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A growing literature uses value-added (VA) models to quantify principals' contributions to improving student outcomes. Principal VA is typically estimated using a connected networks model that includes both principal and school fixed effects (FE) to isolate principal effectiveness from fixed school factors that principals cannot control. While conceptually appealing, high-dimensional FE regression models require sufficient variation to produce accurate VA estimates. Using simulation methods applied to administrative data from Tennessee and New York City, we show that limited mobility of principals among schools yields connected networks that are extremely sparse, where VA estimates are either highly localized or statistically unreliable. Employing a random effects shrinkage estimator, however, can alleviate estimation error to increase the reliability of principal VA.
We provide a descriptive analysis of within-school and neighborhood similarity in high school applications in New York City. We depart from prior work by examining similarity in applications to specific schools rather than preferences for school characteristics. We find surprisingly low similarity within schools and neighborhoods, but substantial variation by race and prior achievement. White and Asian students are more likely to have choices in common relative to Black and Hispanic students, a difference that persists after controlling for achievement and location. Likewise, higher-achieving students are more likely to have choices in common, conditional on other student characteristics and location. An implication is that students’ likelihood of attending high school without any peers from their middle school or neighborhood varies by student background.
We provide novel evidence on the causal impacts of student absences in middle and high school on state test scores, course grades, and educational attainment using a rich administrative dataset that tracks the date and class period of each absence. We use two similar but distinct identification strategies that address potential endogeneity due to time-varying student-level shocks by exploiting within-student, between-subject variation in class-specific absences. We also leverage information on the timing of absences to show that absences that occur after the annual window for state standardized testing do not affect test scores, providing a further check of our identification strategy. Both approaches yield similar results. We nd that absences in middle and high school harm contemporaneous student achievement and longer-term educational attainment: On average, missing 10 classes reduces math or English Language Arts test scores by 3-4% of a standard deviation and course grades by 17-18% of a standard deviation. 10 total absences across all subjects in 9th grade reduce both the probability of on-time graduation and ever enrolling in college by 2%. Learning loss due to school absences can have profound economic and social consequences.
At least 25 million K-12 students in the U.S.—disproportionately children of color from low-income families—have been physically out of school for a full year due to the COVID-19 pandemic. These children are at risk of significant academic, social, mental, and physical harm now and in the long-term. We must determine how to help all students gain access to safe, in-person schooling. In this interdisciplinary Viewpoint, we review the literature about the association between school reopening and COVID-19 transmission rates, and about the political, social, and environmental conditions that shape families’ and teachers’ choices to return to in-person schooling. Even though schools can safely be opened with appropriate mitigation measures, we find four reasons for schooling hesitancy: high community transmission rates; the Trump administration’s politicization of school re-openings in Summer 2020; long-term histories of mutual mistrust and racialized disinvestment in urban districts; and rational calculation about vulnerability due to the social determinants of health that have led Black and Latinx parents disproportionately to keep their children at home and White families disproportionately to send their children to school. Given the deep interconnections between the social determinants of health and of learning, and between schooling hesitancy and community vulnerability, stark inequities in in-person schooling access and take-up are likely to persist. In addition to ramping up safe and speedy school reopening now, we must make a long-term commitment to supporting schools as both sites of and contributors to public health, especially in historically marginalized communities.
Standardized assessments are widely used to determine access to educational resources with important consequences for later economic outcomes in life. However, many design features of the tests themselves may lead to psychological reactions influencing performance. In particular, the level of difficulty of the earlier questions in a test may affect performance in later questions. How should we order test questions according to their level of difficulty such that test performance offers an accurate assessment of the test taker's aptitudes and knowledge? We conduct a field experiment with about 19,000 participants in collaboration with an online teaching platform where we randomly assign participants to different orders of difficulty and we find that ordering the questions from easiest to most difficult yields the lowest probability to abandon the test, as well as the highest number of correct answers. Consistent results are found exploiting the random variation of difficulty across test booklets in the Programme for International Student Assessment (PISA), a triannual international test, for the years of 2009, 2012, and 2015, providing additional external validity. We conclude that the order of the difficulty of the questions in tests should be considered carefully, in particular when comparing performance between test-takers who have faced different order of questions.
Principals (policy makers) have debated the progress in U. S. student performance for a half century or more. Informing these conversations, survey agents have administered seven million psychometrically linked tests in math and reading in 160 waves to national probability samples of selected cohorts born between 1954 and 2007. This study is the first to assess consistency of results by agency. We find results vary by agent, but consistent with Flynn effects, gains are larger in math than reading, except for the most recent period. Non-whites progress at a faster pace. Socio-economically disadvantaged white, black, and Hispanic students make greater progress when tested in elementary school, but that advantage attenuates and reverses itself as students age. We discuss potential moderators.
Scholars differ as to whether populist beliefs are a discourse or an ideology resembling conservatism or liberalism. Research has shown that a belief in popular sovereignty and a distrust of public officials are core components of populism. Its antithesis is defined as Burke’s claim that officials should exercise their own judgment rather than pander to the public. A national probability sample of U. S. adults is asked to respond to six items that form a populist scale, rank themselves on a conservative-liberal scale, and state their views on education issues. The two scales are only moderately correlated, and each is independently correlated with many opinions about contemporary issues. Populism has a degree of coherence that approximates but does not match that of the conservative-liberal dimension.
Local governments spend over 12 billion dollars annually funding the operation of 15,000 public libraries in the United States. This funding supports widespread library use: more than 50% of Americans visit public libraries each year. But despite extensive public investment in libraries, surprisingly little research quantifies the effects of public libraries on communities and children. We use data on the near-universe of U.S. public libraries to study the effects of capital spending shocks on library resources, patron usage, student achievement, and local housing prices. We use a dynamic difference-in-difference approach to show that library capital investment increases children’s attendance at library events by 18%, children’s checkouts of items by 21%, and total library visits by 21%. Increases in library use translate into improved children’s test scores in nearby school districts: a $1,000 or greater per-student capital investment in local public libraries increases reading test scores by 0.02 standard deviations and has no effects on math test scores. Housing prices do not change after a sharp increase in public library capital investment, suggesting that residents internalize the increased cost and improved quality of their public libraries.
This paper takes a novel time series perspective on K-12 school spending. About half of school spending is financed by state government aid to local districts. Because state aid is generally income conditioned, with low-income districts receiving more aid, state aid acts as a mechanism for risk sharing between school districts. We show that temporal inequality, due to state and local business cycles, is prevalent across the income distribution. We estimate a model of local revenue and state aid, and its allocation across districts, and use the parameters to simulate impulse response functions. We find that state aid provides risk sharing for local shocks, although slow speed of adjustment results in temporal inequality. There is little risk sharing for statewide income shocks, and the risk from such shocks to school spending is more severe in low income districts because of their greater reliance on state aid.
Well-documented racial disparities in rates of exclusionary discipline may arise from differences in hard-to-observe student behavior or bias, in which treatment for the same behavior varies by student race or ethnicity. We provide evidence for the presence of bias using statewide administrative data that contain rich details on individual disciplinary infractions. Two complementary empirical strategies identify bias in suspension outcomes. The first uses within-incident variation in disciplinary outcomes across White and under-represented minority students. The second employs individual fixed effects to examine how consequences vary for students across incidents based on the race of the other student involved in the incident. Both approaches find that Black students are suspended for longer than Hispanic or White students, while there is no evidence of Hispanic-White disparities. The similarity of findings across approaches and the ability of individual fixed effect models to account for unobserved characteristics common across disciplinary incidents provide support that remaining racial disparities are likely not driven by behavior.