Understanding behavioral aspects of collective decision-making is an important challenge for economics, and narratives are a crucial group-based mechanism that influences human decision-making. This paper introduces the Character-Role Narrative Framework as a tool to systematically analyze narratives, and applies it to study US climate change policy on Twitter over the 2010-2021 period. We build on the idea of the so-called drama triangle that suggests, within the context of a topic, the essence of a narrative is captured by its characters in one of three essential roles: hero, villain, and victim. We show how this intuitive framework can be easily integrated into an empirical pipeline and scaled up to large text corpora using supervised machine learning. In our application to US climate change policy narratives, we find strong changes in the frequency of simple and complex character-role narratives over time.

Does information influence policy support? We administer a large-scale representative survey with randomised video treatments to test how different policy frames affect citizens’ attitudes towards urban tolls in two large European metropolitan areas without tolls, Berlin-Brandenburg and Paris-Ile de France. Providing information on air pollution increases support by up to 11.4%p, information on climate change and time savings increase support by 7.1 and 6.5 %p, respectively. Treatment effects are stronger in the Paris region, where initial support is lower. We also investigate treatment effect heterogeneity across different socioeconomic characteristics as well as by prior beliefs held about the severity of environmental and traffic problems, and we find weak spillovers of our treatment on the support of other policies. Our findings imply that targeted communication of policy co-benefits can increase policy support across different population groups.

We present a theory linking political and social trust to explain trust erosion in modern societies. Individuals disagree on the seriousness of an externality problem, which leads to diverging policy opinions on how to solve it. As a result, disappointment with the policy rule enacted by the government breeds institutional distrust. Individuals who are more worried blame the government because the rule is too lenient. The less worried blame it because they find the rule too intrusive. Second, this heterogeneity in individuals' concern levels also fuels social distrust. The more individuals are worried, the more they distrust others who are not complying with the rules. Our experimental survey conducted in four European countries shows how these trust dynamics came to the surface during the Covid-19 pandemic. We also find that support for the welfare state erodes alongside sliding trust levels.