Working Papers:
Does Coursework Matter? Uncovering the Role of Skills in the Returns to College
EdWorkingPaper: 26-1484
Abstract
The continuing shift of the U.S. economy toward a high-skill base has increased the demand for college-educated workers. To understand how higher education prepares students for this evolving economy, a large body of literature in labor economics has focused on the causes and consequences of college enrollment, institutional selectivity, and major choice. Much less attention has been paid to a key dimension that shapes the skills students acquire in college—coursework. In this paper, I develop new methods to identify the skills taught in college courses and credibly estimate their causal impacts on labor market outcomes, helping to explain earnings variation among students within the same major. I first scrape and compile a new dataset of detailed course descriptions from Texas public universities. Using a large language model (GPT-4), I extract the skills students are likely to acquire from each course, focusing on two widely taught and consistently identifiable domains: quantitative and writing. I then link these course-level skill measures to Texas administrative records that track students’ educational histories and quarterly earnings. To estimate the returns to coursework-based skills, I implement an instrumental variables strategy that exploits variation in course offerings across cohorts within the same major. I find substantial early-career earnings returns to coursework-based quantitative skills, but no detectable returns to writing skills. These returns are especially large for underrepresented minority (URM) students and for students in less quantitatively intensive majors, suggesting that expanding access to quantitative coursework within majors may serve as a new lever for narrowing racial earnings gaps.
Mobile Internet and Mental Health
(with Lipeng Chen)
Abstract
The past decade has seen a significant worsening of mental health in the U.S., which coincides with the rapid expansion of mobile internet. Combining 3G coverage data from 2007 to 2020 with the Behavioral Risk Factor Surveillance System data, we find that mental health deteriorates following the arrival of 3G internet: a 10-percentage-point increase in 3G coverage raises the number of poor mental health days by 0.02 days per month, corresponding to a 0.6% increase. The effects are larger among younger individuals and women. Regarding mechanisms, we provide evidence that these effects are consistent with increased social media use, reduced offline socializing, and lower physical activity.
Causal Inference in Staggered Adoption Panels: The Correct Comparison Units Depend on Your Intervention
(with Ian Lundberg)
Abstract
A powerful data structure for causal inference is staggered adoption: many units are observed over many time periods, and some units never become treated while other units adopt a treatment at staggered time points. Popular methods that apply in this setting include difference in difference, fixed effects, matching, and synthetic control. All of these methods use untreated or not-yet-treated units to answer the question: what would have happened if the treated unit had not become treated? We show that this question may actually hide two very different causal questions interest. The first question is what would have happened if a treated unit had never become treated over many time points. The second question is what would have happened if a treated unit had not become treated at the particular time point when they in fact became treated. Popular methods such as synthetic control are often interpreted with respect to the former question (about a longitudinal treatment). We show that they actually answer the latter question (about a point-in-time treatment). The distinction is especially relevant if many units become treated at many time points, a common setting when these methods are applied in demography.