Economic Mobility: The Impact of Individual, Parent and Spatial Factors Using National Survey and Administrative Data

跨代的经济和社会流动已成为世界范围内的一个重要政策问题。 然而，研究人员还没有有效地确定这种缺乏流动性的原因，部分原因是缺乏适当的数据。 该项目通过研究童年环境与成年后的收入、教育、生育和监禁之间的关系，探讨缺乏经济和社会流动性的原因。父母的收入和孩子成年后的收入之间的关系与美国儿童缺乏流动性的担忧非常相关。这项研究利用数百万儿童的信息，详细衡量了经济流动性。这项研究的一个关键贡献是，它结合了人口普查局和其他政府机构的众多信息来源，以确定儿童的人口特征，如种族，邻里，家庭结构和父母的教育水平。研究人员还测量了在补贴住房中花费的时间及其对未来收入的影响。利用这些信息，研究人员可以比较儿童环境中的流动水平。除了就业和收入之外，研究人员还研究了这些童年环境对学校教育、地理流动性（前往该国不同地区）、他们何时（以及是否）结婚以及他们在什么年龄生孩子的影响。成年生活。所开发的综合信息，包括数据，可供其他研究人员用于基于证据的政策分析。 这项研究的结果将通过提供改善代际流动性的政策指导来加强美国经济。该项目支持PI正在进行的数据基础设施的开发和分析，该基础设施将全国调查和行政数据-特别是LEHD数据与人口普查微观数据-相结合，使他们能够研究儿童社会经济环境对成人结果的影响，例如教育程度，劳动力市场表现，生育率和监禁。 合并后的数据集提供了详细的（在规模和纵向范围方面）就业和收入，人口统计和家庭特征，居住地点和社区，以及计划参与的信息，为美国数百万人。PI将使用这些数据来探索童年环境对代际经济流动性（IEM）的影响并进行因果推理，包括使用兄弟姐妹的数据来估计年轻时对补贴住房的影响。 对于更广泛的人群，PI将探索个人，家庭和空间因素如何与父母特征相互作用，以确定观察到的IEM，成人监禁，教育程度和生育率的分布。 这些数据允许PI使用最近文献中采用的各种准实验方法来探索工作机制，以确定因果关系。在另一种识别方法中，PI检查了“强制搬迁”的影响，这些搬迁是根据希望IV计划拆除公共住房产生的-提供了另一种将儿童住房与成人结果联系起来的准实验性变化。由于选定的拆除是在不同的社区和都会区，PI可以探索公共住房拆除对青年的长期影响的空间变化。这种异质性的治疗效果，以前没有利用，将有助于我们更好地了解如何童年的情况下与住宅的经验相互作用，影响成年人的福祉。用于联合收割机这些数据集的方法将为可能需要联合收割机人口普查和其他数据集以创建更全面数据集的其他研究人员提供指导。

Economic and social mobility across generations has become an important policy issue world-wide. However, researchers have not effectively identified the causes of this lack of mobility, partly because of lack of appropriate data. This project addresses the causes of lack of economic and social mobility by examining the relationship between childhood circumstances and later adult earnings, education, child-bearing, and incarceration. The relationship between parents' earnings and children's later adult earnings is very relevant to concerns about lack of mobility for American children. Using information for millions of children, this research measures economic mobility in a detailed manner. A key contribution of this research is that it combines numerous sources of information from the Census Bureau and other government agencies to identify demographic characteristics of children such as race, neighborhood, family structure, and level of parents' education. The researchers also measure time spent in subsidized housing and its effect on future earnings. Using this information, the researchers can compare mobility levels across childhood circumstances. In addition to employment and earnings, the researchers also look at the effects of these childhood circumstances on schooling, geographical mobility (to different parts of the country), when (and if) they marry, and at what age they have children in their adult lives. The combined information that is developed, including the data, can be used by other researchers for analysis of policies based on evidence. The results of this research will strengthen the U.S. economy by providing policy guidance on improving intergenerational mobility.This project supports the PIs ongoing development and analysis of data infrastructure that merges national survey and administrative data - - especially LEHD data with Census micro data - - that enables them to study the impact of childhood socioeconomic circumstances on adult outcomes such as educational attainment, labor market performance, fertility, and incarceration. The merged data set provides detailed (in terms of both scale and longitudinal scope) information on employment and earnings, demographics and household characteristics, residential location and neighborhood, and program participation, for millions of individuals across the United States. The PIs will the use these data to explore and make causal inferences of the effects of childhood circumstances on intergenerational economic mobility (IEM), including using data on siblings to estimate of the exposure to subsidized housing when young. For the broader population, the PIs will explore how individual, household, and spatial factors interact with parental characteristics to determine the observed distribution of IEM, adult incarceration, educational attainment, and fertility. The data allow the PIs to explore the mechanisms at work using a variety of quasi-experimental approaches employed in the recent literature to identify causal associations. In another approach to identification, the PIs examine the effects of "forced-moves" stemming from public housing demolitions under the Hope IV program---providing another quasi-experimental variation relating housing for children to adult outcomes. Because selected demolitions were in different neighborhoods and metro areas, the PIs can explore the spatial variation in the long-term effect of public housing demolitions on the youth. This treatment effect of heterogeneity, not previously exploited, will help us to better understand how childhood circumstances interact with residential experiences to affect adult well-being. The method used to combine these data sets will provide a guide for other researchers who may need to combine Census and other data sets to create more comprehensive data sets.