Researchers employed well-established, reduced-form econometric techniques—commonly used to evaluate the effects of events on economic growth rates—to measure the impact of large-scale anti-contagion policies on the growth rate of COVID-19 infections. This technique estimates the total magnitude of policy changes without requiring explicit prior information about fundamental epidemiological parameters, empirically recovering the collective influence of these factors directly from the data. The approach accounts for differences in baseline infection growth rates across subnational locations (affected by time-invariant characteristics like demographics and health systems), systematic temporal patterns unrelated to policy (such as workweek effects), systematic under-surveillance, and changes in diagnostic procedures.