Suchergebnisse

In this work, we evaluate the exposure of Italian regions to the risk associated with the spread of COVID-19 through a two-step value chain approach. First, we estimate the degree of participation of Italian regions in a plurality of value chains linked to consumption, investment and exports. We distinguish between value chains aimed at satisfying essential needs and supply chains activated by needs characterized by a lower level of necessity in line with the restriction measures implemented by the Italian government. Second, we investigate the different levels of contagion risk associated with each value chain and the possibility of reducing it through remote working. An exercise on policy measures implemented by the Italian government during Fall 2020 completes the paper. We find that regions are affected differently by lockdown policies because of their high heterogeneity in the degree of embeddedness within different value chains and because their sectoral contributions to each of them. As a result, Italian regions are associated with very diverse potentials for mitigating contagion risk via remote working practices. Finally, we find evidence that economic and contagion risks positively correlate in non essential value chains, while they are negatively associated in the production of medium-necessity and essential goods and services. In turn, strong lockdowns induce substantially different trade-offs across regions, depending on how regions participate to value chains.

We build a novel computational input-output model to estimate the economic impact of lockdowns in Italy. The key advantage of our framework is to integrate the regional and sectoral dimensions of economic production in a very parsimonious numerical simulation framework. Lockdowns are treated as shocks to available labor supply and they are calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. We show that when estimated on data from the first "hard" lockdown, our model closely reproduces the observed economic dynamics during spring 2020. In addition, we show that the model delivers a good out-of-sample forecasting performance. We also analyze the effects of the second "mild" lockdown in fall of 2020 which delivered a much more moderate negative impact on production compared to both the spring 2020 lockdown and to a hypothetical second "hard" lockdown.

We build a novel computational input-output model to estimate the economic impact of lockdowns in Italy. The key advantage of our framework is to integrate the regional and sectoral dimensions of economic production in a very parsimonious numerical simulation framework. Lockdowns are treated as shocks to available labor supply and they are calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. We show that when estimated on data from the first "hard" lockdown, our model closely reproduces the observed economic dynamics during spring 2020. In addition, we show that the model delivers a good out-of-sample forecasting performance. We also analyze the effects of the second "mild" lockdown in fall of 2020 which delivered a much more moderate negative impact on production compared to both the spring 2020 lockdown and to a hypothetical second "hard" lockdown.