Search EdWorkingPapers by author, title, or keywords.
Improving education and labor market outcomes for low-income students is critical for advancing socioeconomic mobility in the United States. We explore how Massachusetts public high schools affect the longer-term outcomes of low-income students, using detailed longitudinal data. We estimate school value-added impacts on four-year college graduation and earnings. Similar students who attend schools at the 80th percentile of the distribution are 6 percentage points more likely to graduate from a four-year college and earn 13% (or $3,600) more annually at age 30 compared to peers who attend schools at the 20th percentile. We consider how school effectiveness across a range of short-term measures relates to longer-run impacts. Schools that improve students’ test scores and college aspirations improve longer-run outcomes more.
Four-year public colleges may play an important role in supporting intergenerational mobility by providing an accessible path to a bachelor’s degree and increasing students' earnings. Leveraging a midsize state’s GPA- and SAT-based admissions thresholds for the four-year public sector, I use a regression discontinuity design to estimate the effect of four-year public college admissions on earnings and college costs. For low-income students and Black, Hispanic, or Native American students, admission to four-year public colleges increases mean annual earnings by almost $8,000 eight to fourteen years after applying without increasing the private costs of college. The state recovers the cost of an additional four-year public college admission through increased lifetime tax revenue. Expanding access to four-year public colleges may be a particularly effective way to improve the economic outcomes of low-income students and Black, Hispanic, or Native American students.
Does relaxing strict school discipline improve student achievement, or lead to classroom disorder? We study a 2012 reform in New York City public middle schools that eliminated suspensions for non-violent, disorderly behavior, replacing them with less disruptive interventions. Using a difference-in-differences framework, we exploit the sharp timing of the reform and natural variation in its impact to measure the effect of reducing suspensions on student achievement. Math scores of students in more-affected schools rose by 0.05 standard deviations relative to other schools over the three years after the policy change. Reading scores rose by 0.03 standard deviations. Only a small portion of these aggregate benefits can be explained by the direct impact of eliminating suspensions on students who would have been suspended under the old policy. Instead, test score gains are associated with improvements in school culture, as measured by the quality of student-teacher relationships and perceptions of safety at school. We find no evidence of trade-offs between students, with students benefiting even if they were unlikely to be suspended themselves.
Across an array of educational outcomes, evidence suggests that girls outperform boys on average. For example, in Chicago, ninth-grade girls earn math GPAs that are 0.29 points higher than boys on average. This paper examines explanations for this gap, such as girl-boy differences in academic preparation, behaviors and habits, and experiences in math classes. After accounting for these factors, the gender gap in math grades persists. We, then, examine the classroom-level conditions that reduce the gender gap in grades. The gap is smaller in more advanced courses like honors classes and geometry. Further, boys perform more similarly to girls in classes with male teachers. These findings highlight classroom conditions that are more conducive to the academic success of boys.
Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We compare algorithmic and human predictions in the context of a national college advising program, focusing in particular on predicting high-achieving, lower-income students’ college enrollment quality. College advisors slightly outperform a prediction algorithm; however, greater advisor accuracy is concentrated among students with whom advisors had more interactions. The algorithm achieved similar accuracy among students lower in the distribution of interactions, despite advisors having substantially more information. We find no evidence that the advisors or algorithm exhibit bias against vulnerable populations. Our results suggest that, especially at scale, algorithms have the potential to provide efficient, accurate, and unbiased predictions to target scarce social services and resources.
Many policies in higher education are intended to improve college access and degree completion, yet often those policies fall short of their aims by making it difficult for prospective or current college students to access benefits for which they are eligible. Barriers that inhibit access to policy benefits, such as cumbersome paperwork, can weigh more heavily on members of marginalized communities, including racially minoritized students. Such administrative burdens can thus reinforce patterns of inequity. In this paper, we present a conceptual framework for examining administrative burdens embedded in higher education policies that can negatively affect prospective and current college students, especially those who are racially minoritized. With the use of our proposed framework, researchers can improve the understanding of ethnoracial disparities in higher education, inform policymakers’ design of racially equitable policies for higher education, and enable practitioners to implement those policies to promote racial equity.
Colleges across the United States are now placing most or all students directly into college-level courses and providing supplementary, aligned academic support alongside the courses, also known as “corequisite remediation.” Developmental education reforms like corequisite remediation could advance racial and ethnic equity in postsecondary education by facilitating early academic progression. However, there is limited evidence available on differential impacts of corequisite models by race and ethnicity. To better understand the potential for differential impacts of English corequisites for Latinx students, this study leverages data from a randomized control trial across five large urban community colleges across Texas. We also utilize student survey data to develop a deeper understanding of how corequisites shape the experiences of Latinx students in their college-level English courses. Latinx students in our study colleges saw larger benefits from taking corequisite English than non-Latinx students in terms of gateway course completion. The survey findings suggest that corequisites provided an environment where Latinx students felt less academically overwhelmed and less bored relative to patterns observed for traditional DE course enrollees. However, Latinx students in corequisites also reported being less likely to participate in class discussions and ask questions relative to their non-Latinx peers.
Policy makers periodically consider using student assignment policies to improve educational outcomes by altering the socio-economic and academic skill composition of schools. We exploit the quasi-random reassignment of students across schools in the Wake County Public School System to estimate the academic and behavioral effects of being reassigned to a different school and, separately, of shifts in peer characteristics. We rule out all but substantively small effects of transitioning to a different school as a result of reassignment on test scores, course grades and chronic absenteeism. In contrast, increasing the achievement levels of students' peers improves students' math and ELA test scores but harms their ELA course grades. Test score benefits accrue primarily to students from higher-income families, though students with lower family income or lower prior performance still benefit. Our results suggest that student assignment policies that relocate students to avoid the over-concentration of lower-achieving students or those from lower-income families can accomplish equity goals (despite important caveats), although these reassignments may reduce achievement for students from higher-income backgrounds.
Despite recent evidence on the benefits of same-race instructor matching in K-12 and higher education, research has yet to document the incidence of same-race matching in the postsecondary sector. That is, how likely are racially minoritized college students to ever experience an instructor of the same race/ethnicity? Using administrative data from Texas on the universe of community college students, we document the rate of same-race matching overall and across racial groups, the courses in which students are more or less likely to match, the types of instructors students most commonly match to, and descriptive differences in course outcomes across matched and unmatched courses. Understanding each of these measures is critical to conceptualize the mechanisms and outcomes of same-race matching and to drive policy action concerning the diversity of the professoriate.