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Program and policy effects
We study how colleges shape their students' voting habits by linking millions of SAT takers to their college-enrollment and voting histories. To begin, we show that the fraction of students from a particular college who vote varies systematically by the college's attributes (e.g. increasing with selectivity) but also that seemingly similar colleges can have markedly different voting rates. Next, after controlling for students' college application portfolios and pre-college voting behavior, we find that attending a college with a 10 percentage-point higher voting rate increases entrants' probability of voting by 4 percentage points (10 percent). This effect arises during college, persists after college, and is almost entirely driven by higher voting-rate colleges making new voters. College peers' initial voting propensity plays no discernible role.
We examine the fundamental and complex role that time plays in the learning process. We begin by developing a conceptual framework to elucidate the multiple obstacles schools face in converting total time in school into active learning time. We then synthesize the causal research and document a clear positive effect of time on student achievement of small to medium magnitude, but also with likely diminishing marginal returns. Further descriptive analyses reveal how large differences in the length of the school day and year across public schools are an underappreciated dimension of educational inequality in the United States. Finally, our case study of time loss in one urban district demonstrates the potential to substantially increase learning time within existing constraints.
We use a marginal treatment effect (MTE) representation of a fuzzy regression discontinuity setting to propose a novel estimator. The estimator can be thought of as extrapolating the traditional fuzzy regression discontinuity estimate or as an observational study that adjusts for endogenous selection into treatment using information at the discontinuity. We show in a frequentest framework that it is consistent under weaker assumptions than existing approaches and then discuss conditions in a Bayesian framework under which it can be considered the posterior mean given the observed conditional moments. We then use this approach to examine the effects of early grade retention. We show that the benefits of early grade retention policies are larger for students with lower baseline achievement and smaller for low-performing students who are exempt from retention. These findings imply that (1) the benefits of early grade retention policies are larger than have been estimated using traditional fuzzy regression discontinuity designs but that (2) retaining additional students would have a limited effect on student outcomes.
In 2016, the GED® introduced college readiness benchmarks designed to identify testers who are academically prepared for credit-bearing college coursework. The benchmarks are promoted as awarding college credits or exempting “college-ready” GED® graduates from remedial coursework. I show descriptive evidence that those identified as college-ready by these benchmarks enroll and persist in college at significantly higher rates than others who pass the GED® exam, but at lower rates than recent graduates with traditional high school diplomas. Regression discontinuity estimates show that crossing a college readiness threshold does not substantially influence testers' college enrollment or persistence during the two years following their first test attempt. Relatedly, I observe little exam retaking by those who fall narrowly short of the minimum college readiness score thresholds. This contrasts strongly with retaking behavior near the lower GED® passing threshold that determines eligibility for a high school equivalency credential. Those who narrowly fail a GED® subject test are over 100 times more likely to retest than those who fall just short of a college readiness benchmark in the same subject. GED® college readiness benchmarks do not currently appear to promote better college outcomes, but in the absence of more detailed test score information they offer a simple heuristic to predict short-run college enrollment and persistence among GED® graduates, particularly for those who identify educational gain as a primary reason for testing. The results highlight the promise and challenges associated with building pathways for non-traditional students to earn credit for prior learning.
The COVID-19 pandemic’s impact on preschool children’s school readiness skills remains understudied. This research investigates Head Start preschool children’s early numeracy, literacy, and executive function outcomes during a pandemic-affected school year. Study children (N = 336 assessed at fall baseline; N = 237-250 assessed in spring depending on outcome; fall baseline sample: mean age = 51 months; 46% Hispanic; 36% Black Non-Hispanic; 52% female) in a network of Head Start centers in four states (Nevada, New Jersey, Pennsylvania, and Wisconsin) experienced low in-person preschool exposure compared to national pre-pandemic norms. Children experienced fall to spring score gains during the pandemic-affected year of 0.05 SD in executive function, 0.27 SD in print knowledge, and 0.45-0.71 SD in early numeracy skills. Descriptively, for two of the three early numeracy domains measured, spring test score outcomes were stronger among children who attended more in-person preschool. We discuss implications for future research and policy.
Using a rich dataset that merges student-level school records with birth records, and leveraging three alternative identification strategies, we explore how increase in access to charter schools in twelve districts in Florida affects students remaining in traditional public schools (TPS). We consistently find that competition stemming from the opening of new charter schools improves reading—but not math—performance and it also decreases absenteeism of students who remain in the TPS. Results are modest in magnitude.
This study provides the first causal analysis of the impact of expanding Computer Science (CS) education in U.S. K-12 schools on students’ choice of college major and early career outcomes. Utilizing rich longitudinal data from Maryland, we exploit variation from the staggered rollout of CS course offerings across high schools. Our findings suggest that taking a CS course increases students’ likelihood of declaring a CS major by 10 percentage points and receiving a CS BA degree by 5 percentage points. Additionally, access to CS coursework raises students’ likelihood of being employed and early career earnings. Notably, students who are female, low socioeconomic status, or Black experience larger benefits in terms of CS degree attainment and earnings. However, the lower take-up rates of these groups in CS courses highlight a pressing need for targeted efforts to enhance their participation as policymakers continue to expand CS curricula in K-12 education.
An increasing body of robust evidence concludes that corequisite remediation in math and English is a cost-effective alternative to traditional developmental education, offering improved immediate course progression and potentially better persistence and completion. This is the first study to disentangle the impacts of the two main elements of the corequisite model: accelerated college course placement and concurrent academic support. Utilizing a fuzzy regression discontinuity design and variation in Texas colleges' implementation of math corequisites, the study shows that college-level math course placement without additional support increases passing rates by 22 percentage points. This effect rises to 36 percentage points with concurrent developmental support. These findings bolster a growing consensus around the benefits of accelerated developmental education and suggest that a corequisite approach may have significant advantages over removing developmental education requirements entirely.
This study evaluates the unintended consequences of the 2012 suspension ban in New York City. I find that the ban induced a substitution towards classification for students at high risk for suspension—Black students, male students, and those in schools with a high reliance on suspension. I find that disabilities that carry greater stigma and experience greater exclusion from the general education classroom drive the increases in classification. This substitution may benefit students if they are now receiving needed services. Simultaneously, ban-induced classifications may simply serve as a partial substitute for suspension.
This concurrent mixed methods study descriptively explores teacher residency programs (TRPs) across the nation. We examine program and participant survey data from the National Center for Teacher Residencies (NCTR) to identify important TRP structures for resident support. Latent class analysis of program-level data reveals three types of TRPs (locally-funded low tuition, multi-funded multifaceted, and federally-funded post-residency support), while regression models indicate significant relationships between individual program structures and participant (residents, graduates, mentors, and principals) perceptions. Qualitative analyses of multiple open response items across participants details four salient TRP structures: providing extended clinical experience, localizing individual support, offering programmatic training, and teaching practical professional knowledge. Findings inform policymakers on TRP investment, practitioners about program design, and researchers for continued large-scale evidence.