  Many policies in the US are determined locally, e.g., at the county-level.
Local policy regimes provide flexibility between regions, but may become less
effective in the presence of geographic spillovers, where populations
circumvent local restrictions by traveling to less restricted regions nearby.
Due to the endogenous nature of policymaking, there have been few opportunities
to reliably estimate causal spillover effects or evaluate their impact on local
policies. In this work, we identify a novel setting and develop a suitable
methodology that allow us to make unconfounded estimates of spillover effects
of local policies. Focusing on California's Blueprint for a Safer Economy, we
leverage how county-level mobility restrictions were deterministically set by
public COVID-19 severity statistics, enabling a regression discontinuity design
framework to estimate spillovers between counties. We estimate these effects
using a mobility network with billions of timestamped edges and find
significant spillover movement, with larger effects in retail, eating places,
and gyms. Contrasting local and global policy regimes, our spillover estimates
suggest that county-level restrictions are only 54% as effective as statewide
restrictions at reducing mobility. However, an intermediate strategy of
macro-county restrictions -- where we optimize county partitions by solving a
minimum k-cut problem on a graph weighted by our spillover estimates -- can
recover over 90% of statewide mobility reductions, while maintaining
substantial flexibility between counties.
