The impact of confounders, spillovers and interactions on social distancing policy effects estimates

Social distancing policies have been widely used to curb the spread of infectious diseases such as COVID-19, but assessing their effectiveness is challenging. This study shows that widely-used methods to estimate the effects of such policies, like Two-way Fixed Effects and Difference-in-Differences, are highly sensitive to accounting, or failing to account, for the simultaneous adoption of policies and the presence of spillovers across geographies stemming from human movement. By estimating a series of nonparametric models on fine-grained mobility, epidemiological, and policy data from Mexico during the COVID-19 pandemic, this research shows that failing to consider confounders, interactions, and spillovers can change the magnitude and the sign of estimated policy effects, hampering the design of optimal public policies.