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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.

This chapter reviews the growing body of research in economics which concentrates on the education gender gap and its evolution, over time and across countries. The survey first focuses on gender differentials in the historical period that roughly goes from 1850 to the 1940s and documents the deep determinants of the early phase of female education expansion, including preindustrial conditions, religion, and family and kinship patterns. Next, the survey describes the stylized facts of contemporaneous gender gaps in education, from the 1950s to the present day, accounting for several alternative measures of attainment and achievement and for geographic and temporal differentiations. The determinants of the gaps are then summarized, while keeping a strong emphasis on an historical perspective and disentangling factors related to the labor market, family formation, psychological elements, and societal cultural norms. A discussion follows of the implications of the education gender gap for multiple realms, from economic growth to family life, taking into account the potential for reverse causation. Special attention is devoted to the persistency of gender gaps in the STEM and economics fields.

In 2011, Italy introduced board gender quotas in listed companies. Comparing within firms before-after reform changes, we document that quotas are associated with a higher share of female board directors, with higher levels of education of board members and a lower share of elderly members. We then use the reform period as an instrument for the share of female directors and find no significant impact on firms’ performance. Interestingly, we find that the share of female directors is associated to a lower variability of stock market prices. We also run event studies on the stock price reaction to the introduction of gender quotas. A positive effect of the quota law on stock market returns emerges at the date of board’s election. Our results are consistent with gender quotas inducing a beneficial renovation of the board, which is positively received by the market.

We define as populist a party that champions short-term protection policies without regard for their long-term costs. First, we study the demand for populism: we analyze the drivers of the populist vote using individual level data from multiple waves of surveys in Europe. Individual voting preferences are in uenced directly by different measures of economic insecurity and by the decline in trust in traditional parties. However, economic shocks that undermine voters' security and trust in parties also discourage voter turnout, thus mitigating the estimated demand of populism when ignoring this turnout selection. Economic insecurity affects intentions to vote for populist parties and turnout incentives also indirectly because it causes trust in parties to fall. Second, we study the supply side: we find that populist parties are more likely to appear when the drivers of demand for populism accumulate, and more so in countries with weak checks and balances and with higher political fragmentation. The non-populist parties' policy response is to reduce the distance of their platform from that of new populist entrants, thereby magnifying the aggregate supply of populist policies.

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.