Developing computational tools for nonparametric summaries of income mobility

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.