Document Tag: financial aid

Improving the IPEDS Student Average Net Price to be More Relevant for Consumers (2023)

This paper examines the limitations of the current IPEDS Student Average Net Price metric and propose data-driven solutions to better inform consumers—particularly students and families from low-income and marginalized backgrounds—about the true cost of attending college. These recommendations align with #RealCollege’s mission to address critical challenges like food and housing insecurity, child care, transportation, and other basic needs that frequently impact students’ ability to afford higher education.

Buying time: Financial aid allows college students to work less while enrolled (2024)

Many empirical studies have established that financial aid improves college attainment. Few have been able to test why. This study used administrative records of employment and earnings to get a more complete picture of students’ finances during college and test one potential mechanism, that financial aid buys students time by allowing them to work less in off-campus jobs. We studied recipients of New Jersey’s need-based Tuition Aid Grant (TAG). We used the eligibility cutoffs of TAG to identify groups of otherwise similar students who received sharply different amounts of aid. A prior study took the same approach and found that TAG increased on-time graduation rates from public universities. At these schools, 80% of TAG recipients worked at some point during the year. We found that when students received additional aid, on average they reduced earnings dollar for dollar.

College Proximity and College Costs: Is it More Expensive to College Proximity and College Costs: Is it More Expensive to Attend a Far-away College? Attend a Far-away College? (2024)

Many scholars have argued that it may be more costly to attend a far-away college than it is to attend a nearby college. If this is true, students who live in areas where colleges are few and far between may face higher costs than those with ample college options. This study assesses the plausibility of this line of reasoning by examining the association between geographic access to higher education, distance traveled to college, and college costs, as indicated by student debt. Using data from the High School Longitudinal Study of 2009, this study finds that people with lower levels of geographic access travel longer distances to attend college. In addition, people who travel longer distances are more likely to accumulate student debt. Finally, this study finds suggestive evidence that people with lower levels of geographic access tend to accumulate more student debt. These descriptive insights pave the way for future research on this topic. Ultimately, additional research in this area could be one of the keys to understanding and ultimately remedying geographic inequalities in postsecondary outcomes.

The racial wealth gap, financial aid, and college access (2023)

We examine how the racial wealth gap interacts with financial aid in American higher education to generate a disparate impact on college access and outcomes. Retirement savings and home equity are excluded from the formula used to estimate the amount a family can afford to pay. All else equal, omitting those assets mechanically increases the financial aid available to families that hold them. White families are more
likely to own those assets and in larger amounts. We document this issue and explore its relationship with observed differences in college attendance, types of institutions attended, degrees attained, and education debt using data from the Survey of Consumer Finances (SCF), the National Postsecondary Student Aid Study (NPSAS), and the Panel Study of Income Dynamics (PSID). We show that this treatment of assets provides an implicit subsidy worth thousands of dollars annually to students from families with above-median incomes. White
students receive larger subsidies relative to Black students and Hispanic students with similar family incomes, and this gap in subsidies is associated with disadvantages in educational advancement and student loan levels. It may explain 10 percent to 15 percent of white students’ advantage in these outcomes relative to Black students and Hispanic students.

Effects of Universal and Unconditional Cash Transfers on Child Abuse and Neglect (2023)

We estimate the effects of cash transfers on a severe measure of child welfare: maltreatment. To do so, we leverage year-to-year household variation from a universal and unconditional cash transfer, the Alaska Permanent Fund Dividend (PFD). Using linked individual-level administrative data on PFD payments and child maltreatment referrals, we show that an additional $1,000 to families in the first few months of a child’s life reduces the likelihood that a child is referred to Child Protective Services by age three by 2.0 percentage points, or 10 percent, on average. Estimates indicate that additional cash transfers also reduce child mortality.

Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal (2023)

In many settings, interventions may be more effective for some individuals than others, so that targeting interventions may be beneficial. We analyze the value of targeting in the context of a large-scale field experiment with over 53,000 college students, where the goal was to use “nudges” to encourage students to renew their financial-aid applications before a non-binding deadline. We begin with baseline approaches to targeting. First, we target based on a causal forest that estimates heterogeneous treatment effects and then assigns students to treatment according to those estimated to have the highest treatment effects. Next, we evaluate two alternative targeting policies, one targeting students with low predicted probability of renewing financial aid in the absence of the treatment, the other targeting those with high probability. The predicted baseline outcome is not the ideal criterion for targeting, nor is it a priori clear whether to prioritize low, high, or intermediate predicted probability. Nonetheless, targeting on low baseline outcomes is common in practice, for example because the relationship between individual characteristics and treatment effects is often difficult or impossible to estimate with historical data. We propose hybrid approaches that incorporate the strengths of both predictive approaches (accurate estimation) and causal approaches (correct criterion); we show that targeting intermediate baseline outcomes is most effective, while targeting based on low baseline outcomes is detrimental. In one year of the experiment, nudging all students improved early filing by an average of 6.4 percentage points over a baseline average of 37% filing, and we estimate that targeting half of the students using our preferred policy attains around 75% of this benefit.