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October 2019

Michelle Jackson, Stanford University

October 23, 2019 @ 12:00 pm - 1:30 pm PDT
4240 Public Affairs Bldg

A Century of Educational Inequality in the United States

Abstract: The “income inequality hypothesis” holds that rising income inequality affects the distribution of a wide range of social and economic outcomes. Research highlighting the sharp increase in educational inequality in recent decades has fuelled concerns that rising income inequality has had damaging consequences for equality of educational opportunity, even while other researchers have provided descriptive evidence at odds with the income inequality hypothesis. In this paper we track long-term trends in family income inequalities in college enrollment ("enrollment inequality") using all available nationally representative datasets for cohorts born between 1908 and 1995. We show that the trend in enrollment inequality moved in lockstep with the trend in income inequality over the past century. There is one exception to this general finding: for cohorts at risk of serving in the Vietnam War, enrollment inequality was high while income inequality was low. During this period, enrollment inequality was significantly higher for men than for women. Aside from this singular confounding event, evidence on a century of enrollment inequality establishes a strong association between income inequality and enrollment inequality, providing support for the view that rising income inequality is fundamentally changing the distribution of life chances.

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Stefan Wager, Stanford University

October 30, 2019 @ 12:00 pm - 1:30 pm PDT
4240 Public Affairs Bldg

Machine Learning for Causal Inference

Abstract: Given advances in machine learning over the past decades, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible. In this talk, I'll discuss how these machine learning tools can be rigorously integrated into observational study analyses, and how they interact with classical ideas around randomization, semiparametric modeling, double robustness, etc. When deployed carefully, machine learning enables us to develop statistical estimators that reflect the study design more closely than basic linear regression based methods.

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November 2019

Courtney Thomas, UCLA

November 6, 2019 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Distinguishing Distress from Disorder: Black-White Patterns in the Determinants of and Links between Depressive Symptoms and Major Depression

Background: Black Americans experience higher rates of psychological distress but similar or lower rates of psychiatric disorders than Whites. This study aimed to clarify these discordant distress-disorder patterns by (1) assessing whether sociodemographic and psychosocial risk factors varied across outcomes and racial groups and (2) evaluating Black-White differences in the distress-disorder linkage.

Methods: Secondary analysis of the Nashville Stress and Health Study (n=1,246), a community epidemiologic survey of Blacks and Whites in Nashville, Tennessee, was used to assess distress (CES-D depressive symptoms scale) and major depression (MDD; based on the CIDI) was the disorder of interest. Race-stratified models assessed correlates of each outcome and the between distress-disorder association separately among Blacks and Whites; interactions considered potential moderating effects of SES and stress exposure on this association within each group.

Results: Distress and disorder were differentially shaped by risk factors and varied by race. Increases in distress were associated with greater disorder risk among both racial groups. However, a significant interaction between distress and stress exposure indicated that the odds of “chronic” MDD (lifetime and past-year prevalence) depends on the level of stress exposure for Blacks only.

Conclusions: This study informs the “race paradox in mental health” by demonstrating that distress and disorder have etiologies that vary within and across racial groups and the distress-disorder association depends on stress exposure among Black Americans. This has implications for public health practice, as pinpointing the protective mechanisms underlying Blacks’ low disorder rates despite elevated risk exposure may inform more effective avenues of intervention.

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Statistical Computing Part 1

November 6, 2019 @ 1:30 pm - 2:30 pm PST
4240 Public Affairs Bldg

Instructor: Matt Lahmann We'll get you signed up for hoffman2 and TS2. With Hoffman2 and TS2, you'll have state of the art hardware resources and most software you'll ever need for research. RSVP Signup via https://forms.gle/FgGuPdqQdF3RLVzC8

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Courtney Cogburn, Columbia University

November 13, 2019 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Statistical Computing Part 2

November 14, 2019 @ 12:00 pm - 1:00 pm PST

Instructor: Mike Tzen Using a very common data analysis scenario of "Group-(Split)-Apply-Combine", we'll show you how to make the most of cutting edge powerful hardware in both Hoffman2 and TS2. RSVP signup via https://forms.gle/i9fXG2Dcbw8L5AG38

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René D. Flores, The University of Chicago

November 20, 2019 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

December 2019

Emily Smith-Greenway, USC

December 4, 2019 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Life after death: The scale and salience of mortality in sub-Saharan Africa

Abstract: Dramatic reductions in the infant and under-five mortality rates over the last half century are among the global health community’s most notable achievements. The trends are clear and the message is positive: the world today is healthier and safer for young people than it has ever been. Sub-Saharan African countries, in particular, have experienced some of the most dramatic reductions in early life mortality. However, the all-time low infant and under-five mortality rates conceal the pervasiveness by which contemporary populations experience the phenomenon of having an infant or under-five-year-old child die—a life event that can leave parents vulnerable in myriad ways. In this talk I will introduce new population measures that capture the scale at which infant and child deaths are experienced by and dispersed across mothers in contemporary African populations. I will then demonstrate the disadvantage mothers can experience following a child’s death, and will conclude by discussing how I am extending this research with a data collection project in rural Malawi.

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January 2020

Ken Smith, University of Utah

January 8, 2020 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Pablo Barberá, University of Southern California

January 15, 2020 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Hajar Yazdiha, University of Southern California

January 22, 2020 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Hajar Yazdiha, University of Southern California

January 22, 2020 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Nancy Krieger, Harvard University

January 29, 2020 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

February 2020

David A. Siegel, Duke University

February 5, 2020 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg

Marianne Bertrand, University of Chicago

February 12, 2020 @ 12:00 pm - 1:30 pm PST
4240 Public Affairs Bldg
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