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Access and admissions
In-person college advising programs generate large improvements in college persistence and success for low-income students but face numerous barriers to scale. Remote advising models offer a promising strategy to address informational and assistance barriers facing the substantial majority of low-income students who do not have access to community-based advising, yet the existing evidence base on the efficacy of remote advising is limited. We present a comprehensive, multi-cohort experimental evaluation of CollegePoint, a national remote college advising program for high-achieving low- and moderate-income students. Students assigned to CollegePoint are modestly more likely (1.3 percentage points) to attend higher-quality institutions. Results from mechanism experiments we conducted within CollegePoint indicate that moderate changes to the program model, such as a longer duration of advising and modest expansions of the pool of students academically eligible to participate, do not lead to larger program effects. We also capitalize on across-cohort variation in whether students were affected by COVID-19 to investigate whether social distancing required by the pandemic increased the value of remote advising. CollegePoint increased attendance at higher-quality institutions by 3.2 percentage points for the COVID-19-affected cohort. Acknowledgements.
Can families in low-income contexts “pull themselves up by their bootstraps?” In rural Gambia, caregivers with high aspirations for their children, measured before the child starts school, invest substantially more in their children’s education. Despite this, essentially no children are literate or numerate three years later. In contrast, a bundled supply-side intervention administered in these same areas generated large literacy and numeracy gains. Crucially, conditional on receipt of this intervention, high-aspirations children are 25 percent more likely to attain literacy/numeracy than low-aspirations children. We also show how the test score SD metric can mislead when counterfactual learning levels are low.
Despite its increasing importance for educational practices, broadband is not equally accessible among all students. In addition to oft-noted last-mile barriers faced by rural students, there can be wide variation in in-home access between proximate urban and suburban neighborhoods ostensibly covered by the same telecommunications infrastructure. In this paper, we investigate the connection between these disparities and earlier redlining practices by spatially joining two current measures of broadband access with Depression-era residential security maps that graded neighborhoods for real estate investment risk from “Best” to “Hazardous” based in part on racist and classist beliefs. We find evidence that despite internet service providers reporting similar technological availability across neighborhoods, access to broadband in the home generally decreases in tandem with historic neighborhood risk classification. We further find differences in in-home broadband access by race/ethnicity and income level, both across and within neighborhood grades. Our results demonstrate how federally developed housing policies from the prior century remain relevant to the current digital divide and should be considered in discussions of educational policies that require broadband access for success.
The quality of college education is hard for students and employers to observe. Knowing this, in the last 40 years over 1,000 colleges in the US and China alone have changed their names to signal higher quality. We study how these changes affect college choice and labor market performance of college graduates. Using administrative data, we show that colleges which change their names enroll higher-aptitude students and the effects persist over time. These effects are larger for attractive but misleading name changes, and larger among students with less information about the college. In a large resume audit study of the labor market for recent graduates, we find a small, insignificant premium for applicants listing new college names in most jobs, but a penalty in low-pay, low-status jobs. To better understand these results, we analyze scraped online text data, survey data, and other administrative data. These show that while many college applicants lack important information about college quality, employers can see that college name changes lead to an increase in graduate aptitude. Our study demonstrates that signals designed to change perception can have real, lasting impacts on market outcomes.
How have changes in the costs of enrolling for full-time study at public 2-year and 4-year colleges have affected the decisions about whether and where to enroll in college? We exploit local differences in the growth of tuition at community colleges and public 4-year colleges to study the impact of public higher education costs on the postsecondary enrollment decisions of high school graduates over three decades. We model prospective students’ decisions about whether to attend community college, a public 4-year university in their state of residence, other colleges, or no college at all as relative costs change. Unlike institutional analyses, our contribution is not to model how enrollment changes at a particular college or type of college as costs change. But, we draw from the institutional literature to help identify enrollment impacts by instrumenting college costs using policy variation imposed by state appropriations and tuition caps. We estimate that in counties where local community college tuition doubled (about average for the study period), the likelihood of post-secondary enrollment fell by about 0.06, on a mean of about 0.80. In addition to reducing college enrollment overall, rising costs at community colleges diverted other students to 4-year colleges. Rising relative costs of 4-year public colleges similarly diverted some students toward community colleges, but did not limit college attendance in the aggregate. We also find evidence of endogeneity in cost setting at the institution level. Our preferred estimates rely on a control function approach that instruments intertemporal changes in institutional costs using state and local appropriations and state policies to restrict tuition growth.
The COVID-19 pandemic led to an abrupt shift from in-person to virtual instruction in Spring 2020. Using two complementary difference-in-differences frameworks, one that incorporates student fixed effects and another that leverages within-course variation on whether students started their Spring 2020 courses in-person or online, we estimate the impact of this shift on the academic performance of Virginia’s community college students. With both approaches, we find modest negative impacts (four to eight percent) on course completion. Our results suggest that faculty experience teaching a given course online does not mitigate the negative effects of students abruptly switching to online instruction.
We investigate how the presence of a college affects local educational attainment using historical natural experiments in which "runner-up" locations were strongly considered to become college sites but ultimately not chosen for as-good-as-random reasons. While runner-up counties have since had opportunity to establish their own colleges, winners are still more likely to have a college today. Using this variation, we find that winning counties today have college degree attainment rates 58% higher than runner-up counties and have larger shares of employment in high human capital sectors. These effects are not driven primarily by college employees, migration, or local development.
Before the 2020-21 school year, educators, policymakers, and parents confronted the stark and uncertain trade-offs implied by the health, educational, and economic consequences of offering instruction remotely, in person, or through a hybrid of the two. Most public schools in the U.S. chose remote-only instruction and enrollment fell dramatically (i.e., a loss of roughly 1.1 million K-12 students). We examine the impact of these choices on public-school enrollment using unique panel data that combine district-level enrollment trajectories with information on their instructional modes. We find offering remote-only instead of in-person instruction reduced enrollment by 1.1 percentage points (i.e., a 42 percent increase in disenrollment from -2.6 to -3.7 percent). The disenrollment effects of remote instruction are concentrated in kindergarten and, to a lesser extent, elementary schools. We do not find consistent evidence that remote instruction influenced middle or high-school enrollment or that hybrid instruction had an impact.
This paper examines how financial aid reform based on postsecondary institutional performance impacts student choice. Federal and state regulations often reflect concerns about the private, for-profit sector's poor employment outcomes and high loan defaults, despite the sector's possible theoretical advantages. We use student level data to examine how eliminating public subsidies to attend low-performing for-profit institutions impacts students' college enrollment and completion behavior. Beginning in 2011, California tightened eligibility standards for their state aid program, effectively eliminating most for-profit eligibility. Linking data on aid application to administrative payment and postsecondary enrollment records, this paper utilizes a differences-in-differences strategy to investigate students' enrollment and degree completion responses to changes in subsidies. We find that restricting the use of the Cal Grant at for-profit institutions resulted in significant state savings but led to relatively small changes in students' postsecondary trajectories. For older, non-traditional students we find no impact on enrollment or degree completion outcomes. Similarly, for high school graduates, we find that for-profit enrollment remains strong. Unlike the older, non-traditional students, however, there is some evidence of declines in for-profit degree completion and increased enrollment at community colleges among the high school graduates, but these results are fairly small and sensitive to empirical specification. Overall, our results suggest that both traditional and non-traditional students have relatively inelastic preferences for for-profit colleges under aid-restricting policies.
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.