We present evidence that travel by college students, identified by the timing of university spring breaks, contributed to the local spread of COVID-19. Due to the timing of university closures, students at universities with earlier spring breaks traveled and subsequently returned to campus while students at universities with later spring breaks effectively had their breaks canceled. We collect spring break dates for traditional four-year universities and link these universities to smartphone location data. To study the effect of spring break travel on the evolution of confirmed COVID-19 cases and mortality, we use a difference-in-differences identification strategy. Our estimates imply that counties with more early spring break students had higher confirmed case growth rates than counties with fewer early spring break students. We find that the increase in case growth rates peaked two weeks after students returned to campus. Consistent with secondary spread to more vulnerable populations, we find an increase in mortality growth rates that peaked four to five weeks after students returned. We trace destinations and modes of travel for university students and find that students who traveled through airports, to New York City, and to popular Florida destinations contributed more to the spread of COVID-19 than the average early spring break student. Our results suggest that universities have a unique capacity to reduce local COVID-19 spread by altering academic calendars to limit university student travel.
I investigate how personal financial literacy (PFL) education in high school affects federal student loan repayment outcomes after college. I use university-level repayment outcomes to overcome a lack of quality borrower-level data. Changes to state standards have varying impacts on university cohorts because universities differ in their shares of students from adopting states. Using this variation, I find that PFL mandates improve federal student loan repayment and that the effects are largest for first generation and low-income students at public universities. I show that these university-level impacts consistently estimate borrower level improvements. I explore several mechanisms that might explain how PFL mandates increase repayment. The evidence suggests that mandated students are more knowledgeable about the federal financial aid system. However, mandated students are not better at answering questions pertaining to PFL topics nor do they have higher odds of completing college. Further, only high income students reduce federal student loan borrowing as a result of PFL mandates.
We exploit the partial deregulation of New York City taxi medallions to provide a causal estimate of the impact of taxi supply on congestion. We employ taxi trip records to measure historical street-level speed. We find that the roll-out of newly authorized taxis caused a local 8-9% decrease in speed. We estimate an empirical congestion elasticity curve from heterogeneous changes in speed and taxi supply, counted from aerial orthoimagery. Additionally, we provide novel urban sensor data to document a substantial traffic slowdown since 2013. Most of the slowdown in midtown Manhattan is accounted for by new supply from ridehail applications.
I estimate the impact of a supplemental nutrition intervention on math and language arts test scores at low-income elementary schools in the Mississippi Delta. The intervention provided meals to students in order to replicate school breakfast and lunch over the course of the weekend. Using a difference-in-differences design, I estimate the effect of the intervention on the mean and the distribution of test scores. I find that treated students performed better on both language arts and math standardized tests. The average gains stem from a reduction in the share of students achieving at the lowest threshold and shifts toward higher thresholds. I also use administrative daily attendance data to estimate how the intervention affected attendance by day of the week. Attendance on Fridays improved likely due to the transfer incentive, however I find improvements in attendance on Mondays and Tuesdays which is evidence of improved nutrition over the course of the weekend.
We examine the predictors of both long-term expectations of self-employment and future self-employment activities and earnings among the same individuals, with a particular focus on gender differences and the roles of non-cognitive skills. Using longitudinal data from the GMAT Registrant Survey, which includes prospective graduate management students, our analysis involves wide-ranging and novel sets of variables, including work-life balance and job preferences, self-efficacy, confidence, and other non-cognitive skills or characteristics. We find notable differences in the drivers of self-employment and self-employment expectations between men and women, and also large differences in the set of variables that relate to self-employment intentions versus future self-employment outcomes. While preferences for work-life balance matter more for men's expectations, preferences about non-monetary characteristics of the job, such as job security and interesting work, matter more for women. In contrast, regarding actual self-employment, only non-cognitive skills play a substantial role for women, while men are driven mostly by preferences over work-life balance. Confidence in one's quantitative skills influences self-employment decisions, especially for women, and it also affects success in both the self-employed and the traditionally employed sectors, as reflected in earnings.