  We develop a multiple-events model and exploit within and between country
variation in the timing, type and level of intensity of various public policies
to study their dynamic effects on the daily incidence of COVID-19 and on
population mobility patterns across 135 countries. We remove concurrent policy
bias by taking into account the contemporaneous presence of multiple
interventions. The main result of the paper is that cancelling public events
and imposing restrictions on private gatherings followed by school closures
have quantitatively the most pronounced effects on reducing the daily incidence
of COVID-19. They are followed by workplace as well as stay-at-home
requirements, whose statistical significance and levels of effect are not as
pronounced. Instead, we find no effects for international travel controls,
public transport closures and restrictions on movements across cities and
regions. We establish that these findings are mediated by their effect on
population mobility patterns in a manner consistent with time-use and
epidemiological factors.
