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Christopher Cleveland

Christopher Cleveland, Ethan Scherer.

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.

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Christopher Cleveland, Ethan Scherer.

A growing body of research shows that students benefit when they demographically match their teachers. However, little is known about how matching affects social-emotional development. We use student-fixed effects to exploit changes over time in the proportion of teachers within a school grade who demographically match a student to estimate matching's effect on social-emotional measures, test scores, and behavioral outcomes. We find improvements for students in grit and interpersonal self-management when matched to teachers of their race and gender. Black female students drive these effects. We also find that matching reduces absences, especially for Black students. Our findings add to the emerging teacher diversity literature by showing its benefits for Black and female students during a critical stage of development.

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Dylan Lukes, Christopher Cleveland.
Between 1935-1940 the Home Owners' Loan Corporation (HOLC) assigned A (minimal risk) to D (hazardous) grades to neighborhoods that reflected their lending risk from previously issued loans and visualized these grades on color-coded maps, which arguably influenced banks and other mortgage lenders to provide or deny home loans within residential neighborhoods. In this study, we leverage a spatial analysis of 144 HOLC-graded core-based statistical areas (CBSAs) to understand how HOLC maps relate to current patterns of school and district funding, school racial diversity, and school performance. We find that schools and districts located today in historically redlined D neighborhoods have less district per-pupil total revenues, larger shares of Black and non-White student bodies, less diverse student populations, and worse average test scores relative to those located in A, B, and C neighborhoods. Conversely, at the school level, we find that per-pupil total expenditures are better for those schools operating in previously redlined D neighborhoods. Consequently, these schools also have the largest shares of low-income students. Our nationwide results are, on the whole, consistent by region and after controlling for CBSA. Finally, we document a persistence in these patterns across time, with overall positive time trends regardless of HOLC security rating but widening gaps between D vs. A, B, and C outcomes. These findings suggest that education policymakers need to consider the historical implications of redlining and past neighborhood inequality on neighborhoods today when designing modern interventions focused on improving the life outcomes of students of color and students from low-socioeconomic backgrounds.

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