Describe how you pre-defined primary and secondary outcome measures and how you assessed these measures.

To what extent do economic concerns drive anti‐migrant attitudes? Key theoretical arguments extract two central motives: increased labour market competition and the fiscal burden linked to the influx of migrants. This article provides new evidence regarding the impact of material self‐interest on attitudes towards immigrants. It reports the results of a survey experiment embedded in representative surveys in 15 European countries before and after the European refugee crisis in 2014. As anticipated by the fiscal burden argument, it is found that rich natives prefer highly skilled over low‐skilled migration more than low‐income respondents do. Moreover, the study shows that these tax concerns among the wealthy are stronger if fiscal exposure to migration is high. No support is found for the labour market competition argument predicting that natives will be most opposed to migrants with similar skills. The results suggest that highly skilled migrants are preferred over low‐skilled migrants irrespective of natives’ skill levels.

Democratic accountability is characterized as weak in parliamentary systems where voters cannot choose their government directly. We argue that coalition signals about desirable and undesirable coalitions that might be formed after the election help to provide this essential aspect of democratic government. We propose a simple model that identifies the effect of coalition signals on individual vote decisions. Based on survey experiments in two different countries we show how coalition signals change the relative weight of voters' party and coalition considerations. Coalition signals increase the importance of coalition considerations and, at the same time, decrease the importance of party considerations in voters' decision calculus, leading some voters to change their vote intention.

Digital contact tracing apps have been introduced globally as an instrument to contain the COVID-19 pandemic. Yet, privacy by design impedes both the evaluation of these tools and the deployment of evidence-based interventions to stimulate uptake. We combine an online panel survey with mobile tracking data to measure the actual usage of Germany's official contact tracing app and reveal increased uptake rates among vulnerable groups, but lower rates among those with frequent social contact. Using a randomized intervention, we show that informative and motivational video messages have very limited effect on uptake. However, findings from a second intervention suggest that even small monetary incentives can strongly increase uptake and help make digital contact tracing a more effective tool.

We offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multiparty setting.