamic economies. We expect that the marginal effect of property rights in attracting FDI has increased over time with the rate of technological dynamism. We test this using a non-nested multilevel modeling strategy with random coefficients on data from 1970 to 2009. Our results demonstrate that the effect of property rights on attracting FDI is contingent on democratic institutions and that this effect becomes more pronounced over time. This effect holds for both developing and developed countries across all regions.

We generalize standard delegation models to consider policymaking when both information and authority are dispersed among multiple actors. In our theory, the principal may delegate partial authority to a privately informed agent while also reserving some authority for the principal’s use after observing the agent’s decision. Counterintuitively, the equilibrium amount of authority delegated to the agent is increasing in the preference divergence between the principal and agent. We also show that the amount of authority delegated depends upon whether the agent can observe the principal’s own private information (a condition we refer to as “top-down transparency”): this form of transparency increases the authority that must be delegated to the agent to obtain truthful policymaking. Accordingly, such transparency can result in less-informed policymaking. Nonetheless, the principal will sometimes but not always voluntarily choose such transparency.

This paper examines the Trio Presidency and its impact on voting behavior in the Council of the European Union. Trios of Member States cooperate for 18-month periods, with each member holding the Presidency for six months. We study whether belonging to the Trio increases the probability that a Member State votes in favor of a measure. We analyze roll call votes on 1038 legislative acts from January 2007 till June 2014. Conducting probit analyses we present evidence that Member States are indeed more likely to vote in favor during their participation in Trios. We show that this results mainly from the Trio’s agenda-setting powers. Moreover, holding the Presidency itself does not further increase the probability of voting in favor.

Are the social networks of legislators affected more by their political parties or their personal traits? How does the party organization influence the tendency of members to work collectively on a day-to-day basis? In this paper, I explore the determinants of the relationships of legislators in the Brazilian Chamber of Deputies. I use exponential random graph models to evaluate the relative influence of personal traits versus party influence in generating legislator relationships. Despite a focus on personalism in Brazil, the analysis reveals that the effects of political parties on tie formation are roughly equal to the effects of personal traits, suggesting that networks may make political parties much more cohesive than contemporary literature would lead us to believe.

Across a broad range of fields in political science, there are many theoretically interesting dependent variables that can be characterized as compositions. We build on recent work that has developed strategies for modeling variation in such variables over time by extending them to models of time series cross-sectional data. We discuss how researchers can incorporate the influence of contextual variables and spatial relationships into such models. To demonstrate the utility of our proposed strategies, we present a methodological illustration using an analysis of budgetary expenditures in the US states.

Cluster-robust standard errors (as implemented by the eponymous cluster option in Stata) can produce misleading inferences when the number of clusters G is small, even if the model is consistent and there are many observations in each cluster. Nevertheless, political scientists commonly employ this method in data sets with few clusters. The contributions of this paper are: (a) developing new and easy-to-use Stata and R packages that implement alternative uncertainty measures robust to small G, and (b) explaining and providing evidence for the advantages of these alternatives, especially cluster-adjusted t-statistics based on Ibragimov and Müller. To illustrate these advantages, we reanalyze recent work where results are based on cluster-robust standard errors.

How does media influence beliefs, attitudes, and behaviors? While many scholars have studied the effect of media on social and political outcomes, we know surprisingly little about the channels through which this effect operates. I argue that two mechanisms can account for its impact. Media provides new information that persuades individuals to accept it (individual channel), but also, media informs listeners about what others learn, thus facilitating coordination (social channel). Combining a field experiment with a plausibly natural experiment in Mexico, I disentangle these effects analyzing norms surrounding violence against women. I examine the effect of a radio program when it is transmitted privately versus when it is transmitted publicly. I find no evidence supporting the individual mechanism. The social channel, however, increased rejection of violence against women and increased support for gender equality, but unexpectedly increased pessimism about whether violence would decline in the future.

Government instability (cabinet duration) is an important feature of parliamentary democracy. Over time, the research on cabinet duration has improved in technical and theoretical sophistication. However, we note that little attention has been paid to the relationship between governments and the state itself. Our main hypothesis is that state capacity, e.g., factors such as state bureaucratic effectiveness and law and order, shape how easy it is for governments to implement the new policy and thus how well they can achieve policy objectives. We also argue that when state capacity is low, the ability to adequately respond to external shocks goes down, and instability increases. When testing this empirically we find that low state capacity does indeed help us predict an increased risk for early termination—in particular, whether the government ends through a replacement (but not by an early election). Using interaction effects, we also demonstrate that the effect of external shocks, such as an increase in unemployment, is conditional on state capacity. An increase in unemployment only has a significant effect on cabinet stability when state capacity is low, suggesting that the cabinet’s (in)ability to address the economic problems is an important factor for understating cabinet durability.

Although equality figures prominently in many foundational theories of democracy, liberal and electoral conceptions of democracy have dominated empirical political science research on topics like political regimes, democratization and democratic survival. This paper develops the concept of egalitarian democracy as a regime that provides de facto protection of rights and freedoms equally across the population, distributes resources in a way that enables meaningful political participation for all citizens and fosters an environment in which all individuals and social groups can influence political and governing processes. Using new indicators from the Varieties of Democracy project, the paper develops and presents measures of these important concepts, demonstrates their relationship to existing measures, and illustrates their utility for advancing the study of democracy in ways that more fully embrace the richness of democratic theory.

To what extent do survey experimental treatment effect estimates generalize to other populations and contexts? Survey experiments conducted on convenience samples have often been criticized on the grounds that subjects are sufficiently different from the public at large to render the results of such experiments uninformative more broadly. In the presence of moderate treatment effect heterogeneity, however, such concerns may be allayed. I provide evidence from a series of 15 replication experiments that results derived from convenience samples like Amazon’s Mechanical Turk are similar to those obtained from national samples. Either the treatments deployed in these experiments cause similar responses for many subject types or convenience and national samples do not differ much with respect to treatment effect moderators. Using evidence of limited within-experiment heterogeneity, I show that the former is likely to be the case. Despite a wide diversity of background characteristics across samples, the effects uncovered in these experiments appear to be relatively homogeneous.

While many scholars have moved toward using individual-level data to test theories of distributive politics, no studies have ever explicitly examined differences between individual and aggregate analyses of a distributive program. By leveraging nationwide individual-level data on both revealed voter preferences and the actual receipt of particularistic benefits through a contemporary Venezuelan land reform initiative, this article demonstrates that scholars can most effectively test and refine individual-level theories of distributive politics by combining both individual- and macro-level data. There are at least two advantages to doing so. First, comparing and contrasting findings from data at different levels of analysis can enable researchers to paint a more complete picture of distributive targeting. Second, when distributive benefits can be impacted or redirected by subnational politicians, as is common with many distributive programs, individual-level data alone can generate mistaken inferences that are an artifact of competing targeting attempts at different levels of government instead of initial targeting strategies. I demonstrate both of these points and discuss practical and simple recommendations regarding data collection strategies for the purposes of effectively testing theories of distributive politics.

Textual data are plagued by underreporting bias. For example, news sources often fail to report human rights violations. Cook et al. propose a multi-source estimator to gauge, and to account for, the underreporting of state repression events within human codings of news texts produced by the Agence France-Presse and Associated Press. We evaluate this estimator with Monte Carlo experiments, and then use it to compare the prevalence and seriousness of underreporting when comparable texts are machine coded and recorded in the World-Integrated Crisis Early Warning System dataset. We replicate Cook et al.’s investigation of human-coded state repression events with our machine-coded events, and validate both models against an external measure of human rights protections in Africa. We then use the Cook et al. estimator to gauge the seriousness and prevalence of underreporting in machine and human-coded event data on human rights violations in Colombia. We find in both applications that machine-coded data are as valid as human-coded data.

Bivariate probit models are a common choice for scholars wishing to estimate causal effects in instrumental variable models where both the treatment and outcome are binary. However, standard maximum likelihood approaches for estimating bivariate probit models are problematic. Numerical routines in popular software suites frequently generate inaccurate parameter estimates and even estimated correctly, maximum likelihood routines provide no straightforward way to produce estimates of uncertainty for causal quantities of interest. In this note, we show that adopting a Bayesian approach provides more accurate estimates of key parameters and facilitates the direct calculation of causal quantities along with their attendant measures of uncertainty.