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Inequality related to standardized tests in college admissions has long been a subject of discussion; less is known about inequality in non-standardized components of the college application. We analyzed extracurricular activity descriptions in 5,967,920 applications submitted through the Common Application platform. Using human-crafted keyword dictionaries combined with text-as-data (natural language processing) methods, we found that White, Asian American, high-SES, and private school students reported substantially more activities, more activities with top-level leadership roles, and more activities with distinctive accomplishments (e.g., honors, awards). Black, Latinx, Indigenous, and low-income students reported a similar proportion of activities with top-level leadership positions as other groups, although the absolute number was lower. Gaps also lessened for honors/awards when examining proportions, versus absolute number. Disparities decreased further when accounting for other applicant demographics, school fixed effects, and standardized test scores. However, salient differences related to race and class remain. Findings do not support a return to required standardized testing, nor do they necessarily support ending consideration of activities in admissions. We discuss reducing the number of activities that students report and increasing training for admissions staff as measures to strengthen holistic review.
Purpose: Nearly all schools in the United States closed in spring 2020, at the onset of the COVID-19 pandemic. We analyze traditional public and charter school reopenings for the 2020-21 school year in five urban districts. We provide a rich and theoretically grounded description of how and why educational leaders made reopening decisions in each of our case districts.
Research Methods: We used data from a multiple-case study from March 2020 to July 2021. The research team conducted 56 interviews with school, district, and system-level leaders; triangulated with publicly available data; and also drew on interview data from a subsample of parents and guardians in each of our sites. We analyzed these data through qualitative coding and memo writing, and conducted detailed single- and cross-case analyses.
Findings: School system leaders in our case sites generally consulted public health authorities, accounted for state-level health and educational guidance, and engaged with and were responsive to the interests of different stakeholders. Districts’ adherence to and strategic uses of public health guidance, as well as a combination of union-district relations and labor market dynamics, influenced reopening. Parents, city and state lawmakers, and local institutional conditions also played a role, helping to explain differences across cases.
Implications: In contrast to the “politics or science” framing that has dominated research and public discourse on school reopening, we show that local pandemic conditions and local political dynamics both mattered and in fact were interrelated. Our findings have some implications for how educational leaders might navigate future crises.
State takeover of school districts—a form of political centralization that shifts decision-making power from locally elected leaders to the state—has increased in recent years, often with the purported goal of improving district financial condition. Takeover has affected millions of students throughout the U.S. since the first takeover in 1988 and is most common in larger districts and communities serving large shares of low-income students and students of color. While previous research finds takeovers do not benefit student academic achievement on average, we investigate whether takeovers achieve their goal of improving financial outcomes. Using an event study approach, we find takeovers from 1990 to 2019 increased annual school spending by roughly $2,000 per pupil after five years, on average, leading to improvements in financial condition. Increased funding came primarily from state sources and funded districts’ legacy costs. However, takeover did not affect spending for districts with majority-Black student populations—which are disproportionately targeted for takeover—adding to a growing literature suggesting that takeover unequally affects majority-Black communities.
Greater school choice leads to lower demand for private tutoring according to various international studies, but this has not been explicitly tested for the U.S. context. To estimate the causal effect of charter school appearances on neighboring private tutoring prevalence, we employ a comparative event study model combined with a longitudinal matching strategy to accommodate differing treatment years. In contrast to findings from other countries, we estimate that charter schools increase, rather than decrease, tutoring prevalence in the United States. We further find that the effect varies considerably based on the characteristics of the treated neighborhood: areas with the highest income, educational attainment, and proportion Asian show the greatest treatment impacts, while the areas with the least show null effects. Moreover, methodologically this investigation offers a pipeline for flexibly estimating causal effects with observational, longitudinal, geographically located data.
Researchers use test outcomes to evaluate the effectiveness of education interventions across numerous randomized controlled trials (RCTs). Aggregate test data—for example, simple measures like the sum of correct responses—are compared across treatment and control groups to determine whether an intervention has had a positive impact on student achievement. We show that item-level data and psychometric analyses can provide information about treatment heterogeneity and improve design of future experiments. We apply techniques typically used in the study of Differential Item Functioning (DIF) to examine variation in the degree to which items show treatment effects. That is, are observed treatment effects due to generalized gains on the aggregate achievement measures or are they due to targeted gains on specific items? Based on our analysis of 7,244,566 item responses (265,732 students responding to 2,119 items) taken from 15 RCTs in low-and-middle-income countries, we find clear evidence for variation in gains across items. DIF analyses identify items that are highly sensitive to the interventions—in one extreme case, a single item drives nearly 40% of the observed treatment effect—as well as items that are insensitive. We also show that the variation of item-level sensitivity can have implications for the precision of effect estimates. Of the RCTs that have significant effect estimates, 41% have patterns of item-level sensitivity to treatment that allow for the possibility of a null effect when this source of uncertainty is considered. Our findings demonstrate how researchers can gain more insight regarding the effects of interventions via additional analysis of item-level test data.
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 skin color, race, gender, and age in award-winning children’s books widely read in homes, classrooms, and libraries over the last century. We find that more characters with darker skin color appear over time, but the most influential books persistently depict characters with lighter skin color, on average, than other books, even after conditioning on race; we also find that children are depicted with lighter skin than adults on average. 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 present analysis of the supply of, and demand for, books with different levels of representation to better understand the economic behavior that may contribute to 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.
This paper conceptualizes segregation as a phenomenon that emerges from the intersection of public policy and individual decision-making. Contemporary scholarship on complex decision-making describes a two-step process—1) Editing and 2) Selection— and has emphasized the individual decision-maker’s agency in both steps. We build on this work by exploring, both theoretically and empirically, how policy can structure the choices individuals face at each step. We conduct this exploration within the empirical context of enrollment decisions among families in the Wake County Public School System (WCPSS), which used a controlled school choice system to help achieve diversity aims. We first investigate the schooling choice sets that WCPSS constructed for families and then examine families’ schooling selections. We find that families were offered choice sets containing schools varying racial compositions, but that the racial makeup of schools in families’ choice set systematically varied by schooling type and student race/ethnicity. We further show that a majority of families enrolled in their district-assigned default school, with Black and Hispanic families more likely than white or Asian families to attend this option. Finally, we demonstrate that white or Asian families enroll in their default school at lower rates as the share of Black students increases.
How progressive is school spending when spending is measured at the school-level, instead of the district-level? We use the first dataset on school-level spending across schools throughout the United States to ask to what extent progressivity patterns previously examined across districts are amplified, nullified, or reversed, upon disaggregation to schools. We find that progressivity is systematically greater when we conduct a school-level analysis, rather than district-level analysis. This may be surprising, given the traditional view in public economics that local governments cannot effectively redistribute. We thus probe the data for explanations for this pattern, uncovering evidence that federal policies play an important role in driving within-district progressive allocations. In particular, we can explain about 83% of the within-district contribution to progressivity by the federal component of spending plus allocations that are empirically attributable to special education and English language learning programs. Our findings are thus consistent with the traditional view of redistribution being primarily the purview of central governments, operationalized in this context through mandates.
Challenging the conventional wisdom that the spread of democracy was a leading driver of the expansion of primary schooling, recent studies show that democratization in fact did not lead to an average increase in primary school enrollment rates. One reason for this null effect is that there was already considerable provision of primary education before democratization. Still, it is possible that the spread of democracy did impact other aspects of education systems, such as the content of education and the extent to which teaching jobs are politicized. Studying this possibility cross-nationally has been infeasible due to data limitations. To address this gap, we take advantage of an original dataset covering 160 countries from 1945 to 2021 that contains information about these aspects of education. We document that transitions to democracy tend to be preceded by a decline in the politicization of both education content and teaching jobs. However, soon after democratization occurs, this decline usually halts. Counterfactual estimates suggest that democratization roughly halves the degree to which teacher hiring and firing decisions are politicized, but has a smaller impact on the content of education. The empirical patterns that we uncover have important implications for future research.
This is one of the first studies of the mismatch between students’ test scores and teachers’ estimations of those scores in low- and middle-income countries. Prior studies in high-income countries have found strong correlations between these metrics. We leverage data on actual and estimated scores in math and language from India and Bangladesh and find that teachers misestimate their students’ scores and that their estimations reveal their misconceptions about students in most need of support and variability within their class. This pattern is partly explained by teachers’ propensity to overestimate the scores of low-achieving students and to overweight the importance of intelligence. Teachers seem unaware of their errors, expressing confidence in estimations and surprise about their students’ performance once revealed.