Lorenz Meister

Populism, defined by a rhetoric opposing "the people" to "the elites", has reshaped political landscapes across Western democracies. This paper investigates how low social mobility fosters populist attitudes by shaping beliefs and narratives about economic opportunity and fairness. Using new survey and experimental data of 6,600 U.S. residents—including respondent-generated narratives of upward mobility—linked to local mobility measures and electoral outcomes, I document a robust negative relationship between local social mobility and both populist attitudes and support for Donald Trump. Individuals in low-mobility areas perceive national social mobility as lower, and those with such pessimistic beliefs assign a smaller role to effort in the upward mobility narratives they provide. Two survey experiments disentangle these mechanisms: shifting beliefs about mobility rates has no effect on populist attitudes, but weakening beliefs in effort-based mobility significantly increases them, particularly among men, individuals without a college degree, and those lacking personal upward-mobility experience. The findings suggest that limited social mobility fuels populism by eroding faith in meritocratic opportunity.

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.  

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.

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.

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.

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.