Skip to main content

Exploring the Effects of Financial Aid on the Gap in Student Dropout Risks by Income Level

Abstract

Using national survey data and discrete-time logit modeling, this research seeks to understand whether student aid mediates the relationship between parental income and student dropout behavior. Our analysis confirms that there is a gap in dropout rates for low-income students compared with their upper income peers, and suggests that some types of aid are associated with lower risks of dropout. Thus, we examine the interaction between financial aid type and parental income to explore whether, and if so how, different types of aid may reduce the dropout gap by income level group. We find that the receipt of a Pell grant is related to narrowing the dropout gap between students from low- and middle-income groups, although overall the interaction between Pell grant and income is not significant. Loans and work-study aid both have similar effects on student dropout across all income groups. Methodologically, our results demonstrate the need to model dropout behavior temporally and to avoid main-effect bias by incorporating interaction effects.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2

Notes

  1. For more on these, see related sections in Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence by Singer and Willett (2003).

  2. Model fit can be tested with Wald tests and likelihood ratio (LR) tests. Although these two tests may give different answers for small samples, in general the Wald and LR tests are asymptotically equivalent (Greene 2003; Long and Freese 2003).

  3. Although we have evidence of how the receipt of a Pell grant changes the predicted probabilities of dropout for middle income students, the interaction model (results displayed in Table 4) was not a significant improvement (P = .14) compared to the model without these interaction terms (results displayed in Table 3).

References

  • Anderson, M. S., & Hearn, J. C. (1992). Equity issues in higher education outcomes. In W. E. Becker & D. R. Lewis (Eds.), The economics of American higher education. Boston: Kluwer Academic Publishers.

    Google Scholar 

  • Allison, P. D. (1982). Discrete-time methods for the analysis of event histories. In S. Leinhardt (Ed.), Sociological methodology (pp. 61–98). San Francisco: Jossey-Bass.

    Google Scholar 

  • Bean, J. P. (1980). Dropouts and turnover: The synthesis and test of a causal model of student attrition. Research in Higher Education, 12(2), 155–187.

    Article  Google Scholar 

  • College Board. (2004). Trends in student aid. Washington, DC: College Board.

    Google Scholar 

  • College Board. (2005a). Trends in student aid. Washington, DC: College Board.

    Google Scholar 

  • College Board. (2005b). Trends in college pricing. Washington, DC: College Board.

    Google Scholar 

  • Cox, D. R. (1972). Regression models and life-tables (with discussion). Journal of the Royal Statistical Society, 34, 187–220.

    Google Scholar 

  • DesJardins, S. L. (2003). Event history methods: Conceptual issues and application to student departure from college. In J. C. Smart (Eds.), Higher education: Handbook of theory and research (Vol. XVIII, pp. 421–471). Boston: Kluwer Academic Publishers.

    Google Scholar 

  • DesJardins, S. L., Ahlburg, D. A., & McCall, B. P. (1999). An event history model of student departure. Economics of Education Review, 18(3), 375–390.

    Article  Google Scholar 

  • DesJardins, S. L., McCall, B. P., Ahlburg, D. A., & Moye, M. J. (2002). Adding a timing light to the “tool box”. Research in Higher Education, 43(1), 83–114.

    Article  Google Scholar 

  • Dowd, A. C. (2004). Income and financial aid effects on persistence and degree attainment in public colleges. Educational Policy Analysis Archives, 12(21).

  • Greene, W. H. (2003). Economic analysis (5th ed.). Upper Saddle River, NJ: Prentice-Hall.

    Google Scholar 

  • Hu, S., & St. John, E. P. (2001). Student persistence in a public higher education system: Understanding racial and ethnic differences. Journal of Higher Education, 72(3), 265–286.

    Article  Google Scholar 

  • Ishitani, T., & DesJardins, S. L. (2002). A longitudinal investigation of dropout from college in the United States. Journal of College Student Retention, 4(2), 173–201.

    Article  Google Scholar 

  • Jaccard, J. (2001). Interaction effects in logistic regression. Thousand Oaks, Calif: Sage Publications.

    Google Scholar 

  • Long, J. S., & Freese, J. (2003). Regression models for categorical dependent variables using Stata (2nd ed.). College Station, TX: Stata Press.

    Google Scholar 

  • Moline, A. E. (1987). Financial aid and student persistence: An application of causal modeling. Research in Higher Education, 26(2), 130–147.

    Article  Google Scholar 

  • The National Center for Educational Statistics. (2002). 2002 NCES Statistical Standards, http://nces.ed.gov/statprog/2002/stdtoc.asp. Washington, DC: U.S. Department of Education, Institute of Educational Sciences.

  • The National Center for Educational Statistics. (2003). Descriptive summary of 1995–96 Beginning Postsecondary Students: Six Years Later. NCES, Statistical Analysis Report 2003-151. Washington, DC: U.S. Department of Education, Institute of Educational Sciences.

  • 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.

    Article  Google Scholar 

  • Pascarella, E. T., & Terenzini, P. T. (1991). How college affects students: Findings and insights from twenty years of research. California: Jossey-Bass Inc.

    Google Scholar 

  • Paulsen, M. B., and St. John, E. P. (2002) Social class and college costs: Examining the financial nexus between college choice and persistence. Journal of Higher Education, 73(2), 189–236.

    Article  Google Scholar 

  • Peng, S. S., & Fetters, W. B. (1978). Variables involved in withdrawal during the first two years of college: Preliminary findings from The National Longitudinal Study of the high school class of 1972. American Educational Research Journal, 15(3), 361–372.

    Article  Google Scholar 

  • Price, D. V. (2004). Borrowing inequality: Race, class, and student loans. Colorado: Lynne Rienner Publishers, Inc.

    Google Scholar 

  • Singer, J. D., & Willett, J. B. (2003) Applied longitudinal data analysis: Modeling change and event occurrence. New York: Oxford University Press, Inc.

    Google Scholar 

  • Spady, W. G. (1970). Dropouts from higher education: An interdisciplinary review and synthesis. Interchange, 1(1), 64–85.

    Article  Google Scholar 

  • St. John, E. P., & Starkey, J. B. (1995). An alternative to net price: Assessing the influence of prices and subsidies on within-year persistence. Journal of Higher Education, 66(2), 156–186.

    Article  Google Scholar 

  • Thomas, S. L., & Heck, R. H. (2001). Analysis of large-scale secondary data in higher education research: Potential perils associated with complex sampling designs. Research in Higher Education, 42(5), 517–540.

    Article  Google Scholar 

  • Tinto, V. (1975). Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research, 45, 89–125.

    Article  Google Scholar 

  • Tinto, V. (1987). Leaving college: Rethinking the causes and cures of student attrition. Chicago: University of Chicago Press.

    Google Scholar 

  • Tinto, V. (1988). Stages of student departure: Reflections on the longitudinal characteristics of student leaving. Journal of Higher Education, 59(4), 438–455.

    Article  Google Scholar 

  • Tinto, V. (1998). Colleges and communities: Taking research on student persistence seriously. Review of Higher Education, 21(2), 167–177.

    Google Scholar 

  • Tinto, V. (2004). Student retention and graduation: Facing the truth, living with the consequences. Washington, DC: The Pell Institute for the Study of Opportunity in Higher Education.

    Google Scholar 

  • Voorhees, R. A. (1985). Student finances and campus-based financial aid: A structural model analysis of the persistence of high need freshmen. Research in Higher Education, 22(1), 65–92.

    Article  Google Scholar 

  • Walpole, M. (2003). Socioeconomic status and college: How SES affects college experiences and outcomes. Review of Higher Education, 27, 45–73.

    Article  Google Scholar 

  • Willett, J. B., & Singer, J. D. (1991). From whether to when: New methods for studying student dropout and teacher attrition. Review of Educational Research, 61(4), 407–450.

    Article  Google Scholar 

  • Yamaguchi, K. (1991). Event history analysis. Newbury Park, California: Sage Publications.

    Google Scholar 

Download references

Acknowledgements

This research was funded by the American Educational Research Association and the Association for Institutional Research. Their financial support is gratefully acknowledged. The findings and interpretations are, however, the authors’ and do not represent the policies or positions of the funding agencies.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rong Chen.

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Chen, R., DesJardins, S.L. Exploring the Effects of Financial Aid on the Gap in Student Dropout Risks by Income Level. Res High Educ 49, 1–18 (2008). https://doi.org/10.1007/s11162-007-9060-9

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11162-007-9060-9

Keywords

  • Financial aid
  • Income differences
  • Dropout
  • Event history analysis
  • Main-effect bias