Doctoral Dissertation Research in Economics: Residential Segregation and Neighbor-Based Informal Hiring

该奖项的全部或部分资金来自《2021年美国救援计划法案》(公法117-2)。以地点为基础的就业政策，如非正式社区求职，是促进当地就业增长和减少地区经济不平等的常见工具。然而，目前还不清楚是什么让一个社区适合找工作。本研究项目将研究一种这样的机制-邻居网络在求职中的使用。它探讨了哪种类型的社区-种族和教育背景的隔离或融合-更有利于成功找工作。要理清社区对求职的影响是很困难的，部分原因是人们会自己选择社区。研究人员对难民进行了一项政策实验，这些难民被随机分配到全国各地的社区，以克服自我选择的问题。该项目有助于阐明如何利用当地的社会关系，有效地重新安置这些特殊移民，并使他们更快地融入目的地的劳动力市场。其次，通过回答这个问题，这项研究项目将朝着理解社区多样性和繁荣之间的关系迈出一步。这项研究的结果可以为改善劳动力市场的功能提供指导，特别是对那些在种族隔离社区的人。因此，这一结果可能有助于指导降低低收入隔离社区失业率的政策。由于住宅分类，识别社区影响总体上是具有挑战性的。不同的人可能会因为计量经济学家无法观察到的原因而选择住在不同的地方。为了克服这个选择问题并获得因果关系，该项目将利用一个难民安置计划，根据一系列人口特征和住房供应情况，在全国范围内随机分配难民。这一自然实验为新居民和现有居民在社区层面的人口构成引入了有条件的外生变化。该项目将用细粒度的地理数据来补充这一自然实验，以适应社区的大小调整/重新定义。这种灵活性和政策诱导的变化将允许对邻居隔离对在求职中使用邻居网络的影响进行因果估计。通过这样做，这项研究将有助于将经济学文献中的两个领域--社区效应和基于网络的非正式招聘--联系起来，这两个领域在很大程度上是在相互孤立的情况下发展起来的。这项研究的结果将为改善劳动力市场的功能提供指导，特别是对那些在种族隔离社区的人。因此，这一结果可以帮助指导降低低收入隔离社区失业率的政策。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Place-based job policies, such as informal neighborhood job search, are common tools to promote local job growth and reduce regional economic inequality. It is however not clear what makes a neighborhood good for job search. This research project will study one such mechanism---the use of neighbor networks in job search. It explores which type of neighborhood---segregated or integrated, by race and by education---is more conducive to a successful job search. Disentangling the neighborhood effects on job search is difficult partly because people self-select into neighborhoods. The researchers use a policy experiment on refugee that were randomly assigned to neighborhoods throughout the country to overcome the self-selection problem. The project can help shed light on how local social connections can be harnessed for effective resettlement and faster integration of these special immigrants into the labor market at the destination. Second, in answering this question, this research project will be a step towards understanding the relationship between neighborhood diversity and prosperity. The results of this research can provide guidance on improving the functioning of labor markets, especially for those in segregated neighborhoods. The results could therefore help guide policies to reduce unemployment in low income segregated neighborhoods.Identifying neighborhood effects in general is challenging because of residential sorting. Different individuals may choose to live in different places for reasons unobservable to the econometrician. To overcome this selection issue and obtain causality, the project will exploit a refugee settlement program that randomly dispersed refugees across the country, conditional on a set of demographic characteristics and housing availability. This natural experiment introduces conditionally exogenous variation in the demographic composition at the neighborhood level for new and incumbent residents. The project will complement this natural experiment with granular geographical data that accommodate the resizing/redefining of neighborhoods. This flexibility and the policy-induced variation will allow causal estimation of the impacts of neighborhood segregation on the use of neighbor networks in job search. In so doing, the research will help bridge two areas of economic literature – neighborhood effects and networks-based informal hiring – that heretofore have developed largely in isolation from each other. The results of this research will provide guidance on improving the functioning of labor markets, especially for those in segregated neighborhoods. The results could therefore help guide policies to reduce unemployment in low income segregated neighborhoods.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.