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

Harvard University, Cambridge MA

Investigators

Abstract

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.