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This paper studies how school spending impacts student achievement by exploiting the US interstate branching deregulation as state tax revenue shocks. Leveraging school finance data from universal school districts, our difference-in-differences estimation reveals that deregulation leads to an increase in per-pupil total revenue and expenditure. The rise in revenue is primarily attributed to higher state revenues, while the expenditure increase is more prominent in low-income school districts. Using restricted-use student assessments from the Nation’s Report Card, we find that deregulation results in improved student achievement, with no distributional effects evident across students’ ability, race, or free lunch status. We introduce an instrumental variables approach that accounts for dynamic treatment effects and estimate that a one-thousand-dollar increase in per-pupil spending leads to a 0.035 standard deviation improvement in student achievement.
Market-based policies, especially school vouchers, are expanding rapidly and shifting students out of traditional public schools. This essay broadens, deepens, and updates prior critiques of the free market logic in five ways. First, while prior articles have pointed to some of the conditions necessary for efficient market functioning, I provide a more comprehensive list. Second, with an up-to-date literature review, I show that all of these conditions fail to hold to an unusual extent in schooling, relative to other markets. Third, because of these failures, I argue that the strongest critique of the free market approach to schooling comes from the intellectual home of markets—economics. Fourth, I show that the issues leading to inefficiency are the same ones leading to inequity. Fifth, I argue that the analysis points to specific roles for government, which go well beyond those included in new universal school voucher policies, but which are also narrower than the roles of government encompassed in traditional public education. For these reasons, the current direction of policy is off-track and apparently inconsistent with the main criteria on which we evaluate education policy and even with the values that voucher advocates themselves profess.
While it is commonly believed that teachers take more absences than other professionals, few empirical studies have systematically investigated the prevalence of teacher absences in the US. This study documents the level of teacher absences and compares it with other college-educated workers. Using the Monthly Current Population Survey between the 1995 and 2019 school years, we conduct descriptive and regression analysis to estimate the level of teacher absences and the absence gaps between teachers and other college-educated workers. Additional regression analysis using data from the Leave Module of the American Time Use Survey is conducted to explain the gaps in absences between teachers and other observationally similar college-educated workers. The analysis reveals that 7% of teachers are absent at least once weekly, accounting for around 4% of their weekly working time. Compared to observationally similar college-educated workers, teachers take the same, if not less, amount of absences. Further investigation of teachers’ absence behaviour indicates that teachers report fewer demands for absences, have fewer paid leaves, and are more likely to attend work despite needing to be absent. We also find that individuals who prefer fewer absences tend to enter the teaching profession. This study adds to the emerging group of research examining the nature, determinants, and consequences of teacher absences using national-level data. Our findings imply that policymakers may be able to use more support programs to increase teacher attendance.
Noncognitive constructs such as self-e cacy, social awareness, and academic engagement are widely acknowledged as critical components of human capital, but systematic data collection on such skills in school systems is complicated by conceptual ambiguities, measurement challenges and resource constraints. This study addresses this issue by comparing the predictive validity of two most widely used metrics on noncogntive outcomes|observable academic behaviors (e.g., absenteeism, suspensions) and student self-reported social and emotional learning (SEL) skills|for the likelihood of high school graduation and postsecondary attainment. Our ndings suggest that conditional on student demographics and achievement, academic behaviors are several-fold more predictive than SEL skills for all long-run outcomes, and adding SEL skills to a model with academic behaviors improves the model's predictive power minimally. In addition, academic behaviors are particularly strong predictors for low-achieving students' long-run outcomes. Part-day absenteeism (as a result of class skipping) is the largest driver behind the strong predictive power of academic behaviors. Developing more nuanced behavioral measures in existing administrative data systems might be a fruitful strategy for schools whose intended goal centers on predicting students' educational attainment.
We study the progressivity of state funding of school districts under Tennessee’s weighted student funding formula. We propose a simple definition of progressivity based on the difference in exposure to district per-pupil funding between poor and non-poor students. The realized progressivity of district funding in Tennessee is much smaller—only about 17 percent as large—as the formula weights imply directly. The attenuation is driven by the mixing of poor and non-poor students within districts. We further show the components of the Tennessee formula not explicitly tied to student poverty are only modestly progressive. Notably, special education funding is essentially progressivity-neutral for poor students. If we adjust the formula so all factors except individual student poverty receive zero weight and distribute the excess to poor students, we can increase the progressivity of district funding by 124 percent. We interpret this as the opportunity cost of the non-poverty-based funding components, measured in terms of progressivity.
Decentralized matching markets experience high rates of instability due to information frictions. This paper explores the role of these frictions in one of the most unstable markets in the United States, the labor market for first-year school teachers. We develop and estimate a dynamic model of labor mobility that considers non-pecuniary information frictions directly. We find that teachers overestimate the value of hidden amenities and their own preferences for teaching. Improving access to information improves stability by 12% and reduces between-school switching by 18%, but reduces teacher labor supply by over 5%. Compared to each tested alternative, including targeted wage premiums at hard-to-staff schools, bonuses that incentivize retention, and lowered tenure requirements, information revelation improves match quality most.
Career and Technical Education (CTE) has long played a substantial, though controversial, role within America’s public schools. While supporters argue that CTE may increase student engagement and prepare students for success in the workforce, detractors caution that CTE may inhibit students’ access to the rigorous academic coursework needed for college and high-status careers. As students’ time in high school is a relatively fixed resource, this paper seeks to better understand the extent to which CTE is associated with trade-offs within students’ high school curricula. Using a robust statewide longitudinal data system, this study explores the extent to which CTE may limit course taking in a wide range of subjects (including core academic subjects, electives, and Advanced Placement courses). Special attention is paid to how curricular trade-offs may occur differently among different student populations, keeping in mind the legacy of tracking as a long-employed mechanism for reducing opportunity. On average, results indicate that CTE courses do crowd out students’ enrollment in non-CTE elective areas, but that CTE does not lead to large declines in college preparatory coursetaking, though there are nuances for certain student populations. Overall, these findings counter longstanding narratives that CTE participation limits student access to college preparatory coursework.
Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary within persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally viewed as a nuisance under the label of “item parameter drift” (IPD) in the Item Response Theory literature, we argue that IPD may be of substantive interest if it reflects how learning manifests on different items at different rates. In this study, we present a novel application of the Explanatory Item Response Model (EIRM) to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and their standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention on vocabulary knowledge, revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time.
Longitudinal studies can produce biased estimates of learning if children miss tests. In an application to summer learning, we illustrate how missing test scores can create an illusion of large summer learning gaps when true gaps are close to zero. We demonstrate two methods that reduce bias by exploiting the correlations between missing and observed scores on tests taken by the same child at different times. One method, multiple imputation, uses those correlations to fill in missing scores with plausible imputed scores. The other method models the correlations implicitly, using child-level random effects. Widespread adoption of these methods would improve the validity of summer learning studies and other longitudinal research in education.
Criminal activity is seasonal, peaking in the summer and declining through the winter. We provide the first evidence that arrests of children and reported crimes involving children follow a different pattern: peaking during the school year and declining in the summer. We use a regression discontinuity design surrounding the exact start and end dates of the school year to show that this pattern is caused by school: children aged 10-17 are roughly 50% more likely to be involved in a reported crime during the beginning of the school year relative to the weeks before school begins. This sharp increase is driven by student-on-student crimes occurring in school and during school hours. We use the timing of these patterns and a seasonal adjustment to argue that school increases reported crime rates (and arrests) involving 10-17-year-old offenders by 47% (41%) annually relative to a counterfactual where crime rates follow typical seasonal patterns. School exacerbates preexisting sex-based and race-based inequality in reported crime and arrest rates, increasing both the Black-white and male-female gap in reported juvenile crime and arrest rates by more than 40%.