Sara Zobl is a fifth-year PhD student in the sociology department on a National Institute on Aging fellowship and is a Population Studies Center trainee. Sara is also the Michigan RDC’s research assistant, and in that role she has obtained Special Sworn Status and is available to support Michigan RDC research projects with cleaning and analysis of data as well as related tasks.
Sara’s ongoing research focuses broadly on inequality, social stratification mechanisms, the life course, and health and aging in the United States. In her current project, which is the foundation of her dissertation, she maps and compares the life event sequences of Baby Boom and Millennial women to determine how the rapid and significant social and political changes that occurred during the second half of the 20th century—changes that were intended to boost the life chances of all women by giving them access to a wide range of life options not available to prior generations—have played out over time.
Recently, we asked Sara how she became interested in her research. She told us: “As a sociologist, I’m keenly interested in how socioeconomic inequalities are produced and maintained within a society that purports to highly value equality of opportunity, and in the complex relationship between individual agency and structural constraints. My research interests and dissertation topic grew from my own experiences as a first-generation college student and nontraditional student; my interest in the implications of life course disorder is a nod to my (and other women’s) difficulty managing out-of-sequence student-, employee-, and parent roles within educational institutions and workplaces that remain inflexible and fail to accommodate women following nonnormative life paths.”
Sara’s work so far has given her extensive experience with manipulating large, nationally-representative longitudinal datasets and with sequence analysis techniques and has served as a great opportunity for her to hone her Stata skills. She has also presented her findings at the Population Association of America’s annual meeting in Chicago; at the American Sociological Association’s annual meeting in Seattle; and at the Society for Longitudinal and Life Course Studies in Stirling, Scotland.
When we asked Sara where she hopes to take her research in the future, she said: “After completing this work, I plan to explore whether women following nonnormative life paths experience worse physical or mental health outcomes in old age compared to their peers who completed major life events in a more-common order.”
In her free time, Sara enjoys ornamental and vegetable gardening, cooking and baking, and soap making. She also mentioned, “working with my partner Ken on restoring our new (to us!) old house.”
Researchers interested in hiring Sara to assist on a project should make an inquiry via our online form.
CAED invites submissions of paper proposals for its 15th conference on research using enterprise microdata to be held in Ann Arbor, Michigan, May 17-19, 2019. The conference promotes scientific research using enterprise microdata on a variety of topics and from different (and inter) disciplinary approaches. The conference is a unique meeting point for producers and users of these data, including as statisticians, survey methodologists, economists and econometricians, sociologists and other social scientists. It is also an opportunity for policymakers to acquaint themselves with the newest research results and policy evidence and evaluation in this rapidly expanding field.
Joelle Abramowitz is Co-Director of the Michigan RDC and an Assistant Research Scientist in the Survey Research Center, Institute for Social Research at the University of Michigan. She came to ISR in July 2016 after three years at the U.S. Census Bureau where she worked as an economist in the Health and Disability Statistics Branch of the Social, Economic and Housing Statistics Division. Joelle received her Ph.D. in economics from the University of Washington in 2013.
Recently, we asked Joelle how she became interested in her research. She told us: “I took my first economics class as a general requirement my freshman year of college, and when I saw the combination of exploring questions of how individuals and firms make decisions and a math basis, I knew I’d found what made sense to me.” She also told us a unique story about how her dissertation topic developed: “A friend of mine was in her mid-30s and really wanted to start a family, so she was doing online dating to find a spouse. She told me about a guy she was luke-warm about, and then told me about how she’d gone to her doctor and found out she could freeze her eggs. She concluded that while she could continue pursuing a relationship with this man, she had decided to move on to look for better options since she felt more confident about delaying trying to get pregnant. I wondered how many other women felt the same way, and that led me to the main topic of my dissertation, which was how state health insurance mandates that required employers to provide health insurance coverage for assisted reproductive technology affected women’s marriage and fertility timing.”
Joelle also followed another topic in her dissertation that arose from a point of personal interest: “The other topic of my dissertation examined the relationship between working longer hours and body mass index. That was somewhat autobiographical and came about as a way to quantify why I gained weight after I started my first job after college working long hours at a consulting firm.”
Joelle has done a fair amount of work looking at health insurance and medical expenditures. Recently, she examined the effects of the Affordable Care Act young adult provision on marriage and fertility and related outcomes, as well as the effects of the Affordable Care Act state Medicaid expansions on medical expenditures and crowding-out of private health insurance. Her work on marriage and fertility used the Census Bureau’s restricted-use American Community Survey data on survey response date to more precisely identify timing of marriage and fertility with respect to the policy change.
When we asked Joelle where she hoped to take her research in the future, she said: “Prior to joining ISR, my research had mostly focused on younger individuals, either young adults, women of childbearing age, or individuals under age 65, because these were the populations relevant to the policies I was studying. Going forward, I am eager to do more work focusing on older populations. I came to ISR to work on the CenHRS project, linking the Health and Retirement Study to Census Bureau administrative data on respondents’ employers. We’re getting closer to having data that is ready to be used for research, and that is exciting!”
In her free time, Joelle likes to cook, bake, bike, hike, and tango. She also mentioned that she has “the unusual (but not extremely useful) ability to hula hoop for long periods of time.”
My job market paper, Ranking Firms Using Revealed Preference, exploits the intuitive notion that workers move towards more preferred firms. I use this revealed preference approach combined with a standard search-theoretic model to estimate the value of working at essentially each firm in the United States. This approach imposes sufficient structure to map the 1.5 million by 1.5 million matrix of worker flows across all firms in the economy into estimates of firm value.
I then combine the revealed-preference based estimate of firm values with earnings data to decompose the variance of firm-level earnings into a rents and compensating differentials component. This comparison is informative about the role of rents in the labor market because dispersion in earnings that corresponds to dispersion in utility implies that frictions prevent competition from forcing all firms to offer the same level of utility. On the other hand, this comparison is informative about compensating differentials because a high-paying firm that is low-value must have bad nonearnings characteristics, while a low-paying firm that is high-value must have good nonearnings characteristics.
Using the U.S. Census Bureau’s Longitudinal Employer Household Dynamics (LEHD) dataset, I find that both rents and compensating differentials explanations are operative, but compensating differentials are more important than rents. I use my estimates of compensating differentials to show that the presence of compensating differentials increases earnings inequality. If amenities were removed and earnings changed to compensate, then the variance of earnings would fall. The importance of compensating differentials also helps explain why search models cannot generate the observed extent of earnings dispersion in the labor market: some of it does not reflect utility dispersion.