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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.
Although numerous studies document different forms of discrimination in the U.S. public education system, very few provide plausibly causal estimates. Thus, it is unclear to what extent public school principals discriminate against racial and ethnic minorities. Moreover, no studies test for heterogeneity in racial/ethnic discrimination by individual-level resource needs and school-level resource strain – potentially important moderators in the education context. Using a correspondence audit, we examine bias against Black, Hispanic, and Chinese American families in interactions with 52,792 public K-12 principals in 33 states. Our research provides causal evidence that Hispanic and Chinese American families face significant discrimination in initial interactions with principals, regardless of individual-level resource needs. Black families, however, only face discrimination when they have high resource needs. Additionally, principals in schools with greater resource strain discriminate more against Chinese American families. This research uncovers complexities of racial/ethnic discrimination in the K-12 context because we examine multiple racial/ethnic groups and test for heterogeneity across individual- and school-level variables. These findings highlight the need for researchers conducting future correspondence audits to expand the scope of their research to provide a more comprehensive analysis of racial/ethnic discrimination in the U.S.
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.
Educational researchers often report effect sizes in standard deviation units (SD), but SD effects are hard to interpret. Effects are easier to interpret in percentile points, but converting SDs to percentile points involves a calculation that is not transparent to educational stakeholders. We show that if the outcome variable is normally distributed, simply multiplying the SD effect by 37 usually gives an excellent approximation to the percentile-point effect. For students in the middle three-fifths of a normal distribution, this rule of thumb is always accurate to within 1.6 percentile points for effect sizes of up to 0.8 SD. Two examples show that the rule can be just as accurate for empirical effects from real studies. Applying the rule to Kraft’s empirical benchmarks, we find that the least effective third of educational interventions raise scores by 0 to 2 percentile points; the middle third raise scores by 2 to 7 percentile points; and the most effective third raise scores by more than 7 percentile points.
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.
Education leaders must identify valid metrics to predict student long-term success. We exploit a unique dataset containing data on cognitive skills, self-regulation, behavior, course performance, and test scores for 8th-grade students. We link these data to data on students' high school outcomes, college enrollment, persistence, and on-time degree completion. Cognitive tests and survey-based self-regulation measures predict high school and college outcomes. However, these relationships become small and lose statistical significance when we control for test scores and a behavioral index. For leaders hoping to identify the best on-track indicators for college completion, the information collected in student longitudinal data systems better predicts both short- and long-run educational outcomes than these survey-based measures of self-regulation and cognitive skills.
This paper identifies which investments in school facilities help students and are valued by homeowners. Using novel data on school district bonds, test scores, and house prices for 29 U.S. states and a research design that exploits close elections with staggered timing, we show that increased school capital spending raises test scores and house prices on average. However, impacts differ vastly across types of funded projects. Spending on basic infrastructure (such as HVAC) or on the removal of pollutants raises test scores but not house prices; conversely, spending on athletic facilities raises house prices but not test scores. Socio-economically disadvantaged districts benefit more from capital outlays, even conditioning on project type and the existing capital stock. Our estimates suggest that closing the spending gap between high- and low-SES districts and targeting spending towards high-impact projects may close as much as 25% of the observed achievement gap between these districts.
How much school students attend is a powerful indicator of their well-being and a strong predictor of their future success in school. Prior research has documented the myriad in-school and out-of-school factors that contribute to high levels of student absenteeism, many emerging from the root causes of poverty and disengagement. The shift to online learning during the COVID-19 pandemic likely disrupted prior barriers to attendance and may have created new ones. This sequential explanatory mixed-methods study examined student absenteeism during the 2020–2021 school year in Detroit. We used administrative data to show whether and how attendance patterns changed, and we linked family survey and interview data to explain those patterns. We found that 70% of students were chronically absent, with 40% of parents reporting that computer problems contributed to absenteeism. While measures of socioeconomic disadvantage and computer/internet issues were associated with lower attendance and higher probability of chronic absenteeism, reported levels of hardship during the pandemic were not. Despite significant investment in technology, the district’s strategies for engaging students were not sufficient in overcoming economic hardships and the new challenges of online learning.
The COVID-19 pandemic resulted in significant disruption in schooling worldwide. This paper uses global test score data to estimate learning losses. It models the effect of school closures on achievement by predicting the deviation of the most recent results from a linear trend using data from all rounds of the Programme for International Student Assessment. Scores declined by an average of 14 percent of a standard deviation, roughly equal to seven months of learning. Losses were greater for students in schools that faced relatively longer closures, boys, immigrants, and disadvantaged students. Educational losses may translate into significant national income losses over time.