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

Esther Duflo, Abhijit Banerjee and Daniel Keniston

No 13981, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

Keywords: Learning models; Choice modeling; Information acquisition; Illegal behavior; Law enforcement; Crime prevention (search for similar items in EconPapers)
Date: 2019-09
New Economics Papers: this item is included in nep-exp and nep-law
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Citations: View citations in EconPapers (11)

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Working Paper: The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India (2019)
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