Developing computational tools for nonparametric summaries of income mobility

该奖项是作为NSF的社会，行为和经济科学博士后研究奖学金（SPRF）计划的一部分提供的。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在加州大学洛杉矶分校的Brand博士的赞助下，该博士后奖学金支持一位研究计算方法以研究收入流动性的早期职业科学家。广泛的社会科学研究已经记录了收入如何在个人的生命过程中和家庭中的代际变化。大部分研究都依赖于线性模型。该项目通过使用统计学和机器学习中开发的新计算方法扩展了我们对收入流动性的理解。这些方法可以区分高收入和低收入人群的不同模式，并可以更全面地总结分布情况。由于收入对美国家庭的幸福至关重要，该项目的目标与NSF的使命一致，即促进国家健康、繁荣和福利。该项目的第一个目标是可视化美国家庭收入的逐年变化，正如现有的调查数据所记录的那样。一方面，收入可能表现出稳定性：去年的收入可能是今年收入的一个很好的预测因素。另一方面，收入可能是不稳定的：像晋升和裁员这样的事件可能会造成意想不到的变化。过去的工作主要总结了稳定性和波动性的线性模型的手段和方差。这一目标建立在研究分位数模式和允许非线性关系的基础上。该项目的第二个目标是考虑各代人的收入模式。如果儿童在童年时接触到更好的物质条件，这将如何影响他们成年后的收入？为了回答这个因果问题，研究调整了其他可能影响儿童收入暴露和成年收入获得的家庭背景措施。在过去工作的基础上，该项目明确考虑了非线性，使得每增加一美元的收入可能会对低收入家庭和高收入家庭产生不同的影响。除了对代际流动这一实质性专题作出贡献外，该项目的这一组成部分还有助于对连续治疗变量的因果影响进行估计和可视化。这些目标共同带来了计算社会科学的新发展，以解决有关美国家庭收入的经典问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Jennie Brand at the University of California, Los Angeles, this postdoctoral fellowship award supports an early career scientist researching computational methods to study income mobility. Extensive social science research has documented how incomes change over the life course for individuals and across generations within families. Much of that research relies on linear models. This project extends our understanding of income mobility by using new computational methods developed in statistics and machine learning. These methods allow distinct patterns among those with high and low incomes and allow fuller summaries of the distribution. Because income is essential to the well-being of American families, the goals of the project align with NSF’s mission to advance national health, prosperity, and welfare.There are two specific aims. The first aim of the project visualizes year-to-year changes in the incomes of American families, as recorded in existing survey data following those families over time. On one hand, incomes may exhibit stability: income last year may be a good predictor of income this year. On the other hand, incomes may be volatile: events like promotions and layoffs may create unexpected changes. Past work has primarily summarized stability and volatility with linear models for means and variances. This aim builds on that work by examining patterns in quantiles and allowing nonlinear relationships. The second aim of the project considers patterns of incomes across generations. If children were exposed to better material conditions in childhood, how would this affect the incomes that they realize as adults? To answer this causal question, the research adjusts for other measures of family background which may affect childhood income exposure and adult income attainment. Expanding on past work, this project explicitly considers nonlinearities such that each additional dollar of income may have a different effect among low-income as compared with high-income families. In addition to its contribution to the substantive topic of intergenerational mobility, this component of the project contributes to methodology for estimating and visualizing the causal effects of continuous treatment variables. Together, these aims bring new developments in computational social science to bear on classic questions about the incomes of American families.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.