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Recent public discussions and legal decisions suggest that school segregation will remain persistent in the United States, but increased transparency may help monitor spending across schools. These circumstances revive an old question: is it possible to achieve an educational system that is separate but equal—or better—in terms of spending? This question motivates further understanding the measurement of spending progressivity and its association with segregation. Focusing on economic disadvantage, we compare two commonly-used measures of spending progressivity: exposure-based and slope-based. We show that each measure is predicated on different assumptions about the progressivity of within-school resource allocations, and that they are theoretically linked through segregation. We empirically examine school spending progressivity and its properties using nationwide school spending data from the 2018-19 school year. Consistent with our theory, the exposure-based measure is the slope-based measure shrunk inversely by economic school segregation. This property makes more segregated school districts look more progressive on the exposure-based measure, representing a seemingly “separate but better” relationship. However, we show that this provocative pattern may be reversed by relatively modest poor-versus-nonpoor differences in unobserved parental contributions. We discuss implications for the measurement of progressivity, and for theory on public educational investments broadly.
This simulation study examines the characteristics of the Explanatory Item Response Model (EIRM) when estimating treatment effects when compared to classical test theory (CTT) sum and mean scores and item response theory (IRT)-based theta scores. Results show that the EIRM and IRT theta scores provide generally equivalent bias and false positive rates compared to CTT scores and superior calibration of standard errors under model misspecification. Analysis of the statistical power of each method reveals that the EIRM and IRT theta scores provide a marginal benefit to power and are more robust to missing data than other methods when parametric assumptions are met and provide a substantial benefit to power under heteroskedasticity, but their performance is mixed under other conditions. The methods are illustrated with an empirical data application examining the causal effect of an elementary school literacy intervention on reading comprehension test scores and demonstrates that the EIRM provides a more precise estimate of the average treatment effect than the CTT or IRT theta score approaches. Tradeoffs of model selection and interpretation are discussed.
Districts nationwide have revised their educator evaluation systems, increasing the frequency with which administrators observe and evaluate teacher instruction. Yet, limited insight exists on the role of evaluator feedback for instructional improvement. Relying on unique observation-level data, we examine the alignment between evaluator and teacher assessments of teacher instruction and the potential consequences for teacher productivity and mobility. We show that teachers and evaluators typically rate teacher performance similarly during classroom observations, but with significant variability in teacher-evaluator ratings. While teacher performance improves across multiple classroom observations, evaluator ratings likely overstate productivity improvements among the lowest-performing teachers. Evaluators, but not teachers, systematically rate teacher performance lower in classrooms serving higher concentrations of economically disadvantaged students. And while teacher performance improves when evaluators provide more critical feedback about teacher instruction, teachers receiving critical feedback may seek alternative teaching assignments in schools with less critical evaluation settings. We discuss the implications of these findings for the design, implementation and impact of educator evaluation systems.
Billions of dollars are invested in opt-in, educational resources to accelerate students’ learning. Although advertised to support struggling, marginalized students, there is no guarantee these students will opt in. We report results from a school system’s implementation of on-demand tutoring. The take up was low. At baseline, only 19% of students ever accessed the platform, and struggling students were far less likely to opt in than their more engaged and higher achieving peers. We conducted a randomized controlled trial (N=4,763) testing behaviorally-informed approaches to increase take-up. Communications to parents and students together increase the likelihood students access tutoring by 46%, which led to a four-percentage point decrease in course failures. Nonetheless, take-up remained low, showing concerns that opt-in resources can increase—instead of reduce—inequality are valid. Without targeted investments, opt-in educational resources are unlikely to reach many students who could benefit.
We investigate whether and how Achieve Atlanta’s college scholarship and associated services impact college enrollment, persistence, and graduation among Atlanta Public School graduates experiencing low household income. Qualifying for the scholarship of up to $5,000/year does not meaningfully change college enrollment among those near the high school GPA eligibility thresholds. However, scholarship receipt does have large and statistically significant effects on early college persistence (i.e., 14%) that continue through BA degree completion within four years (22%). We discuss how the criteria of place-based programs that support economically disadvantaged students may influence results for different types of students.
Books shape how children learn about society and norms, in part through representation of different characters. We introduce new artificial intelligence methods for systematically converting images into data and apply them, along with text analysis methods, to measure the representation of race, gender, and age in award-winning children’s books from the past century. We find that more characters with darker skin color appear over time, but the most influential books persistently depict a greater proportion of light-skinned characters than other books, even after conditioning on race; we also find that children are depicted with lighter skin than adults. Relative to their growing share of the U.S. population, Black and Latinx people are underrepresented in these same books, while White males are overrepresented. Over time, females are increasingly present but appear less often in text than in images, suggesting greater symbolic inclusion in pictures than substantive inclusion in stories. We then report empirical evidence for predictions about the supply of and demand for representation that would generate these patterns. On the demand side, we show that people consume books that center their own identities. On the supply side, we document higher prices for books that center non-dominant social identities and fewer copies of these books in libraries that serve predominantly White communities. Lastly, we show that the types of children’s books purchased in a neighborhood are related to local political beliefs.
Increased exposure to gender-role information affects a girl's educational performance. Utilizing the classroom randomization in Chinese middle schools, we find that the increased presence of stay-at-home peer mothers significantly reduces a girl's performance in mathematics. This exposure also cultivates gendered attitudes towards mathematics and STEM professions. The influence of peer mothers increases with network density and when the girl has a distant relationship with her parents. As falsification tests against unobserved confounding factors, we find that the exposure to stay-at-home peer mothers does not affect boys' performance, nor do we find that stay-at-home peer fathers affect girls' outcomes.
The role of racial diversity at college campuses has been debated for over a half a century with limited quasi-experimental evidence from classrooms. To fill this void, I estimate the extent that classmate racial compositions affect Hispanic and African-American students at a large and over-subscribed California community college where they are minorities. I find that when minority students are exposed to a greater share of same race classmates, they are more likely to complete the class with a pass and are more likely to enroll in a same subject course the subsequent term. The findings are robust to first-time students with the lowest registration priority vs. all students and different combinations of fixed effects (e.g., student, class, and instructor race).
The disruption of in-person schooling during the Covid-19 pandemic has affected students’ learning, development, and well-being. Students in Latin America and the Caribbean have been hit particularly hard because schools in the region have stayed closed for longer than anywhere else, with long-term expected adverse consequences. Little is known about which factors are associated with the slow in-person return to school in the region and how these factors have had differential effects based on students’ socio-economic status. Combining a longitudinal national survey of the Chilean school system and administrative datasets, we study the supply and demand factors associated with students’ resuming in-person instruction and the socio-economic gaps in school reopening in Chile in 2021. We defined socio-economic status based on parents’ education and household income. Our results show that in-person learning in 2021 was limited mainly by supply factors (i.e., sanitary, administrative, and infrastructure restrictions). However, once the supply restrictions decreased, many low-income students and their families did not resume in-person instruction. We found vast inequalities in face-to-face instruction by school’s socio-economic characteristics. On average, schools in the highest 10% of the socio-economic distribution had three times higher attendance rates than the remaining 90%. We found no significant differences between schools in the lowest 90% of the distribution. After exceptionally long school closures, most school authorities, students, and their families did not return to in-person instruction, particularly those of low socio-economic status. These inequalities in in-person instruction will expand existing disparities in students’ learning and educational opportunities.
We study the importance of job-related and non-job-related factors in students’ college major choices. Using a staggered intervention that allows us to provide students information about many different aspects of majors and to compare the magnitudes of the effects of each piece of information, we show that major choices depend on a wide set of factors. While students do not change their choices when given information about earnings, they do update their choices when told about other aspects of majors. The non-job-related factors, such as a major’s course difficulty and gender composition, are important to students but not well-known to them. We also find that male and female students value different major characteristics in different ways. Lower-ability females flee from majors that they learn are more difficult than they had believed, while other students do not. On the other hand, male students are averse to being taught by female faculty, while female students are not. Overall, our results show that a variety of factors are important for students’ major choices and that different factors matter for male and female students.