The Effect of Large-Scale Anti-Contagion Policies on the Coronavirus (COVID-19) Pandemic

Solomon Hsiang; Daniel Allen; Sebastien Annan-Phan; Kendon Bell; Ian Bolliger; Trinetta Chong; Hannah Druckenmiller; Andrew Hultgren; Luna Yue Huang; Emma Krasovich; Peiley Lau; Jaecheol Lee; Esther Rolf; Jeanette Tseng; Tiffany Wu

Lastly, we include a vector of P c country-specific policy variables for each location and day. These policy variables take on values between zero and one (inclusive) where zero indicates no policy action and one indicates a policy is fully enacted. In cases where a policy variable captures the effects of collections of policies (e.g. museum closures and library closures), a binary policy variable is computed for each, then they are averaged, so the coefficient on these variables are interpreted as the effect if all policies in the collection are fully enacted. In some cases (for Italy and the US), policy data is available at a more spatially granular level than infection data (e.g. city policies and state-level infections in the US). In these cases, we code binary policy variables at the more granular level and use population-weights to aggregate them to the level of the infection data. Thus, policy variables may take on continuous values between zero and one, with a value of one indicating that the policy is fully enacted for the entire population.