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Program and policy effects
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.
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.
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.
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.
School districts historically approached conflict-resolution from a zero-sum perspective: suspend students seen as disruptive and potentially harm them, or avoid suspensions and harm their classmates. Restorative practices (RP) -- focused on reparation and shared ownership of disciplinary justice -- are designed to avoid this trade-off by addressing undesirable behavior without imparting harm. This study examines Chicago Public Schools' adoption of RP. We identify decreased suspensions, improved school climate, and find no evidence of increased classroom disruption. We estimate a 19% decrease in arrests, including for violent offenses, with reduced arrests outside of school, providing evidence that RP substantively changed behavior.
News media plays a crucial role in the student loan policy ecosystem by influencing how policymakers and the public understand the “problem” of student loans. Prior research emphasizes the causal impact of the media on the social construction of policy issues and the lack of knowledge about the authors of news articles. Theory also suggests that it is more difficult for new information to reach people in the core of a social network given their insular relationships. Therefore, we used social network analysis to investigate the college backgrounds for authors of student loan articles published in eight prominent newspapers between 2006 and 2021. We found evidence of a stark status hierarchy among the colleges attended (e.g., over half of the authors attended an Ivy Plus or Public Flagship institution). Our findings also identified a negative relationship between that hierarchy and an innovative practice, the use of racialized language in student loan news articles. We discuss how this status hierarchy might explain current patterns of racialized language in student loan policy and the implications of this relationship for the intersection of status and novel practices.
In response to widening achievement gaps and increased demand for post-secondary education, local and federal governments across the US have enacted policies that have boosted high school graduation rates without an equivalent rise in student achievement, suggesting a decline in academic standards. To the extent that academic standards can shape effort decisions, these trends can have important implications for human capital accumulation. This paper provides both theoretical and empirical evidence of the causal effect of academic standards on student effort and achievement. We develop a theoretical model of endogenous student effort that depends on grading policies, finding that designs that do not account for either the spread of student ability or the magnitude of leniency can increase achievement gaps. Empirically, under a research design that leverages variation from a statewide grading policy and school entry rules, we find that an increase in leniency mechanically increased student GPA without increasing student achievement. At the same time, this policy induced students to increase their school absences. We uncover stark heterogeneity of effects across student ability, with the gains in GPA driven entirely by high ability students and the reductions in attendance driven entirely by low ability students. These differences in responses compound across high school and ultimately widen long-term achievement gaps as measured by ACT scores.
Despite documented benefits to college completion, more than a third of students who initially enroll in college do not ultimately earn a credential. Completing college requires students to navigate both institutional administrative tasks (e.g., registering for classes) and academic tasks within courses (e.g., completing homework). In postsecondary education, several promising interventions have shown that text-based outreach and communication can be a low-cost, easy to implement, and effective strategy for supporting administrative task navigation. In this paper, we report on two randomized controlled trials testing the effect of a text-based chatbot with artificial intelligence (AI) capability on students' academic task navigation in introductory courses (political science and economics). We find the academic chatbot significantly shifted students’ final grades, increasing the likelihood students received a course grade of B or higher by 5-6 percentage points and reduced the likelihood students dropped the course.
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.
Generally, need-based financial aid improves students’ academic outcomes. However, the largest source of need-based grant aid in the United States, the Federal Pell Grant Program (Pell), has a mixed evaluation record. We assess the minimum Pell Grant in a regression discontinuity framework, using Kentucky administrative data. We focus on whether and how year-to-year changes in aid eligibility and interactions with other sources of aid attenuate Pell’s estimated effects on post-secondary outcomes. This evaluation complements past work by assessing explanations for the null or muted impacts found in our analysis and other Pell evaluations. We also discuss the limitations of using regression discontinuity methods to evaluate Pell—or other interventions with dynamic eligibility criteria—with respect to generalizability and construct validity.