JOB MARKET PAPER
Abstract
This paper studies whether low social mobility increases populist sentiment, and whether the relevant channel is pessimism about mobility rates or erosion of the belief that economic success reflects effort. I combine original survey and experimental data from 6,600 U.S. residents with local measures of intergenerational mobility and electoral outcomes. I first document that areas with lower social mobility exhibit higher populist attitudes and greater support for Donald Trump. Respondents in low-mobility areas also perceive national mobility to be lower. Moreover, LLM-based analysis of upward-mobility narratives shows that those who perceive mobility to be low are more likely to view upward mobility as driven by luck rather than effort. To distinguish between these channels, I field two pre-registered survey experiments. Shifting beliefs about the level of social mobility has no detectable effect on populist attitudes. By contrast, weakening beliefs that upward mobility is effort-based significantly increases populist attitudes, especially among men, non-college respondents, and individuals without personal upward-mobility experience. The results suggest that low social mobility fuels populism less through beliefs about how much opportunity exists than through beliefs about whether economic advancement is meritocratic.
PUBLICATIONS
with Daniel Graeber and Panu Poutvaara
European Journal of Political Economy
Abstract
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.
with Lukas Menkhoff and Carsten Schröder
International Review of Financial Analysis
Abstract
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 PAPERS
with Matilda Gettins
Abstract
Populist parties increasingly deploy narratives of social injustice to portray climate policy as elitist and unfair. This paper investigates how such narratives affect public attitudes toward populism and democratic institutions. We conduct a survey experiment with approximately 1,600 respondents in Germany, exposing participants to three common narratives about the distributional costs of climate policy. Our findings show that the narrative emphasizing disproportionate burdens on low-income households significantly increases climate-populist attitudes and reduces satisfaction with democracy. These effects are particularly pronounced among low-income, East German, and conservative voters. By contrast, the narrative that companies can circumvent the cost of climate action fosters climate populism among left-leaning individuals. The results suggest that the framing of climate policy distribution strongly shapes its political acceptance and vulnerability to populist mobilization.
with Daniel Graeber, Carsten Schröder and Sabine Zinn
Abstract
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.
WORK IN PROGRESS
[draft available upon request]
with Daniel Graeber and Neil Murray
Abstract
Economic literature documents that terrorist attacks generate economic effects that far exceed their immediate physical damage, yet the mechanisms behind this disparity remain poorly understood. Using data from the Global Terrorism Database combined with geocoded individual-level measures of risk preferences, we employ a difference-in-differences design comparing individuals living within and outside 25 kilometers of an attack. We find that terrorist attacks induce an immediate and significant decline in risk tolerance among nearby individuals. Leveraging machine learning to classify more than 60,000 news articles, we show that these effects vary systematically with media reporting: Effects are largest for negatively framed and high-coverage attacks. Consistent with changes in risk preferences, we document reductions in risky behaviors such as self-employment and stock market participation. Analysis of mechanisms further indicates that emotions partly explain the observed effects. These results imply that shifts in risk preferences—transmitted primarily through media exposure—contribute substantially to the broader economic costs of terrorism.