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The Impact of No-Loan Policies on Student Economic Diversity at Public Colleges and Universities

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Abstract

To improve college access for low-income students, an increasing number of public colleges and universities have implemented no-loan policies, where student loans are replaced with institutional grant aid that does not require repayment. Using detailed income measures provided by Mobility Report Card data, this study examines the effect of no-loan policies on student economic diversity at public 4-year institutions. Using a difference-in-differences design and the synthetic control method, we found that the adoption of no-loan policies at public institutions increased enrollment shares of low-income students (bottom two family income quintiles). However, the increase was minimal for students from the lowest income quintile, particularly at the most selective institutions. Our findings suggest that although no-loan policies may help improve affordability at public colleges and universities, further efforts are needed to address underrepresentation of students from the lowest part of the income distribution.

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Notes

  1. Net tuition refers to published tuition minus the average amount of total grant aid. Total grant aid includes aid from the federal Pell Grant, the federal Supplemental Educational Opportunity Grant, state grants, institutional grants, and private and employer grants.

  2. The EFC is the minimum amount that students are expected to contribute. It is typically calculated through a federal formula, considering a family’s taxed and untaxed income, assets, benefits (e.g., unemployment benefits), family size, and the number of family members who will attend a postsecondary institution during the year.

  3. Information on the federal poverty line was obtained from the U.S. Department of Health and Human Services (2011).

  4. The specified year refers to the fall semester of the fall-spring academic year. For example, 2000 refers to the 2000–2001 academic year.

  5. The unique identifier of the MRC data is the Super-OPEID code. For most institutions, the Super-OPEID equals a six-digit OPEID assigned by the U.S. Department of Education to identify Title IV participating institutions. For some clusters of similar institutions that cannot be individually identified in the data, the Super-OPEID is a 1–999 code. To merge data between IPEDS and MRC, we first generated a crosswalk between UNITID (i.e., the unique identifier in IPEDS) and Super-OPEID by using six-digit OPEID as a linking identifier since it is available in both data sets. Given that most no-loan policies were implemented at the campus level (i.e., UNITID level), we manually examined the no-loan status of each UNITID within a Super-OPEID, and we defined a Super-OPEID as “treated” if all UNITIDs within this Super-OPEID implemented a no-loan program between 2000 and 2011. For those Super-OPEIDs that consisted of both treated and untreated UNITIDs, we carefully checked whether the untreated UNITIDs were administrative units, graduate schools, or outreach and extension programs that had less than 50 undergraduate students enrolled. If so, we also categorized this Super-OPEID as “treated”; otherwise, we excluded the Super-OPEID from our analytical sample. Since our analysis is based on data at the Super-OPEID level, the word “college” in the rest of the paper stands for a Super-OPEID.

  6. Excluded college systems include the Arizona University System, the University of Illinois System, Indiana University System, the University of Maryland System (except University College) and Baltimore City Community College, Texas A&M University, the University of Tennessee System, and the University of Vermont and State Agricultural College. The enrollment of these systems accounts for 3.6% of total enrollment in the sample.

  7. Racially marginalized groups include non-Hispanic Black, Indian American and Alaska Native, and Hispanic.

  8. To rule out the possibility that enrollment trends were already different before the treatment, we also tested the model specification with the inclusion of state-specific linear time trends to control for potential differential trends in outcomes across states. Although the estimates are less precise, our findings are largely similar to those from the baseline model.

  9. Difference-in-differences results for the 3rd, 4th, and 5th income quintiles are available upon request.

  10. We did not conduct SCM for colleges that adopted the no-loan policy in 2009, because there were not enough post-treatment observations to establish a reliable post-treatment trend. We excluded the College of William & Mary because its synthetic control unit did not fit well with the treated unit during the pre-treatment period.

  11. These merit-based aid states include Delaware, Washington, South Dakota, Michigan, Maryland, Illinois, Idaho, and California (Sjoquist and Winters 2015).

  12. Affirmative action ban states include California, Michigan, Washington, Florida, Nebraska, Arizona, New Hampshire, Oklahoma, and Texas (Blume and Long 2014).

  13. The Goodman-Bacon decomposition is currently only available for strongly balanced panels, where each panel has the same set of time points with non-missing data across all variables in the model. Applying this restriction reduces the sample size to 1,793. Likely owing to this reduction in the number of observations, the decomposition cannot be estimated with the full set of institution- and state-level covariates. Hence, results are reported while only controlling for institution-level covariates. Although not reported, decompositions with alternate specifications, such as including the full set of state-level covariates and a partial set of institution-level covariates, produce similar results.

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Acknowledgements

We are grateful to Dr. Kelly Rosinger and Dr. Karly Ford for their valuable comments and suggestions. We thank Dr. Manuel S. González Canché and participants at the Association for the Study of Higher Education annual conference for constructive feedback. We also thank the Department of Education Policy Studies at the Pennsylvania State University for funding this study through the Student Writing Group Award.

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Authors and Affiliations

  1. Education Policy Innovation Collaborative, Michigan State University, 236C Erickson Hall | 620, Farm Lane, East Lansing, MI, 48824, USA

    Qiong Zhu

  2. Department of Education Policy Studies, Pennsylvania State University, 400 Rackley Building, University Park, PA, 16802, USA

    Junghee Choi & Yi Meng

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Appendix

Appendix

See Figs. 4, 5, 6, 7, 8, 9 and Table 8.

Fig. 4
figure 4

Percentile cutoffs of parental income. Calculated by authors using Opportunity Insight’s Mobility Report Card Data

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Fig. 5
figure 5

Event study of pre- and post-treatment trends in enrollment by income quintiles and institutional selectivity. The figure plots the estimated coefficients of treatment dummies in the event study model. The dots and spikes illustrate the point estimates and 95% confidence intervals, respectively. a Highly selective institutions; b Moderately selective institutions; c Less selective institutions

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Fig. 6
figure 6

Placebo tests for the synthetic control method on enrollment of students from the first income quintile. Grey lines represent the treatment effects of placebo units while blue lines represent the effect for the real treated unit

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Fig. 7
figure 7

Placebo tests for the synthetic control method on enrollment of students from the second income quintile. Grey lines represent the treatment effects of placebo units while blue lines represent the effect for the real treated unit

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Fig. 8
figure 8

Synthetic control method results for institutions that did not experience significant change in enrollment of students from the first income quintile

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Fig. 9
figure 9

Synthetic control method results for institutions that did not experience significant change in enrollment of students from the first income quintile

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Table 8 List of institutions in the donor pool
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Zhu, Q., Choi, J. & Meng, Y. The Impact of No-Loan Policies on Student Economic Diversity at Public Colleges and Universities. Res High Educ 62, 733–764 (2021). https://doi.org/10.1007/s11162-020-09621-9

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