[CV] I am a Ph.D. candidate in sociology and social policy at Princeton University. My research develops and applies statistical and machine learning methods to improve the credibility and precision of research about inequality.
I have shown that housing eviction is more common than previously thought, that sibling and cousin correlations are often misinterpreted with respect to multigenerational social mobility, and that some adolescent and family outcomes are difficult to predict even with the best machine learning techniques. A current project formalizes a gap-closing estimator that combines pre-existing categorical inequality with a causal treatment. The methodological theme across these papers is that precise statement of the estimand—the quantity at the core of the theoretical claim—is an essential starting point for subsequent methodological choices. The estimand links the project to a broader literature, enables estimation by machine learning, and gives meaning to the parameter estimate that results. My research agenda moving forward is to find estimands we have missed in the study of inequality and to develop statistical estimators for those quantities.
In my spare time, I enjoy backpacking in national parks, finding all the trail runs in the Princeton area, and getting to the beach as often as I can. You can reach me at ilundberg [at] princeton.edu.
How prevalent is housing eviction among urban American children? (with Louis Donnelly, Demography, February 2019) [paper][open access][replication code] Ethnographic research suggests that housing eviction has become a common experience among urban renters, particularly those with children. With Louis Donnelly, I use 5 waves of panel data from the Fragile Families and Child Wellbeing Study to estimate that 14.8 % of U.S. children born in 1998-2000 in large cities experienced an eviction by age 15. Estimates are higher for the most disadvantaged subgroups; more than 1 in 4 of those born into deep poverty (below 50 % of the poverty line) were evicted.
Can public policy reduce eviction? (with Sarah Gold, Louis Donnelly, Jeanne Brooks-Gunn, and Sara McLanahan, manuscript in progress). We examine the effect of housing assistance programs on the probability of eviction. Public housing substantially reduces the probability of eviction, which would be 3 times as high for families residing in public housing in our sample if they did not reside in public housing. We argue that policymakers seeking to reduce eviction should expand public housing or invest in future research about whether other assistance programs could be adapted to achieve similar protective effects.
Forthcoming in Demography [preprint]. Winner of 2018 Charles F. Westoff Prize in Demography, Princeton University. Working paper presented at the 2018 Annual Meeting of the Population Association of America and at a 2019 workshop on sibling and cousin correlations held at Oxford University.
What do sibling and cousin correlations tell us about social mobility over many generations? Recent literature suggests that grandparents play a previously unrecognized role in the status attainment process (Mare 2011), casting doubt on the simplicity of classic models of parent-to-child transmission such as those of Blau and Duncan (1967) and Becker and Tomes (1986). Those claiming the largest role for extended families build this evidence by examining cousins' outcomes, arguing that similarities must be attributable to the extended kin that cousins share. I show that this evidence is equally consistent with several alternative theories in which extended kin play no direct role. Two mundane alternatives, measurement error in the outcome variable or a dynamic transmission process that changes across generations, are each sufficient to explain away the observed outcomes. Additionally, I develop a Bayesian estimation approach which reveals that statistical uncertainty about sibling and cousin correlations is substantial. This provides further reason that authors should be cautious when interpreting sibling and cousin correlations. Conclusions clarify that sibling and cousin correlations are informative only in light of an assumed theoretical model.
With Alexandra Killewald, Demography, June 2017, [paper] [open access] [replication materials] [accessible summary]
Does getting or staying married cause men to earn more per hour? Economists have long argued that marriage should increase men's productivity through specialization, as they develop human capital in the workplace while their wives focus on home production. Sociologists have likewise argued that marriage might motivate men to work harder and thus earn more. Against each of these perspectives, Alexandra Killewald and I argue that marriage has no causal effect on men's hourly wages because a time-varying variable confounds the association: the transition to adulthood. Using panel data, we reproduce recent literature showing that hourly wages grow prior to marriage and decline prior to divorce - a nuance missed by fixed-effects models that focus on intercept shifts alone. We then adjudicate between theories of the sources of these trajectories by exploring their empirical predictions for different subgroups. If premarital wage growth is attributable to causal anticipation of marriage, then it should be smaller among men for whom marriage may be less planned: those whose marriage is followed quickly by a birth. However, we find similar premarital wage growth in both groups. Further, men who marry at older ages experience no unusual wage growth prior to marriage. We argue that individuals mature at heterogeneous times, and these heterogeneous latent maturation processes cause both marriage and wage growth.
I completed general exams in three areas of specialization. Princeton's sociology department requires students to demonstrate depth of knowledge in three subfields of our choice before advancing to candidacy. These fields represent areas of focus in which I hope to publish in the future.
Frontiers of causal inference: Nonparametrics, dimensionality, and time
Driven by rising computational power and waves of new sources of "big data," statisticians and computer scientists have rapidly developed new methods for nonparametric statistics and causal inference. Nevertheless, social scientists have only just begun to import these methods to address substantive questions. Under the guidance of Brandon Stewart, I developed a reading list covering new developments in causal inference. I chose to focus on two subfields: nonparametric approaches designed for high-dimensional settings where the functional form of associations may be unknown, and methods for dynamic causal inference in which units are exposed to dynamic treatments that change over time. These two areas of research epitomize a core principle of this emerging field: the best science is achieved when we combine the expertise of social scientists at defining an estimand and arguing for identification assumptions with the abilities of machine learning models to relax estimation assumptions about which humans are less able to reason. In addition to being examined on the reading list, I developed slides for one subset of the readings that bring social science and machine learning together in a beautiful way: tree-based methods for causal inference. (reading list) (tutorial slides)
Inequality and American Families
I designed a syllabus under the guidance of Sara McLanahan for a hypothetical graduate-level course that I could teach sometime in the future, focusing on three areas of family sociology: partnership, parenthood, and labor supply. This syllabus summarizes my substantive area of specialization in family and inequality. (syllabus here)
I completed a general exam in Demography administered by German Rodriguez and Thomas J. Espenshade. The exam covered the basics of demographic methods (following the Preston, Hueveline, and Guillot and the contents of this course) as well as substantive findings from the field (following this reading list).
My teaching experience is in a statistics sequence aimed at Ph.D. students in sociology, cross-listed for undergraduates.
I was a teaching assistant in the full two-semester sequence under Brandon Stewart in the 2016–2017 academic year. I returned to TA the second course of the sequence in spring 2018 under Matthew J. Salganik. In each course, I led a 2-hour section reviewing materials from the course every other week. (link to sample handout on generalized linear models) (links to sample slides on random variables, likelihood inference, binary outcome models, duration models, and missing data)
Course evaluations were universally positive. Out of 29 evaluations, 28 students rated my teaching as "Excellent" (the scale maximum) and 1 student rated it as "Very good." Student comments include:
My committee includes Brandon Stewart (chair), Matthew Salganik, Dalton Conley, and Sara McLanahan. Any of them can serve as references.
My CV is available here.