Seminar Series

Kenneth Dodge, William McDougall Distinguished Professor of Public Policy Studies, Duke University, and William Copeland, Professor of Psychiatry, University of VT, present, "The Great Smoky Mountain Study"

The Great Smoky Mountains Study (GSMS) is a longitudinal, population-based community survey of children and adolescents in North Carolina. The study is part of a collaborative effort between Duke University and the North Carolina State Division of Developmental Disabilities, Mental Health and Substance Abuse Services. The collaborative study started in 1992 and continued until 2015. Important goals of the study were to estimate the number of youth with emotional and behavioral disorders; investigate the persistence of those disorders over time; examine the need for, and use of, services for emotional and behavioral disorders; and identify possible risk factors for developing emotional and behavioral disorders. Drawing from 11 counties in western North Carolina, the screening sample consisted of 4,500 children: 1,500 each aged 9, 11, and 13 years at baseline. The study included both urban and rural sectors, and all the agencies that provide child mental health services in the area. This region is also home to a fairly large American Indian population, and 349 of the youth in the study are enrolled members of the Eastern Band of the Cherokee Nation. These youths represent a population that has been under-represented in mental health research across the country. The GSMS has provided policy-relevant information in the areas of: 1) need for mental health services, 2) risks for emotional and behavioral disorders, 3) outcomes of serious emotional disorders, 4) use of mental health services across sectors and 5) effectiveness of mental health services among cohorts.

Jennie E. Brand, Professor of Sociology and Statistics, UCLA, presents "Uncovering College Effect Heterogeneity using Machine Learning"

Individuals do not respond uniformly to treatments, events, or interventions. Social scientists routinely partition samples into subgroups to explore how the effects of treatments vary by covariates like race, gender, and socioeconomic status. In so doing, analysts determine the key subpopulations based on theoretical priors. Data-driven discoveries are also routine, yet the analyses by which social scientists typically go about them are problematic and seldom move us beyond our expectations, and biases, to explore new meaningful subgroups. Emerging machine learning methods allow researchers to explore sources of variation that they may not have previously considered, or envisaged. In this paper, we use causal trees to recursively partition the sample and uncover sources of treatment effect heterogeneity. We use honest estimation, splitting the sample into a training sample to grow the tree and an estimation sample to estimate leaf-specific effects. Assessing a central topic in the social inequality literature, college effects on wages, we compare what we learn from conventional approaches for exploring variation in effects to causal trees. Given our use of observational data, we use leaf-specific matching and sensitivity analyses to address confounding and offer interpretations of effects based on observed and unobserved heterogeneity. We encourage researchers to follow similar practices in their work on variation in effects.

Lisa Gennetian, Pritzker Associate Professor of Early Learning Policy Studies, Sanford School of Public Policy, Duke University, "Hispanic families and U.S. anti-poverty programs: Policy, practice, and uptake"

Hispanic children account for more than one third of children living in poverty in the U.S. Yet, eligible low-income Hispanic families appear to have lower rates of participation in most public programs designed to reduce poverty. In 2009, for example, Hispanics made up 24 percent of the EITC-eligible population, but only 15 percent of those receiving the federal or state EITC were Hispanics. In recent estimates from the 2018 American Community Survey, only 2 out of 3 native-born Hispanic child households living at the federal poverty level reported receiving SNAP. This presentation will share findings from two related endeavors to understand uptake among Hispanic families and how the design of public policies might be affecting uptake. First, drawing on data from the 2014 panel of the Survey of Income and Program Participation, estimates of EITC uptake from 2013-15 by race and ethnicity are derived adjusting for detailed characteristics of families with state- and year-fixed effects, and with a variety of new variables collected from primary sources to capture EITC marketing and outreach, availability of tax sites, and generosity of related state programs. Second, descriptive analyses of new collated information on state-level policies regarding eligibility determination, documentation requirements, and practices affecting the user-application experience for TANF will be presented in the 13 states that are host to over 80 percent of low income Hispanic children. This is complemented by new primary data collected in 2020 from TANF state and local administrators, and front line workers, in several of these same states regarding their perspectives about serving Hispanic families.

Patrick Heuveline, Professor of Sociology, University of California, Los Angeles presents “I Heard We Had the Best Mortality Rate” Some Old and a New Mortality Indicator for COVID-19 Analyses

Expressing the significance of COVID-19 in a relatable metric is important because public awareness is critical to participation, on which mitigating policies depend. Mortality indicators are among the most salient measures of the impact of COVID-19. Following well-established practices in demography, several CoViD-19 mortality indicators can be derived from the cumulative number of CoViD-19 deaths. The first indicator is an occurrence-exposure rate comparable to the Crude Death Rate. Unstandardized, it may not be appropriate for comparisons between populations that have very different age compositions, but it allows for a direct comparison between CoViD-19 and all causes mortality over periods of any length. The second measure is an indirectly standardized rate which appears to perform quite like a directly standardized rate but without requiring a breakdown of CoViD-19 deaths by age and sex. While age-standardized death rates have excellent properties for tracking the pandemic, those are expressed in underwhelming metrics: deaths per 1,000 or fraction thereof. With extant life tables, reductions in 2020 life expectancies can be estimated. Declines in life expectancies are intuitive indicators, but they are unsuitable for fine-grained tracking of a fast-moving epidemic because their estimation requires an assumption of unchanged future mortality. To avoid making any assumption about future mortality, I introduce a Mean Unfulfilled Lifespan (MUL), defined as the average difference between the actual and otherwise expected ages at death in a recent death cohort. For fine-grained tracking of the pandemic across small areas or over short periods of time, MUL values can be quickly approximated. To illustrate I estimate that using a seven-day rolling window, the MUL peaked at 7.32 years in Lombardy, 8.96 years in Madrid, and 8.93 years in New York, but reached 12.86 years for the entire month of April in Guayas (Ecuador).  

DUPRI's Demography of Aging Training Seminar March 5, 2020 - Ruth Wygle & Anna Holleman

Abstract: Though the primary role of religious congregations is to offer worship services and guidance in the religious lives of their parishioners, they also play a central role in the provision of mental health services in the United States to both parishioners and non-parishioners alike. Approximately a quarter of individuals who experience mental illness turn to religious congregations for assistance (Wang et al. 2003), with about half of these individuals relying only on religious congregations for their mental health needs (Wang et al. 2005). Though past research has identified numerous characteristics of religious congregations that provide mental health services, a key contradiction has arisen concerning the practice of spiritual healing and its relationship to the provision of mental health services (Frenk 2014; Wong et al. 2018). The current study attempts to adjudicate between these past contradictions by operationalizing a reliance on spiritual prayer for the healing of mental disorders, which past research has shown to be in opposition to a reliance on secular medicine. I find that congregations that practice spiritual healing are more likely to offer mental health services that relate specifically to substance abuse disorders. Understanding this relationship is vital for mental health professionals and policy makers who benefit from understanding the ways the orientation of religious congregations towards secular medicine could aid or impede congregations’ willingness to collaborate to assist individuals in need of mental health services.

DUPRI presents Chris Wildeman PhD, Cornell University

Parental incarceration is now sufficiently common and unequally distributed that it could have implications for population health and population health disparities if it has negative effects on children. In this presentation, I present descriptive results from linked administrative data in New York City showing how paternal incarceration during the gestational period is associated with a range of infant outcomes. In so doing, I present the first estimates of the paternal incarceration-child health association using objectively measured indicators of both paternal incarceration and child health.

DUPRI presents Paternal Incarceration and Birth Outcomes: Evidence from New York City, 2010-2016 - Christopher Wildeman

Duke University Population Research Institute Presents Christopher Wildeman, Professor of Policy Analysis and Management and Sociology; Associate Vice Provost for the Social Sciences; Director of the Bronfenbrenner Center for Translational Research; Director of the National Data Archive on Child Abuse and Neglect, Cornell University.

DUPRI presents Jayanti Owens - "What Drives Racial/Ethnic Disparities in School Discipline?"

School suspension and expulsion predict lower school achievement, higher school dropout, and greater interaction with the criminal justice system. Black and Latinx students are respectively 3.2 and 1.3 times more likely than White students to be suspended or expelled. Nonetheless, the causes of these racial/ethnic gaps in discipline remain unclear, due largely to challenges from non-random student sorting into schools/classrooms and difficult-to-observe variation in student behaviors, discipline histories, and classroom situational cues. This study uses an original video vignette experiment with roughly 1,000 U.S. teachers, each linked to administrative data on their school’s characteristics, to disentangle for the first time the roles of three widely-supported mechanisms of Black-White and Latinx-White gaps in school discipline. Tested mechanisms include: 1) between-school sorting (i.e., non-white students disproportionately attend majority-minority and economically disadvantaged schools, which are more punitive to all students), 2) differential behavior perceptions (i.e., comparable behaviors are perceived as worse with non-White vs. White students), and; 3) differential treatment/support (i.e., non-White students are sanctioned more harshly or provided less support for comparable behaviors). (A fourth mechanism, behavior differences, has also been proposed but has gained limited empirical support in prior research and thus is not the focus of the present study.) Findings reveal that between-school sorting plays the largest role in explaining racial disparities in discipline: if White students were to equally attend disadvantaged and minority schools, they would experience similarly high rates of school discipline as Black and Latinx students. Differential behavior perceptions and differential treatment/support also gain some empirical support.

DUPRI's Demography of Aging Training Seminar presents Jessica West, PhD Student in Sociology

Abstract: As the older adult population in the U.S. is expected to double by the year 2030, the health needs of this aging population will likely increase. However, there is limited research on which attributes matter most (or least) to older adults when they decide to seek and utilize care. Research is particularly sparse on the specific needs and priorities of members of the older adult population who face additional challenges in accessing healthcare, such as those with disabilities. People with disabilities experience physical, communication, and attitudinal barriers within the healthcare setting that likely shape their utilization of services, their health outcomes, and their overall satisfaction with their care. In this (very early-stages) presentation, I outline how I plan to address this gap in the literature using the Health and Retirement Study (HRS) and a specific focus on people with hearing, vision, or dual sensory impairments.

DUPRI presents Arnaud Maurel, PhD - "Fertility and Uncertainty"

his paper investigates the impact of economic uncertainty on fertility behavior. We use the fall of the Berlin Wall as a natural experiment that suddenly increased economic uncertainty among women living in East Germany, who underwent a fast transition from a centrally planned economy to a market economy. Using data from the German Socio-Economic Panel, we show that the fall of the Wall resulted in childbearing postponement, which, consistent with a real option framework, is more pronounced early on in the life cycle. We also find that fertility responses to the fall of the Wall were less pronounced for women who report being more shielded from economic uncertainty. We then combine this natural experiment with a dynamic structural model of fertility to estimate the impact of earnings uncertainty on the timing of births, and predict how fertility dynamics would respond to different types of uncertainty shocks.