Policy impact on the global COVID-19 pandemic and unemployment outcomes: A large-scale mixed frequency spatial approach

Current literature often examines the pandemic and its economic consequences in isolation, overlooking their dynamic interactions. The propagation of risk through an interconnected global network is also overlooked. We propose a novel Bayesian mixed-frequency spatial VAR (MF-SVAR) framework to analyse the impacts of containment policies on pandemic spread and unemployment, using weekly new case growth and monthly unemployment rate changes from 68 countries. Our model accounts for spatio-temporal dynamics and identifies significant spillover effects driven by geographical proximity and trade. We find that containment policies significantly reduce virus transmission but have only marginal short-term effects on unemployment. The impact of policies fluctuates weekly, suggesting that implementation timing may play an important role. Our results highlight the need for collaborative and coordinated global suppression efforts to control future pandemic crises, with key risk-vulnerable and risk-spreading countries playing a leading role.