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.

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.