Replication Data for: How to Stop Contagion: Applying Network Science to Evaluate the Effectiveness of Covid-19 Vaccine Distribution Plans

President Trump's haphazard decision to delegate Covid-19 vaccine distribution to US states set up conditions for evaluating state-level vaccine prioritization policies using a quasi-experimental design. Despite agreement on the goal, state-formulated vaccine distribution plans diverged beyond initial priority groups: some prioritized based on mortality risks only (i.e., age), while others also included several high-exposure risk groups. After establishing that this divergence was driven by stochastic rather than systematic factors, I leverage it as an identification strategy to test a key insight from network theory: reducing contagion requires disabling the transmission potential of the most connected actors. Based on this, I argue that early prioritization of high-exposure risk groups, especially public-facing essential workers, led to a greater reduction in Covid-19 cases than prioritization based solely on mortality risks. Analysis of daily Covid-19 data in a matched sample of Oregon and California counties shows strong support for this hypothesis.