Developing computational tools for nonparametric summaries of income mobility

该奖项是作为NSF社会、行为和经济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、工业界或私营部门和政府的科学职业生涯培养有前途的、早期职业博士水平的科学家。SPRF奖项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。国家科学基金会致力于促进科学界所有阶层的科学家参与其研究方案和活动，包括那些来自代表性不足的群体的科学家；博士后阶段被认为是实现这一目标的专业发展的一个重要水平。每个博士后研究员都必须解决推动各自学科领域向前发展的重要科学问题。在加州大学洛杉矶分校Jennie Brand博士的赞助下，这一博士后奖学金奖项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记录了收入如何在个人的一生中以及家庭内的几代人之间发生变化。这些研究很大程度上依赖于线性模型。这个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。这些方法可以在高收入和低收入人群中区分不同的模式，并可以更全面地总结分配情况。由于收入对美国家庭的福祉至关重要，该项目的目标与美国国家科学基金会促进国民健康、繁荣和福祉的使命一致。该项目的第一个目标是可视化美国家庭收入的年复一年变化，记录在跟踪这些家庭一段时间的现有调查数据中。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指标。另一方面，收入可能是不稳定的：升职和裁员等事件可能会带来意想不到的变化。过去的工作主要是用均值和方差的线性模型来总结稳定性和波动性。这个目标建立在这项工作的基础上，通过检查分位数的模式并允许非线性关系。该项目的第二个目标是考虑几代人之间的收入模式。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实现的收入？为了回答这个因果问题，这项研究调整了家庭背景的其他衡量标准，这些衡量标准可能会影响儿童的收入敞口和成人的收入获得。在过去工作的基础上扩展，这个项目明确考虑了非线性，这样每增加一美元的收入可能会对低收入家庭产生与高收入家庭不同的影响。除了对代际流动这一实质性专题作出贡献外，该项目的这一构成部分还有助于评估和可视化持续治疗变量的因果影响的方法。总而言之，这些目标带来了计算社会科学的新发展，与美国家庭收入的经典问题有关。该奖项反映了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 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.