Secondary Outcomes (explanation)
Our secondary outcomes fall into the five aforementioned categories. In the following, we describe every category in more detail:
Revealed attitudes: To move beyond stated attitudes, we give subjects the opportunity to donate to charitable organizations, one for each political dimension (redistribution, globalization, immigration, and climate change). For the amounts donated we also calculate the above polarization measures and conduct analyses analogously to our analysis of the indices outlined above, i.e. test for the 'direct effect' and the 'cross effect'. See our pre-analysis plan for more details on the non-profit organizations and our analyses.
First stage: Our experiment builds on the following first-stage relationship: exposing individuals to a news feed highlighting one of our political dimensions of interest should increase the salience of these dimensions. To assess the existence of this first-stage relationship empirically, we ask participants which of the four political dimensions (redistribution, immigration, globalization, or climate change) they consider to be the most important problem Germany is currently facing. In our analysis, we then test whether individuals in either of our treatment groups are more likely to state the political dimension highlighted in this treatment group as an issue of primary political importance.
Mechanism: We also collect data on the underlying mechanism, i.e. that a shift in the relative salience of political dimensions induces a change in which social groups individuals identify with. We ask all participants whether they consider themselves to be a supporter, an opponent or neither of more redistribution, more immigration, more globalization, and more climate protection. As an example, we consider the case of the redistribution dimension; our approach for the other (sub-) categories is identical. We test whether individuals in the redistribution group are more likely to state that they are either a supporter or an opponent of more redistribution than those in the control group ('direct effect'). Furthermore, we test whether individuals in the redistribution group are less likely to state that they are a supporter or an opponent of more openness than those in the control group ('cross effect').
Affective polarization: To measure affective polarization, we implement a ‘feeling thermometer’ to capture the extent of affective polarization as proposed by Iyengar et al. (2019). We ask participants to rate both supporters and opponents of each of the four dimensions (redistribution, globalization, immigration, and climate change) on a 101-point scale from -50 (cold) to +50 degrees (warm). The measure of affective polarization for individual i is then computed as the difference between the score an individual gives to the group she identifies with herself (in-group) and the score she gives to the out-group. Again, we test for the existence of the 'direct effect' and the 'cross effect'.
1. We may also consider polarization measures based on Esteban and Ray (1994).
2. We also elicit individuals’ partisan political preferences as well as past and future intended voting behavior. We use participants' answers to test whether highlighting the salience of one political dimension induces individuals to vote for a party catering to the highlighted dimension. Again, we consider both the 'direct effect' as well as the 'cross effect'.
3. Finally, we also ask respondents to classify themselves with respect to the traditional political terminology on a political spectrum ranging from very left-wing to very right-wing.
We provide more details on how we construct and use our secondary outcomes in our pre-analysis plan.