BIGDATA: Collaborative Research: IA: Population Reproduction of Poverty at Birth from Surveys, Censuses, and Birth Registrations

从一代人到下一代人向上流动的潜力对民主社会的福祉以及该社会的长期经济生产力至关重要。出生贫困是一种影响超过四分之一的美国儿童的疾病。它是儿童后续发展和生活机会的关键障碍，因此也是打破代际不利循环的关键障碍。母亲和父亲的特点都增加了孩子出生在贫困中的机会。因此，重要的是将母亲和父亲都纳入一代又一代出生时贫穷经历的科学模型。现有的数据和方法不允许在具有代表性的美国人口样本中进行这种建模，特别是在它作为一个日益多种族的社会发展的时候。关于美国每个新生儿的特征，收集的数据有限。在人口普查中，每10年收集一次关于所有个人及其家庭的有限补充数据。每年都会从代表美国人口的随机样本中收集更多的个人和家庭数据。这些样本包括大规模的美国社区调查和众多的中小型抽样调查。这项研究开发和评估了统计估计和模拟方法，以结合所有这些来源的数据来回答关于代际流动性的问题。这项研究将回答关于不同种族和民族出生时贫困的持续程度，以及教育和家庭形成在创造向上流动与持续劣势方面所起的作用的问题。将向研究人员提供用于该项目开发的统计方法的开放源码、用户友好的软件。该项目还培养研究生在统计学和社会科学的大数据方法方面的技能。该项目的目标是开发一种变革性的大数据方法，利用丰富的“传统”数据源，以严格的统计和经验综合的方式建立社会科学理论。没有一个具有全国代表性的数据来源是观察到的母亲-父亲-孩子三合会的出生贫困。相反，三合会在每个人出生时的贫困状况被联系在一个模型中，该模型模拟了四个相互关联的过程：(1)从家庭贫困的出生条件和父母的教育、种族、民族和移民地位预测的教育进步；(2)夫妇的形成和解体；(3)夫妇生育率和未婚妇女的生育率；以及(4)自己孩子出生时的家庭贫困。该项目的“大数据”包括超过1亿的出生人口，结合人口普查、微观人口普查、大规模横断面调查和中小规模的纵向调查数据来源，这些数据来源包括数百万年的就学经历、伴侣配对以及夫妻和(共同居住)的非伴侣出生。与许多大数据应用程序不同，这项研究的这些多个数据来源要么都是完整的枚举，要么都是特定人群的概率样本。该项目开发了联合调查和综合人口和调查估计方法，以提高估计过程的个人行为参数的精度，并开发了一种模拟建模方法，以产生关于四个相关过程出现的因果联系的推论，整合了每个组成部分过程的多个不确定来源。成果和方法方面的进步将通过演讲和同行评议的期刊文章向学术界传播。此外，还将通过项目调查员与新闻媒体的接触和其他论坛进行更广泛的传播，以便与更广泛的政策界接触，了解项目成果及其意义。

The potential for upward mobility from one generation to the next is fundamental to the wellbeing of a democratic society, and to that society's long-term economic productivity. Poverty at birth is a condition affecting more than 1 in 4 American children. It is a critical barrier to children's subsequent development and life chances, and therefore to breaking intergenerational cycles of disadvantage. Both mother's and father's characteristics contribute to the chance that a child will be born into poverty. Therefore it is important to incorporate both the mother and father into the scientific modeling of the experience of poverty at birth from one generation to the next. Existing data and methods do not allow for this modeling in representative samples of the U.S. population, especially as it evolves as an increasingly multi-ethnic society. Limited data are collected on the characteristics of every birth in the United States. Limited additional data on all individuals and their households are collected every 10 years in the Census. Larger amounts of data per individual and household are collected every year from random samples that are representative of the U.S. population. These samples include the large-scale American Community Survey and numerous medium- and small-scale sample surveys. This research develops and evaluates statistical estimation and simulation methods to combine data from all these sources to answer questions about intergenerational mobility. The research will answer questions about the degree of persistence of poverty at birth from one generation to the next across different race and ethnic groups, and about the roles of education and family formation in creating upward mobility versus persistence of disadvantage. Open-source, user-friendly software for the statistical methods developed in the project will be made available to researchers. The project also develops graduate students' skills in Big Data methods in statistics and the social sciences. The project has as its goal the development of a transformative, Big Data approach to exploiting the rich "traditional" data sources to build social-scientific theory in a statistically-rigorous and empirically-comprehensive way. In no single nationally-representative data source is poverty at birth observed for the mother-father-child triad. The triad's poverty statuses at each one's birth are instead linked in a model that simulates four connected processes: (1) educational progress predicted from the birth conditions of household poverty and parents' education, race, ethnicity, and immigrant statuses; (2) couple formation and dissolution; (3) couple fertility and unpartnered women's fertility; and (4) household poverty when their own children are born. The "Big Data" of this project consist of more than 100 million births, combined with census, microcensus, large-scale cross-sectional survey, and medium-scale and smaller-scale longitudinal survey data sources that together include millions of years of exposure to schooling, to partner-matching, and to partnered and (co-residentially) unpartnered births. These multiple sources of data of the study, unlike in many Big Data applications, are all either complete enumerations or probability samples of a well-specified population. The project develops combined-survey and combined population-and-survey estimation methods to estimate with enhanced precision the individual behavioral parameters of the process, and develops a simulation-modeling approach to generating inference about causal associations that emerge from the four connected processes, integrating the multiple sources of uncertainty about each component process. Results and methodological advances will be disseminated to the scholarly community through presentations and peer-reviewed journal articles. Additional, broader dissemination will occur through project investigator contact with the news media and other forums for engagement with the broader policy community about the project results and their significance.