G2TT
来源类型Discussion paper
规范类型论文
来源IDDP13981
DP13981 The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India
Esther Duflo; Abhijit Banerjee; Daniel Keniston
发表日期2019-09-03
出版年2019
语种英语
摘要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.
主题Development Economics
关键词Learning models Choice modeling Information acquisition Illegal behavior Law enforcement Crime prevention
URLhttps://cepr.org/publications/dp13981
来源智库Centre for Economic Policy Research (United Kingdom)
资源类型智库出版物
条目标识符http://119.78.100.153/handle/2XGU8XDN/542862
推荐引用方式
GB/T 7714
Esther Duflo,Abhijit Banerjee,Daniel Keniston. DP13981 The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India. 2019.
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