| 来源类型 | Working Paper |
| 规范类型 | 报告 |
| DOI | 10.3386/w26224 |
| 来源ID | Working Paper 26224 |
| The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India | |
| Abhijit Banerjee; Esther Duflo; Daniel Keniston; Nina Singh | |
| 发表日期 | 2019-09-09 |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | Should police activity 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 | Microeconomics ; Economics of Information ; Other ; Law and Economics ; Development and Growth ; Development |
| URL | https://www.nber.org/papers/w26224 |
| 来源智库 | National Bureau of Economic Research (United States) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/583896 |
| 推荐引用方式 GB/T 7714 | Abhijit Banerjee,Esther Duflo,Daniel Keniston,et al. The Efficient Deployment of Police Resources: Theory and New Evidence from a Randomized Drunk Driving Crackdown in India. 2019. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| w26224.pdf（1030KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
