Optimal policy during an epidemic includes depressing economic activity to slow down the outbreak. Sometimes, these decisions are left to local authorities (e.g. states). This creates an externality, as the outbreak does not respect states' boundaries. The externality directly exacerbates the outbreak. Indirectly, it creates a free-rider problem, because local policymakers pass the cost of fighting the outbreak on to other states. A standard system of distortionary taxes and lump-sum transfers can implement the optimal allocation, with higher tax rates required if states behave strategically. A strategic system of taxes and transfers, rewarding states which depress their economies more than average, improves the outcomes by creating a race-to-the-bottom type of response. In a symmetric equilibrium, the optimal tax rate is lower if states behave strategically.
Opublikowane | Published
Strategic inefficiencies and federal redistribution during uncoordinated response to pandemic waves | European Journal of Political Economy Przeczytaj streszczenie | Read abstract
W toku | Work in progress
The Fragmented United States of America: The impact of scattered lock-down policies on country-wide infections Przeczytaj streszczenie | Read abstract
Fragmented by policies, united by outcomes: This is the picture of the United States that emerges from our analysis of the spatial diffusion of Covid-19 and the scattered lock-down policies introduced by individual states. We first use spatial econometric techniques to document spillovers of new infections across county and state lines, as well as the impact of individual states lock-down policies on infections in neighboring states. We find evidence that new cases diffuse across county lines and that the diffusion across counties was affected by the closure policies of adjacent states. Spatial impulse response functions reveal that the diffusion across counties is persistent. We then develop a spatial version of the epidemiological SIR model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighboring states. We incorporate lock-down policies and calibrate the model to match both the cumulative and the new infections across the 48 contiguous U.S. states and DC. Our results suggest that lax policies in the most lenient states translate into millions of additional infections in the rest of the country. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run.