Gaps in advanced high school coursework by socioeconomic status and geography persist in the United States, even among students with the ability and access to succeed in them. Lack of information on course availability and inaccurate self-perceptions may contribute to these inequities. We report on a large-scale experiment designed to increase Advanced Placement (AP) participation among underrepresented minority students and students attending rural high schools. Students and parents assigned to treatment received personalized outreach via multiple communication channels about APs offered at their high school in which they demonstrated potential to succeed. Outreach increased the probability of AP Exam participation in subjects in which students demonstrated potential to succeed by 1.1 percentage points, a 2.5 percent increase over the control group rate. This, in turn, increased the probability that students scored 3 or higher on those AP Exams by 0.5 percentage points, a 1.4 percent increase over the control group rate. Intervention effects were concentrated among underrepresented minority students attending non-rural high schools and relatively less academically prepared students. The findings indicate that personalized course recommendations can increase equity in advanced high school course participation; however, designing outreach campaigns at scale that engage students is a crucial challenge to their efficacy.
Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy; and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, impacts model performance and the stability of predicted scores. The relative ranking of students’ predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and most complex models.
We examine whether virtual advising – college counseling using technology to communicate remotely – increases postsecondary enrollment in selective colleges. We test this approach using a sample of approximately 16,000 high-achieving, low- and middle-income students identified by the College Board and randomly assigned to receive virtual advising from the College Advising Corps. The offer of virtual advising had no impact on overall college enrollment, but increased enrollment in high graduation rate colleges by 2.7 percentage points (5%), with instrumental variable impacts on treated students of 6.1 percentage points.
Policymakers are increasingly including early-career earnings data in consumer-facing college search tools to help students and families make more informed postsecondary education decisions. We offer new evidence on the degree to which existing college-specific earnings data equip consumers with useful information by documenting the level of selection bias in the earnings metrics reported in the U.S. Department of Education’s College Scorecard. Given growing interest in reporting earnings by college and major, we focus on the degree to which earnings differences across four-year colleges and universities can be explained by differences in major composition across institutions. We estimate that more than 70 percent of the variation in median earnings across institutions is explained by observable factors, and accounting for differences in major composition explains 20-30 percent of the variation in earnings over and above institutional selectivity and student composition. We also identify large variations in the distribution of earnings within colleges; as a result, comparisons of early-career earnings can be extremely sensitive to whether the median, 25th, or 75th percentiles are presented. Taken together, our findings indicate that consumers can easily draw misleading conclusions about institutional quality when using publicly available earnings data to compare institutions.
Little is known about the effects of need-based financial aid disbursed late into college and how students respond when they approach lifetime limits for receiving aid. I exploit changes to federal Pell Grant eligibility rules that reduced the lifetime availability for grant aid from 9 to 6 full-time-equivalent years to examine these questions. Using data from the University System of Georgia and a matched difference-in-differences research design, I compare student outcomes before versus after the rule change for Pell recipients affected and unaffected by the new policy. Risk of aid exhaustion due to the policy change led students to increase their academic effort, as measured by term-over-term re-enrollment and term credits attempted and earned. I find weak evidence that the policy change accelerated time to completion and no evidence that it increased or decreased degree attainment overall. These findings indicate that aid disbursement policies and lifetime aid limits can impact the cost-effectiveness of aid expenditures and the efficiency of college degree production.
Research on college dropout has largely addressed early exit from school, even though a large share of students who do not earn degrees leave after their second year. In this paper, we offer new evidence on the scope of college late departure. Using administrative data from Florida and Ohio, we conduct an event history analysis of the dropout process as a function of credit attainment. Our results indicate that late departure is widespread, particularly at two- and open-admission four-year institutions. We estimate that 14 percent of all entrants to college and one-third of all dropouts completed at least three-quarters of the credits that are typically required to graduate before leaving without a degree. Our results also indicate that the probability of departure spikes as students near the finish line. Amidst considerable policy attention towards improving student outcomes in college, our findings point to promising new avenues for intervention to increase postsecondary attainment.
Performance-based funding models for higher education, which tie state support for institutions to performance on student outcomes, have proliferated in recent decades. Some states have designed these policies to also address educational attainment gaps by including bonus payments for traditionally low-performing groups. Using a Synthetic Control Method research design, we examine the impact of these funding regimes on race-based completion gaps in Tennessee and Ohio. We find no evidence that performance-based funding narrowed race-based completion gaps. In fact, contrary to their intended purpose, we find that performance-based funding widened existing gaps in certificate completion in Tennessee. Across both states, the estimated impacts on associate degree outcomes are also directionally consistent with performance-based funding exacerbating racial inequities in associate degree attainment.
Many selective colleges in the United States consider applicants in the context of their neighborhoods and schools to improve equity in college admissions. However, the efficacy of this practice is unclear because background information is usually not available for all applicants. We examine the impacts of standardizing the collection and presentation of information on educational disadvantage at the high school and neighborhood level for all applicants using a new web-based tool. We leverage the phased rollout across 42 institutions that piloted the tool to estimate its impact on admissions and enrollment in a sample of over 3.6 million applicants. On average, the tool increased the probability of admission for applicants from the most challenging backgrounds by 5 percentage points relative to applicants from the least challenging backgrounds. In contrast, we find no evidence that the tool altered the probability of enrollment in the aggregate, although enrollment impacts vary across institutions. The findings suggest that equipping admission officers with consistent background information on applicants is a necessary but insufficient step towards improving equity in higher education. Complementary policies that tackle enrollment barriers are also needed to increase socioeconomic representation at selective colleges.
Many college-bound students face a tradeoff between attending a more academically selective or affordable institution. We link the universe of SAT-takers with their college enrollment records and financial information thirteen years after high school to examine which of these two factors more strongly predicts early-career financial well-being. Increasing quality of academic fit is associated with stronger financial well-being in adulthood, while college affordability is negatively correlated with annual income and less positively correlated with other outcomes, even after controlling for academic fit and institutional selectivity. These findings suggest that academic fit warrants more consideration than affordability for students who attend college to improve their financial circumstances, while policies that emphasize affordability over academic fit may harm students financially in the long run.