ilundberg [at] princeton.edu
Ph.D. Student, Sociology and Social Policy
With Alexandra Killewald, Demography, June 2017, [paper] [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.
How prevalent is housing eviction among urban American children? (with Louis Donnelly, conditionally accepted) 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 the proportion of urban American children born in 1998-2000 who experience an eviction by age 15.
Methodologically, this paper demonstrates how conclusions depend on untestable assumptions about missing data. Following a set identification approach, we provide a lower bound estimate that shows that eviction is surprisingly common even under assumptions that would tend to dramatically underestimate this quantity. We additionally provide a preferred estimate under more typical assumptions about missing data. Finally, we demonstrate the robustness of our central claims to a nonparametric random forest estimator that relaxes the parametric estimation assumptions of our preferred estimator. Together, these estimates suggest that eviction is alarmingly prevalent.
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.
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 modern approaches to 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 statistics courses aimed at Ph.D. students in sociology.
I precepted Soc 500 (Applied Social Statistics) in Fall 2016 and Soc 504 (Advanced Social Statistics) in Spring 2017, both under Brandon Stewart at Princeton University. In Spring 2018, I am again precepting Soc 504 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)
I currently work most closely with Sara McLanahan, Brandon Stewart, and Matthew Salganik. In college, I collaborated with Alexandra Killewald and was also advised by Christopher Winship. Any of them can serve as references.
My CV is available here.