DP13981 The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India
Should police activity should be narrowly focused and high force, or widely dispersed
but of moderate intensity? Critics of intense “hot spot” policing argue
it primarily displaces, not reduces, crime. But if learning about enforcement
takes time, the police may take advantage of this period to intervene intensively
in the most productive location. We propose a multi-armed bandit model
of criminal learning and structurally estimate its parameters using data from
a randomized controlled experiment on an anti-drunken driving campaign in
Rajasthan, India. In each police station, sobriety checkpoints were either
rotated among 3 locations or fixed in the best location, and the intensity of the
crackdown was cross-randomized. Rotating checkpoints reduced night accidents
by 17%, and night deaths by 25%, while fixed checkpoints had no significant
effects. In structural estimation, we show clear evidence of driver learning and
strategic responses. We use these parameters to simulate environment-specific
optimal enforcement policies.
