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Race, ethnicity and culture
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.
We examine access to high school Ethnic Studies in California, a new graduation requirement beginning in 2029-30. Data from the California Department of Education and the University of California Office of the President indicate that roughly 50 percent of public high school students in 2020-21 attend a school that offers Ethnic Studies or a related course, but as of 2018-19, only 0.2 percent of students were enrolled in such a course. Achieving parity with economics, a current graduation requirement, requires more than doubling the number of Ethnic Studies teachers relative to 2018-19. We also examine school and community factors that predict offering Ethnic Studies and provide descriptive information about the Ethnic Studies teaching force across the state.
The recent spike in book challenges has put school libraries at the center of heated political debates. I investigate the relationship between local politics and school library collections using data on books with controversial content in 6,631 public school libraries. Libraries in conservative areas have fewer titles with LGBTQ+, race/racism, or abortion content and more Christian fiction and discontinued Dr. Seuss titles. This is true even though most libraries have at least some controversial content. I also find that state laws that restrict curricular content are negatively related to some kinds of controversial books. Finally, I present descriptive short-term evidence that book challenges in the 2021-22 school year have had “chilling effects” on the acquisition of new LGBTQ+ titles.
Decisions to invest in human capital depend on people’s time preferences. We show that differences in patience are closely related to substantial subnational differences in educational achievement, leading to new perspectives on longstanding within-country disparities. We use social-media data – Facebook interests – to construct novel regional measures of patience within Italy and the United States. Patience is strongly positively associated with student achievement in both countries, accounting for two-thirds of the achievement variation across Italian regions and one-third across U.S. states. Results also hold for six other countries with more limited regional achievement data.
Does student-teacher match quality exist? Prior work has documented large disparities in teachers' impacts across student types but has not distinguished between sorting and causal effects as the drivers of these disparities. I propose a disparate value-added model and derive a novel measure of teacher quality---revealed comparative advantage---that captures the degree to which teachers affect student outcome gaps. Quasi-experimental changes in teaching staff show that the comparative advantage measure accurately predicts teachers’ disparate impacts: a teacher with a 1 standard deviation in revealed comparative advantage for black students increases black students' test scores by 1 standard deviation and has no effect on non-black students' test scores. Teacher removal and teacher-to-classroom re-allocation simulations show substantial efficiency and equity gains of considering teachers’ comparative advantage.
Teachers’ attitudes and classroom management practices critically affect students’ academic and behavioral outcomes, contributing to the persistent issue of racial disparities in school discipline. Yet, identifying and improving classroom management at scale is challenging, as existing methods require expensive classroom observations by experts. We apply natural language processing methods to elementary math classroom transcripts to computationally measure the frequency of teachers’ classroom management language in instructional dialogue and the degree to which such language is reflective of punitive attitudes. We find that the frequency and punitiveness of classroom management language show strong and systematic correlations with human-rated observational measures of instructional quality, student and teacher perceptions of classroom climate, and student academic outcomes. Our analyses reveal racial disparities and patterns of escalation in classroom management language. We find that classrooms with higher proportions of Black students experience more frequent and more punitive classroom management. The frequency and punitiveness of classroom management language escalate over time during observations, and these escalations occur more severely for classrooms with higher proportions of Black students. Our results demonstrate the potential of automated measures and position everyday classroom management interactions as a critical site of intervention for addressing racial disparities, preventing escalation, and reducing punitive attitudes.
‘QuantCrit’ (Quantitative Critical Race Theory) is a rapidly developing approach that seeks to challenge and improve the use of statistical data in social research by applying the insights of Critical Race Theory. As originally formulated, QuantCrit rests on five principles; 1) the centrality of racism; 2) numbers are not neutral; 3) categories are not natural; 4) voice and insight (data cannot ‘speak for itself); and 5) a social justice/equity orientation (Gillborn et al, 2018). The approach has quickly developed an international and interdisciplinary character, including applications in medicine (Gerido, 2020) and literature (Hammond, 2019). Simultaneously, there has been ferocious criticism from detractors outraged by the suggestion that numbers are anything other than objective and scientific (Airaksinen, 2018). In this context it is vital that the approach establishes some common understandings about good practice; in order to sustain rigor, make QuantCrit accessible to academics, practioners, and policymakers alike, and resist widespread attempts to over-simplify and pillory. This paper is intended to advance an iterative process of expanding and clarifying how to ‘QuantCrit’.
We examine the impact of local labor market shocks and state unemployment insurance (UI) policies on student discipline in U.S. public schools. Analyzing school-level discipline data and firm-level layoffs in 23 states, we find that layoffs have little effect on discipline rates overall. However, effects differ across the UI benefit distribution. At the lowest benefit level ($265/week), a mass layoff increases out-of-school suspensions by 4.5%, with effects dissipating as UI benefits increase. Effects are consistently largest for Black students - especially in predominantly White schools - resulting in increased racial disproportionality in school discipline following layoffs in low-UI states.
We document that recent generations of elementary school teachers are significantly more effective in raising student test scores than those from earlier generations. Measuring teachers’ value-added for Black and white students separately, the improvements in teaching for Black students are significantly larger than those seen for white students. The race-specific improvements in teacher quality are driven by white teachers. Analyses of mechanisms suggest that changing teachers’ biases may be one potential channel. Our results suggest reason for optimism since these teacher quality differences should lead to improved student learning and a narrowing of the Black-white test score gap over time.
Teacher rating scales (TRS) are often used to make service eligibility decisions for exceptional learners. Although TRS are regularly used to identify student exceptionalism either as part of an informal nomination process or through behavioral rating scales, there is little research documenting the between-teacher variance in teacher ratings or the consequences of such rater dependence. To evaluate the possible benefits or disadvantages of using TRS as part of a gifted identification process, we examined the student-, teacher-, and school-level variance in TRS controlling for student ability and achievement to determine the unique information, consistency, and potential bias in TRS. Between 10% and 25% of a students’ TRS score can be attributed to the teacher doing the rating, and between-teacher standard deviations represent an effect size of one-third to one-half standard deviation unit. Our results suggest that TRS are not easily comparable across teachers, making it impossible to set a cut score for admission into a program (or for further screening) that functions equitably across teachers.