Research

How can we improve outcomes for low-income students? We analyze the adult earnings impacts of the largest comprehensive student support program in the United States. 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 $9,000 in taxes between ages 18-65, which compares favorably to the direct cost of the program. Our results are relevant for the growing community school movement and illuminate a possible path for improving economic mobility in low opportunity neighborhoods.

We study the mechanisms underlying recent trends in economic opportunity using anonymized longitudinal data covering nearly the entire U.S. population. We first document a pattern of growing white class gaps and shrinking race gaps in intergenerational mobility. For white children born between 1978 and 1992, intergenerational mobility fell substantially, with improving outcomes for children from high-income families and deteriorating outcomes for children from low-income families (growing white class gaps). Outcomes for Black children improved across all parent income levels in the same birth cohorts, leading to declining Black-white earnings gaps for the majority of Black children (shrinking race gaps). These divergent trends by race and class occur even for children living in the same Census tracts, indicating that they are driven by shocks that affect these groups differently. We then explore the forces underlying these different race and class shocks, showing that changes in intergenerational mobility are closely linked to changes in group-specific parent employment rates. Quasi-experimental estimates based on children who move across counties show that spending an additional year of childhood in a county where parent employment rates in one’s race and class group are declining has negative effects on earnings in adulthood. The effects are group- and cohort-specific, with no effect of parent employment rates in other groups or birth cohorts. These group- and cohort-specific childhood exposure effects suggest that changes to parent employment rates impact outcomes through changes in the childhood environment, not through shared labor market shocks. We conclude by showing that nearly all of the declining race gaps and expanding white class gaps at the national level can be explained by different changes in the childhood environment, with low-income white parents experiencing the largest employment declines during the period we study.

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.

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.