- Matthew A. Lenard
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Matthew A. Lenard
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 elementary and middle-school 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 restrict our identification of peer effects to those students whom the district does not select to switch schools. 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); though these gains may reduce achievement for students from higher-income backgrounds.
Many public school diversity efforts rely on reassigning students from one school to another. While opponents of such efforts articulate concerns about the consequences of reassignments for students’ educational experiences, little evidence exists regarding these effects, particularly in contemporary policy contexts. Using an event study design, we leverage data from an innovative socioeconomic school desegregation plan to estimate the effects of reassignment on reassigned students’ achievement, attendance, and exposure to exclusionary discipline. Between 2000 and 2010, North Carolina’s Wake County Public School System (WCPSS) reassigned approximately 25 percent of students with the goal of creating socioeconomically diverse schools. Although WCPSS’s controlled school choice policy provided opportunities for reassigned students to opt out of their newly reassigned schools, our analysis indicates that reassigned students typically attended their newly reassigned schools. We find that reassignment modestly boosts reassigned students’ math achievement, reduces reassigned students’ rate of suspension, and has no offsetting negative consequences on other outcomes. Exploratory analyses suggest that the effects of reassignment do not meaningfully vary by student characteristics or school choice decisions. The results suggest that carefully designed school assignment policies can improve school diversity without imposing academic or disciplinary costs on reassigned students.
Clustered observational studies (COSs) are a critical analytic tool for educational effectiveness research. We present a design framework for the development and critique of COSs. The framework is built on the counterfactual model for causal inference and promotes the concept of designing COSs that emulate the targeted randomized trial that would have been conducted were it feasible. We emphasize the key role of understanding the assignment mechanism to study design. We review methods for statistical adjustment and highlight a recently developed form of matching designed specifically for COSs. We review how regression models can be profitably combined with matching and note best practice for estimates of statistical uncertainty. Finally, we review how sensitivity analyses can determine whether conclusions are sensitive to bias from potential unobserved confounders. We demonstrate concepts with an evaluation of a summer school reading intervention in Wake County, North Carolina.
Many interventions in education occur in settings where treatments are applied to groups. For example, a reading intervention may be implemented for all students in some schools and withheld from students in other schools. When such treatments are non-randomly allocated, outcomes across the treated and control groups may differ due to the treatment or due to baseline differences between groups. When this is the case, researchers can use statistical adjustment to make treated and control groups similar in terms of observed characteristics. Recent work in statistics has developed matching methods designed for contexts where treatments are clustered. This form of matching, known as multilevel matching, may be well suited to many education applications where treatments are assigned to schools. In this article, we provide an extensive evaluation of multilevel matching and compare it to multilevel regression modeling. We evaluate multilevel matching methods in two ways. First, we use these matching methods to recover treatment effect estimates from three clustered randomized trials using a within-study comparison design. Second, we conduct a simulation study. We find evidence that generally favors an analytic approach to statistical adjustment that combines multilevel matching with regression adjustment. We conclude with an empirical application.