Dynamic Models of Racial Residential Segregation

种族居住隔离的动态模型

• 批准号: 7873477
• 负责人: Elizabeth Eve Bruch
• 金额: $ 20.16万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7873477
• 关键词: Address; Affect; American; Area; Behavioral; Behavioral Model; Censuses; Characteristics; Chicago; Child; Cities; Data; Demographic Factors; Demography; Destinations; Discrimination; Distress; Economics; Family; Family Characteristics; Future; Geography; Health; Household; Housing; Income; Individual; Intervention; Knowledge; Los Angeles; Marketing; Measures; Mechanics; Modeling; Neighborhoods; Persons; Policies; Population; Poverty; Process; Public Housing; Race; Research; Residential Mobility; Resources; Role; Scientist; Simulate; Site; Solutions; Source; Statistical Models; Statutes and Laws; Time; base; cost; data modeling; health disparity; metropolitan; preference; prototype; public health relevance; segregation; simulation; social; tool; urban area; voucher

DESCRIPTION (provided by applicant): This proposal outlines a plan of research to better understand the causes of racial residential segregation in American cities. Racial residential segregation contributes to the formation of high-poverty neighborhoods and has been implicated as an important source of enduring racial health disparities (e.g., Mechanic 2007). Yet social scientists do not know what forces contribute to maintaining residential racial segregation, what the respective importance of these forces are, or how these forces combine. Two obstacles limit accumulation of causal knowledge in this research area. First, we lack a plausible model of how individual demographic factors and neighborhood characteristics simultaneously affect individual mobility decisions. Second, most past research measures potential explanatory factors at the individual or intermediary (e.g., real-estate agents or lenders) level, but do not represent how those factors affect neighborhood racial composition in the aggregate. Our project addresses these problems through a discrete choice statistical model of residential choice and an agent-based simulation of neighborhood formation. The discrete choice models are estimated using the data on residential mobility from the Panel Study of Income Dynamics matched with data from the Decennial Censuses. Our basic model of residential mobility incorporates race and neighborhood racial composition, income and neighborhood housing cost, neighborhood income composition, housing tenure (owning or renting), and household composition; proposed extensions to the model incorporate family wealth, mobility impacts of nearby neighborhood changes, and housing market discrimination. The agent-based model creates simulated cities with the demography, geography, and housing stock of actual urban areas in which household mobility is governed by behavioral rules estimated in the discrete choice model. Taken together, this framework can be used to address questions regarding how preferences for race of neighbors, affordability constraints, housing market discrimination, and metropolitan population composition affect racial segregation. The model can also be used to explore the effects of spatially targeted housing policies on residential segregation, like the demolition of distressed public housing under Hope VI legislation. PUBLIC HEALTH RELEVANCE: Racial residential segregation contributes to the formation of high-poverty neighborhoods and has been implicated as an important source of enduring racial health disparities (e.g., Mechanic 2007). One strategy for reducing health disparities among racial groups, particularly among blacks and whites, would be to reduce racial residential segregation and the corresponding geographic concentration of poverty. The proposed research aims to provide a better understanding of the causes of segregation, suggest solutions for reducing neighborhood racial segregation, and develop new analytical and technical tools for modeling dynamic processes in scientific research.

描述（由申请人提供）：本提案概述了一项研究计划，以更好地了解美国城市种族居住隔离的原因。种族居住隔离有助于形成高度贫困社区，并被认为是持久的种族健康差异的重要来源（例如，Mechanic 2007）。然而，社会科学家不知道是什么力量促成了居住种族隔离的维持，这些力量各自的重要性是什么，或者这些力量是如何结合在一起的。在这一研究领域，有两个障碍限制了因果知识的积累。首先，我们缺乏一个合理的模型来解释个人人口因素和社区特征如何同时影响个人的流动决策。其次，大多数过去的研究都是在个人或中介（如房地产经纪人或贷款人）层面衡量潜在的解释因素，但并不代表这些因素如何影响社区的种族构成。我们的项目通过住宅选择的离散选择统计模型和基于代理的社区形成模拟来解决这些问题。离散选择模型的估计使用来自收入动态小组研究的住宅流动性数据与十年一次的人口普查数据相匹配。我们的住宅流动基本模型包括种族和社区种族构成、收入和社区住房成本、社区收入构成、住房使用权（拥有或租赁）和家庭构成；该模型的扩展包括家庭财富、附近社区变化的流动性影响和住房市场歧视。基于主体的模型创建了具有实际城市地区的人口、地理和住房存量的模拟城市，其中家庭流动性由离散选择模型中估计的行为规则控制。总的来说，这个框架可以用来解决关于邻居种族偏好、负担能力限制、住房市场歧视和大都市人口构成如何影响种族隔离的问题。该模型还可以用于探索空间定向住房政策对居住隔离的影响，如Hope VI立法下的拆除困境公共住房。