To vote, or not to vote? Electoral campaigns and the spread of COVID-19.

The possibility to run fair electoral campaigns is necessary for the legitimization of modern democracies through elections. Yet, during a pandemic, the risk that electoral campaigns would enhance the spread of the disease is substantive. In this paper, we estimate the causal impact of electoral campaigns on the spread of COVID-19. Exploiting plausibly exogenous variation in the schedule of local elections across Italy, we show that the electoral campaign preceding this latter led to a significant worsening of the epidemiological situation related to the disease. Our results strongly highlight the importance of undertaking stringent measures along the entire electoral process to minimize its epidemiological consequences.

What Tears Couples Apart: A Machine Learning Analysis of Union Dissolution in Germany.

This study contributes to the literature on union dissolution by adopting a machine learning (ML) approach, specifically Random Survival Forests (RSF). We used RSF to analyze data on 2,038 married or cohabiting couples who participated in the German Socio-Economic Panel Survey, and found that RSF had considerably better predictive accuracy than conventional regression models. The man's and the woman's life satisfaction and the woman's percentage of housework were the most important predictors of union dissolution; several other variables (e.g., woman's working hours, being married) also showed substantial predictive power. RSF was able to detect complex patterns of association, and some predictors examined in previous studies showed marginal or null predictive power. Finally, while we found that some personality traits were strongly predictive of union dissolution, no interactions between those traits were evident, possibly reflecting assortative mating by personality traits. From a methodological point of view, the study demonstrates the potential benefits of ML techniques for the analysis of union dissolution and for demographic research in general. Key features of ML include the ability to handle a large number of predictors, the automatic detection of nonlinearities and nonadditivities between predictors and the outcome, generally superior predictive accuracy, and robustness against multicollinearity.

Epidemics and trust: The case of the Spanish Flu.

Recent studies argue that major crises can have long-lasting effects on individual behavior. While most studies focused on natural disasters, we explore the consequences of the global pandemic caused by a lethal influenza virus in 1918-19: the so-called "Spanish Flu." This was by far the worst pandemic of modern history, causing up to 100 million deaths worldwide. Using information about attitudes of respondents to the General Social Survey, we find evidence that experiencing the pandemic likely had permanent consequences in terms of individuals' social trust. Our findings suggest that lower social trust was passed on to the descendants of the survivors of the Spanish Flu who migrated to the United States. As trust is a crucial factor for long-term economic development, our research offers a new angle from which to assess current health threats.

Trust and fertility in uncertain times.

Recent studies have shown higher uncertainty to be associated with fertility decline. This study considers the role of social trust as a coping mechanism when general uncertainty increases. We analyse the fertility data of Italian provinces from 2004 to 2013, thereby incorporating the period of economic recession, which unexpectedly and exogenously increased uncertainty across the population. We find a robust and significantly positive impact of social trust on fertility, which is stronger among younger age groups. Moreover, we find that the buffer effect of trust decreases with the level of public childcare provision, suggesting that low trust endowments may be counterbalanced through public policy.