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ABSTRACT Using longitudinal data from the Generations and Gender Surveys (for Bulgaria, France, and Italy), we study the determinants of predicted happiness associated with childbearing and then its role for explaining realized childbearing. “Expected happiness”, as declared by individuals, is indeed a powerful predictor of their fertility behavior. Those who expect to be happier from childbearing indeed have a much higher probability of having a child within the following three years.  But the results also show strong gender and country differences in the level of expected happiness and its effect on fertility behavior. For example, in Italy we see that individuals tend to have a high expected happiness from childbearing, yet realized fertility is low. What separates this study from recent papers considering happiness and fertility is that in the GGS the question about happiness is specific with respect to childbearing. Previous studies tend to focus on overall happiness, which has the drawback of, first, having relatively low variation in responses, and second, it refers to the general level of happiness, which incorporates a whole range of factors, not just children.

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.

We study a setting where anti-discrimination legislation gives rise to adverse selection in the labor market. Firms rely on nonlinear compensation contracts to screen workers who differ in their family/career orientation. This results in a la- bor market equilibrium where career-oriented workers are offered an inefficiently low duration of parental leave. In addition, family-oriented workers are offered lower wages as compared to their equally skilled career-oriented counterparts. We demonstrate the usefulness of mandatory parental leave rules in mitigating the distortion in the labor market and derive conditions under which a Pareto im- provement is possible. We also characterize the optimal parental leave policy and highlight the possibility for parental leave legislation to eliminate the wage penalty of family-oriented workers by supporting pooling employment contracts.

The generous Nordic model of welfare is commonly viewed as an exceptional success, in terms of both equality and economic growth. However, it recently became evident that subgroups of the population with weak labour market attachment and high welfare dependency, such as lone mothers, were vastly overrepresented among the poor. This prompted a workfare reform of the Norwegian welfare system for lone mothers: activity requirements were brought in, time limits imposed and benefit levels raised. To evaluate the reform we introduce an estimator that, unlike the much used difference-in-difference approach, accounts for the fact that policy changes are typically phased in gradually rather than coming into full effect immediately. The results were striking: the workfare reform has not only led to increased earnings and educational attainment but also reduced poverty.

The aim of this paper is to assess the quality of the ranking of institutions obtained with multilevel techniques in presence of different model misspecifications and data structures. Through a Monte Carlo simulation study, we find that it is quite hard to obtain a reliable ranking of the whole effectiveness distribution while, under various experimental conditions, it is possible to identify institutions with extreme performances. Ranking quality increases with increasing intra class correlation coefficient and/or overall sample size. Furthermore, multilevel models where the between and within cluster components of first-level covariates are distinguished perform significantly better than both multilevel models where the two effects are set to be equal and the fixed effect models.

ABSTRACT It is well established that the departure from the parental home of young Italian adults occurs at a particularly late age, especially when compared to northern European countries. Moreover, in Italy a large gap exists between young people’s aspirations and their subsequent realization. This study aims to explore the factors favouring or hampering the successful achievement of residential independence from the family of origin. Using data from the longitudinal surveys “Family and Social Subjects”, carried out by the Italian National Institute of Statistics (Istat) in 2003 and 2007, we analyze leaving home as a mid-term decision-making process. Our results provide empirical evidence that the inability to find a stable job reduces young adults’ autonomy. Net of employment status, attitudes and social norms also have an important effect on the intention to leave the family home. The socio-cultural status of the family of origin specifically favours the successful realization of the behaviour. Notably, this effect is gender-specific, with women more influenced by the mother and men by the father.

We empirically assess the effect of historical slavery on the African American family structure. Our hypothesis is that female single headship among blacks is more likely to emerge in association not with slavery per se, but with slavery in sugar plantations, since the extreme demographic and social conditions prevailing in the latter have persistently affected family formation patterns. By exploiting the exogenous variation in sugar suitability, we establish the following. In 1850, sugar suitability is indeed associated with extreme demographic outcomes within the slave population. Over the period 1880-1940, higher sugar suitability determines a higher likelihood of single female headship. The effect is driven by blacks and starts fading in 1920 in connection with the Great Migration. OLS estimates are complemented with a matching estimator and a fuzzy RDD. Over a linked sample between 1880 and 1930, we identify an even stronger intergenerational legacy of sugar planting for migrants. By 1990, the effect of sugar is replaced by that of slavery and the black share, consistent with the spread of its influence through migration and intermarriage, and black incarceration emerges as a powerful mediator. By matching slaves’ ethnic origins with ethnographic data we rule out any influence of African cultural traditions.

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.