News & Events

For a sample of Central and Eastern European countries, characterized by historically high female labor force participation and currently low fertility rates, we analyze whether fathers’ increased involvement in the family (housework and childcare) has the potential of increasing both fertility and maternal employment. Using two waves of the Generations and Gender Survey, we show that a higher fathers’ involvement in the family increases the subsequent likelihood that the mother has a second child and works full-time. Men’s fertility and work decisions are instead unrelated to mothers’ housework and childcare. We also show that fathers’ involvement in housework plays a more important role than involvement in childcare. The role of fathers’ involvement in housework is confirmed when we consider women who initially wanted or intended to have a child, women whose partner also wanted a child or women who intended to continue working.

We theoretically show that when mothers need to buy childcare services not only if they work but also if they want to search actively for a job, a reduction in the price of childcare will increase their likelihood of searching but may decrease their willingness to accept a job offer and therefore lower employment. We test these predictions empirically by means of a Regression Discontinuity design and find that the introduction in Italy of pre-kindergarten, a much cheaper alternative to day care for 2-year-old children, increased both participation in the labour market and employment of mothers of eligible children. This effect was driven largely by a significant decrease in the stated reservation wage. For a full evaluation of the policy we finally provide evidence that pre-kindergarten did not affect children’s cognitive development as measured at second grade.

This paper aims to investigate whether friends’ and peers’ behavior influence and individual’s entry into marriage and parenthood during the transition to adulthood of young, U.S. adults. After first studying entry into marriage and parenthood as two independent events, we then examine them as interrelated processes, thereby considering them as two joint outcomes of an individual’s unique, underlying family-formation strategy. Using the National Longitudinal Study of Adolescent Health, we engage in a series of discrete time event history models to test whether the larger the number of friends and peers who get married (or have a child), the sooner the individual gets married (or has a child). Results show strong cross-friend effects on entry into parenthood, whereas entry into marriage is only affected by peer effects. Estimates of a multiprocess model show that cross-friend effects on entry into parenthood remain strongly significant even when we control for cross-process unobserved heterogeneity.

ABSTRACT The aim of this paper is to understand how traditional societies faced a period of general crises and more specifically, which behaviours were adopted to limit the increase of local socio-economic inequality. Thus, this paper focuses on a boundary area (the Geradadda) disputed by Milan and Venice that was constantly crossed and occupied by armies during the long period of the Italian Wars (1494-1559). Analysing the management of local finances, and specifically the local commons, it is possible to show the different ways in which these societies organized themselves and, generally, how economic growth occurred in the early modern period.

There is a growing concern that the widespread use of computers, mobile phones and other digital devices before bedtime disrupts our sleep with detrimental effects on our health and cognitive performance. High-speed Internet promotes the use of electronic devices, video games and Internet addiction (e.g., online games and cyberloafing). Exposure to artificial light from tablets and PCs can alterate individuals’ sleep patterns. However, there is little empirical evidence on the causal relationship between technology use near bedtime and sleep. This paper studies the causal effects of access to high-speed Internet on sleep. We first show that playing video games, using PC or smartphones, watching TV or movies are correlated with shorter sleep duration. Second, we exploit historical differences in pre-existing telephone infrastructure that affected the deployment of high-speed Internet across Germany (see Falck et al., 2014) to identify a source of plausibly exogenous variation in access to Broadband. Using this instrumental variable strategy, we find that DSL access reduces sleep duration and sleep satisfaction.

The mounting evidence on the demographics of COVID-19 fatalities points to an overrepresentation of minorities and an underrepresentation of women. Using individual-level, race-disaggregated, and georeferenced death data collected by the Cook County Medical Examiner, we jointly investigate the racial and gendered impact of COVID-19, its timing, and its determinants. Through an event study approach we establish that Blacks individuals are affected earlier and more harshly and that the effect is driven by Black women. Rather than comorbidity or aging, the Black female bias is associated with poverty and channeled by occupational segregation in the health care and transportation sectors and by commuting on public transport. Living arrangements and lack of health insurance are instead found uninfluential. The Black female bias is spatially concentrated in neighborhoods that were subject to historical redlining.

Discussion on the disproportionate impact of COVID-19 on African Americans has been at center stage since the outbreak of the epidemic in the United States. To present day, however, lack of race-disaggregated individual data has prevented a rigorous assessment of the extent of this phenomenon and the reasons why blacks may be particularly vulnerable to the disease. Using individual and georeferenced death data collected daily by the Cook County Medical Examiner, we provide first evidence that race does affect COVID-19 outcomes. The data confirm that in Cook County blacks are overrepresented in terms of COVID-19 related deaths since—as of June 16, 2020—they constitute 35 percent of the dead, so that they are dying at a rate 1.3 times higher than their population share. Furthermore, by combining the spatial distribution of mortality with the 1930s redlining maps for the Chicago area, we obtain a block group level panel dataset of weekly deaths over the period January 1, 2020-June 16, 2020, over which we establish that, after the outbreak of the epidemic, historically lower-graded neighborhoods display a sharper increase in mortality, driven by blacks, while no pretreatment differences are detected. Thus, we uncover a persistence influence of the racial segregation induced by the discriminatory lending practices of the 1930s, by way of a diminished resilience of the black population to the shock represented by the COVID-19 outbreak. A heterogeneity analysis reveals that the main channels of transmission are socioeconomic status and household composition, whose influence is magnified in combination with a higher black share.

In this article we compare two techniques that are widely used in the analysis of life course trajectories, latent class analysis (LCA) and sequence analysis (SA). In particular, we focus on the use of these techniques as devices to obtain classes of individual life course trajectories. We first compare the consistency of the classification obtained via the two techniques using an actual dataset on the life course trajectories of young adults. Then, we adopt a simulation approach to measure the ability of these two methods to correctly classify groups of life course trajectories when specific forms of "random" variability are introduced within pre-specified classes in an artificial dataset. In order to do so, we introduce simulation operators that have a life course and/or observational meaning. Our results contribute on the one hand to outline the usefulness and robustness of findings based on the classification of life course trajectories through LCA and SA, on the other hand to illuminate the potential pitfalls of actual applications of these techniques.

This article provides a picture of long-term developments in the relationship between population and resources in Northern Italy that takes fully into account climate. It analyzes both the slow underlying development of climatic conditions over the centuries  (in the theoretical framework of the Little Ice Age) and the consequences of short-term periods of heightened instability. The most severe famines are shown to be events triggered by climatic and environmental factors operating at a time when the maximum carrying capacity of the system had been reached or, at least, when the population was exerting considerable pressure on the potential for food production. This is the case of the famine of the 1590s, the greatest demographic catastrophe of a non-epidemic nature to strike Northern Italy since the Black Death and up to the end of the eighteenth century. The article also analyzes long-term paths of agrarian innovation, suggesting that most (but not all) of this was consistent with Boserup's idea of chain-reactions of innovations induced by demographic pressure. These processes, though, were too slow to compensate for a rapidly growing population. Finally, the article provides a periodization in which the period between the famine of the 1590s and the great plague pandemic of 1630 is shown to be the crucial turning point in how population dynamics, climate and agrarian innovation interacted.

We consider the case when it is of interest to study the different states experienced over time by a set of subjects, focusing on the resulting trajectories as a whole rather than on the occurrence ofspecific events. Such situation occurs commonly in a variety of settings, for example in social and biomedical studies. Model‐based approaches, such as multistate models or Hidden Markov models, are being used increasingly to analyze trajectories and to study their relationships with a set of explanatory variables. The different assumptions underlying different models typically make the comparison of their performances difficult. In this work we introduce a novel way to accomplish this task, based on microsimulation‐based predictions. We discuss some criteria to evaluate one model and/or to compare competing models with respect to their ability to generate trajectories similar to the observed ones.