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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.

Students' experiences at university prepare them for a future in which they are expected to engage in life-long learning. Self-efficacy theory suggests that a persons beliefs in their capacity to learn will influence their participation in learning. This paper describes development of a new scale to measure self-efficacy for learning (SEL) among university students, designed to be appropriate for both campus-based and online learning, and for administration in a battery of tests on student development. Undergraduate students (n = 265) in a business school in Milan and a department of psychology in Rome participated in the final study. Beginning with a random sample of 200 participants, item response theory and exploratory factor analysis with LISREL were used to identify a 10 item scale to measure SEL. The scales properties were confirmed in a second random sample of 200 participants, using confirmatory factor analysis in LISREL. Correlation with expected grades was, consistent with earlier studies, moderately small (.22), but statistically significant.

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.

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.

Understanding the impact of the COVID-19 pandemic on education requires a solid grasp of the impact of student autonomy on learning. In this paper, we estimate the effect of an increased autonomy policy for higher-performing students on short- and longer-term school outcomes. We exploit an institutional setting with high demand for autonomy in randomly formed classrooms. Identification comes from a natural experiment that allowed higher-achieving students to miss 30 percent more classes without penalty. Using a difference-in-difference-in-differences approach, we find that allowing higher-achieving students to skip class more often improves their performance in high-stakes subjects and increases their university admission outcomes. Higher-achieving students in more academically diverse classrooms exerted more autonomy when allowed to.