The E cient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India

A central question in law and economics is whether, with limited resources, police activity should be narrowly focused and high force, or widely-dispersed but of moderate intensity. Critics of intense “hot spot” enforcement argue that this approach will only lead to crime shifting to other locations or other times. But if law breakers take time to learn that enforcement has begun, the police may take advantage of this period to intervene intensively in the most productive location. We propose a model where criminals progressively learn about policing, and structurally estimate its parameters using a randomized controlled experiment on an anti-drunk driving campaign with the police department in Rajasthan, India. In each station, sobriety checkpoints were either randomly rotated on 3 main routes or fixed in the best route, and the intensity of the crackdown was cross-randomized. We find clear evidence of driver learning about the crackdown and strategically responding to it, causing rotating checkpoints to dominate the fixed location approach. We estimate that crackdowns in rotating locations reduced night accidents in the area covered by a particular police station by 17%, and night deaths by 25% over a two month crackdown and 6 weeks following it.