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Research consistently demonstrates that tutoring interventions have substantial positive effects on student learning. As a result, tutoring has emerged as a promising strategy for addressing COVID-related learning loss and affording greater educational opportunities for students living in poverty. The effectiveness of tutoring programs, however, varies greatly, and these variations may drive differential gains in student learning. Therefore, determining the program characteristics that do and do not drive positive student outcomes will be key to providing guidance for policymakers and practitioners who want to implement high-impact tutoring at scale. Our goal is to highlight the programs, characteristics, and conditions that evidence suggests make for effective tutoring and to create an evidence-based framework for delivering and evaluating tutoring interventions. In addition, we identify promising questions for future research.
In this paper, I review the economics literature on for-profit college education in the United States, assessing what we know about institutional behavior and student outcomes after two decades of research. The many studies reviewed here reveal some consistent patterns. It is clear that for-profits compete with institutions in other sectors, yet they behave differently than their public and nonprofit counterparts. The literature is mixed on the responsiveness of the sector to labor market demands, but any responsiveness does not appear to translate to better student outcomes. The vast majority of studies on employment and earnings gains for students in for-profits find worse outcomes for for-profit students relative to similar students in other sectors. These disappointing results suggest that additional accountability measures may be warranted to protect students and taxpayers.
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.
Despite the growing evidence of informational interventions on college and major choices, we know little about how such light-touch interventions affect the gender gap in STEM majors. Linking survey data to administrative records of Chinese college applicants, we conducted a large-scale randomized experiment to examine the STEM gender gap in the major preference beliefs, application behaviors, and admissions outcomes. We find that female students are less likely to prefer, apply to, and enroll in STEM majors, particularly Engineering majors. In a school-level cluster randomized controlled trial, we provided treated students with major-specific wage information. Students’ major preferences are easily malleable that 39% of treated students updated their preferences after receiving the wage informational intervention. The wage informational intervention has no statistically significant impacts on female students’ STEM-related major applications and admissions. In contrast, those male students in rural areas who likely lack such information are largely shifted into STEM majors as a result of the intervention. We provide supporting evidence of heterogeneous major preferences for extrinsic incentives: even among those students who are most likely to be affected by the wage information (prefer high paying majors and lack the wage information), female students are less responsive to the informational intervention.
We provide theory and evidence about how the design of college financial aid programs affects a variety of high school, college, and life outcomes. The evidence comes from an eight-year randomized trial where 2,587 high school ninth graders received a $12,000 merit-based grant offer. During high school, the program increased their college expectations and non-merit effort but had no effect on merit-related effort (e.g., GPA). After high school, the program increased graduation from two-year colleges only, apparently because of the free college design/framing in only that sector. But we see no effects on incarceration or teen pregnancy. Overall, the results suggest that free college affects student outcomes in ways similar to what advocates of free college suggest and making aid commitments early, well before college starts, increases some forms of high school effort. But we see no evidence that merit requirements are effective. Both the standard human capital model and behavioral economics are required to explain these results.
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.
After increasing in the 1970s and 1980s, time to bachelor’s degree has declined since the 1990s. We document this fact using data from three nationally representative surveys. We show that this pattern is occurring across school types and for all student types. Using administrative student records from 11 large universities, we confirm the finding and show that it is robust to alternative sample definitions. We discuss what might explain the decline in time to bachelor’s degree by considering trends in student preparation, state funding, student enrollment, study time, and student employment during college.