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

大数据：合作研究：IA：调查、人口普查和出生登记中出生时贫困的人口再生产

• 批准号: 1546259
• 负责人: Michael Rendall
• 金额: $ 84.24万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1546259&HistoricalAwards=false
• 关键词: BIGDATA; Collaborative; Research; IA; Population

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.

从一代人到下一代人向上流动的潜力是民主社会福祉的基础，也是社会长期经济生产力的基础。出生贫困是一种影响超过四分之一美国儿童的状况。这是儿童今后发展和生活机会的一个关键障碍，因此也是打破代际不利循环的一个关键障碍。母亲和父亲的性格都增加了孩子出生贫困的可能性。因此，必须将母亲和父亲纳入对一代又一代人出生时贫穷经历的科学建模。现有的数据和方法不允许在美国人口的代表性样本中进行这种建模，特别是当它作为一个日益多民族的社会发展时。收集的关于美国每一个新生儿特征的数据有限。人口普查每10年收集一次关于所有个人及其家庭的有限补充数据。每年从代表美国人口的随机样本中收集每个个人和家庭的大量数据。这些样本包括大规模的美国社区调查和许多中小规模的抽样调查。本研究开发和评估统计估计和模拟方法，将所有这些来源的联合收割机数据结合起来，回答有关代际流动的问题。这项研究将回答关于不同种族和族裔群体从一代到下一代出生时贫困持续程度的问题，以及关于教育和家庭形成在创造向上流动与持续不利地位方面的作用。将向研究人员提供该项目开发的开放源码、方便用户的统计方法软件。该项目还培养了研究生在统计学和社会科学中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，以严谨和全面的方式构建社会科学理论。没有任何一个具有全国代表性的数据来源显示母亲-父亲-子女三位一体的出生贫困。三位一体在每个人出生时的贫困状况在一个模型中被联系起来，该模型模拟了四个相互关联的过程：（1）根据家庭贫困和父母教育、种族、民族和移民状况的出生条件预测的教育进步;（2）夫妇的形成和解除;（3）夫妇生育率和未婚女性的生育率;（4）当自己的孩子出生时家庭贫困。该项目的“大数据”包括1亿多名新生儿，加上人口普查、微型人口普查、大规模横断面调查以及中等规模和较小规模的纵向调查数据来源，这些数据来源包括数百万年来的受教育情况、伴侣匹配情况以及有伴侣和（共同居住）无伴侣的出生情况。与许多大数据应用程序不同，该研究的这些多个数据源都是完整的枚举或指定人群的概率样本。该项目开发了组合调查和组合人口和调查估计方法，以提高过程的个人行为参数的精度，并开发了一种模拟建模方法，以生成有关因果关系的推断，这些因果关系来自四个相连的过程，整合了每个组成过程的多个不确定性来源。将通过介绍和同行评审的期刊文章向学术界传播成果和方法学方面的进展。此外，还将通过项目调查员与新闻媒体和其他论坛的联系，更广泛地传播项目成果及其意义，以便与更广泛的政策界接触。