Based on findings from the literature on campaign effects on the one hand, and the literature on European Parliament elections on the other, we propose a model of European Parliamentary elections in which the campaign shift the calculus of electoral support, making differences in national political allegiances less important and attitudes about the European project more important by informing voters of and getting them interested in European politics. In effect, we argue that the political campaign leading up to the election makes European Parliament elections less second order. While previous studies have demonstrated that EU issues can matter for voting behavior in European Parliament elections, existing research has drawn on post-election surveys that do not enable us to capture campaign effects. Our contribution is to assess the impact of a campaign by utilizing a rolling cross-sectional survey that enables us to track how voters were affected by the campaign. Our findings show that campaigns do have an effect on European Parliament election outcomes, in that they provide information that enables voters to make decisions based on their attitude on European issues, making voter decision-making more dominated by EU issue voting.

Doubts about the integrity of ballot secrecy persist and depress political participation among the American public. Prior experiments have shown that official communications directly addressing these doubts increase turnout among recently registered voters who had not previously voted, but evaluations of similar messages sent by nongovernmental campaigns have yielded only suggestive effects. We build on past research and analyze two large-scale field experiments where a private nonpartisan nonprofit group sought to increase turnout by emphasizing ballot secrecy assurances alongside a reminder to vote in a direct mail voter mobilization campaign during the 2014 midterm election. Our main finding is that a private group’s mailing increases turnout by about 1 percentage point among recently registered nonvoters. This finding is precisely estimated and robust across state political contexts, but is not statistically distinguishable from the effect of a standard voter mobilization appeal. Implications and directions for future research are discussed.

Methodologists and econometricians advocate the partial observability model as a tool that enables researchers to estimate the distinct effects of a single explanatory variable on two partially observable outcome variables. However, we show that when the explanatory variable of interest influences both partially observable outcomes, the partial observability model estimates are extremely sensitive to misspecification. We use Monte Carlo simulations to show that, under partial observability, minor, unavoidable misspecification of the functional form can lead to substantial large-sample bias, even though the same misspecification leads to little or no bias under full observability.

Can electoral reforms such as an independent redistricting commission and the top-two primary create conditions that lead to better legislative representation? We explore this question by presenting a new method for measuring a key indicator of representation—the congruence between a legislator’s ideological position and the average position of her district’s voters. Our novel approach combines two methods: the joint classification of voters and political candidates on the same ideological scale, along with multilevel regression and post-stratification to estimate the position of the average voter across many districts in multiple elections. After validating our approach, we use it to study the recent impact of reforms in California, showing that they did not bring their hoped-for effects.

Lagged dependent variables (LDVs) have been used in regression analysis to provide robust estimates of the effects of independent variables, but some research argues that using LDVs in regressions produces negatively biased coefficient estimates, even if the LDV is part of the data-generating process. I demonstrate that these concerns are easily resolved by specifying a regression model that accounts for autocorrelation in the error term. This actually implies that more LDV and lagged independent variables should be included in the specification, not fewer. Including the additional lags yields more accurate parameter estimates, which I demonstrate using the same data-generating process scholars had previously used to argue against including LDVs. I use Monte Carlo simulations to show that this specification returns much more accurate coefficient estimates for independent variables (across a wide range of parameter values) than alternatives considered in earlier research. The simulation results also indicate that improper exclusion of LDVs can lead to severe bias in coefficient estimates. While no panacea, scholars should continue to confidently include LDVs as part of a robust estimation strategy.

Do coders of political texts incorporate prior beliefs about parties’ issue stances into their coding decisions? We report results from a coding experiment in which ten coders were each given 200 statements on immigration that were extracted from election manifestos. Party labels in these statements were randomly assigned (including a control category without party cues). Coders were more likely to code a statement as pro-immigration if it was attributed to the Greens and less likely choose the anti-immigration category if it was attributed to the populist radical right. No effect was found for mainstream parties of the center-left and center-right. The results also suggest that coders resort to party cues as heuristics when faced with ambiguous policy statements.

Fixed effects estimators are frequently used to limit selection bias. For example, it is well known that with panel data, fixed effects models eliminate time-invariant confounding, estimating an independent variable’s effect using only within-unit variation. When researchers interpret the results of fixed effects models, they should therefore consider hypothetical changes in the independent variable (counterfactuals) that could plausibly occur within units to avoid overstating the substantive importance of the variable’s effect. In this article, we replicate several recent studies which used fixed effects estimators to show how descriptions of the substantive significance of results can be improved by precisely characterizing the variation being studied and presenting plausible counterfactuals. We provide a checklist for the interpretation of fixed effects regression results to help avoid these interpretative pitfalls.

In this research note we replicate, update and expand innovative research by Sniderman et al. conducted in the Netherlands in the late 1990s, and ask whether the relative primacy of cultural compared with economic and safety threats in explaining ethnic prejudice remains true under markedly different national, economic and political contexts. Using two national British surveys conducted in 2011 and 2016, we examine the impact of threat on hostility toward three minority groups. Our results confirm the primacy of cultural threat as the strongest and most consistent predictor of hostility, while demonstrating the more context-specific effects of safety and economic concerns, with safety threats playing an overall more prominent role and increased economic concerns being related to less hostility post-Brexit.