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

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

Electoral cycle bias in the media coverage of corruption news

We investigate the existence of an \u2018electoral cycle bias\u2019 in the timing of media coverage of news with high political salience. In particular, we analyze how the electoral cycles at the regional level influence the coverage of news about corruption scandals in the Italian Regional Health Systems by two important national newspapers having opposite ideology, La Repubblica (left-wing oriented) and Il Giornale (right-wing oriented). Our findings show that Il Giornalesignificantly increases the number of articles about corruption scandals involving left-wing politicians since eight weeks before the elections, while it reduces the number of those about episodes of corruption without any political connection. On the contrary, La Repubblica increases the number of articles about episodes involving right-wing politicians only between four to eight weeks before the elections and it decreases those about no political episodes just the week right before them

Life-course perspective on personality traits and fertility with sequence analysis

We investigate the link between personality traits (PTs) and fertility, accounting for the possible interplay with other key life course events. Using data from German Socio-Economic Panel survey, we build sequence-type representations of fertility, union and job careers between the ages of 20 and 40. We rely on multichannel sequence analysis (MSA) and on the Partitioning around Medoids algorithm to cluster individuals with similar experiences, and relate clusters to PTs via multinomial regression. We also develop a procedure to apply standard and MSA to truncated trajectories. This enables inclusion of individuals whose trajectories were otherwise observed for a limited age span, notably belonging to younger cohorts. We show that PTs relate to these (portions of) life-course trajectories, of which fertility is only one outcome

Revealing “Mafia Inc.”? Financial Crisis, Organized Crime, and the Birth of New Enterprises

We study the investment of organized crime in the legal economy. By using the shock induced on the Italian credit market by the 2007 subprime mortgage crisis, we document how provinces with a high organized crime presence have been impacted less by the crisis in terms of the establishment of new enterprises than provinces with a lower criminal infiltration. We provide evidence that the lower impact of the crisis is consistent with the presence of investments by organized crime in the legal economy. We corroborate this interpretation by comparing our results with the characterization made by the judicial authority of such investments and ruling out possible alternative explanations

Machine-learning techniques for family demography: an application of random forests to the analysis of divorce determinants in Germany

Demographers often analyze the determinants of life-course events with parametric regression-type approaches. Here, we present a class of nonparametric approaches, broadly defined as machine learning (ML) techniques, and discuss advantages and disadvantages of a popular type known as random forest. We argue that random forests can be useful either as a substitute, or a complement, to more standard parametric regression modeling. Our discussion of random forests is intuitive and we illustrate its implementation by analyzing the determinants of divorce with SOEP data for German women entered in a marriage or a cohabitation from 1984 to 2015. The algorithm is able to classify divorce determinants according to their importance, highlighting the most powerful ones, which in our data are partners' overall life satisfaction, their age, and also certain personality traits (i.e., extroversion of the partner and – though with less power – also women's conscientiousness, agreeableness and openness). We are also able to draw partial dependence plots for the main predictors of survival of the relationship

Is it just a matter of personality? On the role of well-being in childbearing behavior

This paper analyses the role played by individual subjective well-being (SWB) in childbearing behavior. We use the German Socio Economic Panel (GSOEP) survey, which contains repeated information about SWB, childbearing events and, importantly, also measures of respondents’ personality, to estimate the way SWB matters for having a(nother) child, con-trolling for personality traits (PTs). We find that SWB positively predicts childbearing for women and men, with the effect significant (and sizable) for both genders only for the second child. Furthermore, we assure that – although PTs are a strong component of SWB variability – the effect of SWB on fertility is not determined by PTs

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

Fuelling Organised Crime: The Mexican War on Drugs and Oil Theft

We show that the Mexican War on Drugs pushed drug cartels into large-scale oil theft. We propose a simple model in which government crackdowns on one criminal sector induce criminal organisations to invest in a new sector. When entering the new sector, challenger organisations with a residual share of the market in the traditional sector may leapfrog incumbent organisations. We bring the model to the data using detailed information on drug cartel presence, oil pipelines, and illegal oil taps across Mexican municipalities. In line with the model predictions, municipalities with oil pipelines witnessed a greater increase in cartel presence than municipalities without pipelines after the crackdown on drugs, and the effect is driven by challenger criminal groups. Within the subset of municipalities with oil pipelines, we observe more illegal oil taps where the political party in favour of anti-drug trafficking policy won local elections by a small margin. Due to specialisation in different criminal sectors, municipalities with pipelines did not witness a surge in violence, but they did experience a decline in socioeconomic condition