Assessing the economic effects of lockdowns in Italy: a dynamic Input-Output approach

The unprecedented lockdown measures implemented by many countries in the wake of the COVID-19 pandemic have created a need for tools to assess their economic costs. For this purpose, we present a novel dynamic input-output modelling framework which we apply to an estimation of the economic impact of lockdowns in Italy. Lockdown measures are treated as shocks to available labor supply, being calibrated on regional and sectoral employment data coupled with the prescriptions of the prime ministerial decrees mandating the closure of specific industries. Using input-output tables for the Italian regions, we estimate the model on data from the first lockdown during spring 2020 and then simulate it to assess the regional and sectoral impacts. We find that, despite the simplicity of our framework, the model is able to reproduce the observed dynamics during the lockdown-induced downturn and subsequent recovery fairly closely for most sectors. This ability to match the empirical data is also confirmed by a small out-of-sample forecasting exercise. We subsequently also simulate the second set of 'softer' lockdown measures implemented during autumn and winter of 2020 in order to evaluate their impact and compare them to the first, 'hard' lockdown. Overall, we believe the simplicity and parsimony of our framework make it suitable for providing quick and reasonably accurate evaluations of the economic effects of different lockdown measures.