Recently, there is a growing interest in understanding how individuals adapt to changing climate conditions and climate-induced extreme weather events. An underexplored question is whether and how climate-related natural hazards affect household saving behavior. For this purpose, we exploit a natural experiment stemming from the European Flood of August 2002. Combining micro data with geo-coded flood maps allows us to analyze the causal impact of flood exposure on household savings within a differences-in-differences setting. We find that flood exposure depresses household saving behavior in the medium run. The most likely explanation is moral hazard induced by massive government support for affected households.

One of the primary objectives of protests and demonstrations is to bring social, political, or economic issues to the attention of politicians and the wider population. While protests can have a mobilizing and persuading effect, they may reduce support for their cause if they are perceived as a threat to public order. In this study, we look at how local or spontaneously organised xenophobic demonstrations affect concerns about hostility towards foreigners and worries about immigration among natives in Germany. We use a regression discontinuity design to compare the attitudes of individuals interviewed in the days immediately before a large far-right demonstration and individuals interviewed in the days immediately after that demonstration. Our results show that large right-wing demonstrations lead to a substantial increase in worries about hostility towards foreigners of 13.7% of a standard deviation. In contrast, worries about immigration are not affected by the demonstrations, indicating that the protesters are not successful in swaying public opinion in their favour. In the heterogeneity analyses, we uncover some polarisation in the population: While worries about hostility against foreigners increase and worries about immigration decrease in left-leaning regions, both types of worries increase in districts where centre-right parties are more successful. Lastly, we also show that people become more politically interested in response to protests, mainly benefiting left-wing parties, and are more likely to wish to donate money to help refugees.

Behavioral economics has so far largely avoided discussing the psychological origins of preferences, as well as their relation to needs. This has not only restricted interdisciplinary exchange, but also significantly limits the predictive capabilities of models. For example, the revealed preference approach can only reliably predict repeating choices, while needing large amounts of observations for calibration. In this paper, I show how unifying preferences with the psychological concept of needs strengthens economic models, by developing a decision-making framework for well-being assessment and choice prediction. To present the direct merit of this approach, I show how this framework yields a systematic approximation scheme, which is able to solve limitations of current approaches by describing new alternatives, non-repeating choices, or otherwise unobservable desires. Meanwhile, the approximation scheme requires less observations on an individual level than current approaches. I achieve this by constructing a hierarchical dependency between human motivations and preferences through the language of needs. I show the basic feasibility of the approximation scheme through simulations on random populations. In practice, the framework is applicable in situations where individuals exert choices only once and measuring preferences is expensive, like evaluating policy proposals or predicting decisions under technological change

Current time allocation and household production models face three major weaknesses: First, they only describe the average time allocation. Thus, information about the order of activities is lost. Therefore, it is impossible to describe the influence of activities on later ones. Such interactions are likely pervasive, and can significantly alter behavior. Second, they are unable to describe the effort allocation of individuals, although effort influences one’s time allocation. Thereby, they are either unable or very limited in describing labor productivity or multitasking although individuals frequently multitask. Through the omission of interactions and effort allocation, current models yield biased descriptions of e.g. price and time elasticities. Third, they require strong assumptions, such as perfect foresight or periodic environments, and thus cannot describe behavior in unpredictable environments, like reactions to external shocks. In this paper, I provide a remedy for these shortcomings by developing a dynamical model of procedurally rational decision making. The basic idea of the model is a feedback loop between experienced utility, decision utility, and activities. In applications of the model, I show how introducing a work-leisure interaction and multitasking significantly changes elasticities and how nonmarginal external shocks cause short-term demand surges, none of which can be described by current time allocation models.