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Numerous high-profile efforts have sought to “turn around” low-performing schools. Evidence on the effectiveness of school turnarounds, however, is mixed, and research offers little guidance on which models are more likely to succeed. We present a mixed-methods case study of turnaround efforts led by the Blueprint Schools Network in three schools in Boston. Using a difference-in-differences framework, we find that Blueprint raised student achievement in ELA by at least a quarter of a standard deviation, with suggestive evidence of comparably large effects in math. We document qualitatively how differential impacts across the three Blueprint schools relate to contextual and implementation factors. In particular, Blueprint’s role as a turnaround partner (in two schools) versus school operator (in one school) shaped its ability to implement its model. As a partner, Blueprint provided expertise and guidance but had limited ability to fully implement its model. In its role as an operator, Blueprint had full authority to implement its turnaround model, but was also responsible for managing the day-to-day operations of the school, a role for which it had limited prior experience.
Empirical evidence demonstrates that publicly funded adult health insurance through the Affordable Care Act (ACA) has had positive effects on low-income adults. We examine whether the ACA’s Medicaid expansions influenced child development and family functioning in low-income households. We use a difference-in-differences framework that exploits cross-state policy variation, and focus on children in low-income families from a nationally representative, longitudinal sample followed from kindergarten to fifth grade. The ACA Medicaid expansions improved children’s reading test scores by approximately 2 percent (0.04 SD). Potential mechanisms for these effects within families are more time spent reading at home, less parental help with homework, and eating dinner together. We find no effects for children’s math test scores or socioemotional skills.
A wide research base has documented the unequal access to and enrollment in K-12 gifted and talented services and other forms of advanced learning opportunities. This study extends that knowledge base by integrating multiple population-level datasets to better understand correlates of access to and enrollment in gifted and talented services, seventh-grade Algebra 1, and eighth-grade Geometry. Results show that states vary widely with some serving 20% of their students as gifted while others serve 0%. Similarly, within-district income segregation, income-related achievement gaps, and the percent of parents with a college degree are the dominant predictors of a school offering these opportunities and the size of the school population served.
Brown v. Board (1954) catalyzed a nationwide effort by the federal judiciary to desegregate public schools by court order, representing a major achievement for the U.S. civil rights movement. Four decades later, courts began dismissing schools from desegregation decrees in a staggered fashion, causing their racial homogeneity to rise. I leverage this exogenous source of variation in the racial mix of schools released from court orders between 1990 and 2014 to explore two key aspects of how whites react to attending schools with students of color. First, contemporaneous survey data indicate that as schools re-segregated, white students in these schools expressed more favorable attitudes towards black and Latino students. Second, present-day voter records from six Southern states of white students in schools that re-segregated show that they are significantly more likely to identify with the more racially liberal party -- the Democrats -- today. The findings are consistent with white students experiencing resegregation as a reduction in social threat, and indicate that school desegregation efforts may have caused life-long shifts among white students toward racial and political conservatism.
Growing evidence shows that a student's growth mindset (the belief that intelligence is malleable) can benefit their academic achievement. However, due to limited information, little is known about how a teachers’ growth mindset affects their students’ academic achievement. In this paper, we study the impact of teacher growth mindset on academic achievement for a nationwide sample of 8th and 10th grade students in Chile in 2017. Using a student fixed effect model that exploits data from two subject teachers for each student, we find that being assigned to a teacher with a growth mindset increases standardized test scores by approximately 0.02 standard deviations, with larger effects on students with high GPAs and particularly on students in low socioeconomic schools.
A growing body of research shows that students benefit when they are demographically similar to their teachers. However, less is known about how matching affects social-emotional development. We investigate the effect of teacher-student race and gender matching for middle school students in six charter management organizations. Using a student fixed effects strategy exploiting changes over time in the proportion of demographic matching in a school-grade, we estimate matching’s effect on self-reports of interpersonal and intrapersonal social-emotional skills, test scores, and behavioral outcomes. We find improvements for Black and female students in interpersonal self-management and grit when they are matched to demographically similar teachers. We also find demographic matching leads to reductions in absences for Black students and improved math test scores for females. Our findings add to the emerging teacher diversity literature by showing its benefits for Black and female students during a critical stage of social-emotional development in their lives.
Research consistently demonstrates that tutoring interventions have substantial positive effects on student learning. As a result, tutoring has emerged as a promising strategy for addressing COVID-related learning loss and affording greater educational opportunities for students living in poverty. The effectiveness of tutoring programs, however, varies greatly, and these variations may drive differential gains in student learning. Therefore, determining the program characteristics that do and do not drive positive student outcomes will be key to providing guidance for policymakers and practitioners who want to implement high-impact tutoring at scale. Our goal is to highlight the programs, characteristics, and conditions that evidence suggests make for effective tutoring and to create an evidence-based framework for delivering and evaluating tutoring interventions. In addition, we identify promising questions for future research.
A growing literature uses value-added (VA) models to quantify principals' contributions to improving student outcomes. Principal VA is typically estimated using a connected networks model that includes both principal and school fixed effects (FE) to isolate principal effectiveness from fixed school factors that principals cannot control. While conceptually appealing, high-dimensional FE regression models require sufficient variation to produce accurate VA estimates. Using simulation methods applied to administrative data from Tennessee and New York City, we show that limited mobility of principals among schools yields connected networks that are extremely sparse, where VA estimates are either highly localized or statistically unreliable. Employing a random effects shrinkage estimator, however, can alleviate estimation error to increase the reliability of principal VA.
We provide a descriptive analysis of within-school and neighborhood similarity in high school applications in New York City. We depart from prior work by examining similarity in applications to specific schools rather than preferences for school characteristics. We find surprisingly low similarity within schools and neighborhoods, but substantial variation by race and prior achievement. White and Asian students are more likely to have choices in common relative to Black and Hispanic students, a difference that persists after controlling for achievement and location. Likewise, higher-achieving students are more likely to have choices in common, conditional on other student characteristics and location. An implication is that students’ likelihood of attending high school without any peers from their middle school or neighborhood varies by student background.
We provide novel evidence on the causal impacts of student absences in middle and high school on state test scores, course grades, and educational attainment using a rich administrative dataset that tracks the date and class period of each absence. We use two similar but distinct identification strategies that address potential endogeneity due to time-varying student-level shocks by exploiting within-student, between-subject variation in class-specific absences. We also leverage information on the timing of absences to show that absences that occur after the annual window for state standardized testing do not affect test scores, providing a further check of our identification strategy. Both approaches yield similar results. We nd that absences in middle and high school harm contemporaneous student achievement and longer-term educational attainment: On average, missing 10 classes reduces math or English Language Arts test scores by 3-4% of a standard deviation and course grades by 17-18% of a standard deviation. 10 total absences across all subjects in 9th grade reduce both the probability of on-time graduation and ever enrolling in college by 2%. Learning loss due to school absences can have profound economic and social consequences.