探索城乡监禁差距：美国地方监狱监禁率的驱动因素
Exploring the Urban - Rural Incarceration Divide: Drivers of Local Jail Incarceration Rates in the U.S
摘要：
随着美国监禁率的持续增长，大量的研究主要集中在了解联邦和州监狱增长的决定因素和驱动因素上。然而，每年有1100万人入狱的地方监狱系统，尽管对社区的影响要广泛得多，但引起的研究关注较少。由维拉司法研究所（Vera）进行的初步时间趋势分析发现了按地理位置划分的县监狱监禁率的差异。与监禁是一种城市现象的假设相反，维拉发现，在过去的几十年里，许多城市地区的审前监禁率有所下降，而农村地区的审前监禁率则有所上升或保持不变。为了发现导致农村地区监狱持续增长的因素，Vera与Two Sigma的Data Clinic合作，这是一个基于志愿者的计划，利用员工的数据科学专业知识。使用2000 - 2013年的县监狱数据和县特定的人口统计学，政治，社会经济，监狱和监狱人口变量，指定了一个广义估计方程（GEE）模型来解释县内随时间的相关性。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都是当地监狱率的重要预测因素。此外，模型残差的地理调查显示，观察到的发病率比预期的高得多（和低得多）的县集群县变量的条件。
英文摘要：
As the rate of incarceration in the United States continues to grow, a large body of research has been primarily focused on understanding the determinants and drivers of federal and state prison growth. However, local jail systems, with 11 million admissions each year, have generated less research attention even though they have a far broader impact on communities. Preliminary time trend analysis conducted by the Vera Institute of Justice (Vera) uncovered disparities in county jail incarceration rates by geography. Contrary to assumptions that incarceration is an urban phenomenon, Vera discovered that during the past few decades, pretrial jail rates have declined in many urban areas whereas rates have grown or remained flat in rural counties. In an effort to uncover the factors contributing to continued jail growth in rural areas, Vera joined forces with Two Sigma's Data Clinic, a volunteer-based program that leverages employees' data science expertise. Using county jail data from 2000 - 2013 and county-specific demographic, political, socioeconomic, jail and prison population variables, a generalized estimating equations (GEE) model was specified to account for correlations within counties over time. The results revealed that county-level poverty, police expenditures, and spillover effects from other county and state authorities are all significant predictors of local jail rates. In addition, geographic investigation of model residuals revealed clusters of counties where observed rates were much higher (and much lower) than expected conditioned upon county variables.
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