Unraveling the controversial effect of Covid-19 on college students' performance.

We disentangle the channels through which Covid-19 has affected the performance of university students by setting up an econometric strategy to identify separately changes in both teaching and evaluation modes, and the short and long term effects of mobility restrictions. We exploit full and detailed information from the administrative archives of one among the first universities to be shut down since the virus spread from Wuhan. The results help solving the inconsistencies in the literature by providing evidence of a composite picture where negative effects such as those caused by the sudden shift to remote learning and by the exposure to mobility restrictions, overlap to opposite effects due to a change in evaluation methods and home confinement during the exam's preparation. Such overlap of conflicting effects, weakening the signaling role of tertiary education, would add to the learning loss by further exacerbating future consequences on the "Covid" generation.

Beyond the Covid-19 pandemic: remote learning and education inequalities.

Is remote learning associated with education inequalities? We use PISA 2018 data from five European countries-France, Germany, Italy, Spain and the United Kingdom-to investigate whether education outcomes are related to the possession of the resources needed for distance learning. After controlling for a wide set of covariates, fixed effects, different specifications and testing the stability of coefficients, we find that remote learning is positively associated with average education outcomes, but also with strong and significant education inequalities. Our results show that negative gaps are larger where online schooling is more widespread, across countries, locations, and school types. More generally, remote learning inequalities appear to be associated with technological network externalities: they increase as digital education spreads. Policy makers must guarantee to all students and schools the possession of the resources needed for remote learning, but to reach this goal efficiently they must adapt their actions to the characteristics of countries, areas and school systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s10663-022-09556-7.

Working from home and income inequality: risks of a 'new normal' with COVID-19.

In the current context of the COVID-19 pandemic, working from home (WFH) became of great importance for a large share of employees since it represents the only option to both continue working and minimise the risk of virus exposure. Uncertainty about the duration of the pandemic and future contagion waves even led companies to view WFH as a 'new normal' way of working. Based on influence function regression methods, this paper explores the potential consequences in the labour income distribution related to a long-lasting increase in WFH feasibility among Italian employees. Results show that a positive shift in WFH feasibility would be associated with an increase in average labour income, but this potential benefit would not be equally distributed among employees. Specifically, an increase in the opportunity to WFH would favour male, older, high-educated, and high-paid employees. However, this 'forced innovation' would benefit more employees living in provinces have been more affected by the novel coronavirus. WFH thus risks exacerbating pre-existing inequalities in the labour market, especially if it will not be adequately regulated. As a consequence, this study suggests that policies aimed at alleviating inequality, like income support measures (in the short run) and human capital interventions (in the long run), should play a more important compensating role in the future.

Identifying policy challenges of COVID-19 in hardly reliable data and judging the success of lockdown measures.

Identifying structural breaks in the dynamics of COVID-19 contagion is crucial to promptly assess policies and evaluate the effectiveness of lockdown measures. However, official data record infections after a critical and unpredictable delay. Moreover, people react to the health risks of the virus and also anticipate lockdowns. All of this makes it complex to quickly and accurately detect changing patterns in the virus's infection dynamic. We propose a machine learning procedure to identify structural breaks in the time series of COVID-19 cases. We consider the case of Italy, an early-affected country that was unprepared for the situation, and detect the dates of structural breaks induced by three national lockdowns so as to evaluate their effects and identify some related policy issues. The strong but significantly delayed effect of the first lockdown suggests a relevant announcement effect. In contrast, the last lockdown had significantly less impact. The proposed methodology is robust as a real-time procedure for early detection of the structural breaks: the impact of the first two lockdowns could have been correctly identified just the day after they actually occurred.