This paper uses a college-by-graduate degree fixed effects estimator to evaluate the returns to 19 different graduate degrees for men and women. We find substantial variation across degrees, and evidence that OLS overestimates the returns to degrees with high average earnings and underestimates the returns to degrees with low average earnings. Second, we decompose the impacts on earnings into effects on wage rates and effects on hours. For most degrees, the earnings gains come from increased wage rates, though hours play an important role in some degrees, such as medicine, especially for women. Third, we estimate the net present value and internal rate of return for each degree, which account for the time and monetary costs of degrees. We show annual earnings and hours worked while enrolled in graduate school vary a lot by gender and degree. Finally, we provide descriptive evidence that gains in overall job satisfaction and satisfaction with contribution to society vary substantially across degrees.
This paper documents differences across higher education courses in the coverage of frontier knowledge. Applying natural language processing (NLP) techniques to the text of 1.7M syl- labi and 20M academic articles, we construct the “education-innovation gap,” a syllabus’s rel- ative proximity to old and new knowledge. We show that courses differ greatly in the extent to which they cover frontier knowledge. Instructors play a big role in shaping course content; instructors who are active researchers teach more frontier knowledge. More selective and bet- ter funded schools, and those enrolling socio-economically advantaged students, teach more frontier knowledge. Students from these schools are more likely to complete a doctoral degree, produce more patents, and earn more after graduation.
What are the effects of school closures during the Covid-19 pandemic on children’s education? Online education is an imperfect substitute for in-person learning, particularly for children from low-income families. Peer effects also change: schools allow children from different socio-economic backgrounds to mix together, and this effect is lost when schools are closed. Another factor is the response of parents, some of whom compensate for the changed environment through their own efforts, while others are unable to do so. We examine the interaction of these factors with the aid of a structural model of skill formation. We find that school closures have a large, persistent, and unequal effect on human capital accumulation. High school students from low-income neighborhoods suffer a learning loss of 0.4 standard deviations after a one-year school closure, whereas children from high-income neighborhoods initially remain unscathed. The channels operating through schools, peers, and parents all contribute to growing educational inequality during the pandemic.
Many centralized school admissions systems use lotteries to ration limited seats at oversubscribed schools. The resulting random assignment is used by empirical researchers to identify the effects of schools on outcomes like test scores. I first find that the two most popular empirical research designs may not successfully extract a random assignment of applicants to schools. When are the research designs able to overcome this problem? I show the following main results for a class of data-generating mechanisms containing those used in practice: The first-choice research design extracts a random assignment under a mechanism if the mechanism is strategy-proof for schools. In contrast, the other qualification instrument research design does not necessarily extract a random assignment under any mechanism. The former research design is therefore more compelling than the latter. Many applications of the two research designs need some implicit assumption, such as large-sample approximately random assignment, to justify their empirical strategy.
We estimate the returns to a broad set of graduate degrees. To control for heterogeneity in preferences and ability, we use fixed effects for combinations of field-specific undergraduate and graduate degrees obtained by the last time we observe an individual. Basically, we compare earnings before the graduate degree to earnings after it. Using National Science Foundation data, we find large differences across graduate fields in earnings effects. The returns often depend on the undergraduate major. The contribution of occupational upgrading to the earnings gain varies across degrees. Finally, simple regression-based estimates of returns to graduate fields are often highly misleading.
As children reach adolescence, peer interactions become increasingly central to their development, whereas the direct influence of parents wanes. Nevertheless, parents may continue to exert leverage by shaping their children's peer groups. We study interactions of parenting style and peer effects in a model where children's skill accumulation depends on both parental inputs and peers, and where parents can affect the peer group by restricting who their children can interact with. We estimate the model and show that it can capture empirical patterns regarding the interaction of peer characteristics, parental behavior, and skill accumulation among US high school students. We use the estimated model for policy simulations. We find that interventions (e.g., busing) that move children to a more favorable neighborhood have large effects but lose impact when they are scaled up because parents' equilibrium responses push against successful integration with the new peer group.
A growing number of school districts use centralized assignment mechanisms to allocate school seats in a manner that reflects student preferences and school priorities. Many of these assignment schemes use lotteries to ration seats when schools are oversubscribed. The resulting random assignment opens the door to credible quasi-experimental research designs for the evaluation of school effectiveness. Yet the question of how best to separate the lottery-generated randomization integral to such designs from non-random preferences and priorities remains open. This paper develops easily-implemented empirical strategies that fully exploit the random assignment embedded in a wide class of mechanisms, while also revealing why seats are randomized at one school but not another. We use these methods to evaluate charter schools in Denver, one of a growing number of districts that combine charter and traditional public schools in a unified assignment system. The resulting estimates show large achievement gains from charter school attendance. Our approach generates efficiency gains over ad hoc methods, such as those that focus on schools ranked first, while also identifying a more representative average causal effect. We also show how to use centralized assignment mechanisms to identify causal effects in models with multiple school sectors.