Ride-sharing and Crime: Evidence from U.S. Cities

• This paper studies the causal impact of ride-sharing services on crime across major U.S. cities. • We employ a staggered difference-in-differences framework that leverages variation in the timing of Uber and Lyft entry. • We use daily crime data for finer analysis of urban crime than prior studies using annual or monthly FBI data. • We find a stronger reduction in DUI, motor vehicle theft, and vandalism, however no consistent effects on assault or robbery. • Our findings show ride-sharing can serve as a public safety tool, especially for nighttime and location-specific crimes. This paper investigates the causal impact of ride-sharing services on crimes across major US cities using daily and monthly level crime data from the Crime Open Database. Employing a staggered difference-in-differences approach, we leverage the temporal variation in Uber and Lyft market entry to isolate their effects on criminal activity. Our analysis reveals a modest post-implementation decline in overall crimes, with more pronounced downward effects for motor vehicle theft, weapon law violations, vandalism, and DUI offenses, while assault and robbery rates show inconsistent patterns. We also find evidence that ride-sharing reduces crime during nighttime, in open spaces, and on weekends, underscoring the spatial and temporal variation in crime patterns. By highlighting these nuanced effects, our findings extend the existing literature, which has predominantly relied on lower-frequency FBI Uniform Crime Reporting data and narrower offense categories. To address methodological concerns, we utilize the Callaway and Sant’Anna estimator to correct for bias from negative weights in traditional models. Robustness checks confirm the stability of our findings.