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Over the past three decades, children from low-income families and those from more affluent families have increasingly been attending different public schools. While recent work has helped us understand patterns of income segregation between districts and schools within districts, we know very little about segregation of students as they experience school: in the classroom. In this paper, we attempt to advance knowledge of trends in the segregation of students by income at the classroom level. We make use of detailed, student-level administrative data from North Carolina which provides a measure of a student’s free/reduced price lunch eligibility, which we refer to as economically disadvantaged (ED) status, along with information on classroom assignments. Since we know the ED status of each student in each classroom, we assess whether ED students are assigned to classes in the same pattern as other students or if are clustered/segregated into different classrooms. We know very little about the magnitude of income-based segregation, and almost nothing about whether this has changed over time, so we provide novel evidence on the question of whether segregation of students by socioeconomic status has increased within schools. We find that within-school segregation has risen by about 10 percent between 2007 and 2014 in elementary and middle schools we study. Further, we find that segregation of ED students within schools is correlated with the level of segregation between schools in districts, and this relationship grew stronger over our panel.
This study reports achievement levels and fall-to-spring gains in grades K to 8 for three groups of English Learners (ELs): (a) ever-ELs who were ever eligible for service; (b) current-ELs who continue to require service; and (b) dually-identified students eligible for both EL and Special Education services. I leverage unique data that include 186,139 ever-ELs and their never-EL peers in 1,520 schools. All three EL groups have lower mean math and reading achievement than the average of all students in kindergarten. Compared to the all-student average, ever-ELs and current-ELs make larger gains in the early grades but smaller gains in the middle grades. Dually-identified students make smaller gains than the all-student average across all grades in math and in kindergarten and 1st grade in reading. The expanding achievement gaps in the middle grades suggest better academic support is urgently needed for all multilingual students, especially dually-identified students.
We estimate the longer-run effects of attending an effective high school (one that improves a combination of test scores, survey measures of socio-emotional development, and behaviours in 9th grade) for students who are more versus less educationally advantaged (i.e., likely to attain more years of education based on 8th-grade characteristics). All students benefit from attending effective schools. However, the least advantaged students experience the largest improvements in high-school graduation, college-going, and school-based arrests. These patterns are driven by the least advantaged students benefiting the most from school impacts on the non-test-score dimensions of school quality. However, while there is considerable overlap in the effectiveness of schools attended by more and less advantaged students, it is the most advantaged students that are most likely to attend highly effective schools. These patterns underscore the importance of quality schools, and the non-test score components of quality schools, for improving the longer-run outcomes for less advantaged students.
Homelessness is rising among public school students in large cities across the US. Using nine years of student-level administrative data, we examine how homelessness affects students’ mathematics and attendance outcomes within the Los Angeles Unified School District, including the differential effects of homelessness based on duration and timing of their homeless experiences. Results using inverse probability of treatment weighting find that homeless students score 0.13 SD lower on math assessments and miss 5.8 additional days of school than students who never experience homeless. Results suggest that current homelessness has larger negative impacts on math achievement and attendance than former homelessness, and that transitory homelessness has larger negative impacts than persistent homelessness on the same outcomes.
Despite the significant influence that peer motivation is likely to have on educational investments during high school, it is difficult to test empirically since exogenous changes in peer motivation are rarely observed. In this paper, I focus on the 2012 introduction of Deferred Action for Childhood Arrivals (DACA) to study a setting in which peer motivation changed sharply for a subset of high school students. DACA significantly increased the returns to schooling for undocumented youth, while leaving the returns for their peers unchanged. I find that DACA induced undocumented youth to invest more in their education, which also had positive spillover effects on ineligible students (those born in the US) who attended high school with high concentrations of DACA-eligible youth. JEL Codes: I26, H52, J15
One of the controversies surrounding charter schools is whether these schools may either “cream skim” high-performing students from traditional public schools or “pushout” low-achieving students or students with discipline histories, leaving traditional public schools to educate the most challenging students. We use these terms strictly for brevity and acknowledge that many of the reasons that students are labeled high- or low-performing academically or behaviorally are beyond the control of the student. In this study, we use longitudinal statewide data from Tennessee and North Carolina and linear probability models to examine whether there is evidence consistent with these selective enrollment practices. Because school choice programs managed by districts (magnet and open enrollment programs) have a similar ability to cream skim and pushout students, we also examine these outcomes for these programs. Across the various school choice programs, magnet schools have the most evidence of cream skimming, but this might be expected as they often have selective admissions. For charter schools, we do not find patterns in the data consistent with cream skimming, but we do find evidence consistent with pushout behaviors based on discipline records. Finally, some have raised concerns that students may be pushed out near accountability test dates, but our results suggest no evidence consistent with this claim.
We examine U.S. children whose parents won the lottery to trace out the effect of financial resources on college attendance. The analysis leverages federal tax and financial aid records and substantial variation in win size and timing. While per-dollar effects are modest, the relationship is weakly concave, with a high upper bound for amounts greatly exceeding college costs. Effects are smaller among low-SES households, not sensitive to how early in adolescence the shock occurs, and not moderated by financial aid crowd-out. The results imply that households derive consumption value from college and household financial constraints alone do not inhibit attendance.
Growing experimental evidence demonstrates that low-touch informational, nudge, and virtual advising interventions are ineffective at improving postsecondary educational outcomes for economically-disadvantaged students at scale. Intensive in-person college advising programs are a considerably higher-touch and more resource intensive strategy; some programs provide students with dozen of hours of individualized assistance starting in high school and continuing through college, and can cost thousands of dollars per student served. Despite the magnitude of this investment, causal evidence on these programs' impact is quite limited, particularly for programs that serve Hispanic students, the fastest growing segment of U.S. college enrollees. We contribute new evidence on the impact of intensive college advising programs through a multi-cohort RCT of College Forward, which provides individualized advising from junior year of high school through college for a majority Hispanic student population in Texas. College Forward leads to a 7.5 percentage point increase in enrollment in college, driven entirely by increased enrollment at four-year universities. Students who receive College Forward advising are nearly 12 percentage points more likely to persist to their third year of college. While more costly and harder to scale than low-touch interventions, back of the envelope calculations suggest that the benefit from increased college graduation likely induced by the program outweighs operating costs in less than two years following college completion.
This article asks whether small changes to community college courses and programs can help improve student outcomes. We use administrative data from the California Community College system, including millions of student records and detailed course-level information for most career-technical education programs in the state. We construct a summary measure of each program’s flexibility, incorporating many components of the availability and scheduling of its courses. We show considerable variation in this flexibility measure across programs and over time. An increase in a program’s flexibility is associated with increases in enrollment and completions, but not with changes in its completion rate.
The estimation of test score “gaps” and gap trends plays an important role in monitoring educational inequality. Researchers decompose gaps and gap changes into within- and between-school portions to generate evidence on the role schools play in shaping these inequalities. However, existing decomposition methods assume an equal-interval test scale and are a poor fit to coarsened data such as proficiency categories. This leaves many potential data sources ill-suited for decomposition applications. We develop two decomposition approaches that overcome these limitations: an extension of V, an ordinal gap statistic, and an extension of ordered probit models. Simulations show V decompositions have negligible bias with small within-school samples. Ordered probit decompositions have negligible bias with large within-school samples but more serious bias with small within-school samples. More broadly, our methods enable analysts to (1) decompose the difference between two groups on any ordinal outcome into portions within- and between some third categorical variable, and (2) estimate scale-invariant between-group differences that adjust for a categorical covariate.