Publications
Restrictions to Civil Liberties in a Pandemic and Satisfaction with Democracywith Daniel Graeber and Panu Poutvaara
European Journal of Political Economy
[Paper] [Working Paper]
In times of crises, democracies face the challenge of balancing effective interventions with civil liberties. This study examines Germany’s response during the early stages of the COVID-19 pandemic, focusing on the interplay between civil liberties and public health goals. Using state-level variation in mobility restrictions, we employ a difference-in-differences design to show that stay-at-home orders notably increased satisfaction with democracy and shifted political support towards centrist parties. Individuals who were exposed to the authoritarian regime of the German Democratic Republic show the largest reactions, underscoring the endogeneity of preferences for state intervention.
European Journal of Political Economy
[Paper] [Working Paper]
In times of crises, democracies face the challenge of balancing effective interventions with civil liberties. This study examines Germany’s response during the early stages of the COVID-19 pandemic, focusing on the interplay between civil liberties and public health goals. Using state-level variation in mobility restrictions, we employ a difference-in-differences design to show that stay-at-home orders notably increased satisfaction with democracy and shifted political support towards centrist parties. Individuals who were exposed to the authoritarian regime of the German Democratic Republic show the largest reactions, underscoring the endogeneity of preferences for state intervention.
Working Papers
Stock Market Participation, Work from Home, and Inequalitywith Lukas Menkhoff and Carsten Schröder
[Working Paper]
Stock market participation among working household heads jumped upwards in the year 2020, in Germany by about 25%. A major cause is the required use of work from home (WfH). We show this by repeating a benchmark study with demanding data requests and adding WfH to the explanatory variables. Moreover, we implement an instrumental variables estimation based on industry-specific levels of WfH-capacity. The transmission channels seem to work via increased available time and time flexibility. Moreover, we show that WfH makes the stock market accessible to a broader population, including lower income groups, which may contribute to lower income inequality.
[Working Paper]
Stock market participation among working household heads jumped upwards in the year 2020, in Germany by about 25%. A major cause is the required use of work from home (WfH). We show this by repeating a benchmark study with demanding data requests and adding WfH to the explanatory variables. Moreover, we implement an instrumental variables estimation based on industry-specific levels of WfH-capacity. The transmission channels seem to work via increased available time and time flexibility. Moreover, we show that WfH makes the stock market accessible to a broader population, including lower income groups, which may contribute to lower income inequality.
Work in Progress
Risk Aversion in the Shadow of Terror
with Daniel Graeber and Neil Murray
We show that terror attacks influence the risk preferences of individuals beyond the immediate victims, potentially explaining the significant economic losses associated with such events, despite limited physical damage. Combining data from the Global Terrorism Database and the German Socio-Economic Panel, we employ a difference-in-differences approach to compare individuals living within a 25-kilometer radius of an attack with those outside it. Our findings reveal that terror attacks lead to an immediate and significant reduction in risk tolerance among exposed individuals. The magnitude of this effect varies with the sentiment and reach of attack-related news coverage. We also observe changes in risky behaviors, such as decreased likelihood of self-employment or stock ownership, and demonstrate that reduced happiness mediates the link between terror exposure and shifts in risk preferences. These results suggest that changes in risk attitudes may help explain the broader economic costs of terrorism.
with Daniel Graeber and Neil Murray
We show that terror attacks influence the risk preferences of individuals beyond the immediate victims, potentially explaining the significant economic losses associated with such events, despite limited physical damage. Combining data from the Global Terrorism Database and the German Socio-Economic Panel, we employ a difference-in-differences approach to compare individuals living within a 25-kilometer radius of an attack with those outside it. Our findings reveal that terror attacks lead to an immediate and significant reduction in risk tolerance among exposed individuals. The magnitude of this effect varies with the sentiment and reach of attack-related news coverage. We also observe changes in risky behaviors, such as decreased likelihood of self-employment or stock ownership, and demonstrate that reduced happiness mediates the link between terror exposure and shifts in risk preferences. These results suggest that changes in risk attitudes may help explain the broader economic costs of terrorism.
Random Forests for Labor Market Analysis: Balancing Precision and Interpretabilitywith Daniel Graeber, Carsten Schröder and Sabine Zinn
Machine learning methods are becoming increasingly popular due to their predictive power. However, the results are sometimes not as straight-forward to interpret compared to classic regression models, for example. In this paper, we address this trade-off by comparing the predictive performance of random forests and logistic regressions to analyze labor market vulnerabilities during the COVID-19 pandemic, and a global surrogate model to enhance our understanding of the complex dynamics. Our study shows that especially in the presence of non-linearities and feature interactions, random forests outperform regressions both in predictive accuracy and interpretability, yielding policy-relevant insights on vulnerable groups affected by labor market disruptions.
Machine learning methods are becoming increasingly popular due to their predictive power. However, the results are sometimes not as straight-forward to interpret compared to classic regression models, for example. In this paper, we address this trade-off by comparing the predictive performance of random forests and logistic regressions to analyze labor market vulnerabilities during the COVID-19 pandemic, and a global surrogate model to enhance our understanding of the complex dynamics. Our study shows that especially in the presence of non-linearities and feature interactions, random forests outperform regressions both in predictive accuracy and interpretability, yielding policy-relevant insights on vulnerable groups affected by labor market disruptions.