Search EdWorkingPapers
Search EdWorkingPapers by author, title, or keywords.
Search
Multiple outcomes of education
Involvement with the juvenile justice system carries immense personal costs to youth: 30% of detained youth drop out of school (relative to 5% nationally) and 55% are re-arrested within one year. These personal costs are compounded by societal costs – both directly in $214,000 of expenses per confined youth per year – and indirectly in lost social and economic productivity. While much of the extant research on the “school-to-prison pipeline” focuses on school disciplinary practices such as suspension, less attention has been given to understanding the impact of school referrals to the juvenile justice system on students’ relationship with school. Using novel administrative data from North Carolina, we link 3 years of individual educational and disciplinary infraction records to juvenile justice system records to identify the effect of juvenile justice referrals for school-based offenses on student academic and behavioral outcomes. We find that, even for the same offense type and circumstance, relative to students only punished for infractions internally in the school, students referred to juvenile justice experience lower academic achievement, increased absenteeism, and are more likely to be involved in future juvenile system contact. We show that these juvenile referrals are not inevitable and instead reflect a series of discretionary choices made by school administrators and law enforcement. Moreover, we examine demographic disparities in school-based referrals to juvenile justice and find that female students, Black students, and economically disadvantaged students are more likely to receive referrals even for the same offense type and circumstances.
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.
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.
We leverage log data from an educational app and two-way text message records from over 3,500 students during the summers of 2019 and 2020, along with in-depth interviews in Spanish and English, to identify patterns of family engagement with educational technology. Based on the type and timing of technology use, we identify several distinct profiles of engagement, which we group into two categories: Independent Users who engage with technology-based educational software independently, and Interaction-Supported Users who use two-way communications to support their engagement. We also find that as the demands of families from schools increased during the COVID-19 pandemic, Spanish-speaking families were significantly more likely than English-speaking families to engage with educational technology across all categories of families, particularly as Interaction-Supported Users.
Using data from nearly 1.2 million Black SAT takers, we estimate the impacts of initially enrolling in an Historically Black College and University (HBCU) on educational, economic, and financial outcomes. We control for the college application portfolio and compare students with similar portfolios and levels of interest in HBCUs and non-HBCUs who ultimately make divergent enrollment decisions - often enrolling in a four-year HBCU in lieu of a two-year college or no college. We find that students initially enrolling in HBCUs are 14.6 percentage points more likely to earn a BA degree and have 5 percent higher household income around age 30 than those who do not enroll in an HBCU. Initially enrolling in an HBCU also leads to $12,000 more in outstanding student loans around age 30. We find that some of these results are driven by an increased likelihood of completing a degree from relatively broad-access HBCUs and also relatively high-earning majors (e.g., STEM). We also explore new outcomes, such as credit scores, mortgages, bankruptcy, and neighborhood characteristics around age 30.
The improvement of low-performing school systems is one potential strategy for mitigating educational inequality. Some evidence suggests districtwide reform may be more effective than school-level change, but limited research examines district-level turnaround. There is also little scholarship examining the effects of turnaround reforms on outcomes beyond the first few years of implementation, on outcomes beyond test scores, or on the effectiveness of efforts to replicate district improvement successes beyond an initial reform context. We study these topics in Massachusetts, home to the Lawrence district representing a rare case of demonstrated improvements in the early years of state takeover and turnaround and where state leaders have since intervened in three other contexts as a result. We use statewide student-level administrative data (2006-07 to 2018-19) and event study methods to estimate medium-term reform impacts on test and non-test outcomes across four Massachusetts-based contexts: Lawrence, Holyoke, Springfield, and Southbridge. We find substantial district improvement was possible although sustaining the rate of gains was more complicated. Replicating gains in new contexts was also possible but not guaranteed.
Although existing research suggests that students benefit on a range of outcomes when they enroll in early algebra classes, policy efforts that accelerate algebra enrollment for large numbers of students often have negative effects. Explanations for this apparent contradiction often emphasize the potential role of teacher and peer effects, which could create positive effects for individual students placed into early algebra that would not translate to larger-scale policies. We use detailed data from Oregon that contain information on the teachers and peers to whom students are exposed in order to investigate these explanations. Our regression discontinuity analyses replicate key findings from prior studies, indicating that placement in eighth-grade algebra boosts student achievement in math and English language arts. We then demonstrate that eighth-grade algebra placement positively affects the achievement level of students’ classmates, as well as the years of experience and value added of students’ math teachers. The effects on peer composition that we observe are large enough to plausibly explain the majority of the effects of eighth-grade algebra on student test scores.
This study reports the findings from a year-long randomized evaluation assessing the impact of assigning 62 classrooms in Nigeria to receive either blocked or interleaved math problem sets. Blocked practice sessions focused on a single skill at a time. Interleaved problem sets alternated between different skills within a practice session. On tests of short-term retention, interleaved practice increased test scores by 0.29 standard deviations. In contrast, we find no evidence that interleaving improves average performance on a cumulative assessment measuring retention of material over the academic year. We find some evidence of large impacts on the cumulative assessment at the bottom of the distribution, but these impacts appear to be offset by negative impacts at the top.
Noncognitive constructs such as self-e cacy, social awareness, and academic engagement are widely acknowledged as critical components of human capital, but systematic data collection on such skills in school systems is complicated by conceptual ambiguities, measurement challenges and resource constraints. This study addresses this issue by comparing the predictive validity of two most widely used metrics on noncogntive outcomes|observable academic behaviors (e.g., absenteeism, suspensions) and student self-reported social and emotional learning (SEL) skills|for the likelihood of high school graduation and postsecondary attainment. Our ndings suggest that conditional on student demographics and achievement, academic behaviors are several-fold more predictive than SEL skills for all long-run outcomes, and adding SEL skills to a model with academic behaviors improves the model's predictive power minimally. In addition, academic behaviors are particularly strong predictors for low-achieving students' long-run outcomes. Part-day absenteeism (as a result of class skipping) is the largest driver behind the strong predictive power of academic behaviors. Developing more nuanced behavioral measures in existing administrative data systems might be a fruitful strategy for schools whose intended goal centers on predicting students' educational attainment.