"Renal Relationships: Understanding Living Kidney Donor Relationship Patterns"
Abstract: Who do we turn to in times of need? Traditionally, social support research has shown a strong preference to rely on strong ties in these scenarios - often, even when weak ties might be better positioned to help. However, this conclusion has recently been challenged by Small (2017), who argues that people often rely on weak ties for emotional support in stressful times, preferring to avoid more complicated strong ties. This suggests that the types of ties we activate in times of need varies by the situation. In this study, we apply this framework to the study of living donor kidney transplantation (LDKT), effectively asking: How does this behavior differ when the stakes are potentially life and death? Using a variety of primary and secondary datasets, we compare the distribution of LDKT ties to the distribution of ties who would be likely able to help, then seek to explain these relative utilization patterns as a function of medical fundamentals, social/spatial relationships, and qualitative reasoning invoked by survey respondents. Our preliminary findings show that LDKT patterns are primarily driven by social relationship quality, and far less by medical fundamentals such as the potential donors' health or genetic relationship to the patient.
"Can Labor Market Discrimination Explain Racial Disparities in Schooling? Evidence from WWII"
Abstract: Can the racial gap in labor market earnings explain black-white disparities in the schooling of the next generation? To answer this, we exploit the large increase in labor demand in markets that received WWII defense industry contracts. This increase in labor demand combined with a policy that prohibited discrimination by race and ethnicity in the defense industries resulted in significant increases in African American earnings and declines in the racial gap in earnings between 1940 and 1950. This was achieved largely via occupational upgrading among African Americans into semi-skilled professions. In contrast with women, whose progress in the labor market was largely reversed in short order, this occupational upgrading persisted for African Americans. We argue that this persistence is consistent with declines in statistical discrimination. Moreover, we find that in these same labor markets, the next generation of African Americans invested relatively more in their human capital, as measured by greater years of schooling and a decline in the black-white schooling gap. We explore three reasons why reductions in the black white earnings gap might lead to reductions in the black white schooling gap of the next generation. First, this would relax the financial constraint faced by many African American families, allowing their children to remain in school longer. Second, occupational upgrading might have increased the returns to human capital among African Americans. Finally, there may be political responses that result in changes in public funding and provision of schooling and other public goods that affect the human capital accumulation of the next generation of African Americans. We find evidence consistent with the first explanation only. We conclude that efforts to further reduce the racial gap in schooling might consider labor market interventions.
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.
Co-sponsored with the Social Stratification, Inequality and Mobility Working Group
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.
