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Discussion of the rising price of higher education and associated student debt in America has been a key feature of political discourse in recent memory, with renewed interest sparked by the announcement of the student loan forgiveness plan. Federal student debt has increased by 756% since 1995, and total student debt tripled from 2007 to 2022. Concurrently, state support for public universities fell by 18% from 2000 to 2015. This phenomenon has drawn interest in the literature, with works by Jaquette and Curs (2015), Bound et al. (2016), Deming and Walters (2017), Webber (2017), and Mathias (2022) examining the effect of state disinvestment on higher education pricing and enrollment. This paper uses data from IPEDS to examine Colorado's College Opportunity Fund, which eliminated state appropriations to Colorado universities in 2006. I advance the literature by being the first to employ quasi-experimental methods, using a synthetic control identification strategy to measure the impact of this funding shock on enrollment and tuition revenue recuperation by Colorado universities. I find that Hispanic enrollment increased by 3 percentage points relative to the synthetic counterfactual, and that tuition revenue increased by 42% as a result of the policy. These results are robust to threats to identification, and placebo tests conrm the validity of the design. These findings provide robust evidence of the pitfalls of state disinvestment in higher education, and the consequences for students who are left to foot the bill.
Inequality related to standardized tests in college admissions has long been a subject of discussion; less is known about inequality in non-standardized components of the college application. We analyzed extracurricular activity descriptions in 5,967,920 applications submitted through the Common Application platform. Using human-crafted keyword dictionaries combined with text-as-data (natural language processing) methods, we found that White, Asian American, high-SES, and private school students reported substantially more activities, more activities with top-level leadership roles, and more activities with distinctive accomplishments (e.g., honors, awards). Disparities decrease when accounting for other applicant demographics, school fixed effects, and standardized test scores. Still, salient differences remain, especially those related to first-generation applicants. Implications and recommendations for college admissions policy and practice are discussed.
College attendance has increased significantly over the last few decades, but dropout rates remain high, with fewer than half of all adults ultimately obtaining a postsecondary credential. This project investigates whether one-on-one college coaching improves college attendance and completion outcomes for former low- and middle-income income state aid recipients who attended college but left prior to earning a degree. We conducted a randomized control trial with approximately 8,000 former students in their early- to mid-20s. Half of participants assigned to the treatment group were offered the opportunity to receive coaching services from InsideTrack, with all communication done remotely via phone or video. Intent-to-treat analyses based on assignment to coaching shows no impacts on college enrollment and we can rule out effects larger than a two-percentage point (5%) increase in subsequent Fall enrollment.
We examine the potential to expand and diversify the production of university STEM degrees by shifting the margin of initial enrollment between community colleges and 4-year universities. Our analysis is based on statewide administrative microdata from the Missouri Department of Higher Education and Workforce Development covering enrollees in all public postsecondary institutions statewide. We find that the potential for shifting the enrollment margin to expand degree production in STEM fields is modest, even at an upper bound, because most community college students are not academically prepared for bachelor’s degree programs in STEM fields. We also find that shifting the enrollment margin is unlikely to improve racial/ethnic diversity among university STEM degree recipients. This is because community college students at the enrollment margin are less diverse than their peers who enter universities directly.
Detroit students who obtain a college degree overcome many obstacles to do so. This paper reports the results of a randomized evaluation of a program meant to provide support to low-income community college students. The Detroit Promise Path (DPP) program was designed to complement an existing College Promise scholarship, providing students with coaching, summer engagement, and financial incentives. The evaluation found that students offered the program enrolled in more semesters and earned more credits compared with those offered the scholarship alone. However, at the three-year mark, there were no discernable impacts on degrees earned. This paper examines systemic barriers to degree completion and offers lessons for the design of interventions to increase equity in postsecondary attainment.
We administer a survey to study students' preferences for relative performance feedback in an introductory economics class. To do so, we elicit students' willingness to pay for/avoid learning their rank on a midterm exam. Our results show that 10% of students are willing to pay to avoid learning their rank. We also find that female students are willing to pay $1 more than male students. We also confirm that beliefs about academic performance do not predict preferences for information. After randomizing access to information about rank, students report needing more study hours to achieve their desired grade and being less likely in the top half of the ability distribution in the class. These effects are driven by stronger effects from people who overestimated their midterm rank compared to those who underestimated their performance. We do not find an overall effect of learning about rank performance on final course grade. We also confirm that students' preferences for feedback do not interfere with their belief updating.
Given the spike of homicides in conflict zones of Colombia after the 2016 peace agreement, I study the causal effect of violence on college test scores. Using a difference-in-difference design with heterogeneous effects, I show how this increase in violence had a negative effect on college learning, and how this negative effect is mediated by factors such as poverty, college major, degree type, and study mode. A 10% increase in the homicide rate per 100,000 people in conflict zones of Colombia, had a negative impact on college test scores equivalent to 0.07 standard deviations in the English section of the test. This negative effect is larger in the case of poor and female students who saw a negative effect of approximately 0.16 standard deviations, equivalent to 3.4 percentage points out of the final score. Online and short-cycle students suffer a larger negative effect of 0.14 and 0.19 standard deviations respectively. This study provides among the first evidence of the negative effect of armed conflict on college learning and offers policy recommendations based on the heterogeneous effects of violence.
Four-year public colleges may play an important role in supporting intergenerational mobility by providing an accessible path to a bachelor’s degree and increasing students' earnings. Leveraging a midsize state’s GPA- and SAT-based admissions thresholds for the four-year public sector, I use a regression discontinuity design to estimate the effect of four-year public college admissions on earnings and college costs. For low-income students and Black, Hispanic, or Native American students, admission to four-year public colleges increases mean annual earnings by almost $8,000 eight to fourteen years after applying without increasing the private costs of college. The state recovers the cost of an additional four-year public college admission through increased lifetime tax revenue. Expanding access to four-year public colleges may be a particularly effective way to improve the economic outcomes of low-income students and Black, Hispanic, or Native American students.
Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models -- one predicting course completion, the second predicting degree completion. Our results show that algorithmic bias in both models could result in at-risk Black students receiving fewer success resources than White students at comparatively lower-risk of failure. We also find the magnitude of algorithmic bias to vary within the distribution of predicted success. With the degree completion model, the amount of bias is nearly four times higher when we define at-risk using the bottom decile than when we focus on students in the bottom half of predicted scores. Between the two models, the magnitude and pattern of bias and the efficacy of basic bias mitigation strategies differ meaningfully, emphasizing the contextual nature of algorithmic bias and attempts to mitigate it. Our results moreover suggest that algorithmic bias is due in part to currently-available administrative data being less useful at predicting Black student success compared with White student success, particularly for new students; this suggests that additional data collection efforts have the potential to mitigate bias.
As affirmative action loses political feasibility, many universities have implemented race-neutral alternatives like top percent policies and holistic review to increase enrollment among disadvantaged students. I study these policies’ application, admission, and enrollment effects using University of California administrative data. UC’s affirmative action and top percent policies increased underrepresented minority (URM) enrollment by over 20 percent and less than 4 percent, respectively. Holistic review increases implementing campuses’ URM enrollment by about 7 percent. Top percent policies and holistic review have negligible effects on lower-income enrollment, while race-based affirmative action modestly increased enrollment among very low-income students. These findings highlight the enrollment gaps between affirmative action and its most common race-neutral alternatives and reveal that available policies do not substantially affect universities’ socioeconomic composition.