Search EdWorkingPapers
Search EdWorkingPapers by author, title, or keywords.
Search
Program and policy effects
School buses may be a critical education policy lever, breaking the link between schools and neighborhoods and facilitating access to school choice. Yet little is known about the commute for bus riders, including the average length of the bus ride or whether long commutes harm academic outcomes. We begin to fill this gap using data from New York City to explore the morning commutes of over 120,000 bus riders. We find that long bus rides are uncommon and that those with long bus rides are disproportionately Black and more likely to attend charter or district-choice schools. We find deleterious effects of long bus rides on attendance and chronic absenteeism of district-choice students.
Underrepresented minority (URM) college students have been steadily earning degrees in relatively less-lucrative fields of study since the mid-1990s. A decomposition reveals that this widening gap is principally explained by rising stratification at public research universities, many of which increasingly enforce GPA restriction policies that prohibit students with poor introductory grades from declaring popular majors. We investigate these GPA restrictions by constructing a novel 50-year dataset covering four public research universities' student transcripts and employing a staggered difference-in-difference design around the implementation of 29 restrictions. Restricted majors’ average URM enrollment share falls by 20 percent, which matches observational patterns and can be explained by URM students’ poorer average pre-college academic preparation. Using first-term course enrollments to identify students who intend to earn restricted majors, we find that major restrictions disproportionately lead URM students from their intended major toward less-lucrative fields, driving within-institution ethnic stratification and likely exacerbating labor market disparities.
Principals (policymakers) disagree as to whether U. S. student performance has changed over the past half century. To inform conversations, agents administered seven million psychometrically linked tests in math (m) and reading (rd) in 160 survey waves to national probability samples of cohorts born between 1954 and 2007. Estimated change in standard deviations (sd) per decade varies by agent (m: -0.10sd to 0.27sd, rd: -0.02sd to 0.12sd). Consistent with Flynn effects, median trends show larger gains in m (0.19sd) than rd (0.04sd), though rates of progress for cohorts born since 1990 have increased in rd but slowed in m. Greater progress is shown by students tested at younger ages (m: 0.31sd, rd: 0.08sd) than when tested in middle years of schooling (m: 0.17sd, rd: 0.03sd) or toward end of schooling (m: 0.06sd, rd: 0.02sd). Young white students progress more slowly (m: 0.28sd, rd: 0.09sd) than Asian (m: 46sd, rd: 0.28sd), black (m: 0.36sd, rd: 0.19sd) and Hispanic (m: 0.29sd, rd: 0.13sd) students. These ethnic differences generally attenuate as students age. Young students in the bottom quartile of the SES distribution show greater progress than those in the top quartile (difference in m: 0.08sd, in rd: 0.15sd), but the reverse is true for older students. Moderators likely include not only changes in families and schools but also improvements in nutrition, health care, and protection from contagious diseases and environmental risks. International data suggest that subject and age differentials may be due to moderators more general than just the United States.
In a randomized trial that collects text as an outcome, traditional approaches for assessing treatment impact require that each document first be manually coded for constructs of interest by human raters. An impact analysis can then be conducted to compare treatment and control groups, using the hand-coded scores as a measured outcome. This process is both time and labor-intensive, which creates a persistent barrier for large-scale assessments of text. Furthermore, enriching ones understanding of a found impact on text outcomes via secondary analyses can be difficult without additional scoring efforts. Machine-based text analytic and data mining tools offer one potential avenue to help facilitate research in this domain. For instance, we could augment a traditional impact analysis that examines a single human-coded outcome with a suite of automatically generated secondary outcomes. By analyzing impacts across a wide array of text-based features, we can then explore what an overall change signifies, in terms of how the text has evolved due to treatment. In this paper, we propose several different methods for supplementary analysis in this spirit. We then present a case study of using these methods to enrich an evaluation of a classroom intervention on young children’s writing. We argue that our rich array of findings move us from “it worked” to “it worked because” by revealing how observed improvements in writing were likely due, in part, to the students having learned to marshal evidence and speak with more authority. Relying exclusively on human scoring, by contrast, is a lost opportunity.
Paraeducators perform multiple roles in U.S. classrooms, including among others preparing classroom activities, working with students individually and in small groups, supporting individualized programming for students with disabilities, managing classroom behavior, and engaging with parents and communities. Yet, little research provides insights into this key group of educators. This study combines an analysis of national administrative data to describe the paraeducator labor market with a systematic review of collective bargaining agreements and other job-defining documents in ten case-study districts. We find a large and expanding labor market of paraeducators, far more diverse along ethnic and racial lines than certified teachers but with far lower wages, fewer performance incentives, less professional development, and fewer opportunities for advancement within the profession.
Nearly half of students who enter college do not graduate. The majority of efforts to increase college completion have focused on supporting students before or soon after they enter college, yet many students drop out after making significant progress towards their degree. In this paper, we report results from a multi-year, large-scale experimental intervention conducted across five states and 20 broad-access, public colleges and universities to support students who are late in their college career but still at risk of not graduating. The intervention provided these “near-completer” students with personalized text messages that encouraged them to connect with campus-based academic and financial resources, reminded them of upcoming and important deadlines, and invited them to engage (via text) with campus-based advisors. We find little evidence that the message campaign affected academic performance or attainment in either the full sample or within individual higher education systems or student subgroups. The findings suggest low-cost nudge interventions may be insufficient for addressing barriers to completion among students who have made considerable academic progress.
Personnel evaluation systems have historically failed to identify and remediate low-performing teachers. In 2012, Chicago Public Schools implemented an evaluation system that incorporated remediation and dismissal plans for low-rated teachers. Regression discontinuity estimates indicate that the evaluation reform increased the exit of low-rated tenured teachers by 50 percent. The teacher labor supply available to replace low-rated teachers was higher performing on multiple dimensions, and instrumental variables estimates indicate that policy-induced exit of low-rated teachers significantly improved teacher quality in subsequent years. Policy simulations show that the teacher labor supply in Chicago is sufficient to remove significantly more low-performing teachers.
We assess whether a light-touch intervention can increase socioeconomic and racial diversity in undergraduate Economics. We randomly assigned over 2,200 students a message with basic information about the Economics major; the basic message combined with an emphasis on the rewarding careers or financial returns associated with the major; or no message. Messages increased the proportion of first generation or underrepresented minority (URM) students majoring in Economics by five percentage points. This effect size was sufficient to reverse the gap in Economics majors between first generation/URM students and students not in these groups. Effect sizes were larger and more precise for better-performing students and first generation students. Extrapolating to the full sample, the treatment would double the proportion of first generation and underrepresented minority students majoring in Economics.
We combine a large multi-site randomized control trial with administrative and survey data to demonstrate that intensive advising during high school and college leads to large increases in bachelor's degree attainment. Novel causal forest methods suggest that these increases are driven primarily by improvements in the quality of initial enrollment. Program effects are consistent across sites, cohorts, advisors, and student characteristics, suggesting the model is scalable. While current and proposed investments in postsecondary education focus on cutting costs, our result suggest that investment in advising is likely to be a more efficient route to promote bachelor's degree attainment.