Doctoral Dissertation Research: Skill-Based Sorting into Neighborhoods and Schools

博士论文研究：基于技能的社区和学校分类

• 批准号: 1904077
• 负责人: Robert Sampson
• 金额: $ 1.59万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1904077&HistoricalAwards=false
• 关键词: Doctoral; Dissertation; Research; Skill; Based

The core objective of this project is to evaluate whether, in an era of market-based, choice-oriented urban policy, parents' cognitive and socioemotional skills facilitate access to neighborhood and school contexts conducive to their children's skill growth and educational attainment. Highly skilled parents deploy distinct strategies to cultivate their children's intellectual and socioemotional development, which in turn promotes intergenerational mobility. Yet we know little about whether and how parents' skills facilitate their children's access to two domains that also shape intergenerational mobility: neighborhoods and schools. In an era of housing market and educational enrollment changes that foster increased choice and information access (e.g., Section 8 housing vouchers, school choice policies), do parents' intellectual and socioemotional skills predict entry into neighborhood and school contexts conducive to their children's skill growth and educational success? Armed with more choices and more information than ever before, parents with higher cognitive and socioemotional skill levels may be more likely to raise their children in higher-quality neighborhoods and place them into higher-quality schools, even when comparing parents with similar demographic backgrounds, economic circumstances, and educational attainment levels. In turn, access to higher-quality neighborhoods and schools may meaningfully shape children's subsequent cognitive and socioemotional skill growth and their likelihood of high school graduation and college enrollment. Findings from investigation of these propositions will contribute to debates over opportunity and urban policy.To investigate the questions of this project, two kinds of statistical models will be built: (1) discrete choice models of neighborhood and school selection and (2) value-added models of neighborhood and school effects on children's cognitive and socioemotional growth. The primary data source includes approximately 1,000 time-varying, geocoded residential and school enrollment histories, collected over about ten years in the 2000s. These data will be combined with individual-level measures of cognitive and socioemotional skills, demographic characteristics, and educational attainment, as well as time-varying household-level measures of income and wealth and neighborhood- and school-level quality data derived from census, geographic information system, and administrative (i.e., state and local government) sources.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目的核心目标是评估在一个以市场为基础、以选择为导向的城市政策时代，父母的认知和社会情感技能是否有助于进入社区和学校环境，从而有利于其子女的技能成长和教育成就。高技能的父母采用独特的战略来培养子女的智力和社会情感发展，这反过来又促进了代际流动。然而，我们对父母的技能是否以及如何促进孩子进入两个领域知之甚少，这两个领域也影响了代际流动：社区和学校。在一个住房市场和教育入学变化的时代，这些变化促进了更多的选择和信息获取（例如，第8节住房券，学校选择政策），父母的智力和社会情感技能预测进入社区和学校环境有利于他们的孩子的技能成长和教育成功？拥有比以往更多的选择和更多的信息，具有更高认知和社会情感技能水平的父母可能更有可能在更高质量的社区抚养孩子，并将他们送入更高质量的学校，即使与具有相似人口结构的父母进行比较也是如此。背景、经济状况和教育程度水平。反过来，进入更高质量的社区和学校可能会有意义地塑造儿童随后的认知和社会情感技能的发展，以及他们高中毕业和大学入学的可能性。 本课题的课题是构建两种统计模型：（1）邻里和学校选择的离散选择模型;（2）邻里和学校对儿童认知和社会情感成长的影响的增值模型。 主要数据源包括大约1,000个随时间变化的、地理编码的住宅和学校入学历史，这些历史是在21世纪初的大约10年里收集的。这些数据将与认知和社会情感技能，人口统计特征和教育程度的个人水平测量，以及收入和财富的随时间变化的家庭水平测量以及来自人口普查，地理信息系统和行政管理（即，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Skill-Based Contextual Sorting: How Parental Cognition and Residential Mobility Produce Unequal Environments for Children

• DOI: 10.1007/s13524-020-00866-8
• 发表时间: 2020-03
• 期刊: Demography
• 影响因子: 3.5
• 作者: Jared N. Schachner;R. Sampson