Author: 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
Title: The Effect of Large-Scale Anti-Contagion Policies on the Coronavirus (COVID-19) Pandemic Document date: 2020_3_27
ID: gtfx5cp4_50
Snippet: where θ 0 is the average growth rate absent policy, policy t is a binary variable describing whether a policy is deployed at time t, and θ is the average effect of the policy on growth rate g. t is a mean-zero disturbance term that captures inter-period changes not described by policy t . Using this approach, infections each day are treated as the initial condition for integrating Equation 4 through to the following day. We compute the first di.....
Document: where θ 0 is the average growth rate absent policy, policy t is a binary variable describing whether a policy is deployed at time t, and θ is the average effect of the policy on growth rate g. t is a mean-zero disturbance term that captures inter-period changes not described by policy t . Using this approach, infections each day are treated as the initial condition for integrating Equation 4 through to the following day. We compute the first differences log(I t ) − log(I t−1 ) using active infections where they are available, otherwise we use cumulative infections, noting that they are almost identical during this early period (except in China, where we use active infections). We then match these data to policy variables that we construct using the novel data sets we assemble and apply a reduced-form approach to estimate a version of Equation 6, although the actual expression has additional terms detailed below.
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