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Program and policy effects
Using detailed classroom-level data for North Carolina, we build on previous research to examine racial gaps in access to high-quality teachers. We calculate the exposure of White, Black and Hispanic students to teachers with various characteristics in 4th grade, 7th grade math and English, and 10th grade math and English. We find that across the state White students enjoy sizable advantages over both Black and Hispanic students in the form of higher exposure to teachers with strong credentials and lower exposure to teachers with weak credentials. Remarkably, we also find this pattern of White advantage in most individual counties, with the largest White advantage occurring in the largest counties by enrollment. A decomposition of the White advantages shows that the bulk of them can be attributed to differences across counties and differences between schools within counties. Only in 10th grade are differences across classrooms within schools important in explaining the White advantage.
An administrative rule allowed students who failed an exam to retake it shortly after, triggering strong `teach to the test' incentives to raise these students' test scores for the retake. We develop a model that accounts for truncation and find that these students score 0.14 standard deviations higher on the retest. Using a regression discontinuity design, we estimate thirty percent of these gains persist to the following year. These results provide evidence that test-focused instruction or `cramming' raises contemporaneous performance, but a large portion of these gains fade-out. Our findings highlight that persistence should be accounted for when comparing educational interventions.
This study investigates whether a principal’s likelihood of hiring a teacher of color is sensitive to the racial composition of students in the school. We used an administrative dataset from Texas including 59,157 principal observations and 662,997 teacher observations spanning 2000 to 2017 in order to consider whether or not the disappearing diversity from a majority white school is a factor in principals’ decisions to hire teachers of color. We examined the hiring patterns of principals within schools where 50% of the students were white and compared the probability that a nonwhite teacher would be hired as the homogeneity of the student body increased (that is, as increasing proportions of the student body were white). We found that white principals were less likely to hire teachers of color as the proportion of white students approached 100%. This study provides initial evidence that teacher hires are not only sensitive to the principal’s race but also to the racial composition of the student body. Specifically, as the diversity of the student body disappears, so too does the principal’s likelihood of hiring a teacher of color.
Millions of high school students who take an Advanced Placement (AP) course in one of over 30 subjects can earn college credit by performing well on the corresponding AP exam. Using data from four metro-Atlanta public school districts, we find that 15 percent of students’ AP courses do not result in an AP exam. We predict that up to 32 percent of the AP courses that do not result in an AP exam would result in a score of 3 or higher, which generally commands college credit at colleges and universities across the United States. Next, we examine disparities in AP exam-taking rates by demographics and course taking patterns. Most immediately policy relevant, we find evidence consistent with the positive impact of school district exam subsidies on AP exam-taking rates. In fact, students on free and reduced-price lunch (FRL) in the districts that provide a higher subsidy to FRL students than non-FRL students are more likely to take an AP exam than their non-FRL counterparts, after controlling for demographic and academic covariates.
Before the 2020-21 school year, educators, policymakers, and parents confronted the stark and uncertain trade-offs implied by the health, educational, and economic consequences of offering instruction remotely, in person, or through a hybrid of the two. Most public schools in the U.S. chose remote-only instruction and enrollment fell dramatically (i.e., a loss of roughly 1.1 million K-12 students). We examine the impact of these choices on public-school enrollment using unique panel data that combine district-level enrollment trajectories with information on their instructional modes. We find offering remote-only instead of in-person instruction reduced enrollment by 1.1 percentage points (i.e., a 42 percent increase in disenrollment from -2.6 to -3.7 percent). The disenrollment effects of remote instruction are concentrated in kindergarten and, to a lesser extent, elementary schools. We do not find consistent evidence that remote instruction influenced middle or high-school enrollment or that hybrid instruction had an impact.
This paper examines how financial aid reform based on postsecondary institutional performance impacts student choice. Federal and state regulations often reflect concerns about the private, for-profit sector's poor employment outcomes and high loan defaults, despite the sector's possible theoretical advantages. We use student level data to examine how eliminating public subsidies to attend low-performing for-profit institutions impacts students' college enrollment and completion behavior. Beginning in 2011, California tightened eligibility standards for their state aid program, effectively eliminating most for-profit eligibility. Linking data on aid application to administrative payment and postsecondary enrollment records, this paper utilizes a differences-in-differences strategy to investigate students' enrollment and degree completion responses to changes in subsidies. We find that restricting the use of the Cal Grant at for-profit institutions resulted in significant state savings but led to relatively small changes in students' postsecondary trajectories. For older, non-traditional students we find no impact on enrollment or degree completion outcomes. Similarly, for high school graduates, we find that for-profit enrollment remains strong. Unlike the older, non-traditional students, however, there is some evidence of declines in for-profit degree completion and increased enrollment at community colleges among the high school graduates, but these results are fairly small and sensitive to empirical specification. Overall, our results suggest that both traditional and non-traditional students have relatively inelastic preferences for for-profit colleges under aid-restricting policies.
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy; and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, impacts model performance and the stability of predicted scores. The relative ranking of students’ predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and most complex models.
Is public housing bad for children? Critics charge that public housing projects concentrate poverty and create neighborhoods with limited opportunities, including low-quality schools. However, whether the net effect is positive or negative is theoretically ambiguous and likely to depend on the characteristics of the neighborhood and schools compared to origin neighborhoods. In this paper, we draw on detailed individual-level longitudinal data on students moving into New York City public housing and examine their academic outcomes over time. Exploiting plausibly random variation in the precise timing of entry into public housing, we estimate credibly causal effects of public housing using both difference-in-differences and event study designs. We find credibly causal evidence of positive effects of moving into public housing on student test scores, with larger effects over time. Stalled academic performance in the first year of entry may reflect, in part, disruptive effects of residential and school moves. Neighborhood matters: effects are larger for students moving out of low-income neighborhoods or into higher-income neighborhoods, and these students move to schools with higher average test scores and lower shares of economically disadvantaged peers. We also find some evidence of improved attendance outcomes and reduction in incidence of childhood obesity for boys following public housing residency. Our study results refute the popular belief that public housing is bad for kids and probe the circumstances under which public housing may work to improve academic outcomes for low-income students.
School bullying is widespread and has substantial social costs. One in five U.S. high school students report being bullied each school year and these students face greater risks of serious mental health challenges that extend into adulthood. As the COVID-19 pandemic forced most students into online education, many have worried that cyberbullying prevalence would grow dramatically. We use data from Google internet searches to examine changing bullying patterns as COVID-19 disrupted in-person schooling. Pre-pandemic historical patterns show that internet searches provide useful information about actual bullying behavior. Real-time data then shows that searches for school bullying and cyberbullying both dropped about 30-40 percent as schools shifted to remote learning in spring 2020. This drop is sustained through the fall and winter of the 2020-21 school year, though the gradual return to in-person instruction partially returns bullying searches to pre-pandemic levels. These results highlight how in-person interaction is an important mechanism underlying not only in-person school bullying, but also cyberbullying. We discuss how this otherwise damaging shock to students and schools provides insight into the mixed impact of the pandemic on student well-being.
Covid-19-induced school closures generated great interest in tutoring as a strategy to make up for lost learning time. Tutoring is backed by a rigorous body of research, but it is unclear whether it can be delivered effectively remotely. We study the effect of teacher-student phone call interventions in Kenya when schools were closed. Schools (n=105) were randomly assigned for their 3rd, 5th and 6th graders (n=8,319) to receive one of two versions of a 7-week weekly math-focused intervention—5-minute accountability checks or 15-minute mini-tutoring sessions—or to the control group. Although calls increased student perceptions that teachers cared, accountability checks had no effect on math performance up to four months after the intervention and tutoring decreased math achievement among students who returned to their schools after reopening. This was, in part, because the relatively low-achieving students most likely to benefit from calls were least likely to return and take in-person assessments. Tutoring substituted away from more productive uses of time, at least among returning students. Neither intervention affected enrollment. Tutoring remains a valuable tool but to avoid unintended consequences, careful attention should be paid to aligning tutoring interventions with best practices and targeting interventions to those who will benefit most.