DP13981 The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India
|Author(s):||Abhijit Banerjee, Esther Duflo, Daniel Keniston|
|Publication Date:||September 2019|
|Date Revised:||September 2019|
|Keyword(s):||Choice Modeling, Crime Prevention, Illegal behavior, Information Acquisition, law enforcement, Learning Models|
|Programme Areas:||Development Economics|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=13981|
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.