Skip to main content
Log in

To Apply or Not to Apply: FAFSA Completion and Financial Aid Gaps

  • Published:
Research in Higher Education Aims and scope Submit manuscript

Abstract

In the United States, college students must complete the Free Application for Student Federal Aid (FAFSA) to access federal aid. However, many eligible students do not apply and consequently forgo significant amounts of financial aid. If students have perfect information about aid eligibility, we would expect that all eligible students complete FAFSA and no aid would go unclaimed. Using data from the National Postsecondary Student Aid Survey, I estimate a multinomial logit model which controls for all variables that contribute to aid eligibility and other student characteristics that may deter FAFSA completion. I find that students who are lower middle income, white, male and independent from parents are less likely to complete FAFSA even when they are eligible for aid. Using propensity score matching, I find that each year applicants forgo $9,741.05 in total aid (including grant and loan aid) which includes $1,281.00 of Pell Grants, $2,439.50 of the balance subsidized student loans, $1,986.65 of the balance of unsubsidized student loans, and $1,016.04 of institutional grants. These aid totals aggregate to $24 billion annually.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Bettinger et al. (2012) conduct an interesting, natural experiment to measure the effect of complexity and information asymmetry on the probability a student completes FAFSA. Partnering with H & R Block, a tax preparation company, the authors assist students completing FAFSA. The authors divide students into three groups. The first group is paired with an H & R Block employee who calculates the expected family contribution (EFC) for the student and then helps the student complete FAFSA. For the second group, the employee calculates the student’s EFC only, and the third group receives no help but a brochure explaining the benefits of college. The students in the first group are more likely to apply for federal aid and enroll in college.

  2. The EFC is the government’s estimate of how much the student or student’s family can contribute to the student’s education. The federal government uses a formula that incorporates family income and size.

  3. This imputation is done “by regression using dependency, family size, income, and number in college.” While these imputed observations must be treated with caution, the NCES does include all components of the federal aid formula so there should be no concern about omitted variable bias. These data construct a helpful counterfactual to estimate how much aid a student would have received if he would have completed FAFSA.

  4. The difference between independent and dependent students is very important when studying federal financial aid. A student is considered independent if he or she is over the age of 24, has dependents, is married, is a graduate student, is a military veteran, is orphaned, was in foster care, a court in the student’s state of residence has declared the student an emancipated minor, or is homeless. Exceptions to this policy can be made by the Department of Education by request (Department of Education 2015). Otherwise the federal government classifies the student as a dependent. If the student is an independent, then the government uses the student’s income to determine need. If the student is a dependent then the government uses parents’ income to determine need.

  5. In this study, I define the term financial aid gap to be the difference in financial aid between students who complete FAFSA and students who do not apply for federal financial aid.

  6. National Center for Education Statistics (2012).

  7. The economics of financial aid literature is still divided regarding the effects of finanical aid. While some papers show significant effects of aid on education outcomes, other researchers find that aid does not affect student behavior. Notable studies include Bruce and Carruthers (2014), Carruthers and Welch (2015), Fitzpatrick and Jones (2012), Rubin (2011), and Sjoquist and Winters (2015).

  8. King (2004) presents summary statistics from the 1999-2000 wave of the National Postsecondary Student Aid Survey (NPSAS). Characteristics that are negatively correlated with FAFSA completion include if the student is considered an independent, income, full or part time enrollment, and the type of school to which a student enrolls. The NPSAS inputs an estimated expected family contribution for non-applicants. Using these data, the author concludes that many students who do not complete FAFSA, would have been eligible for financial aid.

  9. I limit my sample to undergraduate students who are American citizens, attend only one institution during the school year, and attend a four-year public or private not-for profit institution. I drop observations that are over the age of 65 and under the age of 15 (120 observations). I also drop observations of students whose institutions reported a “sticker price” tuition rate less than $100 for full-time students (20 observations) and attend universities with headcount enrollment less than 100 students (50 observations). The tuition observations for these institutions were probably mistakes because they are large, well known universities whose tuition prices are much greater than expected.

  10. There are a number of reasons that even with perfect information and low application costs, we would still expect some Pell-eligible students to choose not to apply despite their eligibility. For example, a student may have received a “full ride” scholarship because of academic merit or athletic talent. Students also differ in the desire to take out student loans, in fact many students have “loan aversion” in the application process (Cadena and Keys 2013; Field 2009). Also many institutions and state merit aid programs require FAFSA completion and the portfolio of schools may induce a student to complete FAFSA in case of attending a higher tuition school.

  11. While Federal aid is means tested, income is only one component of the EFC and thus if students have complete information, then EFC should be statistically significant while income should not. However with incomplete information, a student may incorrectly estimate her EFC.

  12. For this paper, I focus on Pell eligibility since a student who is Pell Eligible will also have access to other forms of federal and institutionally sponsored aid.

  13. I choose a multinomial probit model instead of a multinomial logit model because the logit model requires the independent of irrelevant alternatives assumption which this scenario potentially violates because eligibility and FAFSA completion are too closely related. An alternative model is the nested logit, however the estimated coefficents are difficult to interpret and this model is usually used as a response to failure of IIA, which is not required of multinomial probit.

  14. Using a dummy variable for dependent students and including institutional characteristics could potentially introduce endogeneity into the models. For robustness, I estimated all models with and without the dependent student indicator and institutional characteristics. There were no statistically significant changes in the coefficient estimates.

  15. Recall that the NPSAS contains observation of only individuals who matriculate into college. These results may be different if the data included both students and those who never attend college.

References

  • Abadie, A., & Imbens, G. W. (2006). Large sample properties of matching estimators for average treatment effects. Econometrica, 74(1), 235–267.

    Article  Google Scholar 

  • Alon, S. (2007). The influence of financial aid in leveling group differences in graduating from Elite Institutions. Economics of Education Review, 26(3), 296–311.

    Article  Google Scholar 

  • Alon, S. (2011). Who benefits most from financial aid? The heterogeneous effect of need-based grants on Students’ College Persistence. Social Science Quarterly, 40(6), 1494–1505.

    Google Scholar 

  • Avery, C., & Hoxby, C. M. (2004). Do and should financial aid packages affect students’ college choices? In C. M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 239–303). Chicago: The University of Chicago Press.

    Chapter  Google Scholar 

  • Avery, C., & Kane, T. J. (2004). Student perceptions of college opportunities: The Boston COACH program. In Caroline M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 355–394). Chicago, IL: The University of Chicago Press.

    Chapter  Google Scholar 

  • Bell, A. D., Rowan-Kenyon, Heather T., & Perna, L. W. (2009). College knowledge of 9th and 11th grade students: Variation by school and state context. Journal of Higher Education, 80(6), 663–685.

    Article  Google Scholar 

  • Bettinger, E. P. (2004). How financial aid affects persistence. In Caroline M. Hoxby (Ed.), College choices: The economics of where to go, when to go, and how to pay for it (pp. 207–239). Chicago, IL: The University of Chicago Press.

    Chapter  Google Scholar 

  • Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2012). The role of application assistance and information in college decisions: Results from the H & R block FAFSA experiment. The Quarterly Journal of Economics, 127(3), 1205–1242.

    Article  Google Scholar 

  • Bird, K., & Castleman, B. L. (2016). Here today, gone tomorrow? Investigating rates and patterns of financial aid renewal among college students. Research in Higher Education, 57(4), 395–422.

    Article  Google Scholar 

  • Boschung, M. D., Sharpe, D. L., & Abdel-Ghany, M. (1998). Racial, ethnic, and gender differences in postsecondary financial aid awards. Economics of Education Review, 17(2), 219–222.

    Article  Google Scholar 

  • Bruce, D., & Carruthers, C. K. (2014). Jackpot? The impact of lottery scholarships on enrollment in tennessee. Journal of Urban Economics, 81, 30–44.

    Article  Google Scholar 

  • Bryson, A., Dorsett, R., & Purdon, S. (2002). The use of propensity score matching in the evaluation of active labour market policies. Department for Work and Pensions Working Paper No. 4.

  • Cadena, B. C., & Keys, B. J. (2013). Can self-control explain avoiding free money: Evidence from interest-free student loans. Review of Economics and Statistics, 95(4), 1117–1129.

    Article  Google Scholar 

  • Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31–72.

    Article  Google Scholar 

  • Carruthers, C. K., & Welch, J. G. (2015). Not whether, but where? Pell grants and college choices. University of Tennessee Department of Economics Working Paper, No. 2015-4.

  • Castleman, B. L., Page, L. C., & Schooley, K. (2014). The forgotten summer: Does the offer of college counseling after high school mitigate summer melt among college-intending, low-income high school graduates? Journal of Policy Analysis and Management, 33(2), 320–344.

    Article  Google Scholar 

  • Cornwell, C., Mustard, D. B., & Sridhar, D. J. (2006). The enrollment effects of merit-based financial aid: Evidence from Georgia’s HOPE program. Journal of Labor Economics, 24(4), 761–786.

    Article  Google Scholar 

  • Dee, T. S. (2004). Are there civic returns to education? Journal of Public Economics, 88(9–10), 1697–1720.

    Article  Google Scholar 

  • Deming, D., & Dynarski, S. (2010). College aid. In Phillip B. Levine & David J. Zimmerman (Eds.), Targeting investments in children: Fighting poverty when resources are limited (pp. 283–302). Chicago, IL: The University of Chicago Press.

    Chapter  Google Scholar 

  • Department of Education. (2015). The EFC formula. http://ifap.ed.gov/efcformulaguide/attachments/090214EFCFormulaGuide1516.pdf.

  • Dynarski, S. (2000). Hope for whom? Financial aid for the middle class and its impact on college attendance. National Tax Journal, 53(3), 629–661.

    Article  Google Scholar 

  • Dynarski, S. (2008). Building the stock of college-educated labor. Journal of Human Resources, 43(3), 576–610.

    Article  Google Scholar 

  • Dynarski, S., & Scott-Clayton, J. (2006). The cost of complexity in federal student aid: Lessons from optimal tax theory and behavioral economics. National Tax Journal, 59(2), 319–356.

    Article  Google Scholar 

  • Eide, E. R., & Showalter, M. H. (2011). Estimating the relation between health and education: What do we know and what do we need to know? Economics of Education Review, 30(5), 778–791.

    Article  Google Scholar 

  • Fenske, R. H., Porter, J. D., & DuBrock, C. P. (2000). Tracking financial aid and persistence of women, minority, and needy students in science, engineering, and mathematics. Research in Higher Education, 41(1), 67–94.

    Article  Google Scholar 

  • Field, E. (2009). Educational debt burden and career choice: Evidence from a financial aid experiment at NYU law school. American Economic Journal: Applied Economics, 1(1), 1–21.

    Google Scholar 

  • Fitzpatrick, M. D., & Jones, D. (2012). Higher education, merit-based scholarships, and post-baccalaureate migration. NBER Working Paper, No. 18530.

  • Fuller, W. C., Manski, C. F., & Wise, D. A. (1982). New evidence on the economic determinants of postsecondary schooling choices. The Journal of Human Resources, 17(4), 477–498.

    Article  Google Scholar 

  • Heckman, J. J., Ichimura, H., & Todd, P. E. (1997). Matching as an Econometric evaluation estimator: Evidence from evaluating a training programme. The Review of Economic Studies, 64(4), 605–654.

    Article  Google Scholar 

  • Heckman, J. J., Ichimura, H., & Todd, P. E. (1998). Matching as an econometric evaluation estimator. The Review of Economic Studies, 65(2), 261–294.

    Article  Google Scholar 

  • Imbens, G. W. (2004). Nonparametric estimation of average treatment effects under exogeneity: A review. The Review of Economics and Statistics, 86(1), 4–29.

    Article  Google Scholar 

  • Kim, D. (2004). The effect of financial aid on students’ college choice: Differences by racial groups. Research in Higher Education, 45(1), 43–70.

    Article  Google Scholar 

  • King, J. E. (2004). Missed opportunities: Students who do not apply for financial aid. Washington, DC: American Council on Education Issue Brief.

    Google Scholar 

  • Leslie, L. L., & Brinkman, P. T. (1987). Student price response in higher education. Journal of Higher Education, 58(2), 181–204.

    Article  Google Scholar 

  • Lovenheim, M. F., & Owens, E. G. (2014). Does federal financial aid affect college enrollment?: Evidence from drug offenders and the Higher Education Act of 1998. Journal of Urban Economics, 81, 1–13.

    Article  Google Scholar 

  • McKinney, L., & Novak, H. (2013). The relationship between FAFSA filing and persistence among first-year community college students. Community College Review, 41(1), 63–85.

    Article  Google Scholar 

  • McKinney, L., & Novak, H. (2015). FAFSA filing among first-year college students: Who files on time, who doesn’t, and why does it matter? Research in Higher Education, 56(1), 1–28.

    Article  Google Scholar 

  • McPherson, M. S., & Schapiro, M. O. (1991). Does Student aid affect college enrollment? New evidence on a persistent controversy. The American Economic Review, 81(1), 309–318.

    Google Scholar 

  • Miller, B. (2015). The right FAFSA form. Inside higher education. https://www.insidehighered.com/views/2015/02/24/essay-calls-different-approach-fafsa-reform.

  • Novak, H., & McKinney, L. (2011). The consequences of leaving money on the table: Examining persistence among students who do not file a FAFSA. Journal of Student Financial Aid, 41(3), 5–23.

    Google Scholar 

  • Roy, A. D. (1951). Some thoughts on the distribution of earnings. Oxford Economic Papers, 3(2), 135–146.

    Google Scholar 

  • Rubin, R. B. (2011). The pell and the poor: A regression-discontinuity analysis of on-time college enrollments. Research in Higher Education, 52(7), 675–692.

    Article  Google Scholar 

  • Rudin, D. B. (1974). Estimating causal effects of treatments in randomized and nonrandomized studies. Journal of Educational Psychology, 66(5), 688–701.

    Article  Google Scholar 

  • Scott-Clayton, J. (2012). Information barriers and financial aid policy. National Bureau of Economic Research Working Paper, No. 17811.

  • Singell, L. D. (2004). Come and stay awhile: Does financial aid effect retention conditioned on enrollment at a Large Public University. Economics of Education Review, 23(5), 459–471.

    Article  Google Scholar 

  • Smith, J. A., & Todd, P. E. (2005). Does matching overcome LaLonde’s critique of nonexperimental estimators? Journal of Econometrics, 125(1–2), 305–353.

    Article  Google Scholar 

  • St John, E. P., & Noell, J. (1989). The effects of student financial aid on access to higher education: An analysis of progress with special consideration of minority enrollment. Research in Higher Education, 30(6), 563–581.

    Article  Google Scholar 

  • Sjoquist, D. L., & Winters, J. V. (2015). State merit-based financial aid programs and college attainment. Journal of Regional Science, 55(3), 364–390.

    Article  Google Scholar 

  • van der Klaauw, W. (2002). Estimating the effect of financial aid offers on college enrollment: A regression-discontinuity approach. International Economic Review, 43(3), 1249–1287.

    Article  Google Scholar 

Download references

Acknowledgment

I thank David Mustard, Christopher Cornwell, Ian Schmutte, Jonathan Williams, and Michael Walker for helpful comments and advice. This research benefited from a Summer Dissertation Fellowship sponsored by the Graduate School of the University of Georgia. I also appreciate the comments of seminar and conference participants at the University of Georgia, City University of New York, the Association of Education Finance and Policy, the Southern Economic Association, and the Midwestern Economic Association.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael S. Kofoed.

Additional information

The views expressed herein are those of the author and do not reflect the position of the United States Military Academy, the Department of the Army, or the Department of Defense.

Appendix: Construction of Expected Family Contribution

Appendix: Construction of Expected Family Contribution

This section will describe in detail how the government calculates the EFC including the formula and the variables that the government uses. Also, this section will explain rules that the Department of Education proscribes to individual colleges and universities who calculate the cost of attendance for their college.

The EFC is a summation of two types of financial assets: income and savings. The Department of Education requires colleges to take into account income when calculating the EFC for all applicants. For a student’s savings and assets to be exempted from inclusion in the EFC, the student (or her parents) must either have an adjusted gross income (AGI) less than $50,000, not be required to file an IRS Form 1040, be a dislocated worker, or received a means-tested federal benefit.

To calculate the income component of EFC, the student must report her and her parents’ (if dependent) AGI from the previous year tax form. The federal government then allows the following to be deducted from the reported AGI: federal taxes paid, state taxes paid, Social Security allowance for both parents, and the income protection allowance. The income protection allowance is a function of total family members and the number of college students in the household. The difference between AGI and the exceptions equals the portion of income that counts towards the EFC. If the student is a dependent, then this process is used for both student and parent income and the sum of the two equals the portion of the EFC from income. If the student is an independent then the parents’ contribution is considered to be zero.

If a student does not qualify for the simplified EFC formula (income only), then the government adjusts the EFC for student’s and family’s savings and net worth. The federal government considers the student’s and family’s cash savings (including college savings), investments (not including 401k or pension funds, annuities, non-education IRA, or the value of a home), and net worth of a family own business or investment farm. This sum equals the student’s and family’s net worth. Finally, the government allows an adjustment for education savings and asset protection. This allowance depends on the age of the oldest parent and is increasing with age. Subtracting the asset protection allowance from the family’s net worth yields the family’s discretionary net worth. Students are not allowed to adjust their net worth for asset protection.

Finally, to calculate the student’s and family’s contribution from assets, the government multiplies the student’s net worth by 0.20 and the family’s discretionary net worth by 0.12. To calculate the final EFC, the government sums the contributions from income and the contributions from assets.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kofoed, M.S. To Apply or Not to Apply: FAFSA Completion and Financial Aid Gaps. Res High Educ 58, 1–39 (2017). https://doi.org/10.1007/s11162-016-9418-y

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-016-9418-y

Keywords

JEL Classification

Navigation