  Ridesourcing is popular in many cities. Despite its theoretical benefits, a
large body of studies have claimed that ridesourcing also brings (negative)
externalities (e.g., inducing trips and aggravating traffic congestion).
Therefore, many cities are planning to enact or have already enacted policies
to regulate its use. However, these policies' effectiveness or impact on
ridesourcing demand and traffic congestion is uncertain. To this end, this
study applies difference-in-differences (i.e., a regression-based causal
inference approach) to empirically evaluate the effects of the congestion tax
policy on ridesourcing demand and traffic congestion in Chicago. It shows that
this congestion tax policy significantly curtails overall ridesourcing demand
but marginally alleviates traffic congestion. The results are robust to the
choice of time windows and data sets, additional control variables, alternative
model specifications, alternative control groups, and alternative modeling
approaches (i.e., regression discontinuity in time). Moreover, considerable
heterogeneity exists. For example, the policy notably reduces ridesourcing
demand with short travel distances, but such an impact is gradually attenuated
as the distance increases.
