Exploring the Urban–Rural Incarceration Divide: Drivers of Local Jail Incarceration Rates in the United States
Exploring the Urban–Rural Incarceration Divide: Drivers of Local Jail Incarceration Rates in the United States
复制标题
探索城乡监禁差距：美国当地监狱监禁率的驱动因素
DOI：
10.1080/15228835.2017.1417955
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发表时间：
2017
影响因子：
1.5
通讯作者：
Christian Henrichson
中科院分区：
文献类型：
--
作者：
R. W. Riley;Jacob Kang;Chris Mulligan;Vinod Valsalam;Soumyo Chakraborty;Christian Henrichson
摘要 大量研究的重点是通过联邦和州监狱增长的视角来了解美国的大规模监禁情况。然而，每年收监 1100 万人的当地监狱系统尽管对社区影响广泛，但受到的研究关注却较少。维拉司法研究所 (Vera) 进行的初步分析发现，县监狱监禁率存在地域差异。与监禁是一种城市现象的假设相反，维拉发现，近几十年来，许多城市地区的审前监禁率有所下降或保持不变，而农村县的审前监禁率却有所上升。为了找出导致农村地区监狱持续增长的因素，Vera 与 Two Sigma 的数据诊所合作，这是一个基于志愿者的项目，利用了 Two Sigma 员工的数据科学专业知识。使用广义估计方程 (GEE) 模型对 2000 年至 2013 年当地监狱率的决定因素进行了研究，以解释各县内随时间变化的相关性。结果显示，县级贫困、警察支出以及其他县和州当局的溢出效应是当地监狱率的重要预测因素。对模型残差的调查揭示了一些县的集群，其中观察到的比率远高于根据县变量确定的预期。
ABSTRACT A large body of research has focused on understanding mass incarceration in the United States through the lens of federal and state prison growth. However, local jail systems, with 11 million admissions each year, have received less research attention despite their broad impact on communities. Preliminary analysis conducted by the Vera Institute of Justice (Vera) uncovered geographical disparities in county jail incarceration rates. Contrary to assumptions that incarceration is an urban phenomenon, Vera discovered that, in recent decades, pretrial jail rates have declined or remained flat in many urban areas, whereas rates have grown in rural counties. In an effort to uncover factors contributing to continued jail growth in rural areas, Vera joined forces with Two Sigma’s Data Clinic, a volunteer-based program that leverages Two Sigma employees’ data science expertise. Determinants of local jail rates from 2000–2013 were examined using a generalized estimating equations (GEE) model to account for correlations within counties over time. Results revealed that county-level poverty, police expenditures, and spillover effects from other county and state authorities are significant predictors of local jail rates. Investigation of model residuals revealed clusters of counties where observed rates were much higher than expected conditioned upon county variables.