We build a novel interregional computational input–output model to assess the economic impact of lockdowns in Italy. Lockdowns are modeled as shocks to labor supply, calibrated on regional and sectoral employment data coupled with the prescriptions of government decrees. When estimated on data from the first lockdown, our model closely reproduces the observed economic dynamics during spring 2020. We also show that the model delivers a good out-of-sample performance during fall and winter 2020 and demonstrate that it can be used to analyze counterfactual scenarios.
Users may download and/or print one copy to facilitate their private study or for non-commercial research and may forward the link to others for similar purposes. Users may not engage in further distribution of this material or use it for any profit-making activities or any other form of commercial gain.
We employ a new macroeconomic–epidemiological agent-based model to evaluate the “lives vs livelihoods” trade-off brought to the fore by coronavirus disease (Covid-19). An infectious disease spreads across the network of agents’ social and economic contacts and feeds back on the economic dimension of the model through various channels such as employment and consumption demand. Under a lockdown (LD) scenario, the model is able to closely reproduce the epidemiological dynamics of the first wave of the Covid-19 epidemic in the Lombardy region of Italy. We find that, in the presence of non-pharmaceutical interventions, there is no trade-off between lives and macroeconomic losses as a stricter LD eventually leads to superior outcomes along both dimensions. We also evaluate the efficacy of various macroeconomic stabilization policies designed to counteract the downturn generated by the epidemic and LD measures. In an agent-based setting we gain additional insights on the way in which such policies impact not only on gross domestic product but also, for instance, on firms’ defaults and relative prices. Liquidity support for firms, a short-time working scheme with compensation for workers, and direct transfer to households stand out as the most effective policy tools.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.