Job market paper:
Segregation and Marriage
With Jamie Gracie and Sonya Porter
Abstract
Americans rarely marry outside of their own race or class group. We use anonymized data covering nearly the entire U.S. population to study the sources of marital homophily by parental income, or “class,” and race, focusing on white-Black marriage. We distinguish between two explanations for marital homophily: a lack of exposure to people of different backgrounds versus a preference to marry within group. Despite similar levels of marital homophily by race and class, homophily by class is driven largely by residential segregation, whereas racial homophily is not. We analyze the role of residential segregation in partial equilibrium with an instrument for exposure based on race- and class-specific sex ratios in childhood neighborhoods. Increased exposure to opposite-sex members of other class groups leads to a substantial increase in interclass marriage, but increased exposure to other race groups has no detectable impact on white-Black interracial marriage. To quantify the impact of specific desegregation policies in general equilibrium, we develop and estimate a spatial model of the marriage market. Policies that reduce residential segregation can have large effects on interclass marriage with implications for the dynamics of income across generations.
Research in progress:
Can Individualized Student Supports Improve Economic Outcomes for Children in High Poverty Schools?
With Jamie Gracie and Sonya Porter
Abstract
How can we improve outcomes for low-income students? We analyze the adult earnings impacts of the largest comprehensive student support program in the US. Communities in Schools (CIS) places a “navigator” in high-poverty schools who provides an integrated system of supports to students, including academic (e.g., tutoring), economic (e.g., access to food assistance, housing), and mentoring. In 2023, CIS worked with 1.8 million students in 3,750 schools. Using later-treated CIS schools as a control, we estimate that four years of exposure to CIS generates a $1,500 (6% of control mean) increase in earnings at age 30. Effects are larger for students from low-income families and are driven by a reduction in non-employment and an increase in the probability of having a low-paying job. Each child exposed to four years of CIS is expected to pay an additional $10,000 in taxes between ages 18-65, which compares favorably to the program cost. Our results are relevant for the growing community school movement and illuminate a possible path for improving economic mobility in low opportunity neighborhoods.
Growing Class Gaps, Shrinking Race Gaps: Economic and Sociological Mechanisms Underlying Recent Trends in Intergenerational Mobility
With Raj Chetty, Will Dobbie, Sonya Porter, Crystal Yang
NBER SI 2023
What Explains Temporal and Geographic Variation in the Early US Coronavirus Pandemic?
With Hunt Allcott, Levi Boxell, Jacob Conway, Billy Ferguson, Matthew Gentzkow
Media: Vox | Forbes
Abstract
We provide new evidence on the drivers of the early US coronavirus pandemic. We combine an epidemiological model of disease transmission with quasi-random variation arising from the timing of stay-at-home-orders to estimate the causal roles of policy interventions and voluntary social distancing. We then relate the residual variation in disease transmission rates to observable features of cities. We estimate significant impacts of policy and social distancing responses, but we show that the magnitude of policy effects is modest, and most social distancing is driven by voluntary responses. Moreover, we show that neither policy nor rates of voluntary social distancing explain a meaningful share of geographic variation. The most important predictors of which cities were hardest hit by the pandemic are exogenous characteristics such as population and density.
Publications:
Within-industry agglomeration of occupations: Evidence from census microdata
With Thomas Klier and Thomas Walstrum
Journal of Regional Science 59.5 (2019): 910-930
Abstract
This study uses worker-level data on industry, occupation, and place of work to explore differences in the spatial properties of production, administrative, and R&D occupation groups within industries. To measure differences, we calculate location quotients at the local labor market level and the Duranton and Overman (2005) agglomeration index for each group. We find appreciable differences in the spatial distribution of occupation groups within most manufacturing industries, with R&D occupations consistently exhibiting the highest degree of spatial concentration. Our results are consistent with the core theoretical and empirical results in the agglomeration literature.
Any opinions and conclusions expressed herein are those of the author and do not represent the views of the U.S. Census Bureau. The Census Bureau has ensured appropriate access and use of confidential data and has reviewed these results for disclosure avoidance protection (Project 7519874: Segregation and Marriage CBDRB-FY23-CES014-009, Student Supports CBDRB-FY23-CES014-028, Class and Race Gaps CBDRB-FY22-CES010-004, CBDRB-FY23-0375)