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Research in Higher Education

, Volume 59, Issue 3, pp 273–301 | Cite as

Financial Aid and College Persistence: Do Student Loans Help or Hurt?

  • Serge Herzog
Article

Abstract

Using data from two freshmen cohorts at a public research university (N = 3730), this study examines the relationship between loan aid and second-year enrollment persistence. Applying a counterfactual analytical framework that relies on propensity score (PS) weighting and matching to address selection bias associated with treatment status, the study estimates that loan aid exerts a significant negative effect on persistence for students from low-income background (i.e., Pell eligible), and those taking up high amounts of loans in order to meet total cost of attendance, including students who exhausted the available amount of subsidized loan aid. However, no significant incremental effect associated with unsubsidized loan aid, net of subsidized loan aid, could be detected. The estimated effect of loan aid on persistence controls for first-year academic experience and takes into account 26 factors related to loan selection and persistence in order to match students with loan aid to a counterfactual case in covariate adjusted regression. Comparison with results from non-matched-sample analysis suggests selection bias may mask the negative effect of loans detected with matched-sample estimation. Validity of covariates determining the loan selection process and criteria for acceptable balance in the matched data are discussed, and implications for future research are addressed.

Keywords

Student persistence Financial aid Loans Low income Causal inference estimation Propensity score 

References

  1. AASCU. (2006, August). Student debt burden. Policy Matters, 3(8). Retrieved January 15, 2017, from http://www.aascu.org/uploadedFiles/AASCU/Content/Root/PolicyAndAdvocacy/PolicyPublications/StudentDebtBurden.pdf.
  2. Adelman, C. (2004). Principal indicators of student academic histories in postsecondary education, 1972-2000. Washington, DC: U.S. Department of Education.Google Scholar
  3. Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. Washington, DC: U.S. Department of Education.Google Scholar
  4. Alon, S. (2005). Model mis-specification in assessing the impact of financial aid on academic outcomes. Research in Higher Education, 46(1), 109–125.CrossRefGoogle Scholar
  5. Allison, P. (2012). When can you safely ignore multicollinearity? Statistical Horizons. Retrieved February 12, 2017 from https://statisticalhorizons.com/multicollinearity.
  6. Angrist, J. D. (2003). Randomized trials and quasi-experiments in education research. NBER reporter no. 4. Cambridge, MA: National Bureau of Economic Research.Google Scholar
  7. Angrist, J., Autor, D., Hudson, S., & Pallais, A. (2014, December). Leveling up: Early results from a randomized evaluation of post-secondary aid. NBER Working Paper 20800. Cambridge, MA: National Bureau of Economic Research.Google Scholar
  8. Astin, A. W. (1993). What matters in college? Four critical years revisited. San Francisco: Jossey-Bass.Google Scholar
  9. Attewell, P., Heil, S., & Reisel, L. (2012). What is academic momentum? And does it matter? Educational Evaluation and Policy Analysis, 34, 27–44.CrossRefGoogle Scholar
  10. Austin, P. C. (2011). An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivariate Behavioral Research, 46, 399–424.CrossRefGoogle Scholar
  11. Avery, C., & Turner, S. (2012). Student loans: Do college students borrow too much—Or not enough? The Journal of Economic Perspectives, 26(1), 165–192.CrossRefGoogle Scholar
  12. Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a casual model of student attrition. Research in Higher Education, 12(2), 155–187.CrossRefGoogle Scholar
  13. Bean, J. P. (1983). The application of a model of turnover in work organization to the student attrition process. Review of Higher Education, 6(2), 129–148.CrossRefGoogle Scholar
  14. Bettinger, E. P., Long, B. T., Oreopoulos, P., & Sanbonmatsu, L. (2009, September). The role of simplification and information in college decisions: Results from the H&R Block FAFSA experiment. NBER working paper 15361. Cambridge, MA: National Bureau of Economic Research.Google Scholar
  15. Booij, A. S., Leuven, E., & Oosterbeek, H. (2012). The role of information in the take-up of student loans. Economics of Education Review, 31, 33–44.CrossRefGoogle Scholar
  16. Bowman, N. A., & Herzog, S. (eds.) (2014). Methodological Advances and Issues in Studying College Impact. New Directions for Institutional Research no. 161, San Francisco: Jossey-Bass.Google Scholar
  17. Braunstein, A., McGrath, M., & Pescatrice, D. (2001). Measuring the impact of financial factors on college persistence. Journal of College Student Retention, 2, 191–203.CrossRefGoogle Scholar
  18. Bresciani, M. J., & Carson, L. (2002). A study of undergraduate persistence by unmet need and percentage of gift aid. NASAP Journal, 40, 104–123.Google Scholar
  19. Brown, M., Haughwout, A., Lee, D., Scally, J., & van der Klaaw, W. (2014, April). Measuring student debt and its performance. Staff Report No. 668. New York: Federal Reserve Bank of New York.Google Scholar
  20. Cadena, B. C., & Keys, B. J. (2013). Can self-control explain avoiding free money? Evidence from interest-free student loans. The Review of Economics and Statistics, 95(4), 1117–1129.CrossRefGoogle Scholar
  21. Caison, A. L. (2007). Analysis of institutionally specific retention research: A comparison between survey and institutional database methods. Research in Higher Education, 48, 435–451.CrossRefGoogle Scholar
  22. Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22, 31–72.CrossRefGoogle Scholar
  23. Chen, R. (2008). Financial aid and student dropout in higher education: A heterogeneous research approach. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (pp. 209–239). New York: Springer.CrossRefGoogle Scholar
  24. Chen, R., & DesJardins, S. L. (2008). Exploring the effects of financial aid on the gap in student dropout risks by income level. Research in Higher Education, 49, 1–18.CrossRefGoogle Scholar
  25. Chen, R., & DesJardins, S. L. (2010). Investigating the impact of financial aid on student dropout risks: Racial and ethnic differences. The Journal of Higher Education, 81, 179–208.CrossRefGoogle Scholar
  26. Chen, J., & Zerquera, D. (2011). “A methodological review of studies on effects of financial aid on college student success. Paper prepared for the 36th annual conference of the Association for Education Finance and Policy.Google Scholar
  27. Cho, S. H., Xu, Y., & Kiss, D. E. (2015). Understanding student loan decisions: A literature review. Family and Consumer Sciences Research Journal, 43, 229–243.CrossRefGoogle Scholar
  28. Chudry, F., Foxall, G., & Pallister, J. (2011). Exploring attitudes and predicting intentions: Profiling student debtors using an extended theory of planned behavior. Journal of Applied Social Psychology, 41, 119–149.CrossRefGoogle Scholar
  29. Cruce, T. M. (2009). A note on the calculation and interpretation of the Delta-p statistic for categorical independent variables. Research in Higher Education, 50, 608–622.CrossRefGoogle Scholar
  30. DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (2002). Simulating the longitudinal effects of changes in financial aid on student departure from college. Journal of Human Resources, 37, 653–679.CrossRefGoogle Scholar
  31. Dowd, A. C. (2004, May 12). Income and financial aid effects on persistence and degree attainment in public college. Education Policy Analysis Archives, 12(21). Retrieved March 18, 2005 from http://epaa.asu.edu/epaa/v12n21/.
  32. Dowd, A. C. (2008). Dynamic interactions and intersubjectivity: Challenges to causal modeling in studies of college student debt. Review of Educational Research, 78, 232–259.CrossRefGoogle Scholar
  33. Dowd, A. C., & Coury, T. (2006). The effects of loans on the persistence and attainment of community college students. Research in Higher Education, 47, 33–62.CrossRefGoogle Scholar
  34. Elliot, W., & Nam, I. (2013). Reducing student loan debt through parents’ college savings (Vol. 49, pp. 265–286). St Louis, MO: Center for Social Development at Washington University.Google Scholar
  35. Gross, J. P., Hossler, D., Ziskin, M., & Berry, M. S. (2015). Institutional merit-based aid and student departure: A longitudinal analysis. The Review of Higher Education, 38, 221–250.CrossRefGoogle Scholar
  36. Haynes, R. M. (2008). The impact of financial aid on postsecondary persistence: A review of the literature. NASFAA Journal of Student Financial Aid, 37, 30–35.Google Scholar
  37. Heckman, J. J. (2000). Causal parameters and policy analysis in economics: A twentieth century retrospective. The Quarterly Journal of Economics, 115, 45–97.CrossRefGoogle Scholar
  38. Herzog, S. (2005). Measuring determinants of student return vs. dropout/stopout vs. transfer: A first-to-second year analysis of new freshmen. Research in Higher Education, 46(8), 883–928.CrossRefGoogle Scholar
  39. Herzog, S. (2008, January). Estimating the influence of financial aid on student retention. Education working paper archive. Fayetteville, AR: University of Arkansas, Department of Education Reform.Google Scholar
  40. Herzog, S. (2014). The propensity score analytical framework: An overview and institutional research example. In. N.A. Bowman & S. Herzog (Eds.), Methodological advances and issues in studying college impact. New Directions for Institutional Research no. 161. San Francisco: Jossey-Bass.Google Scholar
  41. Ho, D. E., Imai, K., King, G., & Stuart, E. (2011). MatchIt: Nonparametric preprocessing for parametric causal inference. Journal of Statistical Software, 42(8), 1–28.CrossRefGoogle Scholar
  42. Holland, P. W. (1986). Statistics and causal inference. Journal of the American Statistical Association, 81, 945–960.CrossRefGoogle Scholar
  43. Hossler, D., Ziskin, M., Gross, J., Kim, S., & Cekic, O. (2009). Student aid and its role in encouraging persistence. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (pp. 389–425). New York: Springer.CrossRefGoogle Scholar
  44. Institute for College Access & Success. (2015, October). Student debt and the class of 2014: The project on student debt. 10th annual report. Washington, DC: Institute for College Access & Success.Google Scholar
  45. Ipsos & Sallie Mae. (2012). How America pays for college 2012. Retrieved Feb 12, 2017 from http://news.salliemae.com/sites/salliemae.newshq.businesswire.com/files/publication/file/HowAmericaPays2012.pdf.
  46. Jackson, B. A., & Reynolds, J. R. (2013). The price of opportunity: Race, student loan debt, and college achievement. Sociological Inquiry, 83, 335–368.CrossRefGoogle Scholar
  47. Johnson, M. T. (2013). Borrowing constraints, college enrollment, and delayed entry. Journal of Labor Economics, 31, 669–725.CrossRefGoogle Scholar
  48. Jones-White, D. R., Radcliffe, P. M., Lorenz, L. M., & Soria, K. M. (2014). Priced out? The influence of financial aid on the educational trajectories of first-year students starting college at a large research university. Research in Higher Education, 55, 329–350.CrossRefGoogle Scholar
  49. Kane, L. (2016, January 12). Student loan debt in the US has topped $1.3 trillion. Business Insider. Retrieved May 19, 2016 from http://www.businessinsider.com/student-loan-debt-state-of-the-union-2016-1.
  50. Kim, D. (2007). The effect of loans on students’ degree attainment: Differences by student and institutional characteristics. Harvard Educational Review, 77, 64–100.CrossRefGoogle Scholar
  51. Lechner, M. (1999, December). Identification and estimation of causal effects of multiple treatments under the conditional independence assumption. Discussion paper no. 91. Bonn, Germany: Institute for the Study of Labor (IZA).Google Scholar
  52. Looney, A., & Yannelis, C. (2015, September). A crisis in student loans? How changes in the characteristics of borrowers and in the institutions they attend contributed to rising loan defaults. Brookings papers on economic activity. Washington, DC: The Brookings Institution.Google Scholar
  53. Luna De La Rosa, M. (2006). Is opportunity knocking? Low income students’ perceptions of college and financial aid. American Behavioral Scientist, 49(12), 1670–1686.CrossRefGoogle Scholar
  54. Marx, B. M., & Turner, L. J. (2015, January). Borrowing trouble? Student loans, the cost of borrowing, and implications for the effectiveness of need-based grant aid. NBER working paper 20850. Cambridge, MA: National Bureau of Economic Research.Google Scholar
  55. McKinney, L., & Backschneider Burridge, A. (2015). Helping or hindering? The effects of loans on community college student persistence. Research in Higher Education, 56, 299–324.CrossRefGoogle Scholar
  56. Mendoza, P. (2012). Should I work or should I borrow? A counterfactual analysis on the effect of working while in enrolled and debt on baccalaureate completion. Journal of Student Financial Aid, 42, 25–59.Google Scholar
  57. Mortenson, T. (1999, October). Unmet and overmet financial need of undergraduate students. Postsecondary Education Opportunity, 88, 1–10.Google Scholar
  58. Murnane, R. J., & Willett, J. B. (2010). Methods matter: Improving causal inference in educational and social science research. New York: Oxford University Press.Google Scholar
  59. Pascarella, E. T., & Terenzini, P. T. (1980). Predicting freshman persistence and voluntary dropout decisions from a theoretical model. Journal of Higher Education, 51(1), 60–75.CrossRefGoogle Scholar
  60. Pascarella, E. T., & Terenzini, P. T. (2005). How college affects students: A third decade of research. San Francisco: Jossey-Bass.Google Scholar
  61. Pike, G. R., Hansen, M. J., & Lin, C. (2011). Using instrument variables to account for selection effects in research on first-year programs. Research in Higher Education, 52, 194–214.CrossRefGoogle Scholar
  62. Reynolds, C. L., & DesJardins, S. L. (2010). The use of matching methods in higher education research. In J. C. Smart (Ed.), Higher education: Handbook of theory and research (Vol. 24, pp. 47–104). New York: Springer.CrossRefGoogle Scholar
  63. Robb, C. A., Moody, B., & Adbel-Ghany, M. (2011–2012). College student persistence to degree: The burden of debt. Journal of College Student Retention, 13, 431–456.Google Scholar
  64. Rosenbaum, P. R. (2002). Observational studies. New York: Springer.CrossRefGoogle Scholar
  65. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.CrossRefGoogle Scholar
  66. Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79, 516–524.CrossRefGoogle Scholar
  67. Rubin, D. B. (2001). Using propensity scores to help design observational studies: Application to the tobacco litigation. Health Services and Outcomes Research Methodology, 2, 169–188.CrossRefGoogle Scholar
  68. Rubin, D. B. (2006). Matched sampling for causal inference. New York: Cambridge University Press.CrossRefGoogle Scholar
  69. Scott-Clayton, J., & Minaya, V. (2016). Should student employment be subsidized? Conditional counterfactuals and the outcomes of work-study participation. Economics of Education Review, 52, 1–18.CrossRefGoogle Scholar
  70. Sekhon, J. S. (2011). Multivariate and propensity score matching software with automated balance optimization: The matching package for R. Journal of Statistical Software, 42(7), 1–22.CrossRefGoogle Scholar
  71. Singell, L. D., Jr. (2002). Merit, need, and student self selection: Is there discretion in the packaging of aid at a large public university? Economics of Education Review, 21(5), 445–454.CrossRefGoogle Scholar
  72. Soria, K. M., Weiner, B., & Lu, E. C. (2014). Financial decisions among undergraduate students from low-income and working-class social backgrounds. Journal of Student Financial Aid, 44, 2–23.Google Scholar
  73. Stanton-Salazar, R. D. (1997). A social capital framework for understanding the socialization of racial minority children and youths. Harvard Educational Review, 67(1), 1–40.CrossRefGoogle Scholar
  74. Stuart, E. A. (2010). Matching methods for causal inference: A review and look forward. Statistical Science, 25, 1–21.CrossRefGoogle Scholar
  75. Stuart, E. A., & Rubin, D. B. (2008). Best practices in quasi-experimental designs. In J. W. Osborne (Ed.), Best practices in quantitative methods (pp. 155–176). Thousand Oaks, CA: Sage.CrossRefGoogle Scholar
  76. The Institute for College Access & Success. (2016). Private loans: Facts and trends. Retrieved February 12, 2017 from http://bit.lv/28WOv4Q.
  77. Tinto, V. (1982). Limits of theory and practice in student attrition. Journal of Higher Education, 53(6), 687–700.CrossRefGoogle Scholar
  78. Tinto, V. (1993). Leaving college: Rethinking the causes and cures of student attrition. Chicago, IL: University of Chicago Press.Google Scholar
  79. Titus, M. A. (2007). Detecting selection bias, using propensity score matching, and estimating treatment effects. Research in Higher Education, 48, 487–521.CrossRefGoogle Scholar
  80. Trent, W. T., Lee, H. S., & Owens-Nicholson, D. (2006). Perceptions of financial aid among students of color: Examining the role(s) of self-concept, locus of control, and expectations. American Behavioral Scientist, 49(12), 1739–1759.CrossRefGoogle Scholar
  81. Welbeck, R., Diamond, J., Mayer, A., & Richburg-Hayes, L. (2014). Piecing together the college affordability puzzle. MDRC report. New York: MDRC.Google Scholar
  82. Ziskin, M., Fischer, M. A., Torres, V., Pellicciotti, B., & Player-Sanders, J. (2014). Working students’ perceptions of paying for college: Understanding the connection between financial aid and work. The Review of Higher Education, 37(4), 429–467.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Institutional AnalysisUniversity of Nevada, RenoRenoUSA

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