Americans rarely marry outside their race or class group, a pattern with well-documented implications for inequality and intergenerational mobility. Limited exposure—or interactions with members of other groups—may partly explain these low intergroup marriage rates. We instrument for exposure using variation in childhood neighborhoods based on whether other race and class groups had more opposite-sex children of similar age. Exposure increases interclass (high- and low-parent-income) marriage but has no detectable effect on interracial (White and Black) marriage. A spatial marriage market model predicts that residential segregation—one of many forms of exposure—accounts for more than one third of marital sorting by class but less than 5% by race.

We show that intergenerational mobility changed rapidly by race and class in recent decades and use these trends to study the causal mechanisms underlying changes in economic mobility. For white children in the U.S. born between 1978 and 1992, earnings increased for children from high-income families but decreased for children from low-income families, increasing earnings gaps by parental income ("class") by 30%. Earnings increased for Black children at all parental income levels, reducing white-Black earnings gaps for children from low-income families by 30%. Class gaps grew and race gaps shrank similarly for non-monetary outcomes such as educational attainment, standardized test scores, and mortality rates. Using a quasi-experimental design, we show that the divergent trends in economic mobility were caused by differential changes in childhood environments, as proxied by parental employment rates, within local communities defined by race, class, and childhood county. Outcomes improve across birth cohorts for children who grow up in communities with increasing parental employment rates, with larger effects for children who move to such communities at younger ages. Children's outcomes are most strongly related to the parental employment rates of peers they are more likely to interact with, such as those in their own birth cohort, suggesting that the relationship between children's outcomes and parental employment rates is mediated by social interaction. Our findings imply that community-level changes in one generation can propagate to the next generation and thereby generate rapid changes in economic mobility.

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.

Children from low-income families face persistent educational and economic disadvantages. This paper studies Communities In Schools (CIS), a program that places coordinators in high-poverty schools to connect struggling students with personalized support. CIS is the largest program of its kind in the US, reaching 2 million students each year—nearly three times the size of Head Start—and, unlike most programs of this scale, is funded largely by private philanthropy and local governments. Using the staggered rollout of CIS, we find that the program boosts test scores for struggling students, and that these improvements persist, ultimately increasing high-school completion and adult earnings. These long-run effects can be closely forecast from changes in short-run outcomes, with non-cognitive measures playing a central role. CIS emphasizes personalization as a core feature of its model, and our results are consistent with this claim: coordinators tailor services to distinct student needs, yet students with different needs still see similarly large long-run gains. CIS delivers returns that compare favorably to other major education interventions, such as class-size reductions.

Over the past half-century, U.S. four-year colleges have shifted from majority-male to majority-female. As college men have become increasingly scarce, college women have maintained stable marriage rates by marrying economically well-off non-college men. Meanwhile, economic outcomes for other non-college men have sharply declined, accompanied by a drop in marriage rates for non-college women. Both historical evidence and cross-area comparisons suggest that worsening male outcomes primarily undermine the marriage prospects of non-college women. The gap in marriage rates between college- and non-college women is more than 50% smaller in areas where men have the lowest rates of joblessness and incarceration.

Why do students in high-poverty schools perform worse academically and in the labor market than their peers in low-poverty schools? We show that a key difference is how regularly students attend school. We use transitory shocks to absences arising from factors like respiratory illnesses to estimate the effect of improving school-wide attendance. Among high-poverty schools, moving from the 75th to the 25th percentile of the absence distribution would raise high school graduation rates by 2.3 percentage points and increase average earnings at age 25 by $3,600. Our findings suggest that up to one-third of the achievement gap between low- and high-poverty schools can be attributed to absenteeism. We show that policies aimed at improving attendance in high-poverty schools can close these disparities in outcomes. We estimate that nationwide adoption of "Communities in Schools", the country's largest student support program, could reduce the achievement gap by 20%, primarily through its effect on attendance.